Miracle-dz/UniFAR-FTUs huggingface dataset:
First Genotype–Phenotype Study in TBX4 Syndrome: Gain-of-Function Mutations Causative for Lung Disease

Rationale: Despite the increasing number of TBX4 mutations underlying pulmonary arterial hypertension (PAH), genotype-phenotype associations are lacking and may provide important insights. Methods: We assembled a multi-center cohort of 137 patients harboring TBX4 sequence variants and investigated the effect of missense variants (n = 42) using a novel luciferase reporter assay containing T-BOX binding motifs. We sought genotype-phenotype correlations and undertook a comparative analysis with PAH patients genotyped via the NIHR BioResource - Rare Diseases (NBR) Study harboring BMPR2 mutations (n = 162) or without any identified mutations in PAH risk genes (n = 741). Results: Functional assessment of TBX4 missense variants revealed gain- (n=8) or loss-of-function effects (n=23) with a similar wide phenotypic spectrum ranging from early-onset developmental lung disease to adult-onset PAH. Variants located in the T-BOX and nuclear localization domains associated with earlier presentation (p = 0.002) and increased incidence of interstitial lung disease (p = 0.001). Event-free survival (death or transplantation) was marginally lower in the T-BOX group (p = 0.049) with adjustment for age and sex. Compared to patients with BMPR2 and no mutations, TBX4 mutation carriers were diagnosed at a younger age (p < 0.001) and had worse baseline lung function (FEV1, FVC) (p < 0.001). Conclusions: We demonstrated that TBX4 syndrome is not strictly the result of haploinsufficiency but can also be caused by gain-of-function. The pleiotropic effects of TBX4 in lung disease may be in part explained by the differential effect of pathogenic mutations located in critical protein domains.
Fuzzy Logic based Individual Crop Advisory System based on Weather Input Data

Weather has the most significant influence on agriculture. Various weather phenomenon such as cloudiness, precipitation, temperature, and wind have significant influence on agri-management decisions, management practices and cost of cultivation. Therefore, there is need for development of crop specific advisory to an individiual farmers based on various crop parameters such as type of crop, age of crop, location of crop etc. However, existing advisories are region specific and gives vague advisory and also not available for specific location. This paper is focused on giving overview of development of fuzzy logic based to generate automatic advisory based on type of crop, location of crop and age of the crop. The expert knowledge base available with the agricultural scientists will be utilized for developing automatic crop advisory system using machine learning approach such as Fuzzy Logic. This system is developed with the help of PHP and GUI of weather advisory is made in MATLAB. Testing of realistic weather data and randomly generated weather data is done and Success Rate of both data was calculated.
SRPCN: Structure Retrieval based Point Completion Network

Given partial objects and some complete ones as references, point cloud completion aims to recover authentic shapes. However, existing methods pay little attention to general shapes, which leads to the poor authenticity of completion results. Besides, the missing patterns are diverse in reality, but existing methods can only handle fixed ones, which means a poor generalization ability. Considering that a partial point cloud is a subset of the corresponding complete one, we regard them as different samples of the same distribution and propose Structure Retrieval based Point Completion Network (SRPCN). It first uses k-means clustering to extract structure points and disperses them into distributions, and then KL Divergence is used as a metric to find the complete structure point cloud that best matches the input in a database. Finally, a PCN-like decoder network is adopted to generate the final results based on the retrieved structure point clouds. As structure plays an important role in describing the general shape of an object and the proposed structure retrieval method is robust to missing patterns, experiments show that our method can generate more authentic results and has a stronger generalization ability.
Longitudinally Modulated Dynamo Action in Simulated M-dwarf Stars

M-dwarf stars are well known for the intense magnetic activity that many of them exhibit. In cool stars with near-surface convection zones, this magnetic activity is thought to be driven largely by the interplay of convection and the large-scale differential rotation and circulations it establishes. The highly nonlinear nature of these flows yields a fascinatingly sensitive and diverse parameter space, with a wide range of possible dynamics. We report here on a set of three global MHD simulations of rapidly rotating M2 (0.4 M ⊙) stars. Each of these three models established nests of vigorous convection that were highly modulated in longitude at low latitudes. Slight differences in their magnetic parameters led each model to disparate dynamo states, but the effect of the convective nest was a unifying feature. In each case, the action of longitudinally modulated convection led to localized (and in one case, global) reversals of the toroidal magnetic field, as well as the formation of an active longitude, with enhanced poloidal field amplitudes and flux emergence.
Cross-Channel Attention-Based Target Speaker Voice Activity Detection: Experimental Results for the M2met Challenge

DukeECE. As the highly overlapped speech exists in the dataset, we employ an x-vector-based target-speaker voice activity detection (TS-VAD) to find the overlap between speakers. Firstly, we separately train a single-channel model for each of the 8 channels and fuse the results. In addition, we also employ the cross-channel self-attention to further improve the performance, where the non-linear spatial correlations between different channels are learned and fused. Experimental results on the evaluation set show that the single-channel TS-VAD reduces the DER by over 75% from 12.68% to 3.14%. The multi-channel TS-VAD further reduces the DER by 28% and achieves a DER of 2.26%. Our final submitted system achieves a DER of 2.98% on the AliMeeting test set, which ranks 1st in the M2MET challenge. In this challenge, our team is denoted as A41.
Fair Interpretable Representation Learning with Correction Vectors

Neural network architectures have been extensively employed in the fair representation learning setting, where the objective is to learn a new representation for a given vector which is independent of sensitive information. Various representation debiasing techniques have been proposed in the literature. However, as neural networks are inherently opaque, these methods are hard to comprehend, which limits their usefulness. We propose a new framework for fair representation learning that is centered around the learning of"correction vectors", which have the same dimensionality as the given data vectors. Correction vectors may be computed either explicitly via architectural constraints or implicitly by training an invertible model based on Normalizing Flows. We show experimentally that several fair representation learning models constrained in such a way do not exhibit losses in ranking or classification performance. Furthermore, we demonstrate that state-of-the-art results can be achieved by the invertible model. Finally, we discuss the law standing of our methodology in light of recent legislation in the European Union.
Classically optimized variational quantum eigensolver with applications to topological phases

Variational quantum eigensolver (VQE) is regarded as a promising candidate of hybrid quantum-classical algorithm for the near-term quantum computers. Meanwhile, VQE is confronted with a challenge that statistical error associated with the measurement as well as systematic error could significantly hamper the optimization. To circumvent this issue, we propose classically-optimized VQE (co-VQE), where the whole process of the optimization is efficiently conducted on a classical computer. The efficacy of the method is guaranteed by the observation that quantum circuits with a constant (or logarithmic) depth are classically tractable via simulations of local subsystems. In co-VQE, we only use quantum computers to measure nonlocal quantities after the parameters are optimized. As proof-of-concepts, we present numerical experiments on quantum spin models with topological phases. After the optimization, we identify the topological phases by nonlocal order parameters as well as unsupervised machine learning on inner products between quantum states. The proposed method maximizes the advantage of using quantum computers while avoiding strenuous optimization on noisy quantum devices. Furthermore, in terms of quantum machine learning, our study shows an intriguing approach that employs quantum computers to generate data of quantum systems while using classical computers for the learning process.
Think-Aloud Verbalizations for Identifying User Experience Problems: Effects of Language Proficiency with Chinese Non-Native English Speakers

Subtle patterns in users’ think-aloud (TA) verbalizations (i.e., utterances) are shown to be telltale signs of user experience (UX) problems and used to build artificial intelligence (AI) models or AI-assisted tools to help UX evaluators identify UX problems automatically or semi-automatically. Despite the potential of such verbalization patterns, they were uncovered with native English speakers. As most people who speak English are non-native speakers, it is important to investigate whether similar patterns exist in non-native English speakers’ TA verbalizations. As a first step to answer this question, we conducted think-aloud usability testing with Chinese non-native English speakers and native English speakers using three common TA protocols. We compared their verbalizations and UX problems that they encountered to understand the effects of language and TA protocols. Our findings show that both language groups had similar amounts and proportions of verbalization categories, encountered similar problems, and had similar verbalization patterns that indicate UX problems. Furthermore, TA protocols did not significantly affect the correlations between verbalizations and problems. Based on the findings, we present three design implications for UX practitioners and the design of AI-assisted analysis tools.
Spectrum Sensing for Cognitive Radio Networks over fading channels

Due to rapid advancements in wireless communication and broader application of these wireless networks in the world, efficient utilization of the spectrum has been a persuasive issue for researchers. This has enabled the development of an intelligent network that can adapt to varying channel conditions by analyzing available spectrum frequency band and increasing the efficiency of an otherwise underutilized spectrum. This paper focuses on the spectrum sensing function of the Cognitive Radio in order to detect and utilize empty spaces in the spectrum without creating interference to the primary user. In this paper a quantitative analysis of two broader groups of spectrum sensing techniques namely Energy detection and Matched filter detection has been presented. A performance analysis based on the Probability of detection and probability of false alarming at different SNR levels is conducted under different fading channel models i.e. Additive White Guassian Noise (AWGN), flat fading and Rayleigh fading channels. A comparison between the above mentioned spectrum sensing techniques proofs low probability of false alarm, when Matched filter detection is used.
On the non-vanishing of modular $L$-values and fourier coefficients of cusp forms

We prove a non-vanishing result of modular L -values with quadratic twists, where the quadratic discriminants are in a short interval. Us- ing this fact and Waldspurger’s theorem, we improve the results of Balog-Ono[The chebotarev density theorem in short intervals and some questions of Serre, Journal of number theory. 91(2):356-371(2001)] on the non-vanishing of Fourier coeﬃcients of half-integral weight eigenform.
Efficient Logistic Regression with Local Differential Privacy

—Internet of Things devices are expanding rapidly and generating huge amount of data. There is an increasing need to explore data collected from these devices. Collaborative learning provides a strategic solution for the Internet of Things settings but also raises public concern over data privacy. In recent years, large amount of privacy preserving techniques have been developed based on differential privacy and secure multi-party computation. A major challenge of collaborative learning is to balance disclosure risk and data utility while maintaining high computation efﬁciency. In this paper, we proposed privacy preserving logistic regression model using matrix encryption approach. The secure scheme achieves local differential privacy and can be implemented for both vertical and horizontal partitioning scenarios. Moreover, cross validation is investigated to generate robust model results without increasing the communication cost. Simulation illustrates the high efﬁciency of proposed scheme to analyze dataset with millions of records. Experimental evaluations further demonstrate high model accuracy while achieving privacy protection
Empirical Analysis and Offering of Various Historical Spot Instance Datasets

Public cloud service vendors provide a surplus of computing resources at a cheaper price as a spot instance. The first spot instance provider, Amazon Web Services (AWS), releases the history of spot price changes so that users can estimate the availability of spot instances, and it triggered lots of research work in literature. However, a change in spot pricing policy in 2017 rendered a large portion of spot instance availability analysis work obsolete. Instead, AWS publishes new spot instance datasets, the spot placement score, and the interruption frequency for the previous month, but the new datasets received far less attention than the spot price history dataset. Furthermore, the datasets only provide the current dataset without historical information, and there are few restrictions when querying the dataset. In addition, the various spot datasets can provide contradicting information about spot instance availability of the same entity at the same time quite often. In this work, we develop a web-based spot instance data-archive service that provides historical information of various spot instance datasets. We describe heuristics how we overcame limitations imposed when querying multiple spot instance datasets. We conducted a real-world evaluation measuring spot instance fulfillment and interruption events to identify a credible spot instance dataset, especially when different datasets represent contradictory information. Based on the findings of the empirical analysis, we propose SpotScore, a new spot instance metric that provides a spot recommendation score based on the composition of spot price savings, spot placement score, and inter-ruption frequency. The data-archive service with the SpotScore is now publicly available as a web service to speed up system research in the spot instance community and to improve spot instance usage with a higher level of availability and cost savings.
Riemannian Score-Based Generative Modelling

Score-based generative models (SGMs) are a powerful class of generative models that exhibit remarkable empirical performance. Score-based generative modelling (SGM) consists of a ``noising'' stage, whereby a diffusion is used to gradually add Gaussian noise to data, and a generative model, which entails a ``denoising'' process defined by approximating the time-reversal of the diffusion. Existing SGMs assume that data is supported on a Euclidean space, i.e. a manifold with flat geometry. In many domains such as robotics, geoscience or protein modelling, data is often naturally described by distributions living on Riemannian manifolds and current SGM techniques are not appropriate. We introduce here Riemannian Score-based Generative Models (RSGMs), a class of generative models extending SGMs to Riemannian manifolds. We demonstrate our approach on a variety of manifolds, and in particular with earth and climate science spherical data.
Theoretical characterization of uncertainty in high-dimensional linear classification

Being able to reliably assess not only the accuracy but also the uncertainty of models’ predictions is an important endeavor in modern machine learning. Even if the model generating the data and labels is known, computing the intrinsic uncertainty after learning the model from a limited number of samples amounts to sampling the corresponding posterior probability measure. Such sampling is computationally challenging in high-dimensional problems and theoretical results on heuristic uncertainty estimators in high-dimensions are thus scarce. In this manuscript, we characterize uncertainty for learning from a limited number of samples of high-dimensional Gaussian input data and labels generated by the probit model. In this setting, the Bayesian uncertainty (i.e. the posterior marginals) can be asymptotically obtained by the approximate message passing algorithm, bypassing the canonical but costly Monte Carlo sampling of the posterior. We then provide a closed-form formula for the joint statistics between the logistic classifier, the uncertainty of the statistically optimal Bayesian classifier and the ground-truth probit uncertainty. The formula allows us to investigate the calibration of the logistic classifier learning from a limited amount of samples. We discuss how over-confidence can be mitigated by appropriately regularizing.
An Olfactory EEG Signal Classification Network Based on Frequency Band Feature Extraction

Classification of olfactory-induced electroencephalogram (EEG) signals has shown great potential in many fields. Since different frequency bands within the EEG signals contain different information, extracting specific frequency bands for classification performance is important. Moreover, due to the large inter-subject variability of the EEG signals, extracting frequency bands with subject-specific information rather than general information is crucial. Considering these, the focus of this letter is to classify the olfactory EEG signals by exploiting the spectral-domain information of specific frequency bands. In this letter, we present an olfactory EEG signal classification network based on frequency band feature extraction. A frequency band generator is first designed to extract frequency bands via the sliding window technique. Then, a frequency band attention mechanism is proposed to optimize frequency bands for a specific subject adaptively. Last, a convolutional neural network (CNN) is constructed to extract the spatio-spectral information and predict the EEG category. Comparison experiment results reveal that the proposed method outperforms a series of baseline methods in terms of both classification quality and inter-subject robustness. Ablation experiment results demonstrate the effectiveness of each component of the proposed method.
Improved Bounds for Fractional Online Matching Problems

Online bipartite matching with one-sided arrival and its variants have been extensively studied since the seminal work of Karp, Vazirani, and Vazirani (STOC 1990). Motivated by real-life applications with dynamic market structures, e.g. ride-sharing, two generalizations of the classical one-sided arrival model are proposed to allow non-bipartite graphs and to allow all vertices to arrive online. Namely, online matching with general vertex arrival is introduced by Wang and Wong (ICALP 2015), and fully online matching is introduced by Huang et al. (JACM 2020). In this paper, we study the fractional versions of the two models. We improve three out of the four state-of-the-art upper and lower bounds of the two models. For fully online matching, we design a $0.6$-competitive algorithm and prove no algorithm can be $0.613$-competitive. For online matching with general vertex arrival, we prove no algorithm can be $0.584$-competitive. Moreover, we give an arguably more intuitive algorithm for the general vertex arrival model, compared to the algorithm of Wang and Wong, while attaining the same competitive ratio of $0.526$.
A Summary of COVID-19 Datasets

This research presents a review of main datasets that are developed for COVID-19 research. We hope this collection will continue to bring together members of the computing community, biomedical experts, and policymakers in the pursuit of effective COVID-19 treatments and management policies. Many organizations, such as the World Health Organization (WHO), John Hopkins, National Institute of Health (NIH), COVID-19 open science table and such, in the world, have made numerous datasets available to the public. However, these datasets originate from a variety of different sources and initiatives. The purpose of this research is to summarize the open COVID-19 datasets to make them more accessible to the research community for health systems design and analysis. We also discuss the numerous resources introduced to support text mining applications throughout the COVID-19 literature; more precisely, we discuss the corpora, modelling resources, systems, and shared tasks introduced for COVID-19.
DEVO: Depth-Event Camera Visual Odometry in Challenging Conditions

We present a novel real-time visual odometry framework for a stereo setup of a depth and high-resolution event camera. Our framework balances accuracy and robustness against computational efficiency towards strong performance in challenging scenarios. We extend conventional edge-based semi-dense visual odometry towards time-surface maps obtained from event streams. Semi-dense depth maps are generated by warping the corresponding depth values of the extrinsically calibrated depth camera. The tracking module updates the camera pose through efficient, geometric semi-dense 3D-2D edge alignment. Our approach is validated on both public and self-collected datasets captured under various conditions. We show that the proposed method performs comparable to state-of-the-art RGB-D camera-based alternatives in regular conditions, and eventually outperforms in challenging conditions such as high dynamics or low illumination.
Generalized L-product for hight order tensors with applications using GPU computation

In this paper, we will present a generalization of the L-tensor product (L-product) including generalization of the well known tensor cosine and T-products that were defined for third-order tensors and based on fast Fourier transform and discrete cosine transform (DCT). We will give some applications on tensor completion. To solve some optimization problems linked with the problem of tensor completion, we will use the Proximal Gradient Algorithm (PGA) to solve some derived optimization problems. Numerical tests are given to show the effectiveness of the proposed methods and also present some tests using GPU computation.