October 2022 arXiv papers — page 119

Debsuvra Mukhopadhyay, Jayakrishnan M. P. Nair, G. S. Agarwal

Cavity-mediated magnon-magnon coupling can lead to a transfer of spin-wave excitations between two spatially separated magnetic samples. We enunciate how the application of a two-photon parametric drive to the cavity can lead to stark amplification in this transfer efficiency. The recurrent multiphoton absorption by the cavity opens up an infinite ladder of

Hadi Reisizadeh, Anand Gokhale, Behrouz Touri, Soheil Mohajer

To design algorithms that reduce communication cost or meet rate constraints and are robust to communication noise, we study convex distributed optimization problems where a set of agents are interested in solving a separable optimization problem collaboratively with imperfect information sharing over time-varying networks. We study the almost sure convergen

Shuangzhi Li, Zhijie Wang, Felix Juefei-Xu, Qing Guo

Object detection through LiDAR-based point cloud has recently been important in autonomous driving. Although achieving high accuracy on public benchmarks, the state-of-the-art detectors may still go wrong and cause a heavy loss due to the widespread corruptions in the real world like rain, snow, sensor noise, etc. Nevertheless, there is a lack of a large-sca

Haodong Duan, Jiaqi Wang, Kai Chen, Dahua Lin

Graph convolution networks (GCN) have been widely used in skeleton-based action recognition. We note that existing GCN-based approaches primarily rely on prescribed graphical structures (ie., a manually defined topology of skeleton joints), which limits their flexibility to capture complicated correlations between joints. To move beyond this limitation, we p

Guanrui Li, Xinyang Liu, Giuseppe Loianno

Human-robot interaction will play an essential role in various industries and daily tasks, enabling robots to effectively collaborate with humans and reduce their physical workload. Most of the existing approaches for physical human-robot interaction focus on collaboration between a human and a single ground or aerial robot. In recent years, very little prog

Souvik Dhara, Julia Gaudio, Elchanan Mossel, Colin Sandon

Spectral algorithms are some of the main tools in optimization and inference problems on graphs. Typically, the graph is encoded as a matrix and eigenvectors and eigenvalues of the matrix are then used to solve the given graph problem. Spectral algorithms have been successfully used for graph partitioning, hidden clique recovery and graph coloring. In this p

Yequan Wang, Jiawen Deng, Aixin Sun, Xuying Meng

Recently, amounts of works utilize perplexity~(PPL) to evaluate the quality of the generated text. They suppose that if the value of PPL is smaller, the quality(i.e. fluency) of the text to be evaluated is better. However, we find that the PPL referee is unqualified and it cannot evaluate the generated text fairly for the following reasons: (i) The PPL of sh

Zhaoxuan Zhang, Xiaoguang Han, Bo Dong, Tong Li

We present a deep reinforcement learning method of progressive view inpainting for colored semantic point cloud scene completion under volume guidance, achieving high-quality scene reconstruction from only a single RGB-D image with severe occlusion. Our approach is end-to-end, consisting of three modules: 3D scene volume reconstruction, 2D RGB-D and segmenta

Shuyan Zhang, Jie Gong, Daniel Xiao, B. Reeja Jayan

A workflow is presented for performing pair distribution function (PDF) analysis of defected materials using structures generated from atomistic simulations. A large collection of structures, which differ in the types and concentrations of defects present, are obtained through energy minimization with an empirical interatomic potential. Each of the structure

Baolin Li, Siddharth Samsi, Vijay Gadepally, Devesh Tiwari

Online inference is becoming a key service product for many businesses, deployed in cloud platforms to meet customer demands. Despite their revenue-generation capability, these services need to operate under tight Quality-of-Service (QoS) and cost budget constraints. This paper introduces KAIROS, a novel runtime framework that maximizes the query throughput

Mohammed Ayman Shalaby, Charles Champagne Cossette, James Richard Forbes, Jerome Le Ny

Ultra-Wideband (UWB) systems are becoming increasingly popular for indoor localization, where range measurements are obtained by measuring the time-of-flight of radio signals. However, the range measurements typically suffer from a systematic error or bias that must be corrected for high-accuracy localization. In this paper, a ranging protocol is proposed al

Susmita Seal, Sudeshna Basu, Julio Becerra Guerrero, Juan Miguel Villegas Yeguas

In this work, we study non-rough norms in L(X,Y), the space of bounded linear operators between Banach spaces X and Y. We prove that L(X,Y) has non-rough norm if and only if X* and Y have non-rough norm. We show that the injective tensor product of X and Y has non-rough norm if and only if both X and Y have non-rough norm. We also give an example to show tha

Clare D'Cruz, Mousumi Mandal, J. K. Verma

In this paper we extend a result of Cowsik on set-theoretic complete intersection and a result Huneke, Morales and Goto and Nishida about Noetherian symbolic Rees algebras of ideals. As applications, we show that the symbolic Rees algebras of the following ideals are Noetherian and the ideals are set-theoretic complete intersections: (a) the edge ideal of a

Adrian She, Henry Yuen

We study unitary property testing, where a quantum algorithm is given query access to a black-box unitary and has to decide whether it satisfies some property. In addition to containing the standard quantum query complexity model (where the unitary encodes a binary string) as a special case, this model contains "inherently quantum" problems that have no clas

Ting Su, Jiongtao Zhu, Xin Zhang, Dong Zeng

For medical cone-beam computed tomography (CBCT) imaging, the native receptor array of the flat-panel detector (FPD) is usually binned into a reduced matrix size. By doing so, the signal readout speed can be increased by over 4-9 times at the expense of sacrificing the spatial resolution by at least 50%-67%. Clearly, such tradition poses a main bottleneck in

Tao Yang, Jinghao Deng, Xiaojun Quan, Qifan Wang

Fine-tuning large pre-trained language models on downstream tasks is apt to suffer from overfitting when limited training data is available. While dropout proves to be an effective antidote by randomly dropping a proportion of units, existing research has not examined its effect on the self-attention mechanism. In this paper, we investigate this problem thro

Wei Quan, Denise Gorse

This paper extends boolean particle swarm optimization to a multi-objective setting, to our knowledge for the first time in the literature. Our proposed new boolean algorithm, MBOnvPSO, is notably simplified by the omission of a velocity update rule and has enhanced exploration ability due to the inclusion of a 'noise' term in the position update rule that p

Sarmad Mehrdad, Farah E. Shamout, Yao Wang, S. Farokh Atashzar

Unrecognized patient deterioration can lead to high morbidity and mortality. Most existing deterioration prediction models require a large number of clinical information, typically collected in hospital settings, such as medical images or comprehensive laboratory tests. This is infeasible for telehealth solutions and highlights a gap in deterioration predict

Enze Xu, Jingwen Zhang, Jiadi Li, Qianqian Song

Alzheimer's disease (AD) is a heterogeneous, multifactorial neurodegenerative disorder characterized by beta-amyloid, pathologic tau, and neurodegeneration. There are no effective treatments for Alzheimer's disease at a late stage, urging for early intervention. However, existing statistical inference approaches of AD subtype identification ignore the pathol

Kohei Uehara, Tatsuya Harada

In real-world object recognition, there are numerous object classes to be recognized. Conventional image recognition based on supervised learning can only recognize object classes that exist in the training data, and thus has limited applicability in the real world. On the other hand, humans can recognize novel objects by asking questions and acquiring knowl

Jennifer Lawson, Elena Braverman

We consider a logistic differential equation subject to impulsive delayed harvesting, where the deduction information is a function of the population size at the time of one of the previous impulses. A close connection to the dynamics of high-order difference equations is used to conclude that while the inclusion of a delay in the impulsive condition does no

Thomas A. Trainor

Identified-hadron (PID) $p_t$ spectra from 13 TeV $p$-$p$ collisions are compared with a two-component (soft+hard) model (TCM) that accurately distinguishes jet-related hadron production (hard component) from nonjet projectile-nucleon dissociation (soft component). The present $p$-$p$ study is similar to and is guided by recent TCM studies of PID spectra fro

Haitong Huang, Xinghua Xue, Cheng Liu, Ying Wang

Soft errors in large VLSI circuits pose dramatic influence on computing- and memory-intensive neural network (NN) processing. Understanding the influence of soft errors on NNs is critical to protect against soft errors for reliable NN processing. Prior work mainly rely on fault simulation to analyze the influence of soft errors on NN processing. They are acc

Sunjae Kwon, Zonghai Yao, Harmon S. Jordan, David A. Levy

This paper proposes a new natural language processing (NLP) application for identifying medical jargon terms potentially difficult for patients to comprehend from electronic health record (EHR) notes. We first present a novel and publicly available dataset with expert-annotated medical jargon terms from 18K+ EHR note sentences ($MedJ$). Then, we introduce a

Zohreh HajiAkhondi-Meybodi, Arash Mohammadi, Ming Hou, Elahe Rahimian

Coded/uncoded content placement in Mobile Edge Caching (MEC) has evolved as an efficient solution to meet the significant growth of global mobile data traffic by boosting the content diversity in the storage of caching nodes. To meet the dynamic nature of the historical request pattern of multimedia contents, the main focus of recent researches has been shif

Zhigen Zhao, Xin Xing

We consider the multiple testing of the general regression framework aiming at studying the relationship between a univariate response and a p-dimensional predictor. To test the hypothesis of the effect of each predictor, we construct an Angular Balanced Statistic (ABS) based on the estimator of the sliced inverse regression without assuming a model of the c

Chaerin Kong, DongHyeon Jeon, Ohjoon Kwon, Nojun Kwak

Fashion attribute editing is a task that aims to convert the semantic attributes of a given fashion image while preserving the irrelevant regions. Previous works typically employ conditional GANs where the generator explicitly learns the target attributes and directly execute the conversion. These approaches, however, are neither scalable nor generic as they

Stephan Humeniuk, Yuan Wan, Lei Wang

Direct sampling from a Slater determinant is combined with an autoregressive deep neural network as a Jastrow factor into a fully autoregressive Slater-Jastrow ansatz for variational quantum Monte Carlo, which allows for uncorrelated sampling. The elimination of the autocorrelation time leads to a stochastic algorithm with provable cubic scaling (with a pote

Ziyin Zeng, Yongyang Xu, Zhong Xie, Wei Tang

Large-scale point cloud semantic segmentation is an important task in 3D computer vision, which is widely applied in autonomous driving, robotics, and virtual reality. Current large-scale point cloud semantic segmentation methods usually use down-sampling operations to improve computation efficiency and acquire point clouds with multi-resolution. However, th

Rui Tang, Kouhei Watanabe, Masahiro Fujita, Hanzhi Tang

Optical phase shifters are essential elements in photonic integrated circuits (PICs) and function as a direct interface to program the PIC. Non-volatile phase shifters, which can retain information without a power supply, are highly desirable for low-power static operations. Here a non-volatile optical phase shifter is demonstrated by driving a III-V/Si hybr

Qian Wang

We systematically study the chaotic signatures in a quantum many-body system consisting of an ensemble of interacting two-level atoms coupled to a single-mode bosonic field, the so-called extended Dicke model. The presence of the atom-atom interaction also leads us to explore how the atomic interaction affects the chaotic characters of the model. By analyzin

Yuanyuan Ma, Tiejun Lv, Gaofeng Pan, Yunfei Chen

This work investigates the secrecy outage performance of the uplink transmission of a radio-frequency (RF)-free-space optical (FSO) hybrid cooperative satellite-aerial-terrestrial network (SATN). Specifically, in the considered cooperative SATN, a terrestrial source (S) transmits its information to a satellite receiver (D) via the help of a cache-enabled aer

Peixue Zhao, Fei Huang

An edge-colored graph is \emph{rainbow }if no two edges of the graph have the same color. An edge-colored graph $G^c$ is called \emph{properly colored} if every two adjacent edges of $G^c$ receive distinct colors in $G^c$. A \emph{strongly edge-colored} graph is a proper edge-colored graph such that every path of length $3$ is rainbow. We call an edge-colore

Kien Nguyen, Hugo Proença, Fernando Alonso-Fernandez

In this survey, we provide a comprehensive review of more than 200 papers, technical reports, and GitHub repositories published over the last 10 years on the recent developments of deep learning techniques for iris recognition, covering broad topics on algorithm designs, open-source tools, open challenges, and emerging research. First, we conduct a comprehen

Chen Sun, Wayne M. Saslow

Experiment has established that spin-glasses can support a steady-state spin current $\vec{j}_{i}$. However, the accepted theory of spin glass dynamics permits oscillations but no steady-state spin current. Onsager's irreversible thermodynamics implies that the spin current is proportional to the gradient of a magnetization. We argue, however, that the magno

Tzu-Mo Kuo

We study unparametrized conformal circles, or called conformal geodesics, study diffeomorphisms mapping conformal circles to conformal circles in pseudo-Riemannian conformal manifolds. We show that such local diffeomorphisms are conformal local diffeomorphisms. Our result extends the result of Yano and Tomonaga. We also present a holographic interpretation f

Changjia Ma, Zhichao Han, Tingrui Zhang, Jingping Wang

Robot swarm is a hot spot in robotic research community. In this paper, we propose a decentralized framework for car-like robotic swarm which is capable of real-time planning in cluttered environments. In this system, path finding is guided by environmental topology information to avoid frequent topological change, and search-based speed planning is leverage

Suchetha Cooray, Tsutomu T. Takeuchi, Daichi Kashino, Shuntaro A. Yoshida

We report the discovery of a two-dimensional Galaxy Manifold within the multi-dimensional luminosity space of local galaxies. The multi-dimensional luminosity space is constructed using 11 bands that span from far ultraviolet to near-infrared for redshift < 0.1 galaxies observed with GALEX, SDSS, and UKIDSS. The two latent parameters are sufficient to expres

Ziyi Wu, Nikita Dvornik, Klaus Greff, Thomas Kipf

Understanding dynamics from visual observations is a challenging problem that requires disentangling individual objects from the scene and learning their interactions. While recent object-centric models can successfully decompose a scene into objects, modeling their dynamics effectively still remains a challenge. We address this problem by introducing SlotFo

Y. -M. Robin Hu, Elena A. Ostrovskaya, Eliezer Estrecho

We theoretically investigate the dynamics of wave packets in a generic, non-Hermitian, optically anisotropic exciton-polariton system that exhibits degeneracies of its complex-valued eigenenergies in the form of pairs of exceptional points in momentum space. We observe the self-acceleration and reshaping of the wave packets governed by their eigenenergies. W

LUX Collaboration, D. S. Akerib, S. Alsum, H. M. Araújo

Dual-phase xenon time projection chamber (TPC) detectors offer heightened sensitivities for dark matter detection across a spectrum of particle masses. To broaden their capability to low-mass dark matter interactions, we investigated the light and charge responses of liquid xenon (LXe) to sub-keV nuclear recoils. Using neutron events from a pulsed Adelphi De

Ana Shirley Monteiro, Regivan Santiago, Radko Mesiar, Marisol Gomez

In this paper we develop the idea of abstract homogeneity in the context of interval-valued (IV) functions endowed with admissible orders and investigate some of its properties.

Nathaniel Simon, Allen Z. Ren, Alexander Piqué, David Snyder

Unmanned aerial vehicles (UAVs) are finding use in applications that place increasing emphasis on robustness to external disturbances including extreme wind. However, traditional multirotor UAV platforms do not directly sense wind; conventional flow sensors are too slow, insensitive, or bulky for widespread integration on UAVs. Instead, drones typically obse

Qingyun Zeng

We study various problems arising in higher differential geometry using {\it derived Lie $\infty$-groupoids and algebroids}.We first study Lie $\infty$-groupoids in various categories of derived geometric objects in differential geometry, including derived manifolds, derived analytic spaces, derived noncommutative spaces, and derived Banach manifolds. We con

Yuya Takeuchi

The $Q$-prime curvature is a local pseudo-Einstein invariant on CR manifolds defined by Case and Yang, and Hirachi. Its integral, the total $Q$-prime curvature, gives a non-trivial global CR invariant. On the other hand, Marugame has constructed a family of global CR invariants via renormalized characteristic forms, which contains the total $Q$-prime curvatu

CMS, TOTEM Collaborations

The Precision Proton Spectrometer (PPS) of the CMS and TOTEM experiments collected 107.7 fb$^{-1}$ in proton-proton (pp) collisions at the LHC at 13 TeV (Run 2). This paper describes the key features of the PPS alignment and optics calibrations, the proton reconstruction procedure, as well as the detector efficiency and the performance of the PPS simulation.

Ken Fong, Ramona Leewe, Qiwen Zheng

TRIUMF ISAC 1 tuning controllers operate using minimum seeking sliding mode controller to minimize the reflected power in their cavities. As with all mini-mum seeking algorithms, chatter present in the control-ler can degrade its performance and cause necessary mechanical wear. By observing the rate at which the minimizing function approaches the sliding sur

Fengli Xu, Lingfei Wu, James A. Evans

This paper provides a detailed description of the data collection and machine learning model used in our recent PNAS paper "Flat Teams Drive Scientific Innovation" Xu et al. [2022a]. Here, we discuss how the features of scientific publication can be used to estimate the implicit hierarchy in the corresponding author teams. Besides, we also describe the metho

Shaoming Guo, Hong Wang, Ruixiang Zhang

In this paper, we first generalize the work of Bourgain and state a curvature condition for H\"ormander-type oscillatory integral operators, which we call Bourgain's condition. This condition is notably satisfied by the phase functions for the Fourier restriction problem and the Bochner-Riesz problem. We conjecture that for H\"ormander-type oscillatory integ

V. Calisti, I. Lucardesi, J. -F. Scheid

We study the shape differentiability of a general functional depending on the solution of a bidimensional stationary Stokes-Elasticity system, with respect to the reference domain of the elastic structure immersed in a viscous fluid. The differentiability with respect to reference elastic domain variations are considered under shape perturbations with diffeo

Iván Ongay-Valverde, Franklin D. Tall

We introduce a new topological generalization of the $\sigma$-projective hierarchy, not limited to Polish spaces. Earlier attempts have replaced $^{\omega}\omega$ by $^{\kappa}\kappa$, for $\kappa$ regular uncountable, or replaced countable by $\sigma$-discrete. Instead we close the usual $\sigma$-projective sets under continuous images and perfect preimages

François Impens, David Guéry-Odelin

Synchronization is a major concept in nonlinear physics. In a large number of systems, it is observed at long times for a sinusoidal excitation. In this paper, we design a transiently non-sinusoidal driving to reach the synchronization regime more quickly. We exemplify an inverse engineering method to solve this issue on the classical Van der Pol oscillator.

Kin Wai Ng, Frederick Mubang, Lawrence O. Hall, John Skvoretz

Forecasting social media activity can be of practical use in many scenarios, from understanding trends, such as which topics are likely to engage more users in the coming week, to identifying unusual behavior, such as coordinated information operations or PumpNDump efforts. To evaluate a new approach to forecasting, it is important to have baselines against

Jiachang Liu, Chudi Zhong, Boxuan Li, Margo Seltzer

Over the last century, risk scores have been the most popular form of predictive model used in healthcare and criminal justice. Risk scores are sparse linear models with integer coefficients; often these models can be memorized or placed on an index card. Typically, risk scores have been created either without data or by rounding logistic regression coeffici

Chen Sun, Wannan Yang, Thomas Jiralerspong, Dane Malenfant

In real life, success is often contingent upon multiple critical steps that are distant in time from each other and from the final reward. These critical steps are challenging to identify with traditional reinforcement learning (RL) methods that rely on the Bellman equation for credit assignment. Here, we present a new RL algorithm that uses offline contrast

Bowen Zhang, Zhi Tian, Quan Tang, Xiangxiang Chu

We explore the capability of plain Vision Transformers (ViTs) for semantic segmentation and propose the SegVit. Previous ViT-based segmentation networks usually learn a pixel-level representation from the output of the ViT. Differently, we make use of the fundamental component -- attention mechanism, to generate masks for semantic segmentation. Specifically,

Bagus Tris Atmaja, Zanjabila, Suyanto, Akira Sasou

This paper addresses issues on cough-based COVID-19 detection. We propose a cross-dataset transfer learning approach to improve the performance of COVID-19 detection by incorporating cough detection, cough segmentation, and data augmentation. The first aimed at removing non-cough signals and cough signals with low probability. The second aimed at segregating

Andrew Eberhardt, Alvaro Zamora, Michael Kopp, Tom Abel

Scalar field dark matter offers an interesting alternative to the traditional WIMP dark matter picture. Astrophysical and cosmological simulations are useful to constraining the mass of the dark matter particle in this model. This is particularly true at low mass where the wavelike nature of the dark matter particle manifests on astrophysical scales. These s

Manish K. Singh, D. Venkatramanan, Sairaj Dhople

For power grids predominantly featuring large synchronous generators (SGs), there exists a significant body of work bridging optimization and control tasks. A generic workflow in such efforts entails: characterizing the steady state of control algorithms and SG dynamics; assessing the optimality of the resulting operating point with respect to an optimal dis

Jielin Qiu, Franck Dernoncourt, Trung Bui, Zhaowen Wang

Livestream videos have become a significant part of online learning, where design, digital marketing, creative painting, and other skills are taught by experienced experts in the sessions, making them valuable materials. However, Livestream tutorial videos are usually hours long, recorded, and uploaded to the Internet directly after the live sessions, making

Prithul Sarker, Nasif Zaman, Alireza Tavakkoli

The relative afferent asymmetry between two eyes can be diagnosed using swinging flashlight test, also known as the alternating light test. This remains one of the most used clinical tests to this day. Despite the swinging flashlight test's straightforward approach, a number of factors can add variability into the clinical methodology and reduce the measurem

Paul Minter

The regularity theory of the Campanato space $\mathcal{L}^{(q,λ)}_k(Ω)$ has found many applications within the regularity theory of solutions to various geometric variational problems. Here we extend this theory from single-valued functions to multi-valued functions, adapting for the most part Campanato&#39;s original ideas (\cite{campanato}). We also give a

Nazneen Rajani, Weixin Liang, Lingjiao Chen, Meg Mitchell

With the advent of Transformers, large language models (LLMs) have saturated well-known NLP benchmarks and leaderboards with high aggregate performance. However, many times these models systematically fail on tail data or rare groups not obvious in aggregate evaluation. Identifying such problematic data groups is even more challenging when there are no expli

Milo Moses

We establish an analogue of Pontryagin duality for modules over compact discrete valuation rings $R$. Namely, we define the dual of a topological $R$ module to be its continuous $R$-module homomorphisms into $K/R$, the quotient module of the fraction field by its ring of integers. It is established that for locally compact $R$-modules the double dual map is

Rishikesh Ranade, Chris Hill, Lalit Ghule, Jay Pathak

In this paper we show that our Machine Learning (ML) approach, CoMLSim (Composable Machine Learning Simulator), can simulate PDEs on highly-resolved grids with higher accuracy and generalization to out-of-distribution source terms and geometries than traditional ML baselines. Our unique approach combines key principles of traditional PDE solvers with local-l

An Yan, Jiacheng Li, Wanrong Zhu, Yujie Lu

Contrastive Language-Image Pretraining (CLIP) efficiently learns visual concepts by pre-training with natural language supervision. CLIP and its visual encoder have been explored on various vision and language tasks and achieve strong zero-shot or transfer learning performance. However, the application of its text encoder solely for text understanding has be

Peiye Zhuang, Bliss Chapman, Ran Li, Oluwasanmi Koyejo

In the experimental sciences, statistical power analyses are often used before data collection to determine the required sample size. However, traditional power analyses can be costly when data are difficult or expensive to collect. We propose synthetic power analyses; a framework for estimating statistical power at various sample sizes, and empirically expl

Nidhin Harilal, Rohan Patil

Convolutional neural networks (CNNs) have revolutionized the field of deep neural networks. However, recent research has shown that CNNs fail to generalize under various conditions and hence the idea of capsules was introduced in 2011, though the real surge of research started from 2017. In this paper, we present an overview of the recent advances in capsule

Michal Byra, Ziemowit Klimonda, Piotr Jarosik

Homodyned K (HK) distribution has been widely used to describe the scattering phenomena arising in various research fields, such as ultrasound imaging or optics. In this work, we propose a machine learning based approach to the estimation of the HK distribution parameters. We develop neural networks that can estimate the HK distribution parameters based on t

Ling Li, David Thorsley, Joseph Hassoun

While vision transformers have achieved impressive results, effectively and efficiently accelerating these models can further boost performances. In this work, we propose a dense/sparse training framework to obtain a unified model, enabling weight sharing across various token densities. Thus one model offers a range of accuracy and throughput tradeoffs for d

Ali Omrani, Brendan Kennedy, Mohammad Atari, Morteza Dehghani

Existing word embedding debiasing methods require social-group-specific word pairs (e.g., "man"-"woman") for each social attribute (e.g., gender), which cannot be used to mitigate bias for other social groups, making these methods impractical or costly to incorporate understudied social groups in debiasing. We propose that the Stereotype Content Model (SCM),

Kevin Leung

The surfaces of most metals immersed in aqueous electrolytes have a several-nanometer-thick oxide/hydroxide surface layer. This gives rise to the existence of both metal|oxide and oxide|liquid electrotlyte interfaces, and makes it challenging to correlate atomic length-scale structures with electrochemical properties such the potential-of-zero-charge (PZC).

Bhupesh Bishnoi

In this research work, roll-to-roll chemical vapor deposited graphene device electronic transport properties are benchmarked to elucidate and comprehend mobility degradation in the real-world commercial application of graphene devices. Multifarious device design morphology in the graphene and two-dimensional material with diverse background materials composi

Peiye Zhuang, Jia-bin Huang, Ayush Saraf, Xuejian Rong

Image composition aims to blend multiple objects to form a harmonized image. Existing approaches often assume precisely segmented and intact objects. Such assumptions, however, are hard to satisfy in unconstrained scenarios. We present Amodal Instance Composition for compositing imperfect -- potentially incomplete and/or coarsely segmented -- objects onto a

Z. E. Krix, O. P. Sushkov

Recent observations of superconductivity in Moire graphene have lead to an intense interest in that system, with subsequent studies revealing a more complex phase diagram including correlated insulators and ferromagnetic phases. Here we propose an alternate system, electrostatically patterned bilayer graphene (PBG), in which a supermodulation is induced via

Marwa Gaser, Manuel Mager, Injy Hamed, Nizar Habash

Data sparsity is one of the main challenges posed by code-switching (CS), which is further exacerbated in the case of morphologically rich languages. For the task of machine translation (MT), morphological segmentation has proven successful in alleviating data sparsity in monolingual contexts; however, it has not been investigated for CS settings. In this pa

Oishee Banerjee

We prove that the cohomology of the moduli space of morphisms of a fixed finite degree from a smooth projective curve $C$ of genus $g$ to a complete simplicial toric variety $\mathbb{P}(\Sigma)$, denoted by the rational polyhedral fan $\Sigma$, stabilizes. As an arithmetic consequence we obtain a resolution of the Batyrev-Manin conjecture for toric varieties

Peiye Zhuang, Liqian Ma, Oluwasanmi Koyejo, Alexander G. Schwing

Recent work on 3D-aware image synthesis has achieved compelling results using advances in neural rendering. However, 3D-aware synthesis of face dynamics hasn't received much attention. Here, we study how to explicitly control generative model synthesis of face dynamics exhibiting non-rigid motion (e.g., facial expression change), while simultaneously ensurin

Isha Slavin, Daniel McKenzie

Comparison-Based Optimization (CBO) is an optimization paradigm that assumes only very limited access to the objective function f(x). Despite the growing relevance of CBO to real-world applications, this field has received little attention as compared to the adjacent field of Zeroth-Order Optimization (ZOO). In this work we propose a relatively simple method

Raymond Cheng, Christopher Felder

This work explores several aspects of interpolating sequences for $\ell^p_A$, the space of analytic functions on the unit disk with $p$-summable Maclaurin coefficients. Much of this work is communicated through a Carlesonian lens. We investigate various analogues of Gramian matrices, for which we show boundedness conditions are necessary and sufficient for i

V. Daniel Elvira, Steven Gottlieb, Oliver Gutsche, Benjamin Nachman

Software and Computing (S&C) are essential to all High Energy Physics (HEP) experiments and many theoretical studies. The size and complexity of S&C are now commensurate with that of experimental instruments, playing a critical role in experimental design, data acquisition/instrumental control, reconstruction, and analysis. Furthermore, S&C often plays a lea

Bhargav Rele, Caleb Hogan, Sevvandi Kandanaarachchi, Catherine Leigh

Many water-quality monitoring programs aim to measure turbidity to help guide effective management of waterways and catchments, yet distributing turbidity sensors throughout networks is typically cost prohibitive. To this end, we built and compared the ability of dynamic regression (ARIMA), long short-term memory neural nets (LSTM), and generalized additive

Conor Snedeker

The following work is a preprint collection of formal proofs regarding the convergence properties of the AdaBoost machine learning algorithm's classifier and margins. Various math and computer science papers have been written regarding conjectures and special cases of these convergence properties. Furthermore, the margins of AdaBoost feature prominently in t

Daniel Green

The physics of de Sitter space is essential to our understanding of our cosmological past, present, and future. It forms the foundation for the statistical predictions of inflation in terms of quantum vacuum fluctuations that are being tested with cosmic surveys. In addition, the current expansion of the universe is dominated by an apparently constant vacuum

Adrián González Casanova, Lizbeth Peñaloza, Arno Siri-Jégousse

In this article, we introduce a random (directed) graph model for the simultaneous forwards and backwards description of a rather broad class of Cannings models with a seed bank mechanism. This provides a simple tool to establish a sampling duality in the finite population size, and obtain a path-wise embedding of the forward frequency process and the backwa

Konstantin Tikhomirov

The size-Ramsey number $\hat r(G')$ of a graph $G'$ is defined as the smallest integer $m$ so that there exists a graph $G$ with $m$ edges such that every $2$-coloring of the edges of $G$ contains a monochromatic copy of $G'$. Answering a question of Beck, Rodl and Szemeredi showed that for every $n\geq 1$ there exists a graph $G'$ on $n$ vertices each of de

Maria Gordina, Tai Melcher, Dan Mikulincer, Jing Wang

We study large deviations for random walks on stratified (Carnot) Lie groups. For such groups, there is a natural collection of vectors which generates their Lie algebra, and we consider random walks with increments in only these directions. Under certain constraints on the distribution of the increments, we prove a large deviation principle for these random

Hyunchai Jeong, Jin Tian, Elias Bareinboim

Identifying the effects of new interventions from data is a significant challenge found across a wide range of the empirical sciences. A well-known strategy for identifying such effects is Pearl's front-door (FD) criterion (Pearl, 1995). The definition of the FD criterion is declarative, only allowing one to decide whether a specific set satisfies the criter

Ben Hutchinson, Jason Baldridge, Vinodkumar Prabhakaran

Questions regarding implicitness, ambiguity and underspecification are crucial for understanding the task validity and ethical concerns of multimodal image+text systems, yet have received little attention to date. This position paper maps out a conceptual framework to address this gap, focusing on systems which generate images depicting scenes from scene des

Zhiyuan She, Pei Ge, Huan Lei

One important problem in constructing the reduced dynamics of molecular systems is the accurate modeling of the non-Markovian behavior arising from the dynamics of unresolved variables. The main complication emerges from the lack of scale separations, where the reduced dynamics generally exhibits pronounced memory and non-white noise terms. We propose a data

Marcela S. Melara

The Software Supply Chain Attribute Integrity, or SCAI (pronounced "sky"), specification proposes a data format for capturing functional attribute and integrity information about software artifacts and their supply chain. SCAI data can be associated with executable binaries, statically- or dynamically-linked libraries, software packages, container images, so

Zahra Esmaeilbeig, Kumar Vijay Mishra, Arian Eamaz, Mojtaba Soltanalian

Intelligent reflecting surface (IRS) is a rapidly emerging paradigm to enable non-line-of-sight (NLoS) wireless transmission. In this paper, we focus on IRS-aided radar estimation performance of a moving hidden or NLoS target. Unlike prior works that employ a single IRS, we investigate this problem using multiple IRS platforms and assess the estimation perfo

Edward De Brouwer

Referred to as the third rung of the causal inference ladder, counterfactual queries typically ask the "What if ?" question retrospectively. The standard approach to estimate counterfactuals resides in using a structural equation model that accurately reflects the underlying data generating process. However, such models are seldom available in practice and o

Xi Ye, Guillaume-Alexandre Bilodeau

Different conditional video prediction tasks, like video future frame prediction and video frame interpolation, are normally solved by task-related models even though they share many common underlying characteristics. Furthermore, almost all conditional video prediction models can only achieve discrete prediction. In this paper, we propose a unified model th

A. Belley, T. Miyagi, S. R. Stroberg, J. D. Holt

As experimental searches for neutrinoless double-beta ($0\nu\beta\beta$) decay are entering a new generation, with hopes to completely probe the inverted mass hierarchy, the need for reliable nuclear matrix elements, which govern the rate of this decay, is stronger than ever. Since a large discrepancy in results is typically found with nuclear modela, a larg

M. J. Mazur, S. Metchev, R. A. Brown, R. Gupta

We present the technical design, construction and testing of the Colibri telescope array at Elginfield Observatory near London, Ontario, Canada. Three 50-cm telescopes are arranged in a triangular array and are separated by 110-160 metres. During operation, they will monitor field stars at the intersections of the ecliptic and galactic plane for serendipitou

Zhe Zhang, Guanghui Lan

While Nesterov&#39;s Accelerated Gradient Descent (AGD) efficiently solves constrained problems when the constraint set $X \subseteq \bbr^n$ is simple and easy to project onto, it remains an open question whether function-constrained problems $\min_{x \in X} \{F(x) : g(x) \leq 0\}$ can be solved as efficiently as unconstrained problems in terms of oracle com

Lorenzo Miretti, Thomas Kühne, Alper Schultze, Wilhelm Keusgen

By trading coverage and hardware complexity for abundance of spectrum, sub-THz mobile access networks are expected to operate under highly directive and relatively spectrally inefficient transmission regimes, while still offering enormous capacity gains over current sub-6GHz alternatives. Building on this assumption, and supported by extensive indoor directi

Mikael Henaff, Roberta Raileanu, Minqi Jiang, Tim Rocktäschel

In recent years, a number of reinforcement learning (RL) methods have been proposed to explore complex environments which differ across episodes. In this work, we show that the effectiveness of these methods critically relies on a count-based episodic term in their exploration bonus. As a result, despite their success in relatively simple, noise-free setting

Yuan Gao, Chih-Wei Chang, Sagar Mandava, Raanan Marants

Magnetic Resonance Imaging (MRI) is increasingly incorporated into treatment planning, because of its superior soft tissue contrast used for tumor and soft tissue delineation versus computed tomography (CT). However, MRI cannot directly provide mass density or relative stopping power (RSP) maps required for proton radiotherapy dose calculation. To demonstrat