July 2023 arXiv papers — page 113

Weipeng Zhuo, Ka Ho Chiu, Jierun Chen, Ziqi Zhao

Floor labels of crowdsourced RF signals are crucial for many smart-city applications, such as multi-floor indoor localization, geofencing, and robot surveillance. To build a prediction model to identify the floor number of a new RF signal upon its measurement, conventional approaches using the crowdsourced RF signals assume that at least few labeled signal s

Xinyi Bai, Ze Wang, Lu Yang, Hong Cheng

The virtual viewpoint is perceived as a new technique in virtual navigation, as yet not supported due to the lack of depth information and obscure camera parameters. In this paper, a method for achieving close-up virtual view is proposed and it only uses optical flow to build parallax effects to realize pseudo 3D projection without using depth sensor. We dev

Jason J. Huang, Yao Yang, Daniel Weinstock, Colin R. Bundschu

Furthering the understanding of the catalytic mechanisms in the oxygen reduction reaction (ORR) is critical to advancing and enabling fuel cell technology. In this work, we use multimodal operando synchrotron X-ray diffraction (XRD) and resonant elastic X-ray scattering (REXS) to investigate the interplay between the structure and oxidation state of a Co-Mn

Peter Warren, Nandhini Raju, Abhilash Prasad, Shajahan Hossain

We report significantly improved accuracy of grain boundary segmentation using Convolutional Neural Networks (CNN) trained on a combination of real and generated data. Manual segmentation is accurate but time-consuming, and existing computational methods are faster but often inaccurate. To combat this dilemma, machine learning models can be used to achieve t

Ronen Weiss, Diego Lonardoni, Stefano Gandolfi

In this work we derive a systematic short-range expansion of the many-body wave function. At leading order, the wave function is factorized to a zero-energy $s$-wave correlated pair and spectator particles, while terms that include energy derivatives and larger orbital angular momentum two-body functions appear at subleading orders. The validity of the expan

Hitesh Mohapatra, Soumya Ranjan Mishra

AI Tool is a large language model (LLM) designed to generate human-like responses in natural language conversations. It is trained on a massive corpus of text from the internet, which allows it to leverage a broad understanding of language, general knowledge, and various domains. AI Tool can provide information, engage in conversations, assist with tasks, an

Seongjun Yang, Gibbeum Lee, Jaewoong Cho, Dimitris Papailiopoulos

This paper presents "Predictive Pipelined Decoding (PPD)," an approach that speeds up greedy decoding in Large Language Models (LLMs) while maintaining the exact same output as the original decoding. Unlike conventional strategies, PPD employs additional compute resources to parallelize the initiation of subsequent token decoding during the current token dec

Dian Xie, Chunhua Zhu, Sufen Guo, Helei Liu

The actinides, such as the uranium (U) element, are typically synthesized through the rapid neutron-capture process (r-process), which can occur in core-collapse supernovae or double neutron star mergers. There exist nine r-process giant stars exhibiting conspicuousUabundances, commonly referred to as U-rich giants. However, the origins of these U-rich giant

Jaewoong Cho, Kartik Sreenivasan, Keon Lee, Kyunghoo Mun

Contrastive learning has gained significant attention as a method for self-supervised learning. The contrastive loss function ensures that embeddings of positive sample pairs (e.g., different samples from the same class or different views of the same object) are similar, while embeddings of negative pairs are dissimilar. Practical constraints such as large m

Cecily Sahai. C, Sampath Kumar. S, Arputha Jose. T

Let $C_{k}$ (resp. $P_{k}$) denote the cycle (resp. path) of length $k$. In this paper, we examine the necessary and sufficient conditions for the existence of a $(8; p, q)$-decomposition of tensor product and wreath product of complete graphs.

Xinru Han, Hailang Qin, Tianluo Pan, Bin Guo

Superconducting pairing symmetry are crucial in understanding the microscopic superconducting mechanism of a superconductor. Here we report the observation of a twofold superconducting gap symmetry in an interfacial superconductor Bi$_{2}$Te$_{3}$/FeTe, by employing quasiparticle interference (QPI) technique in scanning tunneling microscopy and macroscopic m

Yu Han, Zhongyan Hu, Kaige Wang, Wei Zhao

Electroosmotic flow (EOF) is a ubiquitous phenomenon at the solid-liquid interface when an external electric field is applied. Despite its prevalence, the characteristics and mechanisms of EOF driven by an alternating current (AC) electric field, particularly within complex chemical environments, have remained insufficiently understood, owing primarily to a

Anton Xue, Rajeev Alur, Eric Wong

Explanation methods for machine learning models tend not to provide any formal guarantees and may not reflect the underlying decision-making process. In this work, we analyze stability as a property for reliable feature attribution methods. We prove that relaxed variants of stability are guaranteed if the model is sufficiently Lipschitz with respect to the m

WeiQin Chuah, Ruwan Tennakoon, Reza Hoseinnezhad, David Suter

Deep learning techniques often perform poorly in the presence of domain shift, where the test data follows a different distribution than the training data. The most practically desirable approach to address this issue is Single Domain Generalization (S-DG), which aims to train robust models using data from a single source. Prior work on S-DG has primarily fo

Ben S. Southworth, Thomas A. Manteuffel

The standard goal for an effective algebraic multigrid (AMG) algorithm is to develop relaxation and coarse-grid correction schemes that attenuate complementary error modes. In the nonsymmetric setting, coarse-grid correction $\Pi$ will almost certainly be nonorthogonal (and divergent) in any known standard product, meaning $\|\Pi\| > 1$. This introduces a ne

Yipeng Leng, Qiangjuan Huang, Zhiyuan Wang, Yangyang Liu

Diffusion probabilistic models (DPMs) have shown remarkable results on various image synthesis tasks such as text-to-image generation and image inpainting. However, compared to other generative methods like VAEs and GANs, DPMs lack a low-dimensional, interpretable, and well-decoupled latent code. Recently, diffusion autoencoders (Diff-AE) were proposed to ex

Beilei Cui, Minqing Zhang, Mengya Xu, An Wang

Noisy label problems are inevitably in existence within medical image segmentation causing severe performance degradation. Previous segmentation methods for noisy label problems only utilize a single image while the potential of leveraging the correlation between images has been overlooked. Especially for video segmentation, adjacent frames contain rich cont

O-joung Kwon, Xiaopan Lian

In this paper, we consider a variant of dichromatic number on digraphs with prescribed sets of arcs. Let $D$ be a digraph and let $Z_1, Z_2$ be two sets of arcs in $D$. For a subdigraph $H$ of $D$, let $A(H)$ denote the set of all arcs of $H$. Let $\mu(D, Z_1, Z_2)$ be the minimum number of parts in a vertex partition $\mathcal{P}$ of $D$ such that for every

Kien X. Nguyen, Liying Zheng, Ashley L. Hawke, Robert E. Carey

It is necessary to analyze the whole-body kinematics (including joint locations and joint angles) to assess risks of fatal and musculoskeletal injuries in occupational tasks. Human pose estimation has gotten more attention in recent years as a method to minimize the errors in determining joint locations. However, the joint angles are not often estimated, nor

Li-Tong Deng, Yong-Xiong Li

Let $F$ be a real quadratic number field with discriminant $D$ and $\mathcal{O}_F$ the ring of integers in $F$. Let $\chi_F$ be the Dirichlet character associated to $F/\mathbb{Q}$. Write $L(\chi_F,s)$ for the Dirichlet L-function of $\chi_F$. By an induction argument for imprimitive Dirichlet L-values, we get several $2$-divisibility results on $L(\chi_F,-1

Joshua Zahl

We consider the $L^p$ mapping properties of maximal averages associated to families of curves, and thickened curves, in the plane. These include the (planar) Kakeya maximal function, the circular maximal functions of Wolff and Bourgain, and their multi-parameter analogues. We propose a framework that allows for a unified study of such maximal functions, and

Elizabeth Z. C. Tan, Caroline Chaux, Emmanuel Soubies, Vincent Y. F. Tan

We design algorithms for Robust Principal Component Analysis (RPCA) which consists in decomposing a matrix into the sum of a low rank matrix and a sparse matrix. We propose a deep unrolled algorithm based on an accelerated alternating projection algorithm which aims to solve RPCA in its nonconvex form. The proposed procedure combines benefits of deep neural

Shi-Sheng Huang, Zi-Xin Zou, Yi-Chi Zhang, Hua Huang

The recent neural surface reconstruction by volume rendering approaches have made much progress by achieving impressive surface reconstruction quality, but are still limited to dense and highly accurate posed views. To overcome such drawbacks, this paper pays special attention on the consistent surface reconstruction from sparse views with noisy camera poses

Ian Char, Jeff Schneider

Deep reinforcement learning (RL) has shown immense potential for learning to control systems through data alone. However, one challenge deep RL faces is that the full state of the system is often not observable. When this is the case, the policy needs to leverage the history of observations to infer the current state. At the same time, differences between th

Chenglong Ma, Zilong Li, Junping Zhang, Yi Zhang

Sparse-view computed tomography (CT) is a promising solution for expediting the scanning process and mitigating radiation exposure to patients, the reconstructed images, however, contain severe streak artifacts, compromising subsequent screening and diagnosis. Recently, deep learning-based image post-processing methods along with their dual-domain counterpar

Hao Wang, Jiatai Lin, Danyi Li, Jing Wang

Mitosis detection is one of the fundamental tasks in computational pathology, which is extremely challenging due to the heterogeneity of mitotic cell. Most of the current studies solve the heterogeneity in the technical aspect by increasing the model complexity. However, lacking consideration of the biological knowledge and the complex model design may lead

Ziru Zhang, Xuling Zhang, Guangzhi Zhu, Yuyang Wang

In the era of Internet of Things (IoT), Digital Twin (DT) is envisioned to empower various areas as a bridge between physical objects and the digital world. Through virtualization and simulation techniques, multiple functions can be achieved by leveraging computing resources. In this process, Mobile Cloud Computing (MCC) and Mobile Edge Computing (MEC) have

Ewan Cameron

Guided by the principles of differential privacy protection the Australian Bureau of Statistics modifies the data summaries from the Australian Census provided through TableBuilder to researchers at approved institutions. This modification algorithm includes the injection of a small degree of artificial noise to every nonzero cell count followed by the suppr

Lai Wei, Qin Li, Sadman Sadeed Omee, Jianjun Hu

Crystal structure prediction (CSP) is now increasingly used in the discovery of novel materials with applications in diverse industries. However, despite decades of developments, the problem is far from being solved. With the progress of deep learning, search algorithms, and surrogate energy models, there is a great opportunity for breakthroughs in this area

Junyi Xie

There are three aims of this note. The first one is to report some advances around the dynamical Mordell-Lang (=DML) conjecture. Second, we generalize some known results. For example, the Dynamical Mordell-lang conjecture was known for endomorphisms of $\mathbb{A}^2$ over $\overline{\mathbb{Q}}$. We generalize this result to all endomorphisms of $\mathbb{A}^

Daning Huang, Muhammad Bayu Prasetyo, Yin Yu, Junyi Geng

The accurate modeling and control of nonlinear dynamical effects are crucial for numerous robotic systems. The Koopman formalism emerges as a valuable tool for linear control design in nonlinear systems within unknown environments. However, it still remains a challenging task to learn the Koopman operator with control from data, and in particular, the simult

Chen Zhao

Given a proper holomorphic surjective morphism $f:X\rightarrow Y$ from a compact K\"ahler manifold to a compact K\"ahler manifold, and a Nakano semipositive holomorphic vector bundle $E$ on $X$, we prove Koll\'ar type vanishing theorems on cohomologies with coefficients in $R^qf_\ast(\omega_X(E))\otimes F$, where $F$ is a $k$-positive vector bundle on $Y$. T

Hao Yang, Nan Cheng, Ruijin Sun, Wei Quan

This paper proposes an novel knowledge-driven approach for resource allocation in device-to-device (D2D) networks using a graph neural network (GNN) architecture. To meet the millisecond-level timeliness and scalability required for the dynamic network environment, our proposed approach incorporates the deep unrolling of the weighted minimum mean square erro

Lang Zeng, Jipeng Zhang, Wei Chen, Ying Ding

The aim of dynamic prediction is to provide individualized risk predictions over time, which are updated as new data become available. In pursuit of constructing a dynamic prediction model for a progressive eye disorder, age-related macular degeneration (AMD), we propose a time-dependent Cox survival neural network (tdCoxSNN) to predict its progression using

Gopika K., Shantanu Desai

Self-interacting dark matter (SIDM) has been proposed as an alternative to the standard collisionless cold dark matter to explain the diversity of galactic rotation curves and core-cusp problems seen at small scales. Here, we estimate the constraints on SIDM for a sample of 11 relaxed galaxy groups with X-ray observations from Chandra and XMM-Newton. We fit

C. A. S. Almeida, F. C. E. Lima

Adopting the premise that the expected value of the quantum fluctuating metric is linear, i.e., $\langle g^{\mu\nu}\rangle=\alpha g^{\mu\nu}$, we analyze the modified gravity theory induced by the Einstein-Hilbert action coupled to a matter field. This approach engenders the $f(R,T)$ gravity used to investigate the braneworld. In this scenario, considering a

Vladimir V Kryzhniy

This paper presents a novel approach to construct regularizing operators for severely ill-posed Fredholm integral equations of the first kind by introducing parametrized discretization. The optimal values of discretization and regularization parameters are computed simultaneously by solving a minimization problem formulated based on a regularization paramete

Martha Allen, Michael Filaseta

Let $u_{2j}$ be the product of the odd positive integers $< 2j$. For $n$ an integer $\ge 1$, define \[ f(x)=\sum_{j=0}^{n}a_j\frac{x^{2j}}{u_{2j+2}}, \] where the $a_j$'s are arbitrary integers with $|a_0|=1$. In 1929, I. Schur established a general theorem about the factorization of $f(x)$ in the case that $|a_{n}| = 1$. We establish a more general result i

Luz B. Valenzuela-Narvaez, Wladimir A. Carlosviza-Amanqui, Fred Torres-Cruz

DataProAnalytica is a powerful application for analyzing vaccination data in health care professionals. Through visualizations and multiple correspondence analysis, it uncovers meaningful relationships between variables and categories. The results provide valuable information for improving vaccination strategies. While there are limitations, the potential of

Noah Kravitz, Mitchell Lee

A conjecture of Pasteczka, generalizing the classical Hermite--Hadamard Inequality, states that if $\Omega \subseteq \mathbb{R}^d$ is a compact convex domain such that $\Omega$ and $\partial \Omega$ have the same center of mass, then for every convex function $f: \Omega \to \mathbb{R}^d$, the average value of $f$ on $\Omega$ is less than or equal to the aver

Hiroshi Fukui, Taiki Miyagawa, Yusuke Morishita

We propose a conceptually simple and thus fast multi-object tracking (MOT) model that does not require any attached modules, such as the Kalman filter, Hungarian algorithm, transformer blocks, or graph networks. Conventional MOT models are built upon the multi-step modules listed above, and thus the computational cost is high. Our proposed end-to-end MOT mod

Zichao Dong, Hang Ji, Weikun Zhang, Xufeng Huang

Occupancy prediction tasks focus on the inference of both geometry and semantic labels for each voxel, which is an important perception mission. However, it is still a semantic segmentation task without distinguishing various instances. Further, although some existing works, such as Open-Vocabulary Occupancy (OVO), have already solved the problem of open voc

Véronique Brousseau-Couture, Xavier Gonze, Michel Côté

The electronic structure of semiconductors and insulators is affected by ionic motion through electron-phonon interaction, yielding temperature-dependent band gap energies and zero-point renormalization (ZPR) at absolute zero temperature. For polar materials, the most significant contribution to the band gap ZPR can be understood in terms of the Fr\"ohlich m

Wei Zhang

Polar codes have attracted the attention of numerous researchers in the past decade due to their excellent performance. However, their performance at short block lengths under standard successive cancellation decoding is far from desirable. An effective method to improve the performance at short lengths is CRC precoding followed by successive-cancellation li

Fiona Draxler, Henrike Weingärtner, Maximiliane Windl, Albrecht Schmidt

Captions provide language learners with a scaffold for comprehension and vocabulary acquisition. Past work has proposed several enhancements such as keyword highlights for increased learning gains. However, little is known about learners' experience with enhanced captions, although this is critical for adoption in everyday life. We conducted a survey and foc

Hui Wei, Weihua Miao, Fushun Li

Memory is an important cognitive function for humans. How a brain with such a small power can complete such a complex memory function, the working mechanism behind this is undoubtedly fascinating. Engram theory views memory as the co-activation of specific neuronal clusters. From the perspective of graph theory, nodes represent neurons, and directed edges re

J. Talukdar, D. Blume

We consider an array of $N_e$ non-interacting qubits or emitters that are coupled to a one-dimensional cavity array with tunneling energy $J$ and non-linearity of strength $U$. The number of cavities is assumed to be larger than the number of qubits. Working in the two-excitation manifold, we focus on the bandgap regime where the energy of two excited qubits

H. Hasegawa, M. R. Argall, N. Aunai, R. Bandyopadhyay

There is ample evidence for magnetic reconnection in the solar system, but it is a nontrivial task to visualize, to determine the proper approaches and frames to study, and in turn to elucidate the physical processes at work in reconnection regions from in-situ measurements of plasma particles and electromagnetic fields. Here an overview is given of a variet

Andrei K. Svinin

The main object of study in this paper is the well-known Somos-4 recurrence. We prove a theorem that any sequence generated by this equation also satisfies Gale-Robinson one. The corresponding identity is written in terms of its companion elliptic sequence. An example of such relationship is provided by the second-order linear sequence which, as we prove usi

Akitaka Matsumura, Kenji Nishihara

We consider the Cauchy problem for one-dimensional p-system with damping of space-dependent coefficient. This system models the compressible flow through porous media in the Lagrangean coordinate. Our concern is an asymptotic behavior of solutions, which is expected to be the diffusion wave based on the Darcy law. To show this expectation, the problem is ref

Shuwei Zhou, Xiaoying Zhuang

This paper proposes a phase field model (PFM) for describing hydraulic fracture propagation in transversely isotopic media. The coupling between the fluid flow and displacement fields is established according to the classical Biot poroelasticity theory while the phase field model characterizes the fracture behavior. The proposed method uses a transversely is

Milo Bechtloff Weising

We construct and study an explicit simultaneous $\mathscr{Y}$-eigenbasis of Ion and Wu's standard representation of the $^+$stable-limit double affine Hecke algebra for the limit Cherednik operators $\mathscr{Y}_i$. This basis arises as a generalization of Cherednik's non-symmetric Macdonald polynomials of type $GL$. We utilize links between $^+$stable-limit

Omar A. Cruz, Gustavo Ortega, Carlos Segovia

We present the unoriented versions of the Schur and Bogomolov multipliers associated with a finite group $G$. We show that the unoriented Schur multiplier is isomorphic to the second cohomology group $H^2(G;\ZZ_2)$. We define the unoriented Bogomolov multiplier as the quotient of the unoriented Schur multiplier by the subgroup generated by classes over the d

Connor Toups, Rishi Bommasani, Kathleen A. Creel, Sarah H. Bana

Machine learning is traditionally studied at the model level: researchers measure and improve the accuracy, robustness, bias, efficiency, and other dimensions of specific models. In practice, the societal impact of machine learning is determined by the surrounding context of machine learning deployments. To capture this, we introduce ecosystem-level analysis

Lixi Zhou, K. Selçuk Candan, Jia Zou

Storing tabular data to balance storage and query efficiency is a long-standing research question in the database community. In this work, we argue and show that a novel DeepMapping abstraction, which relies on the impressive memorization capabilities of deep neural networks, can provide better storage cost, better latency, and better run-time memory footpri

Xiaoying Zhuang, Shuwei Zhou, Mao Sheng, Gensheng Li

In the hydraulic fracturing of natural rocks, understanding and predicting crack penetrations into the neighboring layers is crucial and relevant in terms of cost-efficiency in engineering and environmental protection. This study constitutes a phase field framework to examine hydraulic fracture propagation in naturally-layered porous media. Biot's poroelasti

Quentin Henry, François Larrouturou

This work deals with the tail and ``failed'' tail sectors of the conservative dynamics for compact binary systems at the 5PN order. We employ the Fokker Lagrangian method with dimensional regularization, and our results for the tail sector are perfectly consistent with the previous EFT computations. As for the ``failed'' tail sector, we have good hopes that

Gene Freudenburg, Takanori Nagamine

This paper studies the class of unique factorial domains $B$ over an algebraically closed field $k$ which are affine or unirational over $k$ and which admit an effective unmixed $\mathbb{Z}^{d-1}$-grading with $B_0=k$, where $d$ is the dimension of $B$. Geometrically, these correspond to factorial affine $k$-varieties with an unmixed torus action of complexi

Chi Zhang, Phelan Yu, Arian Jadbabaie, Nicholas R. Hutzler

We propose a method to measure time-reversal symmetry violation in molecules that overcomes the standard quantum limit while leveraging decoherence-free subspaces to mitigate sensitivity to classical noise. The protocol does not require an external electric field, and the entangled states have no first-order sensitivity to static electromagnetic fields as th

Tianyu Zhao, Mojtaba Taherisadr, Salma Elmalaki

Achieving fairness in sequential-decision making systems within Human-in-the-Loop (HITL) environments is a critical concern, especially when multiple humans with different behavior and expectations are affected by the same adaptation decisions in the system. This human variability factor adds more complexity since policies deemed fair at one point in time ma

Ondřej Čertík, John E. Pask, Isuru Fernando, Rohit Goswami

We introduce \texttt{featom}, an open source code that implements a high-order finite element solver for the radial Schr\"odinger, Dirac, and Kohn-Sham equations. The formulation accommodates various mesh types, such as uniform or exponential, and the convergence can be systematically controlled by increasing the number and/or polynomial order of the finite

André L. Marchildon, David W. Zingg

Gaussian processes provide probabilistic surrogates for various applications including classification, uncertainty quantification, and optimization. Using a gradient-enhanced covariance matrix can be beneficial since it provides a more accurate surrogate relative to its gradient-free counterpart. An acute problem for Gaussian processes, particularly those th

Matheus Guedes de Andrade, Jake Navas, Inès Montaño, Don Towsley

The experimental realization of quantum information systems will be difficult due to how sensitive quantum information is to noise. Overcoming this sensitivity is central to designing quantum networks capable of transmitting quantum information reliably over large distances. Moreover, the ability to characterize communication noise in quantum networks is cru

Bruce X. B. Yu, Zhi Zhang, Yongxu Liu, Sheng-hua Zhong

3D human pose estimation has been researched for decades with promising fruits. 3D human pose lifting is one of the promising research directions toward the task where both estimated pose and ground truth pose data are used for training. Existing pose lifting works mainly focus on improving the performance of estimated pose, but they usually underperform whe

Junyao Li, Ming-Yang Zhuang, Yue Shen

The galaxy CID-42 (CXOC J100043.1+020637.2) at z=0.359 has been proposed to contain a promising candidate for a gravitational wave (GW) recoiling supermassive black hole (SMBH), a slingshot SMBH from a triple-SMBH interaction, or a kpc-scale dual Active Galactic Nuclei (AGN). These claims were primarily based on a pair of bright cores separated by $\sim 0.''

Joao P. A. Dantas, Samara R. Silva, Vitor C. F. Gomes, Andre N. Costa

AsaPy is a custom-made Python library designed to simplify and optimize the analysis of aerospace simulation data. Instead of introducing new methodologies, it excels in combining various established techniques, creating a unified, specialized platform. It offers a range of features, including the design of experiment methods, statistical analysis techniques

Fernando Vega, Abdoljalil Addeh, M. Ethan MacDonald

In this work, a denoising Cycle-GAN (Cycle Consistent Generative Adversarial Network) is implemented to yield high-field, high resolution, high signal-to-noise ratio (SNR) Magnetic Resonance Imaging (MRI) images from simulated low-field, low resolution, low SNR MRI images. Resampling and additive Rician noise were used to simulate low-field MRI. Images were

Yan Wen, Chen Gao, Lingling Yi, Liwei Qiu

Automated Machine Learning (AutoML) techniques have recently been introduced to design Collaborative Filtering (CF) models in a data-specific manner. However, existing works either search architectures or hyperparameters while ignoring the fact they are intrinsically related and should be considered together. This motivates us to consider a joint hyperparame

Xilin Liang

There have been numerous attempts, in the last decades, to understand the origin of the unexpectedly large transverse single-spin asymmetry ($A_{N}$) observed in inclusive hadron productions at forward rapidities in transversely polarized $p^{\uparrow}$+$p$ collisions at different center-of-mass energies ($\sqrt{s}$). The current theoretical frameworks aimed

Andressa Paola Cordeiro, Alexandre Tavares Baraviera, Alex Jenaro Becker

We study Markov tree-shifts given by $k$ transition matrices, one for each of its $k$ directions. We provide a method to characterize the complexity function for these tree-shifts, used to calculate the tree entropies defined by Ban and Chang arXiv:1509.08325 and Petersen and Salama arXiv:1712.02251. Moreover, we compare these definitions of entropy in order

Kankana Paul, Saibal Roy, Andreas Amann

A wideband vibration energy harvester with multiple nonlinear forces is investigated. The nonlinearities are due to repulsive magnets and hardening springs, which gives rise to multistabilities between a number of energy branches. Not all branches are accessible by a simple up or down sweep of the driving frequency and in particular the highest energy branch

Jake Richter, Bao-An Li

Given the significant advancement in Bayesian inference of nuclear Equation of State (EOS) from gravitational wave and X-ray observations of neutron stars (NSs), especially since GW170817, is there a data-driven and robust empirical formula for the radius $R_{1.4}$ of canonical NSs in terms of the characteristic EOS parameters (features)? What is the single

Ping Chen, Tusheng Zhang

In this paper, we establish a large deviation principle for the conservative stochastic partial differential equations, whose solutions are related to stochastic differential equations with interaction. The weak convergence method and the contraction principle in the theory of large deviations play an important role.

Sam Allen, Johanna Ziegel, David Ginsbourger

Rank and PIT histograms are established tools to assess the calibration of probabilistic forecasts. They not only check whether an ensemble forecast is calibrated, but they also reveal what systematic biases (if any) are present in the forecasts. Several extensions of rank histograms have been proposed to evaluate the calibration of probabilistic forecasts f

Lukas Haas, Michal Skreta, Silas Alberti, Chelsea Finn

Planet-scale image geolocalization remains a challenging problem due to the diversity of images originating from anywhere in the world. Although approaches based on vision transformers have made significant progress in geolocalization accuracy, success in prior literature is constrained to narrow distributions of images of landmarks, and performance has not

Deepen Garg, I. Y. Dodin

This paper explores the hybridization of linear metric perturbations with linear electromagnetic (EM) perturbations in non-magnetized plasma for a general background metric. The local wave properties are derived from first principles for inhomogeneous plasma, without assuming any symmetries of the background metric. First, we derive the effective (``oscillat

Rich Ryan

Workers separate from jobs, search for jobs, accept jobs, and fund consumption with their wages. Firms recruit workers to fill vacancies. Search frictions prevent firms from instantly hiring available workers. Unemployment persists. These features are described by the Diamond-Mortensen-Pissarides modeling framework. In this class of models, how unemployment

Emilio Ferrara

The Butterfly Effect, a concept originating from chaos theory, underscores how small changes can have significant and unpredictable impacts on complex systems. In the context of AI fairness and bias, the Butterfly Effect can stem from a variety of sources, such as small biases or skewed data inputs during algorithm development, saddle points in training, or

Yujie Zeng, Yiming Huang, Qiang Wu, Linyuan Lü

Simplicial complexes have recently been in the limelight of higher-order network analysis, where a minority of simplices play crucial roles in structures and functions due to network heterogeneity. We find a significant inconsistency between identifying influential nodes and simplices. Therefore, it remains elusive how to characterize simplices' influence an

Yanir Marmor, Alex Abbey, Yuval Shahar, Osnat Mokryn

Interaction-driven modeling of diseases over real-world contact data has been shown to promote the understanding of the spread of diseases in communities. This temporal modeling follows the path-preserving order and timing of the contacts, which are essential for accurate modeling. Yet, other important aspects were overlooked. Various airborne pathogens diff

Ketan R. Sand, Daniela Breitman, Daniele Michilli, Victoria M. Kaspi

FRB 20180916B is a repeating Fast Radio Burst (FRB) with a 16.3-day periodicity in its activity. In this study, we present morphological properties of 60 FRB 20180916B bursts detected by CHIME/FRB between 2018 August and 2021 December. We recorded raw voltage data for 45 of these bursts, enabling microseconds time resolution in some cases. We studied variati

Silke Britzen, Michal Zajaček, Gopal-Krishna, Christian Fendt

The combined study of the flaring of Active Galactic Nuclei (AGN) at radio wavelengths and pc-scale jet kinematics with Very Long Baseline Interferometry (VLBI) has led to the view that i) the observed flares are associated with ejections of synchrotron blobs from the core, and ii) most of the flaring would follow a one-to-one correlation with the component

Dan Gorbonos, Nir S. Gov, Iain D. Couzin

Animals must constantly make decisions on the move, such as when choosing among multiple options, or "targets", in space. Recent evidence suggests that this results from a recursive feedback between the (vectorial) neural representation of the targets and the resulting motion defined by this consensus, which then changes the egocentric neural representation

David G. Russell

The sample of exoplanets from the NASA Exoplanet Archive with equilibrium temperature less than 600 K and with low uncertainty for both mass and radius measurements is found to have a desert in the mass-radius distribution consistent with predictions from the core-accretion scenario. This sub-Saturn mass-radius desert is almost completely barren of any plane

Gonzalo Jiménez

We prove the Forking Path Conjecture for all but one element in the symmetric group $S_4$. Two specific paths in the rex graph of that element give a counterexample for the conjecture. We propose a refined conjecture for the longest element of any $S_n$.

Sanae Amani, Khushbu Pahwa, Vladimir Braverman, Lin F. Yang

Recently, DARPA launched the ShELL program, which aims to explore how experience sharing can benefit distributed lifelong learning agents in adapting to new challenges. In this paper, we address this issue by conducting both theoretical and empirical research on distributed multi-task reinforcement learning (RL), where a group of $N$ agents collaboratively s

George D McDonald, Lujendra Ojha

Haumea, thought to be the Kuiper Belt's 3rd most massive object, has a fast 3.92 hr rotational period, resulting in its shape as a triaxial ellipsoid. Here, we make the first detailed predictions of Haumea's surface morphology, considering in particular effects stemming from its unique shape. Given observations have indicated Haumea's surface to be predomina

Sara Hatami Gazani, Matthew Tucsok, Iraj Mantegh, Homayoun Najjaran

UAV-based intelligent data acquisition for 3D reconstruction and monitoring of infrastructure has experienced an increasing surge of interest due to recent advancements in image processing and deep learning-based techniques. View planning is an essential part of this task that dictates the information capture strategy and heavily impacts the quality of the 3

Isha Garg, Deepak Ravikumar, Kaushik Roy

Deep neural networks are over-parameterized and easily overfit the datasets they train on. In the extreme case, it has been shown that these networks can memorize a training set with fully randomized labels. We propose using the curvature of loss function around each training sample, averaged over training epochs, as a measure of memorization of the sample.

Eric Easthope

I present "SnakeSynth," a web-based lightweight audio synthesizer that combines audio generated by a deep generative model and real-time continuous two-dimensional (2D) input to create and control variable-length generative sounds through 2D interaction gestures. Interaction gestures are touch and mobile-compatible with analogies to strummed, bowed, and pluc

Amirali Madani, Anil Maheshwari

We study the problem of distance-preserving graph compression for weighted paths and trees. The problem entails a weighted graph $G = (V, E)$ with non-negative weights, and a subset of edges $E^{\prime} \subset E$ which needs to be removed from G (with their endpoints merged as a supernode). The goal is to redistribute the weights of the deleted edges in a w

Ajaykrishnan Nageswaran, Prakash Narayan

For a given function of user data, a querier must recover with at least a prescribed probability, the value of the function based on a user-provided query response. Subject to this requirement, the user forms the query response so as to minimize the likelihood of the querier guessing a list of prescribed size to which the data value belongs based on the quer

Guocheng Feng, Huaiyu Cai, Wei Quan

The COVID-19 pandemic has led to the emergence of Long COVID, a cluster of symptoms that persist after infection. Long COVID patients may also experience mental health challenges, making it essential to understand individuals' emotional and mental well-being. This study aims to gain a deeper understanding of Long COVID individuals' emotional and mental well-

Arif Shahriar, Rohan Saha, Denilson Barbosa

Relation extraction (RE) is the task of extracting relations between entities in text. Most RE methods extract relations from free-form running text and leave out other rich data sources, such as tables. We explore RE from the perspective of applying neural methods on tabularly organized data. We introduce a new model consisting of Convolutional Neural Netwo

Juan Sebastián Cañas, Maria Paula Toro-Gómez, Larissa Sayuri Moreira Sugai, Hernán Darío Benítez Restrepo

Global change is predicted to induce shifts in anuran acoustic behavior, which can be studied through passive acoustic monitoring (PAM). Understanding changes in calling behavior requires the identification of anuran species, which is challenging due to the particular characteristics of neotropical soundscapes. In this paper, we introduce a large-scale multi

Raul Isea

The present work shows that it is possible to analytically solve a general model to explain the transmission dynamics of SARS-CoV-2. First, the within-host model is described, and later a between-host model, where the coupling between them is the viral load of SARS-CoV-2. The within-host model describes the equations involved in the life cycle of SARS-CoV-2,

G. M. Merz, Y. Liu, C. J. Burke, P. D. Aleo

The next generation of wide-field deep astronomical surveys will deliver unprecedented amounts of images through the 2020s and beyond. As both the sensitivity and depth of observations increase, more blended sources will be detected. This reality can lead to measurement biases that contaminate key astronomical inferences. We implement new deep learning model

José E. Chacón, Javier Fernández Serrano

The number of modes in a probability density function is representative of the complexity of a model and can also be viewed as the number of subpopulations. Despite its relevance, there has been limited research in this area. A novel approach to estimating the number of modes in the univariate setting is presented, focusing on prediction accuracy and inspire

C. I. Kvande, D. B. Hill, D. Blume

The Hamiltonian for the one-dimensional SSH chain is one of the simplest Hamiltonians that supports topological states. This work considers between one and three finite SSH chains with open boundary conditions that either share a lattice site (or cavity), which -- in turn -- is coupled to a two-level emitter, or are coupled to the same two-level emitter. We

Suyash Verma, Muhammad Saif Ullah Khalid, Arman Hemmati

The correspondence of secondary spanwise structures and pressure gradient is numerically evaluated for a foil, performing heaving and pitching motion, at a range of phase offsets (90$^\circ$ $\le \phi \le$ 270$^\circ$) and reduced frequency (0.32 $\le St_c \le$ 0.56). The Reynolds number is $Re =$ 8000. The wake is shown to be dominated by secondary hairpin-