Miracle-dz/UniFAR-FTUs huggingface dataset:
COMPARISON OF DRY ETCH TECHNIQUES FOR GAN

Dry etching of GaN in Cl2/H2/CH4/Ar has been compared using electron cyclotron resonance (ECR), inductively coupled plasma (ICP), and reactive ion etch (RIE) systems. GaN etch rates and surface morphology were obtained as a function of RF power, and showed significant improvements under high density plasma conditions.
A Near-Optimal Algorithm for Bilevel Empirical Risk Minimization

Bilevel optimization problems, which are problems where two optimization problems are nested, have more and more applications in machine learning. In many practical cases, the upper and the lower objectives correspond to empirical risk minimization problems and therefore have a sum structure. In this context, we propose a bilevel extension of the celebrated SARAH algorithm. We demonstrate that the algorithm requires O (( n + m ) 12 ε − 1 ) gradient computations to achieve ε -stationarity with n + m the total number of samples, which improves over all previous bilevel algorithms. Moreover, we provide a lower bound on the number of oracle calls required to get an approximate stationary point of the objective function of the bilevel problem. This lower bound is attained by our algorithm, which is therefore optimal in terms of sample complexity.
Comparison of equivariant cohomological dimensions

We compare three definitions of the equivariant cohomological dimension of a group with operators, coming from Takasu, Adamson and Bredon relative group cohomologies, giving examples of strict inequality in all cases where it can occur. We prove and make use of Stallings--Swan type results which characterise the groups of equivariant cohomological dimension one. Some of our examples are relevant to Farber's problem which asks for an algebraic characterisation of the topological complexity of discrete groups. In particular, the topological complexity of a group is not in general given by a relative cohomological dimension of the product relative to the diagonal subgroup.
Cross-Domain Label Propagation for Domain Adaptation with Discriminative Graph Self-Learning

Domain adaptation manages to transfer the knowledge of well-labeled source data to unlabeled target data. Many recent efforts focus on improving the prediction accuracy of target pseudo-labels to reduce conditional distribution shift. In this paper, we propose a novel domain adaptation method, which infers target pseudo-labels through cross-domain label propagation, such that the underlying manifold structure of two domain data can be explored. Unlike existing cross-domain label propagation methods that separate domain-invariant feature learning, affinity matrix constructing and target labels inferring into three independent stages, we propose to integrate them into a unified optimization framework. In such way, these three parts can boost each other from an iterative optimization perspective and thus more effective knowledge transfer can be achieved. Furthermore, to construct a high-quality affinity matrix, we propose a discriminative graph self-learning strategy, which can not only adaptively capture the inherent similarity of the data from two domains but also effectively exploit the discriminative information contained in well-labeled source data and pseudo-labeled target data. An efficient iterative optimization algorithm is designed to solve the objective function of our proposal. Notably, the proposed method can be extended to semi-supervised domain adaptation in a simple but effective way and the corresponding optimization problem can be solved with the identical algorithm. Extensive experiments on six standard datasets verify the significant superiority of our proposal in both unsupervised and semi-supervised domain adaptation settings.
Jazznet: A Dataset of Fundamental Piano Patterns for Music Audio Machine Learning Research

This paper introduces the jazznet Dataset, a dataset of fundamental jazz piano music patterns for developing machine learning (ML) algorithms in music information retrieval (MIR). The dataset contains 162520 labeled piano patterns, including chords, arpeggios, scales, and chord progressions with their inversions, resulting in more than 26k hours of audio and a total size of 95GB. The paper explains the dataset’s composition, creation, and generation, and presents an open-source Pattern Generator using a method called Distance-Based Pattern Structures (DBPS), which allows researchers to easily generate new piano patterns simply by defining the distances between pitches within the musical patterns. We demonstrate that the dataset can help researchers benchmark new models for challenging MIR tasks, using a convolutional recurrent neural network (CRNN) and a deep convolutional neural network. The dataset and code are available via: https://github.com/tosiron/jazznet.
Data Lakes: A Survey of Functions and Systems

Data lakes are becoming increasingly prevalent for Big Data management and data analytics. In contrast to traditional ‘schema-on-write’ approaches such as data warehouses, data lakes are repositories storing raw data in its original formats and providing a common access interface. Despite the strong interest raised from both academia and industry, there is a large body of ambiguity regarding the definition, functions and available technologies for data lakes. A complete, coherent picture of data lake challenges and solutions is still missing. This survey reviews the development, architectures, and systems of data lakes. We provide a comprehensive overview of research questions for designing and building data lakes. We classify the existing approaches and systems based on their provided functions for data lakes, which makes this survey a useful technical reference for designing, implementing and deploying data lakes. We hope that the thorough comparison of existing solutions and the discussion of open research challenges in this survey will motivate the future development of data lake research and practice.
False negative probability in iGSR detection: a Bayesian approach

This article discusses the detection of inorganic gunshot residue (iGSR) particles through scanning electron microscopy with energy dispersive X-ray spectrometer (SEM/EDS) in the discovery step. We calculated the probability that all characteristic inorganic gunshot residue particles (iGSR) go undetected and their dependence on the image pixel resolution setup. We built and validated a iGSR particle detection model that relates particle size with equipment registers, and we applied it to 1,174 samples analyzed by a forensic science laboratory. Our results indicate that the probability of missing all characteristic iGSR particles is below 5% for pixel sizes below $0.32 \mu m^{2}$. These values indicate that pixel sizes as great as the double in area that is commonly used in laboratory casework, $0.16 \mu m^{2}$, are effective for an initial scanning of a sample as it yields good rates of detection of characteristic particles, which might exponentially reduce laboratory workload.
Cardiac Disease Diagnosis on Imbalanced Electrocardiography Data Through Optimal Transport Augmentation

In this paper, we focus on a new method of data augmentation to solve the data imbalance problem within imbalanced ECG datasets to improve the robustness and accuracy of heart disease detection. By using Optimal Transport, we augment the ECG disease data from normal ECG beats to balance the data among different categories. We build a Multi-Feature Transformer (MF-Transformer) as our classification model, where different features are extracted from both time and frequency domains to diagnose various heart conditions. Our results demonstrate 1) the classification models’ ability to make competitive predictions on five ECG categories; 2) improvements in accuracy and robustness reflecting the effectiveness of our data augmentation method.
Tensor Networks Meet Neural Networks: A Survey

—Tensor networks (TNs) and neural networks (NNs) are two fundamental types of data modeling approaches. TNs have been proposed as a solution to the curse of dimensionality faced by large-scale tensors by converting an exponential number of dimensions to polynomial complexity. Thus, they have attracted many studies in the ﬁelds of quantum physics and machine learning. On the other hand, NNs are computing systems inspired by the biological NNs that constitute human brains. Recently, NNs and their variants have achieved outstanding performance in various applications, e.g., computer vision, natural language processing, and robotics research. Interestingly, although these two types of networks come from different observations, they are inextricably linked via the common intrinsic multilinearity structure underlying both TNs and NNs. Consequently, a signiﬁcant number of intellectual sparks regarding combinations of TNs and NNs have burst out. The combinations described as “tensor networks meet neural networks” are termed tensorial neural networks (TNNs) in this paper. This survey introduces TNNs based on three aspects. 1) Network Compression. TNs can greatly reduce parameters in NNs and satisfy the idea of constructing effective NNs. 2) Information Fusion. TNs can naturally and effectively enhance NNs with their ability to model the interactions among multiple modalities, views, or sources of various data. 3) Quantum Circuit Simulation. TNs can assist in designing and simulating quantum neural networks (QNNs). This survey also investigates methods for improving TNNs, examines useful toolboxes for implementing TNNs, and attempts to document TNN development and highlight its potential future directions. To the best of our knowledge, this is the ﬁrst comprehensive survey to bridge the connections among NNs, TNs, and quantum circuits. We provide a curated list of TNNs at https://github.com/tnbar/awesome-tensorial-neural-networks.
DSE Stock Price Prediction using Hidden Markov Model

Stock market forecasting is a classic problem that has been thoroughly investigated using machine learning and artificial neural network based tools and techniques. Interesting aspects of this problem include its time reliance as well as its volatility and other complex relationships. To combine them, hidden markov models (HMMs) have been utilized to anticipate the price of stocks. We demonstrated the Maximum A Posteriori (MAP) HMM method for predicting stock prices for the next day based on previous data. An HMM is trained by analyzing the fractional change in the stock price as well as the intraday high and low values. It is then utilized to produce a MAP estimate across all possible stock prices for the next day. The approach demonstrated in our work is quite generalized and can be used to predict the stock price for any company, given that the HMM is trained on the dataset of that company's stocks dataset. We evaluated the accuracy of our models using some extensively used accuracy metrics for regression problems and came up with a satisfactory outcome.
Fast Temporal Wavelet Graph Neural Networks

Spatio-temporal signals forecasting plays an important role in numerous domains, especially in neuroscience and transportation. The task is challenging due to the highly intricate spatial structure, as well as the non-linear temporal dynamics of the network. To facilitate reliable and timely forecast for the human brain and traffic networks, we propose the Fast Temporal Wavelet Graph Neural Networks (FTWGNN) that is both time- and memory-efficient for learning tasks on timeseries data with the underlying graph structure, thanks to the theories of multiresolution analysis and wavelet theory on discrete spaces. We employ Multiresolution Matrix Factorization (MMF) (Kondor et al., 2014) to factorize the highly dense graph structure and compute the corresponding sparse wavelet basis that allows us to construct fast wavelet convolution as the backbone of our novel architecture. Experimental results on real-world PEMS-BAY, METR-LA traffic datasets and AJILE12 ECoG dataset show that FTWGNN is competitive with the state-of-the-arts while maintaining a low computational footprint. Our PyTorch implementation is publicly available at https://github.com/HySonLab/TWGNN
New dynamic subgrid-scale heat flux models for large-eddy simulation of thermal convection based on the general gradient diffusion hypothesis

Three new dynamic tensor thermal diffusivity subgrid-scale (SGS) heat flux (HF) models are proposed for large-eddy simulation of thermal convection. The constitutive relations for the proposed modelling approaches represent the most general explicit algebraic formulations possible for the family of SGS HF models constructed using the resolved temperature gradient and SGS stress tensor. As a result, these three new models include a number of previously proposed dynamic SGS HF models as special cases. In contrast to the classical dynamic eddy thermal diffusivity SGS HF model, which strictly requires the SGS heat flux be aligned with the negative of the resolved temperature gradient, the three new models proposed here admit more degrees of freedom, and consequently provide a more realistic geometrical and physical representation of the SGS HF vector. To validate the proposed models, numerical simulations have been performed based on two benchmark test cases of neutrally and unstably stratified horizontal channel flows.
A Herculean task: Classical simulation of quantum computers

In the effort to develop useful quantum computers simulating quantum machines with conventional computing resources is a key capability. Such simulations will always face limits preventing the emulation of quantum computers of substantial scale but by pushing the envelope as far as possible through optimal choices of algorithms and hardware the value of the simulator tool is maximized. This work reviews the state-of-the-art numerical simulation methods i.e. the classical algorithms that emulate quantum computer evolution under specific operations. We focus on the mainstream state-vector and tensor-network paradigms while briefly mentioning alternative methods. Moreover we review the diverse applications of simulation across different facets of quantum computer development such as understanding the fundamental difference between quantum and classical computations exploring algorithm design spaces for quantum advantage predicting quantum processor performance at the design stage and characterizing fabricated devices efficiently for fast iterations. This review complements recent surveys on today's tools and implementations here we seek to acquaint the reader with an essential understanding of the theoretical basis of classical simulations detailed discussions on the advantages and limitations of different methods and the demands and challenges arising from practical use cases.
Mathematical methods of statistics

In this text about statistical mathematical theory, Harald Cramer joins two major lines of development in the field: while British and American statisticians were developing the science of statistical inference, French and Russian probablists transformed the classical calculus of probability into a rigorous and purely mathematical theory. The result of Cramer's work is an exposition of the mathematical methods of modern statistics that set the standard that others have since sought to follow. The first part of the book is an introduction to the fundamental concept of a distribution and of integration with respect to a distribution. The second part contains the general theory of random variables and probability distributions while the third is devoted to the theory of sampling statistical estimation and tests of significance.
Entropy-aware non-oscillatory high-order finite volume methods using the Dafermos entropy rate criterion

Finite volume methods are popular tools for solving time-dependent partial differential equations, especially hyperbolic conservation laws. Over the past 40 years a popular way of enlarging their robustness was the enforcement of global or local entropy inequalities. This work focuses on a different entropy criterion proposed by Dafermos almost 50 years ago, stating that the weak solution should be selected that dissipates a selected entropy with the highest possible speed. We show that this entropy rate criterion can be used in a numerical setting if it is combined with the theory of optimal recovery. To date, this criterion has only seen limited use in Finite-Volume schemes and to the authors knowledge this work is the first in which this criterion is applied to a Finite-Volume scheme whose accuracy is based on reconstruction from mean values. This leads to a new family of schemes based on reconstruction providing an alternative to the popular ENO and WENO schemes.
Universality laws for Gaussian mixtures in generalized linear models

Let $(x_{i}, y_{i})_{i=1,\dots,n}$ denote independent samples from a general mixture distribution $\sum_{c\in\mathcal{C}}\rho_{c}P_{c}^{x}$, and consider the hypothesis class of generalized linear models $\hat{y} = F(\Theta^{\top}x)$. In this work, we investigate the asymptotic joint statistics of the family of generalized linear estimators $(\Theta_{1}, \dots, \Theta_{M})$ obtained either from (a) minimizing an empirical risk $\hat{R}_{n}(\Theta;X,y)$ or (b) sampling from the associated Gibbs measure $\exp(-\beta n \hat{R}_{n}(\Theta;X,y))$. Our main contribution is to characterize under which conditions the asymptotic joint statistics of this family depends (on a weak sense) only on the means and covariances of the class conditional features distribution $P_{c}^{x}$. In particular, this allow us to prove the universality of different quantities of interest, such as the training and generalization errors, redeeming a recent line of work in high-dimensional statistics working under the Gaussian mixture hypothesis. Finally, we discuss the applications of our results to different machine learning tasks of interest, such as ensembling and uncertainty
Modeling Polypharmacy and Predicting Drug-Drug Interactions using Deep Generative Models on Multimodal Graphs

Latent representations of drugs and their targets produced by contemporary graph autoencoder models have proved useful in predicting many types of node-pair interactions on large networks, including drug-drug, drug-target, and target-target interactions. However, most existing approaches model either the node's latent spaces in which node distributions are rigid or do not effectively capture the interrelations between drugs; these limitations hinder the methods from accurately predicting drug-pair interactions. In this paper, we present the effectiveness of variational graph autoencoders (VGAE) in modeling latent node representations on multimodal networks. Our approach can produce flexible latent spaces for each node type of the multimodal graph; the embeddings are used later for predicting links among node pairs under different edge types. To further enhance the models' performance, we suggest a new method that concatenates Morgan fingerprints, which capture the molecular structures of each drug, with their latent embeddings before preceding them to the decoding stage for link prediction. Our proposed model shows competitive results on three multimodal networks: (1) a multimodal graph consisting of drug and protein nodes, (2) a multimodal graph constructed from a subset of the DrugBank database involving drug nodes under different interaction types, and (3) a multimodal graph consisting of drug and cell line nodes. Our source code is publicly available at https://github.com/HySonLab/drug-interactions.
Computer generated Lyapunov functions for a class of nonlinear systems

The problem of constructing Lyapunov functions for a class of nonlinear dynamical systems is considered. The problem is reduced to the construction of a polytope satisfying some conditions. A generalization of the concept of sector condition that it makes possible to evaluate a given nonlinear function by using a set of piecewise-linear functions is proposed. This improvement greatly reduces the conservatism in the stability analysis of nonlinear systems. Two algorithms for constructing such polytopes are proposed, and two examples are shown to demonstrate the usefulness of the results. >
Multi-scalar theories of gravity with direct matter couplings and their parametrized post-Newtonian parameters

We study theories of gravity including, in addition to the metric, several scalar fields in the gravitational sector. The particularity of this work is that we allow for direct couplings between these gravitating scalars and the matter sector, which can generally be different for the source and the probe of gravity, in addition to the universal interactions generated by the Jordan frame metric. The weak gravity regime of this theory, which would describe solar-system experiments, is studied using the parametrized post-Newtonian (PPN) formalism. We derive the expression of the ten parameters of this formalism. Among them, ζ 3 and ζ 4 are modified with respect to their values in the theories without direct couplings. This fact holds even after eliminating the direct couplings between the gravitating scalars and the energy density of the source, by redefinition of the Jordan frame. All other PPN parameters are insensitive to the direct couplings once in the correctly identified Jordan frame. When direct couplings are different for the source and the probe of gravity, they make non-relativistic probes deviate from the geodesics of the PPN metric in this frame, already at Newtonian order. Such couplings would thus be directly detectable and would have been excluded by experiments. This shows that, contrary to the claims in the recent literature, it is impossible to screen the presence of gravitating scalars relying only on a curved target space and direct couplings to matter.
Coherent feedback control of quantum correlations in cavity magnomechanical system with magnon squeezing

We address a scheme to enhance the quantum correlations in cavity opto-magnomechanical system by using the coherent feedback loop in the presence of magnon squeezing. The proposed coherent feedback-control allows a significant enhancement of the entanglement of three bipartite subsystems, i.e., photon-phonon, photon-magnon and phonon-magnon. We also study the Einstein-Podolsky-Rosen steering and one-way steering in the presence of thermal effects without imposing additional conditions of asymmetric losses or noises in the subsystems. Furthermore, we investigate the sensitiveness of the scheme to the magnon squeezing, and its performance in non-ideal situations in which losses and noises are taken into account.
Efficient Approximation of Certain and Possible Answers for Ranking and Window Queries over Uncertain Data (Extended version)

Uncertainty arises naturally in many application domains due to, e.g., data entry errors and ambiguity in data cleaning. Prior work in incomplete and probabilistic databases has investigated the semantics and efficient evaluation of ranking and top-k queries over uncertain data. However, most approaches deal with top-k and ranking in isolation and do represent uncertain input data and query results using separate, incompatible data models. We present an efficient approach for under- and over-approximating results of ranking, top-k, and window queries over uncertain data. Our approach integrates well with existing techniques for querying uncertain data, is efficient, and is to the best of our knowledge the first to support windowed aggregation. We design algorithms for physical operators for uncertain sorting and windowed aggregation, and implement them in PostgreSQL. We evaluated our approach on synthetic and real world datasets, demonstrating that it outperforms all competitors, and often produces more accurate results.