October 2022 arXiv papers — page 34

Piyush Behre, Naveen Parihar, Sharman Tan, Amy Shah

Segmentation for continuous Automatic Speech Recognition (ASR) has traditionally used silence timeouts or voice activity detectors (VADs), which are both limited to acoustic features. This segmentation is often overly aggressive, given that people naturally pause to think as they speak. Consequently, segmentation happens mid-sentence, hindering both punctuat

Hammed Olawale Fatoyinbo, David J. W. Simpson

The border-collision normal form is a canonical form for two-dimensional, continuous maps comprised of two affine pieces. In this paper we provide a guide to the dynamics of this family of maps in the non-invertible case where the two pieces fold onto the same half-plane. We identify parameter regimes for the occurrence of key bifurcation structures, such as

Qinglin Niu, Jian Liu, Yuanlong Guo, Chang Xu

The nucleon-nucleon short-range correlation NN-SRC is one of the key issues of nuclear physics, which typically manifest themselves in high-momentum components of the nuclear momentum distributions. In this letter, the nuclear spectral functions based on the axially deformed relativistic mean-field model are developed to involve the NN-SRC. With the spectral

Qiang Chen, Ting Gao, Fengli Yan

Complex numbers are widely used in both classical and quantum physics, and play an important role in describing quantum systems and their dynamical behavior. In this paper we study several measures of imaginarity of quantum states in the framework of resource theory, such as the measures based on $l_{1}$ norm, and convex function, etc. We also investigate th

Conghan Dong

In this note, we prove that for a complete noncompact three dimensional Riemannian manifold with bounded sectional curvature, if it has uniformly positive scalar curvature, then there is a uniform lower bound on the injectivity radius.

Song Yang, Houari Sahraoui

In model-driven engineering (MDE), UML class diagrams serve as a way to plan and communicate between developers. However, it is complex and resource-consuming. We propose an automated approach for the extraction of UML class diagrams from natural language software specifications. To develop our approach, we create a dataset of UML class diagrams and their En

Chi Sin Tang, Shengwei Zeng, Jing Wu, Shunfeng Chen

Two-dimensional (2D) perovskite oxide interfaces are ideal systems where diverse emergent properties can be uncovered.The formation and modification of polaronic properties due to short-range strong charge-lattice interactions of 2D interfaces remains hugely intriguing.Here, we report the direct observation of small-polarons at the LaAlO3/SrTiO3 (LAO/STO) co

Austin M. Ferrenti, Maxime A. Siegler, Shreenanda Ghosh, Xin Zhang

BaCo2(AsO4)2 (BCAO) has seen extensive study since its initial identification as a proximate Kitaev quantum spin liquid candidate. Thought to be described by the highly anisotropic XXZ-J_1-J_3 model, the ease with which magnetic order is suppressed in the system indicates proximity to a spin liquid phase. Upon chemical tuning via partial arsenic substitution

Alexander Edmonds, Aleksandar Nikolov, Toniann Pitassi

We study two basic statistical tasks in non-interactive local differential privacy (LDP): learning and refutation. Learning requires finding a concept that best fits an unknown target function (from labelled samples drawn from a distribution), whereas refutation requires distinguishing between data distributions that are well-correlated with some concept in

Jiebao Zhang, Wenhua Qian, Rencan Nie, Jinde Cao

Deep neural networks are vulnerable to adversarial attacks. Most $L_{0}$-norm based white-box attacks craft perturbations by the gradient of models to the input. Since the computation cost and memory limitation of calculating the Hessian matrix, the application of Hessian or approximate Hessian in white-box attacks is gradually shelved. In this work, we note

Qiming Luo, Tinggui Zhang, Xiaofen Huang, Naihuan Jing

We present a scheme for teleporting an unknown, two-particle entangled state with a message from a sender (Alice) to a receiver (Bob) via a six-particle entangled channel. We also present another scheme for teleporting an unknown one-particle entangled state with a message transmitted in a two-way form between the same sender and receiver via a five-qubit cl

Jacob K Goeree

I introduce a concave function of allocations and prices -- the economy's potential -- which measures the difference between utilitarian social welfare and its dual. I show that Walrasian equilibria correspond to roots of the potential: allocations maximize weighted utility and prices minimize weighted indirect utility. Walrasian prices are "utility clearing

Jiangchao Liu, Jierui Liu, Peng Di, Diyu Wu

Context sensitivity is essential for achieving the precision in inter-procedural static analysis. To be (fully) context sensitive, top-down analysis needs to fully inline all statements of the callees at each callsite, leading to statement explosion. Compositional analysis, which inlines summaries of the callees, scales up but often loses precision, as it is

Zunian Luo

Electric vehicle adoption is considered to be a promising pathway for addressing climate change. However, the market for charging stations suffers from a market failure: a lack of EV sales disincentives charging station production, which in turn inhibits mass EV adoption. Charging station subsidies are often discussed as policy levers that can stimulate char

Chi Sin Tang, Shengwei Zeng, Caozheng Diao, Jing Wu

The effects of atomic-scale disorder and charge (de)localization holds significant importance,and they provide essential insights in unravelling the role that strong and weak correlations play in condensed matter systems.For perovskite oxide heterostructures,while disorders introduced via various external stimuli have strong influences on the (de)localizatio

Andrey Pototsky, Ivan S. Maksymov

We investigate nonlinear periodic and solitary two-dimensional rolling waves in a falling two-layer liquid film in the regime of non-zero Reynolds numbers. At any flow rate, a falling two-layer liquid film is known to be linearly unstable with respect to long-wave deformations of the liquid-air surface and liquid-liquid interface. Two different types of zero

Bao Wang, Yang Liu, Zunli Yuan, Nan Liang

We construct a three-dimensional and redshift-evolutionary X-ray and ultraviolet ($L_X-L_{UV}$) luminosity relation for quasars from the powerful statistic tool called copula, and find that the constructed $L_X-L_{UV}$ relation from copula is more viable than the standard one and the observations favor the redshift-evolutionary relation more than $3\sigma$.

Huayang Li, Deng Cai, Jin Xu, Taro Watanabe

$N$-gram language models (LM) have been largely superseded by neural LMs as the latter exhibits better performance. However, we find that $n$-gram models can achieve satisfactory performance on a large proportion of testing cases, indicating they have already captured abundant knowledge of the language with relatively low computational cost. With this observ

Shunsuke Yamada, Tomohito Otobe, David Freeman, Anatoli Kheifets

Theoretical investigation is conducted of high-order harmonic generation (HHG) in silicon thin films to elucidate the effect of light propagation in reflected and transmitted waves. The first-principles simulations are performed of the process in which an intense pulsed light irradiates silicon thin films up to 3 $\mu$m thickness. Our simulations are carried

Matias D. Cattaneo, Rajita Chandak, Jason M. Klusowski

We develop a theoretical framework for the analysis of oblique decision trees, where the splits at each decision node occur at linear combinations of the covariates (as opposed to conventional tree constructions that force axis-aligned splits involving only a single covariate). While this methodology has garnered significant attention from the computer scien

B. S. Balakrishna

FLRW equations are analyzed in a universe with a cosmic scalar background that is spatially uniform but time-varying. Some solvable potentials to the combined dynamics in such a universe are presented, that are consistent with the scalar dynamics as a consequence of energy momentum conservation. Certain potentials are found to provide very good fits to type

Caroline Wang, Garrett Warnell, Peter Stone

While combining imitation learning (IL) and reinforcement learning (RL) is a promising way to address poor sample efficiency in autonomous behavior acquisition, methods that do so typically assume that the requisite behavior demonstrations are provided by an expert that behaves optimally with respect to a task reward. If, however, suboptimal demonstrations a

Yuchen Zhuang, Yinghao Li, Jerry Junyang Cheung, Yue Yu

We study the problem of extracting N-ary relation tuples from scientific articles. This task is challenging because the target knowledge tuples can reside in multiple parts and modalities of the document. Our proposed method ReSel decomposes this task into a two-stage procedure that first retrieves the most relevant paragraph/table and then selects the targe

Yiyu Zhang, Dasari Venkatakrishnarao, Michel Bosman, Wei Fu

Two-dimensional (2D) semiconductors are promising channel materials for continued downscaling of complementary metal-oxide-semiconductor (CMOS) logic circuits. However, their full potential continues to be limited by a lack of scalable high-k dielectrics that can achieve atomically smooth interfaces, small equivalent oxide thicknesses (EOT), excellent gate c

A. M León, J. W. González, H. Rosner

Ca$_{3}$Ru$_{2}$O$_{7}$ is a fascinating material that displays physical properties governed by spin-orbit interactions and structural distortions, showing a wide range of remarkable electronic phenomena. Here, we present a density-functional-based analysis of the interplay among degrees of freedom, such as magnetism, Coulomb repulsion (Hubbard-U), and struc

Mukund Rungta, Janvijay Singh, Saif M. Mohammad, Diyi Yang

In a fair world, people have equitable opportunities to education, to conduct scientific research, to publish, and to get credit for their work, regardless of where they live. However, it is common knowledge among researchers that a vast number of papers accepted at top NLP venues come from a handful of western countries and (lately) China; whereas, very few

J. J. Kas, J. J. Rehr

Intrinsic inelastic losses in x-ray spectra originate from excitations in an interacting electron system due to a suddenly created core-hole. These losses characterize the features observed in x-ray photoemission spectra (XPS), as well Here we present a complementary {\it ab initio} real-space Green's function (RSGF) generalization of the Langreth cumulant i

G. Lusztig

Let H be a connected reductive group over an algebraically closed field. We define a surjective map from the set CS(H) of unipotent character sheaves on H (up to isomorphism) to the set of strata of H. To do this we use the generalized Springer correspondence. We also give a new parametrization of CS(H) in terms of data coming from bad characteristic. Some m

Bikash Kanungo, Nelson D. Rufus, Vikram Gavini

We present an efficient and systematically convergent approach to all-electron real-time time-dependent density functional theory (TDDFT) calculations using a mixed basis, termed as enriched finite element (EFE) basis. The EFE basis augments the classical finite element basis (CFE) with compactly supported numerical atom centered basis, obtained from atomic

Yunzhe Zhou, Zhengling Qi, Chengchun Shi, Lexin Li

In this article, we propose a novel pessimism-based Bayesian learning method for optimal dynamic treatment regimes in the offline setting. When the coverage condition does not hold, which is common for offline data, the existing solutions would produce sub-optimal policies. The pessimism principle addresses this issue by discouraging recommendation of action

Dexin Kong, Nan Yu, Yun Yuan, Guohong Fu

Emotion Cause Extraction in Conversations (ECEC) aims to extract the utterances which contain the emotional cause in conversations. Most prior research focuses on modelling conversational contexts with sequential encoding, ignoring the informative interactions between utterances and conversational-specific features for ECEC. In this paper, we investigate the

John Jurkiewicz, Peter Hinow

The presence of debris in Earth's orbit poses a significant risk to human activity in outer space. This debris population continues to grow due to ground launches, loss of external parts from space ships, and uncontrollable collisions between objects. A computationally feasible continuum model for the growth of the debris population and its spatial distribut

Dongmei Han, Na Wang, Meihong Wang, Zhongzhong Qin

Remote state preparation enables one to create and manipulate a quantum state based on the shared entanglement between distant nodes. Here, we experimentally demonstrate remote preparation and manipulation of squeezed light. By performing homodyne projective measurement on one mode of the continuous variable entangled state at Alice's station, a squeezed sta

Lukas Huber, Jean-Jacques Slotine, Aude Billard

In robotics motion is often described from an external perspective, i.e., we give information on the obstacle motion in a mathematical manner with respect to a specific (often inertial) reference frame. In the current work, we propose to describe the robotic motion with respect to the robot itself. Similar to how we give instructions to each other (go straig

Ziyu Shu, Alireza Entezari

Background and Objective: The success of neural networks in a number of image processing tasks has motivated their application in image reconstruction problems in computed tomography (CT). While progress has been made in this area, the lack of stability and theoretical guarantees for accuracy, together with the scarcity of high-quality training data for spec

Yi Huang, Xianlin Zeng, Ziyang Meng, Jian Sun

This paper develops a distributed primal-dual algorithm via event-triggered mechanism to solve a class of convex optimization problems subject to local set constraints, coupled equality and inequality constraints. Different from some existing distributed algorithms with the diminishing step-sizes, our algorithm uses the constant step-sizes, and is shown to a

Judah Luberto, Emily C. Martin, Peter McGill, Alexie Leauthaud

Gravitational microlensing has the potential to provide direct gravitational masses of single, free-floating brown dwarfs, independent of evolutionary and atmospheric models. The proper motions and parallaxes of nearby brown dwarfs can be used to predict close future alignments with distant background stars that cause a microlensing event. Targeted astrometr

Qiao Sun, Xin Huang, Brian C. Williams, Hang Zhao

Interactive traffic simulation is crucial to autonomous driving systems by enabling testing for planners in a more scalable and safe way compared to real-world road testing. Existing approaches learn an agent model from large-scale driving data to simulate realistic traffic scenarios, yet it remains an open question to produce consistent and diverse multi-ag

Hang Yuan, Monica Olvera de la Cruz

Stokesian Dynamics is a well-established computational method for simulating dynamics of many particles suspended in a conventional passive fluid medium. Active fluids composed of self-propelling particles with broken time reversal symmetry permit the emergence of a so-called odd viscosity. In this work, we extended the conventional Stokesian Dynamics formal

Douglas Mateus da Silva, Dani Gamerman

Preferential sampling is a common feature in geostatistics and occurs when the locations to be sampled are chosen based on information about the phenomena under study. In this case, point pattern models are commonly used as the probability law for the distribution of the locations. However, analytic intractability of the point process likelihood prevents its

Sina Baharlouei, Fatemeh Sheikholeslami, Meisam Razaviyayn, Zico Kolter

This work concerns the development of deep networks that are certifiably robust to adversarial attacks. Joint robust classification-detection was recently introduced as a certified defense mechanism, where adversarial examples are either correctly classified or assigned to the "abstain" class. In this work, we show that such a provable framework can benefit

Isaac Wasserman

Generative Adversarial Networks (GANs) have been shown to aid in the creation of artificial data in situations where large amounts of real data are difficult to come by. This issue is especially salient in the computational linguistics space, where researchers are often tasked with modeling the complex morphologic and grammatical processes of low-resource la

Puyang Zhao, Wei Tian, Lefu Xiao, Xinhui Liu

Bitcoin is the most common cryptocurrency involved in cyber scams. Cybercriminals often utilize pseudonymity and privacy protection mechanism associated with Bitcoin transactions to make their scams virtually untraceable. The Ponzi scheme has attracted particularly significant attention among Bitcoin fraudulent activities. This paper considers a multi-class

C. Cortes-Parra, R. Martinez, J. S. Alvarado

We present an extension $U(1)_{X}$ to the Standard Model that reproduces the lepton mass structures determined by the experiments. In the charged sector, we introduced effective operators of dimension $n = 7$ to generate the mass of the electron, which is null at tree-level due to the $X$ charge. In the neutral sector, we added three sterile right-handed neu

Kyumin Park, Keon Lee, Daeyoung Kim, Dongyeop Kang

Even with recent advances in speech synthesis models, the evaluation of such models is based purely on human judgement as a single naturalness score, such as the Mean Opinion Score (MOS). The score-based metric does not give any further information about which parts of speech are unnatural or why human judges believe they are unnatural. We present a novel sp

Sudhandar Balakrishnan, Yihao Fang, Xioadan Zhu

The invention of transformer-based models such as BERT, GPT, and RoBERTa has enabled researchers and financial companies to finetune these powerful models and use them in different downstream tasks to achieve state-of-the-art performance. Recently, a lightweight alternative (approximately 0.1% - 3% of the original model parameters) to fine-tuning, known as p

Isaac Wasserman

Convolutional neural network-based medical image classifiers have been shown to be especially susceptible to adversarial examples. Such instabilities are likely to be unacceptable in the future of automated diagnoses. Though statistical adversarial example detection methods have proven to be effective defense mechanisms, additional research is necessary that

Luca Foppiano, Pedro Baptista de Castro, Pedro Ortiz Suarez, Kensei Terashima

The automatic extraction of materials and related properties from the scientific literature is gaining attention in data-driven materials science (Materials Informatics). In this paper, we discuss Grobid-superconductors, our solution for automatically extracting superconductor material names and respective properties from text. Built as a Grobid module, it c

Zhaoyuan Yang, Zhiwei Xu, Jing Zhang, Richard Hartley

In this work, we formulate a novel framework for adversarial robustness using the manifold hypothesis. This framework provides sufficient conditions for defending against adversarial examples. We develop an adversarial purification method with this framework. Our method combines manifold learning with variational inference to provide adversarial robustness w

Dajun Du, Changda Zhang, Chen Peng, Minrui Fei

When traditional pole-dynamics attacks (TPDAs) are implemented with nominal models, model mismatch between exact and nominal models often affects their stealthiness, or even makes the stealthiness lost. To solve this problem, our current paper presents a novel stealthy measurement-aided pole-dynamics attacks (MAPDAs) method with model mismatch. Firstly, the

Li Zeng, Xiaoliang Wan, Tao Zhou

In this work, we propose adaptive deep learning approaches based on normalizing flows for solving fractional Fokker-Planck equations (FPEs). The solution of a FPE is a probability density function (PDF). Traditional mesh-based methods are ineffective because of the unbounded computation domain, a large number of dimensions and the nonlocal fractional operato

Zhiyong Si, Mingyi Wang, Yunxia Wang

This paper presents a Crank-Nicolson leap-frog (CNLF) scheme for the unsteady incompressible magnetohydrodynamics (MHD) equations. The spatial discretization adopts the Galerkin finite element method (FEM), and the temporal discretization employs the CNLF method for linear terms and the semi-implicit method for nonlinear terms. The first step uses Stokes sty

Sergiu Klainerman, Jeremie Szeftel

This a brief introduction to the sequence of works \cite{KS:Kerr}, \cite{GKS-2022}, \cite{KS-GCM1}, \cite{KS-GCM2} and \cite{Shen} which establish the nonlinear stability of Kerr black holes with small angular momentum. We are delighted to dedicate this article to Demetrios Christodoulou for whom we both have great admiration. The first author would also lik

Dan-Cornelius Savu, Andrew J. Higgins

The structural stability of a lightsail under the laser flux necessary for interstellar flight is studied analytically and numerically. A sinusoidal perturbation is introduced into a two-dimensional thin-film sail to determine if the sail remains stable or if the perturbations grow in amplitude. A reflective material that gives specular reflection of the las

Romain Billet, Binglin Zeng, James Lockhart, Mike Gattrell

Switchable hydrophilicity solvents (SHSs) are solvents defined by their ability to switch from their hydrophobic form to a hydrophilic form when put in contact with an acidic trigger such as $CO_2$. As a consequence, SHSs qualify as promising alternatives to volatile organic compounds during the industrial solvent extraction processes, as greener and inexpen

Sebastian Casalaina-Martin, Lisa Marquand, Zheng Zhang

There are two types of involutions on a cubic threefold: the Eckardt type (which has been studied by the first named and the third named authors) and the non-Eckardt type. Here we study cubic threefolds with a non-Eckardt type involution, whose fixed locus consists of a line and a cubic curve. Specifically, we consider the period map sending a cubic threefol

Pablo Villalobos, Anson Ho, Jaime Sevilla, Tamay Besiroglu

We investigate the potential constraints on LLM scaling posed by the availability of public human-generated text data. We forecast the growing demand for training data based on current trends and estimate the total stock of public human text data. Our findings indicate that if current LLM development trends continue, models will be trained on datasets roughl

Zhishuai Guo, Rong Jin, Jiebo Luo, Tianbao Yang

In this paper, we tackle a novel federated learning (FL) problem for optimizing a family of X-risks, to which no existing FL algorithms are applicable. In particular, the objective has the form of $\mathbb E_{z\sim S_1} f(\mathbb E_{z'\sim S_2} \ell(w; z, z'))$, where two sets of data $S_1, S_2$ are distributed over multiple machines, $\ell(\cdot)$ is a pair

Seungwhan Moon, Andrea Madotto, Zhaojiang Lin, Alireza Dirafzoon

We present IMU2CLIP, a novel pre-training approach to align Inertial Measurement Unit (IMU) motion sensor recordings with video and text, by projecting them into the joint representation space of Contrastive Language-Image Pre-training (CLIP). The proposed approach allows IMU2CLIP to translate human motions (as measured by IMU sensors) into their correspondi

Jorge J. Betancor, Estefanía Dalmasso, Pablo Quijano, Roberto Scotto

In this paper we give a criterion to prove boundedness results for several operators from $H^1((0,\infty),\gamma_\alpha)$ to $L^1((0,\infty),\gamma_\alpha)$ and also from $L^\infty((0,\infty),\gamma_\alpha)$ to $\BMO((0,\infty),\gamma_\alpha)$, with respect to the probability measure $d\gamma_\alpha (x)=\frac{2}{\Gamma(\alpha+1)} x^{2\alpha+1} e^{-x^2} dx$ o

Leijie Zhang, Ye Shi, Yu-Cheng Chang, Chin-Teng Lin

Distributed fuzzy neural networks (DFNNs) have attracted increasing attention recently due to their learning abilities in handling data uncertainties in distributed scenarios. However, it is challenging for DFNNs to handle cases in which the local data are non-independent and identically distributed (non-IID). In this paper, we propose a federated fuzzy neur

Yoojin Choi, Mostafa El-Khamy, Jungwon Lee

We propose a novel method for training a conditional generative adversarial network (CGAN) without the use of training data, called zero-shot learning of a CGAN (ZS-CGAN). Zero-shot learning of a conditional generator only needs a pre-trained discriminative (classification) model and does not need any training data. In particular, the conditional generator i

Felicia Ruppel, Florian Faion, Claudius Gläser, Klaus Dietmayer

Transformers have recently been utilized to perform object detection and tracking in the context of autonomous driving. One unique characteristic of these models is that attention weights are computed in each forward pass, giving insights into the model's interior, in particular, which part of the input data it deemed interesting for the given task. Such an

Vladimir Dotsenko, Xabier García-Martínez

We prove that if, for a nontrivial variety of non-associative algebras, every subalgebra of every free algebra is free and $I^2$ is an ideal whenever $I$ is an ideal, then this variety coincides with the variety of all Lie algebras.

Keiichi Maeda, Miho Kawabata

Type Iax Supernovae (SNe Iax) form a class of peculiar SNe Ia, whose early-phase spectra share main spectral line identifications with canonical SNe Ia but with higher ionization and much lower line velocities. Their late-time behaviors deviate from usual SNe Ia in many respects; SNe Iax keep showing photospheric spectra over several 100 days and the luminos

Hasna Nhaila, Maria Merzouqi, Elkebir Sarhrouni, Ahmed Hammouch

The Hyperspectral image (HSI) contains several hundred bands of the same region called the Ground Truth (GT). The bands are taken in juxtaposed frequencies, but some of them are noisily measured or contain no information. For the classification, the selection of bands, affects significantly the results of classification, in fact, using a subset of relevant b

Hasna Nhaila, Elkebir Sarhrouni, Ahmed Hammouch

The Remote sensing provides a synoptic view of land by detecting the energy reflected from Earth's surface. The Hyperspectral images (HSI) use perfect sensors that extract more than a hundred of images, with more detailed information than using traditional Multispectral data. In this paper, we aim to study this aspect of communication in the case of passive

Soyoung Yoon, Sungjoon Park, Gyuwan Kim, Junhee Cho

Research on Korean grammatical error correction (GEC) is limited, compared to other major languages such as English. We attribute this problematic circumstance to the lack of a carefully designed evaluation benchmark for Korean GEC. In this work, we collect three datasets from different sources (Kor-Lang8, Kor-Native, and Kor-Learner) that covers a wide rang

Andrew Tai

This paper finds the testable implications of the core in an exchange economy with unit demand when agents' preferences are unobserved. To do so, I develop a model of aggregate matchings in which the core is falsifiable; the identifying assumption is that agents' preferences are solely determined by observable characteristics. I find conditions that

Magda Dettlaff, Michael A. Henning, Jerzy Topp

A graph is $\alpha$-excellent if every vertex of the graph is contained in some maximum independent set of the graph. In this paper, we present two characterizations of the $\alpha$-excellent $2$-trees.

Yifan Zhang, Joe Kileel

We present an alternating least squares type numerical optimization scheme to estimate conditionally-independent mixture models in $\mathbb{R}^n$, without parameterizing the distributions. Following the method of moments, we tackle an incomplete tensor decomposition problem to learn the mixing weights and componentwise means. Then we compute the cumulative d

Etsuko Itou, Kei Iida

We obtain the equation of state (EoS) and the sound velocity for 2-color QCD at low temperature and high density and find that in the superfluid phase, $c_s^2/c^2 >1/3$, where $1/3$ is the value at the relativistic limit. Several independent Monte Carlo studies on 2-color QCD have been conducted intensively in recent years. These works have shown a clear evi

Pete Riley

Sunspot number (SSN) is an important - albeit nuanced - parameter that can be used as an indirect measure of solar activity. Predictions of upcoming active intervals, including the peak and timing of solar maximum can have important implications for space weather planning. Forecasts for the strength of solar cycle 25 have varied considerably, from it being v

Zhiqi Zhang, Nitin Bansal, Changjiang Cai, Pan Ji

Synthetic datasets are often used to pretrain end-to-end optical flow networks, due to the lack of a large amount of labeled, real-scene data. But major drops in accuracy occur when moving from synthetic to real scenes. How do we better transfer the knowledge learned from synthetic to real domains? To this end, we propose CLIP-FLow, a semi-supervised iterati

Pedro D. Manrique, Frank Huo, Sara El Oud, Minzhang Zheng

Online communities featuring 'anti-X' hate and extremism, somehow thrive online despite moderator pressure. We present a first-principles theory of their dynamics, which accounts for the fact that the online population comprises diverse individuals and evolves in time. The resulting equation represents a novel generalization of nonlinear fluid physics and ex

Tetsutaro Shibata

We study the one-dimensional nonlocal elliptic equation of Kirchhoff type with logarithmic Kirchhoff function. We establish the precise asymptotic formulas for the solution $u_\lambda(x)$ as $\lambda \to \infty$. Here, $\lambda > 0$ is the bifurcation parameter.

Sudhanshu Ranjan, Dheeraj Mekala, Jingbo Shang

Multilingual transformer language models have recently attracted much attention from researchers and are used in cross-lingual transfer learning for many NLP tasks such as text classification and named entity recognition. However, similar methods for transfer learning from monolingual text to code-switched text have not been extensively explored mainly due t

Lahari Poddar, György Szarvas, Cheng Wang, Jorge Balazs

Task-oriented dialogue systems in industry settings need to have high conversational capability, be easily adaptable to changing situations and conform to business constraints. This paper describes a 3-step procedure to develop a conversational model that satisfies these criteria and can efficiently scale to rank a large set of response candidates. First, we

Kelly Marchisio, Ali Saad-Eldin, Kevin Duh, Carey Priebe

Bilingual lexicons form a critical component of various natural language processing applications, including unsupervised and semisupervised machine translation and crosslingual information retrieval. We improve bilingual lexicon induction performance across 40 language pairs with a graph-matching method based on optimal transport. The method is especially st

Niharika S. D'Souza, Hongzhi Wang, Andrea Giovannini, Antonio Foncubierta-Rodriguez

In a complex disease such as tuberculosis, the evidence for the disease and its evolution may be present in multiple modalities such as clinical, genomic, or imaging data. Effective patient-tailored outcome prediction and therapeutic guidance will require fusing evidence from these modalities. Such multimodal fusion is difficult since the evidence for the di

Pouria Mistani, Samira Pakravan, Rajesh Ilango, Sanjay Choudhry

We present a highly scalable strategy for developing mesh-free neuro-symbolic partial differential equation solvers from existing numerical discretizations found in scientific computing. This strategy is unique in that it can be used to efficiently train neural network surrogate models for the solution functions and the differential operators, while retainin

Jacob Imola, Amrita Roy Chowdhury, Kamalika Chaudhuri

Locally differentially private (LDP) graph analysis allows private analysis on a graph that is distributed across multiple users. However, such computations are vulnerable to data poisoning attacks where an adversary can skew the results by submitting malformed data. In this paper, we formally study the impact of poisoning attacks for graph degree estimation

Cody Milne, Arunima Singh, Tathagata Biswas

Ultra-wide band gap (UWBG) materials are poised to play an important role in the future of power electronics. Devices made from UWBG materials are expected to operate at higher voltages, frequencies, and temperatures than current silicon and silicon carbide based devices; and can even lead to significant miniaturization of such devices. In the UWBG field, al

Madelyn Leembruggen, Jovana Andrejevic, Arshad Kudrolli, Chris H. Rycroft

We develop an irregular lattice mass-spring-model (MSM) to simulate and study the deformation modes of a thin elastic ribbon as a function of applied end-to-end twist and tension. Our simulations reproduce all reported experimentally observed modes, including transitions from helicoids to longitudinal wrinkles, creased helicoids and loops with self-contact,

Karina Meneses-Cime, Bilin Aksun-Guvenc, Levent Guvenc

This paper proposes the use of an on-demand, ride hailed and ride-Shared Autonomous Vehicle (SAV) service as a feasible solution to serve the mobility needs of a small city where fixed route, circulator type public transportation may be too expensive to operate. The presented work builds upon our earlier work that modeled the city of Marysville, Ohio as an e

Evangelia Gazaki

For an abelian variety $A$ over a field $k$ the author defined in \cite{Gazaki2015} a Bloch-Beilinson type filtration $\{F^r(A)\}_{r\geq 0}$ of the Chow group of zero-cycles, $\text{CH}_0(A)$, with successive quotients related to a Somekawa $K$-group. In this article we show that this filtration behaves well with respect to isogeny, and in particular if $n:A

Andrew S. Toms

Let $A$ be a unital simple separable exact C$^*$-algebra which is approximately divisible and of real rank zero. We prove that the set of positive elements in $A$ with a fixed non-compact Cuntz class has vanishing homotopy groups. Combined with work of S. Zhang for the case of compact elements, this gives a complete calculation of the homotopy groups of Cunt

Paul E. Ciesielski, Richard H. Johnson

Fine et al. (2016, hereafter F16) investigated the potential role of Sumatra Island, as well as the Malay Peninsula and Java, in creating terrain-induced circulations over the Indian Ocean (IO) that subsequently develop into tropical cyclones (TCs). Applying sophisticated vortex tracking software to 2.5 yrs of model analyses, F16 found four regions downstrea

Tanner Fiez, Sergio Gamez, Arick Chen, Houssam Nassif

Adaptive experimental design methods are increasingly being used in industry as a tool to boost testing throughput or reduce experimentation cost relative to traditional A/B/N testing methods. This paper shares lessons learned regarding the challenges and pitfalls of naively using adaptive experimentation systems in industrial settings where non-stationarity

James Lee Hu, Mohammadreza Ebrahimi, Weifeng Li, Xin Li

Deep learning-based adversarial malware detectors have yielded promising results in detecting never-before-seen malware executables without relying on expensive dynamic behavior analysis and sandbox. Despite their abilities, these detectors have been shown to be vulnerable to adversarial malware variants - meticulously modified, functionality-preserving vers

Craig W. Hogle, Daniel Dominguez, Mark Dong, Andrew Leenheer

Experiments with trapped ions and neutral atoms typically employ optical modulators in order to control the phase, frequency, and amplitude of light directed to individual atoms. These elements are expensive, bulky, consume substantial power, and often rely on free-space I/O channels, all of which pose scaling challenges. To support many-ion systems like tra

Michael Carl

Horizontal (automatic) and vertical (control) processes have been observed and reported for a long time in translation production. Schaeffer and Carl's Monitor Model integrates these two processes into one framework, assuming that priming mechanisms underlie horizontal/automatic processes, while vertical/monitoring processes implement consciously accessible

Terrance D. Savitsky, Julie Gershunskaya, Mark Crankshaw

We propose a novel Bayesian framework for the joint modeling of survey point and variance estimates for count data. The approach incorporates an induced prior distribution on the modeled true variance that sets it equal to the generating variance of the point estimate, a key property more readily achieved for continuous data response type models. Our count d

D. Fargion, P. G. De Sanctis Lucentini, M. Y. Khlopov

We consider the recent results on UHECR (Ultra High Energy Cosmic Ray), clustering, composition, distribution in the sky, from the energy of several EeV with the dipole anisotropy up to the highest ones. We have suggested since 2008 and we reconfirm here that UHECR at 40 up to 70 EeV are mostly made by light and lightest nuclei. The remarkable Virgo absence

Leon Riesebos, Kenneth R. Brown

Modern quantum computers rely heavily on real-time control systems for operation. Software for these systems is becoming increasingly more complex due to the demand for more features and more real-time devices to control. Unfortunately, testing real-time control software is often a complex process, and existing simulation software is not usable or practical

Kishaloy Halder, Josip Krapac, Dmitry Goryunov, Anthony Brew

Ensuring safety of the products offered to the customers is of paramount importance to any e- commerce platform. Despite stringent quality and safety checking of products listed on these platforms, occasionally customers might receive a product that can pose a safety issue arising out of its use. In this paper, we present an innovative mechanism of how a lar

M. R. Rostami, S. S. Kia

This paper proposes an algorithm for Federated Learning (FL) with a two-layer structure that achieves both variance reduction and a faster convergence rate to an optimal solution in the setting where each agent has an arbitrary probability of selection in each iteration. In distributed machine learning, when privacy matters, FL is a functional tool. Placing

Banafsheh Rafiee, Sina Ghiassian, Jun Jin, Richard Sutton

In this paper, we explore an approach to auxiliary task discovery in reinforcement learning based on ideas from representation learning. Auxiliary tasks tend to improve data efficiency by forcing the agent to learn auxiliary prediction and control objectives in addition to the main task of maximizing reward, and thus producing better representations. Typical

Mário Cardoso, Pedro Saleiro, Pedro Bizarro

Anti-money laundering (AML) regulations mandate financial institutions to deploy AML systems based on a set of rules that, when triggered, form the basis of a suspicious alert to be assessed by human analysts. Reviewing these cases is a cumbersome and complex task that requires analysts to navigate a large network of financial interactions to validate suspic

Maxim Braverman, Ahmad Reza Haj Saeedi Sadegh

In this paper, we construct a smooth vector bundle over the deformation to the normal cone $\text{DNC}(V,M)$ through a rescaling of a vector bundle $E\to V$, which generalizes the construction of the spinor rescaled bundle over the tangent groupoid by Nigel Higson and Zelin Yi. We also provide an equivariant version of their construction. As the main applica