Miracle-dz/UniFAR-FTUs huggingface dataset:
HIV and COVID-19: review of clinical course and outcomes

Background:Understanding the relationship between HIV and SARS-CoV-2 has important public health implications. Objective:To summarize current research on COVID-19 among people with HIV (PWH) as published through 15 July 2021. Methods: We conducted a search of PubMed, Scopus, preprint databases (medRxiv, bioRxiv), and the references of publications found using key terms relevant to COVID-19 (‘COVID-19’ OR ‘SARS-CoV-2’ OR ‘coronavirus’) AND to HIV (‘HIV’ OR ‘Human Immunodeficiency Virus’ OR ‘AIDS’ OR ‘Acquired Immunodeficiency Syndrome’). We summarized all articles that reported data or opinions on SARS-CoV-2 and HIV coinfection. Conclusions: Although many initial case series and cohort studies found no increased risk for SARS-CoV-2 infection or severe COVID-19 outcomes among PWH, recent studies have signaled an increased risk for severe COVID-19 disease progression even in the setting of well-controlled HIV. Whether this is due to the increased prevalence of comorbidities in PWH and other social determinants of health is unknown. These conflicting findings highlight the continued need for COVID-19 related research among PWH that addresses COVID-19 disease course as well as exacerbation of existing comorbidities already disproportionately represented among PWH.
What’s in a Name? Answer Equivalence For Open-Domain Question Answering

A flaw in QA evaluation is that annotations often only provide one gold answer. Thus, model predictions semantically equivalent to the answer but superficially different are considered incorrect. This work explores mining alias entities from knowledge bases and using them as additional gold answers (i.e., equivalent answers). We incorporate answers for two settings: evaluation with additional answers and model training with equivalent answers. We analyse three QA benchmarks: Natural Questions, TriviaQA, and SQuAD. Answer expansion increases the exact match score on all datasets for evaluation, while incorporating it helps model training over real-world datasets. We ensure the additional answers are valid through a human post hoc evaluation.
ITERATING THE COFINALITY- 
$\omega $
 CONSTRUCTIBLE MODEL

Abstract We investigate iterating the construction of $C^{*}$ , the L-like inner model constructed using first order logic augmented with the “cofinality $\omega $ ” quantifier. We first show that $\left (C^{*}\right )^{C^{*}}=C^{*}\ne L$ is equiconsistent with $\mathrm {ZFC}$ , as well as having finite strictly decreasing sequences of iterated $C^{*}$ s. We then show that in models of the form $L[U]$ we get infinite decreasing sequences of length $\omega $ , and that an inner model with a measurable cardinal is required for that.
Perturbation CheckLists for Evaluating NLG Evaluation Metrics

Natural Language Generation (NLG) evaluation is a multifaceted task requiring assessment of multiple desirable criteria, e.g., fluency, coherency, coverage, relevance, adequacy, overall quality, etc. Across existing datasets for 6 NLG tasks, we observe that the human evaluation scores on these multiple criteria are often not correlated. For example, there is a very low correlation between human scores on fluency and data coverage for the task of structured data to text generation. This suggests that the current recipe of proposing new automatic evaluation metrics for NLG by showing that they correlate well with scores assigned by humans for a single criteria (overall quality) alone is inadequate. Indeed, our extensive study involving 25 automatic evaluation metrics across 6 different tasks and 18 different evaluation criteria shows that there is no single metric which correlates well with human scores on all desirable criteria, for most NLG tasks. Given this situation, we propose CheckLists for better design and evaluation of automatic metrics. We design templates which target a specific criteria (e.g., coverage) and perturb the output such that the quality gets affected only along this specific criteria (e.g., the coverage drops). We show that existing evaluation metrics are not robust against even such simple perturbations and disagree with scores assigned by humans to the perturbed output. The proposed templates thus allow for a fine-grained assessment of automatic evaluation metrics exposing their limitations and will facilitate better design, analysis and evaluation of such metrics. Our templates and code are available at https://iitmnlp.github. io/EvalEval/
Physics-based machine learning for modeling stochastic IP3-dependent calcium dynamics

We present a machine learning method for model reduction which incorporates domain-specific physics through candidate functions. Our method estimates an effective probability distribution and differential equation model from stochastic simulations of a reaction network. The close connection between reduced and fine scale descriptions allows approximations derived from the master equation to be introduced into the learning problem. This representation is shown to improve generalization and allows a large reduction in network size for a classic model of inositol trisphosphate (IP3) dependent calcium oscillations in non-excitable cells.
DHA: End-to-End Joint Optimization of Data Augmentation Policy, Hyper-parameter and Architecture

Automated machine learning (AutoML) usually involves several crucial components, such as Data Augmentation (DA) policy, Hyper-Parameter Optimization (HPO), and Neural Architecture Search (NAS). Although many strategies have been developed for automating these components in separation, joint optimization of these components remains challenging due to the largely increased search dimension and the variant input types of each component. In parallel to this, the common practice of searching for the optimal architecture first and then retraining it before deployment in NAS often suffers from low performance correlation between the searching and retraining stages. An end-to-end solution that integrates the AutoML components and returns a ready-to-use model at the end of the search is desirable. In view of these, we propose DHA, which achieves joint optimization of Data augmentation policy, Hyper-parameter and Architecture. Specifically, end-to-end NAS is achieved in a differentiable manner by optimizing a compressed lower-dimensional feature space, while DA policy and HPO are regarded as dynamic schedulers, which adapt themselves to the update of network parameters and network architecture at the same time. Experiments show that DHA achieves state-of-the-art (SOTA) results on various datasets and search spaces. To the best of our knowledge, we are the first to efficiently and jointly optimize DA policy, NAS, and HPO in an end-to-end manner without retraining.
GradTS: A Gradient-Based Automatic Auxiliary Task Selection Method Based on Transformer Networks

A key problem in multi-task learning (MTL) research is how to select high-quality auxiliary tasks automatically. This paper presents GradTS, an automatic auxiliary task selection method based on gradient calculation in Transformer-based models. Compared to AUTOSEM, a strong baseline method, GradTS improves the performance of MT-DNN with a bert-base-cased backend model, from 0.33% to 17.93% on 8 natural language understanding (NLU) tasks in the GLUE benchmarks. GradTS is also time-saving since (1) its gradient calculations are based on single-task experiments and (2) the gradients are re-used without additional experiments when the candidate task set changes. On the 8 GLUE classification tasks, for example, GradTS costs on average 21.32% less time than AUTOSEM with comparable GPU consumption. Further, we show the robustness of GradTS across various task settings and model selections, e.g. mixed objectives among candidate tasks. The efficiency and efficacy of GradTS in these case studies illustrate its general applicability in MTL research without requiring manual task filtering or costly parameter tuning.
Accelerated ground-state cooling of an optomechanical resonator via shortcuts to adiabaticity

Ground-state cooling of mechanical resonators is an important task in quantum optomechanics, because it is a necessary prerequisite for the creation, manipulation, and application of macroscopic mechanical coherence. Here, we propose a transient-state scheme to accelerate ground-state cooling of a mechanical resonator in a three-mode loop-coupled optomechanical system via shortcuts to adiabaticity (STA). We consider four kinds of coupling protocols and calculate the evolution of the mean phonon number of the mechanical resonator in both the adiabatic and STA cases. We verify that the ground-state cooling of the mechanical resonator can be achieved with the STA method in a much shorter period. The STA method can be generalized to accelerate other adiabatic processes in cavity optomechanics, and hence this work will open up a realm of fast optomechanical manipulations.
Who shapes crisis communication on Twitter? An analysis of influential German-language accounts during the COVID-19 pandemic

Twitter is becoming an increasingly important platform for disseminating information during crisis situations, such as the COVID-19 pandemic. Effective crisis communication on Twitter can shape the public perception of the crisis, influence adherence to preventative measures, and thus affect public health. Influential accounts are particularly important as they reach large audiences quickly. This study identifies influential German-language accounts from almost 3 million German tweets collected between January and May 2020 by constructing a retweet network and calculating PageRank centrality values. We capture the volatility of crisis communication by structuring the analysis into seven stages based on key events during the pandemic and profile influential accounts into roles. Our analysis shows that news and journalist accounts were influential throughout all phases, while government accounts were particularly important shortly before and after the lockdown was instantiated. We discuss implications for crisis communication during health crises and for analyzing long-term crisis data.
Learning Statistical Representation with Joint Deep Embedded Clustering

— One of the most promising approaches for unsupervised learning is combining deep representation learning and deep clustering. Some recent works propose to simultaneously learn representation using deep neural networks and perform clustering by deﬁning a clustering loss on top of embedded features. However, these approaches are sensitive to imbalanced data and out-of-distribution samples. As a consequence, these methods optimize clustering by pushing data close to randomly initialized cluster centers. This is problematic when the number of instances varies largely in different classes or a cluster with few samples has less chance to be assigned a good centroid. To overcome these limitations, we introduce StatDEC, a new unsupervised framework for joint statistical representation learning and clustering. In our framework, we simultaneously train two deep learning models, a deep statistics network that captures the data distribution, and a deep clustering network that learns embedded features and performs clustering by explicitly deﬁning a clustering loss. Speciﬁcally, the clustering network and learning representation network both take advantage of our proposed statistics pooling layer that represents mean, variance, and cardinality to handle the out-of-distribution samples and class imbalance. Our experiments show that using these representations, one can considerably improve results on imbalanced image clustering across a variety of image datasets. Moreover, the learned representations generalize well when transferred to the out-of-distribution dataset.
Disproof of a conjecture of Erd\H{o}s and Simonovits on the Tur\'an number of graphs with minimum degree 3

In 1981, Erd\H{o}s and Simonovits conjectured that for any bipartite graph $H$ we have $\mathrm{ex}(n,H)=O(n^{3/2})$ if and only if $H$ is $2$-degenerate. Later, Erd\H{o}s offered 250 dollars for a proof and 500 dollars for a counterexample. In this paper, we disprove the conjecture by finding, for any $\varepsilon>0$, a $3$-regular bipartite graph $H$ with $\mathrm{ex}(n,H)=O(n^{4/3+\varepsilon})$.
TEASEL: A Transformer-Based Speech-Prefixed Language Model

Multimodal language analysis is a burgeoning field of NLP that aims to simultaneously model a speaker's words, acoustical annotations, and facial expressions. In this area, lexicon features usually outperform other modalities because they are pre-trained on large corpora via Transformer-based models. Despite their strong performance, training a new self-supervised learning (SSL) Transformer on any modality is not usually attainable due to insufficient data, which is the case in multimodal language learning. This work proposes a Transformer-Based Speech-Prefixed Language Model called TEASEL to approach the mentioned constraints without training a complete Transformer model. TEASEL model includes speech modality as a dynamic prefix besides the textual modality compared to a conventional language model. This method exploits a conventional pre-trained language model as a cross-modal Transformer model. We evaluated TEASEL for the multimodal sentiment analysis task defined by CMU-MOSI dataset. Extensive experiments show that our model outperforms unimodal baseline language models by 4% and outperforms the current multimodal state-of-the-art (SoTA) model by 1% in F1-score. Additionally, our proposed method is 72% smaller than the SoTA model.
Learning Indoor Inverse Rendering with 3D Spatially-Varying Lighting

In this work, we address the problem of jointly estimating albedo, normals, depth and 3D spatially-varying lighting from a single image. Most existing methods formulate the task as image-to-image translation, ignoring the 3D properties of the scene. However, indoor scenes contain complex 3D light transport where a 2D representation is insufficient. In this paper, we propose a unified, learning-based inverse rendering framework that formulates 3D spatially-varying lighting. Inspired by classic volume rendering techniques, we propose a novel Volumetric Spherical Gaussian representation for lighting, which parameterizes the exitant radiance of the 3D scene surfaces on a voxel grid. We design a physics-based differentiable renderer that utilizes our 3D lighting representation, and formulates the energy-conserving image formation process that enables joint training of all intrinsic properties with the re-rendering constraint. Our model ensures physically correct predictions and avoids the need for ground-truth HDR lighting which is not easily accessible. Experiments show that our method outperforms prior works both quantitatively and qualitatively, and is capable of producing photorealistic results for AR applications such as virtual object insertion even for highly specular objects.
Are subject-specific models necessary to predict patellar tendon fatigue life? A finite element modelling study

Abstract Patellar tendinopathy is an overuse injury that occurs from repetitive loading of the patellar tendon in a scenario resembling that of mechanical fatigue. As such, fatigue-life estimates provide a quantifiable approach to assess tendinopathy risk and may be tabulated using nominal strain (NS) or finite element (FE) models with varied subject-specificity. We compared patellar tendon fatigue-life estimates from NS and FE models of twenty-nine athletes performing countermovement jumps with subject-specific versus generic geometry and material properties. Subject-specific patellar tendon material properties and geometry were obtained using a data collection protocol of dynamometry, ultrasound, and magnetic resonance imaging. Three FE models were created for each subject, with: subject-specific (hyperelastic) material properties and geometry, subject-specific material properties and generic geometry, and generic material properties and subject-specific geometry. Four NS models were created for each subject, with: subject-specific (linear elastic) material properties and moment arm, generic material properties and subject-specific moment arm, subject-specific material properties and generic moment arm, and generic material properties and moment arm. NS- and FE-modelled fatigue-life estimates with generic material properties were poorly correlated with their subject-specific counterparts (r2 ≤0.073), while all NS models overestimated fatigue life compared to the subject-specific FE model (r2 ≤0.223). Furthermore, FE models with generic tendon geometry were unable to accurately represent the heterogeneous strain distributions found in the subject-specific FE models or those with generic material properties. These findings illustrate the importance of incorporating subject-specific material properties and FE-modelled strain distributions into fatigue-life estimations.
Speaker-Oriented Latent Structures for Dialogue-Based Relation Extraction

Dialogue-based relation extraction (DiaRE) aims to detect the structural information from unstructured utterances in dialogues. Existing relation extraction models may be unsatisfactory under such a conversational setting, due to the entangled logic and information sparsity issues in utterances involving multiple speakers. To this end, we introduce SOLS, a novel model which can explicitly induce speaker-oriented latent structures for better DiaRE. Specifically, we learn latent structures to capture the relationships among tokens beyond the utterance boundaries, alleviating the entangled logic issue. During the learning process, our speaker-specific regularization method progressively highlights speaker-related key clues and erases the irrelevant ones, alleviating the information sparsity issue. Experiments on three public datasets demonstrate the effectiveness of our proposed approach.
A safety evaluation of sucroferric oxyhydroxide for the treatment of hyperphosphatemia

ABSTRACT Introduction Hyperphosphatemia is a common complication as chronic kidney disease (CKD) progresses and most patients undergoing dialysis are prescribed oral phosphate binder therapy to control serum phosphate concentrations. Sucroferric oxyhydroxide is an iron-based phosphate binder approved for the treatment of hyperphosphatemia in CKD patients on dialysis. Areas covered This article reviews key safety and effectiveness data for sucroferric oxyhydroxide from both prospective clinical trials and real-world observational studies. Expert opinion Sucroferric oxyhydroxide potently binds dietary phosphate in the gastrointestinal (GI) tract, resulting in effective reduction of serum phosphate concentrations with a relatively low daily pill burden. Data from clinical trials and real-world observational studies show sucroferric oxyhydroxide has a favorable safety and tolerability profile. The most frequent side effects observed with sucroferric oxyhydroxide are GI-related, mainly discolored (black) stools and mild or moderate transient diarrhea, both of which are manageable. There is minimal systemic iron absorption from sucroferric oxyhydroxide, and therefore the drug is associated with a low risk of iron accumulation. Sucroferric oxyhydroxide also displays low potential for drug–drug interactions with other commonly prescribed oral medications. Overall, sucroferric oxyhydroxide offers an effective and well-tolerated treatment option for the management of hyperphosphatemia.
What causes the absence of pulsations in Central Compact Objects in supernova remnants?

Most young neutron stars belonging to the class of Central Compact Objects (CCOs) in supernova remnants do not have known periodicities. We investigated seven such CCOs to understand the common reasons for the absence of detected pulsations. Making use of XMM-Newton, Chandra, and NICER observations, we perform a systematic timing and spectral analysis to derive updated sensitivity limits for both periodic signals and multi-temperature spectral components that could be associated with radiation from hotspots on the neutron star surface. Based on these limits, we then investigated for each target the allowed viewing geometry that could explain the lack of pulsations. We find that it is unlikely (< 10−6) to attribute that we do not see pulsations to an unfavorable viewing geometry for five considered sources. Alternatively, the carbon atmosphere model, which assumes homogeneous temperature distribution on the surface, describes the spectra equally well and provides a reasonable interpretation for the absence of detected periodicities within current limits. The unusual properties of CCOs with respect to other young neutron stars could suggest a different evolutionary path, as that proposed for sources experiencing episodes of significant fallback accretion after the supernova event.
Exploring pretreatment with ionic liquids for enzymatic cellulose hydrolysis

The efficient hydrolysis of cellulose, which is one major component of plant biomass, is crucial for the economic production of 2nd and 3rd generation biofuels. Enzymatic cellulose hydrolysis allows for the selective conversion of cellulose to glucose at moderate reaction conditions. The efficiency of enzymatic cellulose hydrolysis is primarily limited by the accessibility of the cellulose polymer to cellulases due to the highly organized and recalcitrant cellulose structure. Therefore, cellulose is pretreated to increase the availability of cellulose for cellulases. For cellulose pretreatment ionic liquids, as new generation of solvents, are attractive because some of them can dissolve cellulose and thereby open the highly organized cellulose structure. Dissolved cellulose can then be precipitated by addition of water, leading to an amorphous regenerated cellulose structure with large pores. Therefore, regenerated cellulose can be hydrolyzed much more efficiently with strongly increased reaction rates. The enzymatic heterogeneous hydrolysis of undissolved, regenerated cellulose with a commercial cellulase preparation was characterized and the effect of residual ionic liquid from the pretreatment was analyzed. The ionic liquid strongly reduced the cellulase activity but the enzyme stability was maintained. Therefore, regenerated cellulose was hydrolyzed much faster, also in the presence of residual ionic liquid. However, with increasing amounts of ionic liquid, the yields were reduced. To improve the hydrolysis of regenerated cellulose a semi-empiric mathematical model was developed. This was successfully used to rationally optimize the cellulase mixture and increase the yield by 10% points. An even further improved process was achieved by combining a chemical hydrolysis step with the enzymatic hydrolysis to achieve nearly quantitative conversion in only 5 hours. In addition to the heterogeneous hydrolysis of undissolved cellulose, the homo-
The significances and clinical implications of cholesterol components in human breast cancer

Breast cancer is one the most common malignancies and leading cause of cancer-related mortality in women. Recent studies suggested that hypercholesterolemia may be the potential modifiable risk factors for breast cancer. Cholesterol was well-known for its strong association with cardiovascular disease for long. Moreover, solid evidence has been provided by different studies to illustrate the correlation between lipid and incidence in multiple cancers. Although the conclusion remains controversial or sometimes contrary, which may be due to the multifactorial nature of the disease and the disparity of ethnic population, it is critical to elucidate the relationship between specific cholesterol components in certain population and the exact underlying mechanism of the lipid-associated signaling pathway in breast cancer. The implications of dysregulated lipoproteins as therapeutic targets or options for breast cancer provide novel strategies for us in combating with this malignant disease, which may be achieved by manipulating lipid levels with pharmacological compounds.