November 2022 arXiv papers — page 134

Arusarka Bose, Zili Zhou, Guandong Xu

Increasing number of COVID-19 research literatures cause new challenges in effective literature screening and COVID-19 domain knowledge aware Information Retrieval. To tackle the challenges, we demonstrate two tasks along withsolutions, COVID-19 literature retrieval, and question answering. COVID-19 literature retrieval task screens matching COVID-19 literat

Dan Qin

Based on a nationwide labour-force survey data, this paper investigates the influence of cultural variance on migrants' settlement intention in China. By using dialectal distance as a proxy for cultural distance, we find strong evidence for the negative effects of cultural distance on migrants' settlement intention. By further investigation into sub-samples

Moritz Korte-Stapff, Drew Yarger, Stilian Stoev, Tailen Hsing

Leveraging available measurements of our environment can help us understand complex processes. One example is Argo Biogeochemical data, which aims to collect measurements of oxygen, nitrate, pH, and other variables at varying depths in the ocean. We focus on the oxygen data in the Southern Ocean, which has implications for ocean biology and the Earth's carbo

Dipendra Jha, K. V. L. V. Narayanachari, Ruifeng Zhang, Justin Liao

Despite the huge advancement in knowledge discovery and data mining techniques, the X-ray diffraction (XRD) analysis process has mostly remained untouched and still involves manual investigation, comparison, and verification. Due to the large volume of XRD samples from high-throughput XRD experiments, it has become impossible for domain scientists to process

Weslley da Silva Pereira, Ali Lotfi, Julien Langou

Generating 2-by-2 unitary matrices in floating-precision arithmetic is a delicate task. One way to reduce the accumulation error is to use less floating-point operations to compute each of the entries in the 2-by-2 unitary matrix. This paper shows an algorithm that reduces the number of operations to compute the entries of a Givens rotation. Overall, the new

Liang Peng, Boqi Li, Wenhao Yu, Kai Yang

Autonomous driving confronts great challenges in complex traffic scenarios, where the risk of Safety of the Intended Functionality (SOTIF) can be triggered by the dynamic operational environment and system insufficiencies. The SOTIF risk is reflected not only intuitively in the collision risk with objects outside the autonomous vehicles (AVs), but also inher

Kaanapuli Ramkumar, Harishyam Kumar, Pankaj Jain

We study the fusion of a proton with a nucleus with the emission of two photons at low incident energy of the order of eV or smaller. We use a step model for the repulsive potential between proton and the nuclei. We consider the reaction both in free space and inside a medium. We make a simple model for the medium by assuming a hard wall potential beyond a c

A. A. Ovchinnikov

We study the local lattice integrable regularization of the Sine-Gordon model written down in terms of the lattice Bose-operators. We show that the local spin Hamiltonian obtained from the six-vertex model with alternating inhomogeneities in fact leads to the Sine-Gordon model in the low-energy limit. We show that the Bethe Ansatz results for this model lead

Albertus Hariwangsa Panuluh

This research investigate that science process skills significantly improve after doing some practicum activities. The research population are fifth semester physics education students and the research sample are fifth semester physics education students who was doing electricity and magnetism experiment C class course. We used two questionnaires, the first

Eddy Hudson, Ishan Durugkar, Garrett Warnell, Peter Stone

Given a dataset of expert agent interactions with an environment of interest, a viable method to extract an effective agent policy is to estimate the maximum likelihood policy indicated by this data. This approach is commonly referred to as behavioral cloning (BC). In this work, we describe a key disadvantage of BC that arises due to the maximum likelihood o

K. Ton, G. Kestler, D. Filin, C. Cheung

Neutral atoms trapped in the evanescent optical potentials of nanotapered optical fibers are a promising platform for developing quantum technologies and exploring fundamental science, such as quantum networks and quantum electrodynamics. Building on the successful advancements with trapped alkali atoms, here we demonstrate a state-insensitive optical dipole

Rudy Rodsphon

We provide a short account of a classical folklore result in connection to the local index theorem, which identifies the short-time limit of the JLO cocycle of the Dirac operator to de Rham current obtained by the cap product of the fundamental class with the \^A-genus.

Robin K. S. Hankin

The free algebra is an interesting and useful algebraic object. Here I introduce "freealg", an R package which furnishes computational support for free algebras. The package uses the standard template library's "map" class for efficiency, which uses the fact that the order of the terms is algebraically immaterial. The package follows "disordR" discipline. I

Tuan N. Tang, Jungin Park, Kwonyoung Kim, Kwanghoon Sohn

Online Temporal Action Localization (On-TAL) aims to immediately provide action instances from untrimmed streaming videos. The model is not allowed to utilize future frames and any processing techniques to modify past predictions, making On-TAL much more challenging. In this paper, we propose a simple yet effective framework, termed SimOn, that learns to pre

Lei Kou, Xiao-dong Gong, Yi Zheng, Xiu-hui Ni

Three-phase PWM voltage-source rectifier (VSR) systems have been widely used in various energy conversion systems, where current sensors are the key component for state monitoring and system control. The current sensor faults may bring hidden danger or damage to the whole system; therefore, this paper proposed a random forest (RF) and current fault texture f

Jincheng Hu, Yang Lin, Liang Chu, Zhuoran Hou

The high emission and low energy efficiency caused by internal combustion engines (ICE) have become unacceptable under environmental regulations and the energy crisis. As a promising alternative solution, multi-power source electric vehicles (MPS-EVs) introduce different clean energy systems to improve powertrain efficiency. The energy management strategy (E

Zachary Kincaid, Nicolas Koh, Shaowei Zhu

This paper presents a theory of non-linear integer/real arithmetic and algorithms for reasoning about this theory. The theory can be conceived as an extension of linear integer/real arithmetic with a weakly-axiomatized multiplication symbol, which retains many of the desirable algorithmic properties of linear arithmetic. In particular, we show that the conju

Dorit Aharonov, Xun Gao, Zeph Landau, Yunchao Liu

We give a polynomial time classical algorithm for sampling from the output distribution of a noisy random quantum circuit in the regime of anti-concentration to within inverse polynomial total variation distance. This gives strong evidence that, in the presence of a constant rate of noise per gate, random circuit sampling (RCS) cannot be the basis of a scala

Eduardo Stella, Celso L. Ladera

Adiabatic Invariance (AdI), Hill Equation formalism (HEF), and the Least Action Principle (LAP), three relevant tools of theoretical physics are here separately applied to a one-dimensional parametric oscillator of time-variable frequency that depends on an integer parameter Lambda. This oscillator is subjected to a perturbation which is a functional of the

Rudy Rodsphon

This article aims to explore new perspectives offered by Kasparov's recent work on transverse index theory in the context of actions of compact Lie groups on a manifold, and hints at potential connections to the work of Berline-Vergne and Paradan-Vergne.

Rui Chen, Oktay Gunluk, Andrea Lodi, Guanyi Wang

Online platforms increasingly rely on sequential decision-making algorithms to allocate resources, match users, or control exposure, while facing growing pressure to ensure fairness over time. We study a general online decision-making framework in which a platform repeatedly makes decisions from possibly non-convex and discrete feasible sets, such as indivis

Rudy Rodsphon

This is a first investigation by the author of the similarity between Quillen's superconnection formalism, his constructions of (periodic) cyclic cocycles via algebra cochains on a bar construction, and Kasparov bimodules for KK-theory. In this article, we do so by deriving a slight extension of the Mathai-Quillen Thom form via a bivariant JLO cocycle. The m

Takuya Mieno, Mitsuru Funakoshi, Shunsuke Inenaga

A trie $\mathcal{T}$ is a rooted tree such that each edge is labeled by a single character from the alphabet, and the labels of out-going edges from the same node are mutually distinct. Given a trie $\mathcal{T}$ with $n$ edges, we show how to compute all distinct palindromes and all maximal palindromes on $\mathcal{T}$ in $O(n)$ time, in the case of integer

Zhun Deng, He Sun, Zhiwei Steven Wu, Linjun Zhang

AI methods are used in societally important settings, ranging from credit to employment to housing, and it is crucial to provide fairness in regard to algorithmic decision making. Moreover, many settings are dynamic, with populations responding to sequential decision policies. We introduce the study of reinforcement learning (RL) with stepwise fairness const

Rudy Rodsphon

This short note establishes a relationship between a generalized version of the Radul residue cocycle introduced in former works of the author and the Connes-Moscovici residue cocycle, and discusses the applicability of such a formula to manifolds with conical singularities, where zeta functions of Fuchs-type pseudodifferential operators may exhibit double o

Takuma Usuzaki

Vision Transformer (ViT) has achieved outstanding results in computer vision. Although there are many Transformer-based architectures derived from the original ViT, the dimension of patches are often the same with each other. This disadvantage leads to a limited application range in the medical field because in the medical field, datasets whose dimension is

Kopal Garg, Jennifer Yu, Tina Behrouzi, Sana Tonekaboni

Identifying change points (CPs) in a time series is crucial to guide better decision making across various fields like finance and healthcare and facilitating timely responses to potential risks or opportunities. Existing Change Point Detection (CPD) methods have a limitation in tracking changes in the joint distribution of multidimensional features. In addi

Yik-Cheung Tam, Jiacheng Xu, Jiakai Zou, Zecheng Wang

Performance of spoken language understanding (SLU) can be degraded with automatic speech recognition (ASR) errors. We propose a novel approach to improve SLU robustness by randomly corrupting clean training text with an ASR error simulator, followed by self-correcting the errors and minimizing the target classification loss in a joint manner. In the proposed

Yifei Zhou, Zilu Li, Abhinav Shrivastava, Hengshuang Zhao

Modern retrieval system often requires recomputing the representation of every piece of data in the gallery when updating to a better representation model. This process is known as backfilling and can be especially costly in the real world where the gallery often contains billions of samples. Recently, researchers have proposed the idea of Backward Compatibl

Hiroki Iida, Naoaki Okazaki

IR models using a pretrained language model significantly outperform lexical approaches like BM25. In particular, SPLADE, which encodes texts to sparse vectors, is an effective model for practical use because it shows robustness to out-of-domain datasets. However, SPLADE still struggles with exact matching of low-frequency words in training data. In addition

Ben Kane, Daejun Kim

In this paper, we consider the decomposition of theta series for lattice cosets of ternary lattices. We show that the natural decomposition into an Eisenstein series, a unary theta function, and a cuspidal form which is orthogonal to unary theta functions correspond to the theta series for the genus, the deficiency of the theta series for the spinor genus fr

A. Tamii, L. Pellegri, P. -A. Söderström, D. Allard

Photo-nuclear reactions of light nuclei below a mass of $A=60$ are studied experimentally and theoretically by the PANDORA (Photo-Absorption of Nuclei and Decay Observation for Reactions in Astrophysics) project. Two experimental methods, virtual-photon excitation by proton scattering and real-photo absorption by a high-brilliance gamma-ray beam produced by

Tavor Z. Baharav, Tze Leung Lai

Data depth, introduced by Tukey (1975), is an important tool in data science, robust statistics, and computational geometry. One chief barrier to its broader practical utility is that many common measures of depth are computationally intensive, requiring on the order of $n^d$ operations to exactly compute the depth of a single point within a data set of $n$

Yuqin Yang, AmirEmad Ghassami, Mohamed Nafea, Negar Kiyavash

We focus on causal discovery in the presence of measurement error in linear systems where the mixing matrix, i.e., the matrix indicating the independent exogenous noise terms pertaining to the observed variables, is identified up to permutation and scaling of the columns. We demonstrate a somewhat surprising connection between this problem and causal discove

Liyuan Hu, Mengbing Li, Chengchun Shi, Zhenke Wu

This paper studies reinforcement learning (RL) in doubly inhomogeneous environments under temporal non-stationarity and subject heterogeneity. In a number of applications, it is commonplace to encounter datasets generated by system dynamics that may change over time and population, challenging high-quality sequential decision making. Nonetheless, most existi

Chang-Jian Zhao

In this paper, we establish an Orlicz log-Aleksandrov-Fenchel inequality by introducing new concepts of mixed volume measure and Orlicz multiple mixed volume measure, and using the Orlicz-Aleksandrov-Fenchel inequality. The Orlicz log-Aleksandrov-Fenchel inequality in special cases yield the classical Aleksandrov-Fenchel inequality and Orlicz log-Minkowski t

Cao-Kha Doan, Thi-Thao-Phuong Hoang, Lili Ju, Katharina Schratz

This paper is concerned with conditionally structure-preserving, low regularity time integration methods for a class of semilinear parabolic equations of Allen-Cahn type. Important properties of such equations include maximum bound principle (MBP) and energy dissipation law; for the former, that means the absolute value of the solution is pointwisely bounded

Lingyuan Gao, Robert B. Wexler, Ruixiang Fei, Andrew M. Rappe

Using first principles calculations, we study the ground-state structure of bulk proton-exchanged lithium niobate, which is also called hydrogen niobate and is widely used in waveguides. Thermodynamics helps to establish the most favorable nonpolar surface as well as the water-deficient and water-rich phases under different ambient conditions, which we refer

Feiyan Zhao, Xiaoxi Xu, Hexiang He, Li Zhang

We report solutions for stable compound solitons in a three-dimensional quasi-phase-matched photonic crystal with the quadratic ($\chi ^{(2)}$) nonlinearity. The photonic crystal is introduced with a checkerboard structure, which can be realized by means of the available technology. The solitons are built as four-peak vortex modes of two types, rhombuses and

Bo Tang, Vijay K. Shah, Vuk Marojevic, Jeffrey H. Reed

Openness and intelligence are two enabling features to be introduced in next generation wireless networks, e.g. Beyond 5G and 6G, to support service heterogeneity, open hardware, optimal resource utilization, and on-demand service deployment. The open radio access network (O-RAN) is a promising RAN architecture to achieve both openness and intelligence throu

Don Krieger, Paul Shepard, David O. Okonkwo

Neuroelectric currents were extracted from free-running magnetoencephalographic (MEG) rest and task recordings from 617 normative subjects (ages: 18-87). State-dependent neuroelectric differential activation (DA) with spatial resolution comparable to that of local field potentials was detected in the majority of this cohort. Rest-high (rest greater than task

Yunsheng Tian, Jie Xu, Yichen Li, Jieliang Luo

Assembly planning is the core of automating product assembly, maintenance, and recycling for modern industrial manufacturing. Despite its importance and long history of research, planning for mechanical assemblies when given the final assembled state remains a challenging problem. This is due to the complexity of dealing with arbitrary 3D shapes and the high

Matthew Harrison-Trainor, Dhruv Kulshreshtha

We work in the setting of Zermelo-Fraenkel set theory without assuming the Axiom of Choice. We consider sets with the Boolean operations together with the additional structure of comparing cardinality (in the Cantorian sense of injections). What principles does one need to add to the laws of Boolean algebra to reason not only about intersection, union, and c

Haoyu Wang

The smallest singular value and condition number play important roles in numerical linear algebra and the analysis of algorithms. In numerical analysis with randomness, many previous works make Gaussian assumptions, which are not general enough to reflect the arbitrariness of the input. To overcome this drawback, we prove the first quantitative universality

Daichi Hiramatsu, Edo Berger, Brian D. Metzger, Sebastian Gomez

We present the largest compilation to date of optical observations during and following fast radio bursts (FRBs). The data set includes our dedicated simultaneous and follow-up observations, as well as serendipitous archival survey observations, for a sample of 15 well-localized FRBs: eight repeating and seven one-off sources. Our simultaneous (and nearly si

Eddy Keming Chen

Two of the most difficult problems in the foundations of physics are (1) what gives rise to the arrow of time and (2) what the ontology of quantum mechanics is. They are difficult because the fundamental dynamical laws of physics do not privilege an arrow of time, and the quantum-mechanical wave function describes a high-dimensional reality that is radically

Jinwuk Seok, Chang Sik Cho

In this study, we propose a global optimization algorithm based on quantizing the energy level of an objective function in an NP-hard problem. According to the white noise hypothesis for a quantization error with a dense and uniform distribution, we can regard the quantization error as i.i.d. white noise. From stochastic analysis, the proposed algorithm conv

Muhammad Abid Anwar, Munir Ali, Dong Pu, Srikrishna Chanakya Bodepudi

The performance of nanoscale electronic devices based on a two-three dimensional (2D-3D) interface is significantly affected by the electrical contacts that interconnect these materials with external circuitry. This work investigates charge transport effects at the 2D-3D ohmic contact coupled with the thermionic injection model for graphene/Si Schottky junct

Han Nguyen, Hai Pham, Sashank J. Reddi, Barnabás Póczos

Despite their popularity in deep learning and machine learning in general, the theoretical properties of adaptive optimizers such as Adagrad, RMSProp, Adam or AdamW are not yet fully understood. In this paper, we develop a novel framework to study the stability and generalization of these optimization methods. Based on this framework, we show provable guaran

Frederik Geth

The existence of strictly positive lower bounds on voltage magnitude is taken for granted in optimal power flow problems. Nevertheless, it is not possible to rely on such bounds for a variety of real-world network optimization problems. This paper discusses a few issues related to 0 V assumptions made during the process of deriving optimization formulations

Jie Zhao, Jinhui Chen, Xu-Guang Huang, Yu-Gang Ma

Ultraperipheral heavy-ion collisions (UPCs) offer unique opportunities to study processes under strong electromagnetic fields. In these collisions, highly charged fast-moving ions carry strong electromagnetic fields that can be effectively treated as photon fluxes. The exchange of photons can induce photonuclear and two-photon interactions, and excite ions.

Osamu Narikiyo

The weak-field Hall conductivity in metals is interpreted in terms of the curvature of the Fermi surface in the main part. In the appendix the orbital magnetic-susceptibility and the magneto-conductivity in metals are discussed focusing on Peierls' area factor.

Jonah Blasiak, Holden Eriksson, Pavlo Pylyavskyy, Isaiah Siegl

The machinery of noncommutative Schur functions is a general approach to Schur positivity of symmetric functions initiated by Fomin-Greene. Hwang recently adapted this theory to posets to give a new approach to the Stanley-Stembridge conjecture. We further develop this theory to prove that the symmetric function associated to any $P$-Knuth equivalence graph

Soumajyoti Sarkar, Kaixiang Lin, Sailik Sengupta, Leonard Lausen

The use of multilingual language models for tasks in low and high-resource languages has been a success story in deep learning. In recent times, Arabic has been receiving widespread attention on account of its dialectal variance. While prior research studies have tried to adapt these multilingual models for dialectal variants of Arabic, it still remains a ch

Hai-Yang Cheng

The decay amplitude analyses of $B^\pm\to \pi^+ \pi^-\pi^\pm$ decays in the Dalitz plot performed by LHCb indicate that CP asymmetry for the dominant quasi-two-body decay $B^\pm\to\rho(770)^0\pi^\pm$ was found to be consistent with zero in all three approaches for the $S$-wave component and that CP-violation effects related to the interference between the $\

Tsogbayar Tsednee, Banzragch Tsednee, Tsookhuu Khinayat

In this work we employ the split-step technique combined with a Legendre pseudospectral representation to solve various time-dependent Gross-Pitaevskii equations (GPE). Our findings based on the numerical accuracy of this approach applied for one-dimensional (1D) and two-dimensional (2D) problems show that it can provide accurate and stable solutions. Moreov

Deeksha Adil, Rasmus Kyng, Richard Peng, Sushant Sachdeva

The $\ell_p$-norm regression problem is a classic problem in optimization with wide ranging applications in machine learning and theoretical computer science. The goal is to compute $x^{\star} =\arg\min_{Ax=b}\|x\|_p^p$, where $x^{\star}\in \mathbb{R}^n, A\in \mathbb{R}^{d\times n},b \in \mathbb{R}^d$ and $d\leq n$. Efficient high-accuracy algorithms for the

Young Myoung Ko, Jin Xu

Motivated by the ongoing COVID-19 pandemic, this paper investigates customers' infection risk by evaluating the overlapping time of a virtual customer with others in queueing systems. Most of the current methodologies focus on characterizing the risk in stationary systems, which may not apply to the more practical time-varying systems. As such, we propose an

Banzragch Tsednee, Tsogbayar Tsednee, Tsookhuu Khinayat

The binary additive hard-sphere mixtures have been studied by the Ornstein-Zernike integral equation coupled with the Martynov-Sarkisov (MS) closure approximation. Virial equation of state is computed in the MS approximation. The excess chemical potential for the mixture is evaluated with a closed-form expression based on correlation functions. The excess He

Chee Kwan Gan, Eng Kang Koh

We propose a size-consistent Gr\"uneisen-quasiharmonic approach (GQA) to calculate the lattice thermal conductivity $\kappa_l$ where the Gr\"uneisen parameters that measure the degree of phonon anharmonicity are calculated directly using first-principles calculations. This is achieved by identifying and modifying two existing equations related to the Slack f

Zhihui Xie, Zichuan Lin, Junyou Li, Shuai Li

The past few years have seen rapid progress in combining reinforcement learning (RL) with deep learning. Various breakthroughs ranging from games to robotics have spurred the interest in designing sophisticated RL algorithms and systems. However, the prevailing workflow in RL is to learn tabula rasa, which may incur computational inefficiency. This precludes

Marcello Miranda, Pierre-Antoine Graham, Valerio Faraoni

We apply to tensor-multi-scalar gravity the effective fluid analysis based on the representation of the gravitational scalar field as a dissipative effective fluid. This generalization poses new challenges as the effective fluid is now a complicated mixture of individual fluids mutually coupled to each other and many reference frames are possible for its des

Shuntaro Okada, Masayuki Ohzeki

Quantum annealing is a heuristic algorithm for searching the ground state of an Ising model. Heuristic algorithms aim to obtain near-optimal solutions with a reasonable computation time. Accordingly, many algorithms have so far been proposed. In general, the performance of heuristic algorithms strongly depends on the instance of the combinatorial optimizatio

Lianyu Hu, Mudi Jiang, Yan Liu, Zengyou He

Although numerous algorithms have been proposed to solve the categorical data clustering problem, how to access the statistical significance of a set of categorical clusters remains unaddressed. To fulfill this void, we employ the likelihood ratio test to derive a test statistic that can serve as a significance-based objective function in categorical data cl

Ho Hsiao, Ed Bennett, Deog Ki Hong, Jong-Wan Lee

Chimera baryons are an important element of strongly coupled theories that provide a microscopic origin for UV complete composite Higgs models (CHMs), since they play the role of top partners in top partial compositeness. In a particular interesting realisation of CHMs based upon an underlying $Sp(4)$ gauge theory, such exotic objects are composed of two fer

Diana C. Rivera-Agudelo, S. L. Tostado

Within the different patterns of the neutrino mixing matrix, the cobimaximal mixing remains a plausible possibility for understanding the flavor structure of neutrinos as it is consistent with current experimental data. Such a pattern is related to a concrete form of the mass matrix, displaying a $\mu-\tau$ reflection symmetry, which has motivated many theor

Isaiah Siegl

For a $(3+1)$-free poset $P$, we define a hybrid of $P$-tableaux and cylindric tableaux called cylindric $P$-tableaux. We introduce $P$-analogs of cylindric Schur functions, defined by a determinantal formula, and prove that they are the weight generating functions of cylindric $P$-tableaux. We deduce that certain sums of the $e$-expansion coefficients of th

Alen Alexanderian, Ruanui Nicholson, Noemi Petra

We consider optimal experimental design (OED) for Bayesian nonlinear inverse problems governed by partial differential equations (PDEs) under model uncertainty. Specifically, we consider inverse problems in which, in addition to the inversion parameters, the governing PDEs include secondary uncertain parameters. We focus on problems with infinite-dimensional

Ryokichi Tanaka

We show that non-elementary random walks on word hyperbolic groups with finite first moment are not noise sensitive in a strong sense for small noise parameters.

Qian Li, Shafiq Joty, Daling Wang, Shi Feng

Sparsity of formal knowledge and roughness of non-ontological construction make sparsity problem particularly prominent in Open Knowledge Graphs (OpenKGs). Due to sparse links, learning effective representation for few-shot entities becomes difficult. We hypothesize that by introducing negative samples, a contrastive learning (CL) formulation could be benefi

Omar Mrani-Zentar, Ryan Simpson, Serdar Yüksel

In decentralized stochastic control (or stochastic team theory) and game theory, if there is a pre-defined order in a system in which agents act, the system is called \textit{sequential}, otherwise it is non-sequential. Much of the literature on stochastic control theory, such as studies on the existence analysis, approximation methods, and on dynamic progra

Hana Gil, Chang Ho Hyun, K. S. Kim

Recent measurement of the parity-violating (PV) asymmetry in the elastic electron scattering on $^{27}$Al target evokes the interest in the distribution of the neutron in the nucleus. In this work, we calculate the neutron skin thickness ($R_{np}$) of $^{27}$Al with nonrelativistic nuclear structure models. We focus on the role of the effective mass, symmetr

Jian-Min Wang, Yu-Yang Songsheng, Yan-Rong Li, Pu Du

There are increasing interests in binary supermassive black holes (SMBHs), but merging binaries with separations smaller than ~1 light days (~10^2 gravitational radii for 10^8 Msun), which are rapidly evolving under control of gravitational waves, are elusive in observations. In this paper, we discuss fates of mini-disks around component SMBHs for three regi

Hongjun Choi, Eun Som Jeon, Ankita Shukla, Pavan Turaga

Mixup is a popular data augmentation technique based on creating new samples by linear interpolation between two given data samples, to improve both the generalization and robustness of the trained model. Knowledge distillation (KD), on the other hand, is widely used for model compression and transfer learning, which involves using a larger network's implici

Raul Enrique Rodriguez Luna, Jose Luis Rosenstiehl Martinez

The purpose of this article is to explore the digital behavior of nature tourism SMEs in the department of Magdalena-Colombia, hereinafter referred to as the region. In this sense, the concept of endogenization as an evolutionary mechanism refers to the application of the discrete choice model as an engine of analysis for the variables to be studied within t

Kanato Goto, Masahiro Nozaki, Kotaro Tamaoka, Mao Tian Tan

We study the dynamical properties of a strongly scrambling quantum circuit involving a projective measurement on a finite-sized region by studying the operator entanglement entropy and mutual information (OEE and BOMI) of the dual operator state that corresponds to this quantum circuit. The time-dependence of the OEE exhibits a new dynamical behavior of oper

Matthew Peterson, Tonia Korves, Christopher Garay, Robyn Kozierok

This report presents the evaluation approach developed for the DARPA Big Mechanism program, which aimed at developing computer systems that will read research papers, integrate the information into a computer model of cancer mechanisms, and frame new hypotheses. We employed an iterative, incremental approach to the evaluation of the three phases of the progr

Chuan Guo, Kamalika Chaudhuri, Pierre Stock, Mike Rabbat

In private federated learning (FL), a server aggregates differentially private updates from a large number of clients in order to train a machine learning model. The main challenge in this setting is balancing privacy with both classification accuracy of the learnt model as well as the number of bits communicated between the clients and server. Prior work ha

Jung Jun Park, Kyunghyun Baek, M. S. Kim, Hyunchul Nha

Quantum search algorithms offer a remarkable advantage of quadratic reduction in query complexity using quantum superposition principle. However, how an actual architecture may access and handle the database in a quantum superposed state has been largely unexplored so far; the quantum state of data was simply assumed to be prepared and accessed by a black-bo

Satwik Kottur, Seungwhan Moon, Aram H. Markosyan, Hardik Shah

People capture photos and videos to relive and share memories of personal significance. Recently, media montages (stories) have become a popular mode of sharing these memories due to their intuitive and powerful storytelling capabilities. However, creating such montages usually involves a lot of manual searches, clicks, and selections that are time-consuming

Chandra Sekhar Mukherjee, Jiapeng Zhang

Community detection in the stochastic block model is one of the central problems of graph clustering. Since its introduction, many subsequent papers have made great strides in solving and understanding this model. In this setup, spectral algorithms have been one of the most widely used frameworks. However, despite the long history of study, there are still u

Fan Yang, Yue Wang, Jian-liang Wu

By a well-known theorem of Thomassen and a planar graph depicted by Voigt, we know that every planar graph is $5$-choosable, and the bound is tight. In 1999, Lam, Xu and Liu reduced $5$ to $4$ on $C_4$-free planar graphs. In the paper, by applying the famous Combinatorial Nullstellensatz, we design an effective algorithm to deal with list coloring problems.

Kameswara Bharadwaj Mantha, Ramanakumar Sankar, Yuping Zheng, Lucy Fortson

Many scientific domains gather sufficient labels to train machine algorithms through human-in-the-loop techniques provided by the Zooniverse.org citizen science platform. As the range of projects, task types and data rates increase, acceleration of model training is of paramount concern to focus volunteer effort where most needed. The application of Transfer

Ou Deng, Qun Jin

This paper explores human behavior in virtual networked communities, specifically individuals or groups' potential and expressive capacity to respond to internal and external stimuli, with assortative matching as a typical example. A modeling approach based on Multi-Agent Reinforcement Learning (MARL) is proposed, adding a multi-head attention function to th

Selim Romero, Tunna Baruah, Rajendra R. Zope

Accurate prediction of spin-state energy difference is crucial for understanding the spin crossover (SCO) phenomena and is very challenging for the density functional approximations, especially for the local and semi-local approximations, due to delocalization errors. Here, we investigate the effect of self-interaction error removal from the local spin densi

Jianyu Wang, Linruize Tang, Jie Chen, Jingdong Chen

Nonnegative Tucker Factorization (NTF) minimizes the euclidean distance or Kullback-Leibler divergence between the original data and its low-rank approximation which often suffers from grossly corruptions or outliers and the neglect of manifold structures of data. In particular, NTF suffers from rotational ambiguity, whose solutions with and without rotation

Zong-Zhi Lin, Thomas D. Pike, Mark M. Bailey, Nathaniel D. Bastian

Network intrusion detection systems (NIDS) to detect malicious attacks continue to meet challenges. NIDS are often developed offline while they face auto-generated port scan infiltration attempts, resulting in a significant time lag from adversarial adaption to NIDS response. To address these challenges, we use hypergraphs focused on internet protocol addres

Zhikang Zhang, Zifan Yu, Suya You, Raghuveer Rao

Motivated by the increasing application of low-resolution LiDAR recently, we target the problem of low-resolution LiDAR-camera calibration in this work. The main challenges are two-fold: sparsity and noise in point clouds. To address the problem, we propose to apply depth interpolation to increase the point density and supervised contrastive learning to lear

Yoh Yamamoto, Tunna Baruah, Po-Hao Chang, Selim Romero

Recently proposed local self-interaction correction (LSIC) method [Zope, R. R. et al., J. Chem. Phys. 151, 214108 (2019)] is a one-electron self-interaction-correction (SIC) method that uses an iso-orbital indicator to apply the SIC at each point in space by scaling the exchange-correlation and Coulomb energy densities. The LSIC method is exact for the one-e

Peiyu Zhuang, Haodong Li, Rui Yang, Jiwu Huang

With the spread of tampered images, locating the tampered regions in digital images has drawn increasing attention. The existing image tampering localization methods, however, suffer from severe performance degradation when the tampered images are subjected to some post-processing, as the tampering traces would be distorted by the post-processing operations.

Ankita Pasad, Bowen Shi, Karen Livescu

Many self-supervised speech models, varying in their pre-training objective, input modality, and pre-training data, have been proposed in the last few years. Despite impressive successes on downstream tasks, we still have a limited understanding of the properties encoded by the models and the differences across models. In this work, we examine the intermedia

Henrique Weber, Mathieu Garon, Jean-François Lalonde

We present a method for estimating lighting from a single perspective image of an indoor scene. Previous methods for predicting indoor illumination usually focus on either simple, parametric lighting that lack realism, or on richer representations that are difficult or even impossible to understand or modify after prediction. We propose a pipeline that estim

Zhikang Zhang, Bruno Machado Trindade, Michael Green, Zifan Yu

Due to the complicated nanoscale structures of current integrated circuits(IC) builds and low error tolerance of IC image segmentation tasks, most existing automated IC image segmentation approaches require human experts for visual inspection to ensure correctness, which is one of the major bottlenecks in large-scale industrial applications. In this paper, w

Jai-chan Hwang, Hyerim Noh

We present the general relativistic electrodynamics and magnetohydrodynamics with a helically coupled scalar field. We consider three component system with the fluid, scalar field and electromagnetic fields with the helical coupling. We derive three exact formulations: the covariant formulation, the ADM formulation, and the fully nonlinear and exact perturba

Shiheng Duan, Shuaiqi Wu, Erwan Monier, Paul Ullrich

Almonds are one of the most lucrative products of California, but are also among the most sensitive to climate change. In order to better understand the relationship between climatic factors and almond yield, an automated machine learning framework is used to build a collection of machine learning models. The prediction skill is assessed using historical rec

G. I. Lehrer, R. B. Zhang

We introduce the notion of a diagram category and discuss its application to the invariant theory of classical groups and super groups, with some indications concerning extensions to quantum groups and quantum super groups. Tensor functors from various diagram categories to categories of representnations are introduced and their properties investigated, lead

Juan Sebastián Salcedo Gallo, Jesús Solano, Javier Hernán García, David Zarruk-Valencia

In this work, we propose a framework relying solely on chat-based customer support (CS) interactions for predicting the recommendation decision of individual users. For our case study, we analyzed a total number of 16.4k users and 48.7k customer support conversations within the financial vertical of a large e-commerce company in Latin America. Consequently,

Chen Yu, Daniel Gildea

AMR parsing is the task that maps a sentence to an AMR semantic graph automatically. We focus on the breadth-first strategy of this task, which was proposed recently and achieved better performance than other strategies. However, current models under this strategy only \emph{encourage} the model to produce the AMR graph in breadth-first order, but \emph{cann

Anton Ratnarajah, Ishwarya Ananthabhotla, Vamsi Krishna Ithapu, Pablo Hoffmann

We propose a novel approach for blind room impulse response (RIR) estimation systems in the context of a downstream application scenario, far-field automatic speech recognition (ASR). We first draw the connection between improved RIR estimation and improved ASR performance, as a means of evaluating neural RIR estimators. We then propose a generative adversar

Xue Chao Feng, Ke Wei Wei, Jie Wu, Xue Zhen Zhai

In this work, we investigate the mass spectrum of $1^{1}D_{2}$ and $1^{3}D_{2}$ meson nonets in the framework of the meson mass matrix and Regge phenomenology. The results are compared with the values from different phenomenological models and may be useful for the assignment of the $1^{1}D_{2}$ and $1^{3}D_{2}$ meson nonets in the future.