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## tex4ht - Bugs: bug #340, Math issues in the ODF export

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## bug #340: Math issues in the ODF export

 Submitted by: Michal Hoftich Submitted on: Tue Nov 22 15:39:54 2016 Category: None Priority: 5 - Normal Severity: 5 - Normal Status: None Privacy: Public Assigned to: None Open/Closed: Open

Sat Nov 26 12:40:11 2016, comment #9:

Thanks Karl, I thought that there is no XSLT processor included.

I think that xtpipes works, for example cross-references doesn't work without it. As I understand it, it combines SAX processor with XSLT or calls to Java.

I guess that all what xtpipes does can be done using Lua and the new Domobject, which will be used in next make4ht version. Maybe the Domobject will be usable also for conversion of mathml to starmath or TeX annotations.

Michal Hoftich <michal_h21>
Thu Nov 24 23:04:12 2016, comment #8:

there is no xslt processor in TL, and not likely to be -- those are huge programs with monumental prerequisites. xquery, xpath, etc. it's a completely different and foreign world from tex.

however, if xslt would be useful, don't let that stop you. people who want to can install it through their system, if they don't already have it. that's the only feasible way to do so, seems to me.

as far as i know, xtpipes is not functional and never has been for anyone except eitan. i've never looked into it what it does, let alone what would be needed to get it working. lots of java, for starters.

Karl Berry <karl>
Thu Nov 24 19:52:33 2016, comment #7:

Thanks Karl.

Yes, maybe it is not a bad idea to contact LO people before I try to write mathml to starmath convertor in Lua.

I have one question. Is any xslt processor included in TeX Live? I know that our xt-pipes include a xslt processor, but does anybody know how it works?

I am interested in Mathml to TeX conversion and found one potential solution [1], which is based on xslt. I would like to try if it could be used to add TeX as annotation for mathml.

Michal Hoftich <michal_h21>
Thu Nov 24 00:01:37 2016, comment #6:

committed the new mathml.4ht and ooffice-mml.4ht to TL.

regarding star(t)math, i suppose only the LO people can help, if they invented it. maybe they'd be willing to work with you on improving the overall situation wrt tex4ht/LO/mathml ...

Karl Berry <karl>
Wed Nov 23 14:52:28 2016, comment #5:

I've just found that LO can read mathml files without prefix, I must had done some mistake previously when it didn't work. The issue with prefix-less mathml is that processing with xtpipes don't work.

I've also found interesting thread [1], where odt export from tex4ht is discussed. It seems that at least 7 years ago improving bugs in mathml import wasn't important for OO devs (I totally understand that they didn't had enough developers, moreover developers who understand mathml). Also it seems that the annotation in StarMath format is required for valid odf file. Which again leads to a question: is there any usable mathml to StartMath converter?

Michal Hoftich <michal_h21>
Wed Nov 23 09:00:20 2016, comment #4:

Yes, you can push it to TL, it fixes the issue with <mfences>. We should leave this issue open, as we should add prefixes to all mathml attributes, and address the LO's mathml handling.

Michal Hoftich <michal_h21>
Wed Nov 23 08:57:07 2016, comment #3:

Thanks Karl. The html+mathml produced from the sample document are valid. I can't find functional ODF validator, the online one [1] gives me "Internal server error". I've tried to validate the mathml files included in the ODF can be validated in the HTML validator. It failed because of math: prefix, when I removed it, then they passed as valid. LibreOffice don't open the mathml files without namespace, so this shouldn't be an issue. So I guess this really is a bug in LibreOffice .

I've also figured out substance of another issue, which I found earlier, but didn't understand. Word can open ODT files, but it cannot display the math from tex4ht by default. But when you open the ODT file in LibreOffice and save it, then it can be read. It seems that LO converts the mathml to its's own format called StarMath, which can be then edited in LO's equation editor. Word seems to understand only the StarMath, so it can read the math in ODT files only after it is added by LO.

I can't find much information about StarMath. There is an element reference [1] and it seems that it is based on Troff's Eqn format [3]. I can't find Mathml to StartMath nor Eqn convertor, so we probably need to rely on LO's Mathml support, or write custom Mathml to StarMath converter.

BTW, I've created simple DOM library for LuaXML, next version of Make4ht will provide Lua filters based on it, in addition to regular expression filters. It can do some really funny stuff.

Michal Hoftich <michal_h21>
Wed Nov 23 00:48:57 2016, comment #2:

p.s. regarding your commit (r200), is this something i should push into TL now? thanks for all ...

Karl Berry <karl>
Wed Nov 23 00:48:08 2016, comment #1:

Hi Michal - regarding whether the mathml fragment is ok, I can only suggest passing it (a whole doc using it) through the W3C (or other) validator and see. It's hard to imagine what could be wrong with it, but who knows. (And whether reporting bugs to libreoffice is worthwhile, I also don't know.)

Karl Berry <karl>
Tue Nov 22 15:39:54 2016, original submission:

When I tried to compile the code from a question on TeX.sx [1], I found several issues in ODT export:

1. The braces from \left are small, they don't cover the three lines in the multi-line equation.

2. Instead of "<" characters at start of array columns, upside-down "?" are displayed.

Ad 1: Definitions of \Configure{left} and \Configure{right} are redefined in ooffice-mml.4ht. It is generated from tex4ht-ooffice.tex. There is a comment in the sources:

> OO doesn't seem to hono mfenced

I've tried to delete the configurations for left and right from ooffice-mml.4ht, so the default mathml configuration was used. This resulted in brackets of correct size, but wrong form. "(" instead of "{" was used, right bracket shouldn't be displayed at all.

I've took a look at the generated mathml code. For each math, one file named "filename-m{count}/content.xml" is created. The automatic size bracket are contained in <mfenced> element, with attributes left and right, where the bracket character is specified. mathml used in odf uses math: prefix on each element, this prefix must be used also on attributes. In our case left and right attributes didn't have this prefix, so they haven't been taken into the account and default brackets are used.

The prefixes are added using \a:mathml command in the tex4ht-mathml.tex, it is empty by default, but ooffice uses mathml: prefix. It is used on all element names and on most attributes, but it is missing on some of them, in particular in all configurations which use <mfenced> element.

I will add the prefix for the attributes to all configurations which use <mfenced> element and remove the configurations of "left" and "right" from ooffice-mml.4ht. But I guess there is much more instances of prefix-less attributes which need to be fixed.

Also, maybe it is worth checking whether all mathml fixes in ooffice-mml.4ht are really useful, or if there were only some minor bugs in mathml.4ht as in this case.

Ad 2: It seems to be a bug in the LibreOffice mathml handling. Minimal example which shows this issue is ${} < c$

This result in following mathml:

this seems like valid code and Firefox for instance has no problem in displaying that. It can be fixed if we add <math:mtext /> tag before <math:mo>.

So my question is: is it really a bug in LO, or is there also some issue with the mathml from tex4ht? If it is bug in tex4ht, can we insert <mtext /> automatically in the place of {} in the math context? Or is some post-processing of the XML needed?

Michal Hoftich <michal_h21>

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# Supernova Remnants

Description:

The collapse of a massive star and the resulting supernova explosion are dramatic events which both complete the stellar life cycle and regulate the structure of the Galaxy's interstellar medium. However, we don't yet fully understand how stars explode; constraints on the many complicated processes which occur during core collapse or thermonuclear detonation are desperately needed. Since we rarely see a nearby star go supernova, our focus is on studying the aftermaths of supernova explosions, namely supernova remnants. By observing this diverse class of objects, we can infer properties of the supernova, the progenitor star, and the progenitor's surroundings. We combine these observations with hydrodynamical models to gain new insight into the micro- and macro-physics of the supernova process, on the properties of supernova progenitors, and on the mechanisms which produce the diversity we see in Galactic supernova remnants.

People:

Dan Patnaude, Patrick Slane, Fred Seward, Paul Plucinsky, Paul Gorenstein, Terrance Gaetz, Kelly Korreck

format: plain


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Homework Help: Expectation Value of Momentum

1. Dec 18, 2011

atomicpedals

I've managed to get myself confused on a seemingly simple point of mathematics. When I calculate the expectation value of momentum in quantum mechanics

$$<p>=\int{\psi* \frac{\hbar}{i} \frac{d}{dx} \psi dx}$$
To what should I be applying the derivative? $\psi$?

2. Dec 18, 2011

Dick

Of course you would apply it to ψ. What else?

3. Dec 18, 2011

atomicpedals

Order of operation would rule out complex conjugate of $\psi$. So, instead of being like <x> which can simplify to

$$<x>=\int{|\psi|^{2}xdx}$$
$$<p_x>=\int{\psi * \frac{\hbar}{i} \frac{d \psi}{dx} dx}$$

4. Dec 18, 2011

Dick

Yes. That's it.

5. Dec 18, 2011

atomicpedals

Right on, thanks.


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# kableExtra v1.1.0

0

0th

Percentile

## Construct Complex Table with 'kable' and Pipe Syntax

Build complex HTML or 'LaTeX' tables using 'kable()' from 'knitr' and the piping syntax from 'magrittr'. Function 'kable()' is a light weight table generator coming from 'knitr'. This package simplifies the way to manipulate the HTML or 'LaTeX' codes generated by 'kable()' and allows users to construct complex tables and customize styles using a readable syntax.

# kableExtra

When we are talking about table generators in R, knitr's kable() function is usually a popular choice because of its ultimate simplicity. Unlike those powerful table rendering engines such as xtable, the philosophy behind knitr::kable() is to make it easy for programmers to use. Just as it claimed in its function description,

This is a very simple table generator. It is simple by design. It is not intended to replace any other R packages for making tables. - Yihui

However, the ultimate simplicity of kable() also brought troubles to some of us, especially for new R users, who may not have a lot of experience on generating tables in R. It is not rare to see people including experienced users asking questions like how to center/left-align a table on Stack Overflow. Also, for me personally, I found myself repeatedly parsing CSS into kable() for some very simple features like striped lines. For LaTeX, it's even worse since I'm almost Stack Overflow dependent for LaTeX... That's why this package kableExtra was created.

I hope with kableExtra, you can

• Use default base kable() (Or a good alternative for markdown tables is pander::pander()) for all simple tables
• Use kable() with kableExtra to generate 90 % of complex/advanced/self-customized/beautiful tables in either HTML or LaTeX
• Only have to mess with raw HTML/LaTeX in the last 10% cases where kableExtra cannot solve the problem

This package can load required LaTeX package automatically in vanilla rmarkdown. For customized rmarkdown templates, it is recommended to load related LaTeX packages manually.

## Features

### Pipable syntax

kableExtra is NOT a table generating package. It is a package that can "add features" to a kable() output using a syntax that every useR loves - the pipes %>%. We see similar approaches to deal with plots in packages like ggvis and plotly. There is no reason why we cannot use it with tables.

### Unified functions for both HTML and PDF

Most functionalities in kableExtra can work in both HTML and PDF. In fact, as long as you specifies format in kable() (which can be set globally through option knitr.table.format), functions in this package will pick the right way to manipulate the table be themselves. As a result, if users want to left align the table, kable(...) %>% kable_styling(position = "left") will work in both HTML and PDF.

## Install

install.packages("kableExtra")

# For dev version
devtools::install_github("haozhu233/kableExtra")

## Basic Usage

library(knitr)
library(kableExtra)
dt <- mtcars[1:5, 1:4]

# HTML table
kable(dt, format = "html", caption = "Demo Table") %>%
kable_styling(bootstrap_options = "striped",
full_width = F) %>%

# LaTeX Table
kable(dt, format = "latex", booktabs = T, caption = "Demo Table") %>%
kable_styling(latex_options = c("striped", "hold_position"),
full_width = F) %>%

## Acknowledgement

I would like to thank colleagues at Hebrew SeniorLife Institute for Aging Research and the Boston Pepper Center for their input. I also would like to appreciate the mentorship from Tom Travison (@tgt75) and all the efforts from the open source community, which help this package keep getting better.


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# Difference between revisions of "Shear Melting of a Colloidal Glass"

Entry: Chia Wei Hsu, AP 225, Fall 2010

Christoph Eisenmann, Chanjoong Kim, Johan Mattsson, and David Weitz, Phys. Rev. Lett. 104, 035502 (2010)

## Background

Colloidal suspensions serve as a model system for glass transition. A colloidal glass exhibit cooperativity and dynamic heterogeneity that are typically seen in glass transitions, as well as its own unique properties such as shear melting. Because of the large particle size, colloids form soft solids and can be fluidized through shear. In order to understand the relationship of shear-induced melting to others (such as melting by increasing temperature or reducing volume fraction), we need to understand the microscopic behavior of shear-melting.

## Experiment

Fig. 1. (a)(b) MSD versus strain. (c)(d) Non-Gaussian parameter.

The authors study sterically stabilized poly(methylmethacrylate) particles (average radius $R=0.6\mu m$) suspended in a mixture of cis-decalin and cycloheptylbromide (which matches the particle density and index of refraction) at a volume fraction of $\phi=0.61\pm0.03$. The colloid particles are fluorescently labeled, and the suspension is contained between two parallel glass plates $40 \mu m$ apart in a shear cell.

A symmetrical triangular time-dependent strain is applied in the $y$ direction with strain amplitude up to $\gamma \approx 0.5$ and strain periods between 25 and 100 $s$. At such shear rate, particle motions are dominated by the imposed shear (as opposed to diffusion). The particle positions $x(t)$ and $y(t)$ are tracked with confocal microscopy during shear, with data collected 2 frames per second. Then the mean displacement due to shear is subtracted.

## Results

Fig. 2 Dynamic cluster for an unsheared system (a) and a sheared system (b). (c) Size histogram of the dynamic clusters. Red: unsheared; blue: sheared.

The authors first calculate an effective mean-squared-displacement (MSD), $\langle\Delta x^2(\Delta t)\rangle_0=\langle ( x ( t_0 + \Delta t)-x(t_0))^2\rangle$, where$t_0$ is the initial time of a half strain-cycle. MSD is compared for different values of $\gamma$ (fig 1a, b). The MSD crosses over from subdiffusive to a steady state diffusive behavior at a transition at $\gamma \approx 0.08$ is observed. At the transition, the distribution of particle displacements $P(\Delta x (\Delta t))$ becomes non-Gaussian, reflecting dynamic heterogeneities. This is measured by the non-Gaussian parameter $\alpha_2$ (fig 1c, d).

Fig. 3. (a) Time evolution of MSD. (b) Diffusion constant versus shear rate. Dashed line: $D=(2/9)R_s^2 \dot{\gamma}$, with $R_s=1.8R$.

To study the cooperative motions in the sheared colloids, the authors look at the dynamic clusters in the sample, and compare it to an unsheared colloidal liquid with comparable diffusion constant (fig 2 a,b). To be more quantitative, they compare the dynamic cluster size distribution of the two (fig 2 c). The sheared glass has a longer tail of larger dynamic clusters. Integrating the data, they obtain an average dynamic cluster size about 2-3 particles, setting the scale of cooperative motion.

Lastly, they measure the diffusion constant$D$ with a linear fit of the MSD data: $\langle \Delta x^2(t)\rangle_y=2Dt$ (fig 3). Interestingly, $D$ is linear with the shear rate $\dot{\gamma}$. They propose to explain this relation with a modified version of the Stokes-Einstein relation. Replace the thermal energy $k_B T$ with the shear energy $(4\pi R_s^3/3)\eta \dot{\gamma}$, the Stokes-Einstein relation becomes $D=(2/9)R_s^2 \dot{\gamma}$. Here $R_s$ is a characteristic radius. In the data $R_s=1.8R$, agreeing with the observation that a cluster contains about 3 particles.

## Soft Matter discussion

Explanation of the glass transition is one of the biggest mysteries in soft matter. Shear melting of a colloidal glass provides another way to investigate this phenomenon. Here strain plays a role similar to temperature, and the critical strain $\gamma \approx 0.08$ is analogous to the glass transition temperature $T_g$. Indeed, under external shear, the particle dynamics is not determined by thermal fluctuation but by the imposed shear. That is also why we can simply swap the thermal energy with shear energy, and modify the Stokes-Einstein relation (which is just an example of fluctuation-dissipation ) to explain the diffusion constant of the sheared system. Further, we note that in this example, shearing enhances the mobility of the particles, and that in similar to the role of active transport in biological systems (see Intracellular transport by active diffusion).


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# Chapter01:Fundamental of Energy - Science and Technology¶

## Ex1.1:pg-36¶

In [7]:
# Given data:
T1=500+273.0 #source temp in kelvin
T2=100+273.0 #sink temperature in kelvin
W=1 # output power in kW

nth=1-(T2/T1) # thermal efficiency

Q1=1/nth # heat supplied in kW

Q2=Q1-W # heat rejected in kW

print " The heat rejected is ",Q2,"kW"

# the answer in book is wrong due to incorrect  value of T1

 The heat rejected is  0.9325 kW

## Ex1.2:pg-37¶

In [14]:
# Given data:
T1=40+273.0 # ambient temp in kelvin
T2=-10+273.0 # freezer temp in kelvin

Q1=T1*(Q2/T2) # heat transfer rate in kJ/s

W=Q1-Q2 # work in kW

print "The least power required is ",round(W,2),"kW"

The least power required is  0.38 kW

## Ex1.3:pg-37¶

In [16]:
# Given data:
Q1=3e4 # heat required in kJ/h
W=2e3 # work required in kJ/h

Q2=Q1-W # heat abstracted from outside in kJ/h

COP=Q1/(Q1-Q2) # COP of heat pump

print "Heat abstracted from outside air is ",Q2,"kJ/h"
print "COP heat pump is ",COP

Heat abstracted from outside air is  28000.0 kJ/h
COP het pump is  15.0

## Ex1.4:pg-38¶

In [17]:
# Given data:
T11=320+273.0 # temp at source 1 in kelvin
Q1=10000.0 # heat transfer rate at source 1 in kJ/min
T12=65+273.0 # temp at source 2 in kelvin
Q2=120000.0 #  heat transfer rate at source 2 in kJ/min
T2=35+273.0 # temp of surrounding in kelvin

n1=1-(T2/T11) # efficiency by source 1
n2=1-(T2/T12) # efficiency by source 2

W1=Q1*n1 # work at source 1 in kJ/min
W2=Q2*n2 # work at source 2 in kJ/min

print "The work done at two sources is W1=",W1," kJ/min W2=",W2,"kJ/min"
print "The larger power is provided by source 2"

The work done at two sources is W1= 4806.07082631  kJ/min W2= 10650.887574 kJ/min
The larger power is provided by source 2


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# Inline Math in Display Mode

I know how to have inline math typeset as in display mode in LaTeX, i.e summations with the index below the sigma, but I'm trying to learn ConTeXt and cannot figure out how to do it.

Any thoughts? Is there a way to do it in ConTeXt?

-
Does this link wiki.contextgarden.net/Math#Display_math_mode answer your question? –  N.N. Aug 29 '11 at 17:08
I believe that \displaystyle and \limits work also in ConTeXt. Do you have a non working example? –  egreg Aug 29 '11 at 17:08
@egreg Thanks. \displaystyle does work. Maybe I had a typo in my original? I don't know. Anyway thanks. I appreciate it. –  Doug Moore Aug 29 '11 at 17:14

## 1 Answer

These two ways should work also in ConTeXt:

$\displaystyle\sum_{i=1}^n a_i$

$\sum\limits_{i=1}^n a_i$

Probably the second form is preferable, although I usually advise not to put limits above and below in in-line math formulas.

-


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# Fortran Wiki asinh

## Description

asinh(x) computes the inverse hyperbolic sine of x.

## Standard

Fortran 2008 and later

## Class

Elemental function

## Syntax

result = asinh(x)

## Arguments

• x - The type shall be real or complex.

## Return value

The return value is of the same type and kind as x. If x is complex, the imaginary part of the result is in radians and lies between $-\pi/2 \leq \mathop{Im}{\asinh(x)} \leq \pi/2$.

## Example

program test_asinh
real(8), dimension(3) :: x = (/ -1.0, 0.0, 1.0 /)
write (*,*) asinh(x)
end program


text:
# is the sup of functions measurable function?

Good day, remembering the definition: The function $f(x)$ defined on the set $E$ is said to be measurable if the bounded set $E$ is measurable and if the set $E\cap \left \{ x:f(x)> a \right \}$ is measurable for all $a$.

Prove that the least upper bound of a finite or denumerable set of measurable functions is a measurable function.

Can I use this result? If $f(x)$ is a measurable function defined on the set $E$, then the sets $E\cap \left \{ x:f(x) \geq a \right \}$, $E\cap \left \{ x:f(x)= a \right \}$, $E\cap \left \{ x:f(x) \leq a \right \}$ are measurables for all $a$.

• Well, of course you can. The first and the last conditions are in fact both equivalent to the definition of measurable (real-valued or extended-real-valued) function.
– user228113
Sep 26, 2017 at 23:41

You need only show that one of them is measurable for every $a$. To do this, note that if $f$ is the supremum of a sequence $(f_n)$ of functions, then $f(x)>a$ if and only if $f_n(x)>a$ for some $n$. Therefore, if each $f_n$ is measurable, then $$\{x : f(x) > a\} = \bigcup_{n=1}^n\{x : f_n(x)>a\},$$ which shows that $$E\cap \{x : f(x) > a\} = E\cap\left(\bigcup_{n=1}^n\{x : f_n(x)>a\}\right) =\bigcup_{n=1}^n(E\cap \{x : f_n(x)>a\})$$ is a countable union of measurable sets.
It's important that the supremum is taken over countably many functions. To see why, let $N\subseteq[0,1]$ be a Lebesgue nonmeasurable set. Then the characteristic function $1_N$ is not Lebesgue measurable, but the family $\{1_{\{x\}} : x\in N\}$ consists of measurable functions and $1_N$ is their supremum.


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# Rating logic¶

Let’s start with the most important function: finding the cost of a certain call.

The call information comes to CGRateS having the following vital information like subject, destination, start time and end time. The engine will look up the database for the rates applicable to the received subject and destination.

type CallDescriptor struct {
Direction
TOR
Tenant, Subject, Account, Destination
TimeStart, TimeEnd
LoopIndex       // indicates the position of this segment in a cost request loop
CallDuration    // the call duration so far (partial or final)
FallbackSubject // the subject to check for destination if not found on primary subject
RatingPlans
}

When the session manager receives a call start event it will first check if the call is prepaid or postpaid. If the call is postpaid than the cost will be determined only once at the end of the call but if the call is prepaid there will be a debit operation every X seconds (X is configurable).

In prepaid case the rating engine will have to set rates for multiple parts of the call so the LoopIndex in the above structure will help the engine add the connect fee only to the first part. The CallDuration attribute is used to set the right rate in case the rates database has different costs for the different parts of a call e.g. first minute is more expensive (we can also define the minimum rate unit).

The FallbackSubject is used in case the initial call subject is not found in the rating profiles list (more on this later in this chapter).

What are the activation periods?

At one given time there is a set of prices that applay to different time intervals when a call can be made. In CGRateS one can define multiple such sets that will become active in various point of time called activation time. The activation period is a structure describing different prices for a call on different intervals of time. This structure has an activation time, which specifies the active prices for a period of time by one ore more (usually more than one) rate intervals.
type RateInterval struct {
Years
Months
MonthDays
WeekDays
StartTime, EndTime
Weight, ConnectFee
Prices
RoundingMethod
RoundingDecimals
}

type Price struct {
GroupIntervalStart
Value
RateIncrement
RateUnit
}

An RateInterval specifies the Month, the MonthDay, the WeekDays, the StartTime and the EndTime when the RateInterval’s price profile is in effect.

Example: The RateInterval {“Month”: [1], “WeekDays”:[1,2,3,4,5], “StartTime”:”18:00:00”} specifies the Price for the first month of each year from Monday to Friday starting 18:00. Most structure elements are optional and they can be combined in any way it makes sense. If an element is omitted it means it is zero or any.

The ConnectFee specifies the connection price for the call if this interval is the first one of the call.

The Weight will establish which interval will set the price for a call segment if more then one applies to it.

Example: Let’s assume there is an interval defining price for the weekdays and another interval that defines a special holiday rates. As that holiday is also one of the regular weekdays than both intervals are applicable to a call made on that day so the interval with the smaller Weight will give the price for the call in question. If both intervals have the same Weight than the interval with the smaller price wins. It is, however, a good practice to set the Weight for the defined intervals.

The RoundingMethod and the RoundingDecimals will adjust the price using the specified function and number of decimals (more on this in the rates definition chapter).

The Price structure defines the start (GroupIntervalStart) of a section of a call with a specified rate Value per RateUnit diving and rounding the section in RateIncrement subsections.

So when there is a need to define new sets of prices just define new RatingPlans with the activation time set to the moment when it becomes active.

Let’s get back to the engine. When a GetCost or Debit call comes to the engine it will try to match the best rating profile for the given Direction, Tenant, TOR and Subject using the longest Subject prefix method or using the FallbackSubject if not found. The rating profile contains the activation periods that might apply to the call in question.

At this point in rating process the engine will start splitting the call into various time spans using the following criterias:

1. Minute Balances: first it will handle the call information to the originator user acount to be split by available minute balances. If the user has free or special price minutes for the call destination they will be consumed by the call.
2. Activation periods: if there were not enough special price minutes available than the engine will check if the call spans over multiple activation periods (the call starts in initial rates period and continues in another).
3. RateIntervals: for each activation period that apply to the call the engine will select the best rate intervals that apply.
type TimeSpan struct {
TimeStart, TimeEnd
Cost
RatingPlan
RateInterval
MinuteInfo
CallDuration  // the call duration so far till TimeEnd
}

The result of this splitting will be a list of TimeSpan structures each having attached the MinuteInfo or the RateInterval that gave the price for it. The CallDuration attribute will select the right Price from the RateInterval Prices list. The final cost for the call will be the sum of the prices of these times spans plus the ConnectionFee from the first time span of the call.

## User balances¶

The user account contains a map of various balances like money, sms, internet traffic, internet time, etc. Each of these lists contains one or more Balance structure that have a wheight and a possible expiration date.

type UserBalance struct {
Type           // prepaid-postpaid
BalanceMap
UnitCounters
ActionTriggers
}

type Balance struct {
Value
ExpirationDate
Weight
}

CGRateS treats special priced or free minutes different from the rest of balances. They will be called free minutes further on but they can have a special price.

The free minutes must be handled a little differently because usually they are grouped by specific destinations (e.g. national minutes, ore minutes in the same network). So they are grouped in balances and when a call is made the engine checks all applicable balances to consume minutes according to that call.

When a call cost needs to be debited these minute balances will be queried for call destination first. If the user has special minutes for the specific destination those minutes will be consumed according to call duration.

A standard debit operation consist of selecting a certaing balance type and taking all balances from that list in the weight order to be debited till the total amount is consumed.

CGRateS provide api for adding/substracting user’s money credit. The prepaid and postpaid are uniformly treated except that the prepaid is checked to be always greater than zero and the postpaid can go bellow zero.

Both prepaid and postpaid can have a limited number of free SMS and Internet traffic per month and this budget is replenished at regular intervals based on the user tariff plan or as the user buys more free SMSs (for example).

Another special feature allows user to get a better price as the call volume increases each month. This can be added on one ore more thresholds so the more he/she talks the cheaper the calls.

Finally bonuses can be rewarded to users who received a certain volume of calls.


text:
# Bellovin

Time Limit: 6000/3000 MS (Java/Others)

Memory Limit: 131072/131072 K (Java/Others)

## Description

Peter has a sequence $a_1,a_2,...,a_n$ and he define a function on the sequence -- $F(a_1,a_2,...,a_n)=(f_1,f_2,...,f_n)$, where $f_i$ is the length of the longest increasing subsequence ending with $a_i$.

Peter would like to find another sequence $b_1,b_2,...,b_n$ in such a manner that $F(a_1,a_2,...,a_n)$ equals to $F(b_1,b_2,...,b_n)$. Among all the possible sequences consisting of only positive integers, Peter wants the lexicographically smallest one.

The sequence $a_1, a_2, ..., a_n$ is lexicographically smaller than sequence $b_1, b_2, ..., b_n$, if there is such number $i$ from $1$ to $n$, that $a_k = b_k$ for $1 \le k < i$ and $a_i < b_i$.

## Input

There are multiple test cases. The first line of input contains an integer $T$, indicating the number of test cases. For each test case:

The first contains an integer $n$ $(1 \le n \le 100000)$ -- the length of the sequence. The second line contains $n$ integers $a_1,a_2,...,a_n$ $(1 \le a_i \le 10^9)$.

## Output

For each test case, output $n$ integers $b_1,b_2,...,b_n$ $(1 \le b_i \le 10^9)$ denoting the lexicographically smallest sequence.

## Sample Input

3
1
10
5
5 4 3 2 1
3
1 3 5

## Sample Output

1
1 1 1 1 1
1 2 3

wange2014

## Source

BestCoder Round #84


text:
# What is the history of adding the Clock Hypothesis to Special Relativity?

As far as I understand, Einstein’s original formulation of Special Relativity did not include the Clock Hypothesis, though it was implicitly assumed.

The modern formulation of SR adds a formal statement of the Clock Hypothesis to Einstein’s original set of two postulates.

When was the need to formalize the Clock Hypothesis identified and when did a three postulate presentation of SR first appear in a standard text?

• The history of adding the Clock Hypothesis to Special Relativity might include negative addition, i.e. rejection, thereof... In henk-reints.nl/astro/HR-Twin-paradox-slides.pdf the Clock Hypothesis is shown to be a superfluous assumption by truly explaining the Twin Paradox. I invite you to read it. Jun 1 '21 at 10:01

Introduction of the clock hypothesis is usually associated with Rindler's Special Relativity (1960), and the current debates over it were sparked after Brown defended its necessity in his book Physical Relativity: Spacetime structure from a dynamical perspective (2005). According to Arthur's chapter Minkowski’s Proper Time and the Status of the Clock Hypothesis, one can trace the hypothesis to a remark Einstein made to Sommerfeld (published in 1913):

"In passing I note that, in their insistence that the clock hypothesis is a separate assumption in SR, Rindler and Brown could appeal to the authority of Einstein. For in the notes he made on Minkowski’s original “Raum und Zeit” paper, Arnold Sommerfeld attributes a remark to Einstein that may indicate the origin of the idea that the clock hypothesis is needed – at any rate it is the first statement of it that I have been able to find. Right after mentioning Minkowski’s remark that $$d\tau$$ is not a complete differential, and noting that this shows that the proper times of two motions connecting two world points will generally differ (as discussed in Sect. 2 above), Sommerfeld adds:

"This assertion is based, as Einstein has stressed, on the (unprovable) assumption that the moving clock actually indicates the proper time, i.e. that at each instant it gives the time that corresponds to the instantaneous state of velocity, regarded as constant. The moving clock must naturally have been moved with acceleration (with changes of velocity or direction) in order to be compared with the stationary clock at the world-point $$P$$.""

Arthur and others (see e.g. Valente, What do light clocks say to us regarding the so-called clock hypothesis?) do not consider the clock hypothesis to be on a par with the standard two postulates of special relativity, but classify it with what philosophers call coordinating principles. Those relate theoretical notions to measured quantities, proper time to clock readings in this case, and are not part of the theory proper. They are needed for any theory to make it testable and not just a mathematical abstraction. Here is Arthur's summary of the positions:

"Rindler claims it is an assumption that it is necessary to make in order to get from “purely kinematic laws about acceleration” to the dynamics of really accelerated systems (1966, 28) and Harvey Brown claims something similar in his recent book (Brown 2005, 9)... On the other hand, Jim Hartle holds that Minkowski’s formula for the proper time holds “even for accelerating clocks, i.e., when the velocity is dependent on the time” (Hartle 2003, 62), and he makes no use of the clock hypothesis in his textbook. Roberto Torretti allows that the clock hypothesis “may be viewed as a conventional definition of what we mean by clock accuracy, and hence by physical time” (1996, 96), but argues that “Special Relativity would doubtless have been rejected or, at any rate, deeply modified, if the clock hypothesis were not fulfilled – to a satisfactory approximation – by the timepieces actually used in physical laboratories”...

On these latter views, provided a given process approximates well enough an ideal clock, the clock hypothesis seems to amount to little more than the desideratum that, with the metric locally Minkowskian, the predictions of SR should agree with experimental fact. So the question is, why should it be necessary to state it as an independent hypothesis? Two main sets of considerations have been adduced. As we have seen, one kind of justification has been that, since many natural clocks are subject to accelerations which result in their failing to satisfy the clock hypothesis, we need to appeal to the hypothesis in passing from the kinematics of acceleration of ideal clocks to the dynamics of really moving clocks. A second kind of justification has to do with the different status of Special Relativity within the General Relativistic context, where, as is shown by the example of Weyl’s unified theory of electromagnetism and gravitation, the metric could be locally Minkowskian and yet the rate of clocks could be path-dependent in such a way that the instantaneous rate would depend on the way the clock had been accelerated hitherto, contrary to the clock hypothesis. This, it is argued, proves the independence of the clock hypothesis from the assumption that spacetime is locally Minkowskian."

• Very surprising. I honestly had no idea that the hypothesis is the subject of such rich debate and that while some modern authors include it, others do not. I'm also surprised by how recently the case for necessity was made. I assume that the necessitarians are the minority.
– nwr
Jan 30 '21 at 19:34
• @Nick Well, there is an air of pedantry to it. And Wikipedia is misleading on this, "it has become a standard assumption and is usually included in the axioms of special relativity" is not how I would describe the state of affairs. Jan 31 '21 at 1:17
• Pedantry seems a fair criticism in this case.
– nwr
Jan 31 '21 at 5:52
• I added some markup for your titles. Jan 31 '21 at 15:40


text:
# How do I get the GUI to draw above the kinematic bodies when their z_index is changed?

Currently the Labels in the GUI are below the player and other objects, so the player appears above them. How can this be fixed?

extends Node
var load_1 = true

var Level = null

Level = level.instance()

move_child(Level, 0)
VisualServer.canvas_item_set_z_index($GUI/Label3, 4000) print("I'm here") func _process(delta): getallnodes(self) pass func set_z_index(node): for N in node.get_children(): if N.get_class() == "KinematicBody2D": N.z_index = N.position.y func getallnodes(node): for N in node.get_children(): if N.get_child_count() > 0 and N.get_class() != "RigidBody2D" and N.get_class() != "KinematicBody2D" and N.get_class() != "StaticBody2D": getallnodes(N) else: if N.get_class() == "KinematicBody2D" or N.get_class() == "RigidBody2D": setZOrder_kine(N) print("- "+N.get_name()) if N.get_class() == "StaticBody2D": setZOrder_kine(N) print("- "+N.get_name()) elif N.get_class() == "Sprite": setZOrder(N) print("- "+N.get_name()) func setZOrder_kine(node): for N in node.get_children(): if N.get_class() == "Sprite": var myPos = N.get_global_transform().get_origin() var mySize = N.get_texture().get_height() var bottomOfSprite = (mySize*N.get_scale().y * N.get_parent().get_scale().y)/(2 * N.vframes) N.get_parent().z_index = (myPos.y+bottomOfSprite) func setZOrder(N): var myPos = N.get_global_transform().get_origin() var mySize = N.get_texture().get_height() var bottomOfSprite = (mySize*N.get_scale().y)/(2 * N.vframes) N.z_index = (myPos.y+bottomOfSprite) func _physics_process(delta): if(Level.get_node("Player")):$GUI/Label.text = "Velocity: " + str(Level.get_node("Player").velocity.length()) + ", " + str(Level.get_node("Player").velocity.x) + ", " + str( Level.get_node("Player").velocity.y)
$GUI/Label2.text = "Position: " + str(Level.get_node("Player").position.x) + ", " + str(Level.get_node("Player").position.y) + ", " + str(Level.get_node("Player").z_index)$GUI/Label3.text = "Sword: " + "rotation " + str(Level.get_node("Player").rotation)
\$GUI/Label4.text = "stamina" + str(Level.get_node("Player").stam.current) + "attack_charge: " + str(Level.get_node("Player").stab.charge.current) + "countdown: " + str(Level.get_node("Player").stab_countdown[0]) + "cooldown: " + str(Level.get_node("Player").stab_cooldown[0])


text:
# Chapter 9 - Inequalities and Problem Solving - 9.2 Intersections, Unions, and Compound Inequalities - 9.2 Exercise Set: 32

$(-8,-2]$

#### Work Step by Step

The graph of the given inequality \begin{array}{l}\require{cancel} -8\lt x \le -2 ,\end{array} includes the points from $-8$ (exclusive) to $-2$ (inclusive). In the graph above, a hollowed dot is used for $\lt$ or $\gt$. A shaded dot is used for $\le$ or $\ge.$ In interval notation, this is equivalent to $(-8,-2] .$

After you claim an answer you’ll have 24 hours to send in a draft. An editor will review the submission and either publish your submission or provide feedback.


text:
Models, code, and papers for "Wei Hua":

##### Dual Adaptivity: A Universal Algorithm for Minimizing the Adaptive Regret of Convex Functions

Jun 26, 2019
Lijun Zhang, Guanghui Wang, Wei-Wei Tu, Zhi-Hua Zhou

To deal with changing environments, a new performance measure---adaptive regret, defined as the maximum static regret over any interval, is proposed in online learning. Under the setting of online convex optimization, several algorithms have been successfully developed to minimize the adaptive regret. However, existing algorithms lack universality in the sense that they can only handle one type of convex functions and need apriori knowledge of parameters. By contrast, there exist universal algorithms, such as MetaGrad, that attain optimal static regret for multiple types of convex functions simultaneously. Along this line of research, this paper presents the first universal algorithm for minimizing the adaptive regret of convex functions. Specifically, we borrow the idea of maintaining multiple learning rates in MetaGrad to handle the uncertainty of functions, and utilize the technique of sleeping experts to capture changing environments. In this way, our algorithm automatically adapts to the property of functions (convex, exponentially concave, or strongly convex), as well as the nature of environments (stationary or changing). As a by product, it also allows the type of functions to switch between rounds.

##### Deep Descriptor Transforming for Image Co-Localization

Reusable model design becomes desirable with the rapid expansion of machine learning applications. In this paper, we focus on the reusability of pre-trained deep convolutional models. Specifically, different from treating pre-trained models as feature extractors, we reveal more treasures beneath convolutional layers, i.e., the convolutional activations could act as a detector for the common object in the image co-localization problem. We propose a simple but effective method, named Deep Descriptor Transforming (DDT), for evaluating the correlations of descriptors and then obtaining the category-consistent regions, which can accurately locate the common object in a set of images. Empirical studies validate the effectiveness of the proposed DDT method. On benchmark image co-localization datasets, DDT consistently outperforms existing state-of-the-art methods by a large margin. Moreover, DDT also demonstrates good generalization ability for unseen categories and robustness for dealing with noisy data.

* Accepted by IJCAI 2017
##### Learning to Simulate Human Movement

Mar 03, 2020
Hua Wei, Zhenhui Li

Modeling how human moves on the space is useful for policy-making in transportation, public safety, and public health. The human movements can be viewed as a dynamic process that human transits between states (e.g., locations) over time. In the human world where both intelligent agents like humans or vehicles with human drivers play an important role, the states of agents mostly describe human activities, and the state transition is influenced by both the human decisions and physical constraints from the real-world system (e.g., agents need to spend time to move over a certain distance). Therefore, the modeling of state transition should include the modeling of the agent's decision process and the physical system dynamics. In this paper, we propose to model state transition in human movement through learning decision model and integrating system dynamics. In experiments on real-world datasets, we demonstrate that the proposed method can achieve superior performance against the state-of-the-art methods in predicting the next state and generating long-term future states.

* 9 pages, 6 figures
##### Theoretical Foundation of Co-Training and Disagreement-Based Algorithms

Aug 15, 2017
Wei Wang, Zhi-Hua Zhou

Disagreement-based approaches generate multiple classifiers and exploit the disagreement among them with unlabeled data to improve learning performance. Co-training is a representative paradigm of them, which trains two classifiers separately on two sufficient and redundant views; while for the applications where there is only one view, several successful variants of co-training with two different classifiers on single-view data instead of two views have been proposed. For these disagreement-based approaches, there are several important issues which still are unsolved, in this article we present theoretical analyses to address these issues, which provides a theoretical foundation of co-training and disagreement-based approaches.

##### On the Consistency of AUC Pairwise Optimization

Jul 02, 2014
Wei Gao, Zhi-Hua Zhou

AUC (area under ROC curve) is an important evaluation criterion, which has been popularly used in many learning tasks such as class-imbalance learning, cost-sensitive learning, learning to rank, etc. Many learning approaches try to optimize AUC, while owing to the non-convexity and discontinuousness of AUC, almost all approaches work with surrogate loss functions. Thus, the consistency of AUC is crucial; however, it has been almost untouched before. In this paper, we provide a sufficient condition for the asymptotic consistency of learning approaches based on surrogate loss functions. Based on this result, we prove that exponential loss and logistic loss are consistent with AUC, but hinge loss is inconsistent. Then, we derive the $q$-norm hinge loss and general hinge loss that are consistent with AUC. We also derive the consistent bounds for exponential loss and logistic loss, and obtain the consistent bounds for many surrogate loss functions under the non-noise setting. Further, we disclose an equivalence between the exponential surrogate loss of AUC and exponential surrogate loss of accuracy, and one straightforward consequence of such finding is that AdaBoost and RankBoost are equivalent.

##### Dropout Rademacher Complexity of Deep Neural Networks

Jul 02, 2014
Wei Gao, Zhi-Hua Zhou

Great successes of deep neural networks have been witnessed in various real applications. Many algorithmic and implementation techniques have been developed, however, theoretical understanding of many aspects of deep neural networks is far from clear. A particular interesting issue is the usefulness of dropout, which was motivated from the intuition of preventing complex co-adaptation of feature detectors. In this paper, we study the Rademacher complexity of different types of dropout, and our theoretical results disclose that for shallow neural networks (with one or none hidden layer) dropout is able to reduce the Rademacher complexity in polynomial, whereas for deep neural networks it can amazingly lead to an exponential reduction of the Rademacher complexity.

* 20 pagea
##### On the Doubt about Margin Explanation of Boosting

Aug 28, 2013
Wei Gao, Zhi-Hua Zhou

Margin theory provides one of the most popular explanations to the success of \texttt{AdaBoost}, where the central point lies in the recognition that \textit{margin} is the key for characterizing the performance of \texttt{AdaBoost}. This theory has been very influential, e.g., it has been used to argue that \texttt{AdaBoost} usually does not overfit since it tends to enlarge the margin even after the training error reaches zero. Previously the \textit{minimum margin bound} was established for \texttt{AdaBoost}, however, \cite{Breiman1999} pointed out that maximizing the minimum margin does not necessarily lead to a better generalization. Later, \cite{Reyzin:Schapire2006} emphasized that the margin distribution rather than minimum margin is crucial to the performance of \texttt{AdaBoost}. In this paper, we first present the \textit{$k$th margin bound} and further study on its relationship to previous work such as the minimum margin bound and Emargin bound. Then, we improve the previous empirical Bernstein bounds \citep{Maurer:Pontil2009,Audibert:Munos:Szepesvari2009}, and based on such findings, we defend the margin-based explanation against Breiman's doubts by proving a new generalization error bound that considers exactly the same factors as \cite{Schapire:Freund:Bartlett:Lee1998} but is sharper than \cite{Breiman1999}'s minimum margin bound. By incorporating factors such as average margin and variance, we present a generalization error bound that is heavily related to the whole margin distribution. We also provide margin distribution bounds for generalization error of voting classifiers in finite VC-dimension space.

* Artificial Intelligence 203:1-18 2013
* 35 pages
##### Multi-View Active Learning in the Non-Realizable Case

Oct 29, 2010
Wei Wang, Zhi-Hua Zhou

The sample complexity of active learning under the realizability assumption has been well-studied. The realizability assumption, however, rarely holds in practice. In this paper, we theoretically characterize the sample complexity of active learning in the non-realizable case under multi-view setting. We prove that, with unbounded Tsybakov noise, the sample complexity of multi-view active learning can be $\widetilde{O}(\log\frac{1}{\epsilon})$, contrasting to single-view setting where the polynomial improvement is the best possible achievement. We also prove that in general multi-view setting the sample complexity of active learning with unbounded Tsybakov noise is $\widetilde{O}(\frac{1}{\epsilon})$, where the order of $1/\epsilon$ is independent of the parameter in Tsybakov noise, contrasting to previous polynomial bounds where the order of $1/\epsilon$ is related to the parameter in Tsybakov noise.

* 22 pages, 1 figure
##### 3D Quasi-Recurrent Neural Network for Hyperspectral Image Denoising

Mar 10, 2020
Kaixuan Wei, Ying Fu, Hua Huang

In this paper, we propose an alternating directional 3D quasi-recurrent neural network for hyperspectral image (HSI) denoising, which can effectively embed the domain knowledge -- structural spatio-spectral correlation and global correlation along spectrum. Specifically, 3D convolution is utilized to extract structural spatio-spectral correlation in an HSI, while a quasi-recurrent pooling function is employed to capture the global correlation along spectrum. Moreover, alternating directional structure is introduced to eliminate the causal dependency with no additional computation cost. The proposed model is capable of modeling spatio-spectral dependency while preserving the flexibility towards HSIs with arbitrary number of bands. Extensive experiments on HSI denoising demonstrate significant improvement over state-of-the-arts under various noise settings, in terms of both restoration accuracy and computation time. Our code is available at https://github.com/Vandermode/QRNN3D.

* Accepted by IEEE Transactions on Neural Network and Learning System (TNNLS), 2020
##### Global-Local Metamodel Assisted Two-Stage Optimization via Simulation

Oct 13, 2019
Wei Xie, Yuan Yi, Hua Zheng

To integrate strategic, tactical and operational decisions, the two-stage optimization has been widely used to guide dynamic decision making. In this paper, we study the two-stage stochastic programming for complex systems with unknown response estimated by simulation. We introduce the global-local metamodel assisted two-stage optimization via simulation that can efficiently employ the simulation resource to iteratively solve for the optimal first- and second-stage decisions. Specifically, at each visited first-stage decision, we develop a local metamodel to simultaneously solve a set of scenario-based second-stage optimization problems, which also allows us to estimate the optimality gap. Then, we construct a global metamodel accounting for the errors induced by: (1) using a finite number of scenarios to approximate the expected future cost occurring in the planning horizon, (2) second-stage optimality gap, and (3) finite visited first-stage decisions. Assisted by the global-local metamodel, we propose a new simulation optimization approach that can efficiently and iteratively search for the optimal first- and second-stage decisions. Our framework can guarantee the convergence of optimal solution for the discrete two-stage optimization with unknown objective, and the empirical study indicates that it achieves substantial efficiency and accuracy.

##### BERT-based Ranking for Biomedical Entity Normalization

Aug 09, 2019
Zongcheng Ji, Qiang Wei, Hua Xu

Developing high-performance entity normalization algorithms that can alleviate the term variation problem is of great interest to the biomedical community. Although deep learning-based methods have been successfully applied to biomedical entity normalization, they often depend on traditional context-independent word embeddings. Bidirectional Encoder Representations from Transformers (BERT), BERT for Biomedical Text Mining (BioBERT) and BERT for Clinical Text Mining (ClinicalBERT) were recently introduced to pre-train contextualized word representation models using bidirectional Transformers, advancing the state-of-the-art for many natural language processing tasks. In this study, we proposed an entity normalization architecture by fine-tuning the pre-trained BERT / BioBERT / ClinicalBERT models and conducted extensive experiments to evaluate the effectiveness of the pre-trained models for biomedical entity normalization using three different types of datasets. Our experimental results show that the best fine-tuned models consistently outperformed previous methods and advanced the state-of-the-art for biomedical entity normalization, with up to 1.17% increase in accuracy.

* 9 pages, 1 figure, 4 tables
##### Optimal Control of a Differentially Flat 2D Spring-Loaded Inverted Pendulum Model

Nov 17, 2019
Hua Chen, Patrick M. Wensing, Wei Zhang

This paper considers the optimal control problem of an extended spring-loaded inverted pendulum (SLIP) model with two additional actuators for active leg length and hip torque modulation. These additional features arise naturally in practice, allowing for consideration of swing leg kinematics during flight and active control over stance dynamics. On the other hand, nonlinearity and the hybrid nature of the overall SLIP dynamics introduce challenges in the analysis and control of the model. In this paper, we first show that the stance dynamics of the considered SLIP model are differentially flat, which has a strong implication regarding controllability of the stance dynamics. Leveraging this powerful property, a tractable optimal control strategy is developed. This strategy enables online solution while also treating the hybrid nature of the SLIP dynamics. Together with the optimal control strategy, the extended SLIP model grants active disturbance rejection capability at any point during the gait. Performance of the proposed control strategy is demonstrated via numerical tests and shows significant advantage over existing methods.

##### A Continuously Growing Dataset of Sentential Paraphrases

Aug 01, 2017
Wuwei Lan, Siyu Qiu, Hua He, Wei Xu

A major challenge in paraphrase research is the lack of parallel corpora. In this paper, we present a new method to collect large-scale sentential paraphrases from Twitter by linking tweets through shared URLs. The main advantage of our method is its simplicity, as it gets rid of the classifier or human in the loop needed to select data before annotation and subsequent application of paraphrase identification algorithms in the previous work. We present the largest human-labeled paraphrase corpus to date of 51,524 sentence pairs and the first cross-domain benchmarking for automatic paraphrase identification. In addition, we show that more than 30,000 new sentential paraphrases can be easily and continuously captured every month at ~70% precision, and demonstrate their utility for downstream NLP tasks through phrasal paraphrase extraction. We make our code and data freely available.

* 11 pages, accepted to EMNLP 2017
##### A Physics-based Noise Formation Model for Extreme Low-light Raw Denoising

Mar 28, 2020
Kaixuan Wei, Ying Fu, Jiaolong Yang, Hua Huang

Lacking rich and realistic data, learned single image denoising algorithms generalize poorly to real raw images that do not resemble the data used for training. Although the problem can be alleviated by the heteroscedastic Gaussian model for noise synthesis, the noise sources caused by digital camera electronics are still largely overlooked, despite their significant effect on raw measurement, especially under extremely low-light condition. To address this issue, we present a highly accurate noise formation model based on the characteristics of CMOS photosensors, thereby enabling us to synthesize realistic samples that better match the physics of image formation process. Given the proposed noise model, we additionally propose a method to calibrate the noise parameters for available modern digital cameras, which is simple and reproducible for any new device. We systematically study the generalizability of a neural network trained with existing schemes, by introducing a new low-light denoising dataset that covers many modern digital cameras from diverse brands. Extensive empirical results collectively show that by utilizing our proposed noise formation model, a network can reach the capability as if it had been trained with rich real data, which demonstrates the effectiveness of our noise formation model.

* Accepted to CVPR 2020 (oral); code is available at https://github.com/Vandermode/NoiseModel
##### A Probabilistic Simulator of Spatial Demand for Product Allocation

Jan 09, 2020
Porter Jenkins, Hua Wei, J. Stockton Jenkins, Zhenhui Li

Connecting consumers with relevant products is a very important problem in both online and offline commerce. In physical retail, product placement is an effective way to connect consumers with products. However, selecting product locations within a store can be a tedious process. Moreover, learning important spatial patterns in offline retail is challenging due to the scarcity of data and the high cost of exploration and experimentation in the physical world. To address these challenges, we propose a stochastic model of spatial demand in physical retail. We show that the proposed model is more predictive of demand than existing baselines. We also perform a preliminary study into different automation techniques and show that an optimal product allocation policy can be learned through Deep Q-Learning.

* 8 pages, The AAAI-20 Workshop on Intelligent Process Automation
##### A Survey on Traffic Signal Control Methods

Apr 17, 2019
Hua Wei, Guanjie Zheng, Vikash Gayah, Zhenhui Li

Traffic signal control is an important and challenging real-world problem, which aims to minimize the travel time of vehicles by coordinating their movements at the road intersections. Current traffic signal control systems in use still rely heavily on oversimplified information and rule-based methods, although we now have richer data, more computing power and advanced methods to drive the development of intelligent transportation. With the growing interest in intelligent transportation using machine learning methods like reinforcement learning, this survey covers the widely acknowledged transportation approaches and a comprehensive list of recent literature on reinforcement for traffic signal control. We hope this survey can foster interdisciplinary research on this important topic.

* 30 pages
##### A Compositional Textual Model for Recognition of Imperfect Word Images

Nov 27, 2018
Wei Tang, John Corring, Ying Wu, Gang Hua

Printed text recognition is an important problem for industrial OCR systems. Printed text is constructed in a standard procedural fashion in most settings. We develop a mathematical model for this process that can be applied to the backward inference problem of text recognition from an image. Through ablation experiments we show that this model is realistic and that a multi-task objective setting can help to stabilize estimation of its free parameters, enabling use of conventional deep learning methods. Furthermore, by directly modeling the geometric perturbations of text synthesis we show that our model can help recover missing characters from incomplete text regions, the bane of multicomponent OCR systems, enabling recognition even when the detection returns incomplete information.

##### Matrix Linear Discriminant Analysis

Sep 24, 2018
Wei Hu, Weining Shen, Hua Zhou, Dehan Kong

We propose a novel linear discriminant analysis approach for the classification of high-dimensional matrix-valued data that commonly arises from imaging studies. Motivated by the equivalence of the conventional linear discriminant analysis and the ordinary least squares, we consider an efficient nuclear norm penalized regression that encourages a low-rank structure. Theoretical properties including a non-asymptotic risk bound and a rank consistency result are established. Simulation studies and an application to electroencephalography data show the superior performance of the proposed method over the existing approaches.

##### On the Resistance of Nearest Neighbor to Random Noisy Labels

Sep 13, 2018
Wei Gao, Bin-Bin Yang, Zhi-Hua Zhou

Nearest neighbor has always been one of the most appealing non-parametric approaches in machine learning, pattern recognition, computer vision, etc. Previous empirical studies partly shows that nearest neighbor is resistant to noise, yet there is a lack of deep analysis. This work presents the finite-sample and distribution-dependent bounds on the consistency of nearest neighbor in the random noise setting. The theoretical results show that, for asymmetric noises, k-nearest neighbor is robust enough to classify most data correctly, except for a handful of examples, whose labels are totally misled by random noises. For symmetric noises, however, k-nearest neighbor achieves the same consistent rate as that of noise-free setting, which verifies the resistance of k-nearest neighbor to random noisy labels. Motivated by the theoretical analysis, we propose the Robust k-Nearest Neighbor (RkNN) approach to deal with noisy labels. The basic idea is to make unilateral corrections to examples, whose labels are totally misled by random noises, and classify the others directly by utilizing the robustness of k-nearest neighbor. We verify the effectiveness of the proposed algorithm both theoretically and empirically.

* 35 pages
##### Segmentation of ultrasound images of thyroid nodule for assisting fine needle aspiration cytology

Nov 03, 2012
Jie Zhao, Wei Zheng, Li Zhang, Hua Tian

The incidence of thyroid nodule is very high and generally increases with the age. Thyroid nodule may presage the emergence of thyroid cancer. The thyroid nodule can be completely cured if detected early. Fine needle aspiration cytology is a recognized early diagnosis method of thyroid nodule. There are still some limitations in the fine needle aspiration cytology, and the ultrasound diagnosis of thyroid nodule has become the first choice for auxiliary examination of thyroid nodular disease. If we could combine medical imaging technology and fine needle aspiration cytology, the diagnostic rate of thyroid nodule would be improved significantly. The properties of ultrasound will degrade the image quality, which makes it difficult to recognize the edges for physicians. Image segmentation technique based on graph theory has become a research hotspot at present. Normalized cut (Ncut) is a representative one, which is suitable for segmentation of feature parts of medical image. However, how to solve the normalized cut has become a problem, which needs large memory capacity and heavy calculation of weight matrix. It always generates over segmentation or less segmentation which leads to inaccurate in the segmentation. The speckle noise in B ultrasound image of thyroid tumor makes the quality of the image deteriorate. In the light of this characteristic, we combine the anisotropic diffusion model with the normalized cut in this paper. After the enhancement of anisotropic diffusion model, it removes the noise in the B ultrasound image while preserves the important edges and local details. This reduces the amount of computation in constructing the weight matrix of the improved normalized cut and improves the accuracy of the final segmentation results. The feasibility of the method is proved by the experimental results.

* 15pages,13figures


text:
## LatinX in AI (LXAI) Research at ICML 2021

### Maria Luisa Santiago · Miguel Alonso Jr · Laura Montoya · William Berrios · Fiorela Manco Fernández · Diana Diaz · Vinicius Caridá · LOURDES RAMIREZ CERNA · Pedro Braga · Gabriel Ramos · Leonel Rozo · Walter Mayor · Vanessa Gilede · Dennis Hernando Núñez Fernández · Erick Mendez Guzman · Paola Cascante-Bonilla

Abstract:

Launched in January 2018, leaders from academia and industry in Artificial Intelligence, Education, Research, Engineering, and Social Impact banded together to create a group that would be focused on “Creating Opportunity for LatinX in AI.”

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##### Schedule
 Mon 8:00 a.m. - 8:15 a.m. Opening Remarks (Remark) Miguel Alonso Jr 🔗 Mon 8:15 a.m. - 8:45 a.m. Marynel Vázquez (Talk) Marynel Vázquez 🔗 Mon 8:45 a.m. - 8:55 a.m. Marynel Vázquez Q&A (Q&A) Marynel Vázquez 🔗 Mon 8:55 a.m. - 9:25 a.m. Spotlights 1 (Spotlight) Presentation: Computation of Discrete Flows Over Networks via Constrained Wasserstein Barycenters Sparse Data Generation for Particle-Based Simulation of Hadronic Jets in the LHC Image Inpainting Applied to Art: Completing Escher’s Print Gallery Anticipating faults by predicting non-linearity of environment variables with neural networks: a case study in semiconductor manufacturing OCDE: Odds Conditional Density Estimator Deep Neural Network Uncertainty Estimation with Stochastic Inputs for Robust Aerial Navigation Policies 🔗 Mon 8:55 a.m. - 9:00 a.m. Computation of Discrete Flows Over Networks via Constrained Wasserstein Barycenters (Poster)    We study a Wasserstein attraction approach for solving dynamic mass transport problems over networks. In the transport problem over networks, we start with a distribution over the set of nodes that needs to be "transported" to a target distribution accounting for the network topology. We exploit the specific structure of the problem, characterized by the computation of implicit gradient steps, and formulate an approach based on discretized flows. As a result, our proposed algorithm relies on the iterative computation of constrained Wasserstein barycenters. We show how the proposed method finds approximate solutions to the network transport problem, taking into account the topology of the network, the capacity of the communication channels, and the capacity of the individual nodes. Ferran Arque · Cesar Uribe 🔗 Mon 9:00 a.m. - 9:05 a.m. Sparse Data Generation for Particle-Based Simulation of Hadronic Jets in the LHC (Poster)    We develop a generative neural network for the generation of sparse data in particle physics using a permutation-invariant and physics-informed loss function. The input dataset used in this study consists of the particle constituents of hadronic jets due to its sparsity and the possibility of evaluating the network's ability to accurately describe the particles and jets properties. A variational autoencoder composed of convolutional layers in the encoder and decoder is used as the generator. The loss function consists of a reconstruction error term and the Kullback-Leibler divergence between the output of the encoder and the latent vector variables. The permutation-invariant loss on the particles' properties is combined with two mean-squared error terms that measure the difference between input and output jets mass and transverse momentum, which improves the network's generation capability as it imposes physics constraints, allowing the model to learn the kinematics of the jets. Breno Orzari · Thiago Tomei · Maurizio Pierini · Mary Touranakou · Javier Duarte · Raghav Kansal · Dimitrios Gunopulos · jean-roch vlimant 🔗 Mon 9:05 a.m. - 9:10 a.m. Image Inpainting Applied to Art: Completing Escher’s Print Gallery (Poster)    This work presents the first stages of a research in inpainting suited for art reconstruction. We introduce M.C Escher’s Print Gallery lithography as a use-case example. This artwork presents a void on its center and additionally, it follows a challenging mathematical structure that needs to be preserved by the inpainting method. We present our work so far and our future line of research. Lucia Cipolina Kun 🔗 Mon 9:10 a.m. - 9:15 a.m. Anticipating faults by predicting non-linearity of environment variables with neural networks: a case study in semiconductor manufacturing (Poster)    The present work proposes a neural network model capable of anticipating possible faults in a semiconductor manufacturing plant by predicting non-linearity spikes in sensor data. Early detection of significant variation can be crucial for identifying machinery degradation or issues in the process itself. We use non-linearity as it is not affected by regular process changes and autocorrelation, thus avoiding false-positives in the neural network caused by changes in demand and the presence of control systems. The developed model is able to predict up to 30min of future non-linearity with loss ≤ 0.5. Furthermore, the proposed model is flexible enough to present itself as a starting point for future work in the field of fault detection in other areas. Mateus Melchiades · Gabriel Ramos · Lincoln Schreiber 🔗 Mon 9:15 a.m. - 9:20 a.m. OCDE: Odds Conditional Density Estimator (Poster)    Conditional density estimation (CDE) models can be useful for many statistical applications, especially because the full conditional density is estimated instead of point estimates, revealing more information about the uncertainty of random variables. In this paper, we propose a new methodology called Odds Conditional Density Estimator (OCDE) to estimate conditional densities in a supervised learning scheme. The main idea is that it is very difficult to estimate p{x,y} and p{x} in order to have the conditional density p_{y|x}, but by introducing an instrumental distribution, we transform the CDE problem into a problem of odds estimation, or similarly, training a binary probabilistic classifier. We demonstrate how OCDE works using simulated data and then test its performance against other known state-of-the-art CDE methods in real data. Overall, OCDE is competitive compared with these methods in real datasets. Alex Aki Okuno · Felipe Polo 🔗 Mon 9:20 a.m. - 9:25 a.m. Deep Neural Network Uncertainty Estimation with Stochastic Inputs for Robust Aerial Navigation Policies (Poster)    It is well-known in the literature that uncertainty estimation methods are required in robotic autonomous systems that include deep learning (DL) components to assess the confidence in the outputs. However, to successfully deploy DL components in autonomous systems, they should also handle uncertainty at the input rather than only at the output. In this paper, we present a method to account for uncertainty at the input of Bayesian Deep Learning control policies for Aerial Navigation. Our experiments show that the proposed method improves the robustness of the navigation policy in out-of-distribution (OoD) scenarios. Fabio Arnez Yagualca · Huascar Espinoza · François Terrier 🔗 Mon 9:25 a.m. - 9:35 a.m. Spotlights 1 Q&A (Q&A) Leonel Rozo 🔗 Mon 9:35 a.m. - 9:45 a.m. Break 🔗 Mon 9:45 a.m. - 10:15 a.m. Celia Cintas (Talk) Celia Cintas 🔗 Mon 10:15 a.m. - 10:25 a.m. Celia Cintas Q&A (Q&A) Celia Cintas 🔗 Mon 10:25 a.m. - 10:55 a.m. Spotlights 2 (Spotlights) Presentation: A multiple strategy for plant species identification using images of leaf texture Ceramic Cracks Segmentation with Deep Learning Towards Explainable Deep Reinforcement Learning for Traffic Signal Control Spatial Attention Adapted to a LSTM Architecture with Frame Selection for Human Action Recognition in Videos Minimum-Delay Adaptation in Non-Stationary Reinforcement Learning via Online High-Confidence Change-Point Detection Generalized linear tree: a flexible algorithm for predicting continuous variables 🔗 Mon 10:25 a.m. - 10:30 a.m. A multiple strategy for plant species identification using images of leaf texture (Poster)    In our planet there are thousands of plant species, being important to catalog these to help in the biodiversity preservation. However, identifying various plant species is not an easy task, even for specialists. Methods of computer vision for identifying plant species are interesting solutions for these difficulties. This work aims to analyze the efficiency of texture feature extraction methods applied in the identification of plant species by means of images of its leaves. For this, different texture descriptors were applied in three different databases. The obtained results indicate that local phase quantization (LPQ)-based methods achieve great efficiency and robustness. Additionally, the combination of LPQ-based methods with a segmentation based fractal texture analysis (SFTA) has increased the correct classification rate in all databases. Igor Luidji Turra · Sérgio Francisco Silva · Douglas Cordeiro · Núbia Da Silva 🔗 Mon 10:30 a.m. - 10:35 a.m. Ceramic Cracks Segmentation with Deep Learning (Poster)    Cracks are pathologies whose appearance in ceramic tiles can cause various types of scratches due to the coating system losing water tightness and impermeability functions. Manual inspection is the most common method for this problem. However, it depends on the knowledge and experience of those who perform the analysis and demands a long time to map the entire area and high cost. These inspections require special equipment when they are at high altitudes, and the integrity of the inspector is at risk. Thus, there exists a need for automated optical inspection to find faults in ceramic tiles. This work focuses on the segmentation of cracks in ceramic images. We propose an architecture for segmenting cracks in facades with Deep Learning that includes a pre-processing step. We also propose the Ceramic Crack Database, a set of images to segment defects in ceramic tiles. The results show that the proposed architecture for ceramic crack segmentation achieves promising performance. Gerivan Junior · Janderson Ferreira · Cristian Millán · Ramiro Ruiz · Alberto Junior · Bruno Fernandes 🔗 Mon 10:35 a.m. - 10:40 a.m. Towards Explainable Deep Reinforcement Learning for Traffic Signal Control (Poster)    Deep reinforcement learning has shown potential for traffic signal control. However, the lack of explainability has limited its use in real-world conditions. In this work, we present a Deep Q-learning approach, with the SHAP framework, able to explain its policy. Our approach can explain the impact of features on each action, which promotes the understanding of how the agent behaves in the face of different traffic conditions. Furthermore, our approach improved travel time, waiting time, and speed by 21.49%, 27.97%, 20.87%, compared to fixed-time traffic signal controllers. Lincoln Schreiber · Gabriel Ramos · Ana Lucia Cetertich Bazzan 🔗 Mon 10:40 a.m. - 10:45 a.m. Spatial Attention Adapted to a LSTM Architecture with Frame Selection for Human Action Recognition in Videos (Poster)    Action recognition in videos is currently a topic of interest in the area of computer vision, due to potential applications such as: multimedia indexing, surveillance in public spaces, among others. In this work we propose an attention mechanism adapted to a CNN--LSTM base architecture. To carry out the training and testing phases, we used the HMDB-51 and UCF-101 datasets. We evaluate the performance of our system using accuracy as the evaluation metric, obtaining $57.3\%$ and $90.4\%$ for HMDB-51 and UCF-101 respectively. Carlos Ismael Orozco · María Elena Buemi · Julio Jacobo Berlles 🔗 Mon 10:45 a.m. - 10:50 a.m. Minimum-Delay Adaptation in Non-Stationary Reinforcement Learning via Online High-Confidence Change-Point Detection (Poster)    Non-stationary environments are challenging for reinforcement learning algorithms. If the state transition and/or reward functions change based on latent factors, the agent is effectively tasked with optimizing a behavior that maximizes performance over a possibly infinite random sequence of Markov Decision Processes (MDPs), each of which drawn from some unknown distribution. We call each such MDP a context. We introduce an algorithm that analyzes a possibly infinite stream of data and computes, in real-time, high-confidence change-point detection statistics that reflect whether novel, specialized policies need to be created and deployed to tackle novel contexts, or whether previously-optimized ones might be reused. We show that (i) this algorithm minimizes the delay until unforeseen changes to a context are detected, thereby allowing for rapid responses; and (ii) it bounds the rate of false alarm, which is important in order to minimize regret. Lucas Alegre · Ana Lucia Cetertich Bazzan · Bruno C. da Silva 🔗 Mon 10:50 a.m. - 10:55 a.m. Generalized linear tree: a flexible algorithm for predicting continuous variables (Poster)    Tree-based models are popular among regression methods to predict continuous variables. Also, Generalized Linear Models (GLMs) are pretty standard in many statistical applications and provide a generalization to many of the most commonly applied statistical procedures. However, in most regression tree methods, there is only one theoretical model associated for prediction in the final nodes, like multiple linear regression, logistic regressions, polynomial models, Poisson models, among others. We, therefore, propose a new tree method in which we estimate a GLM in each leaf node of the estimated tree including variable selection, new hyperparameters optimization, and tree pruning. Our method (GLT) has shown to be competitive compared to other well-known regression methods in real datasets, with the advantages and estimation flexibility provided by GLMs. Alberto Rodrigues Ferreira · Alex Aki Okuno 🔗 Mon 10:55 a.m. - 11:05 a.m. Spotlight 2 Q&A (Q&A) Cesar Uribe 🔗 Mon 11:05 a.m. - 12:05 p.m. Mentoring Session 1 (Mentoring) Pedro Braga · Diana Diaz · Vinicius Caridá 🔗 Mon 12:05 p.m. - 1:05 p.m. Long (lunch + sponsors) break (Break)  link » 🔗 Mon 1:05 p.m. - 1:35 p.m. Estevam R. Hruschka Jr (Talk) Estevam R. Hruschka Jr 🔗 Mon 1:35 p.m. - 1:45 p.m. Estevam R. Hruschka Jr Q&A (Talk) Estevam R. Hruschka Jr 🔗 Mon 1:45 p.m. - 2:15 p.m. Spotlights 3 (Spotlight) 🔗 Mon 1:45 p.m. - 1:50 p.m. Mask-net: Detection of Correct Use of Masks Through Computer Vision (Poster)    This paper focuses on creating a system for recognizing the correct use of a mask through computer vision techniques. Research was carried out with aims of establishing the criteria for the creation of custom datasets, which were used to train, validate and test a pair of deep learning models, Mask-net and I-Mask-net. Both were designed with similar architectures, making use of Transfer Learning Techniques. The results given by training showed that the fine tuning carried out was adequate, while the tests carried out showed that the models have an acceptable level of accuracy, reaching 85.47% for Mask-net and 85.96% for I-Mask-net, additionally supported by the obtained precision, recall and F1-Score calculations. Alexander Kalen · Alberto Landi · Nicolas Araque · Alejandro Marcano 🔗 Mon 1:50 p.m. - 1:55 p.m. Community pooling: LDA topic modeling in Twitter (Poster)    Social networks play a fundamental role in propagation of information and news. Characterizing the content of the messages becomes vital for tasks like fake news detection or personalized message recommendation. However, Twitter posts are short and often less coherent than other text documents, which makes it challenging to apply text mining algorithms efficiently. We propose a new pooling scheme for topic modeling in Twitter, which groups tweets whose authors belong to the same community on the retweet network into a single document. Our findings contribute to an improved methodology for identifying the latent topics in a Twitter dataset, without modifying the basic machinery of a topic decomposition model. In particular, we used Latent Dirichlet Allocation (LDA) and empirically showed that this novel method achieves better results than previous pooling methods in terms of cluster quality, document retrieval tasks, supervised machine learning classification and overall run time. Federico Albanese 🔗 Mon 1:55 p.m. - 2:00 p.m. Effects of personality traits in predicting grade retention of Brazilian students (Poster)    Student's grade retention is a key issue faced by many education systems, especially those in developing countries. In this paper, we seek to gauge the relevance of students' personality traits in predicting grade retention in Brazil. For that, we used data collected in 2012 and 2017, in the city of Sertãozinho, countryside of the state of São Paulo, Brazil. The surveys taken in Sertãozinho included several socioeconomic questions, standardized tests, and a personality test. Moreover, students were in grades 4, 5, and 6 in 2012. Our approach was based on training machine learning models on the surveys’ data to predict grade retention between 2012 and 2017 using information from 2012 or before, and then using some strategies to quantify personality traits' predictive power. We concluded that, besides proving to be fairly better than a random classifier when isolated, personality traits contribute to prediction even when using socioeconomic variables and standardized tests results. Lucka G Gianvechio · Carmen Toledo · Jonathan Ferreira · Felipe Polo · Renato Vicente 🔗 Mon 2:00 p.m. - 2:05 p.m. Aspect-based Sentiment Analysis using BERT with Disentangled Attention (Poster)    Aspect-Based Sentiment Analysis (ABSA) tasks aim to identify consumers' opinions about different aspects of products or services. BERT-based language models have been used successfully in applications that require a deep understanding of the language, such as sentiment analysis. This paper investigates the use of disentangled learning to improve BERT-based textual representations in ABSA tasks. Motivated by the success of disentangled representation learning in the field of computer vision, which aims to obtain explanatory factors of the data representations, we explored the recent DeBERTa model (Decoding-enhanced BERT with Disentangled Attention) to disentangle the syntactic and semantics features from a BERT architecture. Experimental results show that incorporating disentangled attention and a simple fine-tuning strategy for downstream tasks outperforms state-of-the-art models in ABSA's benchmark datasets. Ricardo Marcacini 🔗 Mon 2:05 p.m. - 2:10 p.m. Convolutional Neural Networks Evaluation for COVID-19 Classification on Chest Radiographs (Poster)    Early identification of patients with COVID-19 is essential to enable adequate treatment and to reduce the burden on the health system. The gold standard for COVID-19 detection is the use of RT-PCR tests. However, due to the high demand for tests, these can take days or even weeks in some regions of Brazil. Thus, an alternative for the detection of COVID-19 is the analysis of Chest X-rays (CXR). This paper proposes the evaluation of convolutional neural networks to identify pneumonia due to COVID-19 in CXR. The proposed methodology consists of an evaluation of six convolutional architectures pre-trained with the ImageNet dataset: InceptionResNetV2, InceptionV3, MovileNetV2, ResNet50, VGG16, and Xception. The obtained results for our methodology demonstrate that the Xception architecture presented a superior performance in the classification of CXR, with an Accuracy of 85.64%, Sensitivity of 85.71%, Specificity of 85.65%, F1-score of 85.49%, and an AUC of 0.9648. Felipe Zeiser · Cristiano André da Costa · Gabriel Ramos 🔗 Mon 2:10 p.m. - 2:15 p.m. Vehicle-counting with automatic Region-of-Interest and Driving-Trajectory detection (Poster)    Vehicle counting systems can help with vehicle analysis and traffic incident detection. Unfortunately, most existing methods require some level of human input to identify the Region of interest (ROI), movements of interest, or to establish a reference point or line to count vehicles from traffic cameras. This work introduces a method to count vehicles from traffic videos that automatically identifies the ROI for the camera, as well as the driving trajectories of the vehicles. This makes the method feasible to use with Pan-Tilt-Zoom cameras, which are frequently used in developing countries. Preliminary results indicate that the proposed method achieves an average intersection over the union of 57.05% for the ROI and a mean absolute error of just 17.44% at counting vehicles of the traffic video cameras tested. Malolan Vasu · Christian Lopez 🔗 Mon 2:15 p.m. - 2:25 p.m. Spotlights 3 Q&A (Q&A) Juan Banda 🔗 Mon 2:25 p.m. - 2:35 p.m. Break 🔗 Mon 2:35 p.m. - 3:05 p.m. Prof. Ana L.C. Bazzan (Talk) Ana Lucia Cetertich Bazzan 🔗 Mon 3:05 p.m. - 3:15 p.m. Prof. Ana L.C. Bazzan Q&A (Q&A) Ana Lucia Cetertich Bazzan 🔗 Mon 3:15 p.m. - 3:45 p.m. Spotlights 4 (Spotlights) 🔗 Mon 3:15 p.m. - 3:20 p.m. A Tree-Adaptation Mechanism for Covariate and Concept Drift (Poster)    Although Machine Learning algorithms are solving tasks of ever-increasing complexity, gathering data and building training sets remains an error-prone, costly, and difficult problem. However, reusing knowledge from related previously-solved tasks enables reducing the amount of data required to learn a new task. We here propose a method for reusing a tree-based model learned in a source task with abundant data in a target task with scarce data. We perform an empirical evaluation showing that our method is useful, especially in scenarios where the labels are unavailable in the target task. Felipe Leno da Silva · Renato Vicente 🔗 Mon 3:20 p.m. - 3:25 p.m. GAN-based Data Mapping for Model Adaptation (Poster)    Although Machine Learning algorithms are solving tasks of ever-increasing complexity, gathering data and building training sets remains an error prone, costly, and difficult problem. However, reusing knowledge from related previously-solved tasks enables reducing the amount of data required to learn a new task. We here propose a method for learning a mapping model that maps data from a source task with labeled data to a related target task with only unlabeled data. We perform an empirical evaluation showing that our method achieves performance comparable to a model learned directly in the target task. Felipe Leno da Silva · Ruben Glatt · Renato Vicente 🔗 Mon 3:25 p.m. - 3:30 p.m. Population Dynamics for Discrete Wasserstein Gradient Flows over Networks (Poster)    We study the problem of minimizing a convex function over probability measures supported in a graph. We build upon the recent formulation of optimal transport over discrete domains to propose a method that generates a sequence that provably converges to a minimum of the objective function and smoothly transports mass over the edges of the graph. Moreover, we identify novel relation between Riemannian gradient flows and perturbed best response protocols that provide sufficient conditions for the convergence of the proposed algorithm. Numerical results show practical advantages over existing approaches with respect to the implementability and convergence rates. Gilberto Díaz-García · Cesar Uribe · Nicanor Quijano 🔗 Mon 3:30 p.m. - 3:35 p.m. Model Reference Adaptive Control for Online Policy Adaptation and Network Synchronization (Poster)    We propose an online adaptive synchronization method for leader-follower networks of heterogeneous agents. Synchronization is achieved using a distributed Model Reference Adaptive Control (DMRAC-RL) that enables the improved performance of Reinforcement Learning (RL)-trained policies on a reference model. The leader observes the performance of the reference model, and the followers observe the states and actions of the agents they are connected to, but not the reference model. Notably, both the leader and followers models might differ from the reference model the RL-control policy was trained. DMRAC-RL uses an internal loop that adjusts the learned policy for the agents in the form of an augmented input to solve the distributed control problem. Numerical examples of the synchronization of a network of inverted pendulums support our theoretical findings. Miguel F. Arevalo-Castiblanco · Cesar Uribe · Eduardo Mojica-Nava 🔗 Mon 3:35 p.m. - 3:40 p.m. Computation-Aware Distributed Optimization over Networks: A Hybrid Dynamical Systems Approach (Poster)    We study the robustness properties of computationally-aware dual-based distributed optimization algorithm over networks. Contrary to existing literature, we follow a hybrid dynamical systems approach to analyze the stability properties of the distributed Nesterov's ODE when explicitly taking into account the computational resources and time required by a dual first-order oracle to generates an approximate gradient. We show that in such scenario, the distributed Nesterov's ODE is unstable in the Lyapunov sense, i.e., there exist an arbitrarily bounded perturbation function for which the inexact oracle drives the system unstable. Moreover, we propose modified dynamics that are provable stable and robust, and which provably minimizes smooth and strongly convex functions. Daniel Ochoa · Jorge Poveda · Cesar Uribe 🔗 Mon 3:40 p.m. - 3:45 p.m. Long Short-Term Memory with Slower Information Decay (Poster)    Learning to process long-range dependencies has been a challenge for recurrent neural networks. Despite improvements achieved by long short-term memory (LSTMs), its gating mechanism results in exponential decay of information, limiting their capacity of capturing long-range dependencies. In this work, we present a power law forget gate, which instead has a slower rate of information decay. We propose a power law-based LSTM (pLSTM) based on the LSTM but with a power law forget gate. We test empirically the pLSTM on the copy task, sentiment classification, and sequential MNIST, all with long-range dependency tasks. The pLSTM solves these tasks outperforming an LSTM, specially for long-range dependencies. Further, the pLSTM learns sparser and more robust representations. Hsiang-Yun Chien · Javier Turek · Nicole Beckage · Vy Vo · Christopher Honey · Theodore Willke 🔗 Mon 3:45 p.m. - 3:55 p.m. Spotlights 4 Q&A (Q&A) Gabriel Ramos 🔗 Mon 3:55 p.m. - 4:55 p.m. Mentoring II (Mentoring) Pedro Braga · Diana Diaz · Vinicius Caridá · LOURDES RAMIREZ CERNA · Paola Cascante-Bonilla 🔗 Mon 4:55 p.m. - 5:00 p.m. Closing Remarks (Remarks) Gabriel Ramos 🔗 Mon 5:00 p.m. - Social Gathering  link » 🔗


text:
# Electronic Transitions

1. Dec 13, 2005

### rushil

It's a well known fact from Quantum Mechanics that electrons exist in fixed, quantised energy states. Also, when they move from one energy level to another, they emit or absorb the energy as required by $$\triangle E$$. Also, if a particular photon is absorbed , the apprpriate transition takes place. My question is regarding the second phenomenon. Suppose we consider a H atom s.t. the energy difference between 1st and 2nd energy levels is 13.6 eV and theat between 2nd and 3rd is 3.4 eV . What if we give Energy =
(i} 13.7 eV
(ii)13.3 eV
(iii) 4 eV... etc..??
Do electrons jump and then come back if the energy difference is not appropraite or do they just stay in position and don't jump if the PRECISE Energy is not provided???

2. Dec 13, 2005

### Staff: Mentor

If the incoming photon does not have just the right energy to send an electron from its current energy state to a higher energy state, nothing happens.

3. Dec 13, 2005

### rushil

Nothing happens... or the electron jumps but is not able to reach the required level and instantaneously returns... maybe this is so fast, our present equipments cant measure such a short time interval!!!

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# Dense Subsets of Pseudorandom Sets: The Paper(s)

Green, Tao and Ziegler, in their works on patterns in the primes, prove a general result of the following form: if $X$ is a set, $R$ is a, possibly very sparse, “pseudorandom” susbset of $X$, and $D$ is a dense subset of $R$, then $D$ may be “modeled” by a large set $M$ which has the same density in $X$ as the density of $D$ in $R$.

They use this result with $X$ being the integers in a large interval $\{ 1,\ldots, N\}$, $R$ being the “almost-primes” in $X$ (integers with no small factor), and $D$ being the primes in $X$. Since the almost-primes can be proved to be “pseudorandom” in a fairly strong sense, and since the density of the primes in the almost-primes is at least an absolute constant, it follows that the primes are “indistinguishable” from a large set $M$ containing a constant fraction of all integers. Since such large sets are known to contain arbitrarily long arithmetic progressions, as proved by Szemeredi, Green and Tao are able to prove that the primes too must contain arbitrarily long arithmetic progressions. Such large sets are also known to contain arbitrarily long “polynomial progressions,” as proved by Bergelson and Leibman, and this allows Tao and Ziegler to argue that the primes too much contain arbitrarily long polynomial progressions.

(The above account is not completely accurate, but it is not lying too much.)

As announced last October here, and here, Omer Reingold, Madhur Tulsiani, Salil Vadhan and I found a new proof of this “dense model” theorem, which uses the min-max theorem of game theory (or, depending on the language that you prefer to use, the duality of linear programming or the Hahn Banach theorem) and was inspired by Nisan’s proof of the Impagliazzo hard-core set theorem. In complexity-theoretic applications of the theorem, our reduction has polynomial complexity, while the previous work incurred an exponential loss.

As discussed here and here, we also show how to use the Green-Tao-Ziegler techniques of “iterative partitioning” to give a different proof of Impagliazzo’s theorem.

After long procrastination, we recently wrote up a paper about these results.

In the Fall, we received some feedback from additive combinatorialists that while our proof of the Green-Tao-Ziegler result was technically simpler than the original one, the language we used was hard to follow. (That’s easy to believe, because it took us a while to understand the language in which the original proof was written.) We then wrote an expository note of the proof in the analyst’s language. When we were about to release the paper and the note, we were contacted by Tim Gowers, who, last Summer, had independently discovered a proof of the Green-Tao-Ziegler results via the Hahn-Banach theorem, essentially with the same argument. (He also found other applications of the technique in additive combinatorics. The issue of polynomial complexity, which does not arise in his applications, is not considered.)

Gowers announced his results in April at a talk at the Fields institute in Toronto. (Audio and slides are available online.)

Gowers’ paper already contains the proof presented in the “analyst language,” making our expository note not so useful any more; we have still posted it anyways because, by explaining how one translates from one notation to the other, it can be a short starting point for the computer scientist who is interested in trying to read Gowers’ paper, or for the combinatorialist who is interested in trying to read our paper.


text:
Lemma 15.62.16. Let $R$ be a ring. Let $m \in \mathbf{Z}$. Let $K^\bullet$ be a complex of $R$-modules. Let $R \to R'$ be a faithfully flat ring map. If the complex $K^\bullet \otimes _ R R'$ is $m$-pseudo-coherent (resp. pseudo-coherent), then $K^\bullet$ is $m$-pseudo-coherent (resp. pseudo-coherent).

Proof. We will use without further mention that $- \otimes _ R R'$ is an exact functor and that therefore

$H^ i(K^\bullet ) \otimes _ R R' = H^ i(K^\bullet \otimes _ R R').$

Assume $K^\bullet \otimes _ R R'$ is $m$-pseudo-coherent. Let $n \in \mathbf{Z}$ be the largest integer such that $H^ n(K^\bullet )$ is nonzero; then $n$ is also the largest integer such that $H^ n(K^\bullet \otimes _ R R')$ is nonzero. We will prove the lemma by induction on $n - m$. If $n < m$, then the lemma is true by Lemma 15.62.8. If $n \geq m$, then $H^ n(K^\bullet ) \otimes _ R R'$ is a finite $R'$-module, see Lemma 15.62.3. Hence $H^ n(K^\bullet )$ is a finite $R$-module, see Algebra, Lemma 10.82.2. Choose a finite free $R$-module $E$ and a surjection $E \to H^ n(K^\bullet )$. As $E$ is projective we can lift this to a map of complexes $\alpha : E[-n] \to K^\bullet$. Then the cone $C(\alpha )^\bullet$ has vanishing cohomology in degrees $\geq n$. On the other hand, the complex $C(\alpha )^\bullet \otimes _ R R'$ is $m$-pseudo-coherent, see Lemma 15.62.2. Hence by induction we see that $C(\alpha )^\bullet$ is $m$-pseudo-coherent as a complex of $R$-modules. Applying Lemma 15.62.2 once more we conclude. $\square$

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