May 2022 arXiv papers — page 63

Mehmet Tahir Huyut, Andrei Velichko

Since February 2020, the world has been engaged in an intense struggle with the COVID-19 dis-ease, and health systems have come under tragic pressure as the disease turned into a pandemic. The aim of this study is to obtain the most effective routine blood values (RBV) in the diagnosis and prognosis of COVID-19 using a backward feature elimination algorithm

Gaurav Saha, K. M. Santosh, Anugu Reddy, Ravi Kant

This paper presents the plan of an in-pipe climbing robot that works utilizing an astute transmission part to investigate complex relationship of lines. Standard wheeled/proceeded in-pipe climbing robots are inclined to slip and take while investigating in pipe turns. The instrument helps in accomplishing the main inevitable result of getting out slip and dr

Hong-peng Lu, Hui Tian, Li-yun Zhang, Christoffer Karoff

Context. Stellar coronal mass ejections (CMEs) are the primary driver of the exoplanetary space weather and they could affect the habitability of exoplanets. However, detections of possible stellar CME signatures are extremely rare. Aims. This work aims to detect stellar CMEs from time-domain spectra observed through the LAMOST Medium-Resolution Spectroscopi

Yacine Izza, Alexey Ignatiev, Joao Marques-Silva

Decision trees (DTs) epitomize the ideal of interpretability of machine learning (ML) models. The interpretability of decision trees motivates explainability approaches by so-called intrinsic interpretability, and it is at the core of recent proposals for applying interpretable ML models in high-risk applications. The belief in DT interpretability is justifi

Gyungchoon Go, Se Kwon Kim

We theoretically investigate topological spin transport of the magnon-polarons in bilayer magnet with two-dimensional square lattices. Our theory is motivated by recent reports on the van der Waals magnets which show the reversible electrical switching of the interlayer magnetic order between antiferromagnetic and ferromagnetic orders. The magnetoelastic int

Seonbin Jo, Chanung Park, Jisung Yoon, Woo-Sung Jung

Science and society inevitably interact with each other and evolve together. Studying the trend of science helps recognize leading topics significant for research and establish better policies to allocate funds efficiently. Scholarly societies such as the Korean Physics Society (KPS) also play an important role in the history of science. Figuring out the rol

Gourab Ghatak

Next-generation wireless services are characterized by a diverse set of requirements, to sustain which, the wireless access points need to probe the users in the network periodically. In this regard, we study a novel multi-armed bandit (MAB) setting that mandates probing all the arms periodically while keeping track of the best current arm in a non-stationar

Sai Munikoti, Deepesh Agarwal, Laya Das, Balasubramaniam Natarajan

Graph Neural Networks (GNN) provide a powerful framework that elegantly integrates Graph theory with Machine learning for modeling and analysis of networked data. We consider the problem of quantifying the uncertainty in predictions of GNN stemming from modeling errors and measurement uncertainty. We consider aleatoric uncertainty in the form of probabilisti

GyeongTaek Lee

The aim of path planning is to reach the goal from starting point by searching for the route of an agent. In the path planning, the routes may vary depending on the number of variables such that it is important for the agent to reach various goals. Numerous studies, however, have dealt with a single goal that is predefined by the user. In the present study,

Shyam Sundar Ghoshal, Stephane Junca, Akash Parmar

This article deals with the regularity of the entropy solutions of scalar conservation laws with discontinuous flux. It is well-known [Adimurthi et al., Comm. Pure Appl. Math. 2011] that the entropy solution for such equation does not admit BV regularity in general, even when the initial data belongs to BV. Due to this phenomenon fractional BVs spaces wider

Licheng Tang, Yiyang Cai, Jiaming Liu, Zhibin Hong

Few-shot font generation (FFG), which aims to generate a new font with a few examples, is gaining increasing attention due to the significant reduction in labor cost. A typical FFG pipeline considers characters in a standard font library as content glyphs and transfers them to a new target font by extracting style information from the reference glyphs. Most

Desmond Coles

Let $X$ be a spherical variety. We show that Tevelev and Vogiannou's tropicalization map from $X$ to its tropicalization factors through the Berkovich analytification $X^{\text{an}}$, as in the case for toric varieties. Furthermore we show that the tropicalization is a strong deformation retraction of $X^{\text{an}}$. We also give a strong deformation retrac

Shinsaku Sakaue, Taihei Oki

Greedy best-first search (GBFS) and A* search (A*) are popular algorithms for path-finding on large graphs. Both use so-called heuristic functions, which estimate how close a vertex is to the goal. While heuristic functions have been handcrafted using domain knowledge, recent studies demonstrate that learning heuristic functions from data is effective in man

Kevin Tirta Wijaya, Dong-Hee Paek, Seung-Hyun Kong

Existing point cloud feature learning networks often incorporate sequences of sampling, neighborhood grouping, neighborhood-wise feature learning, and feature aggregation to learn high-semantic point features that represent the global context of a point cloud. Unfortunately, the compounded loss of information concerning granularity and non-maximum point feat

Shinsaku Sakaue, Taihei Oki

Augmenting algorithms with learned predictions is a promising approach for going beyond worst-case bounds. Dinitz, Im, Lavastida, Moseley, and Vassilvitskii~(2021) have demonstrated that a warm start with learned dual solutions can improve the time complexity of the Hungarian method for weighted perfect bipartite matching. We extend and improve their framewo

Bradley Greig, J. Stuart B. Wyithe, Steven G. Murray, Simon J. Mutch

Preparing for the first detection of the cosmic 21-cm signal from large-scale interferometer experiments requires rigorous testing of the data analysis and reduction pipelines. To validate that these pipelines do not erroneously remove or add features that can mimic the cosmic signal (e.g. from side-lobes or large-scale power leakage), we require reionisatio

Fredy Vides, Esteban Segura, Carlos Vargas-Agüero

Time series anomaly detection is an important process for system monitoring and model switching, among other applications in cyber-physical systems. In this document, we present a fast subspace method for time series anomaly detection, with a relatively low computational cost, that has been designed for anomaly detection in real sensor signals corresponding

Masaaki Fukasawa, Ryoji Takano

We develop a variant of rough path theory tailor-made for analyzing a class of financial asset price models known as rough volatility models. As an application, we prove a pathwise large deviation principle (LDP) for a certain class of rough volatility models, which in turn describes the limiting behavior of implied volatility for short maturity under those

Weiguo Zhu, Yongqi Sun, Xintong Yi, Yan Wang

The technology of traffic flow forecasting plays an important role in intelligent transportation systems. Based on graph neural networks and attention mechanisms, most previous works utilize the transformer architecture to discover spatiotemporal dependencies and dynamic relationships. However, they have not considered correlation information among spatiotem

Miles H. Anderson, Alexey Tikan, Aleksandr Tusnin, Johann Riemensberger

We theoretically and experimentally investigate the formation of dissipative coherent structures in Kerr nonlinear optical microresonators, whose integrated dispersion exceeds the free-spectral range. We demonstrate that the presence of any periodic modulation along the resonator's circumference, such as periodically varying dispersion, can excite higher-ord

Le Yang, Junwei Han, Tao Zhao, Nian Liu

Temporal action localization aims at localizing action instances from untrimmed videos. Existing works have designed various effective modules to precisely localize action instances based on appearance and motion features. However, by treating these two kinds of features with equal importance, previous works cannot take full advantage of each modality featur

Jiaxiang Yang, Hanyuan Deng, Zikai Tang, Hechao Liu

The zeroth-order general Randi\'{c} index $R^{0}_{a+1}$ of an $n$-vertices oriented graph $D$ is equal to the sum of $(d^{+}_{u_i})^{a}+(d^{-}_{u_j})^{a}$ over all arcs $u_iu_j$ of $D$, where we denote by $d^{+}_{u_i}$ the out-degree of the vertex $u_i$ and $d^{-}_{u_j}$ the in-degree of the vertex $u_j$, $a$ is an arbitrary real number. In the paper, we det

Murray Moinester

The electric ${\alpha}_{\pi}$ and magnetic ${\beta}_{\pi}$ charged pion Compton polarizabilities are of fundamental interest in the low-energy sector of quantum chromodynamics (QCD). They characterize the induced dipole moments of the pion during ${\gamma}{\pi}$ Compton scattering. Pion polarizabilities affect the shape of the ${\gamma}{\pi}$ Compton scatter

Simin Chen, Hamed Khanpour, Cong Liu, Wei Yang

With the privatization deployment of DNNs on edge devices, the security of on-device DNNs has raised significant concern. To quantify the model leakage risk of on-device DNNs automatically, we propose NNReverse, the first learning-based method which can reverse DNNs from AI programs without domain knowledge. NNReverse trains a representation model to represe

Ahai Chen

A molecular rotation assisted non-sequential double ionization (MR-NSDI) mechanism is identified in the breakup of rotational H$_2$ molecules in a few-cycle intense laser pulse using a semi-classical trajectory Monte Carlo method. Applying a molecular source in an appropriate rotational state could intensively boost NSDI probability, and conclude with an add

Leonid Kitchatinov

This paper proposes the idea that the observed dependence of stellar activity cycles on rotation rate can be a manifestation of a stronger dependence on the effective temperature. Observational evidence is recalled and theoretical arguments are given for the presence of cyclic activity in the case of sufficiently slow rotation only. Slow rotation means proxi

Arnab Mukherjee, Sunandan Gangopadhyay, A. S. Majumdar

We introduce a new model of relativistic quantum analogue of the classical Otto engine in the presence of a perfectly reflecting boundary. A single qubit acts as the working substance interacting with a massless quantum scalar field, with the boundary obeying the Dirichlet condition. The quantum vacuum serves as a thermal bath through the Unruh effect. We ob

Amgad Abdrabou, Lijun Yuan, Wangtao Lu, Ya Yan Lu

Photonic structures with high-$Q$ resonances are essential for many practical applications, and they can be relatively easily realized by modifying ideal structures with bound states in the continuum (BICs). When an ideal photonic structure with a BIC is perturbed, the BIC may be destroyed (becomes a resonant state) or may continue to exist with a slightly d

Tiantian Zhang, Shuichi Murakami

Recently, a series of two-dimensional (2D) nonmagnetic layered materials XSi2Y4 (X=transition metals; Y=pnictogens) having similar crystal structures with transition-metal dichalcogenides (TMDs) were proposed for their potential application value. Like TMDs, we propose that chiral phonon involved valley-selective optical circular dichroism can be also obtain

Teppei Suzuki

Hierarchical clustering is an effective and efficient approach widely used for classical image segmentation methods. However, many existing methods using neural networks generate segmentation masks directly from per-pixel features, complicating the architecture design and degrading the interpretability. In this work, we propose a simpler, more interpretable

Jiajia Chen, Xin Xin, Xianfeng Liang, Xiangnan He

Generating recommendations based on user-item interactions and user-user social relations is a common use case in web-based systems. These connections can be naturally represented as graph-structured data and thus utilizing graph neural networks (GNNs) for social recommendation has become a promising research direction. However, existing graph-based methods

Yihan Hao, Mingliang Zhang, Fei Yin, Linlin Huang

Diagram parsing is an important foundation for geometry problem solving, attracting increasing attention in the field of intelligent education and document image understanding. Due to the complex layout and between-primitive relationship, plane geometry diagram parsing (PGDP) is still a challenging task deserving further research and exploration. An appropri

Asad Mujeeb, Wang Peng

Electricity distribution network system is considered one of the key component of the modern electrical power system. Due to increase in the energy demand, penetration of renewable energy resources into the power system has been extensively increasing in recent years. More and more distributed generations (DGs) are joining the distribution network to create

Shiyan Zhong, Shuo Li, Peter Berczik, Rainer Spurzem

Tidal disruption of stars in dense nuclear star clusters containing supermassive central black holes (SMBH) is modeled by high-accuracy direct N-body simulation. Stars getting too close to the SMBH are tidally disrupted and a tidal disruption event (TDE) happens. TDEs probe properties of SMBH, their accretion disks, and the surrounding nuclear stellar cluste

Yang Yang, Mulei Ma, Hequan Wu, Quan Yu

Mobile communication standards were developed for enhancing transmission and network performance by using more radio resources and improving spectrum and energy efficiency. How to effectively address diverse user requirements and guarantee everyone's Quality of Experience (QoE) remains an open problem. The Sixth Generation (6G) mobile systems will solve this

Vidhya Kamakshi, Narayanan C Krishnan

Domain adaptation techniques have contributed to the success of deep learning. Leveraging knowledge from an auxiliary source domain for learning in labeled data-scarce target domain is fundamental to domain adaptation. While these techniques result in increasing accuracy, the adaptation process, particularly the knowledge leveraged from the source domain, re

Charles Antoine, Julian Talbot

Despite having been studied for decades, first passage processes remain an active area of research. In this contribution we examine a particle diffusing in an annulus with an inner absorbing boundary and an outer reflective boundary. We obtain analytic expressions for the joint distribution of the hitting time and the hitting angle in two and three dimension

Paweł Goldstein, Piotr Hajłasz

It was conjectured that if $f\in C^1(\mathbb{R}^n,\mathbb{R}^n)$ satisfies $\operatorname{rank} Df\leq m<n$ everywhere in $\mathbb{R}^n$, then $f$ can be uniformly approximated by $C^\infty$-mappings $g$ satisfying $\operatorname{rank} Dg\leq m$ everywhere. While in general, there are counterexamples to this conjecture, we prove that the answer is in the pos

Zhen Lin, Shubhendu Trivedi, Jimeng Sun

We develop Temporal Quantile Adjustment (TQA), a general method to construct efficient and valid prediction intervals (PIs) for regression on cross-sectional time series data. Such data is common in many domains, including econometrics and healthcare. A canonical example in healthcare is predicting patient outcomes using physiological time-series data, where

Hang Yang, Haochen Zhao, Zekun Niu, Guoqing Pu

The modeling and prediction of the ultrafast nonlinear dynamics in the optical fiber are essential for the studies of laser design, experimental optimization, and other fundamental applications. The traditional propagation modeling method based on the nonlinear Schr\"odinger equation (NLSE) has long been regarded as extremely time-consuming, especially for d

Hwajin Eom, Sojeong Jung, Wontae Kim

A sufficiently large Schwarzschild AdS black hole with the Hawking temperature has a positive heat capacity and undergoes the Hawking-Page phase transition. Even though an arbitrary isothermal surface of a cavity is introduced, the essential features about the stability and phase transition of the Schwarzschild AdS black hole remain the same. In this paper,

Wei Li, Haohao Wang, Yuanhao Liu, Bin Yang

In this paper, we present the fuzzy monoids and vague monoids by using aggregation operators. The unit interval with a $t$-norm or a $t$-conorm is a special monoid, so we mainly talk about fuzzy subsets of monoids. Firstly, the classification of fuzzy sets based on some special aggregation operators is discussed. At the same time, we give two basic propositi

Tamami Mori, Takashi Onaka, Itsuki Sakon, Mridusmita Buragohain

The observed large variation in the abundance of deuterium (D) in the interstellar medium (ISM) suggests that a significant fraction of D may be depleted into polycyclic aromatic hydrocarbons (PAHs). Signatures of deuteration of PAHs are expected to appear most clearly through C-D stretching modes at 4.4--4.7 micron, whose strengths in emission spectra relat

Shiquan Yang, Xinting Huang, Jey Han Lau, Sarah Erfani

Data artifacts incentivize machine learning models to learn non-transferable generalizations by taking advantage of shortcuts in the data, and there is growing evidence that data artifacts play a role for the strong results that deep learning models achieve in recent natural language processing benchmarks. In this paper, we focus on task-oriented dialogue an

Liang Wang, Ying Li, Jierui Zhang, Xianping Tao

Understanding the evolution of communities in developer social networks (DSNs) around open source software (OSS) projects can provide valuable insights about the socio-technical process of OSS development. Existing studies show the evolutionary behaviors of social communities can effectively be described using patterns including split, shrink, merge, expand,

Jimin Wu

A toy model for ferroic orders through entropy. As the rod/spin concentration (or the volume enclosing the ferroic rods) is variable by a tunable force conjugate to the order parameter, the model exhibits hysteresis associated to some discontinuous isotropic-nematic transitions explained essentially by Onsager's hard-rod model. The uniaxial anisotropy and no

Qinghua Zheng, Jihong Wang, Minnan Luo, Yaoliang Yu

Due to the superior performance of Graph Neural Networks (GNNs) in various domains, there is an increasing interest in the GNN explanation problem "\emph{which fraction of the input graph is the most crucial to decide the model's decision?}" Existing explanation methods focus on the supervised settings, \eg, node classification and graph classification, whil

Xin-Ru Feng, Heng-Chao Li, Rui Wang, Qian Du

Hyperspectral unmixing has been an important technique that estimates a set of endmembers and their corresponding abundances from a hyperspectral image (HSI). Nonnegative matrix factorization (NMF) plays an increasingly significant role in solving this problem. In this article, we present a comprehensive survey of the NMF-based methods proposed for hyperspec

Shinji Tsujikawa

In regularized 4-dimensional Einstein-Gauss-Bonnet (EGB) gravity derived from a Kaluza-Klein reduction of higher-dimensional EGB theory, we study the existence and stability of black hole (BH) solutions on a static and spherically symmetric background. We show that asymptotically-flat hairy BH solutions realized for a spatially-flat maximally symmetric inter

Liang Wang, Zhiwen Zheng, Xiangchen Wu, Baihui Sang

On open source software (OSS) platforms such as GitHub, forking and accepting pull-requests is an important approach for OSS projects to receive contributions, especially from external contributors who cannot directly commit into the source repositories. Having a large number of forks is often considered as an indicator of a project being popular. While exte

Xinyue Shao, Hongzhi Wang, Xiao Zhu, Feng Xiong

Meta-learning is used to efficiently enable the automatic selection of machine learning models by combining data and prior knowledge. Since the traditional meta-learning technique lacks explainability, as well as shortcomings in terms of transparency and fairness, achieving explainability for meta-learning is crucial. This paper proposes FIND, an interpretab

Yulei Wang, Xin Cheng, Zining Ren, Mingde Ding

Current sheets (CSs), long stretching structures of magnetic reconnection above solar flare loops, are usually observed to oscillate, their origins, however, are still puzzled at present. Based on a high-resolution 2.5-dimensional MHD simulation of magnetic reconnection, we explore the formation mechanism of the CS oscillations. We find that large-amplitude

Guoxia Feng, Manli Song

In this paper, we prove restriction theorems for the Fourier-Laguerre transform and establish Strichartz estimates for the Schr\"{o}dinger propagator $e^{-itL_\alpha}$ for the Laguerre operator $L_\alpha=-\Delta-\sum_{j=1}^{n}(\dfrac{2\alpha_j+1}{x_j}\dfrac{\partial}{\partial x_j})+\dfrac{|x|^2}{4}$, $\alpha=(\alpha_1,\alpha_2,\cdots,\alpha_n)\in{(-\frac{1}{

Jason Yoo, Frank Wood

Associative memory plays an important role in human intelligence and its mechanisms have been linked to attention in machine learning. While the machine learning community's interest in associative memories has recently been rekindled, most work has focused on memory recall ($read$) over memory learning ($write$). In this paper, we present BayesPCN, a hierar

Chieh-Yu Chang, Yen-Tsung Chen, Yoshinori Mishiba

In this paper, we study multiple zeta values (abbreviated as MZV's) over function fields in positive characteristic. Our main result is to prove Thakur's basis conjecture, which plays the analogue of Hoffman's basis conjecture for real MZV's. As a consequence, we derive Todd's dimension conjecture, which is the analogue of Zagier's dimension conjecture for c

Wenrui Zhang, Ling Yang, Shijia Geng, Shenda Hong

Unsupervised/self-supervised representation learning in time series is critical since labeled samples are usually scarce in real-world scenarios. Existing approaches mainly leverage the contrastive learning framework, which automatically learns to understand the similar and dissimilar data pairs. Nevertheless, they are restricted to the prior knowledge of co

Haitham Khedr, Yasser Shoukry

We consider the problem of whether a Neural Network (NN) model satisfies global individual fairness. Individual Fairness suggests that similar individuals with respect to a certain task are to be treated similarly by the decision model. In this work, we have two main objectives. The first is to construct a verifier which checks whether the fairness property

Kwokwai Chan, Naichung Conan Leung, Ziming Nikolas Ma

In 2002, Fukaya proposed a remarkable explanation of mirror symmetry detailing the SYZ conjecture by introducing two correspondences: one between the theory of pseudo-holomorphic curves on a Calabi-Yau manifold $\check{X}$ and the multi-valued Morse theory on the base $\check{B}$ of an SYZ fibration $\check{p}: \check{X}\to \check{B}$, and the other between

Xinhan Wang, Huanlai Xing, Fuhong Song, Shouxi Luo

Multi-access edge computing (MEC) and network function virtualization (NFV) are promising technologies to support emerging IoT applications, especially those computation-intensive. In NFV-enabled MEC environment, service function chain (SFC), i.e., a set of ordered virtual network functions (VNFs), can be mapped on MEC servers. Mobile devices (MDs) can offlo

Susumu Naito, Yasunori Taguchi, Kouta Nakata, Yuichi Kato

This paper focuses on anomaly detection for multivariate time series data in large-scale fluid handling plants with dynamic components, such as power generation, water treatment, and chemical plants, where signals from various physical phenomena are observed simultaneously. In these plants, the need for anomaly detection techniques is increasing in order to

Alex S. Leong, Daniel E. Quevedo, Wanchun Liu

In this paper we consider the problem of remote state estimation of a Gauss-Markov process, where a sensor can, at each discrete time instant, transmit on one out of M different communication channels. A key difficulty of the situation at hand is that the channel statistics are unknown. We study the case where both learning of the channel reception probabili

Christian Gourieroux, Joann Jasiak

We show that the mixed causal-noncausal Vector Autoregressive (VAR) processes satisfy the Markov property in both calendar and reverse time. Based on that property, we introduce closed-form formulas of forward and backward predictive densities for point and interval forecasting and backcasting out-of-sample. The backcasting formula is used for adjusting the

Ta-Chung Chi, Ting-Han Fan, Peter J. Ramadge, Alexander I. Rudnicky

Relative positional embeddings (RPE) have received considerable attention since RPEs effectively model the relative distance among tokens and enable length extrapolation. We propose KERPLE, a framework that generalizes relative position embedding for extrapolation by kernelizing positional differences. We achieve this goal using conditionally positive defini

Masato Shirasaki, Takashi Okamoto, Shin'ichiro Ando

We study evolution of single subhaloes with their masses of $\sim10^9 M_\odot$ in a Milky-Way-sized host halo for self-interacting dark matter (SIDM) models. We perform dark-matter-only N-body simulations of dynamical evolution of individual subhaloes orbiting its host by varying self-scattering cross sections (including a velocity-dependent scenario), subha

J. S. Duan

Gray Tin is attracting much more interest as a topological quantum material, which has a precisely controlled composition and can be a material for spintronic devices. However, the spin dynamics in gray Tin is largely unknown. In this paper, we calculate the topological surface state of gray Tin by combining density functional theory and a tight-bind model,

Guanyu Hu, Yishu Xue, Weining Shen

We propose a multidimensional tensor clustering approach for studying how professional basketball players' shooting patterns vary over court locations and game time. Unlike most existing methods that only study continuous-valued tensors or have to assume the same cluster structure along different tensor directions, we propose a Bayesian nonparametric model t

C. McCutcheon, Y. Zeng, Z. -W. Liu, R. G. Izzard

In the single-degenerate scenario of Type Ia supernovae (SNe Ia), the interaction between high-speed ejected material and the donor star in a binary system is expected to lead to mass being stripped from the donor. A series of multi-dimensional hydrodynamical simulations of ejecta-donor interaction have been performed in previous studies most of which adopt

Jiahao Xu, Zihuai Lin

With the rapid development of information nowadays, spectrum resources are becoming more and more scarce, leading to a shift in the research direction from the modulation classification of a single signal to the modulation classification of multiple signals on the same channel. Therefore, the emergence of an effective mixed signals automatic modulation class

Arnob Kumar Ghosh, Tanay Nag

Being motivated by intriguing phenomena such as the breakdown of conventional bulk boundary correspondence and emergence of skin modes in the context of non-Hermitian (NH) topological insulators, we here propose a NH second-order topological superconductor (SOTSC) model that hosts Majorana zero modes (MZMs). Employing the non-Bloch form of NH Hamiltonian, we

Jinwoo Go, Tobin Isaac

The ranking of experiments by expected information gain (EIG) in Bayesian experimental design is sensitive to changes in the model's prior distribution, and the approximation of EIG yielded by sampling will have errors similar to the use of a perturbed prior. We define and analyze \emph{robust expected information gain} (REIG), a modification of the objectiv

Nirjhar Bhattacharjee, Krishnamurthy Mahalingam, Adrian Fedorko, Alexandria Will-Cole

Thin films of Topological insulators (TIs) coupled with ferromagnets (FMs) are excellent candidates for energy-efficient spintronics devices. Here, the effect of crystalline structural disorder of TI on interfacial and magnetic properties of sputter-deposited TI/FM, Bi2Te3/Ni80Fe20, heterostructures is reported. Ni and a smaller amount of Fe from Py was foun

Hyunki Min

We introduce a new method to obstruct Liouville and weak fillability. Using this, we show that various rational homology 3-spheres admit strongly fillable contact structures without Liouville fillings, which extends the result of Ghiggini on a family of Brieskorn spheres. We also make partial progress on a conjecture of Ghiggini and Van-Horn-Morris.

Avanika Narayan, Ines Chami, Laurel Orr, Simran Arora

Foundation Models (FMs) are models trained on large corpora of data that, at very large scale, can generalize to new tasks without any task-specific finetuning. As these models continue to grow in size, innovations continue to push the boundaries of what these models can do on language and image tasks. This paper aims to understand an underexplored area of F

Naoto Haraguchi, Kazunori Hase

The present study investigated the effect of a passive lower-limb exoskeleton on lumbar load and verified the effectiveness of biomechanical analysis for evaluating the physical burden while wearing the exoskeleton. Twelve healthy male participants performed an assembly task under three conditions: standing and high and low sitting while wearing the exoskele

Michael Minyi Zhang

We propose a non-linear, Bayesian non-parametric latent variable model where the latent space is assumed to be sparse and infinite dimensional a priori using an Indian buffet process prior. A posteriori, the number of instantiated dimensions in the latent space is guaranteed to be finite. The purpose of placing the Indian buffet process on the latent variabl

Rishikesh Yadav, Raphaël Huser, Thomas Opitz, Luigi Lombardo

To accurately quantify landslide hazard in a region of Turkey, we develop new marked point process models within a Bayesian hierarchical framework for the joint prediction of landslide counts and sizes. To accommodate for the dominant role of the few largest landslides in aggregated sizes, we leverage mark distributions with strong justification from extreme

Sameen Ahmed Khan, Ramaswamy Jagannathan

We derive a new eight dimensional matrix representation of the Maxwell equations for a linear homogeneous medium and extend it to the case of a linear inhomogneous medium. This derivation starts ab initio with the Maxwell equations and uses arguments based on the algebra of the Pauli matrices. This process leads automatically to the matrix representation bas

Elliott Gordon-Rodriguez, Thomas P. Quinn, John P. Cunningham

Data augmentation plays a key role in modern machine learning pipelines. While numerous augmentation strategies have been studied in the context of computer vision and natural language processing, less is known for other data modalities. Our work extends the success of data augmentation to compositional data, i.e., simplex-valued data, which is of particular

Yichen Yang, Kai Jia, Martin Rinard

We study the impact of player capability on social welfare in congestion games. We introduce a new game, the Distance-bounded Network Congestion game (DNC), as the basis of our study. DNC is a symmetric network congestion game with a bound on the number of edges each player can use. We show that DNC is PLS-complete in contrast to standard symmetric network c

Jo Plested, Musa Phiri, Tom Gedeon

Deep neural networks such as convolutional neural networks (CNNs) and transformers have achieved many successes in image classification in recent years. It has been consistently demonstrated that best practice for image classification is when large deep models can be trained on abundant labelled data. However there are many real world scenarios where the req

Jamie Tayar, Meridith Joyce

The abundances of mixing--sensitive elements including lithium, [C/N], and 12C/13C are known to change near the red giant branch bump. The explanation most often offered for these alterations is double diffusive thermohaline mixing in the stellar interior. In this analysis, we investigate the ability of thermohaline mixing to explain the observed timing of t

Mohammad Tariqul Islam, Jason W. Fleischer

In this paper, we introduce a neighbor embedding framework for manifold alignment. We demonstrate the efficacy of the framework using a manifold-aligned version of the uniform manifold approximation and projection algorithm. We show that our algorithm can learn an aligned manifold that is visually competitive to embedding of the whole dataset.

Armin Straub

Rowland and Zeilberger devised an approach to algorithmically determine the modulo $p^r$ reductions of values of combinatorial sequences representable as constant terms (building on work of Rowland and Yassawi). The resulting $p$-schemes are systems of recurrences and, depending on their shape, are classified as automatic or linear. We revisit this approach,

Ouns El Harzli, Bernardo Cuenca Grau, Ian Horrocks

In recent years, there has been increasing interest in explanation methods for neural model predictions that offer precise formal guarantees. These include abductive (respectively, contrastive) methods, which aim to compute minimal subsets of input features that are sufficient for a given prediction to hold (respectively, to change a given prediction). The c

Minzhao Liu, Junyu Liu, Yuri Alexeev, Liang Jiang

Random quantum circuits have been utilized in the contexts of quantum supremacy demonstrations, variational quantum algorithms for chemistry and machine learning, and blackhole information. The ability of random circuits to approximate any random unitaries has consequences on their complexity, expressibility, and trainability. To study this property of rando

Avishek Ghosh, Abishek Sankararaman

We prove an instance independent (poly) logarithmic regret for stochastic contextual bandits with linear payoff. Previously, in \cite{chu2011contextual}, a lower bound of $\mathcal{O}(\sqrt{T})$ is shown for the contextual linear bandit problem with arbitrary (adversarily chosen) contexts. In this paper, we show that stochastic contexts indeed help to reduce

Neeraj Varshney, Swaroop Mishra, Chitta Baral

Curriculum learning strategies in prior multi-task learning approaches arrange datasets in a difficulty hierarchy either based on human perception or by exhaustively searching the optimal arrangement. However, human perception of difficulty may not always correlate well with machine interpretation leading to poor performance and exhaustive search is computat

Trong Nguyen

On January 1, 2020, California passed the California Consumer Privacy Act (CCPA) by more than 56% of voters intended to enhance privacy rights and consumer protection for residents of California, United States. Since then, more conditions have been added to the Act to support consumers' privacy. In addition, two years after the first effective day of CCPA, c

Skip Garibaldi, Holger P. Petersson, Michel L. Racine

Albert algebras, a specific kind of Jordan algebra, are naturally distinguished objects among commutative non-associative algebras and also arise naturally in the context of simple affine group schemes of type $F_4$, $E_6$, or $E_7$. We study these objects over an arbitrary base ring $R$, with particular attention to the case of the integers. We prove in thi

Jiaze Sun, Jiahui Deng, Yang Li, Nan Han

District energy systems can not only reduce energy consumption but also set energy supply dispatching schemes according to demand. In addition to economic cost, energy consumption and pollutant are more worthy of attention when evaluating combined cooling, heating and power (CCHP) models. In this paper, the CCHP model is established with the objective of eco

Simon Bray, Victor Tänzel, Steffen Wolf

We present two methods to reveal protein-ligand unbinding mechanisms in biased unbinding simulations by clustering trajectories into ensembles representing unbinding paths. The first approach is based on a contact principal component analysis for reducing the dimensionality of the input data, followed by identification of unbinding paths and training a machi

Lesław Rachwał

We extensively motivate the studies of higher-derivative gravities, and in particular we emphasize which new quantum features theories with six derivatives in their definitions possess. Next, we discuss the mathematical structure of the exact on the full quantum level beta functions obtained previously for three couplings in front of generally covariant term

Giorgi Basiashvili, Zain Ul Abideen, Samuel Pagliarini

Numerous security threats are emerging from untrusted players in the integrated circuit (IC) ecosystem. Among them, reverse engineering practices with the intent to counterfeit, overproduce, or modify an IC are worrying. In recent years, various techniques have been proposed to mitigate the aforementioned threats but no technique seems to be adequate to hide

Siddhartha Datta, Nigel Shadbolt

Inspired by recent work on neural subspaces and mode connectivity, we revisit parameter subspace sampling for shifted and/or interpolatable input distributions (instead of a single, unshifted distribution). We enforce a compressed geometric structure upon a set of trained parameters mapped to a set of train-time distributions, denoting the resulting subspace

Estelle Sterrett, Waylon Jepsen, Evan Kim

The current design space of derivatives in Decentralized Finance (DeFi) relies heavily on oracle systems. Replicating market makers (RMMs) provide a mechanism for converting specific payoff functions to an associated Constant Function Market Makers (CFMMs). We leverage RMMs to replicate the approximate payoff of a Black-Scholes covered call option. RMM-01 is

Jinlyu Cao, Herb Fertig, Luis Brey

Plasmons are elementary quantum excitations of conducting materials with Fermi surfaces. In two dimensions they may carry a static dipole moment that is transverse to their momentum which is quantum geometric in nature, the quantum geometric dipole (QGD). We show that this property is also realized for such materials confined in nanowire geometries. Focusing

Matías R. Bender

Solving systems of polynomial equations is a central problem in nonlinear and computational algebra. Since Buchberger's algorithm for computing Gr\"obner bases in the 60s, there has been a lot of progress in this domain. Moreover, these equations have been employed to model and solve problems from diverse disciplines such as biology, cryptography, and roboti

Sara Khosravi, Hossein S. Ghadikolaeiy, Jens Zander, Marina Petrova

Due to the large bandwidth available, millimeter-Wave (mmWave) bands are considered a viable opportunity to significantly increase the data rate in cellular and wireless networks. Nevertheless, the need for beamforming and directional communication between the transmitter and the receiver increases the complexity of the channel estimation and link establishm

Miguel Aguilera, Masanao Igarashi, Hideaki Shimazaki

Most natural systems operate far from equilibrium, displaying time-asymmetric, irreversible dynamics characterized by a positive entropy production while exchanging energy and matter with the environment. Although stochastic thermodynamics underpins the irreversible dynamics of small systems, the nonequilibrium thermodynamics of larger, more complex systems

Huai-Ke Guo, Andrew Miller

A compact object with a mass $\mathcal{O}(1 \sim 1000) M_{\odot}$, such as a black hole of stellar or primordial origin or a neutron star, and a much lighter exotic compact object with a subsolar mass could form a non-standard mini extreme mass ratio inspiral (EMRI) and emit gravitational waves within the frequency band of ground-based gravitational-wave det