April 2023 arXiv papers — page 93

Jiale Chen, Jason Hartline, Onno Zoeter

This paper studies grading algorithms for randomized exams. In a randomized exam, each student is asked a small number of random questions from a large question bank. The predominant grading rule is simple averaging, i.e., calculating grades by averaging scores on the questions each student is asked, which is fair ex-ante, over the randomized questions, but

Apakorn Kengkanna, Masahito Ohue

Graph neural networks (GNNs) demonstrate great performance in compound property and activity prediction due to their capability to efficiently learn complex molecular graph structures. However, two main limitations persist including compound representation and model interpretability. While atom-level molecular graph representations are commonly used because

Jungho Kim, Ziqi Wang, Junho Song

To address the challenges of reliability analysis in high-dimensional probability spaces, this paper proposes a new metamodeling method that couples active subspace, heteroscedastic Gaussian process, and active learning. The active subspace is leveraged to identify low-dimensional salient features of a high-dimensional computational model. A surrogate comput

Fang Zhengxin, Yuan Yi, Zhang Jingyu, Liu Yue

Google AlphaGos win has significantly motivated and sped up machine learning (ML) research and development, which led to tremendous ML technical advances and wider adoptions in various domains (e.g., Finance, Health, Defense, and Education). These advances have resulted in numerous new concepts and technologies, which are too many for people to catch up to a

Guanxun Li, Aaron Smith, Quan Zhou

We show that for any multiple-try Metropolis algorithm, one can always accept the proposal and evaluate the importance weight that is needed to correct for the bias without extra computational cost. This results in a general, convenient, and rejection-free Markov chain Monte Carlo (MCMC) sampling scheme. By further leveraging the importance sampling perspect

Zhemin Zhang, Xun Gong

Recently, Transformers have shown promising performance in various vision tasks. However, the high costs of global self-attention remain challenging for Transformers, especially for high-resolution vision tasks. Local self-attention runs attention computation within a limited region for the sake of efficiency, resulting in insufficient context modeling as th

Jiong Wang, Zhou Zhao, Fei Wu

Set-based face recognition (SFR) aims to recognize the face sets in the unconstrained scenario, where the appearance of same identity may change dramatically with extreme variances (e.g., illumination, pose, expression). We argue that the two crucial issues in SFR, the face quality and burstiness, are both identity-irrelevant and variance-relevant. The quali

Hao Fei, Shengqiong Wu, Jingye Li, Bobo Li

Universally modeling all typical information extraction tasks (UIE) with one generative language model (GLM) has revealed great potential by the latest study, where various IE predictions are unified into a linearized hierarchical expression under a GLM. Syntactic structure information, a type of effective feature which has been extensively utilized in IE co

Zixuan Huang, Varun Jampani, Anh Thai, Yuanzhen Li

We present ShapeClipper, a novel method that reconstructs 3D object shapes from real-world single-view RGB images. Instead of relying on laborious 3D, multi-view or camera pose annotation, ShapeClipper learns shape reconstruction from a set of single-view segmented images. The key idea is to facilitate shape learning via CLIP-based shape consistency, where w

Peiyu Duan, Yuan Xue, Shuo Han, Lianrui Zuo

The meninges, located between the skull and brain, are composed of three membrane layers: the pia, the arachnoid, and the dura. Reconstruction of these layers can aid in studying volume differences between patients with neurodegenerative diseases and normal aging subjects. In this work, we use convolutional neural networks (CNNs) to reconstruct surfaces repr

Vipul Arora, Arnab Bhattacharyya, Clément L. Canonne, Joy Qiping Yang

This paper considers the problem of testing the maximum in-degree of the Bayes net underlying an unknown probability distribution $P$ over $\{0,1\}^n$, given sample access to $P$. We show that the sample complexity of the problem is $\tilde{\Theta}(2^{n/2}/\varepsilon^2)$. Our algorithm relies on a testing-by-learning framework, previously used to obtain sam

Yongquan Yang, Jie Chen, Yani Wei, Mohammad Alobaidi

Precise segmentation of residual tumor in breast cancer (PSRTBC) after neoadjuvant chemotherapy is a fundamental key technique in the treatment process of breast cancer. However, achieving PSRTBC is still a challenge, since the breast cancer tissue and tumor cells commonly have complex and varied morphological changes after neoadjuvant chemotherapy, which in

Yuan Gao, Weidong Liu, Hansheng Wang, Xiaozhou Wang

The rapid emergence of massive datasets in various fields poses a serious challenge to traditional statistical methods. Meanwhile, it provides opportunities for researchers to develop novel algorithms. Inspired by the idea of divide-and-conquer, various distributed frameworks for statistical estimation and inference have been proposed. They were developed to

Yibo Yang, Stephan Mandt

Neural image compression methods have seen increasingly strong performance in recent years. However, they suffer orders of magnitude higher computational complexity compared to traditional codecs, which hinders their real-world deployment. This paper takes a step forward towards closing this gap in decoding complexity by using a shallow or even linear decodi

Zheng Zhao, Kai Xu, Ayut Limphirat, Warintorn Sreethawong

We calculate the masses and leptonic decay widths of the bottomonium $b\bar b$ and charmonium $c\bar c$ states in a constituent quark model where the Cornell-like potential and spin-dependent interaction are employed, with all model parameters predetermined by studying ground and first radial excited states of S- and P-wave heavy quarkonium mesons. By compar

Shengjie He, Qiaozhi Geng, Rong-Xia Hao

Let $G$ be a connected graph. The revised edge Szeged index of $G$ is defined as $Sz^{\ast}_{e}(G)=\sum\limits_{e=uv\in E(G)}(m_{u}(e|G)+\frac{m_{0}(e|G)}{2})(m_{v}(e|G)+\frac{m_{0}(e|G)}{2})$, where $m_{u}(e|G)$ (resp., $m_{v}(e|G)$) is the number of edges whose distance to vertex $u$ (resp., $v$) is smaller than the distance to vertex $v$ (resp., $u$), and

Radha Balakrishnan, Rossen Dandoloff, Avadh Saxena

Topological invariants such as winding numbers and linking numbers appear as charges of topological solitons in diverse nonlinear physical systems described by a unit vector field defined on two and three dimensional manifolds. While the Gauss-Bonnet theorem shows that the Euler characteristic (a topological invariant) can be written as the integral of the G

Shengjie He, Rong-Xia Hao, Hong-Jian Lai, Qiaozhi Geng

A mixed graph $\widetilde{G}$ is obtained from a simple undirected graph $G$, the underlying graph of $\widetilde{G}$, by orienting some edges of $G$. Let $c(G)=|E(G)|-|V(G)|+\omega(G)$ be the cyclomatic number of $G$ with $\omega(G)$ the number of connected components of $G$, $m(G)$ be the matching number of $G$, and $\eta(\widetilde{G})$ be the nullity of

W. Horiuchi, T. Inakura, S. Michimasa, M. Tanaka

Nuclear deformations and density profiles of neutron-rich even-even Zr isotopes are investigated using the Skyrme-Hartree-Fock-Bogoliubov method. Large quadrupole and hexadecapole deformations are predicted along with large enhancement of the total reaction cross sections at the neutron number $N=60$-74. Strong nuclear deformation starting at $N=60$ is induc

Chankyu Joung, Mijin Kim, Taejin Paik, Seong-Ho Kong

Accurate delineation of key waveforms in an ECG is a critical step in extracting relevant features to support the diagnosis and treatment of heart conditions. Although deep learning based methods using segmentation models to locate P, QRS, and T waves have shown promising results, their ability to handle arrhythmias has not been studied in any detail. In thi

M. Sharif, Saba Naz

In this paper, we generate an exact anisotropic gravastar model using gravitational decoupling technique through minimal geometric deformation in the framework of $f(\Re, {T}^{2})$ gravity. This novel model explains an ultra-compact stellar configuration whose internal region is smoothly matched to the exterior region. The developed stellar model satisfies s

Wenbin Zou, Hongxia Gao, Liang Chen, Yunchen Zhang

Stereo image super-resolution aims to improve the quality of high-resolution stereo image pairs by exploiting complementary information across views. To attain superior performance, many methods have prioritized designing complex modules to fuse similar information across views, yet overlooking the importance of intra-view information for high-resolution rec

Jacob Henshaw, Pauli Kehayias, Luca Basso, Michael Jaris

The nitrogen-vacancy (NV) color center in diamond has demonstrated great promise in a wide range of quantum sensing. Recently, there have been a series of proposals and experiments using NV centers to detect spin noise of quantum materials near the diamond surface. This is a rich complex area of study with novel nano-magnetism and electronic behavior, that t

Jinshuai Bai, Gui-Rong Liu, Ashish Gupta, Laith Alzubaidi

Our recent intensive study has found that physics-informed neural networks (PINN) tend to be local approximators after training. This observation leads to this novel physics-informed radial basis network (PIRBN), which can maintain the local property throughout the entire training process. Compared to deep neural networks, a PIRBN comprises of only one hidde

Xiaojian Zhang, Xilei Zhao, Yiming Xu, Ruggiero Lovreglio

Real-time forecasting of travel demand during wildfire evacuations is crucial for emergency managers and transportation planners to make timely and better-informed decisions. However, few studies focus on accurate travel demand forecasting in large-scale emergency evacuations. Therefore, this study develops and tests a new methodological framework for modeli

Sarwan Ali, Taslim Murad, Murray Patterson

Coronaviruses are membrane-enveloped, non-segmented positive-strand RNA viruses belonging to the Coronaviridae family. Various animal species, mainly mammalian and avian, are severely infected by various coronaviruses, causing serious concerns like the recent pandemic (COVID-19). Therefore, building a deeper understanding of these viruses is essential to dev

Diego Figueira, Miguel Romero

We introduce injective semantics for Conjunctive Regular Path Queries (CRPQs), and study their fundamental properties. We identify two such semantics: atom-injective and query-injective semantics, both defined in terms of injective homomorphisms. These semantics are natural generalizations of the well-studied class of RPQs under simple-path semantics to the

Shuyuan Wu, Xuening Zhu, Hansheng Wang

Modern statistical analysis often encounters datasets with large sizes. For these datasets, conventional estimation methods can hardly be used immediately because practitioners often suffer from limited computational resources. In most cases, they do not have powerful computational resources (e.g., Hadoop or Spark). How to practically analyze large datasets

Hong-Xuan Jiang, Yosuke Mizuno, Christian M. Fromm, Antonios Nathanail

We have performed a series of two-dimensional two-temperature general relativistic magnetohydrodynamic simulations of magnetized accretion flows initiated from tori with different sizes and poloidal magnetic loop polarities. In these two temperature simulations, we trace the process of heating electrons through turbulence and reconnection, most of the time t

Muhammad Febrian Rachmadi, Charissa Poon, Henrik Skibbe

In this paper, we propose a novel two-component loss for biomedical image segmentation tasks called the Instance-wise and Center-of-Instance (ICI) loss, a loss function that addresses the instance imbalance problem commonly encountered when using pixel-wise loss functions such as the Dice loss. The Instance-wise component improves the detection of small inst

M. Mirzakhani, H. C. Park, F. M. Peeters, D. R. da Costa

Using a tight-binding model along with the mean-field Hubbard method, we investigate the effect of twisting angle on the magnetic properties of twisted bilayer graphene (tBLG) quantum dots (QDs) with triangular shape and zigzag edges. We consider such QDs in two configurations: when their initial untwisted structure is a perfect AA- or AB-stacked BLG, referr

Jarred Jordan, Daniel Posada, Matthew Gillette, David Zuehlke

A method of near real-time detection and tracking of resident space objects (RSOs) using a convolutional neural network (CNN) and linear quadratic estimator (LQE) is proposed. Advances in machine learning architecture allow the use of low-power/cost embedded devices to perform complex classification tasks. In order to reduce the costs of tracking systems, a

Zi-Yi Pan, Ni Yang, Ming Tang, Peixun Shen

An open source software package for simulating thermal neutron propagation in geometry is presented. In this system, neutron propagation can be treated by either the particle transport method or the ray-tracing method. Supported by an accurate backend scattering physics engine, this system is capable of reproducing neutron scattering experiments in complex g

Robert Lung, Nick Polydorides

We consider the inverse problem of fitting atmospheric dispersion parameters based on time-resolved back-scattered differential absorption Lidar (DIAL) measurements. The obvious advantage of light-based remote sensing modalities is their extended spatial range which makes them less sensitive to strictly local perturbations/modelling errors or the distance to

Fu-Long Hu, Hai-Tao Zhang, Bowen Xu, Zhe Hu

In this paper, a Hankel matrix-based fully distributed algorithm is proposed to address a minimal-time deadbeat consensus prediction problem for discrete-time high-order multi-agent systems (MASs). Therein, each agent can predict the consensus value with the minimum number of observable historical outputs of its own. Accordingly, compared to most existing al

Keith Slevin, Tomi Ohtsuki

The quantum Hall effect is one of the most extensively studied topological effects in solid state physics. The transitions between different quantum Hall states exhibit critical phenomena described by universal critical exponents. Numerous numerical finite size scaling studies have focused on the critical exponent $\nu$ for the correlation length. In such st

Jasmin Kaur, Alvaro Cintas Canto, Mehran Mozaffari Kermani, Reza Azarderakhsh

This survey is the first work on the current standard for lightweight cryptography, standardized in 2023. Lightweight cryptography plays a vital role in securing resource-constrained embedded systems such as deeply-embedded systems (implantable and wearable medical devices, smart fabrics, smart homes, and the like), radio frequency identification (RFID) tags

Hangdong Zhao, Shaleen Deep, Paraschos Koutris

In this paper, we investigate space-time tradeoffs for answering conjunctive queries with access patterns (CQAPs). The goal is to create a space-efficient data structure in an initial preprocessing phase and use it for answering (multiple) queries in an online phase. Previous work has developed data structures that trades off space usage for answering time f

Himadri Chakraborty, Reina Ishikawa, Yuuho Tanaka

In this paper, we introduce some new polynomials associated to linear codes over $\mathbb{F}_{q}$. In particular, we introduce the notion of split complete Jacobi polynomials attached to multiple sets of coordinate places of a linear code over $\mathbb{F}_{q}$, and give the MacWilliams type identity for it. We also give the notion of generalized $q$-colored

Yongtao Zhao, Benzheng Chen, Dong Wu, Rui Cheng

We compare the transport properties of a well-characterized hydrogen plasma for low and high current ion beams. The energy-loss of low current beams can be well understood, within the framework of current stopping power models. However, for high current proton beams, significant energy-loss reduction and collimation is observed in the experiment. We have dev

Joaquin Armijo, Carlton M. Baugh, Peder Norberg, Nelson D. Padilla

We introduce a new scheme based on the marked correlation function to probe gravity using the large-scale structure of the Universe. We illustrate our approach by applying it to simulations of the metric-variation $f(R)$ modified gravity theory and general relativity (GR). The modifications to the equations in $f(R)$ gravity lead to changes in the environmen

Zeming Hao, Shuang Miao

We establish a dynamical nonlinear instability of liquid Lane-Emden stars in $\mathbb{R}^{3}$ whose adiabatic exponents take values in $[1,\frac43)$. Our proof relies on a priori estimates for the free boundary problem of a compressible self-gravitating liquid, as well as a quantitative analysis of the competition between the fastest linear growing mode and

Yujia Yang, Chris Manzie, Ye Pu

Moving horizon estimation (MHE) offers benefits relative to other estimation approaches by its ability to explicitly handle constraints, but suffers increased computation cost. To help enable MHE on platforms with limited computation power, we propose to solve the optimization problem underlying MHE sub-optimally for a fixed number of optimization iterations

Jae-Hoon Kwon, Sin-Myung Lee, Masato Okado

We introduce a category of $q$-oscillator representations over the quantum affine superalgebras of type $D$ and construct a new family of its irreducible representations. Motivated by the theory of super duality, we show that these irreducible representations naturally interpolate the irreducible $q$-oscillator representations of type $X_n^{(1)}$ and the fin

Tomas Dohnal, Dmitry E. Pelinovsky, Guido Schneider

Traveling modulating pulse solutions consist of a small amplitude pulse-like envelope moving with a constant speed and modulating a harmonic carrier wave. Such solutions can be approximated by solitons of an effective nonlinear Schrodinger equation arising as the envelope equation. We are interested in a rigorous existence proof of such solutions for a nonli

Hideshi Ishida, Masaaki Horie, Takahiro Harada, Shingo Mizuno

On a daily basis we stir tee or coffee with a spoon and leave it to rest. We know empirically the larger the stickiness, viscosity, of the fluid, more rapidly its velocity slows down. It is surprising, therefore, that the variation, the decay rate of the velocity, has not been utilized for measuring (kinematic) viscosity of fluids. This study shows that a sp

Letian Wu, Wenyao Zhang, Tengping Jiang, Wankou Yang

In this paper, we propose an embarrassingly simple yet highly effective zero-shot semantic segmentation (ZS3) method, based on the pre-trained vision-language model CLIP. First, our study provides a couple of key discoveries: (i) the global tokens (a.k.a [CLS] tokens in Transformer) of the text branch in CLIP provide a powerful representation of semantic inf

Yurong Zhang, Liulei Li, Wenguan Wang, Rong Xie

Current top-leading solutions for video object segmentation (VOS) typically follow a matching-based regime: for each query frame, the segmentation mask is inferred according to its correspondence to previously processed and the first annotated frames. They simply exploit the supervisory signals from the groundtruth masks for learning mask prediction only, wi

Lior M. Burko, Gaurav Khanna, Subir Sabharwal

We study the evolution of axially-symmetric scalar field perturbations on an extreme Kerr spacetime for initial data with multipole moments $\ell^{\prime}$ higher than the least radiative mode, and we measure modes $\ell$ -- and for the first time also horizon charges -- that are excited by mode coupling interactions. We then find the Ori-Sela prefactors, a

Tian-Xiang Hou, Wei Li

Nonadiabatic geometric quantum computation (NGQC) has emerged as an excellent proposal for achieving fast and robust quantum control against control errors. However, previous NGQC protocols could not be strongly resilient against the noise from decay of bare states in a realistic system, which can be equivalently described by a non-Hermitian Hamiltonian. Her

Yucheng Zhang, Masaki Fukuda, Yasunori Ishii, Kyoko Ohshima

3D object detection has become indispensable in the field of autonomous driving. To date, gratifying breakthroughs have been recorded in 3D object detection research, attributed to deep learning. However, deep learning algorithms are data-driven and require large amounts of annotated point cloud data for training and evaluation. Unlike 2D image labels, annot

Ishfaq Ahmad Bhat, Thudiyangal Mithun, Bishwajyoti Dey

We study numerically the vortex dynamics and vortex-lattice formation in a rotating density-dependent Bose-Einstein condensate (BEC), characterized by the presence of nonlinear rotation. By varying the strength of nonlinear rotation in density-dependent BECs, we calculate the critical frequency, $\Omega_{\text{cr}}$, for vortex nucleation both in adiabatic a

Jiazhang Wang, Tianfu Wang, Bingjie Xu, Oliver Cossairt

We present two methods for fast and precise eye-tracking in VR headsets. Both methods exploit deflectometric information, i.e., the specular reflection of an extended screen over the eye surface.

Zhi-Bin Dai, Hao Zhou, Jin Cao

A Synchronous Photometry Data Extraction (SPDE) program, performing indiscriminate monitors of all stars appearing at the same field of view of astronomical image, is developed by integrating several Astropy affiliated packages to make full use of time series observed by the traditional small/medium aperture ground-based telescope. The complete full-frame st

Yang Chen, Yanan Wang

The conjugate phase retrieval problem concerns the determination of a complex-valued function, up to a unimodular constant and conjugation, from its magnitude observations. It can also be considered as a conjugate phaseless sampling and reconstruction problem in an infinite dimensional space. In this paper, we first characterize the conjugate phase retrieval

Juan C. Perdomo, Tolani Britton, Moritz Hardt, Rediet Abebe

Early warning systems (EWS) are predictive tools at the center of recent efforts to improve graduation rates in public schools across the United States. These systems assist in targeting interventions to individual students by predicting which students are at risk of dropping out. Despite significant investments in their widespread adoption, there remain lar

Diogo Fonseca, Mohammad Safeea, Pedro Neto

Force and proximity sensors are key in robotics, especially when applied in collaborative robots that interact physically or cognitively with humans in real unstructured environments. However, most existing sensors for use in robotics are limited by: 1) their scope, measuring single parameters/events and often requiring multiple types of sensors, 2) being ex

Nicholas J Dobbins, Bin Han, Weipeng Zhou, Kristine Lan

Objective: Identifying study-eligible patients within clinical databases is a critical step in clinical research. However, accurate query design typically requires extensive technical and biomedical expertise. We sought to create a system capable of generating data model-agnostic queries while also providing novel logical reasoning capabilities for complex c

Karen Grigorian, Robert Jarrow

This paper studies the pricing and hedging of derivatives in frictionless and competitive, but incomplete jump-diffusion markets. A unique equivalent martingale measure (EMM) is obtained using filtration reduction to a fictitious complete market. This unique EMM in the fictitious market is uplifted to the original economy using the notion of consistency. For

Qing Mai, Xiaofeng Shao, Runmin Wang, Xin Zhang

Sliced inverse regression (SIR, Li 1991) is a pioneering work and the most recognized method in sufficient dimension reduction. While promising progress has been made in theory and methods of high-dimensional SIR, two remaining challenges are still nagging high-dimensional multivariate applications. First, choosing the number of slices in SIR is a difficult

Yunwei Lu, Sandeep Joshi, Vinh San Dinh, Jens Koch

Gradient Ascent Pulse Engineering (GRAPE) is a popular technique in quantum optimal control, and can be combined with automatic differentiation (AD) to facilitate on-the-fly evaluation of cost-function gradients. We illustrate that the convenience of AD comes at a significant memory cost due to the cumulative storage of a large number of states and propagato

Simeon Simjanovski, Guillaume Gauthier, Matthew J. Davis, Halina Rubinsztein-Dunlop

We demonstrate a method for generating persistent currents in Bose-Einstein condensates by using a Gaussian process learner to experimentally control the stirring of the superfluid. The learner optimizes four different outcomes of the stirring process: (O.I) targeting and (O.II) maximization of the persistent current winding number; and (O.III) targeting and

LHCb collaboration, R. Aaij, A. S. W. Abdelmotteleb, C. Abellan Beteta

A flavor-tagged time-dependent angular analysis of the decay $B_s^{0}\rightarrow\phi\phi$ is performed using $pp$ collision data collected by the LHCb experiment at $\sqrt{s}=13$ TeV, the center-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 6 fb^{-1}. The $\it{CP}$-violating phase and direct $\it{CP}$-violation parameter are measured

Arjun Mani, Ishaan Preetam Chandratreya, Elliot Creager, Carl Vondrick

Modeling the mechanics of fluid in complex scenes is vital to applications in design, graphics, and robotics. Learning-based methods provide fast and differentiable fluid simulators, however most prior work is unable to accurately model how fluids interact with genuinely novel surfaces not seen during training. We introduce SURFSUP, a framework that represen

Shuichi Yokoyama

By refining the method proposed in arXiv:2010.07660, entropy current and entropy density for a relativistic hydrostatic equilibrium system with spherical symmetry are constructed as a non-Noether conserved charge in the Einstein gravity with cosmological constant. It is shown that the constructed entropy density satisfies both the local Euler relation and th

Brian Forrest, John Sawatzky, Aasaimani Thamizhazhagan

Let $G$ be a locally compact group. In this paper, we study various invariant subspaces of the duals of the algebras $A_M(G)$ and $A_{cb}(G)$ obtained by taking the closure of the Fourier algebra $A(G)$ in the multiplier algebra $MA(G)$ and completely bounded multiplier algebra $M_{cb}A(G)$ respectively. In particular, we will focus on various functorial pro

Yijie Shen, Chao He, Zipei Song, Binguo Chen

Skyrmions are topologically protected quasiparticles, originally studied in condensed-matter systems and recently in photonics, with great potential in ultra-high-capacity information storage. Despite the recent attention, most optical solutions require complex and expensive systems yet produce limited topologies. Here we demonstrate an extended family of qu

Martin Mayer

Given a closed manifold of positive Yamabe invariant and for instance positive Morse functions upon it, the conformally prescribed scalar curvature problem raises the question, whether or not such functions can by conformally changing the metric be realised as the scalar curvature of this manifold. As we shall quantify depending on the shape and structure of

Pierre Gleize, Weiyao Wang, Matt Feiszli

Keypoint detection & descriptors are foundational tech-nologies for computer vision tasks like image matching, 3D reconstruction and visual odometry. Hand-engineered methods like Harris corners, SIFT, and HOG descriptors have been used for decades; more recently, there has been a trend to introduce learning in an attempt to improve keypoint detectors. On ins

Nicholas H. Barbara, Ruigang Wang, Ian R. Manchester

This paper presents a policy parameterization for learning-based control on nonlinear, partially-observed dynamical systems. The parameterization is based on a nonlinear version of the Youla parameterization and the recently proposed Recurrent Equilibrium Network (REN) class of models. We prove that the resulting Youla-REN parameterization automatically sati

Kyle Whitcomb, Alexandru Maries, Chandralekha Singh

The importance of science beliefs such as self-efficacy, interest, identity, sense of belonging, perceived recognition and effectiveness of peer interaction in science education has been increasingly recognized in recent years. Here, we use five years of data from a validated survey administered to non-majors during their first year, physics majors throughou

Govert Nijs, Wilke van der Schee

We present an extended Bayesian analysis using Trajectum where the initial condition can now include binary scaling. For the far-from-equilibrium evolution before hydrodynamics we introduce an interpolation between free streaming and a holographically inspired evolution that exhibits fast hydrodynamization. We find strong evidence that binary scaling is inco

Honglin Bao, Misha Teplitskiy

The scientific community discourages authors of research papers from citing papers that did not influence them. Such "rhetorical" citations are assumed to degrade the literature and incentives for good work. While a world where authors cite only substantively appears attractive, we argue that mandating substantive citing may have underappreciated consequence

Harish D. Ramachandran, Julia E. Ford, Amar C. Vutha

Precision spectroscopy of solid-state systems is challenging due to inhomogeneous broadening. We describe a technique -- coherent quantum beats -- that enables the measurement of small frequency shifts within an inhomogeneously broadened distribution while addressing the full ensemble. We show that the technique can be used to obtain improvements in signal s

Chandralekha Singh, Alexandru Maries

Helping students become proficient problem-solvers is one of the primary goals of physics courses. In part 1 of this article, we summarized the vast research on problem-solving relevant for physics instruction, and here we discuss a concrete example of problem solving in the context of waves from introductory physics. The goal of this research was to investi

George Ruppeiner, Alexandru-Mihail Sturzu

The microstructure of black holes is a mystery. There is yet no resolution of basic questions such as what the constituent particles are. We work here with black hole thermodynamics (BHT), and the metric geometry of thermodynamics, which connects to interparticle interactions via the invariant thermodynamic Ricci scalar curvature $R$. $R$ may be calculated w

Merlin Carl

We describe two systems currently being developed that use large language models for the automatized correction of (i) exercises in translating back and forth between natural language and the languages of propositional logic and first-order predicate logic and (ii) exercises in writing simple arguments in natural language in non-mathematical scenarios.

Hamed Amini, Andreea Minca, Oscar Peralta

We endow the classical stochastic fluid process with a duration-dependent Markovian arrival process (DMArP). We show that this provides a flexible model for the revenue of a solar energy generator. In particular, it allows for heavy-tailed interarrival times and for seasonality embedded into the state-space. It generalizes the calendar-time inhomogeneous sto

Anjana Arunkumar, Shubham Sharma, Rakhi Agrawal, Sriram Chandrasekaran

Cross-task generalization is a significant outcome that defines mastery in natural language understanding. Humans show a remarkable aptitude for this, and can solve many different types of tasks, given definitions in the form of textual instructions and a small set of examples. Recent work with pre-trained language models mimics this learning style: users ca

Matthew C. Kelley

The speech signal is a consummate example of time-series data. The acoustics of the signal change over time, sometimes dramatically. Yet, the most common type of comparison we perform in phonetics is between instantaneous acoustic measurements, such as formant values. In the present paper, I discuss the concept of absement as a quantification of differences

Giacomo Medda, Francesco Fabbri, Mirko Marras, Ludovico Boratto

Nowadays, research into personalization has been focusing on explainability and fairness. Several approaches proposed in recent works are able to explain individual recommendations in a post-hoc manner or by explanation paths. However, explainability techniques applied to unfairness in recommendation have been limited to finding user/item features mostly rel

J. A. Aguilar, P. Allison, D. Besson, A. Bishop

Glacial ice is used as a target material for the detection of ultra-high energy neutrinos, by measuring the radio signals that are emitted when those neutrinos interact in the ice. Thanks to the large attenuation length at radio frequencies, these signals can be detected over distances of several kilometers. One experiment taking advantage of this is the Rad

R. H. M. Tsang, A. Piepke, S. Al Kharusi, E. Angelico

Large-scale low-background detectors are increasingly used in rare-event searches as experimental collaborations push for enhanced sensitivity. However, building such detectors, in practice, creates an abundance of radioassay data especially during the conceptual phase of an experiment when hundreds of materials are screened for radiopurity. A tool is needed

Rumpa Dasgupta, Amin Sakzad, Carsten Rudolph, Rafael Dowsley

In this paper, we design and present a novel model called SePEnTra to ensure the security and privacy of energy data while sharing with other entities during energy trading to determine optimal price signals. Furthermore, the market operator can use this data to detect malicious activities of users in the later stage without violating privacy (e.g., deviatio

Xiangyu Xu, Lichang Chen, Changjiang Cai, Huangying Zhan

Direct optimization of interpolated features on multi-resolution voxel grids has emerged as a more efficient alternative to MLP-like modules. However, this approach is constrained by higher memory expenses and limited representation capabilities. In this paper, we introduce a novel dynamic grid optimization method for high-fidelity 3D surface reconstruction

Andy Kweon, Vishnu Hu, Jong Yoon Lim, Trevor Gee

As technology progresses, smart automated systems will serve an increasingly important role in the agricultural industry. Current existing vision systems for yield estimation face difficulties in occlusion and scalability as they utilize a camera system that is large and expensive, which are unsuitable for orchard environments. To overcome these problems, th

Felix Y. Zhou, Andrew Weems, Gabriel M. Gihana, Bingying Chen

Signal transduction and cell function are governed by the spatiotemporal organization of membrane-associated molecules. Despite significant advances in visualizing molecular distributions by 3D light microscopy, cell biologists still have limited quantitative understanding of the processes implicated in the regulation of molecular signals at the whole cell s

Rui Chen, Alvin Shek, Changliu Liu

This paper studies real-time collaborative robot (cobot) handling, where the cobot maneuvers an object under human dynamic gesture commands. Enabling dynamic gesture commands is useful when the human needs to avoid direct contact with the robot or the object handled by the robot. However, the key challenge lies in the heterogeneity in human behaviors and the

Chenru Duan, Yuanqi Du, Haojun Jia, Heather J. Kulik

Transition state (TS) search is key in chemistry for elucidating reaction mechanisms and exploring reaction networks. The search for accurate 3D TS structures, however, requires numerous computationally intensive quantum chemistry calculations due to the complexity of potential energy surfaces. Here, we developed an object-aware SE(3) equivariant diffusion m

John Kobak, Bennett J. Richman, LaJuan Washington, Syed A. Hamza

The prompt and accurate recognition of Continuous Human Activity (CHAR) is critical in identifying and responding to health events, particularly fall risk assessment. In this paper, we examine a multi-antenna radar system that can process radar data returns for multiple individuals in an indoor setting, enabling CHAR for multiple subjects. This requires comb

Xincheng Lin

Four-body systems are studied using an effective field theory with two- and three-body contact interactions. A method to systematically address deep trimers (three-body bound states that are more tightly bound than four-body bound/resonant states) in four-body calculations is developed using a diagrammatic approach. Previous calculations were limited by the

Jamil Tahir-Kheli, Tomas Hlasek, Michal Lojka, Michael S. Osofsky

Fusion reactors are limited by the magnetic field available to confine their plasma. The commercial fusion industry uses the larger magnetic field and higher operating temperature of the cuprate superconductor $\mathbf{YBa_{2}Cu_{3}O_{7-\delta}}$ (YBCO) in order to confine their plasma into a dense volume. A superconductor is a macroscopic quantum state that

Christian Borgs, Geng Zhao

For many random graph models, the analysis of a related birth process suggests local sampling algorithms for the size of, e.g., the giant connected component, the $k$-core, the size and probability of an epidemic outbreak, etc. In this paper, we study the question of when these algorithms are robust against misspecification of the graph model, for the specia

Daniel Golovin, Gabor Bartok, Eric Chen, Emily Donahue

In many software systems, heuristics are used to make decisions - such as cache eviction, task scheduling, and information presentation - that have a significant impact on overall system behavior. While machine learning may outperform these heuristics, replacing existing heuristics in a production system safely and reliably can be prohibitively costly. We pr

Ivan Nasonov, Andrey Galyaev, Andrey Medvedev

The task of intercepting a target moving along a rectilinear or circular trajectory by a Dubins' car is formulated as a time-optimal control problem with an arbitrary direction of the car's velocity at the interception moment. To solve this problem and to synthesize interception trajectories, neural network methods of unsupervised learning based on the Deep

Marc Alexander Kühn, Daniel Omeiza, Lars Kunze

The provision of natural language explanations for the predictions of deep-learning-based vehicle controllers is critical as it enhances transparency and easy audit. In this work, a state-of-the-art (SOTA) prediction and explanation model is thoroughly evaluated and validated (as a benchmark) on the new Sense--Assess--eXplain (SAX). Additionally, we develope

Ming-Chang Lee, Jia-Chun Lin, Volker Stolz

As more connected devices are implemented in a cyber-physical world and data is expected to be collected and processed in real time, the ability to handle time series data has become increasingly significant. To help analyze time series in data mining applications, many time series representation approaches have been proposed to convert a raw time series int

Ravi Sahita, Atish Patra, Vedvyas Shanbhogue, Samuel Ortiz

Multi-tenant computing platforms are typically comprised of several software and hardware components including platform firmware, host operating system kernel, virtualization monitor, and the actual tenant payloads that run on them (typically in a virtual machine, container, or application). This model is well established in large scale commercial deployment

Giovanni Di Meglio, Martin B. Plenio, Susana F. Huelga

We derive a Markovian master equation that models the evolution of systems subject to driving and control fields. Our approach combines time rescaling and weak-coupling limits for the system-environment interaction with a secular approximation. The derivation makes use of the adiabatic time-evolution operator in a manner that allows for the efficient descrip

Jennifer K Bertrand, Craig S Chapman

Do patterns of eye-hand coordination observed during real-world object interactions apply to digital, screen-based object interactions? We adapted a real-world object interaction task (physically transferring cups in sequence about a tabletop) into a two-dimensional screen-based task (dragging-and-dropping circles in sequence with a cursor). We collected gaz