April 2023 arXiv papers — page 55

Cameron C. Yetman

The following annotated bibliography contains a reasonably complete survey of contemporary work in the philosophy of astrophysics. Spanning approximately forty years from the early 1980s to the present day, the bibliography should help researchers entering the field to acquaint themselves with its major texts, while providing an opportunity for philosophers

Jacky Chen Long Chai, Tiong-Sik Ng, Cheng-Yaw Low, Jaewoo Park

Very low-resolution face recognition (VLRFR) poses unique challenges, such as tiny regions of interest and poor resolution due to extreme standoff distance or wide viewing angle of the acquisition devices. In this paper, we study principled approaches to elevate the recognizability of a face in the embedding space instead of the visual quality. We first form

Changhao Li, Luyi Feng, Yang Jeong Park, Jian Yang

Cellular force transmission across a hierarchy of molecular switchers is central to mechanobiological responses. However, current cellular force microscopies suffer from low throughput and resolution. Here we introduce and train a generative adversarial network (GAN) to paint out traction force maps of cell monolayers with high fidelity to the experimental t

Jacob Muldoon, Yogesh N. Joglekar

Since the realization of quantum systems described by non-Hermitian Hamiltonians with parity-time (PT) symmetry, interest in non-Hermitian, quantum many-body models has steadily grown. Most studies to-date map to traditional quantum spin models with a non-Hermiticity that arises from making the model parameters complex or purely imaginary. Here, we present a

Penghui Fu, Zhiqiang Tan

We formulate two classes of first-order algorithms more general than previously studied for minimizing smooth and strongly convex or, respectively, smooth and convex functions. We establish sufficient conditions, via new discrete Lyapunov analyses, for achieving accelerated convergence rates which match Nesterov's methods in the strongly and general convex s

Jasper Barr, Giang T. Nguyen, Oscar Peralta

This paper investigates the convergence of Wong--Zakai approximations to regime-switching stochastic differential equations, generated by a collection of finite-variation approximations to Brownian motion. We extend the results of Nguyen and Peralta (2021) to $\mathbb{R}^d$-valued RSSDE by utilising rough path theoretic tools, acquiring the same modification

Albert Musaelian, Anders Johansson, Simon Batzner, Boris Kozinsky

This work brings the leading accuracy, sample efficiency, and robustness of deep equivariant neural networks to the extreme computational scale. This is achieved through a combination of innovative model architecture, massive parallelization, and models and implementations optimized for efficient GPU utilization. The resulting Allegro architecture bridges th

Zheng-Chu Guo, Andreas Christmann, Lei Shi

In this paper, we study an online learning algorithm with a robust loss function $\mathcal{L}_{\sigma}$ for regression over a reproducing kernel Hilbert space (RKHS). The loss function $\mathcal{L}_{\sigma}$ involving a scaling parameter $\sigma>0$ can cover a wide range of commonly used robust losses. The proposed algorithm is then a robust alternative for

Martin-Isbjörn Trappe, William C. Witt, Sergei Manzhos

Density-potential functional theory (DPFT) is an alternative formulation of orbital-free density functional theory that may be suitable for modeling the electronic structure of large systems. To date, DPFT has been applied mainly to quantum gases in one- and two dimensional settings. In this work, we study the performance of DPFT when applied to real-life sy

Tyler J. Hague, Douglas W. Higinbotham, Spencer Portuese

A discrepancy in the determination of the proton's charge radius, $r_p$, between muonic hydrogen spectroscopy versus classic atomic spectroscopy and electron scattering data has become known as the proton radius puzzle. Extractions of $r_p$ from electron scattering data require determination of the slope of the proton's charge form factor, $G_E^p$, in the li

Miao Dai, Gang Sun, Hongfang Yu, Dusit Niyato

Network slicing endows 5G/B5G with differentiated and customized capabilities to cope with the proliferation of diversified services, whereas limited physical network resources may not be able to support all service requests. Slice admission control is regarded as an essential means to ensure service quality and service isolation when the network is under bu

Mustafa Z. Yousif, Yifan Yang, Haifeng Zhou, Linqi Yu

The present study aims to investigate the effectiveness of plasma actuators in controlling the flow around a finite wall-mounted square cylinder (FWMSC) with a longitudinal aspect ratio of 4. The test is conducted in a small-scale closed return-type wind tunnel. The Reynolds number (${Re}_d$) of the experiments is 500 based on the width of the bluff body and

Yutaka Hosotani

In orbifold gauge theory and gauge-Higgs unification models, gauge anomaly flows with an Aharonov-Bohm phase $\theta_H$ in the fifth dimension. We analyze $SU(2)$ gauge theory with doublet fermions in the flat $M^4 \times (S^1/Z_2)$ spacetime and in the Randall-Sundrum (RS) warped space. With orbifold boundary conditions the $U(1)$ part of gauge symmetry rem

Hyunjin Choi, Hyunjae Lee, Seongho Joe, Youngjune L. Gwon

Encoded representations from a pretrained deep learning model (e.g., BERT text embeddings, penultimate CNN layer activations of an image) convey a rich set of features beneficial for information retrieval. Embeddings for a particular modality of data occupy a high-dimensional space of its own, but it can be semantically aligned to another by a simple mapping

Zhuoran Zheng, Xiuyi Jia

With the development of the medical image field, researchers seek to develop a class of datasets to block the need for medical knowledge, such as \text{MedMNIST} (v2). MedMNIST (v2) includes a large number of small-sized (28 $\times$ 28 or 28 $\times$ 28 $\times$ 28) medical samples and the corresponding expert annotations (class label). The existing baselin

Joseph C. Chapman, Alexander Miloshevsky, Hsuan-Hao Lu, Nageswara Rao

Squeezed light is a crucial resource for continuous-variable (CV) quantum information science. Distributed multi-mode squeezing is critical for enabling CV quantum networks and distributed quantum sensing. To date, multi-mode squeezing measured by homodyne detection has been limited to single-room experiments without coexisting classical signals, i.e., on ``

Yun Wei, Sayan Mukherjee, XuanLong Nguyen

Finite mixture models have long been used across a variety of fields in engineering and sciences. Recently there has been a great deal of interest in quantifying the convergence behavior of the \emph{mixing measure}, a fundamental object that encapsulates all unknown parameters in a mixture distribution. In this paper we propose a general framework for estim

Hui Dou, Shanshan Zhu, Yiwen Zhang, Pengfei Chen

Hyper-parameters optimization (HPO) is vital for machine learning models. Besides model accuracy, other tuning intentions such as model training time and energy consumption are also worthy of attention from data analytic service providers. Hence, it is essential to take both model hyperparameters and system parameters into consideration to execute cross-laye

Ansh Mittal

The various aspects like modeling and interpreting 3D environments and surroundings have enticed humans to progress their research in 3D Computer Vision, Computer Graphics, and Machine Learning. An attempt made by Mildenhall et al in their paper about NeRFs (Neural Radiance Fields) led to a boom in Computer Graphics, Robotics, Computer Vision, and the possib

Lukas Schmid, Olov Andersson, Aurelio Sulser, Patrick Pfreundschuh

Real-time detection of moving objects is an essential capability for robots acting autonomously in dynamic environments. We thus propose Dynablox, a novel online mapping-based approach for robust moving object detection in complex environments. The central idea of our approach is to incrementally estimate high confidence free-space areas by modeling and acco

Masaya Morishita, Tomoyuki Ichikawa, Masaaki A. Tanaka, Motoharu Furuta

We fabricated two types of cobalt-ferrite (001) thin films, insulative Fe-rich cobalt-ferrite CoxFe3-xO4+{\delta} (I-CFO) and conductive Fe-rich cobalt-ferrite CoyFe3-yO4 (C-CFO), with perpendicular magnetic anisotropy (PMA) on MgO (001) substrates. Although the stoichiometric cobalt-ferrite is known as an insulating material, it is found that the conductivi

Hui Wang, Yan-Jun Zhao, Hui-Chen Sun, Xun-Wei Xu

We propose a scheme of using two fixed frequency resonator couplers to tune the coupling strength between two Xmon qubits. The induced indirect qubit-qubit interactions by two resonators could offset with each other, and the direct coupling between two qubits are not necessarily for switching off. The small direct qubit-quibt coupling could effectively suppr

Ryoya Yamasaki, Toshiyuki Tanaka

Kernel-based modal statistical methods include mode estimation, regression, and clustering. Estimation accuracy of these methods depends on the kernel used as well as the bandwidth. We study effect of the selection of the kernel function to the estimation accuracy of these methods. In particular, we theoretically show a (multivariate) optimal kernel that min

Gehang Zhang, Bowen Yu, Jiangxia Cao, Xinghua Zhang

Graph contrastive learning (GCL) has recently achieved substantial advancements. Existing GCL approaches compare two different ``views'' of the same graph in order to learn node/graph representations. The underlying assumption of these studies is that the graph augmentation strategy is capable of generating several different graph views such that the graph v

Zahra Mirzamomen, Marcel Böhme

Some bugs cannot be exposed by program inputs, but only by certain program environments. During execution, most programs access various resources, like databases, files, or devices, that are external to the program and thus part of the program's environment. In this paper, we present a coverage-guided, mutation-based environment synthesis approach of bug-ind

Bruce T. Tsurutani, Gurbax S. Lakhina, Rajkumar Hajra

The Colaba, India ~-1600 nT magnetic spike caused by an interplanetary sheath magnetic field inducing a "dayside R1-field aligned current wedge" during the Carrington magnetic storm proposed by Ohtani (2022, https://doi.org/10.1029/2022JA030596) seems highly improbable. Normal interplanetary magnetic field intensities of ~5 nT have previously been shown to b

V. A. Abakumova, S. L. Lyakhovich

The gauge symmetry is said unfree if the gauge transformation leaves the action functional unchanged provided for the gauge parameters are constrained by the system of partial differential equations. The best known example of this phenomenon is the volume preserving diffeomorphism being the gauge symmetry of unimodular gravity (UG). Various extensions are kn

Lening Li, Zhentian Qian, Jianan Xia, Yawen Wang

This work investigates formal policy synthesis for continuous-state stochastic dynamic systems subject to high-level specifications expressed in linear temporal logic. To learn an optimal policy that maximizes the satisfaction probability, we compose the dynamic system with the automaton translated from the specification and solve an optimal planning problem

Muhammad Abdullah Hanif, Muhammad Shafique

Fault-Aware Training (FAT) has emerged as a highly effective technique for addressing permanent faults in DNN accelerators, as it offers fault mitigation without significant performance or accuracy loss, specifically at low and moderate fault rates. However, it leads to very high retraining overheads, especially when used for large DNNs designed for complex

Jie Jian, Jun Liao, Heguo Liu

The adjoint of a matrix in the Lie algebra associated with a matrix algebra is a fundamental operator, which can be generalized to a more general operator $\varphi_{AB}: X\rightarrow AX-XB$ by two matrices $A$ and $B$. The kernel of the operator is very well-known and it can be found in Gantmacher's book. The formulas for the dimensions of the kernels of arb

Belal Amin, Romario Sameh Samir, Youssef Tarek, Mohammed Ahmed

This study proposes a deep learning model for the classification and segmentation of brain tumors from magnetic resonance imaging (MRI) scans. The classification model is based on the EfficientNetB1 architecture and is trained to classify images into four classes: meningioma, glioma, pituitary adenoma, and no tumor. The segmentation model is based on the U-N

Gyuhak Kim, Changnan Xiao, Tatsuya Konishi, Zixuan Ke

As AI agents are increasingly used in the real open world with unknowns or novelties, they need the ability to (1) recognize objects that (a) they have learned before and (b) detect items that they have never seen or learned, and (2) learn the new items incrementally to become more and more knowledgeable and powerful. (1) is called novelty detection or out-o

STAR Collaboration, M. I. Abdulhamid, B. E. Aboona, J. Adam

Global polarizations ($P$) of $\Lambda$ ($\bar{\Lambda}$) hyperons have been observed in non-central heavy-ion collisions. The strong magnetic field primarily created by the spectator protons in such collisions would split the $\Lambda$ and $\bar{\Lambda}$ global polarizations ($\Delta P = P_{\Lambda} - P_{\bar{\Lambda}} < 0$). Additionally, quantum chromody

Benjamin Ramtoula, Matthew Gadd, Paul Newman, Daniele De Martini

Selecting appropriate datasets is critical in modern computer vision. However, no general-purpose tools exist to evaluate the extent to which two datasets differ. For this, we propose representing images - and by extension datasets - using Distributions of Neuron Activations (DNAs). DNAs fit distributions, such as histograms or Gaussians, to activations of n

H. Aratyn, J. F. Gomes, G. V. Lobo, A. H. Zimerman

We show how the zero-curvature equations based on a loop algebra of $D_4$ with a principal gradation reduce via self-similarity limit to a polynomial Hamiltonian system of coupled Painlev\'e III models with four canonical variables and $D_4^{(1)}$ affine Weyl group symmetry.

Jutta Escher, Kirana Bergstrom, Emanuel Chimanski, Oliver Gorton

Nuclear reaction data required for astrophysics and applications is incomplete, as not all nuclear reactions can be measured or reliably predicted. Neutron-induced reactions involving unstable targets are particularly challenging, but often critical for simulations. In response to this need, indirect approaches, such as the surrogate reaction method, have be

Neil Irwin Bernardo, Jingge Zhu, Jamie Evans

This paper examines the maximum code rate achievable by a data-driven communication system over some unknown discrete memoryless channel in the finite blocklength regime. A class of channel codes, called learning-based channel codes, is first introduced. Learning-based channel codes include a learning algorithm to transform the training data into a pair of e

Shiki Ogata, Shunsaku Kitagawa, Katsuki Kinjo, Kenji Ishida

Recently, a superconducting (SC) transition from low-field (LF) to high-field (HF) SC states was reported in CeRh$_2$As$_2$, indicating the existence of multiple SC states. It has been theoretically noted that the existence of two Ce sites in the unit cell, the so-called sub-lattice degrees of freedom owing to the local inversion symmetry breaking at the Ce

Mathilde Papillon, Sophia Sanborn, Mustafa Hajij, Nina Miolane

The natural world is full of complex systems characterized by intricate relations between their components: from social interactions between individuals in a social network to electrostatic interactions between atoms in a protein. Topological Deep Learning (TDL) provides a comprehensive framework to process and extract knowledge from data associated with the

Anna Ijjas

Using the power of numerical relativity, we show that, beginning from generic initial conditions that are far from flat, homogeneous and isotropic and have a large Weyl curvature, a period of slow contraction rapidly drives spacetime towards vanishingly small Weyl curvature as the total energy density grows, thus providing a dynamical mechanism that satisfie

Bilel Tarchoun, Anouar Ben Khalifa, Mohamed Ali Mahjoub, Nael Abu-Ghazaleh

Real-world adversarial physical patches were shown to be successful in compromising state-of-the-art models in a variety of computer vision applications. Existing defenses that are based on either input gradient or features analysis have been compromised by recent GAN-based attacks that generate naturalistic patches. In this paper, we propose Jedi, a new def

William Evans, Seyed Ali Tabatabaee

In this paper, we study the problems of computing the 1-center, centroid, and 1-median of objects moving with bounded speed in Euclidean space. We can acquire the exact location of only a constant number of objects (usually one) per unit time, but for every other object, its set of potential locations, called the object's uncertainty region, grows subject on

Justin Hastings, David Ubilava

We examine whether harvest-time transitory shifts in employment and income lead to changes in political violence and social unrest in rice-producing croplands of Southeast Asia. Using monthly data from 2010 to 2023 on over 86,000 incidents covering 376 one-degree cells across eight Southeast Asian countries, we estimate a general increase in political violen

Qigang Liang, Xuejun Xu, Shangyou Zhang

The previous proved-bound is $C(1+\frac{H^2}{\delta^2})$ for the condition number of the overlapping domain decomposition $\mathrm{H}(\mathrm{curl};\Omega)$ and $\mathrm{H}(\mathrm{div};\Omega)$ methods, where $H$ and $\delta$ are the sizes of subdomains and overlaps respectively. But all numerical results indicate that the best bound is $C(1+\frac{H}{\delta

Chao Cheng, Fan Li

We consider assessing causal mediation in the presence of a post-treatment event (examples include noncompliance, a clinical event, or death). We identify natural mediation effects for the entire study population and for each principal stratum characterized by the joint potential values of the post-treatment event. We derive the efficient influence function

Christopher Henderson, Maximilian Rezek

We show that there exist traveling wave solutions of the Keller-Segel-FKPP equation, which models a diffusing and logistically growing population subject to chemotaxis. In contrast to previous results, our result is in the strong aggregation regime; that is, we make no smallness assumption on the parameters. The lack of a smallness condition makes $L^\infty$

Nick Williams

Introduction: The value of integrating federal HIV services data with HIV surveillance is currently unknown. Upstream and complete case capture is essential in preventing future HIV transmission. Methods: This study integrated Ryan White, Social Security Disability Insurance, Medicare, Children Health Insurance Programs and Medicaid demographic aggregates fr

Venkat Abhignan

To investigate Casimir electromagnetic interaction in $N$ bodies, we implement multiple $\delta$-function plates with electric and magnetic properties. We use their optical properties to study the Casimir energy between the plates by implementing multiple scattering formalism. We initially solve Green's functions for two and three plates configurations to ob

Naser Talebizadeh Sardari, Bryce Joseph Orloski

Given a compact subset $\Sigma \subset \mathbb{R}$ (or $\mathbb{C}$) with logarithmic capacity greater than zero, we construct an explicit family of probability measures supported on $\Sigma$ such that their closure is all the possible weak limit measures of complete sets of conjugate algebraic integers lying inside $\Sigma$. We give an asymptotic formula fo

Di Xu, Xiang He, Tonghua Su, Zhongjie Wang

Deep neural network (DNN) partition is a research problem that involves splitting a DNN into multiple parts and offloading them to specific locations. Because of the recent advancement in multi-access edge computing and edge intelligence, DNN partition has been considered as a powerful tool for improving DNN inference performance when the computing resources

Shinji Tsujikawa

In a new gravitational theory with the trace anomaly recently proposed by Gabadadze, we study the existence of hairy black hole solutions on a static and spherically symmetric background. In this theory, the effective 4-dimensional action contains a kinetic term of the conformal scalar field related to a new scale $bar{M}$ much below the Planck mass. This pr

Jiadi Du, Tie Luo

With the advances of IoT developments, copious sensor data are communicated through wireless networks and create the opportunity of building Digital Twins to mirror and simulate the complex physical world. Digital Twin has long been believed to rely heavily on domain knowledge, but we argue that this leads to a high barrier of entry and slow development due

Ásgeir Valfells

A 1957 conjecture by Zdzislaw Melzak, that the unit volume polyhedron with least edge length was a triangular right prism, with edge length $2^{2/3}3^{11/6}$. We present a variety of necessary local criteria for any minimizer. In the case that we are restricted to convex polyhedrons we demonstrate that all vertices must be of degree three, the number of tria

W. B. Li

The Thomas Jefferson National Accelerator Facility (JLab) has undertaken the 12 GeV Upgrade to double the accelerating energy of its electron beam. This attracts many interesting proposals to probe the quark-gluon nature of nuclear matter at higher energy, therefore a new set of equipment is required. Experimental Hall C of JLab has planned to construct a ne

Michael P. Landry, Yair N. Minsky, Samuel J. Taylor

We show that every atoroidal endperiodic map of an infinite-type surface can be obtained from a depth one foliation in a fibered hyperbolic 3-manifold, reversing a well-known construction of Thurston. This can be done almost-transversely to the canonical suspension flow, and as a consequence we recover the Handel-Miller laminations of such a map directly fro

G. Abellan, N. Bolivar, I. Vasilev

Negative energy scenarios are the most widely studied for the warp metric. In fact, the prevailing view in the community so far has been that the warp metric necessarily has negative energies. In this work it is shown that the issue of negative energy densities associated with the Alcubierre warp metric with a general form function and similar metrics can be

Xi-Jing Wang, Xiao-Mei Kuang, Yuan Meng, Bin Wang

We analyze the light rays around a static hairy black hole in Horndeski gravity with the use of ray-tracing procedure. We find that a stronger Horndeski hairy parameter corresponds to larger photon sphere as well as critical impact parameter, and wider ranges of photon ring and lensed ring emissions. These influences can be robustly interpreted from the shap

Stefan Küchemann, Steffen Steinert, Natalia Revenga, Matthias Schweinberger

The recent advancement of large language models presents numerous opportunities for teaching and learning. Despite widespread public debate regarding the use of large language models, empirical research on their opportunities and risks in education remains limited. In this work, we demonstrate the qualities and shortcomings of using ChatGPT 3.5 for physics t

Christopher Sims

The PageRank algorithm is used to rank web pages by their importance. Since its development, the PageRank algorithm is a critical and fundamental part of search engines today. PageRank is a graph-based algorithm that ranks pages based on how many other pages link to them. This work develops a variational quantum version of the PageRank algorithm and compares

Hongkuan Zhou, Rajgopal Kannan, Ananthram Swami, Viktor Prasanna

Predicting the throughput of WLAN deployments is a classic problem that occurs in the design of robust and high performance WLAN systems. However, due to the increasingly complex communication protocols and the increase in interference between devices in denser and denser WLAN deployments, traditional methods either have substantial runtime or enormous predi

Jan Kim, Donghi Lee

We produce an example demonstrating that every finitely generated relatively hyperbolic group with respect to a collection of Hopfian subgroups need not be Hopfian. This answers a question of Osin \cite[Problem 5.5]{Osin} in the negative.

Nawsad Ali

In this paper we are to study homogeneous and anisotropic Bianchi type-V universe in presence of bulk viscous fluid source of matter with time function gravitational constant G and cosmological term lambda. The viscosity coefficient is regarded as power function of matter density in the first case whereas in other case it is considered as proportional to the

Tiago S. Domingues, Ronald Coifman, Amir Haji-Akbari

Materials under confinement can possess properties that deviate considerably from their bulk counterparts. Indeed, confinement makes all physical properties position-dependent and possibly anisotropic, and characterizing such spatial variations and directionality is an intense area of focus in experimental and computational studies of confined matter. While

Chris Fields, James F. Glazebrook

We study the relationship between assumptions of state separability and both preparation and measurement contextuality, and the relationship of both of these to the frame problem, the problem of predicting what does not change in consequence of an action. We state a quantum analog of the latter and prove its undecidability. We show how contextuality is gener

Mir Sayed Shah Danish, Zahra Nazari, Tomonobu Senjyu

This study investigates the transformation of energy models to align with machine learning requirements as a promising tool for optimizing the operation of combined cycle power plants (CCPPs). By modeling energy production as a function of environmental and control variables, this methodology offers an innovative way to achieve energy-efficient power generat

Hyeonu Heo, Arkadii Krokhin, Arup Neogi, Zhiming Cui

A reconfigurable phononic crystal (PnC) is proposed where elastic properties can be modulated by rotation of asymmetric solid scatterers immersed in water. The scatterers are metallic rods with cross-section of 120{\deg} circular sector. Orientation of each rod is independently controlled by an external electric motor that allows continuous variation of the

Chi Zhao, Elena Parilina

We propose a general concealed voter model (GCVM), in which individuals interact in two layers and can exchange their opinions in the internal layer. This interaction is not allowed in a concealed voter model (CVM). By exchanging opinions in the internal layer we mean that individuals share their real or internal opinions with their close friends. The proces

Adrian Jones, James V Zidek, Joe Watson

The preferential siting of the locations of monitors of hazardous environmental fields can lead to the serious underestimation of the impacts of those fields. In particular, human health effects can be severely underestimated when standard statistical are applied without appropriate adjustment. This report describes an extensive analysis of the siting of mon

Ruth H. Keogh, Nan van Geloven

Predictions under interventions are estimates of what a person's risk of an outcome would be if they were to follow a particular treatment strategy, given their individual characteristics. Such predictions can give important input to medical decision making. However, evaluating predictive performance of interventional predictions is challenging. Standard way

Ege Erdil, Jaime Sevilla

We find that improvements in speedrunning world records follow a power law pattern. Using this observation, we answer an outstanding question from previous work: How do we improve on the baseline of predicting no improvement when forecasting speedrunning world records out to some time horizon, such as one month? Using a random effects model, we improve on th

Natsuko Hoshi, Makoto Katori, Tom H. Koornwinder, Michael J. Schlosser

The identity by Chaundy and Bullard expresses $1$ as a sum of two truncated binomial series in one variable where the truncations depend on two different non-negative integers. We present basic and elliptic extensions of the Chaundy--Bullard identity. The most general result, the elliptic extension, involves, in addition to the nome $p$ and the base $q$, fou

Marcos Gómez-Rodríguez, Laura Davila-Pena, Balbina Casas-Méndez

One of the practical applications of cooperative transferable utility games involves determining the fee structure for users of a given facility, whose construction or maintenance costs need to be recouped. In this context, certain efficiency and equity criteria guide the considered solutions. This paper analyzes how to allocate the fixed costs of a highway

Weijun Tan, Qi Yao, Jingfeng Liu

Detection of baby cries is an important part of baby monitoring and health care. Almost all existing methods use supervised SVM, CNN, or their varieties. In this work, we propose to use weakly supervised anomaly detection to detect a baby cry. In this weak supervision, we only need weak annotation if there is a cry in an audio file. We design a data mining t

Qinyang He, Yonatan Mintz

A key challenge in sequential decision making is optimizing systems safely under partial information. While much of the literature has focused on the cases of either partially known states or partially known dynamics, it is further exacerbated in cases where both states and dynamics are partially known. Computing heparin doses for patients fits this paradigm

Pengfei Huang, Hao Sun

In this paper, we construct the moduli spaces of filtered $G$-local systems on curves for an arbitrary reductive group $G$ over an algebraically closed field of characteristic zero. This provides an algebraic construction for the Betti moduli spaces in the tame nonabelian Hodge correspondence for vector bundles/principal bundles on noncompact curves. As a di

John Arrington, Reynier Cruz-Torres, Tyler J. Hague, Leiqaa Kurbany

A series of experiments were performed in Hall A of Jefferson Lab in 2018 that used a novel tritium and helium-3 target system. These experiments took advantage of the isospin symmetry of these mirror nuclei to make precise measurements of isospin dependence in both nucleon and nuclear structure. We summarize here the design and properties of these cells, th

Tobias Winkler, Joost-Pieter Katoen

Probabilistic pushdown automata (pPDA) are a natural operational model for a variety of recursive discrete stochastic processes. In this paper, we study certificates - succinct and easily verifiable proofs - for upper and lower bounds on various quantitative properties of a given pPDA. We reveal an intimate, yet surprisingly simple connection between the exi

Xi Lin, Paul Szenher, John D. Martin, Brendan Englot

Route planning is essential to mobile robot navigation problems. In recent years, deep reinforcement learning (DRL) has been applied to learning optimal planning policies in stochastic environments without prior knowledge. However, existing works focus on learning policies that maximize the expected return, the performance of which can vary greatly when the

Zuxiang Situ, Qi Wang, Shuai Teng, Wanen Feng

Deep learning models have become increasingly popular for flood prediction due to their superior accuracy and efficiency compared to traditional methods. However, current machine learning methods often rely on separate spatial or temporal feature analysis and have limitations on the types, number, and dimensions of input data. This study presented a CNN-RNN

Xinwu Chen, Shuai Han, Hai Jiang

In this paper, we investigate how an illegitimate user can use a reconfigurable intelligent surface (RIS) to maximally degrade the secrecy performance of the legitimate system. We first consider the case when the illegitimate user performs eavesdropping only. A problem is formulated to minimize the system secrecy rate by optimizing the phase shifts of the RI

Thilina Pathirana, Gianfranco Nencioni

Multi-access Edge Computing (MEC) is one of the enabling technologies of the fifth generation (5G) of mobile networks. MEC enables services with strict latency requirements by bringing computing capabilities close to the users. As with any new technology, the dependability of MEC is one of the aspects that need to be carefully studied. In this paper, we prop

Charvi Rastogi, Marco Tulio Ribeiro, Nicholas King, Harsha Nori

Large language models are becoming increasingly pervasive and ubiquitous in society via deployment in sociotechnical systems. Yet these language models, be it for classification or generation, have been shown to be biased and behave irresponsibly, causing harm to people at scale. It is crucial to audit these language models rigorously. Existing auditing tool

Hugo A. Akitaya, Frederick Stock

We study a new model of 3-dimensional modular self-reconfigurable robots Rhombic Dodecahedral (RD). By extending results on the 2D analog of this model we characterize the free space requirements for a pivoting move and investigate the $\textit{reconfiguration problem}$, that is, given two configurations $s$ and $t$ is there a sequence of moves that transfor

Abdesslem Layeb

This paper presents two novel deterministic initialization procedures for K-means clustering based on a modified crowding distance. The procedures, named CKmeans and FCKmeans, use more crowded points as initial centroids. Experimental studies on multiple datasets demonstrate that the proposed approach outperforms Kmeans and Kmeans++ in terms of clustering ac

Remi Luschei, Werner Brannath

The population-wise error rate (PWER) is a type I error rate for clinical trials with multiple target populations. In such trials, a treatment is tested for its efficacy in each population. The PWER is defined as the probability that a randomly selected, future patient will be exposed to an inefficient treatment based on the study results. It can be understo

Jonas Kulhanek, Torsten Sattler

Neural Radiance Fields (NeRFs) are a very recent and very popular approach for the problems of novel view synthesis and 3D reconstruction. A popular scene representation used by NeRFs is to combine a uniform, voxel-based subdivision of the scene with an MLP. Based on the observation that a (sparse) point cloud of the scene is often available, this paper prop

Michał R. Przybyłek

Working in Zermelo-Fraenkel Set Theory with Atoms over an $\omega$-categorical $\omega$-stable structure, we show how \emph{infinite} constructions over definable sets can be encoded as \emph{finite} constructions over the Stone-\v{C}ech compactification of the sets. In particular, we show that for a definable set $X$ with its Stone-\v{C}ech compactification

Sebastian Burgos

A classical problem in smooth dynamical systems is known as smooth realization problem. It asks if given a compact manifold $M$, one can construct a volume preserving diffeomorphism with prescribed ergodic properties. We study the decay of correlations for certain dynamical systems with non-uniformly hyperbolic attractors, which natural invariant measure is

Dimitrios Psaltis

The Kerr-Newman metric is the unique vacuum solution of the General Relativistic field equations, in which any singularities or spacetime pathologies are hidden behind horizons. They are believed to describe the spacetimes of massive astrophysical objects with no surfaces, which we call black holes. This spacetime, which is defined entirely by the mass, spin

Venkata Sai Pavan Kumar Vadrevu, Lu Xing, Walid G. Aref

The Skiplist, or skip list, originally designed as an in-memory data structure, has attracted a lot of attention in recent years as a main-memory component in many NoSQL, cloud-based, and big data systems. Unlike the B-tree, the skiplist does not need complex rebalancing mechanisms, but it still shows expected logarithmic performance. It supports a variety o

Valentin-Gabriel Soumah, Prashanth Rao, Philipp Eibl, Maite Taboada

We present the Radar de Parit\'e, an automated Natural Language Processing (NLP) system that measures the proportion of women and men quoted daily in six Canadian French-language media outlets. We outline the system's architecture and detail the challenges we overcame to address French-specific issues, in particular regarding coreference resolution, a new co

Hongjing Xia, Joshua C. Chang, Sarah Nowak, Sonya Mahajan

We used survival analysis to quantify the impact of postdischarge evaluation and management (E/M) services in preventing hospital readmission or death. Our approach avoids a specific pitfall of applying machine learning to this problem, which is an inflated estimate of the effect of interventions, due to survivors bias -- where the magnitude of inflation may

Fabrizio Colombo, Antonino De Martino, Stefano Pinton

Harmonic and polyanalytic functional calculi have been recently defined for bounded commuting operators. Their definitions are based on the Cauchy formula of slice hyperholomorphic functions and on the factorization of the Laplace operator in terms of the Cauchy-Fueter operator $\mathcal{D}$ and of its conjugate $\overline{\mathcal{D}}$. Thanks to the Fueter

Simon Segert, Jonathan Cohen

The ability to learn and predict simple functions is a key aspect of human intelligence. Recent works have started to explore this ability using transformer architectures, however it remains unclear whether this is sufficient to recapitulate the extrapolation abilities of people in this domain. Here, we propose to address this gap by augmenting the transform

Luke Marris, Ian Gemp, Georgios Piliouras

Equilibrium solution concepts of normal-form games, such as Nash equilibria, correlated equilibria, and coarse correlated equilibria, describe the joint strategy profiles from which no player has incentive to unilaterally deviate. They are widely studied in game theory, economics, and multiagent systems. Equilibrium concepts are invariant under certain trans

Sofia Alvarez-Lopez, Annudesh Liyanage, Julian Ding, Raymond Ng

Despite achieving sensitivities capable of detecting the extremely small amplitude of gravitational waves (GWs), LIGO and Virgo detector data contain frequent bursts of non-Gaussian transient noise, commonly known as 'glitches'. Glitches come in various time-frequency morphologies, and they are particularly challenging when they mimic the form of real GWs. G

Mingzhen Shao, Tolga Tasdizen, Sarang Joshi

Homography estimation serves as a fundamental technique for image alignment in a wide array of applications. The advent of convolutional neural networks has introduced learning-based methodologies that have exhibited remarkable efficacy in this realm. Yet, the generalizability of these approaches across distinct domains remains underexplored. Unlike other co

Pedro Foletto Pimenta, Pedro H. C. Avelar, Luis C. Lamb

This paper introduces a new learning-based approach for approximately solving the Kidney-Exchange Problem (KEP), an NP-hard problem on graphs. The problem consists of, given a pool of kidney donors and patients waiting for kidney donations, optimally selecting a set of donations to optimize the quantity and quality of transplants performed while respecting a

Lalithkumar Seenivasan, Mobarakol Islam, Gokul Kannan, Hongliang Ren

Advances in GPT-based large language models (LLMs) are revolutionizing natural language processing, exponentially increasing its use across various domains. Incorporating uni-directional attention, these autoregressive LLMs can generate long and coherent paragraphs. However, for visual question answering (VQA) tasks that require both vision and language proc

Yuhki Hosoya

In this paper, we consider an environment in which the utilitarian theorem for the NM utility function derived by Harsanyi and the utilitarian theorem for Alt's utility function derived by Harvey hold simultaneously, and prove that the NM utility function coincides with Alt's utility function under this setup. This result is so paradoxical that we must presu