Miracle-dz/UniFAR-FTUs huggingface dataset:
Stochastic Hard Thresholding Algorithms for AUC Maximization

In this paper, we aim to develop stochastic hard thresholding algorithms for the important problem of AUC maximization in imbalanced classification. The main challenge is the pairwise loss involved in AUC maximization. We overcome this obstacle by reformulating the U-statistics objective function as an empirical risk minimization (ERM), from which a stochastic hard thresholding algorithm (SHT-AUC) is developed. To our best knowledge, this is the first attempt to provide stochastic hard thresholding algorithms for AUC maximization with a per-iteration cost $\mathcal{O}(bd)$ where $d$ and $b$ are the dimension of the data and the minibatch size, respectively. We show that the proposed algorithm enjoys the linear convergence rate up to a tolerance error. In particular, we show, if the data is generated from the Gaussian distribution, then its convergence becomes slower as the data gets more imbalanced. We conduct extensive experiments to show the efficiency and effectiveness of the proposed algorithms.
Low cost enhanced security face recognition with stereo cameras

This article explores a face recognition alternative which seeks to contribute to resolve current security vulnerabilities in most recognition architectures. Current low cost facial authentication software in the market can be fooled by a printed picture of a face due to the lack of depth information. The presented software creates a depth map of the face with the help of a stereo setup, offering a higher level of security than traditional recognition programs. Analysis of the person's identity and facial depth map are processed through deep convolutional neural networks, providing a secure low cost real-time face authentication method.
The upgrade of EAST Safety and Interlock system

The Experimental Advanced Superconducting Tokamak (EAST), a nation-level large-scale scientific project of China, plays a key role for the research of peaceful utilizations of fusion energy. The safety and interlock system (SIS) is in charge of the supervision and control of all the EAST components involved in the protection of human and tokamak from potential accidents. With the development of physical experiment, the SIS had come close to reaching its limits for expandability. Therefore, a prototype for upgrading EAST SIS has been designed, and a fast architecture based on COTS FPGA is absorbed into the new SIS. This paper presents EAST machine and human protection mechanism and the architecture of the upgrading safety and interlock system.
Algorithms and Hardness for Linear Algebra on Geometric Graphs

For a function <tex>$\mathrm{K}:\mathbb{R}^{d}\times \mathbb{R}^{d}\rightarrow \mathbb{R}_{\geq 0}$</tex>, and a set <tex>$P=\{x_{1},\ldots, x_{n}\}\subset \mathbb{R}^{d}$</tex> of <tex>$n$</tex> points, the K graph <tex>$G_{P}$</tex> of <tex>$P$</tex> is the complete graph on <tex>$n$</tex> nodes where the weight between nodes <tex>$i$</tex> and <tex>$j$</tex> is given by <tex>$\mathrm{K}(x_{i}, x_{j})$</tex>. In this paper, we initiate the study of when efficient spectral graph theory is possible on these graphs. We investigate whether or not it is possible to solve the following problems in <tex>$n^{1+o(1)}$</tex> time for a K-graph <tex>$G_{P}$</tex> when <tex>$d < n^{o(1)}$</tex>: •Multiply a given vector by the adjacency matrix or Laplacian matrix of <tex>$G_{P}$</tex> •Find a spectral sparsifier of <tex>$G_{P}$</tex> •Solve a Laplacian system in <tex>$G_{P}$</tex>'s Laplacian matrix For each of these problems, we consider all functions of the form <tex>$\mathrm{K}(u, v)=f(\Vert u-v\Vert_{2}^{2})$</tex> for a function <tex>$f:\mathbb{R}\rightarrow \mathbb{R}$</tex>. We provide algorithms and comparable hardness results for many such K, including the Gaussian kernel, Neural tangent kernels, and more. For example, in dimension <tex>$d=\Omega(\log n)$</tex>, we show that there is a parameter associated with the function <tex>$f$</tex> for which low parameter values imply <tex>$n^{1+o(1)}$</tex> time algorithms for all three of these problems and high parameter values imply the nonexistence of subquadratic time algorithms assuming Strong Exponential Time Hypothesis (SETH), given natural assumptions on <tex>$f$</tex>. As part of our results, we also show that the exponential dependence on the dimension <tex>$d$</tex> in the celebrated fast multi-pole method of Greengard and Rokhlin cannot be improved, assuming SETH, for a broad class of functions <tex>$f$</tex>. To the best of our knowledge, this is the first formal limitation proven about fast multipole methods.
Generating Synthetic Data for Task-Oriented Semantic Parsing with Hierarchical Representations

Modern conversational AI systems support natural language understanding for a wide variety of capabilities. While a majority of these tasks can be accomplished using a simple and flat representation of intents and slots, more sophisticated capabilities require complex hierarchical representations supported by semantic parsing. State-of-the-art semantic parsers are trained using supervised learning with data labeled according to a hierarchical schema which might be costly to obtain or not readily available for a new domain. In this work, we explore the possibility of generating synthetic data for neural semantic parsing using a pretrained denoising sequence-to-sequence model (i.e., BART). Specifically, we first extract masked templates from the existing labeled utterances, and then fine-tune BART to generate synthetic utterances conditioning on the extracted templates. Finally, we use an auxiliary parser (AP) to filter the generated utterances. The AP guarantees the quality of the generated data. We show the potential of our approach when evaluating on the Facebook TOP dataset for navigation domain.
An X-Ray- and SZ-bright Diffuse Source toward M31: A Local Hot Bridge

We report a large-scale ( ) X-ray-bright and Sunyaev–Zeldovich (SZ)-bright diffuse enhancement toward M31, which might be a Local Hot Bridge connecting the Milky Way (MW) with M31. We subtract the Galactic emission from the all-sky O vii and O viii emission-line measurement survey, and find that the emission of these two ions is enhanced within projected around M31. The mean emission enhancements are 5.6 ± 1.3 L.U. , and 2.8 ± 0.6 L.U. for O vii and O viii, respectively ( for both ions). We also extract the SZ signal around M31, which suggests a surface brightness y of , an enhancement (and a best fit of ). These three measurements trace the hot gas with a temperature , showing similar plateau shapes (flat within , and zero beyond ≈30°). A single-phase assumption leads to a temperature of , which is determined by the O vii/O viii line ratio. Combining X-ray and SZ measurements, we suggest that this feature is unlikely to be the hot halo around M31 (too massive) or in the MW (too high pressure and X-ray bright). The plateau shape may be explained by a cylinder connecting the MW and M31 (the Local Hot Bridge). We constrain its length to be about 400 kpc, with a radius of 120 kpc, a density of , and a metallicity of . The baryon mass is , and the oxygen mass is about , which contribute to the baryon or metal budget of the Local Group.
Neural text normalization leveraging similarities of strings and sounds

We propose neural models that can normalize text by considering the similarities of word strings and sounds. We experimentally compared a model that considers the similarities of both word strings and sounds, a model that considers only the similarity of word strings or of sounds, and a model without the similarities as a baseline. Results showed that leveraging the word string similarity succeeded in dealing with misspellings and abbreviations, and taking into account the sound similarity succeeded in dealing with phonetic substitutions and emphasized characters. So that the proposed models achieved higher F1 scores than the baseline.
The statistical mechanics of near-BPS black holes

Due to the failure of thermodynamics for low temperature near-extremal black holes, it has long been conjectured that a ‘thermodynamic mass gap’ exists between an extremal black hole and the lightest near-extremal state. For non-supersymmetric near-extremal black holes in Einstein gravity with an AdS 2 throat, no such gap was found. Rather, at that energy scale, the spectrum exhibits a continuum of states, up to non-perturbative corrections. In this paper, we compute the partition function of near-BPS black holes in supergravity where the emergent, broken, symmetry is PSU(1, 1|2). To reliably compute this partition function, we show that the gravitational path integral can be reduced to that of a N=4 supersymmetric extension of the Schwarzian theory, which we define and exactly quantize. In contrast to the non-supersymmetric case, we find that black holes in supergravity have a mass gap and a large extremal black hole degeneracy consistent with the Bekenstein–Hawking area. Our results verify a plethora of string theory conjectures, concerning the scale of the mass gap and the counting of extremal micro-states.
Advantage of Deep Neural Networks for Estimating Functions with Singularity on Curves

We develop a theory to elucidate the reason that deep neural networks (DNNs) perform better than other methods. In terms of the nonparametric regression problem, it is well known that many standard methods attain the minimax optimal rate of estimation errors for smooth functions, and thus, it is not straightforward to identify the theoretical advantages of DNNs. This study fills this gap by considering the estimation for a class of non-smooth functions with singularities on smooth curves. Our findings are as follows: (i) We derive the generalization error of a DNN estimator and prove that its convergence rate is almost optimal. (ii) We reveal that a certain class of common models are sub-optimal, including linear estimators and other harmonic analysis methods such as wavelets and curvelets. This advantage of DNNs comes from a fact that a shape of singularity can be successfully handled by their multi-layered structure.
MARTY -- Modern ARtificial Theoretical phYsicist: A C++ framework automating symbolic calculations Beyond the Standard Model

Studies Beyond the Standard Model (BSM) will become more and more important in the near future with a rapidly increasing amount of data from different experiments around the world. The full study of BSM models is in general an extremely time-consuming task involving long and difficult calculations. It is in practice not possible to do exhaustive predictions in these models by hand, in particular if one wants to perform a statistical comparison with data and the SM. Here we present MARTY (Modern ARtificial Theoretical phYsicist), a new C++ framework that fully automates calculations from the Lagrangian to physical quantities such as amplitudes or cross-sections. This framework can fully simplify, automatically and symbolically, physical quantities in a very large variety of models. MARTY can also compute Wilson coefficients in effective theories. This will considerably facilitate the study of BSM models in flavor physics. Contrary to the existing public codes in this field MARTY aims to give a unique, free, open-source, powerful and user-friendly tool for high-energy physicists studying predictive BSM models, in effective or full theories up to the 1-loop level, which does not rely on any external package. With a few lines of code one can gather final expressions that may be evaluated numerically for statistical analysis. Features like automatic generation and manual edition of Feynman diagrams, comprehensive manual and documentation, clear and easy to handle user interface are amongst notable features of MARTY.
Widely-distributed Radar Imaging Based on Consensus ADMM

A widely-distributed radar system is a promising architecture to enhance imaging performance. However, most existing algorithms rely on isotropic scattering assumption, which is only satisfied in colocated radar systems. Moreover, due to noise and imaging model imperfections, artifacts such as layovers are common in radar images. In this paper, a novel l1 -regularized, consensus alternating direction method of multipliers (CADMM) based algorithm is proposed to mitigate artifacts by exploiting the spatial diversity in a widely-distributed radar system. By imposing the consensus constraints on the local images formed by the distributed antenna clusters and solving the resulting distributed optimization problem, the scenario's spatially-invariant common features are retained and the spatially-variant artifacts are mitigated in a data-driven fashion. The iterative procedure will finally converge to a high-quality global image in the consensus of all widely-distributed measurements. The proposed algorithm outperforms the existing joint sparsity-based composite imaging (JSC) algorithm in terms of artifacts mitigation. It can also reduce the computation and storage burden of largescale imaging problems through its distributed and parallelizable optimization scheme.
Deformation of Asymptotic Symmetry Algebras and Their Physical Realizations

This thesis is devoted to the study of the deformation and rigidity of infinite dimensional Lie algebras which are not subject to the Hochschild-Serre factorization theorem. In particular, we consider $bms_{3}$, Virasoro-Kac-Moody and $bms_{4}$ algebras and their central extensions which are respectively obtained as asymptotic and/or near horizon symmetry algebras for Einstein gravity on $3d$ flat, AdS$_{3}$ and $4d$ flat spacetimes. We also explore possible deformations of the Maxwell-BMS algebra, which is obtained as asymptotic symmetry algebra of the Chern-Simons gravity theory invariant under the $2+1$ dimensional Maxwell algebra. We find that these algebras are not rigid and can be deformed into new non isomorphic infinite dimensional (family of) algebras. We study these deformations by direct computations and also by cohomological analysis. We then classify all the algebras obtained through deformation of these algebras as well as all possible central extensions thereof. We propose/conjecture an extension of the Hochschild-Serre factorization theorem for infinite dimensional algebras as well as introducing a new notion of rigidity for family algebras obtained through deformation. We also explore physical realizations and significance of the family of algebras we obtain through the deformation procedure.
Can We Trust Deep Speech Prior?

Recently, speech enhancement (SE) based on deep speech prior has attracted much attention, such as the variational auto-encoder with non-negative matrix factorization (VAE-NMF) architecture. Compared to conventional approaches that represent clean speech by shallow models such as Gaussians with a low-rank covariance, the new approach employs deep generative models to represent the clean speech, which often provides a better prior. Despite the clear advantage in theory, we argue that deep priors must be used with much caution, since the likelihood produced by a deep generative model does not always coincide with the speech quality. We designed a comprehensive study on this issue and demonstrated that based on deep speech priors, a reasonable SE performance can be achieved, but the results might be suboptimal. A careful analysis showed that this problem is deeply rooted in the disharmony between the flexibility of deep generative models and the nature of the maximum-likelihood (ML) training.
Continuous PPG-Based Blood Pressure Monitoring Using Multi-Linear Regression

In this work, we present a photoplethy smography-based blood pressure monitoring algorithm (PPG-BPM) that solely requires a photoplethysmography (PPG) signal. The technology is based on pulse wave analysis (PWA) of PPG signals retrieved from different body locations to continuously estimate the systolic blood pressure (SBP) and the diastolic blood pressure (DBP). The proposed algorithm extracts morphological features from the PPG signal and maps them to SBP and DBP values using a multiple linear regression (MLR) model. The performance of the algorithm is evaluated on the publicly available Multiparameter Intelligent Monitoring in Intensive Care (MIMIC I) database. We utilize 28 data-sets (records) that contain both PPG and brachial arterial blood pressure (ABP) signals. The collected PPG and ABP signals are synchronized and divided into intervals of 30 seconds, called epochs. In total, we utilize 47153 <italic>clean</italic> 30-second epochs for the performance analysis. Out of the 28 data-sets, we use only 2 data-sets with a total of 2677 <italic>clean</italic> 30-second epochs to build the MLR model of the algorithm. For the SBP, a mean absolute error (MAE) of 6.10 mmHg between the arterial line and the PPG-based values are achieved, with a Pearson correlation coefficient <italic>r</italic> = 0.90, <italic>p</italic> = . 001. For the DBP, and an MAE of 4.65 mmHg between the arterial line and the PPG-based values are achieved, with a Pearson correlation coefficient <italic>r</italic> = 0.85, <italic>p</italic> <inline-formula><tex-math notation="LaTeX">$\! <\! $</tex-math></inline-formula> . 001. We also use a binary classifier for the BP values with the positives indicating SBP <inline-formula><tex-math notation="LaTeX">$\geq$</tex-math></inline-formula> 130 mmHg and/or DBP <inline-formula><tex-math notation="LaTeX">$\geq$</tex-math></inline-formula> 80 mmHg and the negatives indicating otherwise. The classifier results generated by the PPG-based SBP and DBP estimates achieve a sensitivity and a specificity of 79.11% and 92.37%, respectively.
VLEngagement: A Dataset of Scientific Video Lectures for Evaluating Population-based Engagement

With the emergence of e-learning and personalised education, the production and distribution of digital educational resources have boomed. Video lectures have now become one of the primary modalities to impart knowledge to masses in the current digital age. The rapid creation of video lecture content challenges the currently established human-centred moderation and quality assurance pipeline, demanding for more efficient, scalable and automatic solutions for managing learning resources. Although a few datasets related to engagement with educational videos exist, there is still an important need for data and research aimed at understanding learner engagement with scientific video lectures. This paper introduces VLEngagement, a novel dataset that consists of content-based and video-specific features extracted from publicly available scientific video lectures and several metrics related to user engagement. We introduce several novel tasks related to predicting and understanding context-agnostic engagement in video lectures, providing preliminary baselines. This is the largest and most diverse publicly available dataset to our knowledge that deals with such tasks. The extraction of Wikipedia topic-based features also allows associating more sophisticated Wikipedia based features to the dataset to improve the performance in these tasks. The dataset, helper tools and example code snippets are available publicly at this https URL
Sound Natural: Content Rephrasing in Dialog Systems

We introduce a new task of rephrasing for a more natural virtual assistant. Currently, virtual assistants work in the paradigm of intent slot tagging and the slot values are directly passed as-is to the execution engine. However, this setup fails in some scenarios such as messaging when the query given by the user needs to be changed before repeating it or sending it to another user. For example, for queries like 'ask my wife if she can pick up the kids' or 'remind me to take my pills', we need to rephrase the content to 'can you pick up the kids' and 'take your pills' In this paper, we study the problem of rephrasing with messaging as a use case and release a dataset of 3000 pairs of original query and rephrased query. We show that BART, a pre-trained transformers-based masked language model with auto-regressive decoding, is a strong baseline for the task, and show improvements by adding a copy-pointer and copy loss to it. We analyze different tradeoffs of BART-based and LSTM-based seq2seq models, and propose a distilled LSTM-based seq2seq as the best practical model.
Adaptive Test Allocation for Outbreak Detection and Tracking in Social Contact Networks

We present a general framework for adaptive allocation of viral tests in social contact networks. We pose and solve several complementary problems. First, we consider the design of a social sensing system whose objective is the early detection of a novel epidemic outbreak. In particular, we propose an algorithm to select a subset of individuals to be tested in order to detect the onset of an epidemic outbreak as fast as possible. We pose this problem as a hitting time probability maximization problem and use submodularity optimization techniques to derive explicit quality guarantees for the proposed solution. Second, once an epidemic outbreak has been detected, we consider the problem of adaptively distributing viral tests over time in order to maximize the information gained about the current state of the epidemic. We formalize this problem in terms of information entropy and mutual information and propose an adaptive allocation strategy with quality guarantees. For these problems, we derive analytical solutions for any stochastic compartmental epidemic model with Markovian dynamics, as well as efficient Monte-Carlo-based algorithms for non-Markovian dynamics. Finally, we illustrate the performance of the proposed framework in numerical experiments involving a model of Covid-19 applied to a real human contact network.
Fault Detection for Covered Conductors With High-Frequency Voltage Signals: From Local Patterns to Global Features

The detection and characterization of partial discharge (PD) are crucial for the insulation diagnosis of overhead lines with covered conductors. With the release of a large dataset containing thousands of naturally obtained high-frequency voltage signals, data-driven analysis of fault-related PD patterns on an unprecedented scale becomes viable. The high diversity of PD patterns and background noise interferences motivates us to design an innovative pulse shape characterization method based on clustering techniques, which can dynamically identify a set of representative PD-related pulses. Capitalizing on those pulses as referential patterns, we construct insightful features and develop a novel machine learning model with a superior detection performance for early-stage covered conductor faults. The presented model outperforms the winning model in a Kaggle competition and provides the state-of-the-art solution to detect real-time disturbances in the field.
Spectral Efficiency of Dense Multicell Massive MIMO Networks in Spatially Correlated Channels

This paper is on the spectral efficiency (SE) of a dense multi-cell massive multiple-input multiple-output (MIMO). The channels are spatially correlated and the multi-slope path loss model is considered. In our framework, the channel state information is obtained by using pilot sequences and the BSs are deployed randomly. First, we study the channel estimation accuracy and its impact on the SE as the BS density increases and the network becomes densified. Second, we consider the special case of uncorrelated channels for which the stochastic geometry framework helps us to simplify the SE expressions, and obtain the minimum value of antenna-UE ratio over which the pilot contamination is dominant rather than the inter- and intra-cell interference. Finally, we provide some insights into the obtained SE for the spatially correlated channels, from a multi-cell processing scheme as well as the single-cell ones in terms of the BS density. Our results show that while all the detectors result in non-increasing SE in terms of the BS density, their area SE increases exponentially as the network becomes densified. Moreover, we conclude that in order to achieve a given SE, the required value of antenna-UE ratio decreases as the level of channel correlation increases.