October 2022 arXiv papers — page 20

Ye Lin Tun, Kyi Thar, Chu Myaet Thwal, Choong Seon Hong

To reduce negative environmental impacts, power stations and energy grids need to optimize the resources required for power production. Thus, predicting the energy consumption of clients is becoming an important part of every energy management system. Energy usage information collected by the clients' smart homes can be used to train a deep neural network to

Shulin He, Huaiwen Zhang, Wei Rao, Kanghao Zhang

Target speaker extraction aims to isolate a specific speaker's voice from a composite of multiple sound sources, guided by an enrollment utterance or called anchor. Current methods predominantly derive speaker embeddings from the anchor and integrate them into the separation network to separate the voice of the target speaker. However, the representation of

Mike Y. M. Lau, Matteo Cantiello, Adam S. Jermyn, Morgan MacLeod

Hot Jupiters are gas giant planets with orbital periods of a few days and are found in 0.1-1% of Sun-like stars. They are expected to be engulfed during their host star's radial expansion on the red giant branch, which may account for observed rapidly rotating and chemically enriched giant stars. We performed 3D hydrodynamical simulations of hot Jupiter engu

Fengjun Yang, Fernando Gama, Somayeh Sojoudi, Nikolai Matni

Designing distributed optimal controllers subject to communication constraints is a difficult problem unless structural assumptions are imposed on the underlying dynamics and information exchange structure, e.g., sparsity, delay, or spatial invariance. In this paper, we borrow ideas from graph signal processing and define and analyze a class of Graph Symmetr

Chaohe Zhang, Xu Chu, Liantao Ma, Yinghao Zhu

Multimodal electronic health record (EHR) data are widely used in clinical applications. Conventional methods usually assume that each sample (patient) is associated with the unified observed modalities, and all modalities are available for each sample. However, missing modality caused by various clinical and social reasons is a common issue in real-world cl

Zhipeng Gao, Xin Xia, David Lo, John Grundy

Software developers have heavily used online question and answer platforms to seek help to solve their technical problems. However, a major problem with these technical Q&A sites is "answer hungriness" i.e., a large number of questions remain unanswered or unresolved, and users have to wait for a long time or painstakingly go through the provided answers wit

Zhipeng Gao, Xin Xia, David Lo, John Grundy

Stack Overflow has been heavily used by software developers to seek programming-related information. More and more developers use Community Question and Answer forums, such as Stack Overflow, to search for code examples of how to accomplish a certain coding task. This is often considered to be more efficient than working from source documentation, tutorials

Daniel Gottesman

I will give an overview of what I see as some of the most important future directions in the theory of fault-tolerant quantum computation. In particular, I will give a brief summary of the major problems that need to be solved in fault tolerance based on low-density parity check codes and in hardware-specific fault tolerance. I will then conclude with a disc

Lu Dai, Bang Wang, Wei Xiang, Yijun Mo

Recently, prompt-tuning has attracted growing interests in event argument extraction (EAE). However, the existing prompt-tuning methods have not achieved satisfactory performance due to the lack of consideration of entity information. In this paper, we propose a bi-directional iterative prompt-tuning method for EAE, where the EAE task is treated as a cloze-s

Georgios Chochlakis, Gireesh Mahajan, Sabyasachee Baruah, Keith Burghardt

Detecting emotions expressed in text has become critical to a range of fields. In this work, we investigate ways to exploit label correlations in multi-label emotion recognition models to improve emotion detection. First, we develop two modeling approaches to the problem in order to capture word associations of the emotion words themselves, by either includi

Karun Adusumilli

We study sequential experiments where sampling is costly and a decision-maker aims to determine the best treatment for full scale implementation by (1) adaptively allocating units between two possible treatments, and (2) stopping the experiment when the expected welfare (inclusive of sampling costs) from implementing the chosen treatment is maximized. Workin

Yusa Wang, Zijian Zhao, Dongjie Hou, Xiongtao Yang

The 100-m X-ray Test Facility of the Institute of High Energy Physics (IHEP) was initially proposed in 2012 for the test and calibration of the X-ray detectors of the Hard X-ray Modulation Telescope (HXMT) with the capability to support future X-ray missions. The large instrument chamber connected with a long vacuum tube can accommodate the X-ray mirror, foc

Atinuke Ademola-Idowu, Baosen Zhang

An increase in the integration of Inverter Based Resources (IBRs) to the electric grid, will lead to a corresponding decrease in the amount of connected synchronous generators, resulting in a decline in the available rotational inertia system-wide. This can lead to pronounced frequency deviations when there are disturbances and faults in the grid. This decli

Ravi Chepuri, István A. Kovács

In the emerging quantum internet, complex network topology could lead to efficient quantum communication and enhanced robustness against failures. However, there are some concerns about complexity in quantum communication networks, such as potentially limited end-to-end transmission capacity. These challenges call for model systems in which the feasibility a

Rui Fan, Erick Delage

This research focuses on the bid optimization problem in the real-time bidding setting for online display advertisements, where an advertiser, or the advertiser's agent, has access to the features of the website visitor and the type of ad slots, to decide the optimal bid prices given a predetermined total advertisement budget. We propose a risk-aware data-dr

Yujie Jin, Xu Chu, Yasha Wang, Wenwu Zhu

Domain generalization (DG) aims at generalizing a classifier trained on multiple source domains to an unseen target domain with domain shift. A common pervasive theme in existing DG literature is domain-invariant representation learning with various invariance assumptions. However, prior works restrict themselves to a radical assumption for realworld challen

Yu Wei Tan, Alden Tan, Nicholas Nge, Anand Bhojan

Classically, rasterization techniques are performed for real-time rendering to meet the constraint of interactive frame rates. However, such techniques do not produce realistic results as compared to ray tracing approaches. Hence, hybrid rendering has emerged to improve the graphics fidelity of rasterization with ray tracing in real-time. We explore the appr

Jia-Xin Ye, Xin-Cheng Wen, Xuan-Ze Wang, Yong Xu

In human-computer interaction, Speech Emotion Recognition (SER) plays an essential role in understanding the user's intent and improving the interactive experience. While similar sentimental speeches own diverse speaker characteristics but share common antecedents and consequences, an essential challenge for SER is how to produce robust and discriminative re

Yi-Hao Ding, Chao-Ping Dong, Lin Wei

This paper classifies all the Dirac series (that is, irreducible unitary representations having non-zero Dirac cohomology) of $E_{7(7)}$. Enhancing the Helgason-Johnson bound in 1969 for the group $E_{7(7)}$ is one key ingredient. Our calculation partially supports Vogan's fundamental parallelepiped (FPP) conjecture. As applications, when passing to Dirac in

Xiang He, Teng Wang, Lei Liu, Jianan Li

The dramatic development of cloud and edge computing allows for better Quality of Service (QoS) in many scenarios by deploying services on cloud and edge servers. Microservice technology is also adopted in these scenarios to decompose complex business logic into many small independent services. Meanwhile, as microservice systems continue to grow, providing s

Cheng Long, Jonathan V. Selinger

Many solid materials and liquid crystals exhibit geometric frustration, meaning that they have an ideal local structure that cannot fill up space. For that reason, the global phase must be a compromise between the ideal local structure and geometric constraints. As an explicit example of geometric frustration, we consider a chiral liquid crystal confined in

Tiannan Gao, Minxian Xu

Wireless sensor network (WSN) has been developed for decades and have performed well in the performance, power consumption, and congestion control. However, the following problems have not been addressed, such as inaccurate cost estimation of device's lifecycle, highly-coupled engineering development, and low utilization of hardware and software resources du

Liding Yao

We construct homotopy formulas for the $\overline\partial$-equation on convex domains of finite type that have optimal Sobolev and H\"older estimates. For a bounded smooth finite type convex domain $\Omega\subset\mathbb C^n$ that has $q$-type $m_q$ for $1\le q\le n$, our $\overline\partial$ solution operator $\mathcal H_q$ on $(0,q)$-forms has (fractional) S

Xiaolin Sun

We propose a new estimator for heterogeneous treatment effects in a partially linear model (PLM) with multiple exogenous covariates and a potentially endogenous treatment variable. Our approach integrates a Robinson transformation to handle the nonparametric component, the Smooth Minimum Distance (SMD) method to leverage conditional mean independence restric

Kleanthis Avramidis, Shanti Stewart, Shrikanth Narayanan

Human perception and experience of music is highly context-dependent. Contextual variability contributes to differences in how we interpret and interact with music, challenging the design of robust models for information retrieval. Incorporating multimodal context from diverse sources provides a promising approach toward modeling this variability. Music pres

Ye Lin Tun, Chu Myaet Thwal, Yu Min Park, Seong-Bae Park

In contrast to centralized model training that involves data collection, federated learning (FL) enables remote clients to collaboratively train a model without exposing their private data. However, model performance usually degrades in FL due to the heterogeneous data generated by clients of diverse characteristics. One promising strategy to maintain good p

Kleanthis Avramidis, Tiantian Feng, Digbalay Bose, Shrikanth Narayanan

Detecting unsafe driving states, such as stress, drowsiness, and fatigue, is an important component of ensuring driving safety and an essential prerequisite for automatic intervention systems in vehicles. These concerning conditions are primarily connected to the driver's low or high arousal levels. In this study, we describe a framework for processing multi

Alberto De Marchi

Regularization and interior point approaches offer valuable perspectives to address constrained nonlinear optimization problems in view of control applications. This paper discusses the interactions between these techniques and proposes an algorithm that synergistically combines them. Building a sequence of closely related subproblems and approximately solvi

Shudi Liang, Xue-ping Wang

In this article, we present two methods to construct 2-uninorms on bounded lattices by using additive generators, which are further used for inducing uninorms, nullnorms, uni-nullnorms and null-uninorms, respectively. We also provide some examples for illustrating the constructing methods of 2-uninorms.

Peipei Liu, Xin Zheng, Hong Li, Jie Liu

Modality representation learning is an important problem for multimodal sentiment analysis (MSA), since the highly distinguishable representations can contribute to improving the analysis effect. Previous works of MSA have usually focused on multimodal fusion strategies, and the deep study of modal representation learning was given less attention. Recently,

J. Divahar, A. J. Roberts, Trent W. Mattner, J. E. Bunder

Many multiscale wave systems exhibit macroscale emergent behaviour, for example, the fluid dynamics of floods and tsunamis. Resolving a large range of spatial scales typically requires a prohibitively high computational cost. The small dissipation in wave systems poses a significant challenge to further developing multiscale modelling methods in multiple dim

Kashyap Patel, Anton Kovalyov, Issa Panahi

This study presents UX-Net, a time-domain audio separation network (TasNet) based on a modified U-Net architecture. The proposed UX-Net works in real-time and handles either single or multi-microphone input. Inspired by the filter-and-process-based human auditory behavior, the proposed system introduces novel mixer and separation modules, which result in cos

Shuhua Yu, Soummya Kar

In this paper, we study secure distributed optimization against arbitrary gradient attack in multi-agent networks. In distributed optimization, there is no central server to coordinate local updates, and each agent can only communicate with its neighbors on a predefined network. We consider the scenario where out of $n$ networked agents, a fixed but unknown

Chengming Hu, Xuan Li, Dan Liu, Xi Chen

Although Deep Neural Networks (DNNs) have shown a strong capacity to solve large-scale problems in many areas, such DNNs with voluminous parameters are hard to be deployed in a real-time system. To tackle this issue, Teacher-Student architectures were first utilized in knowledge distillation, where simple student networks can achieve comparable performance t

Tulja Varun Kondra, Chandan Datta, Alexander Streltsov

Resource theory of imaginarity provides a useful framework to understand the role of complex numbers, which are essential in the formulation of quantum mechanics, in a mathematically rigorous way. In the first part of this article, we study the properties of ``real'' (quantum) operations both in single-party and bipartite settings. As a consequence, we provi

Audra McMillan, Adam Smith, Jon Ullman

In this work, we study local minimax convergence estimation rates subject to $\epsilon$-differential privacy. Unlike worst-case rates, which may be conservative, algorithms that are locally minimax optimal must adapt to easy instances of the problem. We construct locally minimax differentially private estimators for one-parameter exponential families and est

Salman Mohamadi, Gianfranco Doretto, Donald A. Adjeroh

Self supervised learning (SSL) has become a very successful technique to harness the power of unlabeled data, with no annotation effort. A number of developed approaches are evolving with the goal of outperforming supervised alternatives, which have been relatively successful. One main issue in SSL is robustness of the approaches under different settings. In

Mauro E. S. Morales, Pedro C. S. Costa, Giacomo Pantaleoni, Daniel K. Burgarth

Quantum algorithms for simulation of Hamiltonian evolution are often based on product formulae. The fractal methods give a systematic way to find arbitrarily high-order product formulae, but result in a large number of exponentials. On the other hand, product formulae with fewer exponentials can be found by numerical solution of simultaneous nonlinear equati

Yasuyuki Kato, Yukitoshi Motome

Topological defects, called magnetic hedgehogs, realize emergent magnetic monopoles, which are not allowed in the ordinary electromagnetism described by Maxwell's equations. Such monopoles were experimentally discovered in magnets in two different forms: tetrahedral $4Q$ and cubic $3Q$ hedgehog lattices. The spin textures are modulated by the chemical compos

Olle Kjellqvist, Jing Yu

System level synthesis is a promising approach that formulates structured optimal controller synthesis problems as convex problems. This work solves the distributed linear-quadratic regulator problem under communication constraints directly in infinite-dimensional space, without the finite-impulse response relaxation common in related work. Our method can al

Prithviraj Prabhu, Christopher Chamberland

Fault-tolerant quantum computers, with error correction implemented using topological codes, will most likely require lattice surgery protocols in order to implement a universal gate set. Timelike failures during lattice surgery protocols can result in logical failures during the execution of an algorithm. In addition to the spacelike distance of the topolog

Diego Barbosa, Felipe Díaz, Liliana Quintero, Andrés Flórez

The production of heavy mass resonances has been widely studied theoretically and experimentally. Several extensions of the standard model (SM) of particle physics, naturally give rise to a new resonance, with neutral electric charge, commonly referred to as the $\textrm{Z}^{\prime}$ boson. The nature, mass, couplings, and associated quantum numbers of this

Jiaqi Leng, Yuxiang Peng, Yi-Ling Qiao, Ming Lin

We formulate the first differentiable analog quantum computing framework with a specific parameterization design at the analog signal (pulse) level to better exploit near-term quantum devices via variational methods. We further propose a scalable approach to estimate the gradients of quantum dynamics using a forward pass with Monte Carlo sampling, which lead

Qi Zhang, Jiang Zhu, Fengzhong Qu, De Wen Soh

Frequency Modulated Continuous Wave (FMCW) radar has been widely applied in automotive anti-collision systems, automatic cruise control, and indoor monitoring. However, conventional analog-to-digital converters (ADCs) can suffer from significant information loss when strong and weak targets coexist in ranging applications. To address this issue, the Unlimite

Jiaqi. Lin, Hui. Sun, JiaJia Dong

The ubiquitous existence of microbial communities marks the importance of understanding how species interact within the community to coexist and their spatial organization. We study a two-species mutualistic cross-feeding model through a stochastic cellular automaton on a square lattice using kinetic Monte Carlo simulation. Our model encapsulates the essenti

Haizhong Zheng, Rui Liu, Fan Lai, Atul Prakash

One-shot coreset selection aims to select a representative subset of the training data, given a pruning rate, that can later be used to train future models while retaining high accuracy. State-of-the-art coreset selection methods pick the highest importance examples based on an importance metric and are found to perform well at low pruning rates. However, at

Jake Smith

The research community has deemed cookie synchronization detection an inherently challenging task. Studies aiming to identify cookie synchronizations often share high-level design choices, but deviate amongst low-level implementations. For example, the majority of studies label a cookie synchronization iff a user identifier is shared with a third party; howe

Lei Bi, Michael Fulham, Shaoli Song, David Dagan Feng

[18F]-Fluorodeoxyglucose (FDG) positron emission tomography - computed tomography (PET-CT) has become the imaging modality of choice for diagnosing many cancers. Co-learning complementary PET-CT imaging features is a fundamental requirement for automatic tumor segmentation and for developing computer aided cancer diagnosis systems. In this study, we propose

S. Endo, T. Okudaira, R. Abe, H. Fujioka

Largely enhanced parity-violating effects observed in compound resonances induced by epithermal neutrons are currently attributed to the mixing of parity-unfavored partial amplitudes in the entrance channel of the compound states. Furthermore, it is proposed that the same mechanism that enhances the parity-violation also enhances the breaking of time-reversa

Nicolò Michelusi

This paper presents NCOTA-DGD, a Decentralized Gradient Descent (DGD) algorithm that combines local gradient descent with a novel Non-Coherent Over-The-Air (NCOTA) consensus scheme to solve distributed machine-learning problems over wirelessly-connected systems. NCOTA-DGD leverages the waveform superposition properties of the wireless channels: it enables si

Bhawesh Kumar, Anil Palepu, Rudraksh Tuwani, Andrew Beam

Self-supervised models trained with a contrastive loss such as CLIP have shown to be very powerful in zero-shot classification settings. However, to be used as a zero-shot classifier these models require the user to provide new captions over a fixed set of labels at test time. In many settings, it is hard or impossible to know if a new query caption is compa

Sreejith Sasidharan, Pranav Prabha, Devasena Pasupuleti, Anand M Das

Young children are at an increased risk of contracting contagious diseases such as COVID-19 due to improper hand hygiene. An autonomous social agent that observes children while handwashing and encourages good hand washing practices could provide an opportunity for handwashing behavior to become a habit. In this article, we present a human action recognition

Armengol Gasull, Hector Giacomini

For planar polynomials systems the existence of an invariant algebraic curve limits the number of limit cycles not contained in this curve. We present a general approach to prove non existence of periodic orbits not contained in this given algebraic curve. When the method is applied to parametric families of polynomial systems that have limit cycles for some

Yueting Li, Qingyue Wei, Ehsan Adeli, Kilian M. Pohl

The white-matter (micro-)structural architecture of the brain promotes synchrony among neuronal populations, giving rise to richly patterned functional connections. A fundamental problem for systems neuroscience is determining the best way to relate structural and functional networks quantified by diffusion tensor imaging and resting-state functional MRI. As

Wen-Han Zhou, Yun Zhang, Xiaoran Yan, Patrick Michel

The Yarkovsky-O'Keefe-Radzievskii-Paddack (YORP) effect plays an important role in the rotational properties and evolution of asteroids. While the YORP effect induced by the macroscopic shape of the asteroid and by the presence of surface boulders has been well studied, no investigation has been performed yet regarding how craters with given properties influ

Tianqi Liu, Yu Lu, Biqing Zhu, Hongyu Zhao

High-dimensional clustering analysis is a challenging problem in statistics and machine learning, with broad applications such as the analysis of microarray data and RNA-seq data. In this paper, we propose a new clustering procedure called Spectral Clustering with Feature Selection (SC-FS), where we first obtain an initial estimate of labels via spectral clu

Julian Lee, Kyle Cotter, Ibrahim Elsadek, Matthew J Comstock

We study the hydrodynamic coupling of neighboring micro-beads placed in a dual optical trap setup allowing us to precisely control the degree of coupling and directly measure time-dependent trajectories of the entrained beads. Average experimental trajectories of a probe bead entrained by the motion of a neighboring scan bead are compared with theoretical co

Ivan Zanardi, Simone Venturi, Marco Panesi

This work proposes a new machine learning (ML)-based paradigm aiming to enhance the computational efficiency of non-equilibrium reacting flow simulations while ensuring compliance with the underlying physics. The framework combines dimensionality reduction and neural operators through a hierarchical and adaptive deep learning strategy to learn the solution o

Ruigang Wang, Armaghan Zafar, Ian R. Manchester

In this paper, we propose a model predictive control (MPC) method for real-time intervention of spreading processes, such as epidemics and wildfire, over large-scale networks. The goal is to allocate budgeted resources each time step to minimize the risk of an undetected outbreak, i.e. the product of the probability of an outbreak and the impact of that outb

Mikhail Khenner

Solid cylindrical nanowires are vulnerable to a Rayleigh-Plateau-type morphological instability. The instability results in a wire breakup, followed by formation of a chain array of spherical nanoparticles. In this paper, a base model of a morphological instability of a nanowire on a substrate in the applied electric field directed along a nanowire axis is c

Prakhar Kulshreshtha, Konstantinos-Nektarios Lianos, Brian Pugh, Salma Jiddi

We address the problem of detecting and erasing furniture from a wide angle photograph of a room. Inpainting large regions of an indoor scene often results in geometric inconsistencies of background elements within the inpaint mask. To address this problem, we utilize perceptual information (e.g. instance segmentation, and room layout) to produce a geometric

Lei Liu, Wen-Rong Sun, Boris A. Malomed

It is known that rogue waves (RWs) are generated by the modulational instability (MI) of the baseband type. Starting with the Bers-Kaup-Reiman system for three-wave resonant interactions, we identify a specific RW-building mechanism based on MI which includes zero wavenumber in the gain band. An essential finding is that this mechanism works solely under a l

Ryan Schneider, Heman Gharibnejad, Barry I. Schneider

We present a novel approach for solving the time-dependent Schr\"{o}dinger equation (TDSE). The method we propose converts the TDSE to an equivalent Volterra integral equation; introducing a global Lagrange interpolation of the integrand transforms the equation to a linear system, which is then solved iteratively. In this paper, we derive the method, explore

T. Treu, S. H. Suyu, P. J. Marshall

Multiply imaged time-variable sources can be used to measure absolute distances as a function of redshifts and thus determine cosmological parameters, chiefly the Hubble Constant H$_0$. In the two decades up to 2020, through a number of observational and conceptual breakthroughs, this so-called time-delay cosmography has reached a precision sufficient to be

Matthew D. Hoffman, Tuan Anh Le, Pavel Sountsov, Christopher Suter

The problem of inferring object shape from a single 2D image is underconstrained. Prior knowledge about what objects are plausible can help, but even given such prior knowledge there may still be uncertainty about the shapes of occluded parts of objects. Recently, conditional neural radiance field (NeRF) models have been developed that can learn to infer goo

Chin-Yun Yu, Sung-Lin Yeh, György Fazekas, Hao Tang

Recently, diffusion models (DMs) have been increasingly used in audio processing tasks, including speech super-resolution (SR), which aims to restore high-frequency content given low-resolution speech utterances. This is commonly achieved by conditioning the network of noise predictor with low-resolution audio. In this paper, we propose a novel sampling algo

Maciej Borodzik, Beibei Liu, Ian Zemke

We define a link lattice complex for plumbed links, generalizing constructions of Ozsv\'ath, Stipsicz and Szab\'o, and of Gorsky and N\'emethi. We prove that for all plumbed links in rational homology 3-spheres, the link lattice complex is homotopy equivalent to the link Floer complex as an $A_\infty$-module. Additionally, we prove that the link Floer comple

Shaunak A. Mehta, Yeunhee Kim, Joshua Hoegerman, Michael D. Bartlett

Robot arms that assist humans should be able to pick up, move, and release everyday objects. Today's assistive robot arms use rigid grippers to pinch items between fingers; while these rigid grippers are well suited for large and heavy objects, they often struggle to grasp small, numerous, or delicate items (such as foods). Soft grippers cover the opposite e

Heng Huang, Lin Zhao, Xintao Hu, Haixing Dai

Visual attention is a fundamental mechanism in the human brain, and it inspires the design of attention mechanisms in deep neural networks. However, most of the visual attention studies adopted eye-tracking data rather than the direct measurement of brain activity to characterize human visual attention. In addition, the adversarial relationship between the a

Cagin Yunus, William Detmold

Large-time correlation functions have a pivotal role in extracting particle masses from Euclidean lattice field theory calculations, however little is known about the statistical properties of these quantities. In this work, the asymptotic form of the distributions of the correlation functions at vanishing momentum is determined for bosonic interacting latti

Jesse Pajwani, Ambrus Pál

We prove an arithmetic refinement of the Yau-Zaslow formula by replacing the classical Euler characteristic in Beauville's argument by a "motivic Euler characteristic", related to the work of Levine. Our result implies similar formulas for other related invariants, including a generalisation of a formula of Kharlamov and Rasdeaconu on counting real rational

Ivan Stanojević, Vojin Šenk

We define the bidirectional distance profile (BDP) of a convolutional code as the minimum of the distance profiles of the code and its corresponding "reverse" code. We present tables of codes with the optimum BDP (OBDP), which minimize the average complexity of bidirectional sequential decoding algorithms. The computer search is accelerated by the facts that

Kevin Miller, Jeff Calder

We show that uncertainty sampling is sufficient to achieve exploration versus exploitation in graph-based active learning, as long as the measure of uncertainty properly aligns with the underlying model and the model properly reflects uncertainty in unexplored regions. In particular, we use a recently developed algorithm, Poisson ReWeighted Laplace Learning

Amit Acharya

A dual variational principle is defined for the nonlinear system of PDE describing the dynamics of dislocations in elastic solids. The dual variational principle accounting for a specified set of initial and boundary conditions for a general class of PDE is also developed.

Kevin Hu, Retsef Levi, Raphael Yahalom, El Ghali Zerhouni

This paper provides the first large-scale data-driven analysis to evaluate the predictive power of different attributes for assessing risk of cyberattack data breaches. Furthermore, motivated by rapid increase in third party enabled cyberattacks, the paper provides the first quantitative empirical evidence that digital supply-chain attributes are significant

Abhishek Paudel

We present an approach that generates kinodynamically feasible paths for robots using Rapidly-exploring Random Tree (RRT). We leverage motion primitives as a way to capture the dynamics of the robot and use these motion primitives to build branches of the tree with RRT. Since every branch is built using the robot's motion primitives that doesn't lead to coll

S. O'Neil, S. G. Schirmer, F. C. Langbein, C. A. Weidner

A strictly time-domain formulation of the log-sensitivity of the error signal to structured plant uncertainty is presented and analyzed through simple but representative classical and quantum systems. Results demonstrate that across a wide range of physical systems, maximization of performance (minimization of the error signal) asymptotically or at a specifi

Lucile Devin, Daniel Fiorilli, Anders Södergren

We study the harmonically weighted one-level density of low-lying zeros of $L$-functions in the family of holomorpic newforms of fixed even weight $k$ and prime level $N$ tending to infinity. For this family, Iwaniec, Luo and Sarnak proved that the Katz--Sarnak prediction for the one-level density holds unconditionally when the support of the Fourier transfo

Fei Jia, Nithin Rao Koluguri, Jagadeesh Balam, Boris Ginsburg

We introduce TitaNet-LID, a compact end-to-end neural network for Spoken Language Identification (LID) that is based on the ContextNet architecture. TitaNet-LID employs 1D depth-wise separable convolutions and Squeeze-and-Excitation layers to effectively capture local and global context within an utterance. Despite its small size, TitaNet-LID achieves perfor

Xinyu Zhang, Sujit Ghosh

Time series forecasting has been a quintessential topic in data science, but traditionally, forecasting models have relied on extensive historical data. In this paper, we address a practical question: How much recent historical data is required to attain a targeted percentage of statistical prediction efficiency compared to the full time series? We propose t

Hsiang-Yun Sherry Chien, Hanlin Goh, Christopher M. Sandino, Joseph Y. Cheng

Decoding information from bio-signals such as EEG, using machine learning has been a challenge due to the small data-sets and difficulty to obtain labels. We propose a reconstruction-based self-supervised learning model, the masked auto-encoder for EEG (MAEEG), for learning EEG representations by learning to reconstruct the masked EEG features using a transf

Pamela Carreno-Medrano, Dana Kulić, Michael Burke

The ability to adapt to changing environments and settings is essential for robots acting in dynamic and unstructured environments or working alongside humans with varied abilities or preferences. This work introduces an extremely simple and effective approach to adapting neural models in response to changing settings. We first train a standard network using

Raúl Penaguiao, Yannic Vargas

The permutation pattern Hopf algebra is a commutative filtered and connected Hopf algebra. Its product structure stems from counting patterns of a permutation, interpreting the coefficients as permutation quasi-shuffles. The Hopf algebra was shown to be a free commutative algebra and to fit into a general framework of pattern Hopf algebras, via species with

Zhiyu Chen, Jie Zhao, Anjie Fang, Besnik Fetahu

Conversational Question Answering (CQA) aims to answer questions contained within dialogues, which are not easily interpretable without context. Developing a model to rewrite conversational questions into self-contained ones is an emerging solution in industry settings as it allows using existing single-turn QA systems to avoid training a CQA model from scra

Felipe Lobel

This paper provides a comprehensive examination of a Brazilian corporate tax reform targeted at the sector and product level. Difference-in-differences estimates instrumented by sector eligibility show that a 20 percentage point cut on payroll tax rates caused a 9% employment increase at the firm level, mostly driven by small firms. This expansion is not dri

Mark Hallap, Emmanuel Dupoux, Ewan Dunbar

Unsupervised speech representations have taken off, with benchmarks (SUPERB, ZeroSpeech) demonstrating major progress on semi-supervised speech recognition, speech synthesis, and speech-only language modelling. Inspiration comes from the promise of ``discovering the phonemes'' of a language or a similar low-bitrate encoding. However, one of the critical prop

Zoltán M. Balogh, Alexandru Kristály, Francesca Tripaldi

Given $p,N>1,$ we prove the sharp $L^p$-log-Sobolev inequality on noncompact metric measure spaces satisfying the ${\sf CD}(0,N)$ condition, where the optimal constant involves the asymptotic volume ratio of the space. This proof is based on a sharp isoperimetric inequality in ${\sf CD}(0,N)$ spaces, symmetrisation, and a careful scaling argument. As an appl

Kiran Bhaskar, Ajith Kumar, James Bunce, Jacob Pressman

The early detection and tracing of anomalous operations in battery packs are critical to improving performance and ensuring safety. This paper presents a data-driven approach for online anomaly detection in battery packs that uses real-time voltage and temperature data from multiple Li-ion battery cells. Mean-based residuals are generated for cell groups and

Alexander Yu. Cherny

The variational theorem for the scattering length in the presence of the dipole-dipole interaction is developed. The theorem is applied to the spinless dipolar Bose gas in three dimensions. We calculated analytically the long-range tails of the single-particle momentum distribution and static structure factor, and the pair distribution function at short dist

M. Ghanbarzadeh Noudehi, A. Kargaran, N. Azimi-Tafreshi, G. R. Jafari

In social networks, the balance theory has been studied by considering either the triple interactions between the links (structural balance) or the triple interaction of nodes and links (coevolutionary balance). In the structural balance theory, the links are not independent from each other, implying a global effect of this term and it leads to a discontinuo

Pedro Dedin Neto

In this paper, we introduce a new open-source code to find numerical solutions for the neutrino evolution considering neutrino-neutrino interactions, which result in the so-called collective effects. We first describe the theoretical background of this type of evolution, explaining the polarization vector formalism in which we implement our numerical code. W

Giacomo Fiorentini, Itir Onal Ertugrul, Albert Ali Salah

Pain is a serious and costly issue globally, but to be treated, it must first be detected. Vision transformers are a top-performing architecture in computer vision, with little research on their use for pain detection. In this paper, we propose the first fully-attentive automated pain detection pipeline that achieves state-of-the-art performance on binary pa

Cristóbal González Díaz, Hector Carrascosa, Guillermo M. Muñoz Caro, Miguel Ángle Satorre

The motivation to study experimentally CO ice under mimicked interstellar conditions is supported by the large CO gas abundances and ubiquitous presence of CO in icy grain mantles. Upon irradiation in its pure ice form, this highly stable species presents a limited ion and photon-induced chemistry, and an efficient non-thermal desorption. Using infrared spec

Michael L. Littman, Ifeoma Ajunwa, Guy Berger, Craig Boutilier

In September 2021, the "One Hundred Year Study on Artificial Intelligence" project (AI100) issued the second report of its planned long-term periodic assessment of artificial intelligence (AI) and its impact on society. It was written by a panel of 17 study authors, each of whom is deeply rooted in AI research, chaired by Michael Littman of Brown University.

Jesse C. Anderson, Payton Beeler, Mikhail Ovchinnikov, Will Cantrell

The effect of aerosol emissions on the properties and distribution of clouds is a large source of uncertainty in predictions of weather and climate. These aerosol-cloud interactions depend critically on the ability of aerosol particles to activate into cloud condensation nuclei (CCN). A key challenge in modeling CCN activation and the formation of cloud drop

Mingjie Liu, Haoyu Yang, Zongyi Li, Kumara Sastry

Lithography modeling is a crucial problem in chip design to ensure a chip design mask is manufacturable. It requires rigorous simulations of optical and chemical models that are computationally expensive. Recent developments in machine learning have provided alternative solutions in replacing the time-consuming lithography simulations with deep neural networ

Noam Levi, Itay M. Bloch, Marat Freytsis, Tomer Volansky

We introduce Noise Injection Node Regularization (NINR), a method of injecting structured noise into Deep Neural Networks (DNN) during the training stage, resulting in an emergent regularizing effect. We present theoretical and empirical evidence for substantial improvement in robustness against various test data perturbations for feed-forward DNNs when trai

Ryan M. Zbikowski, Calvin W. Johnson

The Lanczos algorithm has proven itself to be a valuable matrix eigensolver for problems with large dimensions, up to hundreds of millions or even tens of billions. The computational cost of using any Lanczos algorithm is dominated by the number of sparse matrix-vector multiplications until suitable convergence is reached. Block Lanczos replaces sparse matri

Severino Trotta, Lucie Flek, Charles Welch

Recent language modeling performance has been greatly improved by the use of external memory. This memory encodes the context so that similar contexts can be recalled during decoding. This similarity depends on how the model learns to encode context, which can be altered to include other attributes, such as style. We construct and evaluate an architecture fo

Amri Wandel

A new solution to the Fermi Paradox is presented: probes or visits from putative alien civilizations have a very low probability until a civilization reaches a certain age (called the Contact Era) after the onset of radio communications. If biotic planets are common, putative advanced civilizations may preferentially send probes to planets with technosignatu