July 2022 arXiv papers — page 112

Marius Costandin

In this paper we study the subset sum problem with real numbers. Starting from the given problem, we formulate a quadratic maximization problem over a polytope, P, which is eventually written as a distance maximization to a fixed point over the polytope. Next, starting from the obtained polytope, we construct an intersection of balls which includes the polyt

Ruhao Wan, Shixin Zhu, Jin Li

MDS codes and self-dual codes are important families of classical codes in coding theory. It is of interest to investigate MDS self-dual codes. The existence of MDS self-dual codes over finite field $F_q$ is completely solved for $q$ is even. In this paper, for finite field with odd characteristic, we construct some new classes of MDS self-dual codes by (ext

João Batista P. Matos, Iury Bessa, Edoardo Manino, Xidan Song

Neural networks are essential components of learning-based software systems. However, their high compute, memory, and power requirements make using them in low resources domains challenging. For this reason, neural networks are often quantized before deployment. Existing quantization techniques tend to degrade the network accuracy. We propose Counter-Example

Alexander B. Balakin, Gleb B. Kiselev

We establish a nonlinear version of the SU(N) symmetric theory, which describes self-consistently the interaction between the gravitational, gauge, vector and pseudoscalar (axion) fields. In the context of this theory the SU(N) symmetric multiplet of vector fields is associated with the color aether, the decay of which in the early Universe produced the cano

Mengjie Shi, Tom Vercauteren, Wenfeng Xia

Photoacoustic (PA) imaging is an emerging hybrid imaging modality that combines rich optical spectroscopic contrast and high ultrasonic resolution and thus holds tremendous promise for a wide range of pre-clinical and clinical applications. Compact and affordable light sources such as light-emitting diodes (LEDs) and laser diodes (LDs) are promising alternat

Yue Song, Nicu Sebe, Wei Wang

EigenDecomposition (ED) is at the heart of many computer vision algorithms and applications. One crucial bottleneck limiting its usage is the expensive computation cost, particularly for a mini-batch of matrices in the deep neural networks. In this paper, we propose a QR-based ED method dedicated to the application scenarios of computer vision. Our proposed

Morgane Ayle, Bertrand Charpentier, John Rachwan, Daniel Zügner

The robustness and anomaly detection capability of neural networks are crucial topics for their safe adoption in the real-world. Moreover, the over-parameterization of recent networks comes with high computational costs and raises questions about its influence on robustness and anomaly detection. In this work, we show that sparsity can make networks more rob

José Oscar González Cervantes, Dante Arroyo Sánchez, Juan Bory Reyes

We first prove a Cauchy's integral theorem and Cauchy type formula for certain inhomogeneous Cimmino system from quaternionic analysis perspective. The second part of the paper directs the attention towards some applications of the mentioned results, dealing in particular with four kinds of weighted Bergman spaces, reproducing kernels, projection and conform

Raziyeh Diyanatnezhad, Alireza Nasr-Isfahani

Let $\Lambda$ be an artin algebra and $\mathcal{C}$ be a functorially finite subcategory of mod$\Lambda$ which contains $\Lambda$ or $D\Lambda$. We use the concept of the infinite radical of $\mathcal{C}$ and show that $\mathcal{C}$ has an additive generator if and only if rad$^\infty_{\mathcal{C}}$ vanishes. In this case we describe the morphisms in powers

Xingzhao Jia, Dongye Changlei, Yanjun Peng

RGB-D SOD uses depth information to handle challenging scenes and obtain high-quality saliency maps. Existing state-of-the-art RGB-D saliency detection methods overwhelmingly rely on the strategy of directly fusing depth information. Although these methods improve the accuracy of saliency prediction through various cross-modality fusion strategies, misinform

I. V. Krainov, A. P. Dmitriev, N. S. Averkiev

We study the influence of inelastic processes on shot noise and the Fano factor for a one-dimensional double-barrier structure, where resonant tunneling takes place between two terminals. Most studies to date have found, by means of various approximate or phenomenological methods, that shot noise is insensitive to dephasing caused by inelastic scattering. In

Zhi Xu, Han Li, Ming Ma

Fluctuations of dynamical quantities are fundamental and inevitable. For the booming research in nanotechnology, huge relative fluctuation comes with the reduction of system size, leading to large uncertainty for the estimates of dynamical quantities. Thus, increasing statistical efficiency, i.e., reducing the number of samples required to achieve a given ac

Kanghee Lee, Junha Lee, Jaesik Park

Point cloud registration methods can effectively handle large-scale, partially overlapping point cloud pairs. Despite its practicality, matching the unbalanced pairs in terms of spatial extent and density has been overlooked and rarely studied. We present a novel method, dubbed UPPNet, for Unbalanced Point cloud Pair registration. We propose to incorporate a

Ekaterina Khramtsova, Guido Zuccon, Xi Wang, Mahsa Baktashmotlagh

Persistent topological properties of an image serve as an additional descriptor providing an insight that might not be discovered by traditional neural networks. The existing research in this area focuses primarily on efficiently integrating topological properties of the data in the learning process in order to enhance the performance. However, there is no e

Van T. Manh, Jianqiao Zhou, Xiaohong Jia, Zehui Lin

Ultrasound (US) is the primary imaging technique for the diagnosis of thyroid cancer. However, accurate identification of nodule malignancy is a challenging task that can elude less-experienced clinicians. Recently, many computer-aided diagnosis (CAD) systems have been proposed to assist this process. However, most of them do not provide the reasoning of the

Lloyd Allison

A library of software for inductive inference guided by the Minimum Message Length (MML) principle was created previously. It contains various (object-oriented-) classes and subclasses of statistical Model and can be used to infer Models from given data sets in machine learning problems. Here transformations of statistical Models are considered and implement

Guodong Liu, Tongling Wang, Shuoxi Zhang, Kun He

Model-Agnostic Meta-Learning (MAML) is a famous few-shot learning method that has inspired many follow-up efforts, such as ANIL and BOIL. However, as an inductive method, MAML is unable to fully utilize the information of query set, limiting its potential of gaining higher generality. To address this issue, we propose a simple yet effective method that gener

Zhongxi Fang, Jianming Huang, Xun Su, Hiroyuki Kasai

The Weisfeiler-Lehman (WL) test is a widely used algorithm in graph machine learning, including graph kernels, graph metrics, and graph neural networks. However, it focuses only on the consistency of the graph, which means that it is unable to detect slight structural differences. Consequently, this limits its ability to capture structural information, which

E. P. Inyang, J. E. Ntibi, E. A. Ibanga, E. S. William

We adopt Hulth\'en plus Hellmann potential as the quark-antiquark interaction potential for predicting the mass spectra of heavy mesons. The adopted potential was made to be temperature-dependent by replacing the screening parameter with Debye mass.The radial Schr\"odinger equation was analytically solved using the series expansion method and energy eigenval

Liang Li, Baihua Zheng, Weiwei Sun

Cross-modal hashing is an important approach for multimodal data management and application. Existing unsupervised cross-modal hashing algorithms mainly rely on data features in pre-trained models to mine their similarity relationships. However, their optimization objectives are based on the static metric between the original uni-modal features, without furt

Zhongweiyang Xu, Xulin Fan, Mark Hasegawa-Johnson

Audio-visual target speech extraction, which aims to extract a certain speaker's speech from the noisy mixture by looking at lip movements, has made significant progress combining time-domain speech separation models and visual feature extractors (CNN). One problem of fusing audio and video information is that they have different time resolutions. Most curre

Briskline Kiruba S, Petchiammal A, D. Murugan

A newly identified coronavirus disease called COVID-19 mainly affects the human respiratory system. COVID-19 is an infectious disease caused by a virus originating in Wuhan, China, in December 2019. Early diagnosis is the primary challenge of health care providers. In the earlier stage, medical organizations were dazzled because there were no proper health a

Wenqiao Zhang, Jiannan Guo, Mengze Li, Haochen Shi

Content-Based Image Retrieval (CIR) aims to search for a target image by concurrently comprehending the composition of an example image and a complementary text, which potentially impacts a wide variety of real-world applications, such as internet search and fashion retrieval. In this scenario, the input image serves as an intuitive context and background fo

H. Netzel, R. Smolec

Thanks to relatively firm mode identification, possible based on period ratios only, High Amplitude Delta Scuti Stars pulsating in at least three radial modes are promising targets for asteroseismic inference. In this study we used the most numerous sample of HADS from the OGLE inner bulge fields that likely pulsate in either three or four radial modes simul

Sreekrishnan Venkateswaran

Computer Science education has been evolving over the years to reflect applied realities. Until about a decade ago, theory of computation, algorithm design and system software dominated the curricula. Most courses were considered core and were hence mandatory; the programme structure did not allow much of a choice or variety. This column analyses why this ch

Chang Yue, Peizhuo Lv, Ruigang Liang, Kai Chen

With the broad application of deep neural networks (DNNs), backdoor attacks have gradually attracted attention. Backdoor attacks are insidious, and poisoned models perform well on benign samples and are only triggered when given specific inputs, which cause the neural network to produce incorrect outputs. The state-of-the-art backdoor attack work is implemen

Bhishma Dedhia, Roshini Balasubramanian, Niraj K. Jha

The Synthetic Control method has pioneered a class of powerful data-driven techniques to estimate the counterfactual reality of a unit from donor units. At its core, the technique involves a linear model fitted on the pre-intervention period that combines donor outcomes to yield the counterfactual. However, linearly combining spatial information at each time

Woongbae Park, Armin Schikorra

Let $\mathcal{N} \subset \mathbb{R}^M$ be a smooth simply connected compact manifold without boundary. A rational homotopy subgroup of $\pi_{N}(\mathcal{N})$ is represented by a homomorphism \[{\rm deg}: \pi_{N}(\mathcal{N}) \to \mathbb{R}.\] For maps $f: \mathbb{S}^N \to \mathcal{N}$ we give a quantitative estimate of its rational homotopy group element ${\

Kexun Zhang, Rui Wang, Xu Tan, Junliang Guo

It is difficult for non-autoregressive translation (NAT) models to capture the multi-modal distribution of target translations due to their conditional independence assumption, which is known as the "multi-modality problem", including the lexical multi-modality and the syntactic multi-modality. While the first one has been well studied, the syntactic multi-m

Akito Iwano, Youhei Yamaji

The relationship between crystal structures and superconducting critical temperatures has attracted considerable attention as a clue to designing higher-$T_{\rm c}$ superconductors. In particular, the relationship between the number $n$ of CuO$_2$ layers in a unit cell of cuprate superconductors and the optimum superconducting transition temperature $T_{\rm

Harbinder Singh, Dinesh Arora, Vinay Kumar

Recent innovations shows that blending of details captured by single Low Dynamic Range (LDR) sensor overcomes the limitations of standard digital cameras to capture details from high dynamic range scene. We present a method to produce well-exposed fused image that can be displayed directly on conventional display devices. The ambition is to preserve details

Zhongweiyang Xu, Romit Roy Choudhury

We consider the problem of audio voice separation for binaural applications, such as earphones and hearing aids. While today's neural networks perform remarkably well (separating $4+$ sources with 2 microphones) they assume a known or fixed maximum number of sources, K. Moreover, today's models are trained in a supervised manner, using training data synthesi

Weiming Zhuang, Yonggang Wen, Shuai Zhang

Federated learning (FL) is an emerging distributed machine learning method that empowers in-situ model training on decentralized edge devices. However, multiple simultaneous training activities could overload resource-constrained devices. In this work, we propose a smart multi-tenant FL system, MuFL, to effectively coordinate and execute simultaneous trainin

Wei-Ning Chen, Ayfer Özgür, Peter Kairouz

We introduce the Poisson Binomial mechanism (PBM), a discrete differential privacy mechanism for distributed mean estimation (DME) with applications to federated learning and analytics. We provide a tight analysis of its privacy guarantees, showing that it achieves the same privacy-accuracy trade-offs as the continuous Gaussian mechanism. Our analysis is bas

Fan Yu, Zhixiang Zhao, Yuchen Wang, Yi Xu

In this technical report, we represent our solution for the Human-centric Spatio-Temporal Video Grounding (HC-STVG) track of the 4th Person in Context (PIC) workshop and challenge. Our solution is built on the basis of TubeDETR and Mutual Matching Network (MMN). Specifically, TubeDETR exploits a video-text encoder and a space-time decoder to predict the star

Meng-Jiun Chiou

As the intermediate-level representations bridging the two levels, structured representations of visual scenes, such as visual relationships between pairwise objects, have been shown to not only benefit compositional models in learning to reason along with the structures but provide higher interpretability for model decisions. Nevertheless, these representat

Chunxu Tang, Beinan Wang, Huijun Wu, Zhenzhao Wang

The demand for data analytics has been consistently increasing in the past years at Twitter. In order to fulfill the requirements and provide a highly scalable and available query experience, a large-scale in-house SQL system is heavily relied on. Recently, we evolved the SQL system into a hybrid-cloud SQL federation system, compliant with Twitter's Partly C

Xin Cao

A new gradient-based optimization approach by automatically scheduling the learning rate has been proposed recently, which is called Binary Forward Exploration (BFE). The Adaptive version of BFE has also been discussed thereafter. In this paper, the improved algorithms based on them will be investigated, in order to optimize the efficiency and robustness of

Chongjie Si, Yuheng Jia, Ran Wang, Min-Ling Zhang

Exploiting label correlations is important to multi-label classification. Previous methods capture the high-order label correlations mainly by transforming the label matrix to a latent label space with low-rank matrix factorization. However, the label matrix is generally a full-rank or approximate full-rank matrix, making the low-rank factorization inappropr

Zhenwei Liu, Junyi Geng, Xikai Dai, Tomasz Swierzewski

With the rapid development of powder-based additive manufacturing, depowdering, a process of removing unfused powder that covers 3D-printed parts, has become a major bottleneck to further improve its productiveness. Traditional manual depowdering is extremely time-consuming and costly, and some prior automated systems either require pre-depowdering or lack a

Hongxia Zhong, Guangyong Zhang, Cheng Lu, Shiyuan Gao

Motivated by the recently synthesized two-dimensional semiconducting MoSi$_2$N$_4$, we systematically investigate the quasiparticle band alignment and exciton in monolayer MA$_2$Z$_4$ (M = Mo, W, Ti; A= Si, Ge; Z = N, P, As) using ab initio GW and Bethe-Salpeter equation calculations. Compared with the results from density functional theory (DFT), our GW cal

Michele Guizzardi, Michele Ghini, Andrea Villa, Luca Rebecchi

In this work, we observe plasmon induced hot electron extraction in a heterojunction between indium tin oxide nanocrystals and monolayer molybdenum disulphide. We study the sample with ultrafast differential transmission exciting the sample at 1750 nm where the intense localized plasmon surface resonance of the indium tin oxide nanocrystals is and where the

Laurentius Leonard, Shunsuke Inenaga, Hideo Bannai, Takuya Mieno

Let two static sequences of strings $P$ and $S$, representing prefix and suffix conditions respectively, be given as input for preprocessing. For the query, let two positive integers $k_1$ and $k_2$ be given, as well as a string $T$ given in an online manner, such that $T_i$ represents the length-$i$ prefix of $T$ for $1 \leq i \leq |T|$. In this paper we ar

Liang Guo, Zheng Luo, Qin Wang, Yazhou Zhang

We formulate and prove a Bott periodicity theorem for an $\ell^p$-space ($1\leq p<\infty$). For a proper metric space $X$ with bounded geometry, we introduce a version of $K$-homology at infinity, denoted by $K_*^{\infty}(X)$, and the Roe algebra at infinity, denoted by $C^*_{\infty}(X)$. Then the coarse assembly map descents to a map from $\lim_{d\to\infty}

Simon M. Thomas, James G. Lefevre, Glenn Baxter, Nicholas A. Hamilton

Pathologists have a rich vocabulary with which they can describe all the nuances of cellular morphology. In their world, there is a natural pairing of images and words. Recent advances demonstrate that machine learning models can now be trained to learn high-quality image features and represent them as discrete units of information. This enables natural lang

Natalie Collina, Eshwar Ram Arunachaleswaran, Michael Kearns

We study Stackelberg equilibria in finitely repeated games, where the leader commits to a strategy that picks actions in each round and can be adaptive to the history of play (i.e. they commit to an algorithm). In particular, we study static repeated games with no discounting. We give efficient algorithms for finding approximate Stackelberg equilibria in thi

Lei Shao, Rui Zhang, Wangjun Lu, Zhucheng Zhang

We investigate the quantum phase transition (QPT) in the XXZ central spin model, which can be described as a spin-1/2 particle coupled to N bath spins. In general, the QPT is supposed to occur only in the thermodynamical limit. In contrast, we present that the central spin model exhibits a normal-to-superradiant phase transition in the limit where the ratio

Jian Chai, Shan Cheng, Yao-hui Ju, Da-cheng Yan

The perturbative QCD (PQCD) approach based on $k_T$ factorization has made a great achievement for the QCD calculation of the hadronic B decays. Regulating the endpoint divergence by the transverse momentum of quarks in the propagators, one can do the perturbation calculation for kinds of diagrams including the annihilation type diagrams. In this paper, we r

Juan A. Cañas, J. Bernal, A. Martín-Ruiz

The equivalence principle of gravity is examined at the quantum level using the diffraction in time of matter waves in two ways. First, we consider a quasi-monochromatic beam of particles incident on a shutter which is removed at time $t = 0$ and fall due to the gravitational field. The probability density exhibits a set of mass-dependent oscillations which

Joao P. A. Dantas, Andre N. Costa, Felipe L. L. Medeiros, Diego Geraldo

This work compares supervised machine learning methods using reliable data from constructive simulations to estimate the most effective moment for launching missiles during air combat. We employed resampling techniques to improve the predictive model, analyzing accuracy, precision, recall, and f1-score. Indeed, we could identify the remarkable performance of

Rahul Mukerjee, Víctor Elvira

Multiple importance sampling (MIS) is an increasingly used methodology where several proposal densities are used to approximate integrals, generally involving target probability density functions. The use of several proposals allows for a large variety of sampling and weighting schemes. Then, the practitioner must choose a given scheme, i.e., sampling mechan

Lin Wu, Deyin Liu, Wenying Zhang, Dapeng Chen

Person re-identification (re-ID) is of great importance to video surveillance systems by estimating the similarity between a pair of cross-camera person shorts. Current methods for estimating such similarity require a large number of labeled samples for supervised training. In this paper, we present a pseudo-pair based self-similarity learning approach for u

Surajit Mandal

In this paper, we studied the geodesics of timelike and null like particles near an improved Schwarzschild black hole. The lapse function has been plotted and was found that only one horizon is possible. The equation of motion and effective potential of test particle have been calculated. This equation has an importance in studying the radial free fall and i

Lin Wu, Lingqiao Liu, Yang Wang, Zheng Zhang

The cross-resolution person re-identification (CRReID) problem aims to match low-resolution (LR) query identity images against high resolution (HR) gallery images. It is a challenging and practical problem since the query images often suffer from resolution degradation due to the different capturing conditions from real-world cameras. To address this problem

Trung Dang, Simon Kornblith, Huy Thong Nguyen, Peter Chin

In this work, we study different approaches to self-supervised pretraining of object detection models. We first design a general framework to learn a spatially consistent dense representation from an image, by randomly sampling and projecting boxes to each augmented view and maximizing the similarity between corresponding box features. We study existing desi

Dalton Bidleman

In this thesis we study toric rank functions for chip firing games and prove special cases of a conjectural Riemann-Roch. The original motivation for an investigation into this area of study came for the adaptation (due to Matt Baker) of Riemann-Roch into a graph theoretic analogue through the use of chip-firing games. Here, we collect known results and pres

Chris W. Patterson

It is shown that there are anomalous bound-state solutions to the two-body Dirac equation for an electron and positron interacting via an electromagnetic potential. These anomalous solutions have quantized coordinates at nuclear distances (fermi) and are orthogonal to the usual atomic positronium bound-states as shown by a simple extension of the Bethe-Salpe

Kowshik Thopalli, Pavan Turaga, Jayaraman J. Thiagarajan

A foundational requirement of a deployed ML model is to generalize to data drawn from a testing distribution that is different from training. A popular solution to this problem is to adapt a pre-trained model to novel domains using only unlabeled data. In this paper, we focus on a challenging variant of this problem, where access to the original source data

Ryo Miyashita, Yoshihiko Susuki, Atsushi Ishigame

Control of warm-water supply for dialysis treatment in a hospital environment is typical of safety-critical control problems. In order to guarantee the continuity of warm-water supply satisfying physical specifications for a wide range of operating conditions, it is inevitable to consider the nonlinearity involved in a dynamic model of a warm-water supply sy

Haoxuan Che, Haibo Jin, Hao Chen

Diabetic retinopathy (DR) and diabetic macular edema (DME) are leading causes of permanent blindness worldwide. Designing an automatic grading system with good generalization ability for DR and DME is vital in clinical practice. However, prior works either grade DR or DME independently, without considering internal correlations between them, or grade them jo

Weijie Yu, Zhongxiang Sun, Jun Xu, Zhenhua Dong

As an essential operation of legal retrieval, legal case matching plays a central role in intelligent legal systems. This task has a high demand on the explainability of matching results because of its critical impacts on downstream applications -- the matched legal cases may provide supportive evidence for the judgments of target cases and thus influence th

Jan Mináč, Lyle Muller, Tung T. Nguyen, Federico W. Pasini

Motivated by studies of oscillator networks, we study the spectrum of the join of several normal matrices with constant row sums. We apply our results to compute the characteristic polynomial of the join of several regular graphs. We then use this theorem to study several problems in spectral graph theory. In particular, we provide some simple constructions

Argenis. J. Mendez

In this work we study some regularity properties associated to the initial value problem (IVP) \begin{equation}\label{main1} \left\{ \begin{array}{ll} \partial_{t}u-\partial_{x_{1}}(-\Delta)^{\alpha/2} u+u\partial_{x_{1}}u=0, \quad 0< \alpha\leq 2,& \\ u(x,0)=u_{0}(x),\quad x=(x_{1},x_{2},\dots,x_{n})\in \mathbb{R}^{n},\, n\geq 2,\quad t\in\mathbb{R},& \\ \e

Tung Nguyen, Aditya Grover

Neural Processes (NPs) are a popular class of approaches for meta-learning. Similar to Gaussian Processes (GPs), NPs define distributions over functions and can estimate uncertainty in their predictions. However, unlike GPs, NPs and their variants suffer from underfitting and often have intractable likelihoods, which limit their applications in sequential de

Tao Li, Zhen Chen

The transfer matrix formalism is widely used in modeling heat diffusion in layered structures.Due to an intrinsic numerical instability issue, which has not yet drawn enough attention to the heat transfer community,this formalism fails at high heating frequencies and/or in thick structures. Inspired by its success in modeling wave propagation, we develop a n

Jicheng Zhang, Yizhou Peng, Haihua Xu, Yi He

Intermediate layer output (ILO) regularization by means of multitask training on encoder side has been shown to be an effective approach to yielding improved results on a wide range of end-to-end ASR frameworks. In this paper, we propose a novel method to do ILO regularized training differently. Instead of using conventional multitask methods that entail mor

Changjian Fu, Shengfei Geng, Pin Liu

The claim in the title is proved.

Yizhou Peng, Yufei Liu, Jicheng Zhang, Haihua Xu

Internal Language Model Estimation (ILME) based language model (LM) fusion has been shown significantly improved recognition results over conventional shallow fusion in both intra-domain and cross-domain speech recognition tasks. In this paper, we attempt to apply our ILME method to cross-domain code-switching speech recognition (CSSR) work. Specifically, ou

Meng-Lin Wu, Venkata Ravi Kiran Dayana, Hau Hwang

A shallow depth-of-field image keeps the subject in focus, and the foreground and background contexts blurred. This effect requires much larger lens apertures than those of smartphone cameras. Conventional methods acquire RGB-D images and blur image regions based on their depth. However, this approach is not suitable for reflective or transparent surfaces, o

Wes Robbins, Zanyar Zohourianshahzadi, Jugal Kalita

Vision-language models can assess visual context in an image and generate descriptive text. While the generated text may be accurate and syntactically correct, it is often overly general. To address this, recent work has used optical character recognition to supplement visual information with text extracted from an image. In this work, we contend that vision

Joshua Cutler, Mateo Díaz, Dmitriy Drusvyatskiy

We analyze a stochastic approximation algorithm for decision-dependent problems, wherein the data distribution used by the algorithm evolves along the iterate sequence. The primary examples of such problems appear in performative prediction and its multiplayer extensions. We show that under mild assumptions, the deviation between the average iterate of the a

Jonathan Gu, David Pal, Kevin Ryan

We propose an auction for online advertising where each ad occupies either one square or two horizontally-adjacent squares of a grid of squares. Our primary application are ads for products shown on retail websites such as Instacart or Amazon where the products are naturally organized into a grid. We propose efficient algorithms for computing the optimal lay

Ned Cooper, Tiffanie Horne, Gillian Hayes, Courtney Heldreth

HCI researchers have been gradually shifting attention from individual users to communities when engaging in research, design, and system development. However, our field has yet to establish a cohesive, systematic understanding of the challenges, benefits, and commitments of community-collaborative approaches to research. We conducted a systematic review and

Pham Hoang Ha, Nguyen Hoang Trang

In this paper, we prove that every $n$-vertex connected $K_{1,5}$-free graph $G$ with $\sigma_4(G)\geq n-1$ contains a spanning tree with at most $5$ leaves and branch vertices in total. Moreover, the degree sum condition "$\sigma_4(G)\geq n-1$" is best possible.

Suhwan Song, Stefan Vuckovic, Youngsam Kim, Hayoung Yu

Density functional simulations of condensed phase water are typically inaccurate, due to the inaccuracies of approximate functionals. A recent breakthrough showed that the SCAN approximation can yield chemical accuracy for pure water in all its phases, but only when its density is corrected. This is a crucial step toward first-principles biosimulations. Howe

Xiaoyan Yang, Yi Qiu, Jie Su, Yalei Huang

The materials exhibiting exchange bias (EB) have been extensively investigated mainly due to their great technological applications in magnetic sensors, but its underlying mechanism remains elusive. Here we report the novel coexistence of in-plane and out-of-plane EB in the Cr-doped Mn3Sn, a non-colinear antiferromagnet with a geometrically frustrated Kagome

Zhen-Yu Li, Guo-Liang Yu, Zhi-Gang Wang, Jian-Zhong Gu

Motivated by the experimental progress in the study of heavy baryons, we investigate the mass spectra of strange single heavy baryons in the $\lambda$-mode, where the relativistic quark model and the infinitesimally shifted Gaussian basis function method are employed. It is shown that the experimental data can be well reproduced by the predicted masses. The

Deyin Liu, Lin Wu, Haifeng Zhao, Farid Boussaid

Deep neural networks (DNNs) are known to be vulnerable to adversarial examples that are crafted with imperceptible perturbations, i.e., a small change in an input image can induce a mis-classification, and thus threatens the reliability of deep learning based deployment systems. Adversarial training (AT) is often adopted to improve robustness through trainin

Yichen Gu, David Blaauw, Joshua Welch

A key problem in computational biology is discovering the gene expression changes that regulate cell fate transitions, in which one cell type turns into another. However, each individual cell cannot be tracked longitudinally, and cells at the same point in real time may be at different stages of the transition process. This can be viewed as a problem of lear

Jieshan Chen, Amanda Swearngin, Jason Wu, Titus Barik

Screen recordings of mobile apps are a popular and readily available way for users to share how they interact with apps, such as in online tutorial videos, user reviews, or as attachments in bug reports. Unfortunately, both people and systems can find it difficult to reproduce touch-driven interactions from video pixel data alone. In this paper, we introduce

Joshua Krissansen-Totton, Jonathan J. Fortney

The Trappist-1 planets provide a unique opportunity to test the current understanding of rocky planet evolution. The James Webb Space Telescope is expected to characterize the atmospheres of these planets, potentially detecting CO$_2$, CO, H$_2$O, CH$_4$, or abiotic O$_2$ from water photodissociation and subsequent hydrogen escape. Here, we apply a coupled a

Ryan Batke, Fangzhou Yu, Jeremy Dao, Jonathan Hurst

In this work, we propose a method to generate reduced-order model reference trajectories for general classes of highly dynamic maneuvers for bipedal robots for use in sim-to-real reinforcement learning. Our approach is to utilize a single rigid-body model (SRBM) to optimize libraries of trajectories offline to be used as expert references in the reward funct

Sayan Das, Promit Ghosal, Yier Lin

We consider the KPZ fixed point starting from a general class of initial data. In this article, we study the growth of the large peaks of the KPZ fixed point at a spatial point $0$ when time $t$ goes to $\infty$ and when $t$ approaches $1$. We prove that for a very broad class of initial data, as $t\to \infty$, the limsup of the KPZ fixed point height functi

Amine Ouasfi, Adnane Boukhayma

We explore a new idea for learning based shape reconstruction from a point cloud, based on the recently popularized implicit neural shape representations. We cast the problem as a few-shot learning of implicit neural signed distance functions in feature space, that we approach using gradient based meta-learning. We use a convolutional encoder to build a feat

Mojdeh Banafsheh, Tomasz A. Wesolowski, Tim Gould, Leeor Kronik

The non-additive kinetic potential $v^{\text{NAD}}$ is a key quantity in density-functional theory (DFT) embedding methods, such as frozen density embedding theory and partition DFT. $v^{\text{NAD}}$ is a bi-functional of electron densities $\rho_{\rm B}$ and $\rho_{\rm tot} = \rho_{\rm A} + \rho_{\rm B}$. It can be evaluated using approximate kinetic-energy

Cesar S Lopez-Monsalvo, Alberto Rubio Ponce

We consider the motion of charged particles in the presence of a Dirac magnetic monopole. We use an extension of Noether's theorem for systems with magnetic forces and integrate explicitly the equations of motion.

Matthijs Jansen, Auday Al-Dulaimy, Alessandro V. Papadopoulos, Animesh Trivedi

As the next generation of diverse workloads like autonomous driving and augmented/virtual reality evolves, computation is shifting from cloud-based services to the edge, leading to the emergence of a cloud-edge compute continuum. This continuum promises a wide spectrum of deployment opportunities for workloads that can leverage the strengths of cloud (scalab

Joshua Brunk, Nathan Jermann, Ryan Sharp, Carl D. Hoover

This paper provides a comparison of current video content extraction tools with a focus on comparing commercial task-based machine learning services. Video intelligence (VIDINT) data has become a critical intelligence source in the past decade. The need for AI-based analytics and automation tools to extract and structure content from video has quickly become

Lina Alasfar, Ramona Gröber, Christophe Grojean, Ayan Paul

Revealing the Higgs pair production process is the next big challenge in high energy physics. In this work, we explore the use of interpretable machine learning and cooperative game theory for extraction of the trilinear Higgs self-coupling in Higgs pair production. In particular, we show how a topological decomposition of the gluon-gluon fusion Higgs pair p

Clive Gomes, Hyejin Park, Patrick Kollman, Yi Song

This project involved participation in the DCASE 2022 Competition (Task 6) which had two subtasks: (1) Automated Audio Captioning and (2) Language-Based Audio Retrieval. The first subtask involved the generation of a textual description for audio samples, while the goal of the second was to find audio samples within a fixed dataset that match a given descrip

Adam L. Gross, Lorenz Falling, Matthew C. Staab, Metzli I. Montero

Chemical modifications such as intercalation can be used to modify surface properties or to further functionalize the surface states of topological insulators. Using ambient pressure X-ray photoelectron spectroscopy, we report copper migration in $\text{Cu}_{x}\text{Bi}_2\text{Se}_3$, which occurs on a timescale of hours to days after initial surface cleavin

Dylan Slack, Satyapriya Krishna, Himabindu Lakkaraju, Sameer Singh

Machine Learning (ML) models are increasingly used to make critical decisions in real-world applications, yet they have become more complex, making them harder to understand. To this end, researchers have proposed several techniques to explain model predictions. However, practitioners struggle to use these explainability techniques because they often do not

Abhinav Kumar, Chenhao Tan, Amit Sharma

Neural network models trained on text data have been found to encode undesirable linguistic or sensitive concepts in their representation. Removing such concepts is non-trivial because of a complex relationship between the concept, text input, and the learnt representation. Recent work has proposed post-hoc and adversarial methods to remove such unwanted con

Scott Wilkinson, Sara L. Ellison, Connor Bottrell, Robert W. Bickley

Post-starburst (PSB) galaxies are defined as having experienced a recent burst of star formation, followed by a prompt truncation in further activity. Identifying the mechanism(s) causing a galaxy to experience a post-starburst phase therefore provides integral insight into the causes of rapid quenching. Galaxy mergers have long been proposed as a possible p

Yi Zhao, Engui Fan

In this paper, we address the existence of global solutions to the Cauchy problem for the integrable nonlocal nonlinear Schr\"{o}dinger (nonlocal NLS) equation with the initial data $q_0(x)\in H^{1,1}(\R)$ with the $L^1(\R)$ small-norm assumption. We rigorously show that the spectral problem for the nonlocal NLS equation admits no eigenvalues or resonances,

John T. Griesmer, Anh N. Le, Thái Hoàng Lê

We prove three results concerning the existence of Bohr sets in threefold sumsets. More precisely, letting $G$ be a countable discrete abelian group and $\phi_1, \phi_2, \phi_3: G \to G$ be commuting endomorphisms whose images have finite indices, we show that (1) If $A \subset G$ has positive upper Banach density and $\phi_1 + \phi_2 + \phi_3 = 0$, then $\p

Bosong Li, Baosen Zhang, Daniel S. Kirschen

This paper discusses how a cyber attack could take advantage of torsional resonances in the shaft of turbo-generators to inflict severe physical damage to a power system. If attackers were able to take over the control of a battery energy storage device, they could modulate the injection of this device at a frequency that matches one of the sub-synchronous r

Raffaello Secchi, Pietro Cassarà, Alberto Gotta

Automatic traffic classification is increasingly important in networking due to the current trend of encrypting transport information (e.g., behind HTTP encrypted tunnels) which prevents intermediate nodes to access end-to-end transport headers. This paper proposes an architecture for supporting Quality of Service (QoS) in hybrid terrestrial and SATCOM netwo

Noah Flemens, Dylan Heberle, Jiaoyang Zheng, Devin J. Dean

Parametric amplifiers have allowed breakthroughs in ultrafast, strong-field, and high-energy density laser science and are an essential tool for extending the frequency range of powerful emerging diode-pumped solid-state laser technology. However, their impact is limited by inherently low quantum efficiency due to nonuniform light extraction. Here we demonst

Dunbar Birnie, Christopher Cheng, Emina Soljanin

We develop new methods of quantifying the impact of photon detector imperfections on achievable secret key rates in Time-Entanglement based Quantum Key Distribution (QKD). We address photon detection timing jitter, detector downtime, and photon dark counts and show how each may decrease the maximum achievable secret key rate in different ways. We begin with