The study was prepared pursuant to a 1990 law in which Congress directed government scientists to prepare and transmit a report to the president and Congress every four years assembling and interpreting findings from the U.S. Global Change Research Program. This initiative draws together work from 13 federal agencies that conduct or use research on global change and its impacts, in areas ranging from agriculture to national defense. The report is part I of the Fourth National Climate Assessment, with more to follow.
Along with many other others, I was concerned that the Trump administration would reject the report's conclusions, thereby emboldening contrarians and giving decision-makers at all levels of government cover to ignore climate change in their policy decisions.
Now that the administration has implicitly accepted the report, the question is whether it will have any impact on federal policy. In my view, the chances of that are slim to none. However, letting the report stand has strengthened the case for action to slow and otherwise respond to climate change across the country, notwithstanding President Trump's reluctance to lead from Washington.
The Climate Science Special Report directly contradicts many statements by Trump administration officials.
The Office of Science and Technology Policy, where this report landed, was created by Congress in 1976 to advise the president and coordinate among agencies on issues ranging from climate change to space exploration. Historically the office's director has also served as science adviser to the president, a post that is subject to Senate confirmation.
During final preparation of the Third National Climate Assessment in 2013, I served as vice chair of the National Climate Assessment Development and Advisory Committee. Our job was to bring together an assessment written by nearly 300 authors and deliver it to the Obama administration in a form that nonscientists could understand, while preserving the integrity of the science that went into it.
As the report neared completion, we were regularly called to meetings in Washington, D.C. with John Holdren, President Barack Obama's science adviser. Our work focused on new climate science studies: We examined recent findings and discussed how they affected our confidence in our key messages and the evidence on which we based each of our conclusions.
President Obama released the full Third National Climate Assessment in the Rose Garden on May 6, 2014. In contrast, last week's release was like trick-or-treating on Halloween and coming to a house with a bowl of candy at the door but nobody home.
That's because the White House Science Office is currently an empty shell. President Trump has yet to nominate a director for the office and has no science adviser. There is no one there to act on the report's findings, or on part II of the new assessment on climate change impacts, risks and adaption, which is scheduled for completion in early 2018.
The Trump administration is not legally required to incorporate the assessment's findings in its policy and budgetary decisions, and its strong tilt toward policies that promote fossil fuels strongly indicates that it will not do so.
Congress will have more trouble dismissing proposed legislation that takes climate change risk into account.
It will be harder for federal agencies to justify barring the mention of climate change from documents and websites or preventing their scientists from giving presentations on climate change.
Shareholders of major corporations can cite the climate science report as a basis for urging CEOs to heed material climate risk.
Local governments will have stronger cases for action to ameliorate impacts of climate change and reduce greenhouse gas emissions. For example, Miami is considering a general obligation bond proposal that would use new property taxes to underwrite a US$200 million down payment on an anticipated $900 million investment to upgrade flood prevention and drainage systems.
The Trump administration will have a harder time making its case in court for proposed actions such as repealing the Clean Power Plan.
In summary, action to mitigate and adapt to climate change has already moved beyond the D.C. Beltway, including quiet steps in red-state rural areas. In my view, the Trump administration's stamp of "non-disapproval" on the climate science report will only fuel these efforts.


Our Strapped design may be just what you looking for if you want something innovative with a lot of airbrushed 3D! This design features an original blade layout with beveled edge effects appearing to look tapered done only with a secondary hand layout. Inside these silver beveled blades is our signature leather detailed to the highest degree and done in dark leather jacket colors to match your gear. These Harley scull logos are all airbrushed and worked into the layout with an overlapping blade. Drop shadows were applied to add dimension and a floating effect to the graphics. Under all this is a blend-fade from jet black to deep kandy apple red back to jet black along the sides of each part. Underneath this true kandy apple red is an ultra flash course metallic which shines through our transparent kandy. There is mostly black on this design and the red areas are deep and subtle until the sun is beating directly on it and our vivid colors are activated.


Want to be notified of new releases in sethm/symon?
See the file COPYING for license.
Symon is a general purpose simulator for systems based on the MOS Technologies 6502 microprocessor and compatibles. Symon is implemented in Java. Its core goals are accuracy, ease of development, clear documentation, and extensive test suites for validating correctness.
Symon simulates a complete system with a 1 MHz NMOS 6502 or CMOS 65C02, 32KB of RAM, 16KB of ROM, a MOS 6551 or Motorola 6850 ACIA, a MOS 6522 VIA, and an experimental 6545 CRTC.
Symon has extensive unit tests to verify correctness, and fully passes Klaus Dormann's 6502 Functional Test Suite as of version 0.8.2 (See this thread on the 6502.org Forums for more information about this functional test suite).
Symon is under constant, active development. Feedback and patches are always welcome.
Symon can simulate multiple 6502 based architectures. At present, three machines are implemented: Symon (the default), MULTICOMP, and a "Simple" machine useful for debugging.
The CRT Controller uses memory address $7000 as the start of Video memory.
It also provides CPU status. Contents of the accumulator, index registers, processor status flags, disassembly of the instruction register, and stack pointer are all displayed.
The console supports font sizes from 10 to 20 points.
Symon can load any appropriately sized ROM image. The Symon architecture expects as 16KB (16384 byte) ROM image, while the MULTICOMP architecture expects an 8KB (8192 byte) ROM image. Images are loaded via the "Load ROM..." action in the "File" menu. The selected ROM file will be loaded into memory at the correct ROM base address.
Memory contents can be viewed (and edited) one page at a time through the Memory Window.
The last 20,000 execution steps are disassembled and logged to the Trace Log Window.
Simulated speeds may be set from 1MHz to 8MHz.
Breakpoints can be set and removed through the Breakpoints window.
This feature is highly experimental. It's possible to open a video window from the "View" menu. This window simulates the output of a MOS 6545 CRT Controller located at address $9000 and $9001.
By default, the 40 x 25 character display uses video memory located at base address $7000. This means that the memory from address $7000 (28672 decimal) to $73E8 (29672 decimal) is directly mapped to video.
The simulated 6545 supports only the straight binary addressing mode of the real 6545, and not the Row/Column addressing mode.
The simulated 6545 has full 16 bit addressing, where the real 6545 has only a 14-bit address bus.
The simulation is done at a whole-frame level, meaning that lots of 6545 programming tricks that were achieved by updating the frame address during vertical and horizontal sync times are not achievable. There is no way (for example) to change the Display Start Address (R12 and R13) while a frame is being drawn.
This program will fill the video screen with all printable characters.
Maven will build Symon, run unit tests, and produce a jar file in the target directory containing the compiled simulator.
When Symon is running, you should be presented with a simple graphical interface.
The simulator requires a ROM image loaded into memory to work properly. Without a ROM in memory, the simulator will not be able to reset, since the reset vector for the 6502 is located in the ROM address space.
By default, any file named rom.bin that exists in the same directory where Symon is launched will be loaded as a ROM image. ROM images can also be swapped out at run-time with the "Load ROM Image..." in the File menu.
The "samples" directory contains a ROM image for the Symon architecture named 'ehbasic.rom', containing Lee Davison's Enhanced 6502 BASIC. This serves as a good starting point for exploration.
Note: Presently, EhBASIC only works with the Symon machine architecture, not with MULTICOMP.
In addition to ROM images, programs in the form of raw binary object files can be loaded directly into memory from "Load Program..." in the File menu.
Programs are loaded starting at addres $0300. After loading the program, the simulated CPU's reset vector is loaded with the values $00, $03, and the CPU is reset.
There are two very simple sample program in the "samples" directory, for testing.
'echo.prg' will echo back anything typed at the console.
'hello.prg' will continuously print "Hello, 6502 World!" to the console.
After loading a program or ROM image, clicking "Run" will start the simulator running.
1.3.0: 24 February, 2018 - Adds support for 65C02 opcodes.
1.2.1: 8 January, 2016 - Remove dependency on Java 8. Now supports compiling and running under Java 1.7.
1.2.0: 3 January, 2016 - Add symbolic disassembly to breakpoints window.
1.1.1: 2 January, 2016 - Minor enhancement: Allows breakpoints to be added with the Enter key.
1.1.0: 31 December, 2015 - Fixed delay loop to better simulate various clock speeds. Added ability to select clock speed at runtime. Status display now shows the next instruction to be executed, instead of the last instruction executed. Added support for breakpoints.
1.0.0: 10 August, 2014 - Added "Simple" machine implementation, pure RAM with no IO. Added Klaus Dormann's 6502 Functional Tests for further machine verification (these tests must be run in the "Simple" machine).
0.9.9.1: 27 July, 2014 - Pressing 'Control' while clicking 'Reset' now performs a memory clear.
0.9.1: 26 January, 2014 - Support for IRQ and NMI handling.
0.9.0: 29 December, 2013 - First pass at a 6545 CRTC simulation.
0.8.5: 30 March, 2013 - ASCII display for memory window. Allows user to select a step count from a drop-down box.
0.8.3: 12 January, 2013 - Added tool-tip text. Memory is no longer cleared when resetting. Fixed swapped register labels.
0.8.2: 01 January, 2013 - Fully passes Klaus Dormann's 6502 Functional Test suite!
0.5: 21 October, 2012 - Able to run Enhanced BASIC for the first time.
1.2: Better breakpoint support. Symbolic debugging of breakpoints.
2.0: Complete rewrite of the UI in JavaFX instead of Swing. Complete assembler and disassembler built in. Ability to attach source code for symbolic debugging.
Feedback (in the form of dialogs, status bar, etc).
Better debugging tools from the UI, including breakpoints and disassembly.
UI needs a ton more polish.
Implement CMOS 65C02 instructions and NMOS / CMOS mode flag.
Allow displaying ACIA status and dumping ACIA buffers, for debugging.
CRTC emulation is very naive. The whole frame is drawn in one CPU step. This should be improved by drawing scan lines during machine steps to approximate real NTSC/PAL refresh rates.
Additional components used in this project are copyright their respective owners.
Symon is free software. It is distributed under the MIT License. Please see the file 'COPYING' for full details of the license.


Rhetoric is the study and practice of ethical persuasion. This ancient discipline brings enduring currency through the ages to the marketplace and civic life to engage today's global community. A major or second major in Rhetoric is ideal if you are interested in law or graduate school, a career in politics, or any vocation where argument and persuasive strategies and skills are important.
Strategies: Join Pre-Law Society at Duquesne; meet with pre-law advisor in the Political Science department; apply for government internships; research the Duquesne post-graduate paralegal certificate program.
Strategies: Become active in public and community programs; search DuqCareerLink for posted PA internships in Harrisburg; play leadership roles in student political organizations and political party groups (e. g., Young Republicans, Young Democrats); volunteer and/or work part-time job in political campaign; volunteer in advocacy programs and action groups (e.g., Human Rights, Green Energy).
Strategies: Take on leadership roles in on-campus advocacy groups (e.g., Animal Welfare Group; Lambda; Amizade); research organizations on the Internet to determine focal interests; do an internship with an advocacy group; become active in local advocacy groups and chapters of national/international groups.
Strategies: Attend the annual spring Non-profit Career Fair co-sponsored by Duquesne; gain experience marketing and fundraising for campus organizations; volunteer as a fundraiser for local charities and groups; intern at event planning firm; develop a strong portfolio of writing; take courses and gain experience in writing for the Internet; volunteer to develop materials for community and non-profit organizations; work with college magazines, radio and TV.
Strategies: Intern in PR firms and ad agencies; attend the Duquesne Job and Internship Fair every semester; write copy for promotions and PR of student organizations; develop excellent communication and diplomatic skills; perfect your public speaking and writing skills; join and take leadership roles in student organizations; attend the regular government and the non-profit job and internship fairs sponsored by Duquesne.
Strategies: Intern with government agencies; participate in community action groups; volunteer in political campaigns in publicity areas; consider a minor or take coursework in Political Science or International Relations; work for election campaigns or legislators; develop strong presentation and persuasion skills; build excellent writing and communication skills in a variety of media.


text:
Howard University Cancels Classes After Ransomware Attack
The incident occurs after the FBI warned the public that hackers often exploit holiday weekends to launch ransomware attacks.
7 Sep 2021, 6:57 p.m.
Howard University in Washington, D.C. has canceled classes for at least one day after a ransomware attack infected the school's IT systems.
The hackers may have tried to exploit the Labor Day weekend—when employees take time off—to attack the school. University staff first noticed the attack on Friday, and shut down the school's network to prevent the infection from spreading.
"We are currently working with leading external forensic experts and law enforcement to fully investigate the incident and the impact," Howard University wrote in a statement. To give the school's IT team more time to address the incident, the school also decided to cancel classes on Tuesday. In addition, the campus's Wi-Fi system has been shut down.
Howard University didn't immediately respond to comment. But the school is likely concerned the hackers still have a presence within the school's IT network. So letting students and staffers connect to the campus network could help the ransomware attack proliferate even more.
How the hackers infiltrated the school's network remains unclear. However, cybercriminals can exploit vulnerabilities in IT systems or use phishing emails to launch a ransomware attack. The goal is to then spread the ransomware to as many computers as possible and encrypt all the information onboard. To free the computers, a victim must then pay up, sometimes in hundreds of thousands of dollars, in order to receive a decryption key.
In some cases, the hackers will also steal confidential information from the victim's network and threaten to leak it out. For now, Howard University says there's no evidence any personal information was accessed, but the investigation remains ongoing.
The incident occurs days after the FBI and US cyber authorities noted an increase in "highly impactful ransomware attacks" in the US occurring on the holidays and the weekends—when offices are normally closed. In response, the FBI published an alert in the run-up to the Labor Day holiday, warning organizations to stay on guard.

redpajama_set_name: RedPajamaCommonCrawl


Sewn Stripe Rugby in heavy jersey. Traditional rugby collar with cotton webbing. Taped shoulders and side vents. Rubber buttons. Elastane re-inforced cuffs for shape retention. Twin-needle seams and hems. WRAP Certification. Rugby leisurewear garments are for leisure use only.


text:
\section{Introduction}
Cross-lingual transfer (CLT) is a critical topic for natural language processing due to the data imbalance between languages.
While models of rich-resource languages (e.g. English) have been applied on various real-world tasks,
the progress on poor-resource languages lags behind.
CLT researches enable the knowledge transfer from the rich-resource languages to the poor-resource languages.

Although the application of CLT is valuable, data labels are expensive or even inaccessible in private and sensitive domains, such as medicine and business.
For example, the diagnostic results of a user's symptoms are private and a company's internal description of users are confidential.
Since short of labels for CLT, even though there are excellent applications in rich-resource languages, it is difficult to benefit the people using poor-resource languages.
Previous CLT researches have not well studied how to leverage knowledge of rich-resource languages without labels.
To define and tackle this problem will benefit both the community and the industry.

In order to reduce the demand of labels, existing works mainly fall into two paradigms as follows.
One paradigm focuses on learning language-agnostic representation and model parameters.
CLT is realized by either aligning parameters of monolingual models or sharing parameters among different languages~\citep{liu-etal-2019-investigating,jacob-mbert-2019,xlmr-conneau-etal-2020-unsupervised,Wang*2020Cross-lingual}.
The objective is to build a unified representation, which is used by downstream tasks, for all the languages.
In this paradigm, although the demand of labels is reduced, it still requires a certain number of labels to adapt the model to a particular language and task.
Besides, models in this paradigm are usually large-scale Transformers~\citep{trm_NIPS2017_3f5ee243} based on mPLMs, which limits their deployment in real-world.
Another paradigm is to leverage machine translation (MT) systems to generate training or testing pseudo-corpus for a specific language~\cite{xnli-conneau-etal-2018}.
For simplicity, we take English as the rich-resource languages in this paper.
\texttt{Translate-train} translates annotated training corpus from English to other languages. Gold labels are directly applied to the translated data. Although labels in poor-resource languages are not required, gold labels in English are still necessary.
On the contrary, \texttt{Translate-test} translates testing corpus from poor-resource languages to English. This method can directly leverage off-the-shelf English models, but it runs a 2-pass inference which highly limits its efficiency.
Both the two CLT paradigms mentioned above require language-specific and task-specific labels, except for the 2-pass \texttt{Translate-test}.
The demand of labels highly limits the reuse of the English knowledge in private and sensitive domains.
Then a question comes up: \textit{Is it possible to perform CLT totally without labels?}

In this paper, we define a novel problem: \textit{safe and label-free cross-lingual transfer from off-the-shelf models (FreeTransfer-X)}. The FreeTransfer-X asks researchers to achieve CLT only with off-the-shelf English models but any labels, as formally defined in Section~\ref{sec:problem_definition}.
To the best knowledge of the authors, it's the first time that the FreeTransfer-X is clearly defined.

To address the FreeTransfer-X, we propose a 2-step knowledge distillation~\citep[KD,][]{hinton2015distilling} framework based on mPLM, as shown in Figure~\ref{fig:overview}.
Given an off-the-shelf model $\theta_{src}$ in the source language (e.g. English), first we take $\theta_{src}$ as the teacher and an mPLM model $\theta_{src}^{m}$ as the student, then train $\theta_{src}^{m}$ on unlabeled corpus $\mathcal{D}_{src}$. Second, we take $\theta_{tgt}^{m}$ as the teacher and train a student $\theta_{tgt}$ on unlabeled corpus $\mathcal{D}_{tgt}$.
This cross-lingual transfer framework is label-free and applicable for any model architecture.
Experimental results demonstrate the effectiveness of the proposed framework on both sentence classification and sequence tagging.

In short, the major contributions of this work include:
\begin{itemize}
    \item A novel cross-lingual transfer problem FreeTransfer-X is defined. The FreeTransfer-X asks researchers to achieve CLT from off-the-shelf models without using labels. It reduces the labeling cost and protects the labels in private domains such as medicine and business.
    \item We propose a 2-step knowledge distillation framework based on mPLMs, e.g. XLM-RoBERTa~\citep{xlmr-conneau-etal-2020-unsupervised}, to address the FreeTransfer-X. It significantly outperforms the NMT baselines on sentence classification and sequence tagging tasks. Besides, it's compatible with heterogeneous networks.
    \item Further analysis indicates abundant research potentials of the proposed framework. To improve the two distillation steps and the mPLM may benefit the framework.
\end{itemize}

\section{Methodology}\label{methodology}

\subsection{Problem Definition}\label{sec:problem_definition}
Denote the source language and the target language as $src$ and $tgt$ respectively.
Given an off-the-shelf model $\theta_{src}$ (e.g. English intent classifier), unlabeled in-domain corpus $\mathcal{D}_{src}$ and unlabeled in-domain corpus $\mathcal{D}_{tgt}$, the objective is to output a model $\theta_{tgt}$ in the target language $tgt$.
For simplicity in this paper, we constrain the target model $\theta_{tgt}$ to be of the same network architecture to the off-the-shelf source model $\theta_{src}$.

\subsection{Basic Framework}
We propose to adopt knowledge distillation~\citep[KD,][]{hinton2015distilling} to address the FreeTransfer-X, since it can transfer knowledge from teacher models without knowing original labels.
In addition, knowledge distillation is free from network architectures and can be applied between heterogeneous networks, which benefits the deployment in various environment.
\subsubsection{Two-Step Knowledge Distillation}
For a specific natural language processing (NLP) task, given a model $\theta_{src}$ and the unlabeled data $\mathcal{D}_{src}$ in the source language $src$ and the unlabeled data $\mathcal{D}_{tgt}$ in the target language $tgt$.
As shown in Figure~\ref{fig:overview}, we propose to train a model $\theta_{tgt}$ in the target language $tgt$ via 2 KD steps:
\begin{enumerate}
    \item Leverage the NLP capability of the off-the-shelf model $\theta_{src}$, e.g. an English sentence classifier $\theta_{en,cls}$. We distill knowledge from the teacher $\theta_{src}$ to the student mPLM $\theta_{src}^m$ on data $\mathcal{D}_{src}$.
    \item Due to the zero-shot cross-lingual transfer capability of the mPLMs, $\theta_{src}^m$ implicitly achieve the NLP capability on the target language $\theta_{tgt}^m$. Then similar to the step 1, we distill knowledge from the teacher $\theta_{tgt}^m$ to the student $\theta_{tgt}$ in the target language $tgt$ on data $\mathcal{D}_{tgt}$.
\end{enumerate}
The proposed framework works for arbitrary network including but not limited to Transformers~\citep{trm_NIPS2017_3f5ee243}, BiLSTM~\citep{bilstm} and CNN~\citep{kim-2014-convolutional}.

\subsubsection{Training Objectives}\label{sec:objectives}
The training is purely based on KD that no other training objectives is included.
We only apply KD between the classification distribution $P_{T}(\cdot)$ and $P_{S}(\cdot)$ of the teacher and the student respectively, which is compatible to arbitrary model architecture.
Freezing the parameters of the teacher, we train the student by minimizing the Kullback-Leibler Divergence~\citep[$Div_{KL}$,][]{Joyce2011} between them.
Denote the prediction category as $\mathcal{C}=[c_0, c_1, ..., c_k]$, then the $Div_{KL}$ can be formalized as,

\begin{equation}\label{eq:KLDiv}
\begin{aligned}
    &Div_{KL}(P_{T}(\mathcal{C}|\cdot)\|P_{S}(\mathcal{C}|\cdot) \\
    &= \sum_{c_i\in\mathcal{\mathcal{C}}}P_{T}(c_i|\cdot)\log{(\frac{P_{T}(c_i|\cdot)}{P_{S}(c_i|\cdot)})}
\end{aligned}
\end{equation}

However, KD objectives of different NLU tasks varies a lot.
We classify NLU tasks into two categories: 1) sentence-level tasks like sentence classification, 2) word-level tasks like sequence tagging.
Given an input example $\mathcal{X}\in\mathcal{D}$ as a sequence of words $\mathcal{X}=[x_0, x_1, ..., x_n]$.
For sentence-level tasks, $\mathcal{X}$'s sentence-level category is $\mathcal{C_{\mathcal{X}}}$.
The teacher model and student model respectively output sentence-level prediction distribution $P_{T}(\mathcal{C_{X}}|\mathcal{X})$ and $P_{S}(\mathcal{C_{X}}|\mathcal{X})$.
For word-level tasks, $\mathcal{X}$'s word-level category is $\mathcal{C}_{x_{i}}, i\in[0,n]$.
Then the KD objective can be written as,

\begin{equation}\label{eq:kd-loss-seq}
\begin{aligned}
    \mathcal{L} &= Div_{KL}(P_{T}(\mathcal{C}|\mathcal{X})\|P_{S}(\mathcal{C}|\mathcal{X})) \\
    &\text{where } \mathcal{C} =
    \begin{cases}
        \mathcal{C_X} & , \text{sentence-level} \\
        \mathcal{C}_{x_i} & , \text{word-level}
    \end{cases}
\end{aligned}
\end{equation}

It's worth noting that word-level $Div_{KL}$ cannot be directly applied for heterogeneous teacher and student models since their tokenizations are different.
In order to align the predictions of teacher and student, we only adopt the prediction on the first sub-word of each word.

\subsection{Enhanced Cross-Lingual Distillation}
To explore the potentials of improving the two KD steps, we propose to enhance them with machine translation (MT) and paraphrase generation (PG).

\subsubsection{Language Balanced Distillation}
During the first KD step that training the mPLM from an English (i.e. the source language) classifier, to leverage the cross-lingual transferarability of mPLM, the conventional method is to train the mPLM only on the English corpus.
However, in our preliminary experiments, we notice that the mPLM's accuracy gap between English and the target languages are very huge.
It's over 5\% between the English target model (94.0) and the average of all target models (88.4), as reported by \textit{2-step KD} in Table~\ref{tab:cls_matis_full_unlabeled}, Appendix~\ref{app:lang_wise_results}.

\begin{figure}[!h]
    \centering
    \includegraphics[width=1.\columnwidth]{figures/balanced_dist_camera.png}
    \caption{Language balanced distillation. Leverage the MT model to translate unlabeled English $\mathcal{D}_{en}$ into target languages $\mathcal{D}^{trans}_{tgt}$. Perform KD on the translated $\mathcal{D}^{trans}_{tgt}$ with $\theta_{en}$'s predicted distribution $P_{en}(\mathcal{C}|\mathcal{X})$.}
    \label{fig:balanced_dist}
\end{figure}

Hence, we propose to translate the unlabeled English corpus $\mathcal{D}_{en}$ to target languages $\mathcal{D}^{trans}_{tgt}$, as depicted by Figure~\ref{fig:balanced_dist}.
Since $\mathcal{D}_{en}$ and $\mathcal{D}^{trans}_{tgt}$ are aligned, source English model's predicted distribution $P_{en}(\mathcal{C}|\mathcal{X})$ of $\mathcal{D}_{en}$ can be directly applied to $\mathcal{D}^{trans}_{tgt}$.
In this way, KD is able to be performed on not only the source language but also the target languages.

As shown in the lower left of Figure~\ref{fig:overview}, the translated $\mathcal{D}^{trans}_{tgt}$ is incorporated in the training of KD step one.

\subsubsection{Language-Specific Data Augmentation}
Inspired by data augmentation for KD~\citep{jiao-etal-2020-tinybert} and multilingual paraphrase generation~\citep{DBLP:journals/corr/abs-1911-03597}, we augment the unlabeled target corpus $\mathcal{D}_{tgt}$ via paraphrasing.

\begin{figure}[!h]
    \centering
    \includegraphics[width=1.\columnwidth]{figures/data_augment_camera.png}
    \caption{Language-specific data augmentation. We paraphrase the target corpus $\mathcal{D}_{tgt}$ into $\mathcal{D}^{para}_{tgt}$ as the augmented training data. KD is then performed on the mixture of $\mathcal{D}_{tgt}$ and $\mathcal{D}^{para}_{tgt}$.}
    \label{fig:data_augment}
\end{figure}

\section{Experiments}\label{sec:experiments}
\subsection{Datasets and Preprocessing}\label{sec:datasets_prep}
\noindent\textbf{MultiATIS++}~\citep{xu-etal-2020-end}
extends the Multilingual ATIS corpus~\citep{8461905} to 9 languages across 4 language families, including Indo-European (English, Spanish, German, French, Portuguese and Hindi), Sino-Tibetan (Chinese), Japonic (Japanese) and Altaic (Turkish).
It provides annotations for intent recognition (sentence classification) and slot filling (sequence tagging) for each languages.
The utterances are professionally translated from English and manually annotated.
MultiATIS++ includes 37,084 training examples and 7,859 testing examples.

\noindent\textbf{MTOP}~\citep{li-etal-2021-mtop}
is a recently released multilingual NLU dataset covering 6 languages: English, German, French, Spanish, Hindi, Thai.
It's also manually annotated for intent recognition (sentence classification) and slot filling (sequence tagging).
MTOP provides a larger corpus consisting of 104,445 examples, of which 10\% is validation set and 20\% is testing set.

For each language, we randomly split both MultiATIS++ and MTOP into two balanced parts: \textit{annotated} and \textit{unannotated}.
The annotated parts are used to train and simulate the off-the-shelf source models while the unannotated parts are used for training the baselines and the proposed 2-step distillation model.
We tokenize Chinese, Japanese and Thai utterances using Jieba\footnote{https://github.com/fxsjy/jieba}, MeCab\footnote{https://github.com/polm/fugashi} and pythainlp\footnote{https://github.com/PyThaiNLP/pythainlp} respectively.

\subsection{Baselines}\label{sec:baselines}
\noindent\textbf{Translate-Test}~\citep{xnli-conneau-etal-2018}
is a machine translation based method.
It performs two-pass inferences to tackle the FreeTransfer-X problem:
1) translate the testing utterances into English (i.e. the source language) from the target language,
2) predict on the translated English utterances with the off-the-shelf English model.

\noindent\textbf{Translate-Train-Pseudo}
is also based on machine translation.
It's a variant of the \textbf{Translate-Train}~\citep{xnli-conneau-etal-2018}, which translates English training examples into target languages and applies English annotations to the translated examples.
However, annotations are not provided in the FreeTransfer-X problem.
Hence, Translate-Train-Pseudo utilizes the prediction of the off-the-shelf English model to pseudoly annotates the translated examples.

\noindent\textbf{Gold-Supervised}
is for reference since it's trained with annotations.
It replaces the first distillation step of the proposed framework with gold-supervised training, in other words, the mPLM is supervised by gold annotations instead of the off-the-shelf English model.
It's supposed to be very strong.

\begin{table*}[ht]
    \centering
    \resizebox{2\columnwidth}{!}{
    \begin{tabular}{ll|ccc|ccc|c}
    \hline
    \multicolumn{2}{c|}{\multirow{2}{*}{Models}} & \multicolumn{3}{c|}{MTOP} & \multicolumn{3}{c|}{MultiATIS++} & \multirow{2}{*}{Avg} \\
    \multicolumn{2}{l|}{} & Transformer & BiLSTM & CNN & Transformer & BiLSTM & CNN & \\ \hline

    \multirow{2}{*}{Reference} & Off-the-shelf En source & 88.3 & 86.2 & 90.5 & 94.4 & 90.8 & 92.7 & 90.5 \\
                                & Gold-supervised target & 78.4 & 65.0 & 79.2 & 84.6 & 85.2 & 86.6 & 79.8 \\ \hline
    \multirow{2}{*}{Baselines} & Translate-test & 69.6 & 66.0 & 73.8 & 86.4 & 80.7 & 86.2 & 77.1 \\
                                & Translate-train-pseudo & 64.2 & 57.9 & 67.4 & 84.7 & 81.2 & 83.2 & 73.1 \\ \hline
    \multirow{3}{*}{Ours} & 2-step KD & 75.1 & 72.3 & 75.6 & 87.7 & 83.8 & 85.0 & 79.9 \\
                          & + Balanced distillation & 79.3 & 75.9 & 77.8 & \textbf{88.9} & 85.2 & 86.2 & 82.2 \\
                          & + Data augmentation & \textbf{79.6} & \textbf{79.1} & \textbf{78.8} & 88.7 & \textbf{86.4} & \textbf{86.9} & \textbf{83.3} \\ \hline
    \end{tabular}
    }
    \caption{Classification accuracy averaged over target languages. MTOP: de, es, fr, hi, th. MultiATIS++: de, es, fr, hi, ja, pt, tr, zh. For simplicity, the architecture of a target model is identical to its corresponding English source model.}
    \label{tab:cls_total_results}
\end{table*}

\subsection{Experiment Settings}
\subsubsection{Model Architectures}
We experiment with three mainstream NLU model architectures to verify the universality of the proposed framework.
They are used as the backbones of the off-the-shelf models $\theta_{src}$ and the output models $\theta_{tgt}$ in target language.

\noindent\textbf{Transformer} encoder~\citep{trm_NIPS2017_3f5ee243} models input sequences fully with Attention mechanism.
We follow the language modeling method of BERT~\citep{devlin-etal-2019-bert}.
We adopt absolute positional encoding.
The contextual representation vector of the first word is used for sentence classification.
Sequence tagging is based on the contextual representation of each word.

\noindent\textbf{Bidirectional LSTM (BiLSTM)}~\citep{bilstm} models input sequences via leveraging two stacked LSTM layers respectively from backward and forward directions.
We take the representation vector of the last word for sentence classification.
Word-level representation is used for sequence tagging like Transformer.

\noindent\textbf{Convolutional Neural Networks (CNN)}~\citep{kim-2014-convolutional} encodes input sequences with CNN modules.
We adopt three kind of 1-D kernels with kernel size of 3, 4 and 5.
Output vectors from all kernels and channels are concatenated as the representation for sentence classification.
Dilated CNN~\citep{strubell-etal-2017-fast} is adopted for sequence tagging.

\subsubsection{Training Details}
English is regarded as the source language in all the experiments.
Off-the-shelf English models are trained on the hold-out annotated English corpus as described in Section~\ref{sec:datasets_prep}.
All the experimented models are controlled in comparable model scale.
AdamW~\citep{loshchilov2018decoupled} is adopted as the optimizer with $\epsilon=1e-8$.
We train the models for 50 epochs and take the checkpoint of the best validation accuracy as the final model.
Table~\ref{tab:num_params_archs} reports the hyper-parameters of the model architectures.

\begin{table}[!h]
    \centering
    \resizebox{.95\columnwidth}{!}{
    \begin{tabular}{l|cccc}
        \hline
         Model & Embed size & Hidden size & \#Layers & \#Params \\
        \hline
         Transformer & 256 & 256 & 4 & 5.3M \\
         BiLSTM & 256 & 512 & 2 & 5.3M \\
         CNN & 256 & 768 & 2 & 5.0M \\
        \hline
    \end{tabular}
    }
    \caption{Hyper-parameters of the experimented models.}
    \label{tab:num_params_archs}
\end{table}

Initial learning rate is decided based on a gradient-based searching heuristics proposed by~\citet{DBLP:journals/corr/Smith15a}, since in our preliminary experiments~\citet{DBLP:journals/corr/Smith15a} stably finds better learning rates than manual searching.
We build vocabularies of 10k words for each language via Byte Pair Encoding (BPE)~\citet{sennrich-etal-2016-neural}.
Experiments are implemented with PyTorch~\citep{NEURIPS2019_bdbca288} and conducted on a single Nvidia V100 32GB GPU.

\subsubsection{Auxiliary Models}
\noindent\textbf{M2M-100}~\citep{JMLR:v22:20-1307} is adopted as the MT system in our experiments.
We apply the 418M model checkpoint from Huggingface\footnote{https://huggingface.co/facebook/m2m100\_418M}.

\noindent\textbf{XLM-RoBERTa}~\citep{xlmr-conneau-etal-2020-unsupervised} is adopted as the mPLM in the proposed 2-step distillation framework.

\subsection{Results}
Average accuracy across languages and models is given in Table~\ref{tab:cls_total_results} and Table~\ref{tab:tag_total_results}.
Language-wise results are provided in Appendix~\ref{app:lang_wise_results}.
\subsubsection{Sentence Classification}

\begin{table*}[ht]
    \centering
    \resizebox{2\columnwidth}{!}{
    \begin{tabular}{ll|ccc|ccc|c}
    \hline
    \multicolumn{2}{c|}{\multirow{2}{*}{Models}} & \multicolumn{3}{c|}{MTOP} & \multicolumn{3}{c|}{MultiATIS++} & \multirow{2}{*}{Avg} \\
    \multicolumn{2}{l|}{} & Transformer & BiLSTM & CNN & Transformer & BiLSTM & CNN & \\ \hline

    \multirow{2}{*}{Reference} & Off-the-shelf En source & 74.8 & 81.1 & 72.1 & 88.4 & 94.0 & 89.1 & 83.3 \\
                                & Gold-supervised target & 64.6 & 68.6 & 63.3 & 71.5 & 76.5 & 74.1 & 69.8 \\ \hline
    \multirow{1}{*}{Baselines} & Translate-test & 37.2 & 41.4 & 34.2 & 24.8 & 38.8 & 40.8 & 36.2 \\
                                & Translate-train-pseudo & 34.4 & 40.4 & 28.6 & 53.9 & 63.1 & 61.8 & 47.0 \\ \hline
    \multirow{1}{*}{Ours} & 2-step KD & \textbf{63.7} & \textbf{67.6} & \textbf{55.7} & \textbf{71.7} & \textbf{76.9} & \textbf{73.5} & \textbf{68.2} \\ \hline

    \end{tabular}
    }
    \caption{Sequence tagging F1 score averaged over target languages. MTOP: de, es, fr, hi, th. MultiATIS++: de, es, fr, hi, ja, pt, tr, zh. For simplicity, the architecture of a target model is identical to its corresponding English source model.}
    \label{tab:tag_total_results}
\end{table*}

\begin{table*}[ht]
    \centering
    \resizebox{.95\textwidth}{!}{
    \begin{tabular}{ll|c|c|cccccccc|c}
    \hline
    \multicolumn{2}{c|}{\multirow{2}{*}{Models}} & Original & Finetuned & \multicolumn{9}{c}{Zero-Shot Cross-Lingual Transfer}          \\
    \multicolumn{2}{c|}{} & en & en & de & es & fr & hi & \textbf{ja} & pt & \textbf{tr} & \textbf{zh} & Avg \\ \hline
    \multicolumn{2}{l|}{Gold-supervised} & - & 97.9 & 97.6 & 97.4 & 97.4 & 92.4 & 90.6 & 97.3 & 83.8 & 92.8 & 93.7 \\ \hline

    \multirow{2}{*}{Transformer} & Naive KD & \multirow{2}{*}{94.4} & 97.2 & 96.9 & 96.8 & 96.2 & 90.8 & 90.1 & 95.6 & 84.3 & 91.3 & 92.8 \\
    & + Balanced distillation & & 97.5 & 97.6 & 96.9 & 96.6 & 95.4 & 96.3 & 96.1 & 90.8 & 97.5 & 95.9\\ \hline
    \multirow{2}{*}{BiLSTM}
    & Naive KD & \multirow{2}{*}{90.8} & 93.2 & 93.5 & 93.5 & 93.2 & 90.4 & 83.4 & 93.5 & 77.2 & 85.0 & 88.7 \\
    & + Balanced distillation & & 92.4 & 93.3 & 93.7 & 92.4 & 91.8 & 91.9 & 93.1 & 86.6 & 92.8 & 92.0 \\ \hline
    \multirow{2}{*}{CNN}
    & Naive KD & \multirow{2}{*}{92.7} & 94.7 & 91.8 & 94.1 & 93.2 & 90.3 & 90.9 & 94.2 & 83.5 & 90.3 & 91.0 \\
    & + Balanced distillation & & 92.8 & 93.2 & 92.9 & 92.4 & 91.8 & 91.6 & 93.2 & 89.2 & 92.9 & 92.2 \\ \hline
    \end{tabular}
    }
    \caption{Classification accuracy of the finetuned mPLM models, i.e. XLM-RoBERTa. Evaluated on MultiATIS++. \textit{Gold-supervised} is trained with gold annotations. \textbf{Bold languages} is not in the Indo-European language family as English.}
    \label{tab:dist_step_1}
\end{table*}

As shown in Table~\ref{tab:cls_total_results}, the proposed 2-step KD framework significantly outperforms the MT baselines on most model architectures, except for the CNN of \textit{Translate-test}.
Although \textit{Translate-test} is strong in a very few cases, it requires 2-pass inference (MT and classification) that results in a high latency.
On the contrary, the proposed framework directly produces classification models in the target languages, which is more efficient.
In addition, the \textit{language-balanced distillation} and \textit{language-specific data augmentation} further enhance our model to a large extent, +2.3\% and +1.1\% respectively.
Language-wise results in Table~\ref{tab:cls_matis_full_unlabeled} demonstrate the robustness of our method across various languages.

To our surprise, the naive \textit{2-step KD} model even performs on par with the \textit{Gold-supervised} reference on average.
We guess it's due to the regularization effects of knowledge distillation that brings a good generalizability to the proposed model.
It implies the proposed framework may be a annotation-free alternative to current zero-shot cross-lingual transfer framework.

However, comparing the results of the English source model and those of the target models in Table~\ref{tab:cls_matis_full_unlabeled}, the cross-lingual transferred models still lag far behind the original English models.
There is a great potential of the proposed framework.

\subsubsection{Sequence Tagging}

On the sequence tagging task, the proposed model beats the baselines by a wide margin.
The MT-based baselines perform very poor on this task due to the error from word-level annotation alignment.
Also because of the alignment error, we do not apply \textit{language balanced distillation} and \textit{language-specific data augmentation} on this task.

As to the comparison with the \textit{Gold-supervised} reference, our model performs slightly worse than it.
It may due to the insufficient knowledge distillation from the teacher to the student, which comes from the discrepancy between teacher's and student's tokenizations.
Although, as described in Section~\ref{sec:objectives}, we perfectly align their prediction at word-level, only the first subword of each word is used for distillation.
More informative subword-level aligning and distillation methods can be explored.
We leave this problem for the future research.
Besides, similar to sentence classification, gap between the English source model and the transferred target models is huge, as shown in Table~\ref{tab:tag_total_results}.

In sum, both experimental results on sentence classification and sequence tagging demonstrate that the proposed model is significantly stronger than MT-based cross-lingual transfer methods.
Furthermore, the proposed model only slightly lags behind or even performs on par with the strong \textit{Gold-supervised} reference, which is not able to address the FreeTransfer-X problem.

\iffalse
\begin{table*}[]
    \centering
    \resizebox{2\columnwidth}{!}{
    \begin{tabular}{ll|c|cccccccccc}
        \hline
        \multicolumn{2}{c|}{\multirow{2}{*}{Models}} & Source & \multicolumn{10}{c}{Targets} \\
         & & en & en & de & es & fr & hi & ja & pt & tr & zh & avg \\
        \hline
        \multirow{2}{*}{Reference} & Gold supervised & \multirow{2}{*}{-} & 96.2 & 96.0 & 92.9 & 95.5 & 83.7 & 89.7 & 94.8 & 81.5 & 91.3 & 91.3 \\
        & Translate-Train (gold-label) & & 93.8 & 90.7 & 91.8 & 92.8 & 83.0 & 74.0 & 92.6 & 76.6 & 89.0 & 87.2 \\
        \hline
        \multirow{2}{*}{Baselines} & Translate-Train (pseudo-label) & \multirow{2}{*}{90.7} & 90.4 & 89.2 & 90.5 & 91.4 & 83.1 & 77.4 & 89.1 & 76.2 & 86.9 & 86.0 \\
        & Translate-Test & & 89.0 & 86.3 & 85.2 & 89.1 & 79.3 & 81.3 & 87.2 & 69.1 & 84.8 & 83.5 \\
        \hline
        \multirow{4}{*}{Ours} & LR-tuned & \multirow{4}{*}{90.7} & 89.4 & 89.6 & 90.6 & 89.7 & 84.4 & 85.4 & 90.1 & 75.1 & 87.1 & 86.8 \\
        & + NMT-enhanced XLM & & 91.3 & 91.0 & 92.6 & 91.7 & 84.0 & 83.9 & 91.4 & 81.8 & 90.9 & 88.7 \\
        & + Data gugmentation & & 92.8 & 92.5 & 92.7 & 91.9 & 87.0 & 83.9 & 92.4 & 80.8 & 90.9 & 89.4 \\

        \hline
    \end{tabular}
    }
    \caption{MultiATIS++ accuracy. Transformer transfer. On original train set.}
    \label{tab:cls_matis_full_original}
\end{table*}

\begin{table*}[]
    \centering
    \resizebox{2\columnwidth}{!}{
    \begin{tabular}{ll|c|cccccccccc}
        \hline
        \multicolumn{2}{c|}{\multirow{2}{*}{Models}} & Source & \multicolumn{10}{c}{Targets} \\
         & & en & en & de & es & fr & hi & ja & pt & tr & zh & avg \\
        \hline
        \multirow{2}{*}{XLM-R} & LR-tuned & \multirow{2}{*}{90.7} & 93.3 & 91.0 & 92.6 & 92.3 & 89.0 & 89.8 & 92.5 & 74.8 & 90.1 & 89.5 \\
        & + NMT-enhanced XLM & & 94.3 & 94.2 & 94.2 & 94.7 & 92.2 & 90.1 & 94.1 & 87.7 & 93.8 & 92.8 \\
        \hline
    \end{tabular}
    }
    \caption{MultiATIS++ accuracy. Source model is naive Transformer, target model is XLM-R.}
    \label{tab:cls_matis_full}
\end{table*}

\begin{table*}[]
    \centering
    \resizebox{.5\columnwidth}{!}{
    \begin{tabular}{l|c}
    \hline
    Datasets & Transformer \\ \hline
    MTOP & 74.8 \\
    MultiATIS++ & 88.4 \\ \hline
    \end{tabular}
    }
    \caption{Performance of the off-the-shelf models.}
    \label{tab:tag_preset_models_211214reports}
\end{table*}

\begin{table*}[]
    \centering
    \resizebox{1.5\columnwidth}{!}{
    \begin{tabular}{ll|c|c}
    \hline
    \multicolumn{2}{c|}{\multirow{2}{*}{Models}} & {MTOP} & {MultiATIS++} \\
    \multicolumn{2}{l|}{} & Transformer & Transformer \\ \hline
    \multirow{1}{*}{References} & Supervised & 68.6 &73.7\\ \hline
    \multirow{1}{*}{Baselines} & Translate-Test & 27.8 &35.0\\
                                & Translate-Train (pseudo-label) & &\\ \hline
    \multirow{1}{*}{Ours} & 2-stage KD & 66.0 &69.2\\ \hline
    \end{tabular}
    }
    \caption{Sequence labeling F1 value. Averaged over all languages. MTOP: 6 languages, MultiATIS++: 9 languages.}
    \label{tab:tag_total_results_211214report}
\end{table*}
\fi

\section{Further Analysis}
In order to explore the potential of the proposed framework, we analyze it in more details.
For simplicity, experiments in this Section are conducted only on the MultiATIS++ sentence classification task.
\subsection{Effects of the Distillation}\label{sec:effect_dist}
\begin{table*}[]
    \centering
    \resizebox{.95\textwidth}{!}{
    \begin{tabular}{ll|c|c|cccccccc|c}
    \hline
    \multicolumn{2}{c|}{\multirow{2}{*}{Models}} & Original & Finetuned & \multicolumn{9}{c}{Zero-Shot Cross-Lingual Transfer}          \\
    \multicolumn{2}{c|}{} & en & en & de & es & fr & hi & ja & pt & tr & zh & Avg \\ \hline
    \multirow{2}{*}{Transformer} & XLM-RoBERTa & \multirow{2}{*}{94.4} & 97.2 & 96.9 & 96.8 & 96.2 & 90.8 & 90.1 & 95.6 & 84.3 & 91.3 & 92.8 \\
    & mBERT & & 96.9 & 88.4 & 92.4 & 93.8 & 81.1 & 85.7 & 94.0 & 73.7 & 83.2 & 86.5 \\ \hline
    \multirow{2}{*}{BiLSTM}
    & XLM-RoBERTa & \multirow{2}{*}{90.8} & 93.2 & 93.5 & 93.5 & 93.2 & 90.4 & 83.4 & 93.5 & 77.2 & 85.0 & 88.7 \\
    & mBERT & & 92.3 & 80.4 & 87.5 & 82.5 & 79.2 & 79.4 & 82.3 & 76.5 & 75.0 & 80.3 \\ \hline
    \multirow{2}{*}{CNN}
    & XLM-RoBERTa & \multirow{2}{*}{92.7} & 94.7 & 91.8 & 94.1 & 93.2 & 90.3 & 90.9 & 94.2 & 83.5 & 90.3 & 91.0 \\
    & mBERT & & 93.3 & 82.6 & 86.9 & 87.9 & 78.1 & 78.7 & 88.2 & 72.9 & 80.9 & 82.0 \\ \hline
    \end{tabular}
    }
    \caption{Classification accuracy of XLM-RoBERTa and mBERT. \textit{Step-1 KD}: off-the-shelf English model -> mPLM. The mPLMs are finetuned and evaluated on MultiATIS++.}
    \label{tab:mbert_step_1}
\end{table*}

\begin{table*}[]
    \centering
    \resizebox{.95\textwidth}{!}{
    \begin{tabular}{ll|c|c|cccccccc|cc}
    \hline
    \multicolumn{2}{c|}{\multirow{2}{*}{Models}} & Original & Transferred & \multicolumn{10}{c}{Transferred Target Languages}          \\
    \multicolumn{2}{c|}{} & en & en & de & es & fr & hi & ja & pt & tr & zh & Avg & $\Delta$ \\ \hline
    \multirow{2}{*}{Transformer} & XLM-RoBERTa & \multirow{2}{*}{94.4} & 94.0 & 94.4 & 93.3 & 90.8 & 85.7 & 81.3 & 92.4 & 76.5 & 87.1 & 87.7 & -5.1\\
    & mBERT & & 95.6 & 86.2 & 91.8 & 92.8 & 79.8 & 81.7 & 90.9 & 72.9 & 80.3 & 84.6 & -1.9 \\ \hline
    \multirow{2}{*}{BiLSTM}
    & XLM-RoBERTa & \multirow{2}{*}{90.8} & 89.8 & 89.8 & 89.7 & 89.5 & 84.1 & 78.1 & 84.9 & 71.5 & 83.1 & 83.8 & -4.9 \\
    & mBERT & & 89.6 & 80.9 & 82.9 & 81.0 & 78.5 & 76.0 & 83.7 & 71.7 & 78.6 & 79.2 & -1.1 \\ \hline
    \multirow{2}{*}{CNN}
    & XLM-RoBERTa & \multirow{2}{*}{92.7} & 90.7 & 87.9 & 88.1 & 87.8 & 85.0 & 83.2 & 86.3 & 75.2 & 86.7 & 85.0 & -6.0 \\
    & mBERT & & 89.1 & 79.4 & 82.9 & 81.5 & 76.0 & 78.2 & 82.4 & 72.0 & 79.5 & 79.0 & -3.0 \\ \hline
    \end{tabular}
    }
    \caption{Classification accuracy of the target models, distilled from XLM-RoBERTa and mBERT respectively. \textit{Step-2 KD}: mPLM -> target model. \textit{$\Delta$}: changes w.r.t Table~\ref{tab:mbert_step_1}. The target models are transferred and evaluated on MultiATIS++.}
    \label{tab:mbert_step_2}
\end{table*}

Table~\ref{tab:dist_step_1} reports the accuracy of the XLM-RoBERTa finetuned from gold annotations, Transformer teacher, BiLSTM teacher and CNN teacher.

First, compare the \textit{Original} with the \textit{Naive KD Finetuned} of each model respectively.
It's very interesting that the accuracy of the student mPLM is consistently higher than its teacher.
The XLM-RoBERTa students gain 2.8\%, 2.4\% and 2.0\% improvement from the \textit{Original} teachers as Transformer, BiLSTM and CNN respectively.
The phenomenon implies the general effectiveness of language modeling of mPLMs.
We conjecture the improvement comes from two aspects:
1) mPLMs' generalizability learn from the large-scale pre-training,
2) the large model scale of mPLMs, which enhances its NLU capability.
Besides, the improvement with respect to the \textit{Original} varies across model architectures.
Especially when compare Transformer (+2.8\%) to CNN (+2.0\%), although the Transformer's student XLM-RoBERTa performs much closer to the \textit{Gold-supervised}, it still improves greater than the CNN's student.
Since the XLM-RoBERTa is Transformer-based network, it implies that the knowledge distillation performs better if the architectures of the teacher and the student are more similar.

Second, under the cross-lingual transfer condition, although the \textit{Gold-supervised} outperforms the \textit{Naive KD} on most target languages, it performs weaker on Turkish (tr).
It demonstrates the better generalizability and few-shot performance of the \textit{Naive KD}, since Turkish is a low-resource language in MultiATIS++.
The number of training examples of Turkish (578) is less than other languages (4488).

Third, the effectiveness of the proposed \textit{language balanced distillation} is very clear.
In the comparison between the \textit{Naive KD} and \textit{+ Balanced distillation}, the accuracy is highly boosted almost on all the target languages.
This improvement is particularly significant on the languages that is not in the same family of English: Hindi (hi), Japanese (ja), Turkish (tr) and Chinese (zh).
A future research topic is to improve \textit{language balanced distillation} on the languages similar to the source language, e.g. European languages to English.
Data selection algorithms may have potentials.

In sum, the proposed framework and distillation method is effective and of strong generalizability.
Future researches on heterogeneous distillation and data selection may benefit the proposed framework.

\subsection{Effects of mPLM Models}\label{sec:effect_mplm}
Table~\ref{tab:mbert_step_1} and Table~\ref{tab:mbert_step_2} respectively reports accuracy of the step-1 KD and step-2 KD in the proposed framework.
According to Table~\ref{tab:mbert_step_2}, the choice of mPLM is critical to the target models' performance.
Performance with XLM-RoBERTa as the mPLM is stronger than with mBERT.
However, there are interesting observations we should notice.

First, observe the performance changes ($\Delta$) of the \textit{Step-2 KD}: from the mPLM teacher to the target model student.
We notice that the performance drop of mBERT is slighter than the XLM-RoBERTa's, based on the results of the average score in Table~\ref{tab:mbert_step_2} minus those in Table~\ref{tab:mbert_step_1}.
It implies that as the capability of mPLM increases, the KD dissipation tends to increase as well.
Similar to the analysis in Section~\ref{sec:effect_dist}, the KD dissipation may come from:
1) the pre-trained language model that the target models lack of,
2) discrepancy between the model size of the mPLM and the target models.
Hence, performance based on XLM-RoBERTa drops more due to its gap to the target models is greater than mBERT's in both the two aspects of discrepancy.
To reduce the KD dissipation, researches should focus on how to reduce the model discrepancy between mPLM and the target model, e.g. improve the language modeling capability of the target model.
Besides, the performance difference among model architectures is consistent, either based on XLM-RoBERTa or mBERT.
It further evidences that the proposed framework is general and works well for different model architectures.

\subsection{Cross-Architecture Transfer}
To analyze the proposed framework in a more general setting, we free the architecture ties of the off-the-shelf English models and the target models to be heterogeneous, that the source and target models can be different.

\begin{figure}[!h]
    \centering
    \includegraphics[width=.8\columnwidth]{figures/cross_model_heatmap_abs.png}
    \caption{Classification accuracy of the target models via cross-architecture transfer, averaged over all target languages. Transfer from rows to columns. \textit{Row}: architectures of source English models, \textit{Column}: architectures of the target models. Experimented on MultiATIS++.}
    \label{fig:cross_arch_heatmap_abs}
\end{figure}

As depicted in Figure~\ref{fig:cross_arch_heatmap_abs}, the transfer performs the best when taking Transformer as both the source and target models.
The worst comes to the transfer between BiLSTM models.
On one side, the advantage of the Transformer architecture may be a reason.
On the other side, it reconfirms the observation that the more similar teacher and student models are, the better transfer performance comes.

Besides, taking the BiLSTM as the source or target model consistently result in lower accuracy, no matter what the corresponding target or source models are.
Hence, we guess the architecture similarity between BiLSTM and the Transformer-based mPLM is lower than that between CNN and the mPLM.
We leave this for future work.

\begin{figure}[!h]
    \centering
    \includegraphics[width=.8\columnwidth]{figures/cross_model_heatmap_rel.png}
    \caption{Accuracy drop from the source English models to the target models, averaged over all target languages. Transfer from rows to columns. \textit{Row}: architectures of source English models, \textit{Column}: architectures of the target models. Experimented on MultiATIS++.}
    \label{fig:cross_arch_heatmap_rel}
\end{figure}

In addition, we study the accuracy drop from the source English models to the target models, as shown in Figure~\ref{fig:cross_arch_heatmap_rel}.
From the perspective of the source model, the drop is the least when BiLSTM is the source.
From the perspective of the target model, the drop is the least when Transformer is the target.
It reveals an asymmetry between the two KD steps with respect to the mPLM.
To reduce the KD dissipation to the largest extent, it seems mPLM should be distilled from a weaker teacher architecture (e.g. BiLSTM) before teaching a stronger student architecture (e.g. Transformer).

In brief, the proposed framework works for heterogeneous cross-lingual transfer.
The future work may focus on how to define the similarity between model architectures and how to evaluate the source-target model pairs.

\iffalse
\section{Related Work}
\paragraph{Model-Agnostic Distillation}
\paragraph{Zero-Shot Cross-Lingual Transfer}
\fi

\section{Conclusions}
In this paper, we define a novel cross-lingual transfer (CLT) problem - FreeTransfer-X, especially for CLT in private scenarios such as medical and business.
The FreeTransfer-X is defined to transfer knowledge from off-the-shelf models in rich-resource languages to poor-resource languages, without labeled corpora.
To address the FreeTransfer-X, we propose a 2-step knowledge distillation (2-step KD) framework based on multilingual pre-trained language models.
In addition, two data augmentation methods for cross-lingual KD are proposed to boost the performance of the 2-step KD framework.
Experimental results clearly demonstrate the effectiveness of the proposed framework.
It's worth noting that the proposed KD framework can be applied between heterogeneous models, which benefits the deployment in different environment.
Further analyses point out various research directions for future work.

redpajama_set_name: RedPajamaArXiv


The scientific programmes of the Matter at Extremes Group cover all classical domains of natural sciences with a significant impact in applied fields such as Earth, environmental and planetary science, catalysis, soft matter, material synthesis, and nanoscience. The introduction to the group's chapter in the annual ESRF Highlights gives an overview of some recent accomplishments.
High pressure studies are possible on all the beamlines in the group, but each beamline is optimised for a specific X-ray technique. Time resolution is becoming increasingly important, and static high pressure studies are now being complemented by a few dynamic compression experiments. The pressure and temperature ranges covered by the different techniques vary, depending not only on the X-ray beam characteristics but also on the sample environment at the beamline. Static pressures beyond 200 GPa and temperatures from 5 K to 5000 K are available on some of the beamlines. High pressure structural studies employ monochromatic (angle-dispersive) diffraction using diamond anvil cells (DAC) (ID27 and ID15B). The DAC techniques are complemented by the Paris-Edinburgh press (ID27 and BM23), and the large volume press (ID06). Time-resolved structural studies of phase transformations down to the microsecond scale will soon be feasible at ID27.
Structural dynamics such as collective excitations in disordered materials and phonons in crystalline materials are investigated by nuclear inelastic scattering (ID18). Furthermore, nuclear resonance scattering allows the study of diffusion and rotational motions directly in the time domain, as well as electronic and magnetic properties and their dynamics in the nanosecond to microsecond time regime.
Local structure, electronic and magnetic properties and their time evolution down to the ns timescale are studied utilising X-ray absorption spectroscopy and X-ray magnetic circular dichroism (ID24 and BM23). Extreme states of matter that can be achieved only during short time intervals (< ns) can also be probed by single bunch acquisition.
The ESRF's mission includes interacting with and supporting European industry. Synchrotron X-rays permit analysis of industrial products under real conditions of manufacture and use, often in real time.
The capabilities of this group's beamlines are used to address issues in environmental science, catalysis, soft matter, material synthesis, and nanoscience.
The individuals listed above and the staff of the Business Development Office are available to help industrial researchers identify appropriate resources at the ESRF.
In-house research runs in parallel to beamline operation, helping us to develop and refine techniques while investigating diverse scientific areas. Details for each beamline are on our in-house research page.
From time to time, staff and users have connections with the following beamlines, which are operated by Collaborative Research Groups (CRGs).


text:
The Launière Lake is a freshwater body crossed by the Launière River in the unorganized territory of Lac-Jacques-Cartier, in the La Côte-de-Beaupré Regional County Municipality, in the administrative region of Capitale-Nationale, in the province of Quebec, in Canada. This body of water is located in the Laurentides Wildlife Reserve.

The area around the lake is indirectly served by the route 175 which passes at  on the east side and runs along the west shore of Jacques-Cartier Lake. A few secondary forest roads serve this area for forestry and recreational tourism activities.

Forestry is the main economic activity in the sector; recreational tourism, second.

The surface of Lac Launière is usually frozen from the beginning of December to the end of March, however the safe circulation on the ice is generally made from mid-December to mid-March.

Geography
Lac Launière has a length of , a width of  and its surface is at an altitude of . This lake sunk between the mountains is made in length by more or less marrying the shape of the letter L.

Lac Launière receives the discharge from Lac La Giroflée on the east side and the discharge from Lac Frazie on the west side.

From the mouth of Lac Launière, the current descends on  following the course of the Launière River. Then the current follows the course of the Jacques-Cartier River for  generally south to the northeast bank of the St. Lawrence river.

Toponymy
According to Isaïe Nantais, this toponym recalls a family of gamekeepers who lived for many years at the lake at Christmas, that is,  southeast of the mouth of Lac Launière. Thomas-Edmond Giroux reports Michel Launier, of Innu origin, had his hunting territory there. It is designated on the map of Commissioner Flynn, of the Crown Lands department, in 1896, in the form "Lac à Launier". The first mention in the form "Lac Launière" was noted on a map in 1915.

The toponym Lac Launière was formalized on December 5, 1968, by the Commission de toponymie du Québec.

See also
 St. Lawrence River
 List of lakes of Canada

References

External links
 Official website of Laurentides Wildlife Reserve

La Côte-de-Beaupré Regional County Municipality
Laurentides Wildlife Reserve

redpajama_set_name: RedPajamaWikipedia


text:
Published on April 8, 2020 by Kate Wisialowski
About the Toxic Substances Control Act List
For decades, loopholes in the EPA's law for testing industrial chemicals—also known as the Toxic Substance Control Act (TSCA)—have allowed thousands of potentially harmful products to enter the environment.
Limitations in the law let manufacturers continue using dangerous materials—including known carcinogens like asbestos—despite the risks to consumers. A quick background on the TSCA can give you an idea of why so many chemicals remain unregulated and present in environmental waters that we rely on as drinking water sources.
What is TSCA?
In 1976, the TSCA was passed to help the EPA gather testing data on commercial chemicals to determine their safety.
The law originally intended to give the EPA power to:
Gather information on whether new and existing chemicals are dangerous
Require manufacturers to test and report on potentially unsafe chemicals, if the EPA does not have enough information available
If these tests reveal that a chemical would cause an "unreasonable risk" of injury to peoples' health or the environment, the EPA can set rules like requiring warning labels or placing a total ban.
How Many Industrial Chemicals Are There?
Part of the TSCA requires the EPA to keep a list of all chemicals made or processed in the United States.
Currently there are over 86,000 chemicals in this inventory—and this doesn't even include chemicals used in pesticides, drugs, and cosmetics, which are regulated by different laws. But unlike pesticides and pharmaceuticals, the chemicals in the TSCA's inventory have historically been considered "safe until proven dangerous."
This has placed the burden on the EPA to prove a chemical's risk rather than relying on chemical manufacturers to prove it is safe during its production.
How Many Chemicals Have Been Tested for Safety?
While the TSCA was strengthened by an update in 2016, it's original language played a big part in why so few chemicals have been tested.
Existing Chemicals
After the TSCA was first passed, the EPA listed 62,000 existing chemicals already in use at the time.
Every existing chemical was considered safe unless the EPA could prove otherwise.
But, according to the original law, chemical producers didn't need to provide safety data to the EPA unless the agency could first show that an "unreasonable risk" might exist or that high exposures were already occurring. A veritable catch-22—how would the agency prove a risk without safety data?
Because of this hurdle, the EPA was only able to review about 1,200 (2%) of existing chemicals for safety in the law's first 15 years—though the agency estimated that closer to 26% were potentially of concern.
New Chemicals
About 20,000 new commercial substances have been added to the inventory since the TSCA was passed. To assess the risk of new chemicals, the TSCA requires companies to notify the EPA about basic information on a product before manufacturing new chemicals.
Based on the original law, the agency had 90 days to determine whether a new chemical presents an "unreasonable risk" that required regulation or manufacturers could go ahead with production. But, there was a big loophole for chemical manufacturers:
The TSCA did not require companies to provide any particular amount of health and safety data.
The agency relied on this information to be voluntarily reported, and there were no penalties for not providing this information. In 2003, the EPA found that 85% of PMNs it received lacked data on health effects.
Paralyzed by limited data and a short 90-day review period, the EPA was only able to take action on less than 10% of 36,600 commercial-use chemicals between 1979 and 2004.
How Many Chemicals Have Been Banned?
Of all the existing chemicals from the original inventory, the EPA has only managed to ban or limit five substances:
Polychlorinated biphenyls (PCBs)
Chlorofluorocarbons (CFCs)
Dioxins
Asbestos (though the ban was later overturned by a court ruling)
The four new chemicals banned by the EPA are all used in metalworking fluids and found to cause cancer when mixed with nitrites—a practice that has been banned since 1984.
Why Doesn't the EPA Regulate More Chemicals?
Aside from the limited safety data chemical manufacturers need to provide the EPA, proving that a chemical would cause an "unreasonable risk" has been another major roadblock to regulation.
The term "unreasonable risk" was not clearly defined by the law, leaving the EPA's safety findings open to being challenged in court.
The EPA also had to show that the benefits of regulating a chemical were greater than the economic losses that a regulation might cause. For example, while asbestos was one of the few substances TSCA managed to ban, this was overturned by courts when manufacturers argued a total ban would hurt the economy and that there were ways asbestos could be used safely.
Will the 2016 TSCA Update Lead to More Regulation?
In 2016, the Frank R. Lautenberg Chemical Safety for the 21st Century Act updated the TSCA to give the EPA more regulatory power by:
Holding chemicals to a higher safety standard of "negligible risk" and "reasonable certainty of no harm"
Requiring risk evaluations for all high-priority substance in the existing chemicals inventory
Mandating the EPA to review all new chemicals and prove a new chemical's safety before it can enter commerce
But the update has a few limitations, as well. There are still tens of thousands of chemicals currently in use that need testing.
The agency needs considerable resources to gather and sift through this massive amount of health and safety data. For example, based on the deadlines set by the update, if the agency finds it needs to test 10% of existing chemicals for risk, this works out to 8,500 evaluations in groups of 20 to be completed every 3.5 years—which would take 1,500 years to complete.
Are There Solutions to Fix the TSCA?
Holding companies accountable for proving the safety of their products instead of relying on the EPA to demonstrate their risk would go a long way in protecting consumers. This is how other industrial products—like pharmaceuticals and food additives—are currently regulated.
In the meanwhile, as the EPA catches up to 40 years worth of chemicals that have been allowed into the economy, there's only so much that can be done about known and emerging contaminants that are already in the environment.
If you are concerned about potential contaminants in your drinking water, Tap Score offers a range of testing kits that can detect thousands of contaminants– from common contaminants like lead to emerging contaminants like PFAS, 1,4-dioxane and beyond.
https://www.epa.gov/laws-regulations/summary-toxic-substances-control-act
https://www.theatlantic.com/health/archive/2016/05/toxic-substances-control-act/484280/
https://fas.org/sgp/crs/misc/RL31905.pdf
https://www.epa.gov/tsca-inventory/about-tsca-chemical-substance-inventory
https://www.nytimes.com/2013/04/14/sunday-review/think-those-chemicals-have-been-tested.html
https://www.epa.gov/assessing-and-managing-chemicals-under-tsca/frank-r-lautenberg-chemical-safety-21st-century-act
http://archive.gao.gov/t2pbat2/152799.pdf
https://www.gao.gov/new.items/d05458.pdf
http://www.chemicalspolicy.org/downloads/TSCA10112-24-03.pdf
https://www.businessinsider.com/epa-only-restricts-9-chemicals-2016-2
https://law.justia.com/cases/federal/appellate-courts/F2/947/1201/153685/
emerging contaminants
forever chemicals
What Do HPC Test Results Actually Mean?
What Do PUR Pitchers Filter Out?
What Do Brita Pitchers Filter Out?
While almost all pathogenic bacteria are heterotrophic, but not all heterotrophic bacteria are pathogenic. Subsequently, heterotrophic plate count (HPC) is an indicator of overall bacterial presence in your water, but...
With thousands of pitcher filters on the market and an intimidating mix of treatment options, it can be challenging to navigate the best filter for your water. To help consumers...


: carousels to careers. Would anyone have any information on this matter?
Frankly, I think it would be a much greater challenge to find a metaphor involving effort and reward that *hasn't* been used for career success. Every possible sports metaphor, for example, has been used, verbally and visually and often.


ALPHARETTA, GA February 2, 2011 - LocumTenens.com has announced the promotion of Debra Creedon Zelnio to the position of Vice President of Marketing. The promotion comes after a highly successful year of leading the marketing department at LocumTenens.com as Executive Director of Marketing.
Zelnio, a native of Medford, NJ, holds a Bachelor of Arts in Communication degree from George Mason University in Fairfax, VA. Before joining LocumTenens.com in June of 2003 Zelnio gained 13 years of marketing experience, first at Inside Washington Publishers in Washington DC, and then in the role of Sr. Marketing Manager for AHC, the CME clinical newsletter division of Thomson Healthcare, located in Atlanta, GA. During her tenure at LocumTenens.com Zelnio managed the successful marketing launches of the surgery and primary care divisions, and in 2006 she led a complete rebranding for LocumTenens.com. Most recently Zelnio managed the complete rebuild and launch of the company's new corporate website and job board in October 2010.
"For over seven years Deb has continuously taken on increasing levels of responsibility and leadership, all while growing her expertise in marketing as well as her overall knowledge about our business," said Shane Jackson, president of LocumTenens.com. "Under her leadership the marketing team has experienced vast growth and has become a very valuable and highly respected resource at LocumTenens.com," said Jackson.
"I'm extremely proud of the marketing team I've helped build here at LocumTenens.com," said Debra Creedon Zelnio. "Their commitment, enthusiasm and creativity has grown our work product exponentially in the last year, and I look forward to future projects that will help take LocumTenens.com to the next level."
Founded in 1995, LocumTenens.com is a full-service physician recruitment firm specializing in emergency medicine, primary care (including family practice, internal medicine, pediatrics and hospitalists), anesthesiology and CRNA, psychiatry, radiology and surgery with U.S. hospitals, medical groups and community health centers. LocumTenens.com operates an internet job board with direct access to thousands of jobs - in all specialties - posted directly by facilities and agencies nationwide. LocumTenens.com is part of the Jackson Healthcare family of companies. To learn more, visit http://www.locumtenens.com/welcome or call 800-562-8663.


Say goodbye to General Motor's dinosaurs.
for others, the cars were a bit outdated in design.
includes this week's test car, the wonderful Chevy Impala SS.
Now, let's go enjoy the SS one last time.
Texas, assembly line in February, 1994.
leaping Impala in the C-pillars.
holder/ashtray that slides out from the dash.
allowed me to tackle the hilly track easily.
down quickly, with the help of the ABS system.
nice, too, if it doesn't command collector price tags yet.
system still worked flawlessly, even when the light came on.
prices listed are manufacturer's suggested retail prices.


I just checked two things off my life to do list!
After living in California for 5 years I finally got to Lake Tahoe to see the snow! It was my first time to ever see snow like that! I can't wait to go back so that I can take lots and lots of pictures. This trip was more about focusing on not falling off the mountain (and Clifftech) so next trip can be more about the photog opportunity!
This is Heavenly. I took my ski lesson there. My instructor said I was a natural and I was feeling pretty confidant until the next day we went to Northstar and I got on the ski lift and suddenly everything got scary! The ski lift is so high up and even though you know you're not going to fall out the thoughts that you could keep rushing in!
I skied my first run!
It was a slow process getting down the mountain and practicing my turns but Cliff was super patient with me and cheered me on all the way! I was grateful I conquered it and he was proud, it was a good day!
But seriously tubing was one of the best parts!
Between getting up and trying not to fall off the mountain or get in other peoples way I took lots of long pauses to look around at the beauty. It was breathtaking!
Hey fella, thanks for all the love and helping me to make all my to do's and dreams come true! You're so cool!


The Philippines got backing from five countries in its position on territorial disputes in the West Philippine Sea (South China Sea), a "huge victory" despite China's fresh refusal to commit to a code of conduct in the sea, the Palace said yesterday.
China refused to discuss the territorial disputes during the Asean-China Summit, one of the highest-level meetings during the 21st Association of Southeast Asian Nations (Asean) Summit in Phnom Penh that ended on Tuesday.
But four Asean members claiming territories in the disputed sea—Brunei, Malaysia, the Philippines and Vietnam—agreed to hold talks in Manila on December 12 to see how they could move forward discussions on their territorial disputes with China.
President Aquino, who on Monday publicly rebuked Cambodian Prime Minister Hun Sen on his statement that Asean leaders had agreed not to "internationalise" the disputes, convinced five other leaders on the wisdom of the Philippine position on the issue.
On the president's intervention, Asean issued a joint communiqué expressing the 10-nation bloc's desire for a peaceful resolution of the territorial disputes, officials said.
The joint communiqué, which did not carry Hun Sen's statement, was issued at the close of the Asean-Japan Summit in Phnom Penh on Monday.
In wrapping up Asean's meeting with Japan, Hun Sen, chairman of the Asean Summit and a close ally of China, said the Asean leaders had reached a consensus that the territorial disputes in the West Philippine Sea should not be "internationalised" and that discussions should be confined between the bloc and China.
Aquino intervened, saying no such consensus had been reached.
"We made our stand clear to all parties with sobriety, dignity and reason. In the end, we did not only deliver our message; we [also] convinced [no fewer] than five countries to agree with our position," Aquino said on his arrival in Manila late Tuesday.
"Before, only two were talking about the importance of the (code of conduct). Now more are saying, 'It's about time formal talks on the code of conduct were started to avert tension in the future,"' he said.
The Philippines, Brunei, Malaysia and Vietnam are pushing for a code of conduct with China to minimise the risk of violent confrontations in the West Philippine Sea, home to major sea lanes vital to global trade and where islets, reefs and atolls are believed to be sitting on vast gas and oil deposits.
China said earlier that it was willing to work on a code of conduct with Asean countries at the proper time, although it doubted whether the regional bloc was the proper forum for discussions on such a code.
Cabinet Secretary Rene Almendras, who accompanied the President to the Asean summit, declined to identify the five countries, saying the discussions happened at the retreat sessions. He said the five were not necessarily members of Asean.
The Philippines stands for the resolution of the maritime disputes with China according to the United Nations Convention on the Law of the Sea (Unclos), which allows coastal states 370 kilometres (200 nautical miles) of exclusive economic zone.
Besides the Philippines, Brunei, Malaysia, Vietnam and Taiwan also claim territory in the sea.
But China insists it has sovereignty over nearly the entire sea and refuses to discuss the overlapping claims with its rivals on any international forum. It insists on one-on-one discussions with the other claimants.
Almendras confirmed that Asean had failed to get any commitment from China on the code of conduct. But he said Foreign Secretary Albert del Rosario would restart talks with his Southeast Asian counterparts soon.
Getting five countries to agree to the Philippine position was a "huge victory" given that it was facing Asia's economic and military superpower, the president said.
"It was like a David and Goliath fight, but we stood our ground. Otherwise, who else could we turn to? Now they know: the Filipino will not back down if he's in the right. The Filipino is good at associating with others, but we will not be oppressed," he said.
Almendras said the exclusion of Hun Sen's remarks from the postsummit joint communiqué was a "success" on the part of the Philippines.
"I think, if you were to ask us what was the success of the Asean trip, it was precisely that—that in the final draft, at least the ones that was shown to us, and the ones that explained in the closing ceremony, that particular line that… there was an agreement that 'South China Sea issues will not be internationalised' was not included," Almendras said.
"It's not the end game. We know that it's still gonna be a long discussion, long struggle, but at the very least, the Philippine position was made very clear to everyone. And we were very, very happy at the turn out of the support for the Philippine position during the East Asia Summit retreat session," he said.
Almendras said that US President Barack Obama and other heads of state or government were "very attentive" when Aquino rose to dispute Hun Sen's remarks and declared that Asean was not the only route available for the Philippines.


text:
Q: GD Library extension not available with this PHP installation disculpen alguien que me pueda ayudar a como instalar esa libreria? es que no busco el archivo php.ini

A: Por lo que veo estás en windows. para encontrar el php.ini puedes hacerlo ejecutando desde la terminal (linea de comandos) el comando
php --ini

El resultado será algo como lo que sigue
C:\xampp\htdocs\PR01-base> php --ini
Configuration File (php.ini) Path: C:\WINDOWS
Loaded Configuration File:         C:\xampp\php\php.ini
Scan for additional .ini files in: (none)
Additional .ini files parsed:      (none)

en C:\xampp\php\php.ini encontrarás el php buscado.
Para instalar la librería GD, solo hay que descomentar (sacar el ;) de la linea siguiente
extension=gd2
Ojala esta info te sume.

redpajama_set_name: RedPajamaStackExchange


text:
setup({ allow_uncaught_exception:true });

[
  1,
  new TypeError(),
  undefined
].forEach(throwable => {
  test(t => {
    let happened = false;
    self.addEventListener("error", t.step_func(e => {
      assert_true(e.message !== "");
      assert_equals(e.filename, new URL("reporterror.any.js", location.href).href);
      assert_greater_than(e.lineno, 0);
      assert_greater_than(e.colno, 0);
      assert_equals(e.error, throwable);
      happened = true;
    }), { once:true });
    self.reportError(throwable);
    assert_true(happened);
  }, `self.reportError(${throwable})`);
});

test(() => {
  assert_throws_js(TypeError, () => self.reportError());
}, `self.reportError() (without arguments) throws`);

redpajama_set_name: RedPajamaGithub


text:
An Interview With The Cleveland-Based Electro-Pop Band, RECESS!
Tag: All Access, All Access Music, All Access Music Group, Artist Interview, From Dust To Gold, Leaving Home, Recess
Look out for electro-pop act Recess. The 4 piece outfit is based out of Cleveland, Ohio and released their debut track "Leaving Home" earlier this year. They also shot a video for the track with director Brad Golowin (Red Sun Rising, The Revivalists) that includes a full orchestra. This song can be found on their newest EP, "From Dust To Gold."
The young alternative act thinks like seasoned musicians and are very particular with vocals and production. Anthony who plays keyboards tells explains, "With the release of our new single "Leaving Home" off of our EP "From Dust to Gold", we are really trying to revolutionize the pop industry and just show how four crazy ridiculous kids can rise from a small town in Cleveland, Ohio, and, by working hard and refusing to give up, show people that it can be done.
"Leaving Home" Official Video: https://www.youtube.com/watch?v=ilEg6-CKBR4
Connect With Recess Here- https://www.instagram.com/recessofficial/
Learn more about Recess in the following All Access interview:
Now that we are into the 5th month of the year, how would you say that 2019 is treating the band so far? What are some goals that you have for this year? How are those New Years Resolutions going?
Wow that's crazy to believe! We are on a good pace, man. It's all about setting short term, reasonable goals and working as a unit towards them. So far, we've dropped our newest EP "From Dust to Gold" on time, as we intended, and our episode of "Who Will Rock You" is set to air any day now, so I would say we are making solid progress.
Can you recall the moment when you thought you could be in this group together? Was it hard to think of a name that you could agree on? Has anything surprised you about this musical journey so far?
Knowing each other for over 10 years, it can honestly be challenging to focus on a certain time in which we knew exactly what we were doing. When we first started, we were a bunch of pre-teen kids wowing local, older crowds by playing classic rock covers. The one thing we all shared since the beginning, however, was a common vision of doing what we were doing on a scale much larger than any one of us could think was possible. We were very confident in our capabilities and potential at a very early age, which helped us all take the process seriously and hold it to utmost importance as we grew as individuals.
How do you think your hometown has influenced the sound and how you all carry yourselves in this group?
The Cleveland music scene is dominated my mega tribute/cover bands, so for us to even play the stages we do every summer is astounding. In a place where the music scene is exclusive, we have really had to grind our way through the process and learn things the hard way the last ten years to even be able to do this interview today. We have had our fair share of empty crowds that taught us how to grow and learn how to keep an audience occupied during the entire duration of a 60 minute set. That process has sharpened our skills and made our sound tighter than anything and we feel it has been an integral part of our music inside the studio and live. Little things like that have shaped us into who we are today.
How did your band name first come together? Was it hard to all agree on one name? What other names were you considering?
All of us were sitting in a room with our parents about 10-11 years ago thinking up a cool band name. Anthony says: "How about Recess?", and that was pretty much it.
I always like to ask bands if you all hang out socially apart from the music? In other words, when you aren't working on music, do you guys enjoy hanging out for fun?
You can always find one of us at the Fondale's house. It is pretty much a living Seinfeld episode in which any one of us characters can barge in at any time for any sporadic reason. We really are an extremely tight knit group and there is not a day that goes by where our GC on our phones isn't buzzing with new ideas, or "power moves" as Slan would call them, being formulated and discussed.
Let's talk about your newest track "Leaving Home." What was the inspiration for it? How creatively involved with the process were you in the video making process?
Leaving Home is actually a rather somber song contrary to what it sounds. The inspiration behind the song is the ironic loneliness that follows musicians no matter how big their crowds become. The entire storyboard process was done internally amongst the band. The vision with an orchestra and an obsessed fan was made at the Fondale's house, but, with the help of Brad Golowin, we were able to get our visions into production and eventually into a real, finished project.
How would you say this song prepares listeners for more music from Recess and for your EP "From Dust to Gold"?
Leaving Home is a track that perfectly epitomizes the band in the shortest, sweetest way. Although the #1 song on our Spotify is "When The Sky Comes", Leaving Home perfectly captures the theatrical side to our musicianship that stems from our love of movies and video games we grew up loving, and shows how unexpected and limitless we really are with our creativity. If you're waiting for new Recess music, expect extremely different sounding tracks constantly and throughout our discography.
What was it like making your EP? Did anything surprise you about the overall process?
With how much we focus on pre-production and conceptualizing a song before hitting the studio, we didn't encounter any surprises or twists with the production process because there was no sacrifice with sounds or anything of that matter in the studio. If there was a sound we wanted to capture, but weren't able to for whatever reason, we had no problem closing the session for the night and attacking it with a fresh mind a few days later. We basically know exactly what the song is supposed to be when it's first written, and we plan carefully and arrange the parts with their frequency ranges and rhythmic patterns already set to get the song to its final form before we even open a Pro Tools session or set up a microphone.
Anthony and Vincent are our primary songwriters. Typically, our best songs have started with a solid verse or chorus melody that either Anthony or Vincent wrote, and they both work together to find a vision and direction from there.
How has the sound changed over the years? What has remained the same?
Discovering our sound has been the part we never felt inclined to rush into or force. Finding your niche in music is not something that happens overnight, so we never really focused on writing songs for marketability or anyone else in general. At the time, when we started the songwriting process more seriously than anything, Imagine Dragons was just starting their ascent to the top. They basically dropped our jaws and showed us that the era of the big band is alive and well, and showed us that you can have a modern/polished pop/rock sound that still delivers and moves people live. As a result we just focused on ourselves and wrote songs about things that we wanted to write about, and, so far, it has resonated well within our fan-base.
Being on a stage with your best friends playing in front of a huge crowd that is singing along to the lyrics you wrote in your notebook in high school is a euphoria that simply cannot be described. There are a lot of moments where we are completely connected spiritually, such as the bridge to "Whats The Gain" during our live show. Our energy and emotion is pouring out so strongly that there is simply no better feeling in the entire world knowing the crowd is feeling the same goosebumps and hair standing on their arms during that moment that we are.
Where can fans see you perform next? What do you think makes for an ideal show for this group?
Our upcoming show is May 26th at The Rock and Roll Hall of Fame in Cleveland, OH. It is our first show in the Cleveland area after a long off season so you can expect plenty of new tricks up our sleeves as well as some unreleased, never before heard songs that we have for our next EP.
How has social media impacted the band? How often are you all on your different sites interacting with fans?
Social media is the hub for our fandom. The best part of our day is tweeting our fans from The Philippines to California, and seeing the Recess family grow on a daily basis. Or as Anthony likes to do it, say something completely nonsensical and ambiguous, and then disappear for hours so the fans are left to fight over what he could have possibly meant. It's honestly an open invitation, 24/7 party on our social media accounts and it can get pretty entertaining to say the least.
We are currently living through a very trying and politically charged time right now so I am curious to know how you all think being musicians and in this band still gives you the most joy in life today? Do you find that your music is an escape to all the current events?
We feel that is the most important role of the arts in general. Our most important job as creators is providing that go-to place for people to escape the negativity that comes with life, and to find solace and happiness in the platform that we have created. We take that job very seriously and hold that role proudly.
What musicians would you love to work with in the future? What artists have really been inspiring this group and your music since day one?
We share inspirations from different branches of the music industry. Our influences go from 80's hair metal to hip hop, so being able to give any one man or woman that distinction is impossible. Ryan Tedder (One Republic), Ed Sheeran, and Charlie Puth are all modern artists we all listen to and are inspired by. Also we cannot forget the late Avicii, and The Rev(Avenged Sevenfold), who showed us the best songs come naturally and from within.
What is the message of your music? What do you want people to take away from your songs?
We really don't try to incorporate a specific meaning or associate something to take from our music as whole, but like to change meanings and tell stories from song to song. Some songs are very heartfelt and personal, such as "Swear to Me" and "Goodbye" off of "From Dust to Gold", while other songs like "What's the Gain" off of our first EP "Boundary Lane" is written within the mind of a foot soldier in an ancient battle front, so its really very open and interpretational writing style we have.
Don't forget to catch our upcoming episodes on Season 2 of Topgolf's "Who Will Rock You", a competition TV show looking for the best unsigned bands in America. It's an awesome opportunity to be a part of and we absolutely cannot wait to show the world what we have. Also, keep on the lookout for tour dates near you and new content. It's all coming quite fast and we're extremely excited to share everything with everyone.
An Interview With Bob and Scott Cerny Known As THE CERNY BROTHERS All About Their Latest Album, 'Looking For The Good Land'!
An Interview With The Pittsburgh Grammy- Nominated Artist and Multi-Instrumentalist, KEVIN GARRETT!


Take a new approach to layering with this fashionable linen-blend field BOSS Black jacket. This jacket has a buckle-fastening stand collar, front button-fastening welt pockets and back vents.
Keep a cool and casual look with this BOSS Green track jacket. This distinctive ombré style has a detachable hood, side slant pockets and ribbed hems.
A lightweight windbreaker BOSS Green jacket with an elements resistant synthetic shell and contrasting inserts and trim along the shoulder, side seams, arm, and zipper placket. A mesh lined upper back and double faced breast wick away excess moisture. Inner collar features dashing stripe pattern. Two front zipper pockets are for security and functionality.
Shaped with an attached hood and drawstring, it's an easy, versatile, wear-everywhere style. A laid-back layer in brushed cotton BOSS Orange jersey.
Reinforced elbows and nylon hood in a hidden zipper neck pouch, brings welcomed functionality. A seasonable interpretation of the naval BOSS Black peacoat in cotton gabardine, complete with shoulder epaulets, large graphic buttons, and waist button tabs for a formidable, cinched silhouette.
This BOSS Black jacket, made of a high quality, subtly quilted synthetic fabric features reinforced shoulder seams and front zipper closure to shield you from the weather.
BOSS Black contemporary motorcycle inspired jacket in a modern techno fabrication. A stand-up collar, shoulder yokes and elbow patches featuring multi-topstitching gives an authentic look, while the fabrication keeps the jacket lightweight as easy to wear.
BOSS Green outdoor jacket in a technical style. Sporty jacket with an integrated hood, rubberized appliqués and a modern fabrication. The fleece lining and narrow sleeve cuffs ensure that this leisure jacket is storm proof. The left upper sleeve sports a zip clip and logo.


This is a horrible system. This item doesn't belong on this page. To print the manual completely, please, download it. The time now is Dry his still Tx Erik. If possible, include photos or video to assist in understanding the problem. Other than these, the keyboard works decently, and the batteries last a long time in it, as I haven't had to change the batteries since I got it.
Page 1 of 1 Start over Page 1 of 1. I have to change them about every 10 days, which is a pain and costs a lot of money for batteries.
The "off" amount and percentage simply kh985w the calculated difference between the seller-provided price for the item elsewhere and the seller's price on eBay. Learn more about Amazon Prime. You lose the USB connection when it feels like it. Solvusoft's close relationship with Microsoft as a Gold Certified Partner enables us to provide best-in-class software solutions that are optimized for performance on Windows operating systems.
Amazon Drive Cloud storage from Amazon. Top Reviews Most recent Top Reviews. If possible, include photos or video to assist in understanding the problem. Got it, continue to print. Feedback If you are a seller for this product, would you like to suggest updates through seller support? Hot Key Definitions Press this button to go back a webpage to launch default home page Press this button Press this button to go forward a page Press this button to open a search window Press this button to launch your default e-mail 6.
For technical assistance visit www.
The keys wore off after two weeks. Terms and kb985e apply. East Dane Designer Men's Fashion. Product information Product Dimensions Share your thoughts with other customers. Got same model from WM a cheaper price. IF you don't you are in trouble. Umzinto, South Africa Distribution: This website is using cookies.
The mouse eats them faster, but that's expected. You may also like.
Another thing that I don't like is that the mouse pointer will randomly jump around the edges of the screen quickly, which has caused me to accidentally close windows that I didn't want closed, really irritating me. The only good thing that Kb9885w can think of about the mouse is that it is comfortable in my hand.
It is an effective combination at first, but a couple of months into using it the mouse started acting weird. Micro Innovations is not responsible for any radio or television ,i caused by using other than recommended cables and connectors or by unauthorized changes or modifications to this equipment.
For more advanced trainees it can be a desktop reference, and a collection of the base knowledge needed to proceed with system and network administration. Keep the software CD in a safe place for future installations. This tool will download and update the correct Bk985w Innovations KBW driver versions automatically, protecting you against installing the wrong KBW drivers.
Enter text from picture: BB kv985w is On. To launch the keyboard settings interface Fig. I'll just use that, though it would be nice to use the MI one. It's a terrible mouse to use for any kind of gaming esp.