Miracle-dz/UniFAR-FTUs huggingface dataset:
Adherence to Remote Cardiac Rehabilitation During the Coronavirus Pandemic: A Retrospective Cohort Analysis

Cardiac Rehabilitation Referral and Participation Rates for Heart Failure With Reduced Ejection Fraction T American College of Cardiology/American Heart Association guidelines recommend exercise training (typically in a cardiac rehabilitation [CR] setting) to improve functional status in patients with symptomatic heart failure with reduced ejection fraction (HFrEF) able to participate (Class I recommendation, level of evidence A). To date, few studies have determined CR referral and enrollment rates in heart failure (HF) patients.1-4 These studies have found that CR referral and participation rates for HF patients are low (10.4% and 2.6-41%, respectively). 1-4 A major barrier to CR referral was the lack of insurance coverage.5,6 In 2014, the Centers for Medicare & Medicaid Services (CMS) initiated coverage of CR for selected patients with HFrEF (ie, those who meet CMS referral criteria). It would be expected that the introduction of this CMS coverage would result in greater CR referral and participation rates. To this point, a previous study reported that CR participation rates were 41% in patients with HFrEF following CMS coverage3; however, referral rates were not reported. Other studies reporting CR referral and participation rates following CMS coverage approval have found low CR participation rates1,4; however, it is unclear whether these patients met CMS entry criteria for CR. Thus, we sought to assess CR referral, enrollment, and graduation rates in patients with HFrEF following introduction of CMS coverage for CR.
Exergy Analysis of High-Temperature Proton Exchange Membrane Fuel Cell Systems

In this study, the performance of two high-temperature proton exchange membrane fuel cell (HT-PEMFC) systems has been investigated by using the method of exergy analysis under different operating conditions, one of the systems is directly fueled by hydrogen (direct hydrogen system), and another by methane (natural gas) with steam methane reforming (reformed hydrogen system). The results indicate that for both systems, a higher fuel cell operating temperature tends to increase the efficiency and power output of the system. However, higher inlet air relative humidity and operating pressure has insignificant influence on improving the quality of both systems. Fuel cell stack is the place where exergy loss is the highest, which means more efforts should be focused on improving the energy exchange efficiency and decreasing the irreversibility of fuel cell to increase the system efficiency.
Proximal Fusion Level Above First Coronal Reverse Vertebrae: An Essential Factor Decreasing the Risk of Adjacent Segment Degeneration in Degenerative Lumbar Scoliosis

Study Design: Retrospective radiological analysis. Objective: To explore whether proximal fusion level above first coronal reverse vertebrae (FCRV) could decrease risk of adjacent segment degeneration (ASD) in degenerative lumbar scoliosis (DLS). Methods: One hundred and 16 DLS patients were divided into 2 groups according to occurrence of ASD: study group (ASD positive group) and control group (ASD negative group). FCRV was defined as the first vertebrae that presents opposite orientation of asymmetric Hounsfield unit (HU) ratio from the other vertebrae within major curve. Incidence of ASD was evaluated in subgroups according to location between FCRV and upper instrumented vertebrae (UIV). Results: The fusion level was shorter in study group than that in control group. There were 12 patients presented FCRV 2-level proximal than SV, 32 patients presented FCRV 1-level proximal than SV, 35 patients presented the same vertebra of FCRV with SV, 23 patients presented FCRV 1-level distal than SV, 14 patients presented FCRV 2-level distal than SV. When UIV located above FCRV, no patient presented ASD, while 15.4% patients presented ASD when UIV located on FCRV. In study group, proximal scoliosis progression was detected in 1 patient (3.9%) when UIV located on FCRV, and 17 patients (29.8%) when UIV located below FCRV. Conclusions: Proximal fusion level above FCRV could decrease the risk of ASD in DLS, especially for the proximal scoliosis progression. FCRV represent the transitional point of the mechanical load on coronal plane, and may be within a more stable condition than stable vertebrae measured from radiographs.
Current molecular and clinical insights into uveal melanoma (Review)

Uveal melanoma (UM) represents the most prominent primary eye cancer in adults. With an incidence of approximately 5 cases per million individuals annually in the United States, UM could be considered a relatively rare cancer. The 90-95% of UM cases arise from the choroid. Diagnosis is based mainly on a clinical examination and ancillary tests, with ocular ultrasonography being of greatest value. Differential diagnosis can prove challenging in the case of indeterminate choroidal lesions and, sometimes, monitoring for documented growth may be the proper approach. Fine needle aspiration biopsy tends to be performed with a prognostic purpose, often in combination with radiotherapy. Gene expression profiling has allowed for the grading of UMs into two classes, which feature different metastatic risks. Patients with UM require a specialized multidisciplinary management. Primary tumor treatment can be either enucleation or globe preserving. Usually, enucleation is reserved for larger tumors, while radiotherapy is preferred for small/medium melanomas. The prognosis is unfavorable due to the high mortality rate and high tendency to metastasize. Following the development of metastatic disease, the mortality rate increases to 80% within one year, due to both the absence of an effective treatment and the aggressiveness of the condition. Novel molecular studies have allowed for a better understanding of the genetic and epigenetic mechanisms involved in UM biological activity, which differs compared to skin melanomas. The most commonly mutated genes are GNAQ, GNA11 and BAP1. Research in this field could help to identify effective diagnostic and prognostic biomarkers, as well as novel therapeutic targets.
Optimizing recombineering in Corynebacterium glutamicum

Owing to the increasing demand for amino acids and valuable commodities that can be produced by Corynebacterium glutamicum, there is a pressing need for new rapid genome engineering tools that improve the speed and efficiency of genomic insertions, deletions, and mutations. Recombineering using the λ Red system in Escherichia coli has proven very successful at genetically modifying this organism in a quick and efficient manner, suggesting that optimizing a recombineering system for C. glutamicum will also improve the speed for genomic modifications. Here, we maximized the recombineering efficiency in C. glutamicum by testing the efficacy of seven different recombinase/exonuclease pairs for integrating single‐stranded DNA and double‐stranded DNA (dsDNA) into the genome. By optimizing the homologous arm length and the amount of dsDNA transformed, as well as eliminating codon bias, a dsDNA recombineering efficiency of 13,250 transformed colonies/109 viable cells was achieved, the highest efficiency currently reported in the literature. Using this optimized system, over 40,000 bp could be deleted in one transformation step. This recombineering strategy will greatly improve the speed of genetic modifications in C. glutamicum and assist other systems, such as clustered regularly interspaced short palindromic repeats and multiplexed automated genome engineering, in improving targeted genome editing.
Influence of Type and Content of Various Comonomers on Long-Chain Branching of Ethene/α-Olefin Copolymers

One polyethylene and nine ethene/α-olefin copolymers differing in amount (0.4−2.9 mol %) and molar mass of the comonomer were characterized by NMR, SEC-MALLS, and rheology. Samples were polymerized using a [Ph2C(2,7-di-t-BuFlu)(Cp)]ZrCl2/MAO catalyst, with octene, octadecene, and hexacosene as comonomers, resulting in polymers of Mw ≈ 190 kg/mol. The comonomer content was determined by melt-state NMR. For the homopolymer 0.37 and 0.30 LCB/molecule were found by NMR and SEC-MALLS, respectively. Rheological quantities, such as the zero shear rate viscosity (η0), increased with LCB as compared to linear samples of the same Mw. The shape of the viscosity function and the linear steady-state elastic compliance ( ) showed a dependence on comonomer content and length. These findings are used to elucidate the various long-chain branching architectures. The highest comonomer content samples behaved like typical linear polymers in rheological experiments, while those with less comonomer contents were found to be lo.
No-Transaction Band Network: A Neural Network Architecture for Efficient Deep Hedging

Deep hedging is a versatile framework for computing the optimal hedging strategy of derivatives in incomplete markets. However, it is subject to the action-dependence problem impeding efficient training because the appropriate hedging action at the next step depends on the current action. To overcome this issue, the authors leverage a no-transaction band strategy, an existing technique that provides optimal hedging strategies for European options and exponential utility. The authors theoretically argue this strategy to be optimal for a wider class of utilities and derivatives, including exotics. Based on the result, the authors propose a no-transaction band network, namely, a neural network architecture that facilitates fast training and precise evaluation of the optimal hedging strategy. Moreover, the authors experimentally demonstrate that, for European and lookback options, their architecture rapidly attains a better hedging strategy compared with a standard feed-forward network. The findings thus have important implications for the practical applications of deep hedging.
Generalised gravitational wave burst generation with generative adversarial networks

We introduce the use of conditional generative adversarial networks (CGANs) for generalised gravitational wave (GW) burst generation in the time domain. Generative adversarial networks are generative machine learning models that produce new data based on the features of the training data set. We condition the network on five classes of time-series signals that are often used to characterise GW burst searches: sine-Gaussian, ringdown, white noise burst, Gaussian pulse and binary black hole merger. We show that the model can replicate the features of these standard signal classes and, in addition, produce generalised burst signals through interpolation and class mixing. We also present an example application where a convolutional neural network (CNN) classifier is trained on burst signals generated by our CGAN. We show that a CNN classifier trained only on the standard five signal classes has a poorer detection efficiency than a CNN classifier trained on a population of generalised burst signals drawn from the combined signal class space.
Class Means as an Early Exit Decision Mechanism

—State-of-the-art neural networks with early exit mechanisms often need considerable amount of training and ﬁne-tuning to achieve good performance with low computational cost. We propose a novel early exit technique based on the class means of samples. Unlike most existing schemes, our method does not require gradient-based training of internal classiﬁers. This makes our method particularly useful for neural network training in low-power devices, as in wireless edge networks. In particular, given a ﬁxed training time budget, our scheme achieves higher accuracy as compared to existing early exit mechanisms. Moreover, if there are no limitations on the training time budget, our method can be combined with an existing early exit scheme to boost its performance, achieving a better trade-off between computational cost and network accuracy.
Hindi-Urdu Adposition and Case Supersenses v1.0

These are the guidelines for the application of SNACS (Semantic Network of Adposition and Case Supersenses; Schneider et al. 2018) to Modern Standard Hindi of Delhi. SNACS is an inventory of 50 supersenses (semantic labels) for labelling the use of adpositions and case markers with respect to both lexical-semantic function and relation to the underlying context. The English guidelines (Schneider et al., 2020) were used as a model for this document. Besides the case system, Hindi has an extremely rich adpositional system built on the oblique genitive, with productive incorporation of loanwords even in present-day Hinglish. This document is aligned with version 2.5 of the English guidelines.
New Public-Key Crypto-System EHT

In this note, an LWE problem with a hidden trapdoor is introduced. It is used to construct an efficient public-key crypto-system EHT. The new system is significantly different from LWE based NIST candidates like FrodoKEM. The performance of EHT compares favorably with FrodoKEM.
Efficient Encoding Algorithm of Binary and Non-Binary LDPC Codes Using Block Triangulation

We propose an efficient encoding algorithm for the binary and non-binary low-density parity-check codes. This algorithm transforms the parity part of the parity-check matrix into a block triangular matrix with low weight diagonal submatrices by row and column permutations in the preprocessing stage. This algorithm determines the parity part of a codeword by block back-substitution to reduce the encoding complexity in the encoding stage. Numerical experiments show that this algorithm has a lower encoding complexity than existing encoding algorithms. Moreover, we show that this algorithm encodes the non-binary cycle codes in linear time.
Hard sphere crystal nucleation and growth near large spherical impurities

We report how large spherical impurities affect the nucleation and growth of hard sphere colloidal crystals. Both the impurities and the colloids are fluorescently labelled polymethylmetacrylate particles and are dispersed in an optically and density matching solvent mixture. Crystal growth, initiated either at the impurity surface, or at the sample bottom, was studied by imaging sequences of two-dimensional xy-slices in the plane of the impurity’s centre of mass with a laser scanning confocal microscope. At least two factors determine whether a large impurity can function as a seed for heterogeneous nucleation: timescales and impurity curvature. The curvature needs to be sufficiently low for crystal nuclei to form on the impurity surface. If bulk crystal growth has already approached the impurity, bulk growth is dominant over growth of crystallites on the impurity surface. Such surface crystallites eventually reorient to adapt to the overall bulk crystal symmetry.
There is More than Meets the Eye: Self-Supervised Multi-Object Detection and Tracking with Sound by Distilling Multimodal Knowledge

Attributes of sound inherent to objects can provide valuable cues to learn rich representations for object detection and tracking. Furthermore, the co-occurrence of audiovisual events in videos can be exploited to localize objects over the image field by solely monitoring the sound in the environment. Thus far, this has only been feasible in scenarios where the camera is static and for single object detection. Moreover, the robustness of these methods has been limited as they primarily rely on RGB images which are highly susceptible to illumination and weather changes. In this work, we present the novel self-supervised MM-DistillNet framework consisting of multiple teachers that leverage diverse modalities including RGB, depth and thermal images, to simultaneously exploit complementary cues and distill knowledge into a single audio student network. We propose the new MTA loss function that facilitates the distillation of information from multimodal teachers in a self-supervised manner. Additionally, we propose a novel self-supervised pretext task for the audio student that enables us to not rely on labor-intensive manual annotations. We introduce a large-scale multimodal dataset with over 113,000 time-synchronized frames of RGB, depth, thermal, and audio modalities. Extensive experiments demonstrate that our approach outperforms state-of-the-art methods while being able to detect multiple objects using only sound during inference and even while moving.
Implications of an improved water equation of state for water-rich planets

Water (H2O), in all forms, is an important constituent in planetary bodies, controlling habitability and influencing geological activity. Under conditions found in the interior of many planets, as the pressure increases, the H-bonds in water gradually weaken and are replaced by ionic bonds. Recent experimental measurements of the water equation of state (EOS) showed both a new phase of H-bonded water ice, ice-VIIt, and a relatively low transition pressure just above 30 GPa to ionic bonded ice-X, which has a bulk modulus 2.5 times larger. The higher bulk modulus of ice-X produces larger planets for a given mass, thereby either reducing the atmospheric contribution to the volume of many exoplanets or limiting their water content. We investigate the impact of the new EOS measurements on the planetary mass–radius relation and interior structure for water-rich planets. We find that the change in the planet mass–radius relation caused by the systematic differences between previous and new experimental EOS measurements is comparable to the observational uncertainties in some planet sizes – an issue that will become more important as observations continue to improve.
Self-Supervised Symmetric Nonnegative Matrix Factorization

Symmetric nonnegative matrix factorization (SNMF) has demonstrated to be a powerful method for data clustering. However, SNMF is mathematically formulated as a non-convex optimization problem, making it sensitive to the initialization of variables. Inspired by ensemble clustering that aims to seek a better clustering result from a set of clustering results, we propose self-supervised SNMF (S3NMF), which is capable of boosting clustering performance progressively by taking advantage of the sensitivity to initialization characteristic of SNMF, without relying on any additional information. Specifically, we first perform SNMF repeatedly with a random positive matrix for initialization each time, leading to multiple decomposed matrices. Then, we rank the quality of the resulting matrices with adaptively learned weights, from which a new similarity matrix that is expected to be more discriminative is reconstructed for SNMF again. These two steps are iterated until the stopping criterion/maximum number of iterations is achieved. We mathematically formulate S3NMF as a constrained optimization problem, and provide an alternative optimization algorithm to solve it with the theoretical convergence guaranteed. Extensive experimental results on 10 commonly used benchmark datasets demonstrate the significant advantage of our S3NMF over 14 state-of-the-art methods in terms of 5 quantitative metrics. The source code is publicly available at https://github.com/jyh-learning/SSSNMF.
Some Finite Sample Properties of the Sign Test

This paper contains two finite-sample results concerning the sign test. First, we show that the sign-test is unbiased with independent, non-identically distributed data for both one-sided and two-sided hypotheses. The proof for the two-sided case is based on a novel argument that relates the derivatives of the power function to a regular bipartite graph. Unbiasedness then follows from the existence of perfect matchings on such graphs. Second, we provide a simple theoretical counterexample to show that the sign test over-rejects when the data exhibits correlation. Our results can be useful for understanding the properties of approximate randomization tests in settings with few clusters.
Part2Whole: Iteratively Enrich Detail for Cross-Modal Retrieval with Partial Query

Text-based image retrieval has seen considerable progress in recent years. However, the performance of existing methods suffers in real life since the user is likely to provide an incomplete description of a complex scene, which often leads to results ﬁlled with false positives that ﬁt the incomplete description. In this work, we introduce the partial-query problem and extensively analyze its inﬂuence on text-based image retrieval. We then propose an interactive retrieval framework called Part2Whole to tackle this problem by iteratively enriching the missing details. Speciﬁcally, an Interactive Retrieval Agent is trained to build an optimal policy to reﬁne the initial query based on a user-friendly interaction and statistical characteristics of the gallery. Compared to other dialog-based methods that rely heavily on the user to feed back differentiating information, we let AI take over the optimal feedback searching process and hint the user with conﬁrmation-based questions about details. Furthermore, since fully-supervised training is often infeasible due to the difﬁculty of obtaining human-machine dialog data, we present a weakly-supervised reinforcement learning method that needs no human-annotated data other than the text-image dataset. Experiments show that our framework signiﬁcantly improves the performance of text-based image retrieval under complex scenes.
Long-Running Speech Recognizer: An End-to-End Multi-Task Learning Framework for Online ASR and VAD

When we use End-to-end automatic speech recognition (E2E-ASR) system for real-world applications, a voice activity detection (VAD) system is usually needed to improve the performance and to reduce the computational cost by discarding non-speech parts in the audio. This paper presents a novel end-to-end (E2E), multi-task learning (MTL) framework that integrates ASR and VAD into one model. The proposed sys-tem, which we refer to as Long-Running Speech Recognizer (LR-SR), learns ASR and VAD jointly from two seperate task-speciﬁc datasets in the training stage. With the assistance of VAD, the ASR performance improves as its connectionist temporal classiﬁcation (CTC) loss function can leverage the VAD alignment information. In the inference stage, the LR-SR sys-tem removes non-speech parts at low computational cost and recognizes speech parts with high robustness. Experimental re-sults on segmented speech data show that the proposed MTL framework outperforms the baseline single-task learning (STL) framework in ASR task. On unsegmented speech data, we ﬁnd that the LR-SR system outperforms the baseline ASR systems that build an extra GMM-based or DNN-based voice activity detector.
Towards a Better Understanding of Social Acceptability

Social contexts play an important role in understanding acceptance and use of technology. However, current approaches used in HCI to describe contextual influence do not capture it appropriately. On the one hand, the often used Technology Acceptance Model and related frameworks are too rigid to account for the nuanced variations of social situations. On the other hand, Goffman’s dramaturgical model of social interactions emphasizes interpersonal relations but mostly overlooks the material (e.g., technology) that is central to HCI. As an alternative, we suggest an approach based on Social Practice Theory. We conceptualize social context as interactions between co-located social practices and acceptability as a matter of their (in)compatibilities. Finally, we outline how this approach provides designers with a better understanding of different types of social acceptability problems and helps finding appropriate solutions.