Miracle-dz/UniFAR-FTUs huggingface dataset:
Hybrid Workload Scheduling on HPC Systems

Traditionally, on-demand, rigid, and malleable applications have been scheduled and executed on separate systems. The ever-growing workload demands and rapidly developing HPC infrastructure trigger the interest of converging these applications on a single HPC system. Although allocating the hybrid workloads within one system could potentially improve system efficiency, it is difficult to balance the tradeoff between the responsiveness of on-demand requests, incentive for malleable jobs, and the performance of rigid applications. In this study, we present several scheduling mechanisms to address the issues involved in co-scheduling on-demand, rigid, and malleable jobs on a single HPC system. We extensively evaluate and compare their performance under various configurations and workloads. Our experimental results show that our proposed mechanisms are capable of serving on-demand workloads with minimal delay, offering incentives for declaring malleability, and improving system performance.
Prior Omission of Dissimilar Source Domain(s) for Cost-Effective Few-Shot Learning

Few-shot slot tagging is an emerging research topic in the field of Natural Language Understanding (NLU). With sufficient annotated data from source domains, the key challenge is how to train and adapt the model to another target domain which only has few labels. Conventional few-shot approaches use all the data from the source domains without considering inter-domain relations and implicitly assume each sample in the domain contributes equally. However, our experiments show that the data distribution bias among different domains will significantly affect the adaption performance. Moreover, transferring knowledge from dissimilar domains will even introduce some extra noises so that affect the performance of models. To tackle this problem, we propose an effective similarity-based method to select data from the source domains. In addition, we propose a Shared-Private Network (SP-Net) for the few-shot slot tagging task. The words from the same class would have some shared features. We extract those shared features from the limited annotated data on the target domain and merge them together as the label embedding to help us predict other unlabelled data on the target domain. The experiment shows that our method outperforms the state-of-the-art approaches with fewer source data. The result also proves that some training data from dissimilar sources are redundant and even negative for the adaption.
To Protect and To Serve? Analyzing Entity-Centric Framing of Police Violence

Framing has significant but subtle effects on public opinion and policy. We propose an NLP framework to measure entity-centric frames. We use it to understand media coverage on police violence in the United States in a new Police Violence Frames Corpus of 82k news articles spanning 7k police killings. Our work uncovers more than a dozen framing devices and reveals significant differences in the way liberal and conservative news sources frame both the issue of police violence and the entities involved. Conservative sources emphasize when the victim is armed or attacking an officer and are more likely to mention the victim's criminal record. Liberal sources focus more on the underlying systemic injustice, highlighting the victim's race and that they were unarmed. We discover temporary spikes in these injustice frames near high-profile shooting events, and finally, we show protest volume correlates with and precedes media framing decisions.
Federated Ensemble Model-based Reinforcement Learning

—Federated learning (FL) is a privacy-preserving machine learning paradigm that enables collaborative training among geographically distributed and heterogeneous users without gathering their data. Extending FL beyond the conventional supervised learning paradigm, federated Reinforcement Learning (RL) was proposed to handle sequential decision-making problems for various privacy-sensitive applications such as autonomous driving. However, the existing federated RL algorithms directly combine model-free RL with FL, and thus generally have high sample complexity and lack theoretical guarantees. To address the above challenges, we propose a new federated RL algorithm that incorporates model-based RL and ensemble knowledge distillation into FL. Speciﬁcally, we utilise FL and knowledge distillation to create an ensemble of dynamics models from clients, and then train the policy by solely using the ensemble model without interacting with the real environment. Furthermore, we theoretically prove that the monotonic improvement of the proposed algorithm is guaranteed. Extensive experimental results demonstrate that our algorithm obtains signiﬁcantly higher sample efﬁciency compared to federated model-free RL algorithms in the challenging continuous control benchmark environments. The results also show the impact of non-IID client data and local update steps on the performance of federated RL, validating the insights obtained from our theoretical analysis.
Media Exposure and the Social Determinants of Health Insurance Coverage in Ethiopia 2011 - 2016

Background: In a push for universal health coverage, Ethiopia introduced two insurance schemes in 2010. Yet coverage rates remain very low. To encourage greater adoption, policymakers require a better understanding of who chooses to enroll and which promotional efforts are most effective in encouraging enrollment. Objective: Using nationally representative Demographic and Health Surveys, this research assessed the social determinants of health insurance coverage, including media exposure, in Ethiopia from 2011-2016. Methods: This research analyzed health insurance coverage and other sociodemographic and media exposure variables using multivariable logistic regression model. Results: Health insurance coverage increased 3.30 times from 1.48% in 2011 to 4.89% in 2016. In both years, coverage was associated with higher education, older age, higher wealth levels, and exposure to newspaper and television. Compared to those with no exposure to newspaper, those with newspaper exposure at least once a week were 1.80 times (2011) and 1.86 times (2016) more likely to be insured. Similar results were obtained for television exposure. Conclusion: Initiatives that target the poor and less educated will be necessary if Ethiopia is to achieve universal health coverage. This research suggests that, to date, newspaper and television mediums have been effective promotion mechanisms for growing enrollment.
Evaluation of the Accuracy of Cuffless Blood Pressure Measurement Devices: Challenges and Proposals

Supplemental Digital Content is available in the text. Several novel cuffless wearable devices and smartphone applications claiming that they can measure blood pressure (BP) are appearing on the market. These technologies are very attractive and promising, with increasing interest among health care professionals for their potential use. Moreover, they are becoming popular among patients with hypertension and healthy people. However, at the present time, there are serious issues about BP measurement accuracy of cuffless devices and the 2021 European Society of Hypertension Guidelines on BP measurement do not recommend them for clinical use. Cuffless devices have special validation issues, which have been recently recognized. It is important to note that the 2018 Universal Standard for the validation of automated BP measurement devices developed by the American Association for the Advancement of Medical Instrumentation, the European Society of Hypertension, and the International Organization for Standardization is inappropriate for the validation of cuffless devices. Unfortunately, there is an increasing number of publications presenting data on the accuracy of novel cuffless BP measurement devices, with inadequate methodology and potentially misleading conclusions. The objective of this review is to facilitate understanding of the capabilities and limitations of emerging cuffless BP measurement devices. First, the potential and the types of these devices are described. Then, the unique challenges in evaluating the BP measurement accuracy of cuffless devices are explained. Studies from the literature and computer simulations are employed to illustrate these challenges. Finally, proposals are given on how to evaluate cuffless devices including presenting and interpreting relevant study results.
Ohm-GPU: Integrating New Optical Network and Heterogeneous Memory into GPU Multi-Processors

Traditional graphics processing units (GPUs) suffer from the low memory capacity and demand for high memory bandwidth. To address these challenges, we propose Ohm-GPU, a new optical network based heterogeneous memory design for GPUs. Specifically, Ohm-GPU can expand the memory capacity by combing a set of high-density 3D XPoint and DRAM modules as heterogeneous memory. To prevent memory channels from throttling throughput of GPU memory system, Ohm-GPU replaces the electrical lanes in the traditional memory channel with a high-performance optical network. However, the hybrid memory can introduce frequent data migrations between DRAM and 3D XPoint, which can unfortunately occupy the memory channel and increase the optical network traffic. To prevent the intensive data migrations from blocking normal memory services, Ohm-GPU revises the existing memory controller and designs a new optical network infrastructure, which enables the memory channel to serve the data migrations and memory requests, in parallel. Our evaluation results reveal that Ohm-GPU can improve the performance by 181% and 27%, compared to a DRAM-based GPU memory system and the baseline optical network based heterogeneous memory system, respectively.
Learning from Language Description: Low-shot Named Entity Recognition via Decomposed Framework

In this work, we study the problem of named entity recognition (NER) in a low resource scenario, focusing on few-shot and zero-shot settings. Built upon large-scale pre-trained language models, we propose a novel NER framework, namely SpanNER, which learns from natural language supervision and enables the identification of never-seen entity classes without using in-domain labeled data. We perform extensive experiments on 5 benchmark datasets and evaluate the proposed method in the few-shot learning, domain transfer and zero-shot learning settings. The experimental results show that the proposed method can bring 10%, 23% and 26% improvements in average over the best baselines in few-shot learning, domain transfer and zero-shot learning settings respectively.
A Scalable Continuous Unbounded Optimisation Benchmark Suite from Neural Network Regression

—For the design of optimisation algorithms that perform well in general, it is necessary to experiment with and benchmark on a range of problems with diverse characteristics. The training of neural networks is an optimisation task that has gained prominence with the recent successes of deep learning. Although evolutionary algorithms have been used for training neural networks, gradient descent variants are by far the most common choice with their trusted good performance on large-scale machine learning tasks. With this paper we contribute CORNN (Continuous Optimisation of Regression tasks using Neural Networks), a large suite that can easily be used to benchmark the performance of any continuous black-box algorithm on neural network training problems. By employing different base regression functions and neural network architectures, problem instances with different dimensions and levels of difﬁculty can be created. We demonstrate the use of the CORNN Suite by comparing the performance of three evolutionary and swarm-based algorithms on a set of over 300 problem instances. With the exception of random search, we provide evidence of performance complementarity between the algorithms. As a baseline, results are also provided to contrast the performance of the best population-based algorithm against a gradient-based approach (Adam). The suite is shared as a public web repository to facilitate easy integration with existing benchmarking platforms.
Qualitative and Quantitative Analysis of Diversity in Cross-document Coreference Resolution Datasets

Cross-document coreference resolution (CDCR) datasets, such as ECB+, contain manually annotated event-centric mentions of events and entities that form coreference chains with identity relations. ECB+ is a state-of-the-art CDCR dataset that focuses on the resolution of events and their descriptive attributes, i.e., actors, location, and date-time. NewsWCL50 is a dataset that annotates coreference chains of both events and entities with a strong variance of word choice and more loosely-related coreference anaphora, e.g., bridging or near-identity relations. In this paper, we qualitatively and quantitatively compare annotation schemes of ECB+ and NewsWCL50 with multiple criteria. We propose a phrasing diversity metric (PD) that compares lexical diversity within coreference chains on a more detailed level than previously proposed metric, e.g., a number of unique lemmas. We discuss the different tasks that both CDCR datasets create, i.e., lexical disambiguation and lexical diversity challenges, and propose a direction for further CDCR evaluation.
Plasma Methylmalonic Acid Concentration in Folic Acid–Supplemented Depressed Patients with Low or Marginal Vitamin B-12: A Randomized Trial

ABSTRACT Background Individuals with low serum vitamin B-12 and high serum folate have higher plasma concentrations of methylmalonic acid (MMA). Whether folic acid (FA) causes an increase in MMA is not known. Objectives We aimed to determine the impact of FA supplementation on plasma MMA concentration in people with low or marginal serum vitamin B-12. Methods We conducted a multicenter double-blind placebo-controlled randomized trial of oral FA (5 mg/d for 12 wk) in middle-aged patients treated with antidepressant medication participating in the FoLATED (Folate Augmentation of Treatment—Evaluation for Depression) trial. Participants defined as having “low” serum vitamin B-12 (vitamin B-12 ≥150 and <220 ng/L) or “marginal” serum vitamin B-12 (vitamin B-12 ≥ 220 and <280 ng/L) were included. The primary outcome of this substudy was MMA at week 12. A mixed-effects linear regression was fitted and reported using the adjusted mean difference (aMD). Results A total of 177 participants were included (85 randomly assigned to placebo and 92 to FA); the mean ± SD age was 46.2 ± 11.8 y, and 112 (63.3%) were female. The MMA analysis included 135 participants and the aMD was −0.01 (95% CI: −0.06, 0.04; P = 0.71). Serum folate was measured on 166 participants and increased in the supplementation group; the aMD was 21.6 μg/L (95% CI: 8.13, 25.02 μg/L; P < 0.001). A total of 117 participants were assessed for RBC folate, which also increased in the supplementation group; the aMD was 461 μg/L (95% CI: 387, 535 μg/L; P < 0.001). Conclusions Supplementation of FA leads to an increase of serum and RBC folate, but does not change plasma MMA concentration in individuals with serum vitamin B-12 between 150 and 280 ng/L. We cannot exclude effects in older people or those with serum vitamin B-12 <150 ng/L. Previously reported associations may arise from effects of impaired vitamin B-12 status on folate metabolism. This trial was registered at www.isrctn. com as ISRCTN37558856.
The Perception of Esthetic Importance of Craniofacial Elements

Supplemental Digital Content is available in the text Abstract The human face is an important human structure, often judged by its esthetics. It serves great purpose, as it is and always has been involved in both cultural matters, such as art, as well as biological matters, such as exhibition of emotion and mate selection. Throughout history, there have been multiple studies on what makes a face attractive. Despite the myriad ways in which the face has been explored academically, there has been a lack of reports on which craniofacial elements are deemed the most important for attractiveness subjectively. To fill up this gap on the perception of the esthetics of the human face, as well as to provide a contemporary view on the matter, this research was conducted. Participants were asked to fill out a questionnaire with a visual analogue scale for each craniofacial element and questions about appearance satisfaction. A total of 299 participants were enrolled in this study. Eyes, hair and teeth were deemed to be the most important craniofacial elements for attractiveness. Women scored teeth, hair, and eyebrows significantly higher than men, men scored higher in satisfaction with their own appearance than women. Older people, married people and people in a relationship care less about the opinion of others regarding their appearance. The results from this study can be used to identify a focus in the future development of esthetically related matters, such as plastic surgery or art and will add to the academic knowledge on the perception of craniofacial beauty.
Insomnia symptoms and chronic pain: Outcomes of an interdisciplinary pain rehabilitation program

Insomnia is a highly prevalent problem among patients with chronic pain. Interdisciplinary pain rehabilitation programs (IPRPs) are a leading treatment option for chronic pain; however, research is limited and existing findings are mixed on the impact of insomnia symptoms on IPRP outcomes and the extent to which insomnia symptoms improve as a result of IPRP treatment.
Pack Together: Entity and Relation Extraction with Levitated Marker

Named Entity Recognition (NER) and Relation Extraction (RE) are the core sub-tasks for information extraction. Many recent works formulate these two tasks as the span (pair) clas-siﬁcation problem, and thus focus on investigating how to obtain a better span representation from the pre-trained encoder. However, a major limitation of existing works is that they ignore the dependencies between spans (pairs). In this work, we propose a novel span representation approach, named Packed Levitated Markers, to consider the dependencies be-tween the spans (pairs) by strategically packing the markers in the encoder. In particular, we propose a group packing strategy to enable our model to process massive spans together to consider their dependencies with limited resources. Furthermore, for those more complicated span pair classiﬁcation tasks, we design a subject-oriented packing strategy, which packs each subject and all its objects into an instance to model the dependencies between the same-subject span pairs. Our experiments show that our model with packed levitated markers outperforms the sequence labeling model by 0.4%-1.9% F1 on three ﬂat NER tasks, beats the token concat model on six NER benchmarks, and obtains a 3.5%-3.6% strict relation F1 improvement with higher speed over previous SOTA models on ACE04 and ACE05. Code and models are publicly available at https://github.com/thunlp/PL-Marker .
Mutations in conserved residues of the myosin chaperone UNC-45 result in both reduced stability and chaperoning activity

Proper muscle development and function depends on myosin being properly folded and integrated into the thick filament structure. For this to occur the myosin chaperone UNC-45, or UNC-45B, must be present and able to chaperone myosin. Here we use a combination of in vivo C. elegans experiments and in vitro biophysical experiments to analyze the effects of six missense mutations in conserved regions of UNC-45/UNC-45B. We found that the phenotype of paralysis and disorganized thick filaments in 5/6 of the mutant nematode strains can likely be attributed to both reduced steady state UNC-45 protein levels and reduced chaperone activity. Interestingly, the biophysical assays performed on purified proteins show that all of the mutations result in reduced myosin chaperone activity but not overall protein stability. This suggests that these mutations only cause protein instability in the in vivo setting and that these conserved regions may be involved in UNC-45 protein stability/ regulation via post translational modifications, protein-protein interactions, or some other unknown mechanism.
A Complex Constrained Total Variation Image Denoising Algorithm with Application to Phase Retrieval

This paper considers the constrained total variation (TV) denoising problem for complex-valued images. We extend the definition of TV seminorms for real-valued images to dealing with complex-valued ones. In particular, we introduce two types of complex TV in both isotropic and anisotropic forms. To solve the constrained denoising problem, we adopt a dual approach and derive an accelerated gradient projection algorithm. We further generalize the proposed denoising algorithm as a key building block of the proximal gradient scheme to solve a vast class of complex constrained optimization problems with TV regularizers. As an example, we apply the proposed algorithmic framework to phase retrieval. We combine the complex TV regularizer with the conventional projection-based method within the constraint complex TV model. Initial results from both simulated and optical experiments demonstrate the validity of the constrained TV model in extracting sparsity priors within complex-valued images, while also utilizing physically tractable constraints that help speed up convergence.
AMH and AMHR2 Involvement in Congenital Disorders of Sex Development

Anti-müllerian hormone (AMH) is 1 of the 2 testicular hormones involved in male development of the genitalia during fetal life. When the testes differentiate, AMH is secreted by Sertoli cells and binds to its specific receptor type II (AMHR2) on the müllerian ducts, inducing their regression. In the female fetus, the lack of AMH allows the müllerian ducts to form the fallopian tubes, the uterus, and the upper part of the vagina. The human AMH gene maps to 19p13.3 and consists of 5 exons and 4 introns spanning 2,764 bp. The AMHR2 gene maps to 12q13.13, consists of 11 exons, and is 7,817 bp long. Defects in the AMH pathway are the underlying etiology of a subgroup of disorders of sex development (DSD) in 46,XY patients. The condition is known as the persistent müllerian duct syndrome (PMDS), characterized by the existence of a uterus and fallopian tubes in a boy with normally virilized external genitalia. Approximately 200 cases of patients with PMDS have been reported to date with clinical, biochemical, and molecular genetic characterization. An updated review is provided in this paper. With highly sensitive techniques, AMH and AMHR2 expression has also been detected in other tissues, and massive sequencing technologies have unveiled variants in AMH and AMHR2 genes in hitherto unsuspected conditions.