text:
Inference for a Complex World
Student Blogs » Inference for a Complex World
A quick glance at the United Nations list of global issues here shows us the myriad of complex and diverse challenges we face today [1]. Mathematical modelling is an excellent tool for enabling us to understand and solve these challenges. With data availability and computational resources rapidly increasing, our models are growing in increasing complexity to match the grand scale of these global issues. However, this introduces significant challenges in accurately calibrating these complex models to precisely emulate the interactions we observe in the real world.
Complex models are often hard to express as a formula, making it impossible to use classical statistical methods to accurately calibrate the model.
Enter approximate Bayesian computation (ABC).
Also known as simulator-based inference, ABC is a class of statistical methods that test a wide range of possible model parameters and compare the resulting output to the observed data. ABC identifies model parameters that consistently provide model results that match the real dataset closely. Thus, ABC enables relatively accurate calibration in scenarios where it was not possible before!
ABC has been used in a variety of applications. It is widely used for applications in biological sciences e.g., in population genetics, ecology, epidemiology, and systems biology [2]. Recently, ABC has been essential in calibrating models to forecast and predict COVID-19 outbreaks in countries [3], or to describe the spread of diseases in a plantation [4]. ABC has helped evaluate energy policy, by enabling a complex Agent-Based Model to accurately forecast the fiscal effects of different feed-in tariff structures in Great Britain [5]. Although airline travel is still limited today, ABC has enabled us to use a complex queuing system to accurately model the foot traffic flow in airports [6].
These are just a few examples where ABC has proven to be invaluable in calibrating models to aid in understanding and solving large problems. The idea behind ABC is intuitive, and effectively leverages the rapid increase in computational power and the availability of large datasets to model processes previously beyond reach. Among many other instruments in a statistician's toolkit, ABC can help us stand tall when faced with the Goliath-like issues we are surrounded by today and tackle them with greater precision!
[1] 'Global Issues Overview', Nov. 19, 2015. https://www.un.org/en/sections/issues-depth/global-issues-overview/ (accessed Feb. 18, 2021).
[2] J. S. Lopes and M. A. Beaumont, 'ABC: a useful Bayesian tool for the analysis of population data', Infect. Genet. Evol., vol. 10, no. 6, pp. 825–832, 2010.
[3] D. J. Warne, A. Ebert, C. Drovandi, W. Hu, A. Mira, and K. Mengersen, 'Hindsight is 2020 vision: a characterisation of the global response to the COVID-19 pandemic', BMC Public Health, vol. 20, no. 1, pp. 1–14, 2020.
[4] A. Varghese, C. Drovandi, A. Mira, and K. Mengersen, 'Estimating a novel stochastic model for within-field disease dynamics of banana bunchy top virus via approximate Bayesian computation', PLOS Comput. Biol., vol. 16, no. 5, pp. 1–23, 2020, doi: 10.1371/journal.pcbi.1007878.
[5] P. Pearce and R. Slade, 'Feed-in tariffs for solar microgeneration: Policy evaluation and capacity projections using a realistic agent-based model', Energy Policy, vol. 116, pp. 95–111, 2018.
[6] A. Ebert, P. Wu, K. Mengersen, and F. Ruggeri, 'Computationally efficient simulation of queues: the R package queuecomputer', ArXiv Prepr. ArXiv170302151, 2017.
Abhishek Varghese
View Abhishek Varghese
Making Elective Surgery More Equitable and Transparent
How Mathematics Became My Personality


text:
American sitcom
Will & Grace is an Emmy winning American television sitcom. The show is set in New York City. It features four best friends, Will Truman (Eric McCormack), Grace Adler (Debra Messing), Jack McFarland (Sean Hayes) and Karen Walker (Megan Mullally). It was first aired in series beginning 1998 and series finale aired in 2006. It ran for 8 seasons for a total of 194 episodes.
David Kohan
Max Mutchnick
Eric McCormack
Megan Mullally
Sean Hayes
Shelley Morrison
Michael Angarano
Theme music composer
Jonathan Wolff
No. of seasons
No. of episodes
210 (list of episodes)
Executive producer(s)
Jhoni Marchinko
Jeff Greenstein
Alex Herschlag
Dave Flebotte
Jon Kinnally
Tracy Poust
Gary Janetti
Greg Malins
Bill Wrubel
John Quaintance
Suzanne Martin
Camera setup
Multi-camera
KoMut Entertainment
Sister Entertainment (1998–2000)
3 Sisters Entertainment (2000–06)
3 Princesses and a P (2017–)
NBC Studios (1998–2006)
Universal Television (2017–)
(U.S.) (1998–2006)
NBCUniversal Television Distribution
(U.S.) (2017–)[1]
MGM International Television Distribution (International)
Original network
480i (4:3 SDTV) (1998–2003)
1080i (16:9 HDTV) (2004–2006, 2017–)
Stereo (1998–2003)
Dolby Digital 5.1 (2004–06, 2017–)
Original release
September 21, 1998 (1998-09-21) –
CastEdit
Eric McCormack as Will Truman
Debra Messing as Grace Adler
Sean Hayes as Jack McFarland
Megan Mullally as Karen Walker
↑ Andreeva, Nellie (January 27, 2017). "NBC Orders Multi-Camera Comedy Pilot From Max Mutchnick & Jeff Astrof As Potential 'Will & Grace' Companion". Retrieved September 22, 2017.
Will & Grace on IMDb
This short article about television can be made longer. You can help Wikipedia by adding to it.
Retrieved from "https://simple.wikipedia.org/w/index.php?title=Will_%26_Grace&oldid=7263291"

redpajama_set_name: RedPajamaCommonCrawl


Empower Clinics Provides Corporate Update and announces new chief Financial Officer – Empower Clinics Inc.
VANCOUVER BC: March 13th, 2019 – EMPOWER CLINICS INC. (CSE: EPW) (Frankfurt 8EC) ("Empower" or the "Company") a growth oriented and diversified medical cannabis company, is pleased to provide an update to recent corporate activities after announcing acquisitions and partnerships intended to re-shape the profile of the Company going forward.
The Company has reached a number of important milestones over the past eight weeks as we continue to re-shape the path forward striving to become a global health & wellness company, serving the needs of patients through our network of physician-staffed health and pain management clinics and CBD Extraction operations.
Launch of CBD Extraction Facility The Company intends to open a fully functioning hemp-based CBD extraction facility in greater Portland, Oregon in 2Q 2019 with the first extraction system having the capacity to produce 6,000kg of extracted product per year with current CBD extract wholesale pricing at $6,500.00 USD per kilogram. Empower has entered into a non-binding term sheet with Aibeida LifeTech Limited ("Aibeida"), whereby Aibeida will sell and license specialized extraction equipment to Empower, provide facility build-out and set-up services, complete licensing & permitting requirements, equipment set-up, testing and activation plus provide ongoing facility management, production and maintenance for the first Empower CBD extraction facility. Aibeida's Director and Chief Scientist Dr. Shuang Xie, PhD. Chemist, Queens University in Canada will oversea and manage the Empower CBD extraction project providing Empower with vertical integration into the CBD supply chain producing world class isolates, distillates and crude oil that have shown superb third party test results in the initial test facility.
Focus on CBD Product Sales Empower has commenced selling it's proprietary line of CBD based products called SOLLIEVO, through its network of company owned clinics in the U.S. Empower's patient base and customers are expected to benefit from access to high margin derivative products, including CBD lotion, tinctures, spectrum oils, capsules, lozenges, patches, e-drinks, topical lotions, gel caps, hemp extract drops and pet elixir hemp extract drops. Patients and customers will be able to access Empower's world-class customer service, home delivery and e-commerce platform.
CBD Market Demand The passing of the US$867 billion Agriculture Improvement Act (the Farm Bill) has legalized hemp and hemp-based products. This has created a massive opportunity for the production and sales of a variety of CBD based products that can provide genuine help and effective relief to millions of people suffering from a variety of serious qualifying conditions. Recent reports and studies indicate the approval of this bill could create a US$20 billion industry by 2022.
Increased Patient Access With a rapidly expanding company-owned clinic network and significant expansion opportunity through the Sun Valley franchise model, Empower anticipates it will grow its total patient list substantially in the years ahead. This is expected to provide greater opportunity for treatment analysis using artificial intelligence (AI), validating the Company as a leader in understanding the efficacy of cannabis-related therapies.
Market Leading Technology Empower utilizes a market-leading patient electronic management and POS system that is HIPAA compliant and provides deep insight to patient care. The Company supports remote patients using its tele-medicine portal, enabling patients who do not live near one of its clinic locations, or are disabled or unable to come to a location, to still benefit from a doctor consultation.
The Company is also pleased to announce the appointment of Mr. Mathew Lee as Chief Financial Officer ("CFO") effective March 11th, 2019. Mr. Lee is a Chartered Professional Accountant with a Bachelor of Commerce Degree from the University of British Columbia and is a member of the Chartered Professional Accountants of British Columbia. Mr. Lee brings a breadth of financial experience in both public and private company operations across various sectors, including mineral resources, cannabis and financial services. Along with the appointment of Mr. Lee, Empower has engaged Invictus Accounting Group LLP to support its public financial reporting and compliance needs. Invictus is a well-regarded Vancouver, B.C. based advisory firm with expertise in accounting services, corporate finance, controllership, due diligence, ERP, financial reporting, internal controls compliance and tax compliance. We look forward to the support of Mr. Lee and Invictus as we grow the business together.
Empower is a leading owner/operator of a network of physician-staffed clinics focused on helping patients improve and protect their health through innovative uses of medical cannabis. Treatment solutions are focused on education, data and efficacy in combination with other modalities to support patients with serious qualifying conditions, such as chronic pain, PTSD, sleep disorders, opioid dependency, epilepsy and more. It is expected that Empower's proprietary product line "Sollievo" will offer patients a variety of delivery methods of doctor recommended cannabidiol (CBD) based product options in its clinics, online and at major retailers. With over 120,000 patients, an expanding clinic footprint, a focus on new technologies, including tele-medicine and an expanded product development strategy, Empower is undertaking new growth initiatives to be positioned as a vertically integrated diverse market-leading service provider for complex patient requirements in 2019 and beyond.
This news release contains certain "forward-looking statements" or "forward-looking information" (collectively "forward looking statements") within the meaning of applicable Canadian securities laws. All statements, other than statements of historical fact, are forward-looking statements and are based on expectations, estimates and projections as at the date of this news release. Forward-looking statements can frequently be identified by words such as "plans", "continues", "expects", "projects", "intends", "believes", "anticipates", "estimates", "may", "will", "potential", "proposed" and other similar words, or information that certain events or conditions "may" or "will" occur. Forward-looking statements in this news release include, but are not limited to, statements regarding the expected terms of the Financing and the use of proceeds thereof. Such statements are only projections, are based on assumptions known to management at this time, and are subject to risks and uncertainties that may cause actual results, performance or developments to differ materially from those contained in the forward-looking statements, including that the Company may be unable to complete the Financing on the terms expected or at all; that the Company may deem it necessary to allocate the proceeds of the Financing other than as provided herein; and other factors beyond the Company's control. No assurance can be given that any of the events anticipated by the forward-looking statements will occur or, if they do occur, what benefits the Company will obtain from them. Readers are cautioned not to place undue reliance on the forward-looking statements in this release, which are qualified in their entirety by these cautionary statements. The Company is under no obligation, and expressly disclaims any intention or obligation, to update or revise any forward-looking statements in this release, whether as a result of new information, future events or otherwise, except as expressly required by applicable laws.


Footage Poker Place – What Exactly Is It? Leading Online video Poker Collection Strategies The sum of your bets likely might depend on which almost poker you're participating. If you ever prefer to acquire great at flick poker it's vital to have the noble flush. So as to start off trying to play online poker you will want to enroll first.
Poker is the perfect bet on system. It is among the most most serious gaming titles while in the current time. It is among the most powerful gaming titles from the show time.
Perfecting video recording poker is literally especially robust. Casino site Hold'em may be a residential home gameplay intended to be dealt through a croupier for gambling establishment customers to spend time playing through the major gambling. You will appreciate that one can find plenty of casino houses and then there will not be any adequate furniture to hold lower restriction gamers (that can be disappointing should you be a beginner to online game).
Also, the casino conducts various tournaments monthly. A few of the on line gambling houses supply you with using the net gaming place slot appliance mmorpgs. If you would like to play at online casinos for real money, you wish to be familiar with very best and worst games so far as player edge.
There's in your precise way the live on-range online casino as soon as you aspiration. Several internet casinos present poker game beside other gambling house computer games. Just what the In-Audience Won't Share With You Video tutorial Poker Set Wherever you eventually opt to play, the most important issue to keep in mind is that when playing in an on-line casino website, ensure it remains affordable and don't chase any loses you have made. The casino's homepage has a complete bunch of transaction choices around. In conclusion, there are numerous web sites in which you may have fun with playing casino game like web based video clip slotsfree.
Be sure to fully grasp the rules of bingo halls, before you commence playing bingo online. If you opt to gamble over the web, you stand an excellent potential of making an item. Most world wide web betting web-sites provide you with perks to usher in new golfers, and some of them have the capacity to be rather large.
There are plenty of charge card computer game freeware internet sites on the net where gamers may possibly download and read and have fun with absolutely free credit card video games. There's an amazing set of helpful strategies about a lot of the attainable inconveniences. Your property is most likely the 1 business banking the game, however your machine isn't manufactured to whip you.
In accord with the modern day around the world niche ailments, it really is looks that there is absolutely no end in idea concerning the 100 % interest in web wagering through the environment. Seriously, mastering slots mmorpgs might supply you with alot more having fun with second. Poker funds may just be gamed on sufficient internet sites on line.
At our at no cost funds for you to get started off enjoying. Except if a visits one of several essential gaming hubs like Vegas, it isn't simple and easy to buy a on line casino that provides a variety of individual styles of casino site game underneath the exactly the same roofing. Right now, casinos have become renowned around the globe. In addition, in a number of primary online betting vendors you're allowed to button from on the internet on line casino wagering to on line sporting activities betting with a similar username and bank account. Most online casinos give you totally free dough signup bonuses as a way to lure new customers as well as to remain well informed concerning the competitiveness.
You can find a pair on-line casinos specialized in poker. Exactly What You Need Do to discover more regarding Video recording Poker Set in place Previous to You're Put Aside In case the gaming is being experienced right now, you may really have to hold off until it's finished, in advance of moving in the next online game. video poker At its highly fact, it's an effortless computer game. Next game starts in a particular time.
It's genuine, you can play any sport of your liking while not having to leave behind your chosen recliner. It's always better to find a game first before playing. You've got to observe how to locate the video game, by rivaling unique professional athletes. Most video tutorial poker adventures take advantage of the combinations of wrists and hands that can be produced from a 52-cards outdoor patio. If you're entirely new to the game, the web is just one of the greatest places from where you are able to learn the basics and the rules of limit hold'em poker.
With far more activity buffs, the recording gameplay adopted the notion of getting 52 price greeting cards which means you can give a level wider amount of folks. Whenever they want you possess considered which form of picture poker video game that you ought to use, you are going to want also to decide on the coin measuring you want to carryout. Knowing the overall game isn't as difficult as you might believe that. Poker games are almost always experienced working with a custom called family table stakes", meaning merely the chips within the family table may be used to wager in a fingers.
On-line poker match will give you a lot of the sought after casino site games like blackjack and video poker. The best way to look for familiar with the sport of poker is find someone who you know knows how to participate in poker and let them help you learn to play. Rehearse only can force you to get best, so it's rather critical you simply training the poker video game.
Ever before you might have opted which type of flick poker computer game that you must use, you can expect to want also to select the coin measuring you want to conduct. Poker experience is only one of the well-liked gambling house mmorpgs in all over the world.


The concept for Fragrance Outlet was simple – offer the widest selection of genuine designer fragrances, sold by knowledgeable, friendly staff at affordable prices. The formula was foolproof, and the store was an overwhelming success. Throughout the next few years, Fragrance Outlet expanded its presence in the fragrance market, opening stores in the best Outlet Centers across the country and Guam. Fragrance Outlet, Inc. continues to grow with expansion plans exceeding 110 stores over the next several years.


In twenty-five years, the Mongol army subjugated more lands and people than the Romans had conquered in four hundred years. Genghis Khan, together with his sons and grandsons, conquered the most densely populated civilization of the thirteenth century…Genghis Khan conquered more than twice as much as any other man in history…At its zenith, the empire covered between 11 and 12 million contiguous square miles, an area about the size of the African continent and considerably larger than North America…The majority of people today live in countries conquered by the Mongols; on the modern map Genghis Khan's conquests include thirty countries with well over 3 billion people.
That's from Jack Weatherford's overstated but fascinating Genghis Khan and the Making of the Modern World.
By the way, the entire Mongol tribe was no more than one million people, with perhaps one hundred thousand warriors.
It is estimated that 17 million people worldwide, including the British Royal Family, Iranian Royalty, and the family of Dracula, are direct descendents of Genghis Khan.
Addendum: Read more here on the nature of the test.


text:
Cyrestis andamanica är en fjärilsart som beskrevs av Wood-mason och De Nicéville 1881. Cyrestis andamanica ingår i släktet Cyrestis och familjen praktfjärilar. Inga underarter finns listade i Catalogue of Life.

Källor

Praktfjärilar
andamanica

redpajama_set_name: RedPajamaWikipedia


Why should you go to the play Night Before Christmas?
It is difficult to find a work of world literature, which equates with the christmas of Christmas with the story of Gogol "The Night Before Christmas."
Bright, funny story, in which sincere love, willingness for any victim for the sake of a loved one, fun, unpredictable adventures and even mysticism of the last night before Christmas were intertwined.
Director A. Bakirov put an absolutely wonderful musical comedy, which also attracts the world of the Christmas night tales with its incredible miracles.
Smith Vakula will go on a search of shoes for the cute, but Oksana, the witch of Soloh will fill up with unsuccessful visitors.
all the empty bags, the devil steals the moon, and the two cummies will wander the snowy village in search of vodka and a good company.
Do not miss! Such adventures happen only once!


By now you're probably familiar with the minor pentatonic scale.
C on the eighth fret of the E string.
We're going to take that scale and play it on one string.
one string is that it forces you to make up melodies in a different way.
which is one fret bigger than a whole step.
then we have the flat third which is a minor third away or three half-steps.
So, you can actually even just move if you want to get used to the distance.
a fifth and a flat seven.
go up a whole step to the fourth, whole step to the fifth.
the flat seven and then a whole step to the root to finish it off.
So, there are no half-steps in the minor pentatonic scale.
fourth, fifth, flat seven, root.
Practice the minor pentatonic scale on one string on your own.
take your time, see what happens, and have fun with it.
You might come up with something that sounds like this.


Author Cincinnati Human Relations Commission.
Title Cincinnati Human Relations Commission black directory : a compendium of civic, civil rights, cultural, educational, historical, media, professional, religious and social groups in Cincinnati / Richard A. Weiland, Chairperson, Board of Commissioners.
Published Cincinnati, Ohio : The Commission, 1987.
Subjects African Americans -- Ohio -- Cincinnati -- Directories.
African Americans -- Ohio -- Cincinnati -- Societies, etc. -- Directories.
Associations, institutions, etc. -- Ohio -- Cincinnati -- Directories.


text:
\section{Appendix}

\subsection{Training details}

\begin{table}[h]
\centering
\caption{Statistics of datasets}
\label{Statistics of dataset}
\begin{tabular}{@{}cccccc@{}}
\toprule
\multicolumn{2}{c}{dataset}      & Training set & Testing set & Development set & Classes \\ \midrule
\multicolumn{2}{c}{CIFAR-100\cite{cifar100}}    & 50000                   & 10000                  & -                  & 100               \\
\multicolumn{2}{c}{ImageNet\cite{DBLP:conf/cvpr/DengDSLL009/imagenet}}     & $\approx$1200k                  & $\approx$100k                  & $\approx$50k               & 1000              \\
\multirow{10}{*}{GLUE\cite{DBLP:conf/iclr/WangSMHLB19/glue}} & CoLA    & 8551                    & 1063                   & 1043               & 2                 \\
                       & SST-2   & 67350                   & 1821                   & 873                & 2                 \\
                       & MRPC    & 3668                    & 1725                   & 408                & 2                 \\
                       & STS-B   & 5749                    & 1377                   & 1379               & 5                 \\
                       & QQP     & 363870                  & 390965                 & 40431              & 2                 \\
                       & MNLI-m  & 390702                  & 9796                   & 9815               & 3                 \\
                       & MNLI-mm & 390702                  & 9847                   & 9832               & 3                 \\
                       & QNLI    & 104743                  & 5461                   & 5463               & 2                 \\
                       & RTE     & 2491                    & 3000                   & 277                & 2                 \\
                       & WNLI    & 635                     & 146                    & 71                 & 2                 \\ \bottomrule
\end{tabular}
\end{table}

\paragraph{Datasets}
Table \ref{Statistics of dataset} shows the statistics used in our experiments. The training set, testing set, and development set refer to the scale of data in each part of the dataset. Table \ref{Statistics of dataset} shows that ImageNet is much bigger than CIFAR-100 in scale. And that's the reason we choose the two datasets, to verify our proposed approach in datasets of different scales. GLUE benchmark can be naturally split into low resource tasks and high resource ones for the similar propose. We divide low-resource tasks and high-resource ones by 100k on a data scale.

We are also concerned about people's consent and privacy in datasets. From the information search from the website, faces in ImageNet have been blurred in order to protect privacy after being blamed to collect information from people without consent. But we do not find a similar announcement on the website of CIFAR-100 and GLUE. Maybe it's because they have pre-processed the data or the data itself contains little privacy information. It's better for the websites to make the announcement of not containing personally identifiable information or offensive content.

\paragraph{Infrastructure}
We implement our models with Pytorch, and our experiments are as follows:

\begin{enumerate}
\item CPU: 256  AMD EPYC 7742 64-Core Processor
\item RAM: 386840MB
\item GPU: 8x GeForce RTX 3090
\item Operating System: Ubuntu 18.04 LTS
\item Tools: Python3.7, tensorflow2,2,0, sklearn 0.23.2
\end{enumerate}

\paragraph{Hyper-parameter search}
In CV experiment, we follow previous works~\cite{CRD}, the settings on CIFAR-100 and ImageNet dataset are the same as these works.
In CIFAR-100 we train the student model by SGD optimizer with a momentum of 0.9, a batch size of 64 and weight decay of $5\times10^{-4}$. The learning rate starts from 0.05 and decays by 10 every 30 epochs after 150 epochs.
And on ImageNet we train the student model by SGD optimizer with a momentum of 0.9, a batch size of 256 and weight decay of $1\times10^{-4}$. The learning rate starts from 0.1 and decays by 10 every 30 epochs after 30 epochs. In the experiment of students trained with the fine-tuned teachers, we train the teacher model along with the student model during the training process with a learning rate of $1\times10^{-3}$.
And in the experiment of a student trained with a teacher with the adapter module, also called adaptive teacher, the learning rate of the trainable part in the teacher model is set to $1\times10^{-4}$. Notably, classification loss from the teacher model is appended to the loss of students by multiplying a hyper-parameter of 0.5.

For NLP, we inherit parameters like maximum sequence length, temperature and batch size  according to setting from previous works~\cite{meta-kd}. We also perform grid search over the sets of the student learning rate $\lambda$ from \{1e-5, 2e-5, 3e-5, 4e-5, 5e-5\}, teacher learning rate $\mu$ from \{2e-6, 1e-5, 2e-5\}, batch size from \{32, 64\}, the weight of KD loss from \{0.3, 0.5, 0.8, 0.9\} for a better performance. We evaluate our methods on dev set of GLUE benchmark.

\section{Introduction}
With the continuous growth of data scale and computing resources, the scale of deep neural networks has rapidly increased, from millions of parameters to billions of parameters~\cite{9343374}. Although these huge models, such as BERT \cite{bert}, GPT-3~\cite{gpt3}, and CLIP \cite{DBLP:conf/icml/RadfordKHRGASAM21/clip}, have powerful multitasking capabilities, they cannot be deployed on some edge devices with limited computing resources, which further limits the environmentally friendly and efficient deployment of deep learning applications.

Knowledge distillation (KD) \cite{DBLP:journals/corr/HintonVD15}, as an important method for model compression, has been widely used in various fields \cite{meta-kd,RCO,CRD,Zhang2022FinetuningGM} of deep learning. This paradigm utilizes a pre-trained teacher network to obtain a student network that is close to the teacher network but with fewer parameters. In the traditional distillation framework \cite{CRD,DBLP:conf/eccv/PassalisT18/PKT,DBLP:conf/cvpr/ParkKLC19/RKD}, the prediction output (soft label) is produced by the
fixed teacher, as shown in Figure \ref{fig:base_kd} .
However, limited by the capacity of the student model, the teacher's knowledge cannot be well transferred to such small models, which makes traditional distillation methods suboptimal \cite{RCO,DBLP:conf/aaai/MirzadehFLLMG20}. Based on this phenomenon, we propose the first research question: \textit{\textbf{RQ1:} How to transfer teachers' knowledge to students more effectively?}

In recent works \cite{park2021learning,RCO,BERT-of-Theseus,DBLP:conf/cikm/RaoQQW0021}, the teacher network and the student network are jointly trained to make the teacher's knowledge more friendly to the students. The online distillation methods, such as DML \cite{DML} and KDCL \cite{KDCL}, usually update most or even all the parameters of the teacher by using the real labels and the feedback information (soft labels) of the students, as shown in Figure ~\ref{fig:fine_tune_kd}. However, in the setting of online distillation, a large teacher network and a student network need to be trained simultaneously for each new downstream task from scratch, which is too time-consuming and inefficient. This  motivates us to consider the following research question: \textit{\textbf{RQ2:} How to make the friendly transfer of knowledge more parameter-efficiently?}

In this paper, we propose a new distillation framework that is parameter-efficient and student-friendly, as shown in Figure~\ref{fig:adapter_kd}.
Our paper forges a connection between two literatures that have evolved  independently: knowledge distillation and parameter-efficient learning. This allows us to leverage the powerful methods of parameter-efficient learning to ensure the friendliness of teachers' knowledge to students, and to update teachers' knowledge representation more efficiently.
Based on extensive experiments and analyses, we show that our framework can utilize the information from ground-truth labels and student supervision to train the adapter modules, and further narrow the gap between the teacher and student models, which makes knowledge transfer easier.

In summary, our contributions are:
\begin{itemize}[leftmargin=12pt]
    \item We propose a parameter-efficient and student-friendly distillation (PESF-KD) framework, where an adapter is deliberately designed for the teacher.

    \item We provide theoretical and experimental evidences to show PESF-KD can facilitate the knowledge transfer by reducing the gap between teacher and student.
    \item We empirically validate the effectiveness and efficiency of our PESF-KD upon several vision and language models compared to existing knowledge distillation methods.

\end{itemize}
\begin{figure*}[t!]
    \subfigure[Vanilla KD]{
    \begin{minipage}[t]{0.31\textwidth}
           \centering
           \includegraphics[width=\textwidth]{base_distil.pdf}
            \label{fig:base_kd}
    \end{minipage}
    }

    \subfigure[KD with fine-tuned teacher]{
    \begin{minipage}[t]{0.31\textwidth}
            \centering
            \includegraphics[width=\textwidth]{fine_tune_distil.pdf}
            \label{fig:fine_tune_kd}
    \end{minipage}
    }

    \subfigure[PESF-KD]{
    \begin{minipage}[t]{0.31\textwidth}
            \centering
            \includegraphics[width=\textwidth]{adapter_distil.pdf}
            \label{fig:adapter_kd}
    \end{minipage}
    }
    \caption{{Comparison between KD, KD w/ fine-tuned teacher, and our PESF-KD. \textbf{\textcolor[RGB]{160,255,232}{Green}} means the parameter needs to be updated, while \textbf{\textcolor[RGB]{160,232,255}{blue}} means not.} (a) Vanilla KD, teachers and students are trained independently, resulting a gap of knowledge transfer. (b) This method is quite like DML~\cite{DML} and other online KD methods, which get better knowledge transfer and need training teachers and students together. (b) PESF-KD updates the parameters of adapter modules of the teacher with ground-truth labels and the feedback from student outputs, while the rest of the parameters of the teacher are all fixed. This method makes knowledge transfer more friendly and effective.
    }
\end{figure*}

\section{Background}
\subsection{Label Smoothing and Knowledge Distillation}
Gradient descent is mostly used to optimize the cross entropy \cite{DBLP:conf/nips/BaumW87} of the hard ground-truth labels and predicted values in deep learning. And in recent years, \citet{DBLP:conf/cvpr/SzegedyVISW16/label_smoothing} introduced \textbf{Label Smoothing} to soften and weight the traditional hard labels with the uniform distribution.
This approach has successfully improved the effectiveness of several deep learning models and has been widely validated in natural language processing (NLP) and computer vision (CV). And to date, this approach has also been used as a training trick to improve the training of models. We provide a mathematical description of the label smoothing process. First we show the original cross-entropy:
\begin{equation}\label{eq1}
H(\boldsymbol{y}, \boldsymbol{p})=\sum_{k=1}^{K}-y_{k} \log \left(p_{k}\right),
\end{equation}
where $y_k$ is "1" for the correct class and "0" for the rest.
Then the label smoothing is achieved by increasing the smoothing parameter $\alpha$ to change $y_k$ to $y_k^{LS}$:
\begin{equation}\label{eq2}
    y_{k}^{L S}=y_{k}(1-\alpha)+\alpha / K
\end{equation}
When a network is trained with label smoothing, the differences between the logits of the correct and incorrect classes become a constant that is dependent on $\alpha$, while knowledge distillation provides dynamic soft labels to let the network learn the distribution of teachers.

\textbf{Knowledge Distillation} (KD) \cite{DBLP:journals/corr/HintonVD15} often employs a pre-trained teacher network with the goal of transferring the teacher's knowledge to a small group of students, as shown in Figure \ref{fig:base_kd}. In the classification task, one of the simplest forms is to provide the soft label information by forwarding the teacher's output. The initial teacher and student model can be defined as: teacher $\boldsymbol{p}(\theta^t)$ and student $\boldsymbol{p}(\theta^s)$, respectively, where $\theta$ is the model parameters and $\boldsymbol{p_k}(\cdot)=\frac{\exp \left(\boldsymbol{z}_{k}(\theta) / \tau\right)}{\sum_{j=1}^{K} \exp \left(\boldsymbol{z}_{j}(\theta) / \tau\right)}$ is the probability predict of the matching label and $K$ is the number of classes and $z_k$ is the logical output of the $k\mathrm{-th}$ class. So the vanilla KD loss measuring the KL-Divergence of teachers and students can be formulated as:
\begin{equation}\label{eq3}
\mathcal{L}_{K L}\left(\boldsymbol{p}(\tau|\theta^s), \boldsymbol{p}(\tau|\theta^t)\right)=\tau^{2} \sum_{j} \boldsymbol{p}_{j}(\tau|\theta^t)\cdot\\\log \frac{\boldsymbol{p}_{j}(\tau|\theta^t)}{\boldsymbol{p}_{j}(\tau|\theta^s)}
\end{equation}
where $\tau$ is the temperature used in KD, which controls how much to rely on the teacher's soft predictions. For simplicity, we use $\mathcal{L}_{K L}$ represent $\mathcal{L}_{K L}\left(\boldsymbol{p}(\tau|\theta^s), \boldsymbol{p}(\tau|\theta^t)\right)$ in the following sections.

\subsection{Parameter-Efficient Training}
Transfer learning from pre-trained models is a general learning paradigm that has been applied to a variety of tasks \cite{DBLP:conf/cvpr/HeZRS16/resnet,bert,vilbert}. The most popular transfer learning method nowadays is to fine-tune all of the model's parameters (full fine-tuning). But full fine-tuning is relatively expensive because parameter adjustment correlates to the need to completely retrain the entire model for each downstream task. Artificially constructed modules with a modest number of parameters are used in approaches like Prompt \cite{DBLP:conf/emnlp/LesterAC21/prompt} and Adapters \cite{DBLP:conf/icml/adapter,DBLP:conf/acl/LiL20/prefix} to achieve a fit between the pre-trained model and the downstream task with parameter-efficient training.

\section{Methodology}\label{sec:method}
\subsection{Knowledge Distillation with Fine-Tuned Teacher}
\textbf{Motivation.}
Compared with label smoothing, knowledge distillation can improve the training of the network for the following two reasons: First, teachers can understand the nuances of different classes, and such inter-class information brings more information than label smoothing and helps students generalize some unseen data. Second, the soft distribution of teachers constrains students' learning directions such that they will not be mistakenly overconfident. For a student network, the information it learns is obtained through this soft label.
Previous works~\cite{park2021learning,DBLP:conf/iccv/ZhuW21a,RCO,DBLP:conf/aaai/MirzadehFLLMG20} pointed out that the mismatch of teacher and student network capacity can cause the \textit{knowledge transfer difficulty} of such soft label~\cite{DBLP:journals/ijcv/GouYMT21}. One of the solutions to reduce transfer difficulty is smoothing the teacher's output by adjusting the temperature \cite{guo2022reducing}.
\citet{DBLP:conf/nips/MullerKH19} indicates that the label smoothness of the target provided by the teacher exerts a great influence on the student network, and the difference information between classes determines whether the student's performance can be improved.
But manual conditioning of the label smoothness by the temperature is quite difficult and may cause the loss of inter-class information when the temperature is too high.
In our preliminary experiments, we found that if the teacher network continues to fine-tune through the ground truth labels with the rest of the settings as the valina KD~\cite{DBLP:journals/corr/HintonVD15}
, teacher's labels will be smoother, and the accuracy of the distilled student network is better, where we leave this competitive setting as the strong benchmark, namely ``\textbf{KD w/ fine-tuned teacher}'', in the main experiments (Section~\ref{exp}).
This phenomenon urges us to explore the relationship between the teacher network and the student network in Section~\ref{kd_relation}.
\\
\textbf{Training Objectives.}
As shown in Figure \ref{fig:fine_tune_kd}, different from vanilla KD~\cite{DBLP:journals/corr/HintonVD15}, our distillation method requires fine-tuning the parameters of the teacher network. The teacher network needs to output soft labels to supervise the student network, it also requires ground-truth labels to training itself.
Take classification task as instance, the corresponding teacher's loss is:
\begin{equation}\label{eq4}
\mathcal{L}_{t}=\mathcal{L}_{task}(\theta^t)=-\sum_{i\in|X|} \sum_{c\in C}
\left[\mathbf{1}\left[\mathbf{y}_{i}=c\right]
\cdot\log p\left(\mathbf{y}_{i}=c \mid\mathbf{x}_{i};
\mathbf{\theta}^{t}\right)\right]  ,
\end{equation}
where $c$ is a class label and $C$ denotes the set of class labels.

In vanilla KD~\cite{DBLP:journals/corr/HintonVD15}, students receive soft label supervision (in Equation~\ref{eq4}) from the teacher as well as label supervision as the following formulation:
\begin{equation}\label{eq5}
\mathcal{L}_{s}=\alpha\mathcal{L}_{KL}+(1-\alpha)\mathcal{L}_{task}(\theta^s),
\end{equation}
where the task loss $\mathcal{L}_{task}$ follows the same format as the teacher network.

\subsection{Parameter-Efficient and Student-Friendly Knowledge Distillation}
\textbf{Motivation.}
As with the same training objective in the previous section, we still need to update the parameters of the teacher network, as shown in Figure \ref{fig:adapter_kd}. Inspired by the method of parameter efficient fine-tuning~\cite{DBLP:conf/icml/adapter}, we introduce the adapter structure only adjusting this part of the parameters and fixing the original teacher network parameters. We take advantage of this approach to achieve parameter efficient and student-friendly fine-tuning.
\\
\textbf{Adapter Module.}
Our PESF-KD has three key properties: (i) it allows teachers and students to learn online while maintaining good performance, (ii) it can automatically update the teacher's output based on the student's output, which reduces the sharpness of teachers (see \S \ref{kd_relation}), and (iii) it adds extra parameters trainable to the pre-trained teacher network while all parameters of the original are fixed, reducing the over-consumption of the fully trained teacher network.
Many online KD methods require retraining more than one teacher networks, so it is desirable to participate in training a teacher network with only a small number of parameters.
To achieve these properties, we propose to adopt a standard adapter module for knowledge distillation.\\
\textbf{Structures.}
Most adapters can be written as proj\_down $\rightarrow$ nonlinear $\rightarrow$ proj\_up architecture. Specifically, the adapter firstly projects the input $h$ to a lower-dimensional space with dimension $r$, utilizing a down-projection weight matrix $\boldsymbol{W}_{\text {down }} \in \mathbb{R}^{d \times r}$.
Then through a nonlinear activation function and then through a up-projection function with weight matrix $\boldsymbol{W}_{\text {up }} \in \mathbb{R}^{r \times d}$ to increase the dimension to the original dimension. Usually, these modules use a residual connection, and the final form is as follows:
\begin{equation}\label{eq6}
\boldsymbol{h} \leftarrow \boldsymbol{h}+f\left(\boldsymbol{h} \boldsymbol{W}_{\text {down }}\right) \boldsymbol{W}_{\text {up }}
\end{equation}

\section{A Closer Look at Teacher-Student Relationship in Distillation}\label{kd_relation}
\subsection{Gap Between Teacher and Student}
The relationship between the teacher network and the student network in KD is rarely discussed. When independently training the model from scratch, the larger model is more likely to output sharper values and obtain better accuracy, while the smaller model is more likely to output smoother values and obtain poorer accuracy ~\cite{DBLP:conf/cvpr/Chen0ZJ21,DBLP:conf/iccv/ZhuW21a,DBLP:conf/aaai/MirzadehFLLMG20,DBLP:conf/iccv/ChoH19}. This phenomenon (i.e., capacity mismatch) significantly exacerbates the difficulty of knowledge transfer in knowledge distillation. An optional solution to measure the difference in the sharpness of the network output between teachers and students is to directly use the entropy of the network output to get the output value of the maximum confidence score.
Differently, we use a simple and intuitive sharpness metric \cite{guo2022reducing} to
get a smooth approximation to the maximum function considering the overall information of each class.
If we use $K$ to denote $K$ classes, the \textbf{Sharpness} is defined as the logarithm of the exponential sum of logits:
\begin{equation}\label{eq7}
    S_{sharpness}=log\sum_{j}^{K} \exp{z_j(\theta)}
\end{equation}
For the difference between teacher and student networks, we use the sharp gap between the two networks as a metric:
\begin{equation}\label{eq8}
    G_{gap}=log\sum_{j}^{K} \exp{(z_j(\theta^t))} - log\sum _{j}^{K}\exp{(z_j(\theta^s))}
\end{equation}

\subsection{How to Narrow the Gap?}
\paragraph{Mathematical Analysis}
As shown in Equation \ref{eq8}, we get the expression for the gap between teachers and students.
In this section, we will explore what factors affect this gap. We first approximate this expression using a Taylor second expansion:
\begin{equation}\label{eq9}
\begin{aligned}
G_{ g a p}=& log\sum_{j}^{K} \exp{(z_j(\theta^t))} - log\sum _{j}^{K}\exp{(z_j(\theta^s))} \\
\approx & \log \left(K+\sum_{j}^{K} z_{j}(\theta^t)+\frac{1}{2} \sum_{j}^{K} z_{j}(\theta^t)^{2}\right) -\log \left(K+\sum_{j}^{K} z_{j}(\theta^s)+\frac{1}{2} \sum_{j}^{K} z_{j}(\theta^s)^{2}\right)
\end{aligned}
\end{equation}
Following Hindon's assumption~\cite{DBLP:journals/corr/HintonVD15} and also through experimental phenomena~\cite{guo2022reducing}, it can be known that the logits of each training sample are approximately zero-meaned so that $\sum_{j}^{K} z_{j}(\theta^s)=\sum_{j}^{K} z_{j}(\theta^t)=0$. So the gap can be rewritten as:
\begin{equation}\label{gap_explain}
\begin{aligned}
G_{ g a p} &=\log \left(K+\frac{1}{2} \sum_{j}^{K}\left(z_{j}(\theta^t)\right)^{2}\right)-\log \left(K+\frac{1}{2} \sum_{j}^{K}\left(z_{j}(\theta^s)\right)^{2}\right) \\
&=\log \left(1+\frac{1}{2 K} \sum_{j}^{K} z_{j}(\theta^t)^{2}\right)-\log \left(1+\frac{1}{2 K} \sum_{j}^{K} z_{j}(\theta^s)^{2}\right)\\
&=\log \left(1+\frac{1}{2} *\sigma_{t}^{2}\right)-\log \left(1+\frac{1}{2} *\sigma_{s}^{2}\right),
\end{aligned}
\end{equation}
where the $\sigma^{2}=\frac{1}{K}\sum_{j}^{K} z_{j}(\theta)^{2}$ is the variance of logits.

Naturally, assuming that the temperature $\tau$ is fixed. Then the change of the gap comes from the change in the variance of teachers' logits and students' logits. Once the these logits become smooth then the corresponding variance becomes smaller, if the logits become sharp then the variance becomes larger. The smoothness of the final logits results in a change in the gap. In the following sections, we compared three distillation methods, namely, vanilla KD (vanilla) with different temperature and our proposed methods -- fine-tuned teacher (train with finetune teacher) and adaptive teacher (train with adapter) to check out how temperature and respective methods affect the gap.

\begin{figure*}[h!]
\centering
\begin{minipage}[t]{0.40\linewidth}
    \centering
    \includegraphics[width=\linewidth]{major_logits.png}
    \caption{Normalized major logits distribution.}
    \label{major_logit}
\end{minipage}
\hspace{0.5em}
\begin{minipage}[t]{0.40\linewidth}
\centering
    \includegraphics[width=\linewidth]{sharpness_gap2.png}
    \caption{Sharpness gap comparison.}
    \label{sharpness_gap}
\end{minipage}\\
\hspace{0.5em}
\begin{minipage}[t]{0.88\linewidth}
\centering
    \includegraphics[width=\linewidth]{acc_merge2.png}
\caption{Comparison of Top-1 accuracy between different smoothing setting and our approaches.}
\label{teacher_acc_cmp}
\end{minipage}

\end{figure*}

\paragraph{Appropriate smoothness can improve the friendliness of knowledge transfer.}
In order to more intuitively show the effect of logits output smoothness on the gap, we show the average major logit distribution in the Figure \ref{major_logit}.
In most cases, the logit has the largest value that represents the model's category prediction, and other tiny values show that the input image is similar to those other categories.
In the real situation, the variance of students' logits will be affected by the variance of teachers' logits, and these two statistics change simultaneously. It is difficult to quantitatively analyze the gap between a fine-tuned teacher and an adaptive teacher, but we can get inspiration from Figure \ref{major_logit} and \ref{sharpness_gap}.
Obviously,  vanilla KD brings more sharp logit output, that is, greater variance. The logit output of fine-tuned teacher is in the middle position compared other two methods while PESF-KD has the smallest variance. It is worth noting that our methods greatly reduce the variance of logits compared to the vanilla KD, and both our proposed methods have relatively small variance.
In Figure \ref{sharpness_gap}, it shows the degree of sharpness for different temperatures in vanilla KD and our methods. Consistent with the analysis of Equation \ref{gap_explain}, since the teacher's output is unchanged, that is, the variance is unchanged, by increasing the temperature, the student's output is indeed smoother, resulting in a larger gap.
Figure \ref{teacher_acc_cmp} shows the Top-1 accuracy of the corresponding methods. In general, lower gaps are associated with higher accuracy (lower temperature, lower gap value), but very close gaps may introduce anomalies (see the case of temperature 2 and 4). This phenomenon shows that smoothing the output of teachers and students within a certain range can improve the friendly transferability of knowledge (in terms of improving the accuracy and reducing the gap). In the face of each sample, it is impossible to simply manually adjust the common temperature to achieve a dynamic smooth output to improve the students' ability to accept the teacher's knowledge. With our methods, the smoothness of the output can be dynamically adjusted  according to the gradient of each sample to achieve better knowledge transfer (compared to the vanilla KD with the best temperature setting, our methods have improved a lot about accuracy, and a large reduction in the gap).
\paragraph{Student-friendly knowledge leads to better performance.}
In this part, we show that trainable part in teacher models can narrow the gap between student and teacher and make knowledge transfer process more student-friendly thus achieving better accuracy.
We further explore the relationship between network consistency (sharpness gap in Figure \ref{sharpness_gap}, KL-Divergence in Figure \ref{kl_loss} \&  \ref{kl_loss_t} and CKA~\cite{CKA} in  Figure \ref{CKA_feature} \& \ref{CKA_logit} ) and accuracy in  Figure \ref{teacher_acc_cmp}. The three metrics mentioned above measure the final degree of consistency of the teacher-student network from different perspectives. A lower sharpness gap represents a closer knowledge representation of the teacher-student, and a lower KL represents the final convergence degree of the lower bound through distillation learning, while the a larger CKA represents larger similarity of the students and teachers.
We get the following interesting findings:
1) From Equation \ref{gap_explain}, it is clear that the gap also decreases when the student network trained with the vanilla KD. This reduction comes from the fact that the output of the student network becomes sharper i.e., more similar to the output of the larger teacher network (both kl-divergence and gap decrease).
2) Both our proposed methods can reduce the gap, and model trained with fine-tuned teacher can bring a great reduction (from 36.3 to 16.8).
Our methods also make the output of teachers and students more consistent (the KL-Divergence, gap of the two methods are significantly lower and the CKA is higher than those of vanilla KD with different temperature).
3) The accuracy of the final student network trained by our two methods is similar, although the gap between them is quite different (32.4 vs. 16.8).
The KL-Divergence (0.42 vs. 0.23), CKA of logits and predictions (almost same) of these two proposed methods are closer, which shows that our methods can guarantee the consistency of teacher and student characteristics.
It also illustrates that gap and KL-Divergence interpret the similarity of output distributions from different perspectives.

To explore the changes in class distribution of logits, we visualize the penultimate layer representation of student model ResNet20 on dataset CIFAR-100 as shown in Figure \ref{tsne}. We randomly choose 7 classes and record the penultimate layer representation in the 230-th epoch and then use T-SNE to project the data in the 2D plane. Classes are marked with different colors.
We observe that clusters in our proposed approach are tighter because the student model is encouraged to learn more information from all other class templates in the training data set by narrowing the sharpness gap between teacher and student networks.
Besides, when looking at the projections, some clusters, i.e., crimson and dark blue ones, are more discernible in our proposed models than in Vanilla KD.

\begin{figure}[]
\centering
\small
\begin{minipage}[t]{0.22\linewidth}
    \centering
    \includegraphics[width=1\linewidth]{kl2.png}
    \caption{kl loss comparison of our proposed approach and vanilla KD.

    }
    \label{kl_loss}
\end{minipage}
\hspace{1em}
\begin{minipage}[]{0.22\linewidth}

    \centering
    \includegraphics[width=1\linewidth]{kl3.png}
    \caption{kl loss comparison of vanilla KD with different temperature.

    }
    \label{kl_loss_t}
\end{minipage}
\hspace{1em}
\begin{minipage}[]{0.22\linewidth}
    \centering
    \includegraphics[width=\linewidth]{CKA.png}
    \caption{CKA of feature ( last layer before classifier ).

    }
    \label{CKA_feature}
\end{minipage}
\hspace{1em}
    \begin{minipage}[]{0.22\linewidth}
    \includegraphics[width=\linewidth]{CKA_logit.png}
    \caption{CKA of logit vanilla KD and ourproposed approach

    }
    \label{CKA_logit}
    \end{minipage}
\end{figure}

\begin{figure*}[t]
\centering
\subfigure[Vanilla KD]{
\begin{minipage}[]{0.3\linewidth}
    \centering
    \includegraphics[width=\linewidth]{vanilla_feat.png}
    \label{visulization of vanilla KD}
\end{minipage}
}
\hspace{0.5em}
\subfigure[Fine-tuned teacher]{
\begin{minipage}[]{0.3\linewidth}
    \centering
    \includegraphics[width=\linewidth]{gt_feat.png}
    \label{visulization of fine-tuned teacher}
\end{minipage}
}
\hspace{0.5em}
\subfigure[PESF-KD]
{\begin{minipage}[]{0.3\linewidth}
    \centering
    \includegraphics[width=\linewidth]{adapter_feat.png}
    \label{visulization of adaptive teacher}
\end{minipage}
}
\caption{Visualize of penultimate layer  representation of Vanilla KD, w/ fine-tuned teacher and PESF-KD with 7 semantically different classes using T-SNE~\cite{van2008visualizing}.
\textbf{Clusters are more tighter and distinguishable in our proposed approach}\label{tsne}
}
\end{figure*}
\section{Experiments}
\label{exp}

\subsection{Datasets and Baselines}
\textbf{Datasets} Two types of tasks incluing image classification (CIFAR-100~\cite{cifar100} and ImageNet~\cite{DBLP:conf/cvpr/DengDSLL009/imagenet}) and natural language understanding (GLUE~\cite{DBLP:conf/iclr/WangSMHLB19/glue} ) are adopted for a series of experiments.
Specifically, we test on two types of NLU datasets according to the amount of training resource.
\textit{\textbf{Low resource}}: MRPC~\cite{mrpc} and STS-B~\cite{sts-b} for Paraphrase Similarity Matching; SST-2~\cite{sst-2} for Sentiment Classification ; RTE~\cite{DBLP:conf/iclr/WangSMHLB19/glue} for the Natural Language Inference.
\textit{\textbf{High Resource}} :QNLI~\cite{qnli} for the Natural Language Inference and QQP
\footnote{https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs}
for Paraphrase Similarity Matching.
\\
\textbf{Baselines}
We report several knowledge distillation methods for comparison, including vanilla KD~\cite{DBLP:journals/corr/HintonVD15}, knowledge distillation via collaborative learning (KDCL) \cite{KDCL}, deep mutual learning (DML) ~\cite{DML}, contrastive representation distillation (CRD)~\cite{CRD}, relational knowledge distillation (RKD)~\cite{DBLP:conf/cvpr/ParkKLC19/RKD} and probabilistic knowledge transfer(PKT) ~\cite{DBLP:conf/eccv/PassalisT18/PKT}.
According to ~\citet{DBLP:journals/ijcv/GouYMT21}, KD methods can be divided into two groups, online distillation and offline distillation.
For a more fine-grained comparison, We further split them into three different kinds, online KD (DML, KDCL), offline KD (vanilla KD, PKT) and representation KD (CRD, RKD).
Besides these methods, we report the result of our method ``w/ Fine-tuned teacher'' and ``PESF-KD'' to support our argument about less sharpness gap helps student to perform better to absorb the knowledge of the teacher.

\begin{table*}[t!]
\small
\caption{Results on CIFAR-100 test set.
We compare the training time consumption (batch time) and the amount of parameters  needed to update (params) and the corresponding accuracy (@1) on various teacher-student combinations. The best results are \textbf{bold}. The second best results are \underline{underlined}.}
\vspace{1em}
\label{result_cifar100}
\resizebox{\textwidth}{!}
{
\begin{tabular}{@{}cccccccccccccc@{}}
\toprule
                                   &                                                               & \multicolumn{3}{c}{resnet56-20} & \multicolumn{3}{c}{resnet110-32} & \multicolumn{3}{c}{vgg13-8}  & \multicolumn{3}{c}{resnet56-vgg8} \\ \midrule
                                   & method                                                        & batch time   & params  & @1     & batch time   & params   & @1     & batch time & params & @1     & batch time   & params    & @1     \\ \midrule
\multirow{2}{*}{offline KD}        & Vanilla KD\cite{DBLP:journals/corr/HintonVD15}                                                    & 0.219        & 1.11M   & 70.95  & 0.319        & 1.89M    & 73.08  & 0.097      & 15.86M & 73.36  & 0.134        & 15.86M    & 73.98  \\
                                   & PKT\cite{DBLP:conf/eccv/PassalisT18/PKT}                                                           & 0.225        & 1.11M   & 71.27  & 0.336        & 1.89M    & 73.67  & 0.120       & 15.86M & 73.40   & 0.139        & 15.86M    & 74.10   \\ \midrule
\multirow{2}{*}{Representation KD} & CRD\cite{CRD}                                                           & 0.234        & 1.18M   & 71.44  & 0.353        & 1.96M    & 73.62  & 0.139      & 16.39M & 73.31  & 0.159        & 15.93M    & 74.06  \\
                                   & RKD~\cite{DBLP:conf/cvpr/ParkKLC19/RKD}                                                           & 0.234        & 1.11M   & 71.47  & 0.846        & 1.89M    & 73.53  & 0.207      & 15.86M & \underline{74.15}  & 0.444        & 15.86M    & 73.35  \\ \midrule
\multirow{2}{*}{online KD}&KDCL~\cite{KDCL}&0.520&4.56M& 70.11&1.035&8.84M&72.87& 0.190 & 53.71M & 73.99&0.433&19.31M& 73.16  \\
& DML\cite{DML}& 0.524        & 4.56M& 69.40  & 0.912        & 8.84M& 72.21  & 0.190       & 53.71M& \textbf{74.18}  & 0.437        & 19.31M & 73.86  \\ \midrule
\multirow{2}{*}{ours (online KD)}               & \begin{tabular}[c]{@{}c@{}}w/ Fine-tuned \\ teacher\end{tabular} & 0.481        & 4.56M &\textbf{71.65}   & 0.819        & 8.84M& \textbf{73.90}    & 0.156      & 53.71M& 73.52  & 0.443        & 19.31M &\textbf{74.40}    \\
                                   & \begin{tabular}[c]{@{}c@{}}PESF-KD\end{tabular}   & 0.228        & 1.16M   & \underline{71.63}  & 0.325        & 1.94M    & \underline{73.86}  & 0.117      & 15.91M & 73.79  & 0.136        & 15.91M    & \underline{74.29}  \\
\bottomrule
\end{tabular}}\end{table*}

\begin{table}[t!]
\small
\centering
\begin{minipage}[t]{0.7\textwidth}
\caption{Results on CIFAR-100 test set. (continue)}
\label{acc_cifar100}
\resizebox{\textwidth}{!}
{
\begin{tabular}{@{}ccccc@{}}
\toprule
teacher                     & resnet56 & resnet110 & vgg13 & resnet56 \\
student                     & resnet20 & resnet32  & vgg8  & vgg8     \\ \midrule
teacher                     & 72.34    & 74.31     & 74.64 & 79.34    \\
student                     & 69.06    & 71.14     & 70.36 & 70.36    \\ \midrule
KD\cite{DBLP:journals/corr/HintonVD15}                          & 70.66    & 73.08     & 72.98 & 73.81    \\
FitNet\cite{journals/corr/RomeroBKCGB14}                      & 69.21    & 71.06     & 71.02 & 70.69    \\
AT\cite{DBLP:conf/iclr/ZagoruykoK17}                          & 70.55    & 72.31     & 71.43 & 71.84    \\
SP\cite{DBLP:conf/iccv/TungM19}                          & 69.67    & 72.69     & 72.68 & 73.34    \\
CC\cite{DBLP:conf/iccv/PengJLZWLZ019}                          & 69.63    & 71.48     & 70.71 & 70.25    \\
VID\cite{DBLP:conf/cvpr/AhnHDLD19}                         & 70.38    & 72.61     & 71.23 & 70.30     \\
AB\cite{DBLP:conf/aaai/HeoLY019a}                          & 69.47    & 70.98     & 70.94 & 70.65    \\
FT\cite{DBLP:conf/nips/KimPK18}                          & 69.84    & 72.37     & 70.58 & 70.29    \\
FSP\cite{DBLP:conf/cvpr/YimJBK17}                         & 69.95    & 71.89     & 70.23 & 73.90     \\
w/ Fine-tuned teacher & \textbf{71.66}    & \textbf{73.89}     & \underline{73.51} & \textbf{74.40}     \\
PESF-KD          & \underline{71.64}    & \underline{73.85}     & \textbf{73.79} & \underline{74.29}    \\ \bottomrule
\end{tabular}
}
\end{minipage}
\end{table}
\begin{table}[t!]
\centering
\begin{minipage}[t]{0.9\textwidth}
\caption{Results on ImageNet test set. ResNet18 is the student model and ResNet50 is the teacher model. We report the averaged results over 3 random seeds. The best results are \textbf{bold}. The second best results are \underline{underlined}.}
\label{result_imagenet}
\centering
\resizebox{1\textwidth}{!}
{
\begin{tabular}{ccccccc}
\toprule
                                   & method            & batch time & params & @1      & KL loss & GAP   \\ \midrule
\multirow{2}{*}{offline KD}        & Vanilla KD\cite{DBLP:journals/corr/HintonVD15}        & 0.46      & 47M& 69.20    &    10.3     &      19.3 \\
                                   & PKT\cite{PKD}& 0.47      &47M& 69.69& 10.3   & 19.2  \\
\multirow{2}{*}{online KD}         & KDCL\cite{KDCL}              & 1.56       & 149M& \textbf{70.17}   & \textbf{6.7}    & 31.8  \\
                                   & DML\cite{DML}               & 1.45      & 149M    & 68.45   & \textbf{6.7}    & \textbf{14.1}  \\
\multirow{2}{*}{Representation KD} & CRD\cite{CRD}               & 0.57       &63M&69.33&10.5  &20.0 \\
                                   & RKD\cite{DBLP:conf/cvpr/ParkKLC19/RKD}&1.53&47M&69.52&9.6&20.5 \\
\multirow{2}{*}{ours (online KD)}&w/ Fine-tuned teacher&1.44&149M&\underline{70.00}& \textbf{6.7}&\underline{14.9}   \\
                                   & PESF-KD           & 0.47      & 54M& 69.96   & 8.9    & 18.6
                                \\
\bottomrule
\end{tabular}
}
\end{minipage}
\end{table}

\subsection{Experimental Setup}

For CV tasks, we follow previous works~\cite{CRD,meta-kd} using various combination of student \& teacher networks. Each pair of student \& teacher networks are from different capacity and architecture, including ResNet~\cite{DBLP:conf/cvpr/HeZRS16/resnet} and VGG~\cite{vggnet}. We run two types of distillations, isomorphic distillation and isomeric distillation.
For isomorphic distillation, we run three different combinations (ResNet56-ResNet20, ResNet 110-ResNet32 and VGG13-VGG8). For isomerism distillation, the results of ResNet-56 to VGG-8 are reported. For NLU tasks, we first fine-tune the pre-trained teacher (12-layers version of BERT-Base) and then train student model (6-layers version of BERT-Base) on each downstream task.
We report \textit{Top 1 Accuracy} (@1) for image classification experiments as a network performance metric.
For NLU tasks, we report the same format as on the GLUE leaderboard.
 For MRPC and QQP we report \textit{F1}. For STS-B, we report \textit{Pearson and Spearman correlation}. For other tasks we report \textit{accuracy}. Besides, to measure network consistency between teachers and students , we report average predicted \textit{KL divergence} (KL), \textit{CKA consistency}~\cite{CKA} (CKA) and \textit{the sharpness gap}~\cite{guo2022reducing} (GAP) as similiarity metrics.

\subsection{Results}
\begin{table*}[t!]
\small
\centering
\caption{Results on the development set of GLUE with our low \& high resource division.
We consider tasks with fewer than 100K as low-resource tasks and others are high-resource tasks. The best results are \textbf{bold}. The second best results are \underline{underlined}. We report the averaged results over 3 random seeds.
}
\label{result_GLUE}
\resizebox{1\textwidth}{!}
{
\begin{tabular}{@{}ccccccccc@{}}
\toprule
                   &          &         & \multicolumn{4}{c}{low resource}                 & \multicolumn{2}{c}{high resource} \\ \midrule
method             & \#Params & Time & \begin{tabular}[c]{@{}c@{}}MRPC\\ $(3.7K)$\end{tabular} & \begin{tabular}[c]{@{}c@{}}STS-B\\ $(5.7K)$\end{tabular} & \begin{tabular}[c]{@{}c@{}}RTE\\ $(2.5K)$\end{tabular} & \begin{tabular}[c]{@{}c@{}}SST-2\\ $(67K)$\end{tabular} & \begin{tabular}[c]{@{}c@{}}QNLI\\ $(105K)$\end{tabular}     &  \begin{tabular}[c]{@{}c@{}}QQP\\ $(364K)$\end{tabular}               \\ \midrule
BERT-base$_{teacher}$            & -        & -       & 91.6 & 90.2/89.8 & 71.4      & 93    & 91.2        & 88.5         \\
BERT-base$_{student}$            & -        & -       & 89.2 & 88.1/87.9 & 67.9      & 91.1  & 88.6        & 86.9         \\ \midrule
Vanilla KD\cite{DBLP:journals/corr/HintonVD15}            & 66M      & 1.00x   & 89.6 & \underline{88.6}/88.2   & 67.7      & 91.2  & 89.0           & 87.3         \\
RCO\cite{RCO}                & >176M      & >2.66x   & \textbf{90.5} & \textbf{88.7}/\underline{88.3} & 67.6     & 91.4  & \textbf{89.7}        & 87.4         \\
TAKD\cite{DBLP:conf/aaai/MirzadehFLLMG20}               & >132M      & >2.00x   & 89.6 & 88.2/88.0 &
\underline{68.5}      & 91.4  & 89.6          & 87.5         \\
DML\cite{DML}                & 176M      & 2.66x   & 89.6 & 88.4/88.1 & 68.4      & \underline{91.5}  & \underline{89.6}            & 87.4         \\
SFTN\cite{park2021learning}               & >176M      & >2.66x   & \underline{89.8} & 88.4/\textbf{88.5}  & \textbf{69.4}      & \underline{91.5}  & 89.5       & 87.5         \\
PKD\cite{PKD}                & 66M      & >1.05x   & 89.4 & \underline{88.6}/88.1 & 67.6      & 91.3  & 89.5          & \textbf{87.8}        \\
w/ Fine-tuned teacher & 176M     & 2.66x   & \underline{89.8} & \underline{88.6}/\underline{88.3} & 68.2      & \textbf{91.7}  & 89.2       & 87.2         \\
PESF-KD   & 66M      & 1.05x   & 89.7 & \underline{88.6}/\underline{88.3} & 68.2      & \underline{91.5}  & 89.1       & \underline{87.7}        \\ \bottomrule
\end{tabular}
}
\end{table*}
\subsubsection{Results on CIFAR-100}
This section compares different KD methods, which can be grouped as offline KD, representation KD, and online KD, and their training running time (batch time), need for updated parameters (params), and the corresponding accuracy., as shown in Table \ref{result_cifar100}.
Encouragingly, our experiments fit our two previous hypotheses.
On the one hand, through the joint training of the teacher network and the student network, the problem of difficult knowledge transfer when the teacher-student capacity does not match can be alleviated. Specifically, the distillation results of the online distillation methods are significantly higher than those of the offline distillation methods, especially in the case of excessive difference in network capacity between teachers and students (vgg13-8) get a huge improvement.
And our method ``w/ Fine-tuned teacher'' achieved the best results among almost all distillation methods, such as 71.65 for resnet56-20 and 73.90 for resnet110-32
and 74.40 for resnet56-vgg8.

On the other hand, through the adapter module, the amount of parameters that teachers need to update can be reduced on the premise of alleviating the problem of capacity mismatch, thereby reducing the cost of training.
As shown in Table \ref{result_cifar100}, the method of online distillation will greatly increase the training cost due to the need to update the parameters of the teacher network and the student network synchronously (see the batch time change).
While PESF-KD significantly reduces the number of parameters that need to be updated for training, i.e., vgg13-8 reduces the number of parameters from the $\sim$ 54M to $\sim$ 16M.
Obviously, it also significantly reduces the time required for training.
Whereas in most combinations of teacher-student architectures on distillation, even if using adaptive teacher has a slight reduction in the best distillation results, such reduction is negligible. To further illustrate the superiority of our methods, we further compare the current typical distillation methods like previous work~\cite{CRD}, as shown in Table \ref{acc_cifar100}. Results of baselines in Table \ref{acc_cifar100} are reported  by \cite{meta-kd}.
The results show that our two proposed methods achieve the best results among these methods under a broad combination of teacher-student architectures in distillation.

\subsubsection{Results on ImageNet}
We further conduct experiments for ResNet (ResNet50, 102.2M to ResNet18, 46.8M) distillation on a lager dataset ImageNet. As shown in Table 3, our methods achieves comparable Top-1 accuracy results compared to other baselines, including SOTA representation KD method CRD (70.00 for fine-tuned teacher; 69.96 for PESF-KD). Notably, compared with other online KD method KDCL (70.17), student trained with fine-tuned teacher show similar top-1 accuracy (fall behind 0.17 point) and the same KL loss. That's partly because both the methods have similar working mechanisms which allow part of the teacher model to be trainable during the student training process. But KDCL fails to fill the sharpness gap between teacher and student (31.8), while our methods are more capable of narrowing the gap (14.9 and 19.2). Besides, the student trained with the adaptive teacher reach similar top-1 accuracy (69.96) compared with fine-tuned teacher (70.00) and only require less than one-third the time (0.47 batch time for adaptive teacher and 1.44 batch time for fine-tuned teacher). That fits our assumption of PESF-KD to improve performance and reduce training time.

\subsubsection{Results on GLUE}

In Table \ref{result_GLUE}, we further explore the effect of the PESF-KD in NLP dataset, and other baselines are reported by \cite{meta-kd}. Time in the Table \ref{result_GLUE} refer to training resources cost, which is the lowest consumption with our PESF-KD compared with other baselines except for vanilla KD.
In \cite{zhang2022fine}, they find that the overly strict regularization of KD is unnecessary in NLP datasets, which will actually reduce the final performance using KD in most cases of GLUE. However, PESF-KD can even bring further improvements to most datasets with KD, probably due to better student learning from teachers.
Compared to vanilla  KD~\cite{DBLP:journals/corr/HintonVD15}, PESF-KD achieve consistent improvement in low-resource situations, showing that the same effect of low-resource adaptability~\cite{DBLP:conf/icml/adapter}.
Similar to the results of CV datasets, our method also obtains similar results and requires minimal training cost compared with baselines on NLP datasets,
and even surpasses other baselines on SST-2,
which verifies the generalizability of our methods.

\begin{figure*}[t]
  \begin{minipage}[t]{0.42\linewidth}
      \centering
      \includegraphics[width=\linewidth]{alpha.png}
      \caption{Influence of Loss weight on results of different KD methods.  \textbf{$\alpha$ = 0.9  is an appropriate choice.}
      \label{alpha_ablation}}
\end{minipage}
  \hspace{5em}
  \begin{minipage}[t]{0.42\linewidth}
      \centering
      \includegraphics[width=\linewidth]{temperature.png}
      \caption{Influence of temperature on results of different KD methods.  \textbf{T=4 is an appropriate choice.}}
      \label{temperature_ablation}
  \end{minipage}
\end{figure*}

\begin{table}[t!]
 \small
 \centering
 \hspace{-1.4em}
\begin{minipage}[t]{0.58\textwidth}
\caption{Ablation on different structures of adapter in VGG. Experiments are performed from CIFAR-100. \textbf{The simple 3-layer adapter shows better performance compared with other parameter efficient method in top 1 accuracy, CKA and time consuming.}}
\label{vgg_ablation}
\resizebox{\textwidth}{!}
{
 \begin{tabular}{@{}ccccc@{}}
 \toprule
 method          & batch time & parameter & top-1 & CKA    \\ \midrule
 adapter \cite{DBLP:conf/icml/adapter}       & 0.137      & 15.95M    & 73.43 & 0.8758 \\
 LoRA \cite{lora}           & 0.166      & 15.95M    & 73.49 & 0.8579 \\
 Scaled PA \cite{toward_unified_view_adapter}    & 0.165      & 15.95M    & 73.13 & 0.8594 \\ \bottomrule
 \end{tabular}
}
\end{minipage}
\hspace{0.3em}
\begin{minipage}[t]{0.4\textwidth}
\caption{Comparison of different methods with PESF-KD. \textbf{It shows that PESF-KD has the potential to enhance distillation performance on other KD methods.}}
\label{ablation of different method}
\centering
\resizebox{1\textwidth}{!}
{
\begin{tabular}{@{}ccc@{}}
\toprule
teacher/student & \multicolumn{2}{c}{resnet56/resnet20} \\ \midrule
method & stadard           & PESF-KD           \\ \midrule
KD~\cite{DBLP:journals/corr/HintonVD15}              & 70.95             & 71.63             \\
PKT~\cite{DBLP:conf/eccv/PassalisT18/PKT}             & 71.27             & 71.74             \\
CRD~\cite{CRD}             & 71.44             & 71.76             \\
RKD~\cite{DBLP:conf/cvpr/ParkKLC19/RKD}             & 71.47             & 71.52             \\ \bottomrule
\end{tabular}}
\end{minipage}
\end{table}

\subsection{Analysis}

\paragraph{Adapter Architecture}
The adapter module is our recipe for success in the above performance comparisons. To explore the influence of adapter structure on classification results, we compare three classic adapter structures and report their top-1 accuracy and CKA consistency.
We use the teacher-student combination of vgg13-8 and conduct comparative experiments on CIFAR-100.
As can be seen from Table \ref{vgg_ablation},
the simple and efficient adapter achieve second  performance in top-1 accuracy, best CKA scores  and time consuming.
Therefore we use simple adapter module in all experiments.

\paragraph{Loss Weight} The hyper-parameter $\alpha$ decides the percent of cross entropy loss between the ground truth labels and student models prediction, and the KL loss between student and teacher models.
Bigger $\alpha$ means a higher percent of kl loss. Figure \ref{alpha_ablation} shows that  bigger $\alpha$ benefits student models in PESF-KD. That's partly support our hypothesis of PESF-KD to promote a easy-learning teacher.

\paragraph{Temperature} Figure \ref{temperature_ablation} shows the performance of student models in differ temperature. In the experiments vanilla KD and our proposed approach has similar trend in different temperatures. And PESF-KD shows better performance in all experiments.

\paragraph{Results combined with other methods} We also test PESF-KD in other methods to verify the potential improvement on other knowledge distillation methods. Table \ref{ablation of different method} shows the results of four knowledge distillation approaches (vanilla KD~\cite{DBLP:journals/corr/HintonVD15}, PKT~\cite{DBLP:conf/eccv/PassalisT18/PKT}, CRD~\cite{CRD} and ~RKD\cite{DBLP:conf/cvpr/ParkKLC19/RKD}) combined with PESF-KD on CIFAR-100.
Compared with standard knowledge distillation approaches, results with  PESF-KD get better performance on top-1 accuracy and indicate that PESF-KD has the potential to become a plug-in method on top of different KD methods.
\section{Conclusion}
In this paper, we present PESF-KD, a novel knowledge distillation framework by applying adapters to optimize the teacher network for better knowledge transfer to the student network. Through detailed analysis, we point out that the decline in sharpness and a better ability to distinguish within classes lead to better knowledge transfer, which leads to better results.
Extensive experiments demonstrate the robustness and effectiveness of our method.
Future works of embedding PESF-KD into existing distillation framework is expected to generalize our methods.

{\small
\bibliographystyle{acl_natbib}

redpajama_set_name: RedPajamaArXiv


Our Angel Wings Sun Catcher is extremely unique, as it connects directly to your own personal experiences, whether it be celebration or sadness. The Angel Wings symbol is related, of course, to the Angelic realm, which symbolises protection, affection, and harmony. Angels provide hope and happiness, which is exactly why this beautiful Sun catcher was created.
It will be sent out to you carefully gift wrapped.


text:
The Fender Stratocaster
Greatest Players
Dave Murray
David Michael Murray was born 23 December 1956 in Edmonton, England in a family that lived in poverty, thus forcing them to constantly move to different areas of London. Murray is a guitarist and songwriter and one of the earliest members of the British heavy metal band Iron Maiden as well as the only musician who has appeared on all of the band's releases together with the group's founder, bassist and primary songwriter Steve Harris. He also considered one of the preeminent characters of the New Wave of British Heavy Metal (better known as NWOBHM) movement of the late 1970s.
In 1976, Murray answered an advertisement in U.K. music magazine Melody Maker offering the Stratocaster that belonged to recently deceased Free guitarist Paul Kossoff. After doublechecking the guitar's serial number, Murray gave the equivalent of $1,400 for it. At the time, that was a princely sum for a person of the working-class. "[Kossoff] used that guitar on a lot of Free" Murray explained to www.gear-vault.com in 2009. "I actually saw him many years ago using it during a Free performance of 'My Brother Jake' on an English television show called Top of the Pops. They were one of my favorite bands, and I had to have that guitar because it belonged to Kossoff. I paid about $1,400 for it, which in 1976 was quite a bit of money. But I didnt care. I just sold everything I had so I could get it. And I used it from then on. It just felt like I was holding a piece of magic, because he used this guitar." On the same year, Murray answered another ad of Melody Maker and was auditioned for Iron Maiden.
Kossoff's Stratocaster, easily identifiable for its unconventional H/S/H configuration, featured a 1957 body and a 1963 rosewood-board neck. Murray added to it a DiMarzio humbucker in the bridge position to obtain a thicker sound and to cancel the sense of movement that is aided by the normally angled bridge pickup.
The Iron Maiden's first eight albums were recorded with this Stratocaster, which followed Murray also on tour from 1978 to 1990. "It was my main guitar," he said, "and I played everything with it: lead and rhythm, clean stuff, heavy stuff. It was real versatile."
This guitar is also at a centre of a debate about whether it's the same white-with-mint-pickguard Stratocaster featured on the cover of Kossoff's solo album "Back Street Crawler" released in 1973. Some say that Murray painted the guitar whereas others claim that it was already black when he acquired it. Given Murray's deep admiration of Kossoff, his understanding of the instrument's significance and the considerable financial sacrifice he had to make to buy it, it is unlikely that he would have had the guitar painted.
Murray switched onto ESP and Jackson guitars in the late '80s and early '90s. He then returned to Fender in 1995 and, since 2010, his main guitars has been a Sunburst California Series Stratocaster loaded with Seymour Duncan Hot Rail blade-type pickups and a Floyd Rose tremolo system. In 2009 his black Strat was honoured with a Signature Series model.
Next player: Bonnie Raitt
Get the latest news, discounts, sales & promotions directly to your inbox.
© 2022 Strat '54.


On August 19, ten days after police murdered Michael Brown in Ferguson, Missouri, a slew of corporate media stories appeared charging that "criminals" and "outside agitators" were responsible for clashes during the protests. CNN alleged that "all sides agree there are a select number of people—distinct from the majority of protesters—who are fomenting violence," quoting a State Highway Patrol Captain, a State Senator, and a former FBI assistant director to confirm this.
So when we hear them say "outside agitators," we know the authorities are getting ready to spill blood. All the better, from their perspective, if people buy into this rhetoric and police themselves so no officer has to get his hands dirty. This is often called for in the name of avoiding violence, but self-policing returns us to the same passivity that enables police violence to occur in the first place. How many people would have even heard about Michael Brown if not for the "criminals" and "agitators" who brought his death to our attention? Self-policing also preserves the impression that we all choose this state of affairs of our own free will, reinforcing the impression that anyone who does not is an outsider.
Still, let's imagine that some of these arrestees who gave out-of-town addresses are in Ferguson for the very first time. Wouldn't that make them outside agitators? Perhaps it would, if the issue was specific to Ferguson alone and they had no stake in it. But in "Chicago, Brooklyn, Washington, San Francisco, Austin, Des Moines, and Huntsville, Alabama" the police have killed black men under identical circumstances. The militarization, brutality, and systematic racism of the police are in effect all around the country, not just in Ferguson. When people are suffering the same forms of oppression everywhere, it makes sense for us to come to each other's assistance, to make common cause.
In this light, it is ironic, if not unexpected, that one of the corporate media stereotypes of the "outside agitator" is the "white anarchist"—as if all anarchists were white. It's no longer considered decorous to call people race traitors, so the allegation is inverted: white people who fight alongside black and brown people must not have their best interests at heart, certainly not as much as the police and corporate media do. Although declaring oneself an anarchist does not magically free a white person of the racism that pervades our society, it is racist indeed to attribute all the unrest in Ferguson to "white anarchists," denying the existence or agency of black and brown participants.


MIT report on Aaron Swartz's prosecution is out, and it's a "whitewash"
MIT's report on its involvement in the prosecution of Aaron Swartz (PDF) has been published. The report does not apportion any blame to the university for Swartz's prosecution, stating the the university operated as a "neutral party."
Taren Stinebrickner-Kauffman, Aaron's partner, vigorously disputes the report's findings, calling it a whitewash, pointing out that MIT provided significant aid to the federal prosecutors who chased Aaron over downloading technical aritcles (which he was entitled to see) from its network, but refused to supply the same documents to the defense team, who desperately needed them. This makes MIT's claim of "neutrality" ring false.
Further, Larry Lessig has posted some preliminary thoughts on MIT's position, pointing out that it turned on a question of authorized or unauthorized access, and that the report says MIT never told the prosecutors that Aaron's access was "unauthorized," suggesting that the prosecutors knew they had no case.
MIT's behavior throughout the case was reprehensible, and this report is quite frankly a whitewash.
Here are the facts: This report claims that MIT was "neutral" — but MIT's lawyers gave prosecutors total access to witnesses and evidence, while refusing access to Aaron's lawyers to the exact same witnesses and evidence. That's not neutral. The fact is that all MIT had to do was say publicly, "We don't want this prosecution to go forward" – and Steve Heymann and Carmen Ortiz would have had no case. We have an institution to contrast MIT with – JSTOR, who came out immediately and publicly against the prosecution. Aaron would be alive today if MIT had acted as JSTOR did. MIT had a moral imperative to do so.
And even now, MIT is still stonewalling. Wired reporter Kevin Poulsen FOIA'd the Secret Service's files on Aaron's case, and judge ordered them to be released. The only reason they haven't been is because MIT has filed an objection. If MIT is at all serious about implementing any reforms to stop this kind of tragedy from happening again, it must stop objecting to the release of information about the case."
TarenSK | MIT report is a whitewash. My statement in response.


Solo Service Group are recruiting a cleaner.
All successful applicants will be subject to Police Vetting.
To apply contact our Vacancy line on 0844 8794809 leaving your name, contact number and clearly stating 'SOL09' as the area of interest, alternatively you can email vacancies@soloservicegroup.com. Do not apply direct to site.


On the second day of her humanitarian aid visit to Lebanon, Grand Duchess Maria Teresa of Luxembourg visited Taanayel town which is connected to Beqaa Governorship. The Grand Duchess firstly went to the headquarters of Arcenciel Foundation in Taanayel. Arcenciel Foundation is a nonprofit humanitarian aid organization. The Duchess then visited the Bar Elias refugee camp in Bekaa Valley and met with beneficiaries who receive education and microfinance support from Arcenciel. The Grand Duchess then visited a safe house where sexual violence victims live.
M Therese ziet er hier weer fantastisch uit, mooi kapsel, magerder,vriendelijk.
After all this work she is trying to sneak back into a square boxey coat - please no.
Her work and personality are commendable.
MT looks very good in jeans! I'm so use to seeing her in Eli Suab, etc. These jeans are very sliming on her.
And is it just me or are all the royals suddenly hugging people now? Has a certain young Duke and his bride now made it "fashionable okay" for every royal house? LOL.
Love everything on her today.The new hairstyle make her looks so young and the smile it is fantastic. She interacts so well with the children.Nice to see her on a solo tour overseas.
Love M T but don't like anything about this outfit. Sorry.
I truly hate the buttons of her jacket, both because of how they are arranged (too 80s for me) and because of the white colour which cheapens the look tremendously. Other than that, Maria Teresa looks good.


text:
Keyword Analysis & Research: list of countries that accept math studies
list of countries that accept math studies 0.94 0.5 75 53 42
countries 1.39 0.4 6198 54 9
that 1 0.1 3160 13 4
accept 1.9 0.5 3562 32 6
math 1.3 0.3 8531 89 4
studies 1.89 0.1 5189 18 7
Keyword Research: People who searched list of countries that accept math studies also searched
list of countries that accept math studies 0.48 0.6 4433 34
Is Poland a good country for mathematics?
A few years ago, Poland was known as a country of culture and the arts. They had a deep desire to excel more in the fields of science and mathematics that led them to where they are now. They are now considered one of the countries that excel the most in the mathematical field.
Which country has the highest reading and math scores?
Country Total Score Reading Score Math Score Science Score South Korea 1623 539 546 538 Japan 1588 520 529 539 Canada 1580 524 527 529 New Zealand 1572 521 519 532 61 more rows ...
How many countries participate in the International Student Assessment Program?
More than 250,000 15-year old students from 41 countries participated in the assessment. The countries included all major industrialized nations (results for Britain were not available) and 11 other nations that chose to participate.
What are some of the best studies about education around the world?
One of the most-reviewed studies regarding education around the world involved 470,000 fifteen-year-old students. Each student was administered tests in math, science, and reading similar to the SAT or ACT exams (standardized tests used for college admissions in the U.S.)
Search Results related to list of countries that accept math studies on Search Engine
The latest ranking of top countries in math, reading, and
mathnasium.com
https://www.mathnasium.com/Pisaworldwiderank
Dec 06, 2016 · The Organization for Economic Co-operation and Development (OECD) released the results of its 2015 global rankings on student performance in mathematics, reading, and science, on the Program for International Student Assessment, or PISA. The PISA is a worldwide exam administered every three years that measures 15-year-olds in 72 countries. About …
list of countries that accept math studies | The latest
microlinkinc.com
https://www.microlinkinc.com/search/list-of-countries-that-accept-math-studies
https://www.insidermonkey.com/blog/15-smartest-countries-in-math-358222/. Jul 02, 2015 · They are now considered one of the countries that excel the most in the mathematical field. 15 Smartest Countries in Math Australia Belgium Canada China. England France Germany Israel Italy Japan... DA: 29 PA: 57 MOZ Rank: 12.
Revealed: Timss 2019 top countries in science and maths
tes.com
https://www.tes.com/magazine/archived/revealed-timss-2019-top-countries-science-and-maths
Dec 08, 2020 · But Singapore topped all the charts in both subjects and at both age groups for the second time running. East Asia also took the other top five places in maths for both age groups thanks to strong scores from Taiwan, South Korea, Japan and Hong Kong.
International Comparison of Math, Reading, and Science
infoplease.com
https://www.infoplease.com/us/education/international-comparison-math-reading-and-science-skills
International Comparison of Math, Reading, and Science Skills Among 15-Year-Olds. More than 250,000 15-year old students from 41 countries participated in the assessment. The countries included all major industrialized nations (results for Britain were not available) and 11 other nations that chose to participate. The test scores are from 2003.
15 Smartest Countries in Math - Insider Monkey
insidermonkey.com
https://www.insidermonkey.com/blog/15-smartest-countries-in-math-358222/
Jul 02, 2015 · They are now considered one of the countries that excel the most in the mathematical field. 15 Smartest Countries in Math Australia Belgium Canada China. England France Germany Israel Italy Japan...
Find countries and universities that recognize the IB
https://www.ibo.org/university-admission/find-countries-and-universities-that-recognize-the-ib/
Albania Argentina Australia Austria Azerbaijan Bahrain Belgium Belize Bolivia Botswana Cameroon Canada Chile China Colombia Costa Rica Croatia Cyprus Czech Republic Denmark Democratic Republic of the Congo Ecuador Egypt El Salvador Estonia Ethiopia Fiji Finland France Georgia Germany Ghana Greece Guatemala
Courses and universities that accept maths studies?? - The
thestudentroom.co.uk
https://www.thestudentroom.co.uk/showthread.php?t=1951490
There are many (worthwhile) courses which will accept Maths Studies. Obviously, more advanced degrees like engineering which require a high standard of maths are less likely to accept maths studies. If you are unsure, or the level of maths required is not specified, email the course director to clarify this issue.
Education Rankings by Country 2021
worldpopulationreview.com
https://worldpopulationreview.com/country-rankings/education-rankings-by-country
*For the full list of countries' educational systems ranked from best to worst, scroll down. Ironically, despite the United States having the best-surveyed education system on the globe, U.S students consistently score lower in math and science than students from many other countries.
List of Countries (Universities) to Study Abroad Without
afterschoolafrica.com
https://www.afterschoolafrica.com/13715/list-of-countries-to-study-abroad-without-toeflielts-for-international-students/
USAInterestingly, few schools in the US are known to exempt students from certain countries from presenting English proficiency test scores. International students whose national language is English often don't need to take the TOEFL/IELTS. It now becomes important for students seeki… math studies
USAInterestingly, few schools in the US are known to exempt students from certain countries from presenting English proficiency test scores. International students whose national language is English often don't need to take the TOEFL/IELTS. It now becomes important for students seeki…
math studies


text:
Q: Increment Core data attribute I have the following relationship in my app:
Product ->> ProductOrder <<- Order
I then have two Obj-c categories in order to etablish these relationship:
ProductOrder+Product
+ (ProductOrder *)addProductToOrderWithProduct:(Product *)product inManagedObjectContext:(NSManagedObjectContext *)context {

    ProductOrder *orderProduct = nil;

    orderProduct = [NSEntityDescription insertNewObjectForEntityForName:@"ProductOrder" inManagedObjectContext:context];

    NSManagedObjectID *productID = [product objectID];

    orderProduct.qnty = product.qnty;
    orderProduct.price = product.price;

    [(Product *)[context objectWithID:productID] addOrderedProductsObject:orderProduct];

    return orderProduct;

}

And then Order+ProductOrder
+ (Order *)addOrderedProductToOrderWithOrderedProduct:(ProductOrder *)orderedProduct inManagedObjectContext:(NSManagedObjectContext *)context {

    Order *order = nil;

   order = [NSEntityDescription insertNewObjectForEntityForName:@"Order" inManagedObjectContext:context];

    NSManagedObjectID *orderedProductID = [orderedProduct objectID];

    [order addOrderProductsObject:(ProductOrder *)[context objectWithID:orderedProductID]];

    return order;

}

I add products to these relationship like so:
   for (Product *prod in [self.fetchedResultsController fetchedObjects]) {

                [[[DataManager sharedInstance] backgroundManagedObjectContext] performBlock:^{

                    ProductOrder *prodOrder = [ProductOrder addProductToOrderWithProduct:prod inManagedObjectContext:[[DataManager sharedInstance] backgroundManagedObjectContext]];

                    Order *order = [Order addOrderedProductToOrderWithOrderedProduct:prodOrder inManagedObjectContext:[[DataManager sharedInstance] backgroundManagedObjectContext]];

                    NSInteger amount = [order.orderNumber integerValue];

                    amount++;

                    order.orderNumber =  [NSString stringWithFormat:@"Order %@", [NSNumber numberWithInteger:amount]];

                    [[DataManager sharedInstance] saveBackgroundContext];
                    [[DataManager sharedInstance] saveMasterContext];

                }];

}

I now want to group these products in headers. I have this orderNumber attribute in my Order entity, I want to increment for every group of products added to the relationship. How would I manage to do this?
Visual example:

A: Your code has a number of problems.
First, is there a particular reason you need different managed object context? If not, eliminate that part. Remember, you can access each managed object's context with
product.managedObjectContext;

Second, you are using ObjectIDs which is really only necessary if you pass objects across contexts. In your method, you extract the ID from product and then call objectWithID to get it back. That does not make any sense at all.
Third, you need a very good reason to have this ProductOrder entity at all. Even if you have not told us, let's assume it is necessary because you want to include, say, different quantities for the products in an order, as well as an ordering number. However, the name you chose is very confusing. Let's call it Item.
Your scheme should now look like this:
Product <---->> Item <<------> Order

You could simply use the Core Data generated methods to add relationships and throw away your categories.
For your table you could fetch the Order entity and inform the datasource as follows:
// number of sections
fetchedObjects.count;

// title for section
Order *order = fetchedObjects[section];
order.name;

// number of rows in section
Order *order = fetchedObjects[section];
order.items.count;

// row data
Order *order = fetchedObjects[section];
NSArray *items = [order.items sortedArrayUsingSortDescriptors:
  @[[NSSortDescriptor sortDescriptorWithKey:@"sequenceNumber" ascending:YES]]];
Item *item = items[indexPath.row];
item.product.name;

redpajama_set_name: RedPajamaStackExchange


1080P Autumn Mountains 23 Wallpapers This Wallpaper was viewed 178 times and downloaded 0 times. 1080P Autumn Mountains 23 Wallpapers Uplaoded by GalaXia in September 26, 2013, 6:16 am . This image have 1600x900px resolution and 465.62 KB of data Approximately 80.94 MB bandwith was consumed for this wallpaper. Last View for this Wallpaper is April 20, 2019, 11:49 am. You can download it for FREE and use it for your wallpaer on your desktop, tablet or other device you have. Have enjoy with this wallpaper :D . Click FULL SIZE for Download This Wallpaper.


Utilizzare un codice sconto per CheBanca semplice : Ti baster scegliere la promozione pi adatta alle tue esigenze dalla nostra lista, e cliccare su "Guarda la..
personalisieren, Werbeanzeigen maßzuschneidern und zu messen sowie die Sicherheit unserer Nutzer zu erhöhen. Many of those stores will accept coupons from their partner store even though it isnt specifically a baby product coupon. The Great American Lobster Fest is the Midwests largest Lobster and Seafood Festival. If you have a coupon for a store whose partner is a baby supply store, you might be in luck. Many organizations will send gift baskets to new mothers with twins. Print these coupons now and clip additional coupons after you print. Enjoy live lobster flown in fresh from the cold waters of the East Coast, live music performances, family-friendly games and activities, unique craft shopping, land food options, and cold beverages.
Not to be combined with any other offer or promotion. Look for printable coupons. Share Share with your friends. Landing Page codici sconto fifa 17 * Offer Type, coupon Code, free Shipping, gift Card. More, see more offers in Foods. From diapers to toys to clothes that wont fit in two weeksyou can spend a lot of money without even realizing. More See more offers in Foods. By using Twitters services you agree to our. Subscribe to our Newsletter. If youre a new parent, you know how expensive baby products can.
Valid for month to month rentals.
Includes 2000 miles per month.
Minimum age to rent is 21 years old.