Miracle-dz/UniFAR-FTUs huggingface dataset:
These Unprecedented Times: The Dynamic Pattern Of COVID-19 Deaths Around The World

In this article, we deal with COVID-19 data to study the trend of the epidemic at the global situation. Choosing the mortality rate as an appropriate metric which measures the relative relation between the cumulative confirmed cases and death cases, we utilize the modified kernel estimator for random density function (Petersen and Muller, 2016) to obtain the density of the mortality rate, and apply Frechet change point detection (Dubey and Muller, 2020) to check if there is any significant change on the dynamic pattern of COVID-19 deaths. The analysis shows that the pattern of global COVID-19 mortality rate seems to have an obvious change at 104 days after first day when the reported death cases exceed 30. Then we discuss the reasons of abrupt change of mortality rate trend from aspects both subjective and objective.
Learning from Human Feedback: Challenges for Real-World Reinforcement Learning in NLP

Large volumes of interaction logs can be collected from NLP systems that are deployed in the real world. How can this wealth of information be leveraged? Using such interaction logs in an offline reinforcement learning (RL) setting is a promising approach. However, due to the nature of NLP tasks and the constraints of production systems, a series of challenges arise. We present a concise overview of these challenges and discuss possible solutions.
Machine Learning Framwork for Performance Anomaly in OpenMP Multi-Threaded Systems

Some OpenMP multi-threaded applications increasingly suffer from performance anomaly owning to shared resource contention as well as software- and hardware-related problems. Such performance anomaly can result in failure and inefficiencies, and are among the main challenges in system resiliency. To minimize the impact of performance anomaly, one must quickly and accurately detect and diagnose the performance anomalies that cause the failures. However, it is difficult to identify anomalies in the dynamic and noisy data collected by OpenMP multi-threaded monitoring infrastructures. This paper presents a novel machine learning framework for performance anomaly in OpenMP multi-threaded systems. To evaluate our framework, the NAS Parallel NPB benchmark, EPCC OpenMP micro-benchmark suite, and Jacobi benchmark are used to test the performance of our framework proposed. The experimental results demonstrate that our framework successfully identifies 90.3\% of injected anomalies of OpenMP multi-threaded applications.
Accurate inference in negative binomial regression

Negative binomial regression is commonly employed to analyze overdispersed count data. With small to moderate sample sizes, the maximum likelihood estimator of the dispersion parameter may be subject to a significant bias, that in turn affects inference on mean parameters. This paper proposes inference for negative binomial regression based on adjustments of the score function aimed at mean and median bias reduction. The resulting estimating equations are similar to those available for improved inference in generalized linear models and, in particular, can be solved using a suitable extension of iterative weighted least squares. Simulation studies show a remarkable performance of the new methods, which are also found to solve in many cases numerical problems of maximum likelihood estimates. The methods are illustrated and evaluated using two case studies: an Ames salmonella assay data set and data on epileptic seizures. Inference based on adjusted scores turns out to be generally preferable to explicit bias correction.
Data Augmentation and Terminology Integration for Domain-Specific Sinhala-English-Tamil Statistical Machine Translation

Out of vocabulary (OOV) is a problem in the context of Machine Translation (MT) in low-resourced languages. When source and/or target languages are morphologically rich, it becomes even worse. Bilingual list integration is an approach to address the OOV problem. This allows more words to be translated than are in the training data. However, since bilingual lists contain words in the base form, it will not translate inflected forms for morphologically rich languages such as Sinhala and Tamil. This paper focuses on data augmentation techniques where bilingual lexicon terms are expanded based on case-markers with the objective of generating new words, to be used in Statistical machine Translation (SMT). This data augmentation technique for dictionary terms shows improved BLEU scores for Sinhala-English SMT.
Entity Linking in 100 Languages

We propose a new formulation for multilingual entity linking, where language-specific mentions resolve to a language-agnostic Knowledge Base. We train a dual encoder in this new setting, building on prior work with improved feature representation, negative mining, and an auxiliary entity-pairing task, to obtain a single entity retrieval model that covers 100+ languages and 20 million entities. The model outperforms state-of-the-art results from a far more limited cross-lingual linking task. Rare entities and low-resource languages pose challenges at this large-scale, so we advocate for an increased focus on zero- and few-shot evaluation. To this end, we provide Mewsli-9, a large new multilingual dataset (this http URL) matched to our setting, and show how frequency-based analysis provided key insights for our model and training enhancements.
Effects mediated by the α7 nicotinic acetylcholine receptor on cell proliferation and migration in rat adipose-derived stem cells

Adipose-derived stem cells (ASCs) are an attractive source for regenerative medicine as they can be easily isolated, rapidly expandable in culture and show excellent in vitro differentiation potential. Acetylcholine (ACh), one of the main neurotransmitters in central and peripheral nervous systems, plays key roles in the control of several physiological processes also in non-neural tissues. As demonstrated in our previous studies, ACh can contribute to the rat ASCs physiology, negatively modulating ASCs proliferation and migration via M2 muscarinic receptor (mAChR) activation. In the present work we show that rat ASCs also express α7 nicotinic receptors (nAChRs). In particular, we have investigated the effects mediated by the selective activation of α7 nAChRs, which causes a reduction of ASC proliferation without affecting cell survival and morphology, and significantly promotes cell migration via upregulation of the CXCR4 expression. Interestingly, the activation of the α7 nAChR also upregulates the expression of M2 mAChR protein, indicating a cooperation between muscarinic and nicotinic receptors in the inhibition of ASC proliferation.
Common Complements of Linear Subspaces and the Sparseness of MRD Codes

We consider the problem of estimating the number of common complements of a family of subspaces over a finite field, in terms of the cardinality of the family and its intersection structure. We derive upper and lower bounds for this number, along with their asymptotic versions as the field size tends to infinity. We use these bounds to describe the general behavior of common complements with respect to sparsity and density, showing that the decisive property is whether or not the number of spaces to be complemented is negligible with respect to the field size. The proof techniques are based on the study of isolated vertices in certain bipartite graphs. By specializing our results to matrix spaces, we answer an open question in coding theory, proving that MRD codes in the rank metric are sparse for all parameter sets as the field grows, with only very few exceptions. We also investigate the density of MRD codes as their column length tends to infinity, obtaining a new asymptotic bound. Using properties of the Euler function from number theory, we then show that our bound improves on known results for most parameter sets. We conclude the paper by establishing two structural properties of the density function of rank-metric codes.
Imagining Grounded Conceptual Representations from Perceptual Information in Situated Guessing Games

In visual guessing games, a Guesser has to identify a target object in a scene by asking questions to an Oracle. An effective strategy for the players is to learn conceptual representations of objects that are both discriminative and expressive enough to ask questions and guess correctly. However, as shown by Suglia et al. (2020), existing models fail to learn truly multi-modal representations, relying instead on gold category labels for objects in the scene both at training and inference time. This provides an unnatural performance advantage when categories at inference time match those at training time, and it causes models to fail in more realistic “zero-shot” scenarios where out-of-domain object categories are involved. To overcome this issue, we introduce a novel “imagination” module based on Regularized Auto-Encoders, that learns context-aware and category-aware latent embeddings without relying on category labels at inference time. Our imagination module outperforms state-of-the-art competitors by 8.26% gameplay accuracy in the CompGuessWhat? zero-shot scenario (Suglia et al., 2020), and it improves the Oracle and Guesser accuracy by 2.08% and 12.86% in the GuessWhat? benchmark, when no gold categories are available at inference time. The imagination module also boosts reasoning about object properties and attributes.
Query Expansion System for the VoxCeleb Speaker Recognition Challenge 2020

In this report, we describe our submission to the VoxCeleb Speaker Recognition Challenge (VoxSRC) 2020. Two approaches are adopted. One is to apply query expansion on speaker verification, which shows significant progress compared to baseline in the study. Another is to use Kaldi extract x-vector and to combine its Probabilistic Linear Discriminant Analysis (PLDA) score with ResNet score.
Chronologic trends of cancer-related lymph node research in PubMed: informetrics analysis

Purpose Given the long history of investigation into cancer and its relevance to the lymph node (LN), it would be meaningful to plot the trends of research on cancer-related LN. Methods Queries such as “cancer,” “lymph node,” and “cancer and lymph node” were submitted to PubMed to collect articles on cancer and LN published between 1945 and 2017. The collected articles were then extracted by an automatic web crawler and examined through informetrics and linguistic analysis. Results The number of articles related to cancer was 2,795,476 and 127,897 articles (4.6%) were found to be relevant to LN. With regard to cancer types, breast cancer was the most studied (37%), followed by gastric cancer (17%). With regard to the subjects in which the surgeon is interested, LN metastasis (57%) was found to be the topic most discussed, followed by LN dissection (22%) and sentinel LN (17%). Publications on LN metastasis gradually increased over time from 1988 to 2017 although those on sentinel LN and LN dissection have stagnated since the early 2000s. Conclusion Although research on cancer was abundant, only a small portion was dedicated to investigating its relevance to LN. Western countries had led the research on cancer-related LN, but Asian countries began to participate as major players, expanding their contributions. While LN metastasis, one of the major cancer-related LN topics, showed a steady increase, those involved in oncologic surgery such as LN dissection and sentinel LN did not.
Mutual Modality Learning for Video Action Classification

The construction of models for video action classification progresses rapidly. However, the performance of those models can still be easily improved by ensembling with the same models trained on different modalities (e.g. Optical flow). Unfortunately, it is computationally expensive to use several modalities during inference. Recent works examine the ways to integrate advantages of multi-modality into a single RGB-model. Yet, there is still room for improvement. In this paper, we explore various methods to embed the ensemble power into a single model. We show that proper initialization, as well as mutual modality learning, enhances single-modality models. As a result, we achieve state-of-the-art results in the Something-Something-v2 benchmark.
The Association Between Anemia of Chronic Inflammation and Alzheimer’s Disease and Related Dementias

Background: Dementia is a spectrum of neurological diseases characterized by memory impairment and cognitive decline with the pathogenesis and effective management remaining elusive. Several studies have identified a correlation between anemia and Alzheimer’s disease and related dementias (ADRD); however, anemia subtypes and association with ADRD have yet to be studied conclusively. Objective: To study an association between ADRD and anemia of chronic inflammation. Methods: We conducted a retrospective case-control study of the patients, diagnosed with ADRD at Brookdale Hospital. Pair-wise comparisons between means of controls and cases in terms of iron studies and laboratory results were performed using a Mann–Whitney U test. Pair-wise comparisons between anemia subgroups (moderate and severe) were performed using a Two Sample proportion Z-Test, where for each couple of normally distributed population. Results: There was a total of 4,517 (1,274 ADRD group; 3,243 Control group) patients. There was significant difference in hemoglobin 10.15 versus 11.04 [p-value <0.001]. Iron studies showed a significant difference in ferritin 395±488.18 versus 263±1023.4 [p < 0.001], total iron binding capacity 225±84.08 versus 266±82.30 [p < 0.001] and serum iron level 64±39.34 versus 53±41.83 [p < 0.001]. Folic acid and vitamin B12 levels were normal in both groups. Severe and moderate anemia in the ADRD group were respectively 6.2% [95% CI: 4.2–8.4] and 13% [95% CI: 9.8–16.2] higher. Overall, incidence of moderate-to-severe anemia was found to be 19% higher in ADRD group [95% CI: 15.8–22.1]. Conclusion: We demonstrated an association between ADRD and anemia of chronic inflammation independent of age, renal function, and HgbA1C levels.
DUDE: Deep Unsigned Distance Embeddings for Hi-Fidelity Representation of Complex 3D Surfaces

High fidelity representation of shapes with arbitrary topology is an important problem for a variety of vision and graphics applications. Owing to their limited resolution, classical discrete shape representations using point clouds, voxels and meshes produce low quality results when used in these applications. Several implicit 3D shape representation approaches using deep neural networks have been proposed leading to significant improvements in both quality of representations as well as the impact on downstream applications. However, these methods can only be used to represent topologically closed shapes which greatly limits the class of shapes that they can represent. As a consequence, they also often require clean, watertight meshes for training. In this work, we propose DUDE - a Deep Unsigned Distance Embedding method which alleviates both of these shortcomings. DUDE is a disentangled shape representation that utilizes an unsigned distance field (uDF) to represent proximity to a surface, and a normal vector field (nVF) to represent surface orientation. We show that a combination of these two (uDF+nVF) can be used to learn high fidelity representations for arbitrary open/closed shapes. As opposed to prior work such as DeepSDF, our shape representations can be directly learnt from noisy triangle soups, and do not need watertight meshes. Additionally, we propose novel algorithms for extracting and rendering iso-surfaces from the learnt representations. We validate DUDE on benchmark 3D datasets and demonstrate that it produces significant improvements over the state of the art.
Chasing Carbon: The Elusive Environmental Footprint of Computing

Given recent algorithm, software, and hardware innovation, computing has enabled a plethora of new applications. As computing becomes increasingly ubiquitous, however, so does its environmental impact. This paper brings the issue to the attention of computer-systems researchers. Our analysis, built on industry-reported characterization, quantifies the environmental effects of computing in terms of carbon emissions. Broadly, carbon emissions have two sources: operational energy consumption, and hardware manufacturing and infrastructure. Although carbon emissions from the former are decreasing thanks to algorithmic, software, and hardware innovations that boost performance and power efficiency, the overall carbon footprint of computer systems continues to grow. This work quantifies the carbon output of computer systems to show that most emissions related to modern mobile and data-center equipment come from hardware manufacturing and infrastructure. We therefore outline future directions for minimizing the environmental impact of computing systems.
Simple and optimal methods for stochastic variational inequalities, I: operator extrapolation

In this paper we first present a novel operator extrapolation (OE) method for solving deterministic variational inequality (VI) problems. Similar to the gradient (operator) projection method, OE updates one single search sequence by solving a single projection subproblem in each iteration. We show that OE can achieve the optimal rate of convergence for solving a variety of VI problems in a much simpler way than existing approaches. We then introduce the stochastic operator extrapolation (SOE) method and establish its optimal convergence behavior for solving different stochastic VI problems. In particular, SOE achieves the optimal complexity for solving a fundamental problem, i.e., stochastic smooth and strongly monotone VI, for the first time in the literature. We also present a stochastic block operator extrapolations (SBOE) method to further reduce the iteration cost for the OE method applied to large-scale deterministic VIs with a certain block structure. Numerical experiments have been conducted to demonstrate the potential advantages of the proposed algorithms. In fact, all these algorithms are applied to solve generalized monotone variational inequality (GMVI) problems whose operator is not necessarily monotone. We will also discuss optimal OE-based policy evaluation methods for reinforcement learning in a companion paper.
Case Series of Ventriculoatrial Shunt placement in Hybrid Room: Reassessment of Ventriculoatrial Shunt

Objective Ventriculoatrial shunt (VAS) remains an alternate option for treatment of hydrocephalus in patients with ventriculoperitoneal shunt (VPS) failure. Unfamiliar anatomy for a neurosurgeon has resulted in the VAS falling out of favor as a treatment option. However, there are unsatisfactory reports on the long-term result of VPS, and VAS has been recently re-evaluated. We are to report the simple way to do the VAS using a peel-away sheath in a hybrid operation room. Methods A jugular vein path was drawn by ultrasound, a small incision was made above the clavicle, and a shunt catheter was tunneled into it. The jugular vein was punctured beside the tunneled catheter with a Seldinger needle under ultrasound guidance. A flexible guide wire was introduced into the vein and 6-Fr peel-away sheath was advanced into the vein along the wire. Under fluoroscopic guidance, the catheter was cut to position approximately mid-level in the atrium. After the guide wire was removed, the distal shunt catheter was passed down. After confirming proper position of the distal catheter under the fluoroscope, the catheter-guiding sheath was pulled out as a peeling-away manner. We performed this surgical procedures in 5 cases. Results All the procedures of the VAS using a peel-away sheath were performed in a hybrid operation room Of 5 patients, 3 patients had the distal catheter failures in the peritoneal cavity and 2 patients had shunt A distal catheter was successfully indwelling in all the cases without any difficulties. After the surgery, neither shunt infection nor thromboembolic event happened. Conclusion VAS using fluoroscopy and a peel-away sheath is a good alternative option for hydrocephalus patients with shunt failure related to peritoneal cavity complications.
Lactate Induces Cisplatin Resistance in S. cerevisiae through a Rad4p-Dependent Process

Cisplatin is a widely used antineoplastic agent that has DNA as the main target, though cellular resistance hampers its therapeutic efficacy. An emerging hallmark of cancer cells is their altered metabolism, characterized by increased glycolysis even under aerobic conditions, with increased lactate production (known as the Warburg effect). Although this altered metabolism often results in increased resistance to chemotherapy, it also provides an opportunity for targeted therapeutic intervention. It has been suggested that cisplatin cytotoxicity can be affected by tumor metabolism, though with varying effects. We therefore sought to better characterize how lactate affects cisplatin sensitivity in the simplified Saccharomyces cerevisiae model. We show that lactate renders yeast cells resistant to cisplatin, independently of growth rate or respiration ability. We further show that histone acetylation is not affected, but histone phosphorylation is decreased in lactate-containing media. Finally, we show that Rad4p, essential for nucleotide excision repair, is required for the observed phenotype and thus likely underlies the mechanism responsible for lactate-mediated resistance to cisplatin. Overall, understanding how lactate modulates cisplatin sensitivity will aid in the development of new strategies to overcome drug resistance.
Shapelet Discovery by Lazy Time Series Classification

As a representation of discriminative features, the time series shapelet has recently received considerable research interest. However, most shapelet-based classification models evaluate the differential ability of the shapelet on the whole training dataset, neglecting characteristic information contained in each instance to be classified and the classwise feature frequency information. Hence, the computational complexity of feature extraction is high, and the interpretability is inadequate. To this end, the efficiency of shapelet discovery is improved through a lazy strategy fusing global and local similarities. In the prediction process, the strategy learns a specific evaluation dataset for each instance, and then the captured characteristics are directly used to progressively reduce the uncertainty of the predicted class label. Moreover, a shapelet coverage score is defined to calculate the discriminability of each time stamp for different classes. The experimental results show that the proposed method is competitive with the benchmark methods and provides insight into the discriminative features of each time series and each type in the data.