May 2022 arXiv papers — page 62

Philip Beck, Lucas Schneider, Roland Wiesendanger, Jens Wiebe

Realizing Majorana bound states in chains of magnetic impurities on $s$-wave superconducting substrates relies on a fine tuning of the energy and hybridization of the single magnetic impurity bound states and of the spin-orbit coupling (SOC). While recent experiments investigate the influence of the former two parameters, the effect of SOC remained experimen

Birger Brietzke, Hans Knüpfer

We consider the onset of pattern formation in an ultrathin ferromagnetic film of the form $\tilde{\Omega}_t := \tilde{\Omega} \times [0,t]$ for $\tilde{ \Omega} \Subset \mathbb{R}^2$ with preferred perpendicular magnetization direction. The relative micromagnetic energy is given by \begin{align} \mathcal{E}[M] &= \int_{\tilde{\Omega}_t} d^2 |\nabla M|^2+ Q \

Nis Meinert, Jakob Gawlikowski, Alexander Lavin

There is a significant need for principled uncertainty reasoning in machine learning systems as they are increasingly deployed in safety-critical domains. A new approach with uncertainty-aware regression-based neural networks (NNs), based on learning evidential distributions for aleatoric and epistemic uncertainties, shows promise over traditional determinis

Jinyu Guo, Kai Shuang, Jijie Li, Zihan Wang

In dialogue state tracking, dialogue history is a crucial material, and its utilization varies between different models. However, no matter how the dialogue history is used, each existing model uses its own consistent dialogue history during the entire state tracking process, regardless of which slot is updated. Apparently, it requires different dialogue his

Rebecca Erbanni, Kishor Bharti, Leong-Chuan Kwek, Dario Poletti

The calculation of off-diagonal matrix elements has various applications in fields such as nuclear physics and quantum chemistry. In this paper, we present a noisy intermediate scale quantum algorithm for estimating the diagonal and off-diagonal matrix elements of a generic observable in the energy eigenbasis of a given Hamiltonian. Several numerical simulat

Jonathan Dubois, Ulf Saalmann, Jan Michael Rost

Preservation of coherence is a fundamental yet subtle phenomenon in open systems. We uncover its relation to symmetries respected by the system Hamiltonian and its coupling to the environment. We discriminate between local and global classes of decoherence-free subspaces for many-body systems through the introduction of "ghost variables". The latter are orth

Andrea Valenti, Davide Bacciu

Disentanglement is a difficult property to enforce in neural representations. This might be due, in part, to a formalization of the disentanglement problem that focuses too heavily on separating relevant factors of variation of the data in single isolated dimensions of the neural representation. We argue that such a definition might be too restrictive and no

Vivien Cabannes, Stefano Vigogna

Classification is often the first problem described in introductory machine learning classes. Generalization guarantees of classification have historically been offered by Vapnik-Chervonenkis theory. Yet those guarantees are based on intractable algorithms, which has led to the theory of surrogate methods in classification. Guarantees offered by surrogate me

Risheng Liu, Xuan Liu, Wei Yao, Shangzhi Zeng

Gradient methods have become mainstream techniques for Bi-Level Optimization (BLO) in learning and vision fields. The validity of existing works heavily relies on solving a series of approximation subproblems with extraordinarily high accuracy. Unfortunately, to achieve the approximation accuracy requires executing a large quantity of time-consuming iteratio

Jintang Li, Ruofan Wu, Wangbin Sun, Liang Chen

The last years have witnessed the emergence of a promising self-supervised learning strategy, referred to as masked autoencoding. However, there is a lack of theoretical understanding of how masking matters on graph autoencoders (GAEs). In this work, we present masked graph autoencoder (MaskGAE), a self-supervised learning framework for graph-structured data

Haokai Hong, Min Jiang, Liang Feng, Qiuzhen Lin

Large-scale multiobjective optimization problems (LSMOPs) refer to optimization problems with multiple conflicting optimization objectives and hundreds or even thousands of decision variables. A key point in solving LSMOPs is how to balance exploration and exploitation so that the algorithm can search in a huge decision space efficiently. Large-scale multiob

R. P. George, Tomis, V. Narayanan, Subhashish Banerjee

We employ a method involving an array detector to measure the transverse spatial variation of the von Neumann Entropy (VNE) associated with the polarization state of light for different light sources including the coherent light from a diode laser, the chaotic light from an LED, the fluorescent emission from a dye widely used as a contrast agent and also the

Johannes Schleischitz

For $n\geq 2$, we determine the Dirichlet spectrum in $\Rn$ with respect to a linear form and the maximum norm as the entire interval $[0,1]$. This natural result improves on recent work of Beresnevich, Guan, Marnat, Ram\'irez and Velani, and complements a subsequent paper by the author where the analogous result was proved for simultaneous approximation. Va

Iris Dominguez-Catena, Daniel Paternain, Mikel Galar

The increasing amount of applications of Artificial Intelligence (AI) has led researchers to study the social impact of these technologies and evaluate their fairness. Unfortunately, current fairness metrics are hard to apply in multi-class multi-demographic classification problems, such as Facial Expression Recognition (FER). We propose a new set of metrics

Tao Fang, Xiying Yuan

The odd-ballooning of a graph $G$, denoted by $G_q$, is the graph obtained from replacing each edge in $G$ by a odd cycle of the same size where the new vertices of the odd cycles are all different. In 2002, Erd\"os et al. determined the extremal graphs of $k$-fan. In 2016, Hou et al. determined extremal graphs of the odd-ballooning of stars for $q\geqslant

Xing Chen, Dongcui Diao, Hechang Chen, Hengshuai Yao

The popular Proximal Policy Optimization (PPO) algorithm approximates the solution in a clipped policy space. Does there exist better policies outside of this space? By using a novel surrogate objective that employs the sigmoid function (which provides an interesting way of exploration), we found that the answer is ``YES'', and the better policies are in fac

Zheyong Fan, Yanzhou Wang, Penghua Ying, Keke Song

We present our latest advancements of machine-learned potentials (MLPs) based on the neuroevolution potential (NEP) framework introduced in [Fan et al., Phys. Rev. B 104, 104309 (2021)] and their implementation in the open-source package GPUMD. We increase the accuracy of NEP models both by improving the radial functions in the atomic-environment descriptor

Pradip Mainali, Ismini Psychoula, Fabien A. P. Petitcolas

In this paper, we present a novel method to compute decision rules to build a more accurate interpretable machine learning model, denoted as ExMo. The ExMo interpretable machine learning model consists of a list of IF...THEN... statements with a decision rule in the condition. This way, ExMo naturally provides an explanation for a prediction using the decisi

Cristiano Capone, Pier Stanislao Paolucci

Humans and animals can learn new skills after practicing for a few hours, while current reinforcement learning algorithms require a large amount of data to achieve good performances. Recent model-based approaches show promising results by reducing the number of necessary interactions with the environment to learn a desirable policy. However, these methods re

Mahdi Godazgar, Simon Guisset

We apply the recent derivations of dual charges in asymptotically flat spacetimes to asymptotically locally AdS spacetimes. In contrast to the results in the flat case, in the AdS case with a Dirichlet boundary the dual charge contribution vanishes at the leading order. However, by focusing on the Taub-NUT-AdS solution, we show that nevertheless, more genera

Joshua Klein, Irem Boybat, Yasir Qureshi, Martino Dazzi

Analog in-memory computing (AIMC) cores offers significant performance and energy benefits for neural network inference with respect to digital logic (e.g., CPUs). AIMCs accelerate matrix-vector multiplications, which dominate these applications' run-time. However, AIMC-centric platforms lack the flexibility of general-purpose systems, as they often have har

Agustinus Kristiadi, Runa Eschenhagen, Philipp Hennig

Monte Carlo (MC) integration is the de facto method for approximating the predictive distribution of Bayesian neural networks (BNNs). But, even with many MC samples, Gaussian-based BNNs could still yield bad predictive performance due to the posterior approximation's error. Meanwhile, alternatives to MC integration tend to be more expensive or biased. In thi

Ting-Yun Hsieh, Babu Baijnath Prasad, Guang-Yu Guo

Transition metal monosilicides CoSi, CoGe, RhSi and RhGe in the chiral cubic B20 structure have recently been found to host unconventional chiral fermions beyond spin-1/2 WFs, and also exhibit exotic physical phenomena such as long Fermi arc surface states, GME and quantized CPGE. Thus, exploring novel spin-related transports in these unconventional chiral f

Minoru Yamashita, Yuki Sato, Yuichi Kasahara, Shigeru Kasahara

A finite residual linear term in the thermal conductivity at zero temperature in insulating magnets indicates the presence of gapless excitations of itinerant quasiparticles, which has been observed in some candidate materials of quantum spin liquids (QSLs). In the organic triangular insulator beta'-EtMe3Sb[Pd(dmit)2]2, a QSL candidate material, the low-temp

Yukiko Tanaka, Hiroyuki Uchida, Takaaki Tanaka, Yuki Amano

Recent high-resolution X-ray spectroscopy revealed possible presence of charge exchange (CX) X-ray emission in supernova remnants (SNRs). Although CX is expected to take place at outermost edges of SNR shells, no significant measurement has been reported so far due to the lack of nearby SNR samples. Here we present an X-ray study of SNR G296.1$-$0.5, which h

Abhishek Dinkar Jagtap, Mattias Heinrich, Marian Himstedt

An increasing number of colonoscopic guidance and assistance systems rely on machine learning algorithms which require a large amount of high-quality training data. In order to ensure high performance, the latter has to resemble a substantial portion of possible configurations. This particularly addresses varying anatomy, mucosa appearance and image sensor c

Marius Bozga, Joseph Sifakis

The paper proposes a method for the correct by design coordination of autonomous driving systems (ADS). It builds on previous results on collision avoidance policies and the modeling of ADS by combining descriptions of their static environment in the form of maps, and the dynamic behavior of their vehicles. An ADS is modeled as a dynamic system involving a s

Yuxin Ren, Benyou Wang, Lifeng Shang, Xin Jiang

Recent work explored the potential of large-scale Transformer-based pre-trained models, especially Pre-trained Language Models (PLMs) in natural language processing. This raises many concerns from various perspectives, e.g., financial costs and carbon emissions. Compressing PLMs like BERT with negligible performance loss for faster inference and cheaper depl

Danyang Liu, Jens Oppliger, Aleš Cahlík, Catherine Witteveen

We demonstrate a straightforward approach to integrating a magnetic field into a low-temperature scanning tunneling microscope (STM) by adhering an NdFeB permanent magnet to a magnetizable sample plate. To render our magnet concept compatible with high-temperature sample cleaning procedures, we make the irreversible demagnetization of the magnet a central pa

Louis Bailly-Salins, Marco Vettori, Thomas Dursap, Philippe Regreny

In this paper we compute and compare the surface energy of various Ga liquid droplets wetting a cylindrical cavity in various configurations. While for some of these configurations the surface energy can be computed explicitely for others numerical computation is needed. Motivated by the results obtained for the cylindrical cavities we explore the case of th

Dianhai Yu, Liang Shen, Hongxiang Hao, Weibao Gong

While modern internet services, such as chatbots, search engines, and online advertising, demand the use of large-scale deep neural networks (DNNs), distributed training and inference over heterogeneous computing systems are desired to facilitate these DNN models. Mixture-of-Experts (MoE) is one the most common strategies to lower the cost of training subjec

Zhixiong Gong, Michael Baudoin

Selective single beam tweezers open tremendous perspectives in microfluidics and microbiology for the micromanipulation, assembly and mechanical properties testing of microparticles, cells and microorganisms. In optics, single beam optical tweezers rely on tightly focused laser beams, generating a three-dimensional (3D) trap at the focal point. In acoustics,

Pratik Gajane, Akrati Saxena, Maryam Tavakol, George Fletcher

Fairness-aware learning aims at satisfying various fairness constraints in addition to the usual performance criteria via data-driven machine learning techniques. Most of the research in fairness-aware learning employs the setting of fair-supervised learning. However, many dynamic real-world applications can be better modeled using sequential decision-making

Boxuan Chen, Ruifeng Zhang, Shaochu Wang, Liqiang Zhang

Pedestrian dead reckoning is a challenging task due to the low-cost inertial sensor error accumulation. Recent research has shown that deep learning methods can achieve impressive performance in handling this issue. In this letter, we propose inertial odometry using a deep learning-based velocity estimation method. The deep neural network based on Res2Net mo

Masakiyo Kitazawa, ShinIchi Esumi, Toshihiro Nonaka

We evaluate the ratio of baryon and electric-charge cumulants at second order from the recent experimental results at $\sqrt{s_{_{NN}}}=200$ GeV by the STAR Collaboration. The baryon number cumulant is reconstructed from the proton number distribution, and effects of the finite acceptance on the transverse momentum are corrected assuming the independent part

Xiang-Mao Ding, Xiang Li

We generalize the determinant representation of the KP $\tau$ functions to the case of the 2D Toda $\tau$ functions. The generating functions for the weighted Hurwitz numbers are a parametric family of 2D Toda $\tau$ functions; for which we give a determinant representation of weighted Hurwitz numbers. Then we can get a finite-dimensional equation system for

Tanmoy Chakraborty, Antariksha Das, Hedser van Brug, Oriol Pietx-Casas

Extended quantum networks are based on quantum repeaters that often rely on the distribution of entanglement in an efficient and heralded fashion over multiple network nodes. Many repeater architectures require multiplexed sources of entangled photon pairs, multiplexed quantum memories, and photon detection that distinguishes between the multiplexed modes. H

Gal Shalom, Eran Treister, Irad Yavneh

We propose a novel quasi-Newton method for solving the sparse inverse covariance estimation problem also known as the graphical least absolute shrinkage and selection operator (GLASSO). This problem is often solved using a second-order quadratic approximation. However, in such algorithms the Hessian term is complex and computationally expensive to handle. Th

Sizhe Xing, Guoqiang Li, Ziwei Li, Nan Chi

We propose and study an efficient photonic CDE method using MZI-based coherent optical matrix multiplication. It improves the compensation performance by about 60% when the tap-length is limited, and only 50% taps of the theoretical value is needed for photonic CDE with 1-dB penalty.

Matteo Fasiello, Tomohiro Fujita, Zvonimir Vlah

We derive exact analytic solutions for density and velocity fields to all orders in Eulerian standard perturbation theory for $\Lambda$CDM cosmology. In particular, we show that density and velocity field kernels can be written in a separable form in time and momenta at each perturbative order. The kernel solutions are built from an analytic basis of momentu

Le Liu, Yanbang Chu, Guang Yang, Yalong Yuan

We report an observation of quantum oscillations (QOs) in the correlated insulators with valley anisotropy of twisted double bilayer graphene (TDBG). The anomalous QOs are best captured in the magneto resistivity oscillations of the insulators at v = -2, with a period of 1/B and an oscillation amplitude as high as 150 k{\Omega}. The QOs can survive up to ~10

Paterne Gahungu, Jean Remy Kubwimana

Air pollution is a major public health problem worldwide although the lack of data is a global issue for most low and middle income countries. Ambient air pollution in the form of fine particulate matter (PM2.5) exceeds the World Health Organization guidelines in Rwanda with a daily average of around 42.6 microgram per meter cube. Monitoring and mitigation s

Daniel Fernández-González

Semantic role labeling (SRL) focuses on recognizing the predicate-argument structure of a sentence and plays a critical role in many natural language processing tasks such as machine translation and question answering. Practically all available methods do not perform full SRL, since they rely on pre-identified predicates, and most of them follow a pipeline s

Laurent Meunier, Raphaël Ettedgui, Rafael Pinot, Yann Chevaleyre

In this paper, we study the problem of consistency in the context of adversarial examples. Specifically, we tackle the following question: can surrogate losses still be used as a proxy for minimizing the $0/1$ loss in the presence of an adversary that alters the inputs at test-time? Different from the standard classification task, this question cannot be red

Yousef A. Alohali, Yassin Abdelsamad, Tamer Mesallam, Fida Almuhawas

Sensorineural hearing loss can be treated using Cochlear implantation. After this surgery using the electrode array impedance measurements, we can check the stability of the impedance value and the dynamic range. Deterioration of speech recognition scores could happen because of increased impedance values. Medicines used to do these measures many times durin

Wonkeun Jo, Dongil Kim

Deep neural networks (DNNs) are one of the most highlighted methods in machine learning. However, as DNNs are black-box models, they lack explanatory power for their predictions. Recently, neural additive models (NAMs) have been proposed to provide this power while maintaining high prediction performance. In this paper, we propose a novel NAM approach for mu

Juhee Son, Jiho Jin, Haneul Yoo, JinYeong Bak

The Annals of Joseon Dynasty (AJD) contain the daily records of the Kings of Joseon, the 500-year kingdom preceding the modern nation of Korea. The Annals were originally written in an archaic Korean writing system, `Hanja', and were translated into Korean from 1968 to 1993. The resulting translation was however too literal and contained many archaic Korean

Guogang Liao, Xuejian Li, Ze Wang, Fan Yang

Online advertising driven by auctions brings billions of dollars in revenue for social networking services and e-commerce platforms. GSP auctions, which are simple and easy to understand for advertisers, have almost become the benchmark for ad auction mechanisms in the industry. However, most GSP-based industrial practices assume that the user click only rel

Matthew Darlington, Kevin D. Glazebrook, David S. Leslie, Rob Shone

In this paper we consider a stochastic game for modelling the interactions between smugglers and a patroller along a border. The problem we examine involves a group of cooperating smugglers making regular attempts to bring small amounts of illicit goods across a border. A single patroller has the goal of preventing the smugglers from doing so, but must pay a

Wenshuai Zhao, Zhiyuan Li, Joni Pajarinen

The number of agents can be an effective curriculum variable for controlling the difficulty of multi-agent reinforcement learning (MARL) tasks. Existing work typically uses manually defined curricula such as linear schemes. We identify two potential flaws while applying existing reward-based automatic curriculum learning methods in MARL: (1) The expected epi

Gerhard Hilgers, Thomas Braunroth, Hans Rabus

The production of two double strand breaks in spatially separated locations on the DNA molecule can cause the loss of a whole DNA loop, which can be of substantial length depending on the geometrical position of the two damaged sites and depends on the degree of correlation between ionisation clusters formed in sites of several nanometres in size. In the fir

Bingzhe Wu, Jintang Li, Junchi Yu, Yatao Bian

Deep graph learning has achieved remarkable progresses in both business and scientific areas ranging from finance and e-commerce, to drug and advanced material discovery. Despite these progresses, how to ensure various deep graph learning algorithms behave in a socially responsible manner and meet regulatory compliance requirements becomes an emerging proble

Vivasha Govinden, Suyash Rijal, Qi Zhang, Yousra Nahas

Nanoscale ferroelectric topologies such as vortices, anti-vortices, bubble patterns etc. are stabilized in thin films by a delicate balance of both mechanical and electrical boundary conditions. A systematic understanding of the phase stability of bubble domains, particularly when the above factors act simultaneously, remains elusive. Here we present first-p

Marija Sakota, Maxime Peyrard, Robert West

Wikipedia is one of the richest knowledge sources on the Web today. In order to facilitate navigating, searching, and maintaining its content, Wikipedia's guidelines state that all articles should be annotated with a so-called short description indicating the article's topic (e.g., the short description of beer is "Alcoholic drink made from fermented cereal

Abhijit Suprem, Sanjyot Vaidya, Suma Cherkadi, Purva Singh

Machine learning models with explainable predictions are increasingly sought after, especially for real-world, mission-critical applications that require bias detection and risk mitigation. Inherent interpretability, where a model is designed from the ground-up for interpretability, provides intuitive insights and transparent explanations on model prediction

Taekyun Kim, Dae San Kim

The aim of this paper is to investigate some properties, recurrence relations and identities involving degenerate hyperharmonic numbers, hyperharmonic numbers and degenerate harmonic numbers. In particular, we derive an explicit expression of the degenerate hyperharmonic numbers in terms of the degenerate harmonic numbers. This is a degenerate version of the

Jean Barbier, TianQi Hou, Marco Mondelli, Manuel Sáenz

We consider the problem of estimating a rank-1 signal corrupted by structured rotationally invariant noise, and address the following question: how well do inference algorithms perform when the noise statistics is unknown and hence Gaussian noise is assumed? While the matched Bayes-optimal setting with unstructured noise is well understood, the analysis of t

Nagita Mehrseresht

We propose an action parsing algorithm to parse a video sequence containing an unknown number of actions into its action segments. We argue that context information, particularly the temporal information about other actions in the video sequence, is valuable for action segmentation. The proposed parsing algorithm temporally segments the video sequence into a

Anthony C. Leung, K. S. Ida Melody, Aaron D. Tranter, Karun V. Paul

Strong nonlinear interactions between single photons have important applications in optical quantum information processing. Demonstrations of these interactions in cold atomic ensembles have largely been limited to exploiting slow light generated using electromagnetically induced transparency (EIT). However, these EIT implementations have limited achievable

Geonho Cha, Ho-Deok Jang, Dongyoon Wee

Managing the dynamic regions in the photometric loss formulation has been a main issue for handling the self-supervised depth estimation problem. Most previous methods have alleviated this issue by removing the dynamic regions in the photometric loss formulation based on the masks estimated from another module, making it difficult to fully utilize the traini

Kh. Helensana Devi, K. Sashikanta Singh, N. Nimai Singh

The paper addresses the effects of the variations of the SUSY breaking scale $m_s$ in the range (2-14) TeV on the three neutrino masses and mixings, in running the renormalization group equations (RGEs) for different input values of high energy seesaw scale $M_R$, in both normal and inverted hierarchical neutrino mass models. The present investigation is a c

Marcus Kalander

Failures and anomalies in large-scale software systems are unavoidable incidents. When an issue is detected, operators need to quickly and correctly identify its location to facilitate a swift repair. In this work, we consider the problem of identifying the root cause set that best explains an anomaly in multi-dimensional time series with categorical attribu

Ioannis Sarridis, Christos Koutlis, Giorgos Kordopatis-Zilos, Ioannis Kompatsiaris

In this paper, we introduce InDistill, a method that serves as a warmup stage for enhancing Knowledge Distillation (KD) effectiveness. InDistill focuses on transferring critical information flow paths from a heavyweight teacher to a lightweight student. This is achieved via a training scheme based on curriculum learning that considers the distillation diffic

Andrei Tudor Patrascu

A fibre bundle viewpoint of gauge field theories is reviewed with focus on a possible quantum interpretation. The fundamental quantum properties of non-separability of state spaces is considered in the context of defining the connection on the fibre bundle, leading to an application of the quantum principles to the geometrical and topological definition of g

Maximilian Nitsch, Rubén Seoane Souto, Martin Leijnse

A Majorana box - two topological superconducting nanowires coupled via a trivial superconductor - is a building block in devices aiming to demonstrate nonabelian physics, as well as for topological quantum computer architectures. We theoretically investigate charge transport through a Majorana box and show that current can be blocked when two Majoranas coupl

Frederik Bopp, Jonathan Rojas, Natalia Revenga, Hubert Riedl

Tunnel-coupled pairs of optically active quantum dots - quantum dot molecules (QDMs) - offer the possibility to combine excellent optical properties such as strong light-matter coupling with two-spin singlet-triplet ($S-T_0$) qubits having extended coherence times. The $S-T_0$ basis formed using two spins is inherently protected against electric and magnetic

Paolo Fittipaldi, Anastasios Giovanidis, Frédéric Grosshans

Future quantum internet aims to enable quantum communication between arbitrary pairs of distant nodes through the sharing of end-to-end entanglement, a universal resource for many quantum applications. As in classical networks, quantum networks also have to resolve problems related to routing and satisfaction of service at a sufficient rate. We deal here wit

Guangyuan Jiang, Manjie Xu, Song-Chun Zhu, Wenjuan Han

Standardized and quantified evaluation of machine behaviors is a crux of understanding LLMs. In this study, we draw inspiration from psychometric studies by leveraging human personality theory as a tool for studying machine behaviors. Originating as a philosophical quest for human behaviors, the study of personality delves into how individuals differ in thin

Robert Laugwitz, Vanessa Miemietz

This paper develops a theory of pretriangulated 2-representations of dg 2-categories. We characterize cyclic pretriangulated 2-representations, under certain compactness assumptions, in terms of dg modules over dg algebra 1-morphisms internal to associated dg 2-categories of compact objects. Further, we investigate the Morita theory and quasi-equivalences fo

Filippo Bracci, Hervé Gaussier

We present different constructions of abstract boundaries for bounded complete (Kobayashi) hyperbolic domains in ${\mathbb C}^d$, $d \geq 1$. These constructions essentially come from the geometric theory of metric spaces. We also present, as an application, some extension results concerning biholomorphic maps.

Paul-Gerhard Reinhard, Daniel Dundas, Phuong Mai Dinh, Marc Vincendon

We investigate the depletion of single-electron states in small molecules under the influence of very short XUV pulses. In N$_2$, for a certain window of XUV energies around 50 eV, we observe a marked occupation inversion, i.e. a situation where depletion of the deepest bound valence electron state is much larger than for any other state. This represents a r

Ferdinando Auricchio, Giuseppe Toscani, Mattia Zanella

In this paper we study a novel Fokker-Planck-type model that is designed to mimic manufacturing processes through the dynamics characterizing a large set of agents. In particular, we describe a many-agent system interacting with a target domain in such a way that each agent/particle is attracted by the center of mass of the target domain with the aim to unif

Yuzhong Chen, Zhenxiang Xiao, Lin Zhao, Lu Zhang

Learning with little data is challenging but often inevitable in various application scenarios where the labeled data is limited and costly. Recently, few-shot learning (FSL) gained increasing attention because of its generalizability of prior knowledge to new tasks that contain only a few samples. However, for data-intensive models such as vision transforme

Dmitri I. Panyushev

Let $\mathfrak g$ be a complex simple Lie algebra and $\mathfrak b=\mathfrak t\oplus\mathfrak u^+$ a fixed Borel subalgebra. Let $\Delta^+$ be the set of positive roots associated with $\mathfrak u^+$ and $\mathcal K\subset\Delta^+$ the Kostant cascade. We elaborate on some constructions related to $\mathcal K$ and applications of $\mathcal K$. This includes

Louis Reboul, Teddy Pichard, Marc Massot

We consider hyperbolic systems of conservation laws with relaxation source terms leading to a diffusive asymptotic limit under a parabolic scaling. We introduce a new class of secondorder in time and space numerical schemes, which are uniformly asymptotic preserving schemes. The proposed Implicit-Explicit (ImEx) approach, does not follow the usual path relyi

Anshu Priya, M V Sangaranarayanan

Using a combinatorial method, the partition functions for two-dimensional nearest neighbour Ising models have been derived for a square lattice of 16 sites in the presence of the magnetic field. A novel hierarchical method of enumeration of all the configurations for any arrangement of sites has been proposed. This enumeration has been executed by a systemat

Timothé Albouy, Davide Frey, Michel Raynal, François Taïani

This paper considers the problem of reliable broadcast in asynchronous authenticated systems, in which n processes communicate using signed messages and up to t processes may behave arbitrarily (Byzantine processes). In addition, for each message m broadcast by a correct (i.e., non-Byzantine) process, a message adversary may prevent up to d correct processes

Michael Janner, Yilun Du, Joshua B. Tenenbaum, Sergey Levine

Model-based reinforcement learning methods often use learning only for the purpose of estimating an approximate dynamics model, offloading the rest of the decision-making work to classical trajectory optimizers. While conceptually simple, this combination has a number of empirical shortcomings, suggesting that learned models may not be well-suited to standar

Yi-Cai Zhang

In the previous work, the concept of critical region in a generalized Aubry-Andr\'{e} model (Ganeshan-Pixley-Das Sarma's model) has been set up. In this work we propose that the critical region can be realized in a one-dimensional flat band lattice system with a quasi-periodic potential. It is found that the above flat band lattice model can be reduced into

Seanie Lee, Bruno Andreis, Kenji Kawaguchi, Juho Lee

Meta-learning approaches enable machine learning systems to adapt to new tasks given few examples by leveraging knowledge from related tasks. However, a large number of meta-training tasks are still required for generalization to unseen tasks during meta-testing, which introduces a critical bottleneck for real-world problems that come with only few tasks, du

T. I. Rajib, S. Sultana

The reductive perturbation approach was used to explore the nonlinear propagation of fast (compressive) and slow (rarefactive) electron-position (EP) magneto-acoustic (EPMA) modes in an EP plasma medium. The solitary wave solution of the Korteweg-de Vries (K-dV) equation is used to identify the basic properties of EP compressional Alfv\'en waves. It is shown

Vikas Raunak, Matt Post, Arul Menezes

Traditional machine translation (MT) metrics provide an average measure of translation quality that is insensitive to the long tail of behavioral problems in MT. Examples include translation of numbers, physical units, dropped content and hallucinations. These errors, which occur rarely and unpredictably in Neural Machine Translation (NMT), greatly undermine

Jiaming Qi, Dongyu Li, Yufeng Gao, Peng Zhou

The robotic manipulation of compliant objects is currently one of the most active problems in robotics due to its potential to automate many important applications. Despite the progress achieved by the robotics community in recent years, the 3D shaping of these types of materials remains an open research problem. In this paper, we propose a new vision-based

Abdullah Abdullah, Martin Holler, Karl Kunisch, Malena Sabate Landman

Isolating different types of motion in video data is a highly relevant problem in video analysis. Applications can be found, for example, in dynamic medical or biological imaging, where the analysis and further processing of the dynamics of interest is often complicated by additional, unwanted dynamics, such as motion of the measurement subject. In this work

Harsh Chaudhari, Matthew Jagielski, Alina Oprea

Secure multiparty computation (MPC) has been proposed to allow multiple mutually distrustful data owners to jointly train machine learning (ML) models on their combined data. However, by design, MPC protocols faithfully compute the training functionality, which the adversarial ML community has shown to leak private information and can be tampered with in poi

Bin Chen, Xia Zhao, Weidong Wang, Peibiao Zhao

In this paper, we introduce the so-called $L_p$ $q$-torsional measure for $p\in\mathbb{R}$ and $q>1$ by establishing the $L_p$ variational formula for the $q$-torsional rigidity of convex bodies without smoothness conditions. Moreover, we achieve the existence of solutions to the $L_p$ Minkowski problem $w.r.t.$ the $q$-torsional rigidity for discrete measur

Soumitra Ghosh, Asif Ekbal, Pushpak Bhattacharyya

The World Health Organization (WHO) has emphasized the importance of significantly accelerating suicide prevention efforts to fulfill the United Nations' Sustainable Development Goal (SDG) objective of 2030. In this paper, we present an end-to-end multitask system to address a novel task of detection of two interpersonal risk factors of suicide, Perceived Bu

Shupeng Song, Gaoping Long, Cong Zhang, Xiangdong Zhang

The statistical mechanical calculation of the thermodynamical properties of non-rotating isolated horizons are studied in the loop quantum gravity framework. By employing the Hawking temperature and horizon mass of isolated horizons as physical inputs, the microcanonical ensemble associated with the system are well established. As a result, the black hole en

Aki Takahashi, Gabriele Pozzato, Anirudh Allam, Vahid Azimi

In this paper, a novel electrochemical model for LiFePO$_4$ battery cells that accounts for the positive particle lithium intercalation and deintercalation dynamics is proposed. Starting from the enhanced single particle model, mass transport and balance equations along with suitable boundary conditions are introduced to model the phase transformation phenom

Jonathan Hermann, Daniel Stremmer, Malgorzata Worek

Since its discovery at the Large Hadron Collider in 2012 the Higgs boson has arguably become the most famous of the Standard Model particles and many measurements have been performed in order to assess its properties. Among others, these include measurements of the Higgs boson's ${\cal CP}$ state which is predicted to be ${\cal CP}$-even. Even though a pure

Wen-Cong Chen

HD49798/RX J0648.0-4418 is a peculiar binary including a hot subdwarf of O spectral type and a compact companion in a 1.55 day orbit. According to the steady spin period derivative $\dot{P}=(-2.17\pm0.01)\times10^{-15} ~\rm s\,s^{-1}$ , the compact object was thought to be a contracting young white dwarf (WD). However, the X-ray luminosity producing by the w

Rabayet Sadnan, Anamika Dubey

With massive penetrations of active grid-edge technologies, distributed computing and optimization paradigm has gained significant attention to solve distribution-level optimal power flow (OPF) problems. However, the application of generic distributed optimization techniques to OPF problems leads to a very large number of macro-iterations or communication ro

Dennis Nieman, Michel Mandjes, Liron Ravner

Our goal is to estimate the characteristic exponent of the input to a L\'evy-driven storage system from a sample of equispaced workload observations. The estimator relies on an approximate moment equation associated with the Laplace-Stieltjes transform of the workload at exponentially distributed sampling times. The estimator is pointwise consistent for any

Matteo Breschi, Rossella Gamba, Ssohrab Borhanian, Gregorio Carullo

Next-generation detectors are expected to be sensitive to postmerger signals from binary neutron star coalescences and thus to directly probe the remnant dynamics. We investigate the scientific potential of postmerger detections with the Einstein Telescope using full Bayesian analyses with the state-of-the-art waveform model ${\tt NRPMw}$. We find that: (i)

Songlin Xu, Guanjie Wang, Ziyuan Fang, Guangwei Zhang

We present HeadText, a hands-free technique on a smart earpiece for text entry by motion sensing. Users input text utilizing only 7 head gestures for key selection, word selection, word commitment and word cancelling tasks. Head gesture recognition is supported by motion sensing on a smart earpiece to capture head moving signals and machine learning algorith

O. Deniz Kose, Yanning Shen

Graph neural networks (GNNs) have been demonstrated to achieve state-of-the-art for a number of graph-based learning tasks, which leads to a rise in their employment in various domains. However, it has been shown that GNNs may inherit and even amplify bias within training data, which leads to unfair results towards certain sensitive groups. Meanwhile, traini

Ali Waqar Azim, Yannis Le Guennec, Marwa Chafii, Laurent Ros

In this communication, we propose a framework for amalgamating optical-orthogonal frequency-division multiplexing (O-OFDM) and O-OFDM with index modulation (O-OFDM-IM) for optical wireless systems. Both schemes individually have some limitations, e.g., O-OFDM does not provide any granularity for spectral efficiency (SE)/energy efficiency (EE) trade-off, and

Su-Hyeon Kim, Hyunjoon Cheon, Yo-Sub Han, Sang-Ki Ko

Due to the practical importance of regular expressions (regexes, for short), there has been a lot of research to automatically generate regexes from positive and negative string examples. We tackle the problem of learning regexes faster from positive and negative strings by relying on a novel approach called `neural example splitting'. Our approach essential

Yuzhong Chen, Yu Du, Zhenxiang Xiao, Lin Zhao

Vision transformer (ViT) and its variants have achieved remarkable successes in various visual tasks. The key characteristic of these ViT models is to adopt different aggregation strategies of spatial patch information within the artificial neural networks (ANNs). However, there is still a key lack of unified representation of different ViT architectures for

Edwin Herrera, Beilun Wu, Evan O'Leary, Alberto M. Ruiz

The orthorhombic compound AuSn4 is compositionally similar to the Dirac node arc semimetal PtSn$_4$. AuSn$_4$ is, contrary to PtSn$_4$, superconducting with a critical temperature of T$_c$ = 2.35 K. Recent measurements present indications for quasi two-dimensional superconducting behavior in AuSn$_4$. Here we present measurements of the superconducting densi