May 2022 arXiv papers — page 20

Andrey Pupasov-Maksimov

The one-dimensional dynamics of identical discrete elements that combine the properties of newtonian mechanical particles and cellular automata are investigated. It is shown that the motion of a cluster of combined discrete elements, which is the simplest observable object of the model, leads to the Feynman chessboard model whose continuous limit gives the D

Hossein Mirzaei, Mohammadreza Salehi, Sajjad Shahabi, Efstratios Gavves

We aim for image-based novelty detection. Despite considerable progress, existing models either fail or face a dramatic drop under the so-called "near-distribution" setting, where the differences between normal and anomalous samples are subtle. We first demonstrate existing methods experience up to 20% decrease in performance in the near-distribution setting

Xiaolin Bu, Zihao Li, Shengxin Liu, Jiaxin Song

Fair allocation of indivisible goods studies allocating $m$ goods among $n$ agents in a fair manner. While fairness is a fundamental requirement in many real-world applications, it often conflicts with (economic) efficiency. This raises a natural and important question: How can we identify the most welfare-efficient allocation among all fair allocations? Thi

Jing-Xuan Zhang, Gen-Shun Wan, Jia Pan

Lip region-of-interest (ROI) is conventionally used for visual input in the lipreading task. Few works have adopted the entire face as visual input because lip-excluded parts of the face are usually considered to be redundant and irrelevant to visual speech recognition. However, faces contain much more detailed information than lips, such as speakers' head p

Fuchuan Tong, Siqi Zheng, Haodong Zhou, Xingjia Xie

While promising performance for speaker verification has been achieved by deep speaker embeddings, the advantage would reduce in the case of speaking-style variability. Speaking rate mismatch is often observed in practical speaker verification systems, which may actually degrade the system performance. To reduce intra-class discrepancy caused by speaking rat

Hechao Chen, Hui Tian, Hao Li, Jianguo Wang

Stellar flares are characterized by sudden enhancement of electromagnetic radiation from the atmospheres of stars. Compared to their solar counterparts, our knowledge on the coronal plasma dynamics of stellar flares and their connection to coronal mass ejections (CMEs) remains very limited. With time-resolved high-resolution spectroscopic observations from t

Dian Wang, Colin Kohler, Xupeng Zhu, Mingxi Jia

We present BulletArm, a novel benchmark and learning-environment for robotic manipulation. BulletArm is designed around two key principles: reproducibility and extensibility. We aim to encourage more direct comparisons between robotic learning methods by providing a set of standardized benchmark tasks in simulation alongside a collection of baseline algorith

Yuanyuan Lian

In this paper, we prove a boundary pointwise regularity for fully nonlinear elliptic equations on cones. In addition, based on this regularity, we give simple proofs of the Liouville theorems on cones.

Joshua Z. Tan, Luke V. Miller

Agreements and contracts are everywhere, but they are built on layers and layers of legal and social institutions. Software is slowly entering into this stack. In this article, we introduce agreement paths, a general model for understanding and decomposing digital agreement systems, and Agreement Engine, an open-source software service for building net-nativ

Bingting Wu, Xiaofang Zhang

Automatic code review (ACR), aiming to relieve manual inspection costs, is an indispensable and essential task in software engineering. The existing works only use the source code fragments to predict the results, missing the exploitation of developer's comments. Thus, we present a Multi-Modal Apache Automatic Code Review dataset (MACR) for the Multi-Modal A

Lajanugen Logeswaran, Yao Fu, Moontae Lee, Honglak Lee

Pre-trained large language models have shown successful progress in many language understanding benchmarks. This work explores the capability of these models to predict actionable plans in real-world environments. Given a text instruction, we show that language priors encoded in pre-trained language models allow us to infer fine-grained subgoal sequences. In

Yibing Wang, Hao Wu, Yong Niu, Jianwen Ding

With the explosive growth of mobile traffic demand, densely deployed small cells underlying macrocells have great potential for 5G and beyond wireless networks. In this paper, we consider the problem of supporting traffic flows with diverse QoS requirements by exploiting three high frequency bands, i.e., the 28GHz band, the E-band, and the Terahertz (THz) ba

Zhen-Xiong Shen, Chuanxun Su, Lixin He

We perform high-throughput first-principles computations to search the high Curie temperature ($T_{\rm C}$) two-dimensional ferromagnetic (2DFM) materials. We identify 79 2DFM materials and calculate their $T_{\rm C}$, in which Co$_2$F$_2$ has the highest $T_{\rm C}$=541K, well above the room temperature. The 79 2DFM materials are classified into different s

Gourav Datta, Souvik Kundu, Zihan Yin, Joe Mathai

Today's high resolution, high frame rate cameras in autonomous vehicles generate a large volume of data that needs to be transferred and processed by a downstream processor or machine learning (ML) accelerator to enable intelligent computing tasks, such as multi-object detection and tracking. The massive amount of data transfer incurs significant energy, lat

Ankur Moitra, Dhruv Rohatgi

Measuring the stability of conclusions derived from Ordinary Least Squares linear regression is critically important, but most metrics either only measure local stability (i.e. against infinitesimal changes in the data), or are only interpretable under statistical assumptions. Recent work proposes a simple, global, finite-sample stability metric: the minimum

Lei Cheng, Feng Yin, Sergios Theodoridis, Sotirios Chatzis

Sparse modeling for signal processing and machine learning has been at the focus of scientific research for over two decades. Among others, supervised sparsity-aware learning comprises two major paths paved by: a) discriminative methods and b) generative methods. The latter, more widely known as Bayesian methods, enable uncertainty evaluation w.r.t. the perf

Antonios Saravanos, Olivia Verni, Ian Moore, Sall Aboubacar

This paper investigates the end-user acceptance of last-mile delivery carried out by autonomous vehicles within the United States. A total of 296 participants were presented with information on this technology and then asked to complete a questionnaire on their perceptions to gauge their behavioral intention concerning acceptance. Structural equation modelin

Midhul Vuppalapati, Kushal Babel, Anurag Khandelwal, Rachit Agarwal

Many applications that benefit from data offload to cloud services operate on private data. A now-long line of work has shown that, even when data is offloaded in an encrypted form, an adversary can learn sensitive information by analyzing data access patterns. Existing techniques for oblivious data access-that protect against access pattern attacks-require

Li Niu, Qingyang Liu, Zhenchen Liu, Jiangtong Li

Object placement assessment (OPA) aims to predict the rationality score of a composite image in terms of the placement (e.g., scale, location) of inserted foreground object. However, given a pair of scaled foreground and background, to enumerate all the reasonable locations, existing OPA model needs to place the foreground at each location on the background

Samir Bouchiba, Salah Kabbaj, Keri Sather-Wagstaff

We investigate the transfer of regularity between commutative, noetherian, local rings through a class of local homomorphisms which we call basically regular. We give numerical characterizations of these maps, investigate their behavior under composition and decomposition, and compare them with Avramov-Foxby-Herzog's weakly regular local homomorphisms.

Siqi Zhang, Yifan Hu, Liang Zhang, Niao He

This paper takes an initial step to systematically investigate the generalization bounds of algorithms for solving nonconvex-(strongly)-concave (NC-SC/NC-C) stochastic minimax optimization measured by the stationarity of primal functions. We first establish algorithm-agnostic generalization bounds via uniform convergence between the empirical minimax problem

Lorenzo Amatucci, Joon-Ha Kim, Jemin Hwangbo, Hae-Won Park

In this work, a non-gaited framework for legged system locomotion is presented. The approach decouples the gait sequence optimization by considering the problem as a decision-making process. The redefined contact sequence problem is solved by utilizing a Monte Carlo Tree Search (MCTS) algorithm that exploits optimization-based simulations to evaluate the bes

J. Thorben Frank, Oliver T. Unke, Klaus-Robert Müller

The application of machine learning methods in quantum chemistry has enabled the study of numerous chemical phenomena, which are computationally intractable with traditional ab-initio methods. However, some quantum mechanical properties of molecules and materials depend on non-local electronic effects, which are often neglected due to the difficulty of model

Jianming Dong, Ruyan Jin, Guohui Lin, Bing Su

Various new scheduling problems have been arising from practical production processes and spawning new research areas in the scheduling field. We study the parallel multi-stage open shops problem, which generalizes the classic open shop scheduling and parallel machine scheduling problems. Given m identical k-stage open shops and a set of n jobs, we aim to pr

Jonathan W. Richardson, Swadha Pandey, Edita Bytyqi, Tega Edo

Achieving the quantum noise targets of third-generation detectors will require 10 dB of squeezed-light enhancement as well as megawatt laser power in the interferometer arms - both of which require unprecedented control of the internal optical losses. In this work, we present a novel optimization approach to gravitational-wave detector design aimed at maximi

Nancy Xu, Giannis Nikolentzos, Michalis Vazirgiannis, Henrik Boström

Image matching is a key component of many tasks in computer vision and its main objective is to find correspondences between features extracted from different natural images. When images are represented as graphs, image matching boils down to the problem of graph matching which has been studied intensively in the past. In recent years, graph neural networks

A. Arhrib, R. Benbrik, M. Krab, B. Manaut

We investigate the light charged Higgs boson production via $pp \rightarrow H^\pm h$ at the Large Hadron Collider (LHC) in the Two-Higgs Doublet Model (2HDM) type-I. By focusing on a scenario where $H$ is the observed Higgs boson of mass 125 GeV, we study the aforementioned Higgs boson production channel and explore its bosonic decay $H^\pm \rightarrow W^\pm

Victor A. Paludetto Magri, Robert D. Falgout, Ulrike M. Yang

Multigrid methods are well suited to large massively parallel computer architectures because they are mathematically optimal and display excellent parallelization properties. Since current architecture trends are favoring regular compute patterns to achieve high performance, the ability to express structure has become much more important. The hypre software

Matija Ćuk, Robert S. French, Mark R. Showalter, Matthew S. Tiscareno

Some of the small inner moons of Uranus have very closely-spaced orbits. Multiple numerical studies have found that the moons Cressida and Desdemona, within the Portia sub-group, are likely to collide in less than 100 Myr. The subsequent discovery of three new moons (Cupid, Perdita, and Mab) made the system even more crowded. In particular, it has been sugge

Jianyang Ren, Wanli Ni, Hui Tian

In this letter, we study a wireless federated learning (FL) system where network pruning is applied to local users with limited resources. Although pruning is beneficial to reduce FL latency, it also deteriorates learning performance due to the information loss. Thus, a trade-off problem between communication and learning is raised. To address this challenge

Michelle Ntampaka, Ana Bonaca, Sownak Bose, Daniel J. Eisenstein

Refereeing is a crucial component of publishing astronomical research, but few professional astronomers receive formal training on how to effectively referee a manuscript. In this article, we lay out considerations and best practices for referees. This document is intended as a tool for early career researchers to develop a fair, effective, and efficient app

Amir Pouya Aghasadeghi, Jan Van den Bussche, Julia Stoyanovich

Temporal graphs represent graph evolution over time, and have been receiving considerable research attention. Work on expressing temporal graph patterns or discovering temporal motifs typically assumes relatively simple temporal constraints, such as journeys or, more generally, existential constraints, possibly with finite delays. In this paper we propose to

Connor Pryor, Charles Dickens, Eriq Augustine, Alon Albalak

In this paper, we introduce Neural Probabilistic Soft Logic (NeuPSL), a novel neuro-symbolic (NeSy) framework that unites state-of-the-art symbolic reasoning with the low-level perception of deep neural networks. To model the boundary between neural and symbolic representations, we propose a family of energy-based models, NeSy Energy-Based Models, and show t

Gheorghe Craciun, Jiaxin Jin, Polly Y. Yu

Systems of differential equations with polynomial right-hand sides are very common in applications. On the other hand, their mathematical analysis is very challenging in general, due to the possibility of complex dynamics: multiple basins of attraction, oscillations, and even chaotic dynamics. Even if we restrict our attention to mass-action systems, all of

Bożena Poncyljusz, Tomasz Bulik, Niraj Dhital, Oleksandr Sushchov

Both the lack of observation of ultra-high energy (UHE) photons and the limitations of the state-of-the-art methodology being applied for their identification motivate studies on alternative approaches to the relevant simulations and the related observational strategies. One of such new approaches is proposed in this report and it concerns new observables al

Gregory Canal, Yancy Diaz-Mercado, Magnus Egerstedt, Christopher Rozell

A brain-computer interface (BCI) is a system that allows a human operator to use only mental commands in controlling end effectors that interact with the world around them. Such a system consists of a measurement device to record the human user's brain activity, which is then processed into commands that drive a system end effector. BCIs involve either invas

Laurent Lessard

Optimization problems in engineering and applied mathematics are typically solved in an iterative fashion, by systematically adjusting the variables of interest until an adequate solution is found. The iterative algorithms that govern these systematic adjustments can be viewed as a control system. In control systems, the output in measured and the input is a

Charles Champagne Cossette, Mohammed Ayman Shalaby, David Saussie, Jerome Le Ny

In multi-robot missions, relative position and attitude information between agents is valuable for a variety of tasks such as mapping, planning, and formation control. In this paper, the problem of estimating relative poses from a set of inter-agent range measurements is investigated. Specifically, it is shown that the estimation accuracy is highly dependent

Johan S. Høye, Dirk Gillespie

The Mean Spherical Approximation (MSA) is a commonly-used thermodynamic theory for computing the energetics of ions in the primitive model (i.e., charged hard-sphere ions in a background dielectric). For the excess chemical potential, however, the early MSA formulations (which were widely adopted) only included the terms needed to compute the mean excess che

Tuhin Samanta, Dmitry V. Matyushov

Molecular dynamics of two water models, SPC/E and TIP3P, at a number of temperatures is used to test the Kivelson-Madden equation connecting single-particle and collective dielectric relaxation times through the Kirkwood factor. The relation is confirmed by simulations and used to estimate the nonlinear effect of the electric field on the dielectric relaxati

Hung Viet Chu

A set $A$ of positive integers is said to be Schreier if either $A = \emptyset$ or $\min A\ge |A|$. We give a bijective map to prove the recurrence of the sequence $(|\mathcal{K}_{n, p, q}|)_{n=1}^\infty$ (for fixed $p\ge 1$ and $q\ge 2$), where $$\mathcal{K}_{n, p, q} \ = \ \{A\subset \{1, \ldots, n\}\,:\, \mbox{either }A = \emptyset \mbox{ or } (\max A-\ma

Julie Choi

There has been a rising interest in graph neural networks (GNNs) for representation learning over the past few years. GNNs provide a general and efficient framework to learn from graph-structured data. However, GNNs typically only use the information of a very limited neighborhood for each node to avoid over-smoothing. A larger neighborhood would be desirabl

Charles Godfrey, Davis Brown, Tegan Emerson, Henry Kvinge

Symmetry is a fundamental tool in the exploration of a broad range of complex systems. In machine learning symmetry has been explored in both models and data. In this paper we seek to connect the symmetries arising from the architecture of a family of models with the symmetries of that family's internal representation of data. We do this by calculating a set

Ganlin Lyu, Chao Chen, Xi Du, Spencer J. Sherwin

One approach to reduce the cost to simulate transitional compressible boundary layer flow is to adopt a near body reduced domain with boundary conditions enforced to be compatible with a computationally cheaper 3D RANS simulation. In such an approach it is desirable to enforce a consistent pressure distribution which is not typically the case when using the

Hongwei Ge, Christopher A Tout, Xuefei Chen, Matthias U Kruckow

Common envelope evolution (CEE) physics plays a fundamental role in the formation of binary systems, such as mergering stellar gravitational wave sources, pulsar binaries and type Ia supernovae. A precisely constrained CEE has become more important in the age of large surveys and gravitational wave detectors. We use an adiabatic mass loss model to explore ho

Hyoungjun Kim, Thomas W. Mattman

The Graph Minor Theorem of Robertson and Seymour implies a finite set of obstructions for any minor closed graph property. We show that there are only three obstructions to knotless embedding of size 23, which is far fewer than the 92 of size 22 and the hundreds known to exist at larger sizes. We describe several other topological properties whose obstructio

Sidney L. Johnson, Athanasios Hatzikoutelis, Christopher L. Smallwood

The COVID-19 pandemic imposed profound changes on the way we think about undergraduate physics education. Online courses became mainstream. Exam formats were reimagined. Digital whiteboards replaced face-to-face discussions. Laboratory classes were outfitted with home-delivered supply kits. And all of us developed a more intimate knowledge of Greek letters a

Jade Master

The composition problem for shortest paths asks the following: given shortest paths on weighted graphs M and N which share a common boundary, find the shortest paths on their union. This problem is a crucial step in any algorithm which uses the divide and conquer method to find shortest paths. This extended abstract details how this problem may be understood

Sebastian Ertel, Wilhelm Stannat

Ensemble Kalman--Bucy filters (EnKBFs) are an important tool in Data Assimilation that aim to approximate the posterior distribution for continuous time filtering problems using an ensemble of interacting particles. In this work we extend a previously derived unifying framework for consistent representations of the posterior distribution to correlated observ

Aditya R. Vaidya, Shailee Jain, Alexander G. Huth

Self-supervised language models are very effective at predicting high-level cortical responses during language comprehension. However, the best current models of lower-level auditory processing in the human brain rely on either hand-constructed acoustic filters or representations from supervised audio neural networks. In this work, we capitalize on the progr

Jacopo Banfi, Lindsey Woo, Mark Campbell

This paper investigates the usefulness of reasoning about the uncertain presence of obstacles during path planning, which typically stems from the usage of probabilistic occupancy grid maps for representing the environment when mapping via a noisy sensor like a stereo camera. The traditional planning paradigm prescribes using a hard threshold on the occupanc

A. Danehkar, M. Parthasarathy

The planetary nebula (PN) IC 4997 is one of a few rapidly evolving objects with variable brightness and nebular emission around a hydrogen-deficient star. In this study, we have determined the physical conditions and chemical abundances of this object using the collisionally excited lines (CELs) and optical recombination lines (ORLs) measured from the medium

Pi-Yueh Chuang, Lorena A. Barba

Though PINNs (physics-informed neural networks) are now deemed as a complement to traditional CFD (computational fluid dynamics) solvers rather than a replacement, their ability to solve the Navier-Stokes equations without given data is still of great interest. This report presents our not-so-successful experiments of solving the Navier-Stokes equations with

Prabhu Vellaisamy, John Paul Shen

Temporal Neural Networks (TNNs) are spiking neural networks that exhibit brain-like sensory processing with high energy efficiency. This work presents the ongoing research towards developing a custom design framework for designing efficient application-specific TNN-based Neuromorphic Sensory Processing Units (NSPUs). This paper examines previous works on NSP

Manuel Olguín Muñoz, Seyed Samie Mostafavi, Vishnu N. Moothedath, James Gross

Experimental research on wireless networking in combination with edge and cloud computing has been the subject of explosive interest in the last decade. This development has been driven by the increasing complexity of modern wireless technologies and the extensive softwarization of these through projects such as a Open Radio Access Network (O-RAN). In this c

Sangeet Sagar, Abhinav Bhatt, Abhijith Srinivas Bidaralli

Defenses against security threats have been an interest of recent studies. Recent works have shown that it is not difficult to attack a natural language processing (NLP) model while defending against them is still a cat-mouse game. Backdoor attacks are one such attack where a neural network is made to perform in a certain way on specific samples containing s

Maria das Neves Rebocho, Nicholas S. Witte

Our work studies sequences of orthogonal polynomials $ \{P_{n}(x)\}_{n=0}^{\infty} $ of the Laguerre-Hahn class, whose Stieltjes functions satisfy a Riccati type differential equation with polynomial coefficients, are subject to a deformation parameter $t$. We derive systems of differential equations and give Lax pairs, yielding non-linear differential equat

Radovan Dermisek, Keith Hermanek, Navin McGinnis, Sangsik Yoon

We show that any new interaction resulting in a chirally-enhanced contribution to the muon magnetic moment necessarily modifies the decay rate of the Higgs boson to muon pairs or generates the muon electric dipole moment. These three observables are highly correlated, and near future measurements of $h\to \mu^+\mu^-$ will carve an ellipse in the plane of dip

Pedro Contreras, Dianela Osorio, Eugeniy Yurievich Beliayev

We use the idea of the Wigner probability distribution (WPD) in a reduced scattering phase space (RPS) for the elastic scattering cross-section, with the help of a Tight-Binding (TB) numerical procedure allowing us to consider the anisotropic quantum effects, to phenomenologically predict several phases in these two novel unconventional superconductors. Unli

Kanun Pokharel, James W. Furness, Yi Yao, Volker Blum

Machine learning techniques have received growing attention as an alternative strategy for developing general-purpose density functional approximations, augmenting the historically successful approach of human designed functionals derived to obey mathematical constraints known for the exact exchange-correlation functional. More recently efforts have been mad

Richard D. P. Grumitt, Biwei Dai, Uros Seljak

We propose a general purpose Bayesian inference algorithm for expensive likelihoods, replacing the stochastic term in the Langevin equation with a deterministic density gradient term. The particle density is evaluated from the current particle positions using a Normalizing Flow (NF), which is differentiable and has good generalization properties in high dime

Canze Zhu, Qiang Tao

In this paper, we consider the three dimensional Cauchy problem of the compressible micropolar viscous flows, we prove the existence of unique global classical solution for smooth initial data with small initial energy but possibly large oscillations, the initial density may allowed to contain vacuum states. Furthermore, the large-time behavior of the soluti

Gideon Amir, Shangjie Yang

We introduce an "intermediate branching number"(IBN) which captures the branching of intermediate growth trees, similar in spirit to the well-studied branching number of exponential growth trees. We show that the IBN is the critical threshold for several random processes on trees, and analyze the IBN on some examples of interest. Our main result is an algori

Yao Liu, Dipendra Misra, Miro Dudík, Robert E. Schapire

We study reinforcement learning (RL) in settings where observations are high-dimensional, but where an RL agent has access to abstract knowledge about the structure of the state space, as is the case, for example, when a robot is tasked to go to a specific room in a building using observations from its own camera, while having access to the floor plan. We fo

Adnan Ben Mansour, Gaia Carenini, Alexandre Duplessis, David Naccache

To date, the most popular federated learning algorithms use coordinate-wise averaging of the model parameters. We depart from this approach by differentiating client contributions according to the performance of local learning and its evolution. The technique is inspired from control theory and its classification performance is evaluated extensively in IID f

Laurence Boxer

We show how to obtain freezing sets for digital images in $\mathbb{Z}^n$ for arbitrary $n$, using the $c_1$ and $c_n$ adjacencies.

Dario Modenini, Alfredo Locarini, Lorenzo Valentini, Alberto Faedi

In this paper, architectures for interplanetary communications that feature the use of a data relay are investigated. In the considered "two-leg" architecture, a spacecraft orbiting the Earth, or in orbit at a Lagrange point, receives data from a deep space probe (leg-1) and relays them towards ground (leg-2). Different wireless technologies for the interpla

Mario Hubert, Charles T. Sebens

We argue that the asymmetry between diverging and converging electromagnetic waves is just one of many asymmetries in observed phenomena that can be explained by a past hypothesis and statistical postulate (together assigning probabilities to different states of matter and field in the early universe). The arrow of electromagnetic radiation is thus absorbed

Abhijeet Vyas, Kamyar Azizzadenesheli

We study the problem of convergence to a stationary point in zero-sum games. We propose competitive gradient optimization (CGO ), a gradient-based method that incorporates the interactions between the two players in zero-sum games for optimization updates. We provide continuous-time analysis of CGO and its convergence properties while showing that in the con

Samuel R Rogers, Jaimie Daure, Philip Shipway, David Stewart

In order to reduce cobalt within the primary circuit of pressurised water reactors (PWRs), wear-resistant steels are being researched and developed. In particular interest is the understanding of galling mechanisms, an adhesive wear mechanism which is particularly prevalent in PWR valves. Here we show that large shear stresses and adhesive transfer occur dur

Ruochen Jiao, Xiangguo Liu, Takami Sato, Qi Alfred Chen

Predicting the trajectories of surrounding objects is a critical task for self-driving vehicles and many other autonomous systems. Recent works demonstrate that adversarial attacks on trajectory prediction, where small crafted perturbations are introduced to history trajectories, may significantly mislead the prediction of future trajectories and induce unsa

Jonathan Laurent, André Platzer

We propose a new approach to automated theorem proving where an AlphaZero-style agent is self-training to refine a generic high-level expert strategy expressed as a nondeterministic program. An analogous teacher agent is self-training to generate tasks of suitable relevance and difficulty for the learner. This allows leveraging minimal amounts of domain know

Yinghao Li, Le Song, Chao Zhang

Weakly supervised named entity recognition methods train label models to aggregate the token annotations of multiple noisy labeling functions (LFs) without seeing any manually annotated labels. To work well, the label model needs to contextually identify and emphasize well-performed LFs while down-weighting the under-performers. However, evaluating the LFs i

C. J. O. Reichhardt, C. Reichhardt

We consider the motion of superconducting vortices and skyrmions on a square substrate near the first commensurate matching field. Slightly above commensuration, a series of dynamic phases appear including interstitial flow, and there is a transition from fluid flow to soliton flow that generates negative differential conductivity. Slightly below commensurat

Hui Wan, Siva Sankalp Patel, J. William Murdock, Saloni Potdar

Dialogue systems can benefit from being able to search through a corpus of text to find information relevant to user requests, especially when encountering a request for which no manually curated response is available. The state-of-the-art technology for neural dense retrieval or re-ranking involves deep learning models with hundreds of millions of parameter

Swarnadeep Majumder, Christopher G. Yale, Titus D. Morris, Daniel S. Lobser

Quantum computing testbeds exhibit high-fidelity quantum control over small collections of qubits, enabling performance of precise, repeatable operations followed by measurements. Currently, these noisy intermediate-scale devices can support a sufficient number of sequential operations prior to decoherence such that near term algorithms can be performed with

Kaiyi Ji, Mingrui Liu, Yingbin Liang, Lei Ying

Bilevel optimization has arisen as a powerful tool for solving a variety of machine learning problems. Two current popular bilevel optimizers AID-BiO and ITD-BiO naturally involve solving one or two sub-problems, and consequently, whether we solve these problems with loops (that take many iterations) or without loops (that take only a few iterations) can sig

Walter Zulehner

We discuss two types of discrete inf-sup conditions for the Taylor-Hood family $Q_k$-$Q_{k-1}$ for all $k\in \mathbb{N}$ with $k\ge 2$ in 2D and 3D. While in 2D all results hold for a general class of hexahedral meshes, the results in 3D are restricted to meshes of parallelepipeds. The analysis is based on an element-wise technique as opposed to the widely u

Nikhil R. Agrawal, Rui Wang

The study of the electrical double layer lies at the heart of colloidal and interfacial science. The standard mean-field Poisson-Boltzmann (PB) theory is incapable of modeling many phenomena originated from ion correlation. An important example is charge inversion or overcharging of electrical double layers in multivalent electrolyte solutions. Existing theo

Ali Eidi, Navid Zehtabiyan-Rezaie, Reza Ghiassi, Xiang Yang

Computational fluid dynamics using the Reynolds-averaged Navier-Stokes (RANS) remains the most cost-effective approach to study wake flows and power losses in wind farms. The underlying assumptions associated with turbulence closures are one of the biggest sources of errors and uncertainties in the model predictions. This work aims to quantify model-form unc

Snigdha Panigrahi, Natasha Stewart, Chandra Sekhar Sripada, Elizaveta Levina

Multi-task learning is frequently used to model a set of related response variables from the same set of features, improving predictive performance and modeling accuracy relative to methods that handle each response variable separately. Despite the potential of multi-task learning to yield more powerful inference than single-task alternatives, prior work in

Tao Meng, Sidi Lu, Nanyun Peng, Kai-Wei Chang

We propose a general and efficient framework to control auto-regressive generation models with NeurAlly-Decomposed Oracle (NADO). Given a pre-trained base language model and a sequence-level boolean oracle function, we propose to decompose the oracle function into token-level guidance to steer the base model in text generation. Specifically, the token-level

Keith Hawkins, Adrian M. Price-Whelan, Allyson A. Sheffield, Aidan Z. Subrahimovic

Stellar streams in the Galactic halo are useful probes of the assembly of galaxies like the Milky Way. Many tidal stellar streams that have been found in recent years are accompanied by a known progenitor globular cluster or dwarf galaxy. However, the Orphan--Chenab (OC) stream is one case where a relatively narrow stream of stars has been found without a kn

Xiang Lisa Li, John Thickstun, Ishaan Gulrajani, Percy Liang

Controlling the behavior of language models (LMs) without re-training is a major open problem in natural language generation. While recent works have demonstrated successes on controlling simple sentence attributes (e.g., sentiment), there has been little progress on complex, fine-grained controls (e.g., syntactic structure). To address this challenge, we de

Lucy Nwosu, Xiangfang Li, Lijun Qian, Seungchan Kim

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes coronavirus disease 2019 (COVID-19). Imaging tests such as chest X-ray (CXR) and computed tomography (CT) can provide useful information to clinical staff for facilitating a diagnosis of COVID-19 in a more efficient and comprehensive manner. As a breakthrough of artificial intelligence (AI),

M. Lynne Saade, Murray Brightman, Daniel Stern, Matthew A. Malkan

Type 2 active galactic nuclei (AGN) show signatures of accretion onto a supermassive black hole through strong, high-ionization, narrow emission lines extended on scales of 100s to 1000s of parsecs, but they lack the broad emission lines from close in to the black hole that characterize type 1 AGN. The lack of broad emission could indicate obscuration of the

C. Phillips, R. Bandyopadhyay, D. J. McComas

The Taylor microscale is a fundamental length scale in turbulent fluids, representing the end of fluid properties and onset of dissipative processes. The Taylor microscale can also be used to evaluate the Reynolds number in classical turbulence theory. Although the solar wind is weakly collisional, it approximately behaves as a magnetohydrodynamic (MHD) flui

Nicolas Clozeau, Marc Josien, Felix Otto, Qiang Xu

We study the Representative Volume Element (RVE) method, which is a method to approximately infer the effective behavior $a_{\text{hom}}$ of a stationary random medium. The latter is described by a coefficient field $a(x)$ generated from a given ensemble $\langle\cdot\rangle$ and the corresponding linear elliptic operator $-\nabla\cdot a\nabla$. In line with

Carole Delporte-Gallet, Panagiota Fatourou, Hugues Fauconnier, Eric Ruppert

We study the ability of different shared object types to solve recoverable consensus using non-volatile shared memory in a system with crashes and recoveries. In particular, we compare the difficulty of solving recoverable consensus to the difficulty of solving the standard wait-free consensus problem in a system with halting failures. We focus on the model

Viresh Ranjan, Minh Hoai

We tackle the task of Class Agnostic Counting, which aims to count objects in a novel object category at test time without any access to labeled training data for that category. All previous class agnostic counting methods cannot work in a fully automated setting, and require computationally expensive test time adaptation. To address these challenges, we pro

Tadashi Kozuno, Wenhao Yang, Nino Vieillard, Toshinori Kitamura

In this work, we consider and analyze the sample complexity of model-free reinforcement learning with a generative model. Particularly, we analyze mirror descent value iteration (MDVI) by Geist et al. (2019) and Vieillard et al. (2020a), which uses the Kullback-Leibler divergence and entropy regularization in its value and policy updates. Our analysis shows

Elias B. Khalil, Christopher Morris, Andrea Lodi

Mixed-integer programming (MIP) technology offers a generic way of formulating and solving combinatorial optimization problems. While generally reliable, state-of-the-art MIP solvers base many crucial decisions on hand-crafted heuristics, largely ignoring common patterns within a given instance distribution of the problem of interest. Here, we propose MIP-GN

Hongzhu Li, Xiangrui Gao, Linhui Feng, Yafeng Deng

Conventional representation learning algorithms for knowledge graphs (KG) map each entity to a unique embedding vector, ignoring the rich information contained in the neighborhood. We propose a method named StarGraph, which gives a novel way to utilize the neighborhood information for large-scale knowledge graphs to obtain entity representations. An incomple

Carlo Graziani, Marieme Ngom

Modern advanced manufacturing and advanced materials design often require searches of relatively high-dimensional process control parameter spaces for settings that result in optimal structure, property, and performance parameters. The mapping from the former to the latter must be determined from noisy experiments or from expensive simulations. We abstract t

J. W. Brooks, M. S. McDonald, A. A. Kaptanoglu

Hall thrusters are susceptible to large-amplitude plasma oscillations that impact thruster performance and lifetime and are also difficult to model. High-speed cameras are a popular tool to study these dynamics due to their spatial resolution and are a popular, nonintrusive complement to in-situ probes. High-speed video of thruster oscillations can be isolat

Paul Almasan, Miquel Ferriol-Galmés, Jordi Paillisse, José Suárez-Varela

The proliferation of emergent network applications (e.g., telesurgery, metaverse) is increasing the difficulty of managing modern communication networks. These applications entail stringent network requirements (e.g., ultra-low deterministic latency), which hinders network operators to manage their resources efficiently. In this article, we introduce the net

Serdar Abut

Computer modeling and simulation is used to analyze system behavior and evaluate strategies for operating in descriptive or predictive modes. In this part of the book, modeling and simulation approaches that have been proposed since the 1970s have been tried to be presented. Simulation models used in social sciences, risk management and cloud-based informati

Shariq Iqbal, Robby Costales, Fei Sha

Despite significant progress on multi-agent reinforcement learning (MARL) in recent years, coordination in complex domains remains a challenge. Work in MARL often focuses on solving tasks where agents interact with all other agents and entities in the environment; however, we observe that real-world tasks are often composed of several isolated instances of l

Xinyang Geng, Hao Liu, Lisa Lee, Dale Schuurmans

Building scalable models to learn from diverse, multimodal data remains an open challenge. For vision-language data, the dominant approaches are based on contrastive learning objectives that train a separate encoder for each modality. While effective, contrastive learning approaches introduce sampling bias depending on the data augmentations used, which can

W. Dednam, S. Tewari, E. B. Lombardi, J. J. Palacios

Low-temperature scanning tunneling microscopy is used here to study dynamic bonding of gold atoms on surfaces under low coordination conditions. In the experiments, using an atomically-sharp gold tip, a gold adatom is deposited onto a gold surface with atomic precision either on the first hollow site near a step edge, or far away from it. Classical molecular