Miracle-dz/UniFAR-FTUs huggingface dataset:
Intrinsic orbital moment and prediction of a large orbital Hall effect in two-dimensional transition metal dichalcogenides

Carrying information using generation and detection of the orbital current, instead of the spin current, is an emerging field of research, where the orbital Hall effect (OHE) is an important ingredient. Here, we propose a new mechanism of the OHE that occurs in {\it non-}centrosymmetric materials. We show that the broken inversion symmetry in the 2D transition metal dichalcogenides (TMDCs) causes a robust orbital moment, which flow in different directions due to the opposite Berry curvatures under an applied electric field, leading to a large OHE. This is in complete contrast to the inversion-symmetric systems, where the orbital moment is induced only by the external electric field. We show that the valley-orbital locking as well as the OHE both appear even in the absence of the spin-orbit coupling. The non-zero spin-orbit coupling leads to the well-known valley-spin locking and the spin Hall effect, which we find to be weak, making the TMDCs particularly suitable for direct observation of the OHE, with potential application in {\it orbitronics}.
Conformal invariance and quantum non-locality in hybrid quantum circuits

We establish the emergence of a conformal field theory (CFT) in a (1+1)-dimensional hybrid quantum circuit right at the measurement-driven entanglement transition, by revealing space-time conformal covariance of entanglement entropies and mutual information for various subregions at different circuit depths. While the evolution takes place in real time, the spacetime manifold of the circuit appears to host a Euclidean field theory with imaginary time. Throughout the paper we investigate Clifford circuits with several different boundary conditions by injecting physical qubits at the spatial and/or temporal boundaries, all giving consistent characterizations of the underlying "Clifford CFT". We emphasize (super)universal results that are consequences solely of the conformal invariance and do not depend crucially on the precise nature of the CFT. Among these are the infinite entangling speed as a consequence of measurement-induced quantum non-locality, and the critical purification dynamics of a mixed initial state.
Towards Palmprint Verification On Smartphones

With the rapid development of mobile devices, smartphones have gradually become an indispensable part of people's lives. Meanwhile, biometric authentication has been corroborated to be an effective method for establishing a person's identity with high confidence. Hence, recently, biometric technologies for smartphones have also become increasingly sophisticated and popular. But it is noteworthy that the application potential of palmprints for smartphones is seriously underestimated. Studies in the past two decades have shown that palmprints have outstanding merits in uniqueness and permanence, and have high user acceptance. However, currently, studies specializing in palmprint verification for smartphones are still quite sporadic, especially when compared to face- or fingerprint-oriented ones. In this paper, aiming to fill the aforementioned research gap, we conducted a thorough study of palmprint verification on smartphones and our contributions are twofold. First, to facilitate the study of palmprint verification on smartphones, we established an annotated palmprint dataset named MPD, which was collected by multi-brand smartphones in two separate sessions with various backgrounds and illumination conditions. As the largest dataset in this field, MPD contains 16,000 palm images collected from 200 subjects. Second, we built a DCNN-based palmprint verification system named DeepMPV+ for smartphones. In DeepMPV+, two key steps, ROI extraction and ROI matching, are both formulated as learning problems and then solved naturally by modern DCNN models. The efficiency and efficacy of DeepMPV+ have been corroborated by extensive experiments. To make our results fully reproducible, the labeled dataset and the relevant source codes have been made publicly available at this https URL.
Federated Residual Learning

We study a new form of federated learning where the clients train personalized local models and make predictions jointly with the server-side shared model. Using this new federated learning framework, the complexity of the central shared model can be minimized while still gaining all the performance benefits that joint training provides. Our framework is robust to data heterogeneity, addressing the slow convergence problem traditional federated learning methods face when the data is non-i.i. d. across clients. We test the theory empirically and find substantial performance gains over baselines.
HCN 3–2 survey towards a sample of local galaxies

We present observations of HCN 3-2 emissions towards 37 local galaxies using 10-m Submillimeter Telescope (SMT). HCN 3-2 emission is detected in 23 galaxies. The correlation of infrared luminosity (LIR) and the luminosity of HCN 3-2 line emission measured in our sample is fitted with a slope of 1.11 and correlation coefficient of 0.91, which follows the linear correlation found in other dense gas tracers in the literatures. Although molecular gas above a certain volume density threshold (i.e., $n_{\rm H_2}\geq$ 10$^4$ cm$^{-3}$) statistically gave similar relation with infrared luminosity, the large scatter of HCN 3-2/HCN1-0 ratios for galaxies with different LIR indicates that dense gas masses estimated from the line luminosities of only one transition of dense gas tracers should be treated with caution for individual galaxies.
DaST: Data-Free Substitute Training for Adversarial Attacks

Machine learning models are vulnerable to adversarial examples. For the black-box setting, current substitute attacks need pre-trained models to generate adversarial examples. However, pre-trained models are hard to obtain in real-world tasks. In this paper, we propose a data-free substitute training method (DaST) to obtain substitute models for adversarial black-box attacks without the requirement of any real data. To achieve this, DaST utilizes specially designed generative adversarial networks (GANs) to train the substitute models. In particular, we design a multi-branch architecture and label-control loss for the generative model to deal with the uneven distribution of synthetic samples. The substitute model is then trained by the synthetic samples generated by the generative model, which are labeled by the attacked model subsequently. The experiments demonstrate the substitute models produced by DaST can achieve competitive performance compared with the baseline models which are trained by the same train set with attacked models. Additionally, to evaluate the practicability of the proposed method on the real-world task, we attack an online machine learning model on the Microsoft Azure platform. The remote model misclassifies 98.35% of the adversarial examples crafted by our method. To the best of our knowledge, we are the first to train a substitute model for adversarial attacks without any real data.
SARS-CoV-2 (COVID-19) by the numbers

The COVID-19 pandemic is a harsh reminder of the fact that, whether in a single human host or a wave of infection across continents, viral dynamics is often a story about the numbers. In this article we provide a one-stop, curated graphical source for the key numbers (based mostly on the peer-reviewed literature) about the SARS-CoV-2 virus that is responsible for the pandemic. The discussion is framed around two broad themes: i) the biology of the virus itself; ii) the characteristics of the infection of a single human host.
Discrete Frames For $L^2({\mathbb R}^{n^2})$ Arising From Tiling Systems On ${\rm GL}_n({\mathbb R})$

A discrete frame for $L^2({\mathbb R}^d)$ is a countable sequence $\{e_j\}_{j\in J}$ in $L^2({\mathbb R}^d)$ together with real constants $0<A\leq B< \infty$ such that $$ A\|f\|_2^2 \leq \sum_{j\in J}|\langle f,e_j \rangle |^2 \leq B\|f\|_2^2,$$ for all $f\in L^2(\mathbb{R}^d)$. We present a method of sampling continuous frames, which arise from square-integrable representations of affine-type groups, to create discrete frames for high-dimensional signals. Our method relies on partitioning the ambient space by using a suitable "tiling system". We provide all relevant details for constructions in the case of ${\rm M}_n({\mathbb R})\rtimes {\rm GL}_n({\mathbb R})$, although the methods discussed here are general and could be adapted to many other settings. Finally, we prove significantly improved frame bounds over the previously known construction for the case of $n=2$.
On an Old Question of Erd\H{o}s and R\'{e}nyi Arising in the Delay Analysis of Broadcast Channels

Consider a broadcast channel with $n$ users, where different users receive different messages, and suppose that each user has to receive $m$ packets. A quantity of interest here, introduced by Sharif and Hassibi (2006-7) \cite{Sh}, \cite{S-H}, is the (\emph{packet}) \emph{delay} $D_{m,n}$, namely the number of channel uses required to guarantee that all users will receive $m$ packets. For the case of a \emph{homogeneous} network, where in each channel use the transmitter chooses a user at random, i.e. with probability $1/n$, and sends him$/$her a packet, the same quantity $D_{m,n}$ had already appeared in the \emph{coupon collector} context, in the works of Newman and Shepp (1960) \cite{N-S} and of Erdős and Renyi (1961) \cite{E-R}. A problem of particular interest in wireless communications, related to the delay $D_{m,n}$, is to determine its behavior as $n$ and $m$ grow large. Regarding this problem, Sharif and Hassibi \cite{Sh}, \cite{S-H} managed to calculated the asymptotics of the mean value $\mathbb{E}[D_{m,n}]$, as $n \to \infty$, for the cases (a) $m = \ln n$ and (b) $m = (\ln n)^{\rho}$, $\rho > 1$. It is remarkable that Erdős and Renyi \cite{E-R} had, also, raised the question of the determination of the asymptotic profile of $D_{m,n}$ for large $m$ and $n$ (in 1961). And in the 1970's the limiting distribution of $D_{m,n}$ for large $m$ and $n$ was determined (in great generality) by Ivchenko \cite{I1} and \cite{I2}. In this article we determine the asymptotics of the moments of $D_{m,n}$ for large $m$ and $n$. We also derive its limiting distribution in the "supercritical case" where $m$ grows faster than $\ln n$ and in the "critical case" $m \sim \beta \ln n$, by an approach which is different from the one used by Ivchenko.
Stochastic Proximal Gradient Algorithm with Minibatches. Application to Large Scale Learning Models

Stochastic optimization lies at the core of most statistical learning models. The recent great development of stochastic algorithmic tools focused significantly onto proximal gradient iterations, in order to find an efficient approach for nonsmooth (composite) population risk functions. The complexity of finding optimal predictors by minimizing regularized risk is largely understood for simple regularizations such as $\ell_1/\ell_2$ norms. However, more complex properties desired for the predictor necessitates highly difficult regularizers as used in grouped lasso or graph trend filtering. In this chapter we develop and analyze minibatch variants of stochastic proximal gradient algorithm for general composite objective functions with stochastic nonsmooth components. We provide iteration complexity for constant and variable stepsize policies obtaining that, for minibatch size $N$, after $\mathcal{O}(\frac{1}{N\epsilon})$ iterations $\epsilon-$suboptimality is attained in expected quadratic distance to optimal solution. The numerical tests on $\ell_2-$regularized SVMs and parametric sparse representation problems confirm the theoretical behaviour and surpasses minibatch SGD performance.
Two-moment scheme for general-relativistic radiation hydrodynamics: a systematic description and new applications

We provide a systematic description of the steps necessary – and of the potential pitfalls to be encountered – when implementing a two-moment scheme within an implicit–explicit (IMEX) scheme to include radiative-transfer contributions in numerical simulations of general-relativistic (magneto-)hydrodynamics (GRMHD). We make use of the M1 closure, which provides an exact solution for the optically thin and thick limits, and an interpolation between these limits. Special attention is paid to the efficient solution of the emerging set of implicit conservation equations. In particular, we present an efficient method for solving these equations via the inversion of a 4 × 4-matrix within an IMEX scheme. While this method relies on a few approximations, it offers a very good compromise between accuracy and computational efficiency. After a large number of tests in special relativity, we couple our new radiation code, frac, with the GRMHD code bhac to investigate the radiative Michel solution, namely, the problem of spherical accretion on to a black hole in the presence of a radiative field. By performing the most extensive exploration of the parameter space for this problem, we find that the accretion’s efficiency can be expressed in terms of physical quantities such as temperature, T, luminosity, L, and black hole mass, M, via the expression $\varepsilon =(L/L_{\rm Edd})/(\dot{M}/\dot{M}_{\rm Edd})= 7.41\times 10^{-7}\left(T/10^6\, \mathrm{K}\right)^{0.22} \left(L/L_\odot \right)^{0.48} \left(M/M_\odot \right)^{0.48}$, where LEdd and $\dot{M}_{\mathrm{Edd}}$ are the Eddington luminosity and accretion rate, respectively. Finally, we also consider the accretion problem away from spherical symmetry, finding that the solution is stable under perturbations in the radiation field.
Minimax optimality of permutation tests

Permutation tests are widely used in statistics, providing a finite-sample guarantee on the type I error rate whenever the distribution of the samples under the null hypothesis is invariant to some rearrangement. Despite its increasing popularity and empirical success, theoretical properties of the permutation test, especially its power, have not been fully explored beyond simple cases. In this paper, we attempt to fill this gap by presenting a general non-asymptotic framework for analyzing the power of the permutation test. The utility of our proposed framework is illustrated in the context of two-sample and independence testing under both discrete and continuous settings. In each setting, we introduce permutation tests based on U-statistics and study their minimax performance. We also develop exponential concentration bounds for permuted U-statistics based on a novel coupling idea, which may be of independent interest. Building on these exponential bounds, we introduce permutation tests which are adaptive to unknown smoothness parameters without losing much power. The proposed framework is further illustrated using more sophisticated test statistics including weighted U-statistics for multinomial testing and Gaussian kernel-based statistics for density testing. Finally, we provide some simulation results that further justify the permutation approach.
On the relation between relative rigid and support tilting

Let B be an extriangulated category with enough projectives and enough injectives. Let C be a fully rigid subcategory of B which admits a twin cotorsion pair ((C,K),(K,D)). The quotient category B/K is abelian, we assume that it is hereditary and has finite length. In this article, we study the relation between support tilting subcategories of B/K and maximal relative rigid subcategories of B. More precisely, we show that the image of any cluster tilting subcategory of B is support tilting in B/K and any support tilting subcategory in B/K can be lifted to a unique relative maximal rigid subcategory in B. We also give a bijection between these two classes of subcategories if C is generated by an object.
Topological Data Analysis in Text Classification: Extracting Features with Additive Information

While the strength of Topological Data Analysis has been explored in many studies on high dimensional numeric data, it is still a challenging task to apply it to text. As the primary goal in topological data analysis is to define and quantify the shapes in numeric data, defining shapes in the text is much more challenging, even though the geometries of vector spaces and conceptual spaces are clearly relevant for information retrieval and semantics. In this paper, we examine two different methods of extraction of topological features from text, using as the underlying representations of words the two most popular methods, namely word embeddings and TF-IDF vectors. To extract topological features from the word embedding space, we interpret the embedding of a text document as high dimensional time series, and we analyze the topology of the underlying graph where the vertices correspond to different embedding dimensions. For topological data analysis with the TF-IDF representations, we analyze the topology of the graph whose vertices come from the TF-IDF vectors of different blocks in the textual document. In both cases, we apply homological persistence to reveal the geometric structures under different distance resolutions. Our results show that these topological features carry some exclusive information that is not captured by conventional text mining methods. In our experiments we observe adding topological features to the conventional features in ensemble models improves the classification results (up to 5\%). On the other hand, as expected, topological features by themselves may be not sufficient for effective classification. It is an open problem to see whether TDA features from word embeddings might be sufficient, as they seem to perform within a range of few points from top results obtained with a linear support vector classifier.
The influence of environment on satellite galaxies in the GAEA semi-analytic model

Reproducing the observed quenched fraction of satellite galaxies has been a long-standing issue for galaxy formation models. We modify the treatment of environmental effects in our state-of-the-art GAlaxy Evolution and Assembly (GAEA) semi-analytic model to improve our modelling of satellite galaxies. Specifically, we implement gradual stripping of hot gas, ram-pressure stripping of cold gas, and an updated algorithm to account for angular momentum exchanges between the gaseous and stellar disc components of model galaxies. Our updated model predicts quenched fractions that are in good agreement with local observational measurements for central and satellite galaxies, and their dependencies on stellar mass and halo mass. We also find consistency between model predictions and observational estimates of quenching times for satellite galaxies, H i, H2 fractions of central galaxies, and deficiencies of H i, H2, SFR of galaxies in cluster haloes. In the framework of our updated model, the dominant quenching mechanisms are hot gas stripping for low-mass satellite galaxies, and AGN feedback for massive satellite galaxies. The ram-pressure stripping of cold gas only affects the quenched fraction in massive haloes with Mh > 1013.5 M⊙, but is needed to reproduce the observed H i deficiencies.
Reconfigurable Intelligent Surfaces-Assisted Multiuser MIMO Uplink Transmission With Partial CSI

This paper considers the application of reconfigurable intelligent surfaces (RISs) (a.k.a. intelligent reflecting surfaces (IRSs)) to assist multiuser multiple-input multiple-output (MIMO) uplink transmission from several multi-antenna user terminals (UTs) to a multi-antenna base station (BS). For reducing the signaling overhead, only partial channel state information (CSI), including the instantaneous CSI between the RIS and the BS as well as the slowly varying statistical CSI between the UTs and the RIS, is exploited in our investigation. In particular, an optimization framework is proposed for jointly designing the transmit covariance matrices of the UTs and the RIS phase shift matrix to maximize the system global energy efficiency (GEE) with partial CSI. We first obtain closed-form solutions for the eigenvectors of the optimal transmit covariance matrices of the UTs. Then, to facilitate the design of the transmit power allocation matrices and the RIS phase shifts, we derive an asymptotically deterministic equivalent of the objective function with the aid of random matrix theory. We further propose a suboptimal algorithm to tackle the GEE maximization problem with guaranteed convergence, capitalizing on the approaches of alternating optimization, fractional programming, and sequential optimization. Numerical results substantiate the effectiveness of the proposed approach as well as the considerable GEE gains provided by the RIS-assisted transmission scheme over the traditional baselines.
AutoTrack: Towards High-Performance Visual Tracking for UAV With Automatic Spatio-Temporal Regularization

Most existing trackers based on discriminative correlation filters (DCF) try to introduce predefined regularization term to improve the learning of target objects, e.g., by suppressing background learning or by restricting change rate of correlation filters. However, predefined parameters introduce much effort in tuning them and they still fail to adapt to new situations that the designer did not think of. In this work, a novel approach is proposed to online automatically and adaptively learn spatio-temporal regularization term. Spatially local response map variation is introduced as spatial regularization to make DCF focus on the learning of trust-worthy parts of the object, and global response map variation determines the updating rate of the filter. Extensive experiments on four UAV benchmarks have proven the superiority of our method compared to the state-of-the-art CPU- and GPU-based trackers, with a speed of ~60 frames per second running on a single CPU. Our tracker is additionally proposed to be applied in UAV localization. Considerable tests in the indoor practical scenarios have proven the effectiveness and versatility of our localization method. The code is available at https://github.com/vision4robotics/AutoTrack.
Distributed function estimation: Adaptation using minimal communication

We investigate whether in a distributed setting, adaptive estimation of a smooth function at the optimal rate is possible under minimal communication. It turns out that the answer depends on the risk considered and on the number of servers over which the procedure is distributed. We show that for the $L_\infty$-risk, adaptively obtaining optimal rates under minimal communication is not possible. For the $L_2$-risk, it is possible over a range of regularities that depends on the relation between the number of local servers and the total sample size.
Towards Deep Learning Models Resistant to Large Perturbations

Adversarial robustness has proven to be a required property of machine learning algorithms. A key and often overlooked aspect of this problem is to try to make the adversarial noise magnitude as large as possible to enhance the benefits of the model robustness. We show that the well-established algorithm called "adversarial training" fails to train a deep neural network given a large, but reasonable, perturbation magnitude. In this paper, we propose a simple yet effective initialization of the network weights that makes learning on higher levels of noise possible. We next evaluate this idea rigorously on MNIST ($\epsilon$ up to $\approx 0.40$) and CIFAR10 ($\epsilon$ up to $\approx 32/255$) datasets assuming the $\ell_{\infty}$ attack model. Additionally, in order to establish the limits of $\epsilon$ in which the learning is feasible, we study the optimal robust classifier assuming full access to the joint data and label distribution. Then, we provide some theoretical results on the adversarial accuracy for a simple multi-dimensional Bernoulli distribution, which yields some insights on the range of feasible perturbations for the MNIST dataset.