Washington, DC -- Association for Community Affiliated Plans (ACAP), National Alliance on Mental Illness (NAMI), Mental Health America, American Psychiatric Association (APA), AIDS United, National Partnership for Women & Families, and Little Lobbyists today filed suit in the U.S. District Court for the District of Columbia to invalidate the short-term, limited-duration insurance (STLDI) plan rule issued last month by three federal agencies. This rule will harm patients and their families as well as others in the health care system by undermining access to quality, affordable coverage, will significantly disrupt insurance markets in states across the country, and threatens to bring back abusive practices that harm consumers specifically prohibited by the Affordable Care Act (ACA).
The groups argue in their complaint that the final rule violates the plain-English meaning of "short-term" by defining it as 364 days instead of three months, as currently allowed, and "limited duration" as up to 36 instead of 12 months. The plaintiffs also argue that the rule arbitrarily reverses previous limits on these plans to create an "alternative" to ACA-compliant plans that Congress did not authorize and that violates the ACA by effectively undercutting ACA plans and making them increasingly unaffordable and unsustainable for consumers who have nowhere else to turn. As such, the plaintiffs believe that the courts will agree that the rule is unlawful.
The rule expands the availability of discriminatory, inadequate short-term "junk" plans, which can: set higher premiums based on age, gender, and health status, deny access to basic benefits, undermine catastrophic protections, deny coverage for any pre-existing condition, and increase uncompensated care for health care providers. Expansion of short-term plans also threatens people's access to quality coverage. Middle-income families with comprehensive coverage will see their premiums increase while limited, medically underwritten plans lure healthy people out of the quality plans that include consumer safeguards. Such plans are not subject to mental health parity nor the non-discrimination rules that protect people with conditions like HIV/AIDS. Additionally, rushing sale of short-term plans will undercut health plans that play by the rules and will confuse consumers when they are signing up for 2019 coverage starting on November 1.
"At a time when suicide and overdose deaths have hit epidemic levels and continue to rise, the last thing we need is a rule that confuses consumers and offers worse mental health and substance use benefits. The STLDI rule not only violates the intent of Congress in the ACA, it also rips away needed treatment and threatens the lives of countless Americans," said Paul Gionfriddo, President and CEO of Mental Health America.
"The plans and protections of the ACA have been life savers for people living with HIV. Discrimination based on pre-existing conditions including HIV was prolific before the ACA. These plans are a giant step backwards and will effectively sanction discrimination by the insurance industry and will deny access to thousands including people living with HIV. Access to essential medications for HIV treatment not only preserve individuals' health, but achieving viral suppression through medications can also prevent new HIV infections. These short term plans are not only a threat to people with HIV, but to our the nation's public health by making it harder to end the HIV epidemic in America," said Jesse Milan, Jr., JD, President & CEO, AIDS United.
"Children with complex medical needs require access to affordable health insurance that covers essential health benefits, pre-existing conditions, and does not impose monetary caps on care. The ACA assures these families that any plan they buy will have these protections. The STLDI rule destroys that assurance. Those who buy these STLDI plans will have necessary care for their children go uncovered and face financial ruin. Those fortunate enough to have ACA-compliant plans will face skyrocketing premiums because of the effects of the STLDI plans on the market. Our children deserve better," said Elena Hung, President of Little Lobbyists.
The plaintiffs represent a broad array of insurers, health care providers, and consumer groups that advocate for patients, particularly those with pre-existing conditions and serious illnesses. A deluge of overwhelmingly negative comments from across the health care sector has warned that short-term plans will leave many enrollees uncovered when they need health care, increase premiums for those who buy quality plans, increase costs to taxpayers, and reduce choices. One analysis found that 98 percent or 335 of 340 health care group comments either raised problems with the rule or expressed outright opposition to it.
The Trump administration's final rule was issued without Congressional approval or public support: a recent survey found that 90 percent of people say it is important that the ACA's pre-existing protections remain law. This final rule unilaterally undermines federal health insurance protections for people with private insurance, especially the 133 million Americans with pre-existing conditions.
The Association for Community Affiliated Plans (ACAP) represents 62 Safety Net Health Plans, which provide health coverage to more than 21 million people in 29 states. Safety Net Health Plans serve their members through Medicaid, Medicare, the Children's Health Insurance Program (CHIP), the Marketplace and other health programs. For more information, visit www.communityplans.net.
The National Alliance on Mental Illness (NAMI) is the nation's largest grassroots mental health organization dedicated to building better lives for the millions of Americans affected by mental illness. Learn more about www.nami.org | facebook.com/nami | twitter.com/namicommunicate | instagram.com/namicommunicate.
Mental Health America – founded in 1909 – is the nation's leading community-based nonprofit dedicated to addressing the needs of those living with mental illness and to promoting the overall mental health of all Americans. Our work is driven by our commitment to promote mental health as a critical part of overall wellness, including prevention services for all, early identification and intervention for those at risk, integrated care, services, and supports for those who need it, with recovery as the goal.
The American Psychiatric Association (APA), founded in 1844, is the oldest medical association in the country. The APA is also the largest psychiatric association in the world with more than 37,800 physician members specializing in the diagnosis, treatment, prevention and research of mental illnesses. APA's vision is to ensure access to quality psychiatric diagnosis and treatment. For more information please visit www.psychiatry.org.
AIDS United has the singular mission of ending the HIV/AIDS epidemic in the United States. We work to achieve this ambitious mission through strategic grantmaking, technical assistance and capacity building services, formative research, and advocacy based on sound public policy analysis. Headquartered in Washington, D.C., AIDS United's staff of public health and policy professionals work daily on building power for HIV efforts at national, regional, and community levels and funding initiatives for improved health outcomes for people living with HIV and prevention strategies for those most likely to be impacted by the epidemic. AIDS United's Public Policy Council (PPC) of 47 HIV/AIDS Service organizations, national and regional coalitions is the largest and longest-running community-based HIV/AIDS domestic policy coalition in the country. For the last 35 years, the PPC has led initiatives to shape and inform federal policies that impact people living with and affected by HIV. AIDS United additionally represents more than 200 grantee and subgrantee AIDS Service Organizations serving people living with HIV throughout the United States.
The National Partnership for Women & Families is a nonprofit, nonpartisan advocacy group dedicated to promoting access to quality, affordable health care, reproductive health and rights, fairness in the workplace, and policies that help women and men meet the dual demands of work and family. More information is available at www.nationalpartnership.org.
Little Lobbyists is an organization working to protect and expand the rights of children with complex medical needs through advocacy, education, and outreach. The group was founded by and is led by families of children with complex medical needs and disabilities requiring specialized and ongoing health care. These families face significant challenges in caring for their children and experience first-hand the impact that federal and state laws and policies have on their lives. The mission of Little Lobbyists is to support, inform, and advocate for this growing community of families and their unique needs. For more information, please visit www.littlelobbyists.org and @LittleLobbyists on Twitter, Facebook and Instagram.


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text:
\section{Introduction}
Pretrained word embeddings are an essential ingredient for high performance on most NLP tasks.
Transformer language models (TLMs)\footnote{Following popular usage, we will informally refer to TLMs similar to the original BERT as ``BERTs'' throughout this work.} such as BERT/mBERT \citep{devlin_bert_2019} RoBERTa/XLM-R, \citep{liu_roberta_2019,conneau_unsupervised_2020}, and ELECTRA \citep{clark_electra_2020} provide state-of-the-art performance, but they expect at least tens of millions of tokens in training data.
High-resource languages like English, Arabic, and Mandarin are able to meet this requirement, but most of the world's languages cannot.
Two major lines of work have arisen in order to address this gap: the first attempts to use multilingual transfer to pool different languages' data together to meet TLMs' data demands, and the second attempts to lower TLMs' data demands by changing their architectures and training regimens.

In this study, we take up work in the latter direction, asking specifically whether (1) vast reduction of model size and (2) incorporation of explicitly supervised, rather than self-supervised, tasks into model pretraining can produce better monolingual TLMs.
The former method is motivated by the intuition that normal-sized TLMs are so large as to be severely overparameterized for low-resource settings, and the latter method is motivated by an intuition that in the absence of large volumes of unlabeled text, signal from a supervised task with linguistic annotations is less likely be redundant to the model.
We find evidence that indicates both methods are helpful: our MicroBERT models produce monolingual embeddings that can outperform comparable multilingual approaches.
We summarize our contributions as follows:
\begin{enumerate}
\item We describe a method for training monolingual BERTs for low-resource settings, MicroBERT, characterized by a small parameter count and multitask pretraining which includes masked language modeling (MLM), part-of-speech (PoS) tagging and dependency syntax parsing.
\item Using evaluations on named-entity recognition (NER) and Universal Dependencies (UD) parsing across 7 diverse languages, we show that this approach is competitive with multilingual methods and often outperforms them for languages unseen by mBERT, even when the only pretraining task is MLM.
Our evaluation reveals a 7\% higher parser LAS and 6\% higher NER F1 on average for unseen languages, with gains up to 18.87\% and 11.81\% for parsing and NER.
\item We release all MicroBERT models trained for this work at \url{https://github.com/lgessler/microbert}{}.
\item We publicly release our code at \url{https://github.com/lgessler/microbert}{} for reproducing our results and as a turnkey facility for training new MicroBERTs.
\end{enumerate}

\section{Previous Work}

At least since the development of pretrained static word embeddings \citep{mikolov_distributed_2013,mikolov_efficient_2013,pennington_glove_2014,bojanowski_enriching_2017}, pretrained word representations have been indispensable resources for NLP models, providing dense numerical representations of tokens' linguistic properties.
Pretrained contextualized embeddings \citep{mccann_learned_2018,peters_deep_2018,devlin_bert_2019} based on the Transformer architecture \citep{vaswani_attention_2017} have since overtaken them in popularity.
Throughout this period, high-resource languages have received the majority of attention, and although interest in low-resource settings has increased in the past few years, there remains a large gap (in terms of linguistic resources, pretrained models, etc.) between low- and high-resource languages \citep{joshi-etal-2020-state}.

\subsection{Multilingual Models} The publication of BERT \citep{devlin_bert_2019} also included a multilingual model, mBERT, trained on 104 languages.
mBERT and other massively multilingual models such as XLM-R \citep{conneau_unsupervised_2020} achieve impressive performance not just on those 104 languages but also in some zero-shot settings (cf., {\it inter alia}, \citealt{pires_how_2019,rogers_primer_2020-3}), despite the fact that models like mBERT do not have any explicit mechanism for inducing shared representations across languages.
However, large language models like XLM-R suffer from the fact that languages necessarily compete for parameters, meaning that barring fortuitous synergies each additional language should tendentially degrade the overall performance of the model for a fixed parameter count.
Moreover, languages with less training data tend to perform more poorly in LMs like XLM-R \citep{wu_are_2020}.

While the majority of multilingual models seek to include many languages, with a large proportion of them being high-resource, there are some low-resource approaches to training multilingual models from scratch where there may not even be any high-resource languages.
For example, \citet{ogueji_small_2021} train an mBERT on data totaling less than 1GB ($\approx$100M tokens) from 11 African languages, and find that their model often outperforms comparable massively multilingual models.

\subsection{Adapting Multilingual Models}
One response to the difficulties posed by massively multilingual models has been to leave aside the goal of fitting ever more languages into a single model, and to investigate whether it would be more fruitful to {\it adapt} pretrained massively multilingual models for a given target language.
Enriching the TLM's vocabulary with additional tokens (e.g.~wordpieces for BERT-style models) has been shown to be helpful because of how it improves tokenization and reduces the rate of out-of-vocabulary tokens \citep{wang_extending_2020,artetxe-etal-2020-cross,chau_parsing_2020,ebrahimi_how_2021}.
Transliteration has also been shown to be beneficial when there are related languages that would not have been able to benefit from transfer in the form of shared representations otherwise, e.g.~between Turkish (Latin script) and related Uyghur (Arabic script) \citep{muller_when_2021,chau_specializing_2021}.
Using adapter modules \citep{houlsby_parameter-efficient_2019} has also proven effective \citep{pfeiffer_adapterhub_2020}.
All these approaches are typically combined with {\it continued pretraining}, where MLM and other pretraining tasks are used to update model weights, and some formulations of continued pretraining are multitask \citep[][{\it inter alia}]{pfeiffer_mad-x_2020,chau_specializing_2021}.

\subsection{Monolingual Models}
Whereas multilingual approaches have tried to address low-resource settings with transfer from high-resource languages, other approaches have investigated the question of how much data is needed for a given level of quality in a BERT-like model, and the question of what alternative training regimens might help reduce this data requirement.

Several studies have examined notable thresholds on dataset size.
\citet{martin_camembert_2020} find in a series of experiments that for French, at least 4GB of text is needed for near-SOTA performance, and \citet{micheli_importance_2020} show further that at least 100MB of text is needed (again for French) for ``well-performing'' models on some tasks.
\citep{micallef_pre-training_nodate} perform similar experiments for a monolingual Maltese BERT, finding that even when trained with only 46M tokens, the monolingual BERT, BERTu, was able to achieve results competitive with an mBERT model adapted with the vocabulary augmentation methods of \citet{chau_parsing_2020}.
\citep{warstadt_learning_2020} train English RoBERTa models on datasets ranging from 1M to 1B tokens and find that while models acquire linguistic features readily on small datasets, they require more data to fully exploit these features in generalization on unseen data.

To our knowledge, there has been little work on examining whether significantly reducing model size could help in the low-resource monolingual setting.
As a baseline, \citet{chau_specializing_2021} and \citet{muller_when_2021} train monolingual BERTs with 6 instead of 12 layers for low-resource languages, but this does not even halve the model's parameter count.
The only exception we were able to find is work from \citet{turc_well-read_2019}, where very small models (as low as 4.4M parameters to BERT base's 110M) are pretrained directly prior to training via distillation, but the condition where the small model is only pretrained and not trained via distillation is not evaluated in their work.

\subsection{Non-TLM Models}
Finally, it is worth noting that while BERT-like TLMs are the clear winner overall for high-resource languages in most tasks, in low-resource settings, other embedding models may be superior.
\citet{arora_contextual_2020} and \citet{ortiz_suarez_monolingual_2020} show that ELMo \citep{peters_deep_2018}, static \citep{pennington_glove_2014}, and even random embeddings are often not too far behind BERT-like TLMs on some tasks even for high-resource languages.
\citet{riabi_can_2021} show that a character-based language model is competitive with mBERT for one low-resource language, NArabizi.

\section{Motivation}
As we have seen, monolingual BERTs trained with standard methods tend to perform poorly when less than 20-40M tokens are available during training, and there is evidence that they do not learn to fully generalize some linguistic patterns without a large ($\approx$1B tokens, \citealt{warstadt_learning_2020}) amount of training data.
However, most popular methods for pretraining BERTs are self-supervised, using only unlabeled text.
This has turned out well for high-resource languages, where unlabeled text is available in far greater quantities than labeled text, to the point where incorporating labeled text into pretraining does not always provide large gains.

However, even in very low-resource settings, it is common for sources of linguistic signal beyond unlabeled text to be available, such as treebanks, interlinearized text, and dictionaries.
It is natural to ask whether using them as data for auxiliary supervised tasks during model pretraining could help monolingual models overcome a lack of unlabeled data, and perhaps even interact synergistically with the main pretraining task, such as MLM.
It is known, for example, that BERTs learn to represent words' parts of speech \citep{rogers_primer_2020-3}, and it seems possible that providing direct supervision for predicting parts of speech may help a model acquire good PoS representations with less data.
This leads us to our first hypothesis \textbf{H1}, that monolingual models should benefit from multitask pretraining with auxiliary tasks incorporating labeled data.

Previous results also lead us to our second hypothesis \textbf{H2}, that in low-resource settings, monolingual BERTs are typically severely overparameterized.
Most BERTs are overparameterized in the sense that they can have modules removed, disabled, or compressed while showing minimal regressions (or sometimes even improvements) \citep{rogers_primer_2020-3}, but in H2 we mean further that there are so many parameters that the model cannot be effectively learned given the amount of data.
As noted in \S2, there appears to be a gap in the literature on whether pretraining a vastly scaled down BERT model could help monolingual BERTs perform better in low-resource settings, and we take up the question in this work.

\section{Approach}
We propose an architecture and training regime for monolingual BERTs which we call MicroBERT.
We keep the basic architecture of BERT, but we reduce encoder layer count to 3, hidden representation size to 100, and number of attention heads to 5.
(Compare this to BERT base's 12, 768, and 12, respectively.)
Excluding prediction heads, this reduces parameter count from 108M\footnote{Obtained from \texttt{\small bert-base-cased} using the \texttt{BertModel} implementation in HuggingFace's \texttt{transformers} library.} to 1.29M, or just 1.19\% of a normal BERT model's size.
After the encoder stack, one dedicated head is used for each task, where each head is provided with the last encoder layer's hidden states.

For training, assume a task set $\mathcal{T} = t_1, \ldots, t_{|\mathcal{T}|}$, corresponding datasets $\mathcal{D} = d_1, \ldots, d_{|\mathcal{T}|}$, and a set of weights for each task $\lambda_1, \ldots, \lambda_{|\mathcal{T}|}$, s.t.~$\sum_i\lambda_i=1$.
To prepare the sequences of batches $\mathcal{B} = b_1, \ldots, b_{|\mathcal{B}|}$ for a given epoch, construct each batch $b_i$ using only instances from exactly one dataset $d_t$, and sample batches so that each dataset $d_t$ is represented at least $\lfloor\lambda_t|\mathcal{B}|\rfloor$ times in $\mathcal{B}$.
Each batch is sent not only to its dataset's corresponding prediction head, but also to any other prediction heads which are compatible with it.
For example, a batch containing dependency syntax labels would be sent to the parsing prediction head, and it would also be sent to the MLM head, since the MLM head only requires unlabeled text.%
\footnote{Actually, matters are a bit more complicated than this.
The MLM head requires representations that included a \texttt{[MASK]} token from the start, whereas other heads require representations from unmasked sequences.
For multitask batches, therefore, the batch must be fed through the encoder stack twice: once with masking, and once without masking.}
If a dataset is exhausted in the course of this procedure, new instances are sampled anew from the beginning of the dataset.
This is a simple means for addressing the fact that some datasets will be much larger than others, which without intervention could have led to one task's parameter updates drowning out others.

We consider three tasks in this work.
The first is MLM implemented as whole-word, dynamic masking, as in RoBERTa \citep{liu_roberta_2019}.
The second is PoS tagging, for which our prediction head is a simple linear projection.
The third is dependency parsing, for which we use a modified form of the biaffine dependency parser of \citet{dozat_biaffine_2017} which has had the encoder LSTM stack removed.
Cross-entropy loss is used for all tasks and summed together: each head produces an associated loss $\ell_i$, which is summed into a single loss $\ell$ which is used to begin backpropagation.
We note that it would be straightforward to add other tasks, though we choose PoS tagging and parsing for this work since PoS tagged and dependency parsed datasets are relatively common for low-resource languages.
This multitask setup is not novel---in fact, \citet{chau_specializing_2021} use the the same three tasks for a similar purpose, though instead of pretraining a BERT from scratch, they use the multitask setup to perform adaptive finetuning on a pretrained multilingual model, and find a negative result.

\section{Experimental Methods}

To evaluate our approach, we train MicroBERT models on several languages and compare them to a variety of baselines.
All our experiments are implemented using AllenNLP \citep{gardner-etal-2018-allennlp}, Transformers \citep{wolf_huggingfaces_2020}, and PyTorch \citep{NEURIPS2019_9015}.
All code and models are available at \url{https://github.com/lgessler/microbert}.

\subsection{Data}
We prepare datasets for seven diverse languages: Wolof, Uyghur, Ancient Greek, Maltese, Coptic, Indonesian, and Tamil.
These languages were selected according to several criteria.
First, two hard requirements were that they needed to have a Universal Dependencies \citep{nivre_universal_2016} treebank with a train, dev, and test split; and that they needed to have a ``large-enough'' source of unlabeled text totaling between 500,000 and 10,000,000 tokens.
Second, languages were prioritized based on phylogenetic diversity: six unrelated language families are represented (Niger--Congo, Turkic, Indo-European, Afro-Asiatic, Austronesian, Dravidian), and languages vary widely in syntax (for example, Uyghur is morphologically rich, while Coptic is morphologically poor).
Third, we sample languages along the spectrum of data quality---for example, some have very high quality tokenization, while others have noisier tokenization.

\begin{table}[]
    \centering
    \small
    \begin{tabular}{l|rrr}
Language   & Unlabeled & UD & NER\\\hline
Wolof      &    517,237 &    9,581 &   10,800 \\
Coptic     &    970,642 &   48,632 &       -- \\
Tamil      &  1,429,735 &   40,236 &  186,423 \\
Indonesian &  1,439,772 &  122,021 &  800,063 \\
Maltese    &  2,113,223 &   44,162 &   15,850 \\
Uyghur     &  2,401,445 &   44,258 &   17,095 \\
Anc. Greek &  9,058,227 &  213,999 &       -- \\
    \end{tabular}
    \caption{Token count for each dataset by language, sorted in order of increasing unlabeled token count. Recall that unlabeled data for Indonesian and Tamil was downsampled, and all other sources of unlabeled data were used in full.}
    \label{tab:token_stats}
\end{table}

For each language, we obtain a UD treebank, a larger unlabeled corpus, and for all languages except Ancient Greek and Coptic, an NER dataset from WikiAnn \citep{pan-etal-2017-cross}.
Unlabeled data for each language was taken from Wikipedia, except for Ancient Greek and Coptic, whose unlabeled corpora were taken from open access digital humanities projects.
Note that the unlabeled corpora for Indonesian and Tamil were downsampled by randomly choosing Wikipedia articles until a quota of around 1.5M tokens was met.
A summary of corpus statistics is given in \Cref{tab:token_stats}, and a full description of the languages' datasets and their preparation is given in \Cref{sec:datasets}.
Note that Uyghur is written in Arabic script; Wolof, Indonesian, and Maltese are written in Latin script; and Tamil, Coptic, and Ancient Greek are written in their own scripts.

\subsection{Conditions}
We compare four baselines, as well as six variants of the MicroBERT approach.

\begin{itemize}
\item \textbf{\textsc{word2vec}{}}: a 100-dimensional static word embedding baseline, motivated by observations that static word embeddings can perform well in low resource settings (cf. \S2).
\item \textbf{\textsc{mbert}{}}: the \textit{bert-base-multilingual-cased} pretrained model. Note that only two of our seven languages (Indonesian and Tamil) have been seen by \textsc{mbert}{}.
\item \textbf{\textsc{mbert-va}{}}: the \textit{bert-base-multilingual-cased} pretrained model adapted in the \textit{vocabulary augmentation} method of \citet{chau_parsing_2020}, where 99 wordpieces are added to the vocabulary and the model is pretrained further.
\item \textbf{$\upmu$\textsc{bert-m}{}, $\upmu$\textsc{bert-mx}{}, $\upmu$\textsc{bert-mxp}{}}: our MicroBERT models with MLM; MLM and XPOS\footnote{In Universal Dependencies parlance, an XPOS tag is a part of speech tag from a language-specific tag inventory, as opposed to a UPOS, which is drawn from a universal tag inventory.} tagging; and MLM, XPOS tagging, and UD parsing used in pretraining. $\upmu$\textsc{bert-mx}{} performs tasks at an 8:1 ratio, and $\upmu$\textsc{bert-mxp}{} performs tasks at an 8:1:1 ratio.
\item \textbf{$\upmu$\textsc{bert4-m}{}, $\upmu$\textsc{bert4-mx}{}, $\upmu$\textsc{bert4-mxp}{}}: like the corresponding MicroBERT models, but approximately 4 times larger, having 200 instead of 100 hidden units; 8 instead of 5 attention heads; and 6 instead of 3 layers.

\end{itemize}

Our $\upmu$\textsc{bert}{} models are all trained for 200 epochs with a batch size of 32 and 8,000 batches per epoch, and we save the model that achieves best MLM performance on the validation split of the unlabeled dataset.
This results in our models being trained on only 20\% of the batches that BERT was, though we hypothesize that due to our smaller model and dataset sizes, this may not be an issue.
A full description of our methods is given in \Cref{sec:conditions}.

\subsection{Evaluation}
To evaluate our pretrained models, we perform NER on the WikiAnn datasets and dependency parsing on the UD datasets for each language--model pair, following previous work \citep[][{\it inter alia}]{chau_parsing_2020,muller_when_2021}.
We choose these tasks because they are common in the literature of TLM evaluation, because datasets are common even in low-resource languages for them, and because they both assess somewhat complementary linguistic information: informally, parsing requires grammatical knowledge, and NER requires semantic and world knowledge.
Combined, they ought to give a holistic view of a model's abilities.

We use common hyperparameter settings to train the evaluation models which allow for fine-tuning of the BERT model at a reduced learning rate.
A standard \citet{dozat_biaffine_2017} parser is used for the parsing evaluation, and a linear chain CRF with stacked LSTM encoders is used for the NER evaluation.
Our metrics for these tasks are LAS and span-based F1 score
respectively.
Gold tokenization is used in both evaluations.
No auxiliary input signals (e.g.~PoS embeddings, morphological feature embeddings, static embeddings) are used.
We forgo auxiliary inputs even though they would likely improve our scores, and even though it means no longer being able to compare our performance directly to numbers reported in some other works, since we believe providing the model's representations as the sole input provides the clearest picture of its quality.%
\footnote{This is motivated by our experience in preliminary experiments of using a parser with these auxiliary inputs, with the result that differences between our models were no larger than 3\% since the auxiliary inputs were contributing so much to the model's performance, obscuring the content of the model representations.
We also notice a similarly small difference between comparable models in other works where auxiliary inputs were used in a parsing evaluation.}
Full descriptions of the evaluation models is available in \Cref{sec:evaluation}.

\def\textsc{tbd}{\textsc{tbd}}
\section{Results}
\begin{table*}[ht]
    \centering
    \tiny
    \begin{tabular}{l|rrrrrrr|r}
                       & Wolof & Coptic & Maltese & Uyghur & An. Gk. & Tamil & Indon. & Avg. \\\hline\hline
        \textsc{word2vec}     & 72.35 & 85.69 & 73.41 & 54.27 & 73.30 & 50.91 & 74.10 & 69.15 \\
        \textsc{mbert}         & 76.40 & 14.43 & 78.18 & 46.30 & 72.30 & \textbf{66.73} & {\bf 78.63} & 61.85 \\
        \textsc{mbert-va}       & 72.94 & 82.11 & 72.69 & 42.97 & 65.89 & 54.92 & 75.67 & 66.74 \\
        \citet{chau_specializing_2021} & 60.12 & & 65.92 & 60.34 & & & \\
        $\upmu$\textsc{bert-m}    & 75.69 & 86.45 & 74.33 & 61.26 & 78.95 & 59.75 & 74.66 & 73.01 \\
        $\upmu$\textsc{bert-mx}    & \textbf{77.83} & \textbf{88.25} & 78.90 & \textbf{65.17} & \textbf{80.55} & 61.00 & 74.69 & \textbf{75.20} \\
        $\upmu$\textsc{bert-mxp}    & 73.30 & 86.35 & 75.11 & 59.98 & 79.08 & 58.05 & 73.28 & 72.16 \\
        $\upmu$\textsc{bert4-m}    & 74.42 & 82.72 & \textbf{79.25} & 57.79 & 79.59 & 61.09 & 74.32 & 72.74 \\
        $\upmu$\textsc{bert4-mx}    & 73.99 & 82.52 & 78.61 & 57.14 & 79.09 & 60.82 & 74.21 & 72.34 \\
        $\upmu$\textsc{bert4-mxp}    & 74.30 & 82.73 & 78.99 & 57.01 & 79.56 & 60.92 & 74.34 & 72.55 \\\hline
        $\upmu$\textsc{bert-mx}{} -- \textsc{mbert-va} & 4.89 & 6.14 & 6.21 & 22.20 & 14.66 & 6.08 & -0.97 & 8.46 \\
    \end{tabular}
    \caption{Labeled attachment score (LAS) by language and model combination for UD parsing evaluation. The final row shows the difference in score between $\upmu$\textsc{bert-mx}{} and \textsc{mbert-va}{}. Results from \citet{chau_specializing_2021}'s half-sized monolingual BERT are included for comparison.}
    \label{tab:parser_results}
\end{table*}

\begin{table*}[ht]
    \centering
    \tiny
    \begin{tabular}{l|rrrrr|r}
                       & Wolof & Maltese & Uyghur & Tamil & Indon. & Avg. \\\hline\hline
        \textsc{word2vec}     & 86.89 & 82.67   & 86.37  &  82.71 & 94.28 & 86.58 \\
        \textsc{mbert}         & 83.79 & 73.71   & 78.40  &  70.47 & 91.04 & 79.48 \\
        \textsc{mbert-va}       & 79.37 & 78.11   & 77.03  &  69.38 & 91.05 & 78.99\\
        $\upmu$\textsc{bert-m}    & \textbf{83.92}  & 75.89  & 81.36  &  \textbf{82.28} & 92.25 & 83.14 \\
        $\upmu$\textsc{bert-mx}    & 81.12 & 84.80   & \textbf{85.45}  &  81.61 & 92.43 & 85.08 \\
        $\upmu$\textsc{bert-mxp}    & 82.21 & \textbf{88.79}   & 82.52  &  82.00 & 92.27 & \textbf{85.56} \\
        $\upmu$\textsc{bert4-m}    & 78.69 & 78.22   & 80.28  &  80.57 & \textbf{93.05} & 82.16 \\
        $\upmu$\textsc{bert4-mx}    & 80.95 & 80.00   & 79.36  &  80.12 & 92.55 & 82.60 \\
        $\upmu$\textsc{bert4-mxp}    & 79.02 & 79.31   & 81.59  &  80.11 & 93.01 & 82.61 \\\hline
        $\upmu$\textsc{bert-mx}{} -- \textsc{mbert}{} & -2.67 & 11.09 &  7.05  &  11.14 &  1.39 & 5.60 \\
    \end{tabular}
    \caption{Span-based F1 score by language and model combination for NER evaluation. The final row shows the difference in score between $\upmu$\textsc{bert-mx}{} and \textsc{mbert}{}. Boldface indicating top performance for a language does not consider \textsc{word2vec}.}
    \label{tab:ner_results}
\end{table*}

Results for the parser evaluation are given in \Cref{tab:parser_results}, and results for the NER evaluation are given in \Cref{tab:ner_results}.
For both tables, we also include additional rows comparing important model pairs.

It is possible to directly compare our parsing evaluation results with those of \citet[][Table 2]{chau_specializing_2021}, whose evaluation methodology we closely follow for parsing.
For our three overlapping languages---Maltese, Uyghur, and Wolof---we find that LAS for mBERT is similar, which establishes that evaluation conditions are comparable.
We include their half-size BERT model's numbers in \Cref{tab:parser_results} for comparison, which were obtained by training a \texttt{bert-base}-sized BERT from scratch on the target language with 6 instead of 12 layers.

\paragraph{Non-DNN Baseline}
First, corroborating prior work, we can see that static word embeddings are competitive for many languages, often outperforming the multilingual models in both tasks, and often performing best overall for NER.

\paragraph{Multilingual Baselines}
Note the generally poor performance of \textsc{mbert-va}{}, which we had hoped would be a baseline stronger than \textsc{mbert}{}, but often underperforms relative to \textsc{mbert}{}.
An exception to this is parsing for Coptic, where \textsc{mbert}{}'s lack of wordpieces for Coptic script causes a high out-of-vocabulary rate, giving \textsc{mbert-va}{} an obvious advantage.
After carefully ruling out implementation errors, we reason that \textsc{mbert-va}{} underperformed because fine-tuning a large BERT can produce unpredictable results \citep{rogers_primer_2020-3} and our hyperparameters for adaptive pretraining may have been suboptimal (\citealt{chau_parsing_2020} perform a hyperparameter search for vocabulary augmentation---see \Cref{sec:evaluation}).
In correspondence with the authors of \citet{chau_parsing_2020}, we discussed our results, and they shared our assessment.
In sum, \textsc{mbert-va}{} appears to produce volatile results without careful hyperparameter selection, which we take to be a result of large model size and small dataset size.

\paragraph{Monolingual Model Size}
We can see that for parsing and NER, the $\upmu$\textsc{bert}{}4 model performs worse in almost all cases than the equivalent $\upmu$\textsc{bert}{} model.
The degradation is -0.27\% on average for \textsc{-m} variants, and -2.86\% on average for \textsc{-mx} variants.
The one language for which the $\upmu$\textsc{bert}{}4 model performs much better on parsing is Maltese, where the $\upmu$\textsc{bert4-m}{} model performs 5\% better than the $\upmu$\textsc{bert-m}{} model, indicating that in this experimental condition greater model size may help, though note that the Chau and Smith's half-BERT does much worse than $\upmu$\textsc{bert4-m}{} showing a 13\% lower score compared to $\upmu$\textsc{bert-m}{} and reversing the trend.
On our two other languages in common with Chau and Smith, we see an 18\% (Wolof) and 5\% (Uyghur) degradation relative to $\upmu$\textsc{bert-mx}{}.
For NER, we similarly observe that the $\upmu$\textsc{bert}{}4 variants have worse average performance than $\upmu$\textsc{bert}{} variants.
We take this all to be strong evidence for H2, that monolingual BERTs trained at common sizes are severely overparameterized in low-resource settings, to the point that large performance degradations are observed.

\paragraph{Parsing}
Considering the five languages unseen by mBERT (all except Tamil and Indonesian), we see in the parsing results that in every case the best monolingual model, usually $\upmu$\textsc{bert-mx}{}, is able to outperform the best multilingual model.
In some cases the difference is very large, such as in Uyghur parsing where there is an absolute gain in 18.87\% LAS, and in others it is within the range of chance, such as in Maltese parsing.
For the languages mBERT has seen, Tamil and Indonesian, \textsc{mbert}{} outperforms the $\upmu$\textsc{bert}{} by several points, though we find it remarkable that $\upmu$\textsc{bert}{} is able to still provide a competitive score despite being trained on very small subsets of Tamil and Indonesian Wikipedia (150K and 600K articles, respectively), which mBERT had full access to.
$\upmu$\textsc{bert-mx}{} performs best of all the models, achieving a score 8.5\% higher than that of \textsc{mbert-va}{} on average.

\paragraph{NER}
Turning now to NER results, we see that in three cases, our $\upmu$\textsc{bert}{} models are able to clearly outperform other models, including Tamil, which \textsc{mbert}{} has seen.
In the other two cases, Indonesian and Wolof, $\upmu$\textsc{bert}{} models technically keep a lead but with margins thin enough to be noise.
For all languages except Maltese however, \textsc{word2vec}{} is able to meet or beat top performance from TLMs.
Taken together with the parsing results, where \textsc{word2vec}{} underperforms, and with the strengths and weaknesses of contextualized and static embeddings in mind, we hypothesize that NER on the WikiAnn dataset may require rote capacities, such as name memorization, instead of sophisticated linguistic knowledge, especially on an automatically-constructed dataset like WikiAnn.

\paragraph{Validation MLM Perplexity}
In order to better understand the effects of our auxiliary tagging and parsing tasks, we examine the validation MLM perplexity of our models during pretraining.
An example of these curves is given in \Cref{fig:uyghur_validation_perplexity}.
We first observe that for all languages, validation MLM perplexity is lower at all times for the multitask models compared to the perplexity curve for the MLM only model.
Moreover, validation MLM perplexity converges more quickly on its asymptotic value for multitask models.
For $\upmu$\textsc{bert-mx}{} in particular, validation MLM perplexity usually comes very close to its final value even within the first 10 epochs of pretraining.
Validation MLM perplexity is only one incomplete measure of model quality, and indeed it is not entirely predictive of downstream performance since $\upmu$\textsc{bert-m}{} sometimes outperforms $\upmu$\textsc{bert-mx}{} and $\upmu$\textsc{bert-mxp}{}.
But we take these results as evidence that our auxiliary tasks are helping our models learn more quickly.
Moreover, while proving this would require additional work, it seems possible from the shapes of the validation curves that for the smallest datasets, multi-task learning (MTL) might be helping models learn more than they could have through MLM alone.

Within validation MLM perplexity, we also see that each language follows one of two patterns: either the perplexity curves for $\upmu$\textsc{bert-mx}{} and $\upmu$\textsc{bert-mxp}{} are nearly identical, or the perplexity curve for $\upmu$\textsc{bert-mxp}{} remains a bit higher than for $\upmu$\textsc{bert-mx}{}.\footnote{The former pattern holds for Wolof, Maltese, Greek, Indonesian, and Tamil, and the latter pattern holds for Uyghur and Coptic.}
With the intuition that more auxiliary tasks ought to make MLM easier, we had hypothesized that if anything the curve for $\upmu$\textsc{bert-mx}{} would have been higher than for $\upmu$\textsc{bert-mxp}{}, but instead the reverse sometimes turned out to be true.
We hypothesize that the difference in task proportions between $\upmu$\textsc{bert-mx}{} and $\upmu$\textsc{bert-mxp}{} might have been partially responsible for this: in the former, 1 in 9 batches are for auxiliary tasks, and in the latter, 2 in 10 batches are for auxiliary tasks.
If this is true, then finding the right proportion of primary and auxiliary tasks during pretraining would be critical for the multitask pretraining approach.

\begin{figure}
    \centering
    \includegraphics[width=\linewidth]{uyghur_validation.png}
    \caption{MLM perplexity vs. epoch for the validation split of the Uyghur dataset.
    The top line is $\upmu$\textsc{bert-m}{}, the middle line is $\upmu$\textsc{bert-mxp}{}, and the lowest line is $\upmu$\textsc{bert-mx}{}.}
    \label{fig:uyghur_validation_perplexity}
\end{figure}

\section{Discussion}

\paragraph{Main Findings}
We take our most important result to be our demonstration that it is possible to train a monolingual BERT from scratch that can compete with and even outperform multilingual models by up to 18\% LAS and 11\% NER F1 using as little as 500,000 tokens and a UD treebank of 44,000 tokens and less than 1\% of the parameters.

\paragraph{Multilingual Baselines}
We chose to use mBERT as a baseline because it is widely used and well studied.
Moreover, given the the architectural homogeneity of mBERT and other leading multilingual LLMs, we additionally believe mBERT is strong enough to be representative of the state of multilingual LLMs for this work.
While \textsc{mbert-va}{} appeared to severely underperform in some cases, we observe that it was still a strong baseline for both tasks (on average 5\% better than \textsc{mbert}{} for parsing, and 0.5\% worse).
In sum, while slightly stronger multilingual baselines may exist, we believe the ones in this work were still strong enough to show the MicroBERT approach holds promise, given that MicroBERTs were able to perform better than multilingual LLMs by several percentage points on average in both tasks.

\paragraph{Hypotheses}
We find strong support in these results for H1, that monolingual TLMs often, though not always, benefit from multitask learning on labeled data in low-resource settings.
We additionally find strong support for H2, that when data is severely limited, typical BERT configurations are harmfully overparameterized.

\paragraph{Future Work}
There remain some unanswered questions in this work.
The addition of the third parsing task proved harmful to performance in most cases, and it is unclear why.
Parsing and XPOS tagging involve much of the same linguistic phenomena, and it seems possible that replacing one of them with a more semantic auxiliary task might have led to better results.
Another possibility is that having loss computed for auxiliary tasks only on {\it some} batches may lead to jerky or suboptimal paths along the loss gradient, a problem which could be mitigated by having batches where only some sequences are suitable for use in auxiliary tasks.

It is natural to ask whether any of the elements of our approach here could find use in multilingual settings.
Reducing the size of multilingual models may not be a promising direction due to the curse of multilinguality \citep{conneau_unsupervised_2020}.
\citet{ogueji_small_2021} show further that even for low-resource multilingual models, size still seems to be important.
As for multitask learning, \citet{chau_specializing_2021} find a negative result for using MTL in multilingual model adaptation, though given the complex nature of MTL, many possible approaches remain untried.

Most languages in the world lack PoS tagged and parsed datasets, and if the MicroBERT approach is to be extended to very low-resource languages, it is likely that other auxiliary tasks would be needed.
We leave this direction to future work, though we speculate that there are plenty of alternatives that may work.
Parallel corpora, often in the form of a Bible translation, are readily available for over a thousand of the world's languages.
High-quality rule-based morphological parsers are sometimes available for very low-resource languages, and their outputs could be used like PoS tags.
Interlinearized texts and dictionaries are also common products of language documentation which are rich in linguistic information.
All of these resources could be adapted for use in an auxiliary task.

\section{Conclusion}

We have shown that it is possible to train monolingual TLMs that are competitive with multilingual models using as little as 500K tokens and a 40K token treebank with greatly reduced model size and multitask learning on PoS tagging and dependency syntax parsing.
While multilingual models did have some advantages over our approach, we observe that our MicroBERT approach has unique strengths for work on low-resource TLMs, including its lack of reliance on successful cross-lingual transfer and radically reduced computational demands for pretraining and downstream use.

We take this result to call into question whether multilingual representation learning can scale down effectively to truly ``low-resource'' languages that have less than a few million tokens in training data.
Sometimes languages like these can be well served by transfer from related languages, even if all languages are low-resource \citep{ogueji_small_2021}, but not all languages may be so lucky: language isolates by definition lack related languages, and small language families are likely less able to benefit from transfer, since transfer tends to be enabled by phylogenetic \citep{nguyen-chiang-2017-transfer} or areal \citep{goyal-etal-2020-contact} relatedness between languages.
While multilingual methods hold much promise, it is important to examine other approaches to low-resource representation learning which, if not strictly better, may at least be complementary.

redpajama_set_name: RedPajamaArXiv


gazing back at me, in love like when our vows were spoken so pure they felt like gifts from our angels.
but I can feel you, and know exactly how you look.
for you to know, that I swept tonight, for you.
For that moment, I would sweep our kitchen floor, all night, for eternity.
I thought this piece was beautiful so I printed it and put it under my bride's pillow last night. (I'd run out of the store-bought cards I usually use.) Anyway, she thought it was sweet but now I've got to get back to my house work.
Don't think this worked out like I'd hoped.
Well, you could always try a bigger font.
Patience had little to do with it, to tell the truth.
It would be a pretty cool trick to have the style copyrighted. I've used it a couple times, sort of loosely. But no, just this piece.
What an amazing love poem–it's beautiful.


text:
Authentic Brands Group shelves IPO, to sell stake in deal that values company at $12.7 billion
Authentic Brands Group Chief Executive Jamie Salter.
Source: Authentic Brands Group
Retail conglomerate Authentic Brands Group will shelve a planned initial public offering and instead sell significant equity stakes in its business to private equity firm CVC Capital, hedge fund HPS Investment Partners and a pool of existing stakeholders.
The deal, which was announced Monday, gives the company a $12.7 billion enterprise value.
Authentic Brands' portfolio companies include apparel retailers Forever 21 and Aeropostale, department store chain Barneys New York, men's suit maker Brooks Brothers and Sports Illustrated magazine. Early next year, its deal to buy sneaker maker Reebok is expected to close, adding another brand to its holdings.
The company had filed for an IPO in early July. But Authentic Brands Chief Executive Jamie Salter said it will now target an IPO date in 2023 or 2024. He said he has signed on to be CEO for five more years.
"The IPO climate is ridiculous," Salter said in a phone interview. "I think we would have gotten a massive valuation … maybe even more than what we sold the business for. But guess what? I'd rather be private."
A wave of retail companies entered the public market in recent months, from eyeglasses maker Warby Parker and fashion rental platform Rent the Runway to eco-friendly shoe brand Allbirds and e-commerce fashion site Lulu's. Investors have favored names that have a strong footing on the internet, allowing some to fetch valuations as if they were high-growth tech companies.
CNBC had reported that Authentic Brands was seeking a valuation of about $10 billion in its public debut.
The transaction with CVC and HPS is expected to close in December, at which point the PE firm and hedge fund will each retain a seat on Authentic Brands' board of directors.
"We plan to work closely with the ABG team to execute on their strategic priorities, particularly around international expansion," said Chris Baldwin, a managing partner at CVC.
BlackRock will keep its position as Authentic Brands' largest shareholder, which it has held since 2019, the company said. Existing investors including U.S. mall owner Simon Property Group, General Atlantic, Leonard Green & Partners, Brookfield and basketball star Shaquille O'Neal will hold on to their equity positions.
When it filed to go public, Authentic Brands reported that its net income in 2020 jumped to $211 million from $72.5 million a year earlier, while its revenue rose about 2% to $489 million.
"We have the same playbook today as we had yesterday," said Salter. "You'll hear about more acquisitions by the end of this year."
CVC recently struck a deal to buy Unilever's tea business. Some of the firm's other portfolio companies include streetwear brand A Bathing Ape and pet goods chain Petco, according to its website. HPS spun out of J.P. Morgan Asset Management in 2016.
Correction: This article has been updated to reflect that the deal values the company at $12.7 billion and to correct the spelling of Chris Baldwin's name.
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Spend or Save: Should I Pay Off My Mortgage or Invest for Retirement?

redpajama_set_name: RedPajamaCommonCrawl


You are here: Home » CFCI Difference » What can a good confession do!
Having been in the CFCI singles community for 4 years I have experienced God work amazingly in my life. Lately, even more closely as my healer and deliverer. At last year's conference Laudate, I learnt what it means to praise in all situations of my life because God is who He is and He never changes.
On hearing the announcement of this year's conference, I was excited, opened the calendar on my phone and saved the dates. As months and days ran by, the conference approached faster than anticipated and my level of excitement kept increasing. A week before the conference while walking down the stairs, I missed a step and twisted my ankle. I sat right there in so much pain and the first thought to cross my mind was "what if it's a fracture, how will I attend the conference?" by then my leg had swollen up. The Lord reminded me of the previous conference Laudate at that very moment, and I began praising and thanking Him for this pain.
That evening I went to the hospital to check on my foot, while I waited for my X-ray report I kept praising God and on receiving my report I was happy and relieved since it was just a sprain. The doctor told me to wear a bandage for a week or two and then I would be fine. However, I was in a lot of pain and walked with a limp.
As the day of the conference commenced and we reached the Old Goa retreat center, I was filled with joy to meet my brothers and sisters from across India. While we were entering I realized the elevator wasn't working compelling me to take the stairs. Now this was rarely painful but I used it as an opportunity to offer this suffering up for lost souls.
Day one of the conference began and through the talks God rebuked me in areas I needed to repent specially Lust of the flesh, of the eyes and pride of life. That night as I retired to bed I went through the examination of the conscience which helped me prepare for a good confession the following day.
The next day as I confessed my sins and said my penance, I experienced the never ending mercy and forgiveness of God. The Lord filled me once again with His grace and I was set free from the chains of sin and at that very moment I received a physical healing from Him. He healed me instantly of my ankle pain. I removed the bandage and could once again walk properly. I jumped and danced before my God during the praise and worship time.
This entry was posted in CFCI Difference, Sharings and tagged National Singles Conference, Santa Cruz, Sharings, Singles, Soul on Fire by Chriselle Mendonca. Bookmark the permalink.
2 Responses to What can a good confession do!
Beautiful article 🙂 heading for a confession today itself!


text:
Rediff.com » News » Decoding Zakir Naik: A TV preacher or a threat?
Decoding Zakir Naik: A TV preacher or a threat?
July 07, 2016 12:57 IST
Islamic preacher Zakir Naik has been in the news lately after it was disclosed that one of the five Bangladeshi terrorists who killed 22 people at a restaurant in Dhaka last week was his follower.
Here is what we know about him:
Naik, a medical doctor by education, not only influenced the Dhaka attackers, even the chief of Islamic State's Hyderabad module told the National Investigation Agency that he was deeply influened by his teachings.
Unlike other preachers who like to dress in traditional clothes, Naik usually wears a three-piece suit (with pants fitting above the ankles) and a tie, but teams it up with a skull cap, and speaks English fluently. Arabic is his other preferred language.
Naik is banned in the United Kingdom and Canada for hate speech. He is also among 16 banned Islamic scholars in Malaysia.
Naik once advised Sania Mirza that she should dress up modestly, even when she is playing tennis. He has also said that wife beating is not necessarily a bad thing, has rationalied stoning, and has said that using a condom is like killing a human being.
His TV channel 'Peace TV' is banned in India but is run in many languages -- Bangla, Urdu and Chinese among others -- and has a 200 million-strong viewership in Bangladesh. It is not known how he funds the TV network. He has around 14 million followers on Facebook.
Controversial statements:
On 9/11: It is a blatant, open secret that this attack on the Twin Towers was done by George Bush himself... Even a fool will know that the 9/11 attacks were an inside job.
On Osama bin Laden: If he is fighting enemies of Islam, I am for him. I don't know him personally. If he is terrorising America, the biggest terrorist, I am with him. Every Muslim should be a terrorist. The thing is that if he is terrorising a terrorist, he is following Islam.
On girls' education: Girls shouldn't be sent to schools where they lose their virginity by the time they pass out. Schools should be shut down.
On homosexuals: They should be killed.
On Islam: It is superior to all other faiths. Non-Muslims should not be allowed to have places of religious worship in an Islamic country.
Related News: Islam, Osama bin Laden, Zakir Naik, Islamic State, National Investigation Agency
From Rs 22 to Rs 900 crore, Vasanthakumar's success story
Bollywood's new box office trend
India has an image problem: Nobel laureate
We don't ban individuals: India on controversial Islamist preacher Zakir Naik
Terror revisits Bangladesh on Eid; 4 dead in blast, encounter on


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text:
Congress presidential poll: Shashi Tharoor's blunder in manifesto, shows distorted map of India
New Delhi [India], September 30 (ANI): Congress presidential candidate Shashi Tharoor has landed in a controversy on Friday as the manifesto shared by him for the election shows a "distorted map of India" in which he has omitted parts of Jammu and Kashmir, and Ladakh.
He shared the distorted map of India, where he talked about 'decentralization of the organization'.
The goof-up by Tharoor, who is running for the president's post of the country's oldest party does not show good precedent.
Later Tharoor rectified the blunder by replacing it with the correct map of India in his election manifesto.
This is not the first time the Lok Sabha MP has shared a distorted map of India. Earlier in 2019, Tharoor shared a 'distorted' map of India on Twitter. The northernmost territory of the country was missing from the map shared by the Congress leader.
In December 2019, he shared the cover of a booklet about the Kerala Congress's protest against the Citizenship Amendment Act (CAA). He later deleted the tweet and rectified his mistake.
Tharoor has issued a manifesto explaining how he intends to rejuvenate the party through decentralisation and internal reorganisation if he wins the election.
"Every party needs leadership, but at all levels and not just at the top. The Congress must empower the party in the states by giving real authority to the PCC Presidents, decentralize authority and truly empower the grassroots office-bearers of the party. We must provide a credible alternative to the BJP 's centralization of authority in its party affairs and in the affairs of governance. Re-imagining the organization, delegating powers to state, district and block leaders and empowering grassroots workers will not only free the new leader from the onerous burdens of over-administration, but help create the strong state leadership that in past eras strengthened the Congress' national appeal," the Congress leader's manifesto reads.
He also promised to increase focus on youth and to give a bigger role to women in the party"The Congress will support leadership by women, for women, in keeping with the "Ladki boon, lad sakti hoon" principle. Women will be encouraged to take up issues that actually matter to women in our society. More positions for women should be reserved in PCCs and in the electoral fray. The party will work to pass the Women's Reservation Bill. In these efforts, the role and capacity of All India Mahila Congress will be strengthened," the manifesto reads.
Tharoor filed his nomination at the All India Congress Committee (AICC) office in the national capital. Before that, he visited the Raj Ghat to pay homage to Mahatma Gandhi.
"There is a need for decentralisation in this party. We are not an election machine for winning elections every five years. We must serve the people of India," Tharoor said.
He said the manifesto explains "how we hope to rejuvenate the party, to decentralize the organisation of the party".
"In my own efforts, I am guided by the crores of party workers who have taken the party through good times and bad and who are once again hoping for a renewed, dynamism and energy in the Congress party," he said addressing a press conference.
"This is what I hope to bring to and through my campaign. I have a vision for Congress, for the internal reorganisation of the congress, which I will send to every delegate. We are pleased that we have the signatures of party colleagues from Kashmir to Kerala and Punjab to Nagaland. I am here to be the voice of all the party workers," Tharoor added.
The senior Congress leader also hit out at the BJP government at the Centre, saying things are "not really functioning properly" in the country.
"We have seen that things are not really functioning properly in our country: demonetisation, historical unemployment, inflation. Congress should be the party to bring change," he said.
Tharoor will primarily take on another veteran leader of the Congress, Mallikarjun Kharge, for the party president post. Jharkhand Congress leader KN Tripathi, too, has filed his nomination.
Earlier, Digvijaya Singh and Ashok Gehlot said that they have dropped out of the race.
The former Madhya Pradesh chief minister visited Kharge at his residence this morning. "I told him that I stand by him and can't even think of contesting against him, I will be his proposer," Singh told reporters.
Nominations for the post of Congress president closed at 3 pm today and results will be declared on October 19. (ANI)


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text:
package gov.sandia.cognition.learning.function.vector;

import gov.sandia.cognition.math.matrix.Vector;
import gov.sandia.cognition.math.matrix.VectorFactory;
import junit.framework.TestCase;
import java.util.Random;

/**
 * Unit tests for EntropyEvaluatorTest.
 *
 * @author krdixon
 */
public class EntropyEvaluatorTest
    extends TestCase
{

    /**
     * Random number generator to use for a fixed random seed.
     */
    public Random random = new Random( 1 );

    /**
     * Default tolerance of the regression tests, {@value}.
     */
    public double EPS = 1e-5;

    /**
     * Tests for class EntropyEvaluatorTest.
     * @param testName Name of the test.
     */
    public EntropyEvaluatorTest(
        String testName)
    {
        super(testName);
    }

    /**
     * Tests the constructors of class EntropyEvaluatorTest.
     */
    public void testConstructors()
    {
        System.out.println( "Constructors" );
        EntropyEvaluator f = new EntropyEvaluator();
        assertNotNull( f );
    }

    /**
     * Test of evaluate method, of class EntropyEvaluator.
     */
    public void testEvaluate()
    {
        System.out.println("evaluate");
        Vector input = VectorFactory.getDefault().copyValues( 0.2, 0.6, 0.2 );
        EntropyEvaluator f = new EntropyEvaluator();
        assertEquals( 1.370951, f.evaluate(input), EPS );

        input.setElement(0, 1.0);
        try
        {
            f.evaluate(input);
            fail( "Values must add to 1.0" );
        }
        catch (Exception e)
        {
            System.out.println( "Good: " + e );
        }

    }

}

redpajama_set_name: RedPajamaGithub


text:
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In-Service Withdrawals from 401(k) Plans
Effective Tax Planning Starts Early
What You Should Know About Sales and Use Tax Exemption Certificates
Hurray for Hollywood-Runaway Production
Expanded Foreign Reporting Requirements for 2011 Tax Filings
More Time to Get Money from Uncle Sam - Extended Telephone Tax Refund Opportunity
By Steven D. Brett, President of Marcum Financial Services LLC
Related: View Tax & Business Services
You may be familiar with the rules for putting money into a 401(k) plan. But are you familiar with the rules for taking your money out? Federal law limits the withdrawal options that a 401(k) plan can offer. But a 401(k) plan may offer fewer withdrawal options than the law allows, and may even provide that you can't take any money out at all until you leave employment. However, many 401(k) plans are more flexible.
First, consider a plan loan
Many 401(k) plans allow you to borrow money from your own account. A loan may be attractive if you don't qualify for a withdrawal, or if you don't want to incur the taxes and penalties that may apply to a withdrawal, or if you don't want to permanently deplete your retirement assets. (Also, you must take any available loans from all plans maintained by your employer before you're even eligible to withdraw your own pretax or Roth contributions from a 401(k) plan because of hardship.)
In general, you can borrow up to one half of your vested account balance (including your contributions, your employer's contributions, and earnings), but not more than $50,000.
You can borrow the funds for up to five years (longer if the loan is to purchase your principal residence). In most cases you repay the loan through payroll deduction, with principal and interest flowing back into your account. But keep in mind that when you borrow, the unpaid principal of your loan is no longer in your 401(k) account working for you.
Withdrawing your own contributions
If you've made after-tax (non-Roth) contributions, your 401(k) plan can let you withdraw those dollars (and any investment earnings on them) for any reason, at any time. You can withdraw your pretax and Roth contributions (that is, your "elective deferrals"), however, only for one of the following reasons—and again, only if your plan specifically allows the withdrawal:
• You attain age 59½
• You become disabled
• The distribution is a "qualified reservist distribution"
• You incur a hardship (i.e., a "hardship withdrawal")
Hardship withdrawals are allowed only if you have an immediate and heavy financial need, and only up to the amount necessary to meet that need. In most plans, you must require the money to:
• Purchase a principal residence or repair a principal residence damaged by an unexpected event (e.g., a hurricane)
• Prevent eviction or foreclosure
• Pay medical bills
• Pay certain funeral expenses
• Pay certain education expenses
• Pay income tax and/or penalties due on the hardship withdrawal itself
Investment earnings aren't available for hardship withdrawal, except for certain pre-1989 grandfathered amounts.
But there are some disadvantages to hardship withdrawals, in addition to the tax consequences described below. You can't take a hardship withdrawal at all until you've first withdrawn all other funds, and taken all nontaxable plan loans, available to you under all retirement plans maintained by your employer. And, in most 401(k) plans, your employer must suspend your participation in the plan for at least six months after the withdrawal, meaning you could lose valuable employer matching contributions. And hardship withdrawals can't be rolled over. So think carefully before making a hardship withdrawal.
Withdrawing employer contributions
Getting employer dollars out of a 401(k) plan can be even more challenging. While some plans won't let you withdraw employer contributions at all before you terminate employment, other plans are more flexible, and let you withdraw at least some vested employer contributions before then. "Vested" means that you own the contributions and they can't be forfeited for any reason. In general, a 401(k) plan can allow you to withdraw vested company matching and profit-sharing contributions if:
• You incur a hardship (your employer has some discretion in how hardship is defined for this purpose)
• You attain a specified age (for example, 59½)
• You participate in the plan for at least five years, or
• The employer contribution has been in the account for a specified period of time (generally at least two years)
Your own pretax contributions, company contributions, and investment earnings are subject to income tax when you withdraw them from the plan. If you've made any after-tax contributions, they'll be nontaxable when withdrawn. Each withdrawal you make is deemed to carry out a pro-rata portion of taxable and any nontaxable dollars.
Your Roth contributions, and investment earnings on them, are taxed separately: if your distribution is "qualified," then your withdrawal will be entirely free from federal income taxes. If your withdrawal is "nonqualified," then each withdrawal will be deemed to carry out a pro-rata amount of your nontaxable Roth contributions and taxable investment earnings. A distribution is qualified if you satisfy a five-year holding period, and your distribution is made either after you've reached age 59½, or after you've become disabled. The five-year period begins on the first day of the first calendar year you make your first Roth 401(k) contribution to the plan.
The taxable portion of your distribution may be subject to a 10% premature distribution tax, in addition to any income tax due, unless an exception applies. Exceptions to the penalty include distributions after age 59½, distributions on account of disability, qualified reservist distributions, and distributions to pay medical expenses.
Rollovers and conversions
Rollover of non-Roth funds
If your in-service withdrawal qualifies as an "eligible rollover distribution," you can roll over all or part of the withdrawal tax free to a traditional IRA or to another employer's plan that accepts rollovers. In general, most in-service withdrawals qualify as eligible rollover distributions except for hardship withdrawals and required minimum distributions after age 70½. If your withdrawal qualifies as an eligible rollover distribution, your plan administrator will give you a notice (a "402(f) notice") explaining the rollover rules, the withholding rules, and other related tax issues. (Your plan administrator will withhold 20% of the taxable portion of your eligible rollover distribution for federal income tax purposes if you don't directly roll the funds over to another plan or IRA.)
You can also roll over ("convert") an eligible rollover distribution of non-Roth funds to a Roth IRA. And some 401(k) plans even allow you to make an "in-plan conversion"--that is, you can request an in-service withdrawal of non-Roth funds, and have those dollars transferred into a Roth account within the same 401(k) plan. In either case, you'll pay income tax on the amount you convert (less any nontaxable after-tax contributions you've made).
Rollover of Roth funds
If you withdraw funds from your Roth 401(k) account, those dollars can only be rolled over to a Roth IRA, or to another Roth 401(k)/403(b)/457(b) plan that accepts rollovers. (Again, hardship withdrawals can't be rolled over.) But be sure to understand how a rollover will affect the taxation of future distributions from the IRA or plan. For example, if you roll over a nonqualified distribution from a Roth 401(k) account to a Roth IRA, the Roth IRA five-year holding period will apply when determining if any future distributions from the IRA are tax-free qualified distributions. That is, you won't get credit for the time those dollars resided in the 401(k) plan.
You should become familiar with the terms of your employer's 401(k) plan to understand your particular withdrawal rights. A good place to start is the plan's summary plan description (SPD). Your employer will give you a copy of the SPD within 90 days after you join the plan.
This material was prepared by Raymond James for use by Steven D. Brett, President of Marcum Financial Services LLC., and Branch Manager of Raymond James Financial Services, Inc. Member FINRA/SIPC. If you have any questions pertaining to this article, please contact Steven D. Brett at 631-414-4020 or by e-mail at steven.brett@marcumfs.com.
This information, developed by an independent third party, has been obtained from sources considered to be reliable, but Marcum Financial Services LLC., an independent firm, and Raymond James Financial Services, Inc. does not guarantee that the foregoing material is accurate or complete. This information is not a complete summary or statement of all available data necessary for making an investment decision and does not constitute a recommendation. The information contained in this report does not purport to be a complete description of the securities, markets, or developments referred to in this material. This information is not intended as a solicitation or an offer to buy or sell any security referred to herein. Investments mentioned may not be suitable for all investors.The material is general in nature. Past performance may not be indicative of future results. Marcum Financial Services LLC., an independent firm, and Raymond James Financial Services, Inc. does not provide advice on tax, legal or mortgage issues. These matters should be discussed with the appropriate professional.
Securities offered through Raymond James Financial Services, Inc., member FINRA/SIPC, an independent broker/dealer, and are not insured by FDIC, NCUA or any other government agency, are not deposits or obligations of the financial institution, are not guaranteed by the financial institution, and are subject to risks, including the possible loss of principal.
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Protesters are taking to the streets in Hong Kong, Chile and Spain for different reasons, but the...
1 ..
Forbes published Food & Beverage Leader Lou Biscotti's January column on the bankruptcy of Borden Dairy Co.
Borden, the more than 150-year-old company that first began delivering milk in bottles and built one of the nation's most recognized brands, has filed for bankruptcy.
Long Island Business News featured Melville Office Managing Partner Carolyn Mazzenga in an executive forecast of what's on the horizon for the new year.
Accounting Today invited Chief Information & Digital Officer Peter Scavuzzo onto a panel to discuss how technology will impact auditing in the future.
Skoda Minotti Joins Marcum LLP
Marcum LLP announced a merger with Skoda Minotti, a market-leading accounting and business advisory firm, effective today. The firm adds 31 partners, more than 190 associates, and four offices to Marcum's Midwest and Florida regions.
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text:
Strophidia directaria är en fjärilsart som beskrevs av Walker. Strophidia directaria ingår i släktet Strophidia och familjen Uraniidae. Inga underarter finns listade i Catalogue of Life.

Källor

Fjärilar
directaria

redpajama_set_name: RedPajamaWikipedia


text:
namespace Bungie.Responses
{
    internal abstract class AbstractMessage<T>
    {
        public T Response { get; set; }
        public int ErrorCode { get; set; }
        public int ThrottleSeconds { get; set; }
        public string ErrorStatus { get; set; }
        public string Message { get; set; }
        public object MessageData { get; set; }
    }
}


RARE OPPORTUNITY TO OWN A PIECE OF BEAUMONT'S HISTORY! ONE OF BEAUMONT'S OLDEST HOMES BEAUTIFULLY RENOVATED FROM TOP TO BOTTOM. This character home was gutted and upgraded to modern standards whilst maintaining its character. Exterior has been totally redone boasting newer vinyl siding & triple pane windows, 35 year shingles, doors, soffits, fascia & eaves, huge rear 2-tier deck & wrap around covered front & side decks. Interior renovations include newer drywall, insulation, white kitchen offering lots of cabinets, pantry, raised eating bar, quality appliances & garden doors, renovated 4-pce bath with jetted soaker tub & tile tub surround, sloped ceilings, crown molding, newer flooring (H/W & ceramic tile), plumbing, electrical, doors, trim, lighting, furnace, water heater, front load W/D, etc. The huge lot is nicely landscaped offering a lovely hedge, shrubs & perennials, newer driveway & walks and chain link fencing. The oversized single garage is insulated, boarded & heated and there is RV parking.
MLS #E4151684 - ACTIVE - 4712 50 Avenue in Beaumont, AB is a Residential Detached Single Family with roughly 1,341 sqft located in the subdivision of None. Built in 1927, this 2 bedroom and 1 bathroom residential property has a lot size of 0.16 acres. This property is currently listed for $399,000. This Residential Detached Single Family comes with Forced Air-1 heating. The exterior features include Fenced, Fruit Trees/Shrubs, Golf Nearby, Landscaped, Playground Nearby, Public Swimming Pool, Public Transportation, Schools, Shopping Nearby, Treed Lot, Corner, Park/Reserve. The Interior features include Dishwasher-Built-In, Dryer, Hood Fan, Oven-Microwave, Refrigerator, Stove-Electric, Washer, Window Coverings, Storage Shed, See Remarks, Freezer. According to statistics, there are currently 206 listings for sale in Beaumont. The average listing price is $463,550, with the highest listing price being $1,850,000. Enjoy your search for a new home. To request a showing, please contact one of our real estate agents.


The project supported the New Zealand Productivity Commission's low emissions economy inquiry, by developing a model to guide its assessments of transition options, opportunities and risks. It modelled the impact of policy and investment strategies, under a range of uncertain future circumstances to evaluate the feasibility of targets, quantify potential mitigation costs and identify robust economic strategies.
Modelling the transition to a lower net emissions New Zealand examined the impacts of decision making under uncertainty by considering how different decisions impact outcomes of interest, such as economic activity and greenhouse gas emissions, under uncertain future states of the world. The modelling occurred in two phases, with the Interim Report considering the impacts of stronger and weaker emissions reductions targets, while the Uncertainty Analysis focused on assessing the performance of strategies under deep uncertainty.
To do so this project utilised outputs from Concept Consulting's Energy in New Zealand Model and Motu Economic and Public Policy Research's Land Use in Rural New Zealand models. Vivid Economics were responsible for developing the modelled scenarios, interpreting model outputs and drafting the reports.


Repeats every week every Sunday until Sun Mar 20 2016.
Upper Canyon Guided Hike, March 13 & 20, 8 a.m. - noon, at Seminole Canyon State Park & Historic Site near Comstock TX. Back-country, two-mile, four-hour hike into the upper area of Seminole Canyon that includes visits to prehistoric rock art sites, historical railroad sites, and the U.S. Army's Detachment of the Black Seminole Indian Scout campsite. Meet at Park Headquarters Building/museum. Tour leaves promptly at 8:15 a.m. Tour fee of $12:00 cash or check (payable to Rock Art Foundation), plus park entry fee of $3.00 per person. Reservations required and begins 30 days before and ends on the Wednesday before the tour departure date. Call the park at (432) 292-4464. Participants must be able to hike, clamber and climb over large boulders, rough uneven ground and dense vegetation. Please only sign up family members. Not suitable for young children (no strollers). Tours are subject to cancellation due to weather conditions. It is recommended to call the park prior to your trip to conform tour. No pets, guns, drugs/alcohol allowed.


text:
Meet the woman behind Emma Watson's viral feminism campaign
December 18, 2014 by Caroline Fairchild published in FORTUNE
Elizabeth Nyamayaro, the brains behind the UN's HeForShe campaign, recruited Watson — and also snagged some of corporate America's top execs — to promote gender equality.
Before actress Emma Watson turned a discussion on gender equality at the United Nations into a viral YouTube video, Elizabeth Nyamayaro, the Senior Advisor to the Executive Director of UN Women, was hard at work.
Nyamayaro is the lesser-known head of the HeForShe campaign, a movement aimed at getting men, not just women, to champion gender equality. In September, Watson, a UN Women Goodwill Ambassador and the spokeswoman for HeForShe, got the campaign an impressive amount of attention — over 6 million hits on YouTube — with an impassioned speech announcing her involvement.
Three months later, now that the excitement surrounding Watson's speech has died down, I checked in with
Nyamayaro to get a sense for what's next for the campaign and how she plans to continue to build off its initial momentum.
What inspired you to launch HeForShe?
I joined UN Women about 11 months ago, and during my orientation, it became really clear that if we were going to advance the conversation about young women, we needed something that was going to change how we look at gender equality in a positive way. We need to make it a global issue. If it remains a women's issue, then progress will remain slow. Men still hold power so you can't really speak about imbalance against women without finding a way to engage men as meaningful partners.
Vew the full article >>


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