October 2023 arXiv papers — page 130

Abdullah Hayajneh, Erchin Serpedin, Mohammad Shaqfeh, Graeme Glass

A major obstacle when attempting to train a machine learning system to evaluate facial clefts is the scarcity of large datasets of high-quality, ethics board-approved patient images. In response, we have built a deep learning-based cleft lip generator designed to produce an almost unlimited number of artificial images exhibiting high-fidelity facsimiles of c

Yinpei Dai, Run Peng, Sikai Li, Joyce Chai

Zero-Shot Object Navigation (ZSON) enables agents to navigate towards open-vocabulary objects in unknown environments. The existing works of ZSON mainly focus on following individual instructions to find generic object classes, neglecting the utilization of natural language interaction and the complexities of identifying user-specific objects. To address the

A. K. Yi, S. Ahn, B. R. Ko, Y. K. Semertzidis

The signal to noise ratio efficiency $\epsilon_{\rm SNR}$ in axion dark matter searches has been estimated using large-statistic simulation data reflecting the background information and the expected axion signal power obtained from a real experiment. This usually requires a lot of computing time even with the assistance of powerful computing resources. Empl

Liliaokeawawa Cothren, Francesco Bullo, Emiliano Dall'Anese

In this paper, we provide a novel contraction-theoretic approach to analyze two-time scale systems, including those commonly encountered in Online Feedback Optimization (OFO). Our framework endows these systems with several robustness properties, enabling a more comprehensive characterization of their behaviors. The primary assumptions are the contractivity

Satoshi Nawata, Yiwen Pan, Jiahao Zheng

We study 2d $\mathcal{N}=(0,2)$ and $\mathcal{N}=(0,4)$ theories derived from compactifying class $\mathcal{S}$ theories on $S^2$ with a topological twist. We present concise expressions for the elliptic genera of both classes of theories, revealing the TQFT structure on Riemann surfaces $C_{g,n}$. Furthermore, our study highlights the relationship between t

Sung-Yi Liao

In additive combinatorics, Erd\"{o}s-Szemer\'{e}di Conjecture is an important conjecture. It can be applied to many fields, such as number theory, harmonic analysis, incidence geometry, and so on. Additionally, its statement is quite easy to understand, while it is still an open problem. In this dissertation, we investigate the Erd\"{o}s-Szemer\'{e}di Conjec

Satoshi Kawanomoto, Michitaro Koike, Fraser Bradfield, Toshihiro Fujii

Extensive air showers induced from high-energy cosmic rays provide a window into understanding the most energetic phenomena in the universe. We present a new method for observing these showers using the silicon imaging detector Subaru Hyper Suprime-Cam (HSC). This method has the advantage of being able to measure individual secondary particles. When paired w

Prathamesh Pawar

One of the prominent problems with processing and operating on text data is the non uniformity of it. Due to the change in the dialects and languages, the caliber of translation is low. This creates a unique problem while using NLP in text data; which is the spell variation arising from the inconsistent translations and transliterations. This problem can als

Sari Ghanem

We prove exterior energy estimates for tensorial non-linear wave equations, where the background metric is a perturbation of the Minkowski space-time, and where the derivatives are the Minkowski covariant derivatives. We obtain bounds in the exterior region of the Minkowski space-time, for the weighted $L^2$ norm on each component, separately, of the covaria

Yuan Zhao, Heng-Jie Hu, Qian-Qian Zhou, Zhang-Cai Qiu

We present numerical results for three-dimensional (3D) solitons with symmetries of the semi-vortex (SV) and mixed-mode (MM) types, which can be created in spinor Bose-Einstein condensates of Rydberg atoms under the action of the spin-orbit coupling (SOC). By means of systematic numerical computations, we demonstrate that the interplay of SOC and long-range

J. X. Lu

Why adding a collinear magnetic field to the electric one on a D3 brane in a system of two parallel separated D3 branes can enhance the open string pair production? How is the open string pair production rate related to the QED ones in the weak field limit? We answer these questions and report the somehow expected but still remarkable relation between the we

Jeongmo Kim, Zijun Wang, Khalid Lahlil, Patrick Davidson

Concentrated colloidal suspensions of nanorods often exhibit liquid-crystalline (LC) behavior. The transition to a nematic LC phase, with long-range orientational order of the particles, is usually well captured by Onsager's theory for hard rods, at least qualitatively. The theory shows how the volume fraction at the transition decreases with increasing aspe

Md Mahbubur Rahman, Ira Ceka, Chengzhi Mao, Saikat Chakraborty

Deep learning vulnerability detection has shown promising results in recent years. However, an important challenge that still blocks it from being very useful in practice is that the model is not robust under perturbation and it cannot generalize well over the out-of-distribution (OOD) data, e.g., applying a trained model to unseen projects in real world. We

Nilay Patel, Rahul Saha, Jeffrey Flanigan

Verifying mathematical proofs is difficult, but can be automated with the assistance of a computer. Autoformalization is the task of automatically translating natural language mathematics into a formal language that can be verified by a program. This is a challenging task, and especially for higher-level mathematics found in research papers. Research paper m

A. Jain, A. V. Metrikine, M. J. M. M. Steenbergen, K. N. van Dalen

Railway transition zones (RTZs) experience higher rates of degradation compared to open tracks, which leads to increased maintenance costs and reduced vailability. Despite existing literature on railway track assessment and maintenance, effective design solutions for RTZs are still limited. Therefore, a robust design criterion is required to develop effectiv

Danilo Amigo, Felipe Lepe, Gonzalo Rivera

In this paper we propose and analyze a virtual element method to approximate the natural frequencies of the acoustic eigenvalue problem with polygonal meshes that allow the presence of small edges. With the aid of a suitable seminorm that depends on the stabilization of the small edges method, we prove convergence and error estimates for the eigenfrequencies

Sari Ghanem

This is a first in a series of papers in which we study the stability of the $(1+n)$-Minkowski space-time, for $n \geq 3$, solution to the Einstein-Yang-Mills equations, in both the Lorenz and harmonic gauges, associated to any arbitrary compact Lie group $G$, and for arbitrary small perturbations. In this first, we prove global stability of the Minkowski sp

Chaofan Huang, V. Roshan Joseph

Importance sampling is a powerful tool for correcting the distributional mismatch in many statistical and machine learning problems, but in practice its performance is limited by the usage of simple proposals whose importance weights can be computed analytically. To address this limitation, Liu and Lee (2017) proposed a Black-Box Importance Sampling (BBIS) a

Jared P. Canright, Suzanne White Brahmia

We report on a study of the effects of laboratory activities that model fictitious laws of physics in a virtual reality environment on (1) students' epistemology about the role of experimental physics in class and in the world; (2) students' self-efficacy; and (3) the quality of student engagement with the lab activities. We create opportunities for students

R. Loek Van Heyningen, Ngoc Cuong Nguyen, Patrick Blonigan, Jaime Peraire

The solution of conservation laws with parametrized shock waves presents challenges for both high-order numerical methods and model reduction techniques. We introduce an r-adaptivity scheme based on optimal transport and apply it to develop reduced order models for compressible flows. The optimal transport theory allows us to compute high-order r-adaptive me

Chandralekha Singh, Akash Levy, Jeremy Levy

After the passage of the US National Quantum Initiative Act in December 2018, the National Science Foundation and the Office of Science and Technology Policy assembled an interagency working group and conducted a workshop titled "Key Concepts for Future Quantum Information Science Learners" that focused on identifying core concepts for future curricular and

Simon Felten

A classical problem in algebraic geometry is to construct smooth algebraic varieties with prescribed properties. In the approach via smoothings, one first constructs a degenerate scheme with the prescribed properties, and then shows the existence of a smooth variety degenerating to this scheme. Logarithmic geometry has given important new impulses to the sec

Rajan Plumley, Sougata Mardanya, Cheng Peng, Johannes Nokelainen

Van der Waals (vdW) magnetic materials are comprised of layers of atomically thin sheets, making them ideal platforms for studying magnetism at the two-dimensional (2D) limit. These materials are at the center of a host of novel types of experiments, however, there are notably few pathways to directly probe their magnetic structure. We report the magnetic or

Jiaxin Jin, Chanwoo Kim

We study linear two-half dimensional Vlasov equations under the logarithmic gravity potential in the half space of diffuse reflection boundary. We prove decay-in-time of the exponential moments with a polynomial rate, which depends on the base logarithm.

L. E. Golub

Theory of weak localization in graphene with Rashba splitting of energy spectrum is developed. Anomalous magnetoresistance caused by weak localization is calculated with account for inter- and intravalley, spin-orbit and spin-valley scattering processes. It is shown that the anomalous magnetoresistance is described by the expression different from the tradit

Shujaut H. Bader, Xiaojue Zhu

The scaling law for the horizontal length scale $\ell$ relative to the domain height $L$, originating from the linear theory of quasi-static magnetoconvection, $\ell/L \sim Q^{-1/6}$, has been verified through two-dimensional (2D) direct numerical simulation (DNS), particularly at high values of the Chandrasekhar number ($Q$). This relationship remains valid

Kaifeng Huang, Yingfeng Xia, Bihuan Chen, Zhuotong Zhou

Open source software brings benefit to software community, but also introduces legal risks caused by license violations, which result in serious consequences such as lawsuits and financial losses. To mitigate legal risks, some approaches have been proposed to identify licenses, detect license incompatibilities and inconsistencies, and recommend licenses. As

Lucas Affonso

In this thesis, we present results from the investigation of two problems, one related to the phase transition of long-range Ising models and the other one associated with the characterization of equilibrium states in quantum spin systems. Due to the long-range nature of the interactions, $J|x-y|^{-\alpha}$, estimates using contours usually found in the lite

Max Hallgren, Wangjian Jian, Jian Song, Gang Tian

We establish geometric regularity for Type I blow-up limits of the K\"ahler-Ricci flow based at any sequence of Ricci vertices. As a consequence, the limiting flow is continuous in time in both Gromov-Hausdorff and Gromov-$W_1$ distance. In particular, the singular sets of each time slice and its tangent cones are close and of codimension no less than $4$.

Wangjian Jian, Jian Song, Gang Tian

We establish the scalar curvature and distance bounds, extending Perelman's work on the Fano K\"ahler-Ricci flow to general finite time solutions of the K\"ahler-Ricci flow. These bounds are achieved by our Li-Yau type and Harnack estimates for weighted Ricci potential functions of the K\"ahler-Ricci flow. We further prove that the Type I blow-ups of the fin

Hongxu Pu, Xincong Yang, Jing Li, Runhao Guo

Ensuring the safety, quality, and timely completion of construction projects is paramount, with construction inspections serving as a vital instrument towards these goals. Nevertheless, the predominantly manual approach of present-day inspections frequently results in inefficiencies and inadequate information management. Such methods often fall short of prov

Wangjian Jian, Jian Song, Gang Tian

In this note, we give a new proof for Perelman's scalar curvature and diameter estimates for the K\"ahler-Ricci flow on Fano manifolds. The proof relies on a new Harnack estimate for a special family of functions in space-time. Our new approach initiates the work in \cite{JST23a} for general finite time solutions of the K\"ahler-Ricci flow.

Antonio Montalban, Rodrigo M. Corder

We investigate the emergence of synchronization in heterogeneous networks of chaotic maps. Our findings reveal that a small cluster of highly connected maps is responsible for triggering the spark of synchronization. After the spark, the synchronized cluster grows in size and progressively moves to less connected maps, eventually reaching a cluster that may

Benjamin Cichy, Jamie Lukos, Mohammad Alam, J. Cortney Bradford

Deep neural networks (DNN) have become increasingly utilized in brain-computer interface (BCI) technologies with the outset goal of classifying human physiological signals in computer-readable format. While our present understanding of DNN usage for BCI is promising, we have little experience in deciphering neural events from dynamic freely-mobile situations

Ravit Sharma, Wojciech Romaszkan, Feiqian Zhu, Puneet Gupta

Researchers have long touted a vision of the future enabled by a proliferation of internet-of-things devices, including smart sensors, homes, and cities. Increasingly, embedding intelligence in such devices involves the use of deep neural networks. However, their storage and processing requirements make them prohibitive for cheap, off-the-shelf platforms. Ov

Hideki Yanaoka

The collocation method uses the Rhie-Chow scheme to find the cell interface velocity by pressure-weighted interpolation. The accuracy of this interpolation method in unsteady flows has not been fully clarified. This study constructs a finite difference scheme for incompressible fluids using a collocated grid in a general curvilinear coordinate system. The ve

Matthew J Simpson, Keeley M Murphy, Scott W McCue, Pascal R Buenzli

Mathematical models describing the spatial spreading and invasion of populations of biological cells are often developed in a continuum modelling framework using reaction-diffusion equations. While continuum models based on linear diffusion are routinely employed and known to capture key experimental observations, linear diffusion fails to predict well-defin

Bangguo Yu, Qihao Yuan, Kailai Li, Hamidreza Kasaei

Visual target navigation is a critical capability for autonomous robots operating in unknown environments, particularly in human-robot interaction scenarios. While classical and learning-based methods have shown promise, most existing approaches lack common-sense reasoning and are typically designed for single-robot settings, leading to reduced efficiency an

Stephen P. Schmidt, Kevin C. Schlaufman, Keyi Ding, Samuel K. Grunblatt

While secondary mass inferences based on single-lined spectroscopic binary (SB1) solutions are subject to $\sin{i}$ degeneracies, this degeneracy can be lifted through the observations of eclipses. We combine the subset of Gaia Data Release (DR) 3 SB1 solutions consistent with brown dwarf-mass secondaries with the Transiting Exoplanet Survey Satellite (TESS)

Riccardo Fogliato, Arun Kumar Kuchibhotla, Zachary Lipton, Daniel Nagin

Many important policy decisions concerning policing hinge on our understanding of how likely various criminal offenses are to result in arrests. Since many crimes are never reported to law enforcement, estimates based on police records alone must be adjusted to account for the likelihood that each crime would have been reported to the police. In this paper,

Matteo Marcoli

We discuss recent progress in the calculation of integrated antenna functions for final-state radiation at N$^3$LO in QCD. Antenna functions are directly extracted from physical matrix elements for the decay of a colour-singlet state into partons. In order to compute their integrated counterparts, which are necessary ingredients for fixed-order calculations

P. Rogozenski, E. Krause, V. Miranda

The increasing statistical precision of photometric redshift surveys requires improved accuracy of theoretical predictions for large-scale structure observables to obtain unbiased cosmological constraints. In $\Lambda$CDM cosmologies, massive neutrinos stream freely at small cosmological scales, suppressing the small-scale power spectrum. In massive neutrino

Ran Tian, Chenfeng Xu, Masayoshi Tomizuka, Jitendra Malik

When operating in service of people, robots need to optimize rewards aligned with end-user preferences. Since robots will rely on raw perceptual inputs like RGB images, their rewards will inevitably use visual representations. Recently there has been excitement in using representations from pre-trained visual models, but key to making these work in robotics

Adyasha Maharana, Prateek Yadav, Mohit Bansal

Analytical theories suggest that higher-quality data can lead to lower test errors in models trained on a fixed data budget. Moreover, a model can be trained on a lower compute budget without compromising performance if a dataset can be stripped of its redundancies. Coreset selection (or data pruning) seeks to select a subset of the training data so as to ma

Philippe Camus, Jonathan Corbett, Sean Crawford, Koby Dering

Low-temperature cryogenics open the door for a range of interesting technologies based on features like superconductivity and superfluidity, low-temperature phase transitions or the low heat capacity of non-metals in the milli-Kelvin range. Devices based on these technologies are often sensitive to small energy depositions as can be caused by environmental r

Catherine Arnett, Tyler A. Chang, James A. Michaelov, Benjamin K. Bergen

Do multilingual language models share abstract grammatical representations across languages, and if so, when do these develop? Following Sinclair et al. (2022), we use structural priming to test for abstract grammatical representations with causal effects on model outputs. We extend the approach to a Dutch-English bilingual setting, and we evaluate a Dutch-E

K. J. Petersen, J. R. Brinkerhoff

Microscopic thermal fluctuations are known to affect the macroscopic and spatio-temporal evolution of a host of physical phenomena central to the study of biological systems, turbulence, and reactive mixtures, among others. In phase-changing fluids metastability and nucleation rates of embryos are known to be non-trivially affected by thermal noise stemming

Ziyue Zou, Pratyush Tiwary

In this study, we present a graph neural network-based learning approach using an autoencoder setup to derive low-dimensional variables from features observed in experimental crystal structures. These variables are then biased in enhanced sampling to observe state-to-state transitions and reliable thermodynamic weights. Our approach uses simple convolution a

Lars Becker, Ohad Klein, Joseph Slote, Alexander Volberg

Let $f$ be an analytic polynomial of degree at most $K-1$. A classical inequality of Bernstein compares the supremum norm of $f$ over the unit circle to its supremum norm over the sampling set of the $K$-th roots of unity. Many extensions of this inequality exist, often understood under the umbrella of Marcinkiewicz-Zygmund-type inequalities for $L^p,1\le p\

M. Rostami, H. Moradian, S. S. Kia

This paper proposes a set of novel optimization algorithms for solving a class of convex optimization problems with time-varying streaming cost function. We develop an approach to track the optimal solution with a bounded error. Unlike the existing results, our algorithm is executed only by using the first-order derivatives of the cost function which makes i

Nils Olsson, Christopher O'Neill, Derek Rawling

Consider the set $M_{a,b} = \{n \in \mathbb Z_{\ge 1} : n \equiv a \bmod b\} \cup \{1\}$ for $a, b \in \mathbb Z_{\ge 1}$. If $a^2 \equiv a \bmod b$, then $M_{a,b}$ is closed under multiplication and known as an arithmetic congruence monoid (ACM). A non-unit $n \in M_{a,b}$ is an atom if it cannot be expressed as a product of non-units, and the atomic densit

William Merrill, Ashish Sabharwal

Recent theoretical work has identified surprisingly simple reasoning problems, such as checking if two nodes in a graph are connected or simulating finite-state machines, that are provably unsolvable by standard transformers that answer immediately after reading their input. However, in practice, transformers' reasoning can be improved by allowing them to us

Nikhil Devanathan, Stephen Boyd

In 1963 Boris Polyak suggested a particular step size for gradient descent methods, now known as the Polyak step size, that he later adapted to subgradient methods. The Polyak step size requires knowledge of the optimal value of the minimization problem, which is a strong assumption but one that holds for several important problems. In this paper we extend P

Bhagya Chembakottu, Heng Li, Foutse Khomh

Prior studies on mobile app analysis often analyze apps across different categories or focus on a small set of apps within a category. These studies either provide general insights for an entire app store which consists of millions of apps, or provide specific insights for a small set of apps. However, a single app category can often contain tens of thousand

Abdiel de Jesús Espinosa-Champo, Gerardo G. Naumis, Pavel Castro-Villarreal

A study of strongly curved graphene magnetization and magnetic susceptibility is carried out. Through a Dirac model complemented with a tight-binding model analysis, we are able to show that mechanical deformations solve the long-standing problem of graphene's theoretically calculated diamagnetic divergence at low temperatures. This suggests that corrugation

Will Dumm, Mary Barker, William Howard-Snyder, William S. DeWitt

In many situations, it would be useful to know not just the best phylogenetic tree for a given data set, but the collection of high-quality trees. This goal is typically addressed using Bayesian techniques, however, current Bayesian methods do not scale to large data sets. Furthermore, for large data sets with relatively low signal one cannot even store ever

Jannik Deuschel, Caleb N. Ellington, Yingtao Luo, Benjamin J. Lengerich

Interpretable policy learning seeks to estimate intelligible decision policies from observed actions; however, existing models force a tradeoff between accuracy and interpretability, limiting data-driven interpretations of human decision-making processes. Fundamentally, existing approaches are burdened by this tradeoff because they represent the underlying d

Elaheh Jafarigol, Theodore Trafalis, Neshat Mohammadi

For over two decades, detecting rare events has been a challenging task among researchers in the data mining and machine learning domain. Real-life problems inspire researchers to navigate and further improve data processing and algorithmic approaches to achieve effective and computationally efficient methods for imbalanced learning. In this paper, we have c

Ancheng Lin, Yusheng Xiang, Jun Li, Mukesh Prasad

Neural Radiance Fields (NeRFs) have shown great potential in modeling 3D scenes. Dynamic NeRFs extend this model by capturing time-varying elements, typically using deformation fields. The existing dynamic NeRFs employ a similar Eulerian representation for both light radiance and deformation fields. This leads to a close coupling of appearance and motion and

Dawen Zhang, Boming Xia, Yue Liu, Xiwei Xu

The World Wide Web, a ubiquitous source of information, serves as a primary resource for countless individuals, amassing a vast amount of data from global internet users. However, this online data, when scraped, indexed, and utilized for activities like web crawling, search engine indexing, and, notably, AI model training, often diverges from the original in

Kang Wang, Yvette Perrott, Richard Arnold, David Huijser

This study focuses on modelling galaxy cluster gas profiles via a semi-parametric nodal approach. While traditional methods like the generalised Navarro-Frenk-White (gNFW) often encounter parameter degeneracy, our flexible node-based method precisely defines a cluster gas pressure profile. Using Planck space telescope data from the Coma region, our model, fo

T. Z. Esirkepov, S. V. Bulanov

A modulation of refractive index can move at the speed of light. How it interacts with an electromagnetic wave? Does it reflect? We show that an incident electromagnetic wave, depending on its frequency either is totally transmitted with a phase shift, or forms a standing wave, or is totally reflected with the frequency upshift. A short incident pulse is con

Ryan Park, Brett S. Scheiner, Mark C. Zammit

Plasma-neutral interactions, including reactive kinetics, are often either studied in 0D using ODE based descriptions, or in multi-dimensional fluid or particle based plasma codes. The latter case involves a complex assembly of procedures that are not always necessary to test effects of underlying physical models and mechanisms for particle-based description

Marzieh Eidi, Sayan Mukherjee

Random walks on graphs are a fundamental concept in graph theory and play a crucial role in solving a wide range of theoretical and applied problems in discrete math, probability, theoretical computer science, network science, and machine learning. The connection between Markov chains on graphs and their geometric and topological structures is the main reaso

Huiyin Xue, Nikolaos Aletras

Scaling pre-trained language models has resulted in large performance gains in various natural language processing tasks but comes with a large cost in memory requirements. Inspired by the position embeddings in transformers, we aim to simplify and reduce the memory footprint of the multi-head attention (MHA) mechanism. We propose an alternative module that

Roberta Angelini, Domenico Larobina, Barbara Ruzicka, Francesco Greco

The occurrence of non-equilibrium transitions between arrested states has recently emerged as an intriguing issue in the field of soft glassy materials. The existence of one such transition has been suggested for aging colloidal clays (Laponite$^{\circledR}$ suspensions) at weight concentration 3.0%, although further experimental evidences are necessary to v

Arda Gulucu, Hasan Sahin

In this study, the structural, electronic and vibrational properties of thinnest possible Cadmium crystal are investigated by performing first-principle calculations. Total energy optimization and dynamic stability calculations reveal that the thinnest possible crystal structure is a double-layered structure consisting of alternating layers formed by trigona

Keith T. Murray

Recurrent neural networks (RNNs) can implement complex computations by leveraging a range of dynamics, such as oscillations, attractors, and transient trajectories. A growing body of work has highlighted the emergence of phase codes, a type of oscillatory activity where information is encoded in the relative phase of network activity, in RNNs trained for wor

T. M. Sitnova, T. Matsuno, Z. Yuan, N. F. Martin

We report the serendipitous discovery of a very metal-poor (VMP) Li-rich giant star ($T_{\rm eff}$ = 4690$\pm$80 K, log g = 1.34$\pm$0.13, [Fe/H] = $-2.43\pm$0.07). We analyse the Li I 6103 and 6707 \r{A} lines accounting for departures from local thermodynamic equilibrium (NLTE) and correcting for 3D effects using literature data, which yields a lithium abu

Zhenhe Zhang, Jun Zheng, Guchuan Zhu

This paper presents a new aggregate power tracking control scheme for populations of thermostatically controlled loads (TCLs). The control design is performed in the framework of partial differential equations (PDEs) based on a late-lumping procedure without truncating the infinite-dimensional model describing the dynamics of the TCL population. An input-out

Hyunjoong Kim, Sean D Lawley

The speed of an exhaustive search can be measured by a cover time, which is defined as the time it takes a random searcher to visit every state in some target set. Cover times have been studied in both the physics and probability literatures, with most prior works focusing on a single searcher. In this paper, we prove an explicit formula for all the moments

Andoni Rodríguez, Cesar Sanchez

Reactive synthesis is the process of using temporal logic specifications in LTL to generate correct controllers, but its use has been restricted to Boolean specifications. Recently, a Boolean abstraction technique allows to translate LTL T specifications that contain literals in theories into equi-realizable LTL specifications. However, no synthesis procedur

Amir Hossein Jalilvand, Faeze S. Banitaba, Seyedeh Newsha Estiri, Sercan Aygun

Sorting is a fundamental operation in various applications and a traditional research topic in computer science. Improving the performance of sorting operations can have a significant impact on many application domains. For high-performance sorting, much attention has been paid to hardware-based solutions. These are often realized with application-specific i

Noémie Jaquier, Leonel Rozo, Tamim Asfour

In the realm of robotics, numerous downstream robotics tasks leverage machine learning methods for processing, modeling, or synthesizing data. Often, this data comprises variables that inherently carry geometric constraints, such as the unit-norm condition of quaternions representing rigid-body orientations or the positive definiteness of stiffness and manip

Connor Paddock, William Slofstra

Mermin and Peres showed that there are boolean constraint systems (BCSs) which are not satisfiable, but which are satisfiable with quantum observables. This has led to a burgeoning theory of quantum satisfiability for constraint systems, connected to nonlocal games and quantum contextuality. In this theory, different types of quantum satisfying assignments c

Ruchira Ray, Marco Avella Medina, Cynthia Rush

Power posteriors "robustify" standard Bayesian inference by raising the likelihood to a constant fractional power, effectively downweighting its influence in the calculation of the posterior. Power posteriors have been shown to be more robust to model misspecification than standard posteriors in many settings. Previous work has shown that power posteriors de

Sumedh A Sontakke, Jesse Zhang, Sébastien M. R. Arnold, Karl Pertsch

Reward specification is a notoriously difficult problem in reinforcement learning, requiring extensive expert supervision to design robust reward functions. Imitation learning (IL) methods attempt to circumvent these problems by utilizing expert demonstrations but typically require a large number of in-domain expert demonstrations. Inspired by advances in th

Rolando Garcia, Anusha Dandamudi, Gabriel Matute, Lehan Wan

Production Machine Learning involves continuous training: hosting multiple versions of models over time, often with many model versions running at once. When model performance does not meet expectations, Machine Learning Engineers (MLEs) debug issues by exploring and analyzing numerous prior versions of code and training data to identify root causes and miti

Min Jun Park, Julio Frigerio, Bob Clapp, Biondo Biondi

Monitoring stored CO2 in carbon capture and storage projects is crucial for ensuring safety and effectiveness. We introduce DeepNRMS, a novel noise-robust method that effectively handles time-lapse noise in seismic images. The DeepNRMS leverages unsupervised deep learning to acquire knowledge of time-lapse noise characteristics from pre-injection surveys. By

Ajay Sridhar, Dhruv Shah, Catherine Glossop, Sergey Levine

Robotic learning for navigation in unfamiliar environments needs to provide policies for both task-oriented navigation (i.e., reaching a goal that the robot has located), and task-agnostic exploration (i.e., searching for a goal in a novel setting). Typically, these roles are handled by separate models, for example by using subgoal proposals, planning, or se

Kushagra Pandey, Maja Rudolph, Stephan Mandt

Diffusion models suffer from slow sample generation at inference time. Therefore, developing a principled framework for fast deterministic/stochastic sampling for a broader class of diffusion models is a promising direction. We propose two complementary frameworks for accelerating sample generation in pre-trained models: Conjugate Integrators and Splitting I

James Anderson, Anton Bernshteyn

We characterize Borel line graphs in terms of 10 forbidden induced subgraphs, namely the 9 finite graphs from the classical result of Beineke together with a 10th infinite graph associated to the equivalence relation $\mathbb{E}_0$ on the Cantor space. As a corollary, we prove a partial converse to the Feldman--Moore theorem, which allows us to characterize

Jalil Chavez-Galaviz, Jianwen Li, Ajinkya Chaudhary, Nina Mahmoudian

Station keeping is an essential maneuver for Autonomous Surface Vehicles (ASVs), mainly when used in confined spaces, to carry out surveys that require the ASV to keep its position or in collaboration with other vehicles where the relative position has an impact over the mission. However, this maneuver can become challenging for classic feedback controllers

Behrad Moniri, Donghwan Lee, Hamed Hassani, Edgar Dobriban

Feature learning is thought to be one of the fundamental reasons for the success of deep neural networks. It is rigorously known that in two-layer fully-connected neural networks under certain conditions, one step of gradient descent on the first layer can lead to feature learning; characterized by the appearance of a separated rank-one component -- spike --

Davi Colli Tozoni, Zizhou Huang, Daniele Panozzo, Denis Zorin

Two-scale topology optimization, combined with the design of microstructure families with a broad range of effective material parameters, is increasingly widely used in many fabrication applications to achieve a target deformation behavior for a variety of objects. The main idea of this approach is to optimize the distribution of material properties in the o

Bowen Pan, Rameswar Panda, SouYoung Jin, Rogerio Feris

We explore the use of language as a perceptual representation for vision-and-language navigation (VLN), with a focus on low-data settings. Our approach uses off-the-shelf vision systems for image captioning and object detection to convert an agent's egocentric panoramic view at each time step into natural language descriptions. We then finetune a pretrained

Alexander Davis, Aidan Chen, Milton Chen, James Davis

Population health surveys are an important tool to effectively allocate limited resources in low resource communities. In such an environment, surveys are often done by local population with pen and paper. Data thus collected is difficult to tabulate and analyze. We conducted a series of interviews and experiments in the Philippines to assess if mobile forms

Zizhou Huang, Daniele Panozzo, Denis Zorin

Mechanical shock is a common occurrence in various settings, there are two different scenarios for shock protection: catastrophic protection (e.g. car collisions and falls) and routine protection (e.g. shoe soles and shock absorbers for car seats). The former protects against one-time events, the latter against periodic shocks and loads. Common shock absorbe

Benjamin Salmon, Alexander Krull

Accurate analysis of microscopy images is hindered by the presence of noise. This noise is usually signal-dependent and often additionally correlated along rows or columns of pixels. Current self- and unsupervised denoisers can address signal-dependent noise, but none can reliably remove noise that is also row- or column-correlated. Here, we present the firs

Pranav Mantini, Shishir K. Shah

Camera tamper detection is the ability to detect unauthorized and unintentional alterations in surveillance cameras by analyzing the video. Camera tampering can occur due to natural events or it can be caused intentionally to disrupt surveillance. We cast tampering detection as a change detection problem, and perform a review of the existing literature with

Chenzhong Yin, Mingxi Cheng, Xiongye Xiao, Xinghe Chen

The collective behavior of a network with heterogeneous, resource-limited information processing units (e.g., group of fish, flock of birds, or network of neurons) demonstrates high self-organization and complexity. These emergent properties arise from simple interaction rules where certain individuals can exhibit leadership-like behavior and influence the c

Sergio I. Bugosen, Carl D. Laird, Robert B. Parker

Alternative formulations for the optimization of chemical process flowsheets are presented that leverage surrogate models and implicit functions to replace and remove, respectively, the algebraic equations that describe a difficult-to-converge Gibbs reactor unit operation. Convergence reliability, solve time, and solution quality of an optimization problem a

Junhui Zhang, Patrick Jaillet

We study variants of the secretary problem, where $N$, the number of candidates, is a random variable, and the decision maker wants to maximize the probability of success -- picking the largest number among the $N$ candidates -- using only the relative ranks of the candidates revealed so far. We consider three forms of prior information about $\mathbf p$, th

Davide Fiaschi, Cristiano Ricci

This paper examines the spatial agglomeration of workers and income in a continuous space-time framework. Local markets feature spatial spillovers and both exogenous and endogenous amenities. Workers relocate to maximise their instantaneous utility, constrained by mobility costs. In the limit of infinite workers, short-run equilibria are described by a parti

Thomas Decker, Ralf Gross, Alexander Koebler, Michael Lebacher

In this paper, we investigate the practical relevance of explainable artificial intelligence (XAI) with a special focus on the producing industries and relate them to the current state of academic XAI research. Our findings are based on an extensive series of interviews regarding the role and applicability of XAI along the Machine Learning (ML) lifecycle in

Nawras Alkassab, Chin-Tser Huang, Tania Lorido Botran

Content Delivery Networks carry the majority of Internet traffic, and the increasing demand for video content as a major IP traffic across the Internet highlights the importance of caching and prefetching optimization algorithms. Prefetching aims to make data available in the cache before the requester places its request to reduce access time and improve the

Steven S. Andrews, H. Steven Wiley, Herbert M. Sauro

Design patterns are generalized solutions to frequently recurring problems. They were initially developed by architects and computer scientists to create a higher level of abstraction for their designs. Here, we extend these concepts to cell biology in order to lend a new perspective on the evolved designs of cells' underlying reaction networks. We present a

Hao-Ping Lee, Yu-Ju Yang, Thomas Serban von Davier, Jodi Forlizzi

Privacy is a key principle for developing ethical AI technologies, but how does including AI technologies in products and services change privacy risks? We constructed a taxonomy of AI privacy risks by analyzing 321 documented AI privacy incidents. We codified how the unique capabilities and requirements of AI technologies described in those incidents genera

Smita Sahu

We present a comprehensive analysis of the coupled scheme introduced in [Springer Proceedings in Mathematics \& Statistics, vol 237. Springer, Cham 2018 \cite{S2018}] for linear and Hamilton-Jacobi equations. This method merges two distinct schemes, each tailored to handle specific solution characteristics. It offers a versatile framework for coupling variou

Shubham Singh, Mrunal Bewoor, Ammar Ranapurwala, Satyam Rai

The work proposes a novel deep-learning framework for the synthesis of three-dimensional MRI volumes from corresponding 3D ultrasound images of the brain, leveraging a modified iteration of the Pix2Pix Generative Adversarial Network (GAN) model. Addressing the formidable challenge of bridging the modality disparity between ultrasound and MRI, this research h