May 2023 arXiv papers — page 132

Xinyu Gong, Sreyas Mohan, Naina Dhingra, Jean-Charles Bazin

In this paper, we study a novel problem in egocentric action recognition, which we term as "Multimodal Generalization" (MMG). MMG aims to study how systems can generalize when data from certain modalities is limited or even completely missing. We thoroughly investigate MMG in the context of standard supervised action recognition and the more challenging few-

Quanxue Gao, Qianqian Wang, Han Lu, Wei Xia

Although numerous clustering algorithms have been developed, many existing methods still leverage k-means technique to detect clusters of data points. However, the performance of k-means heavily depends on the estimation of centers of clusters, which is very difficult to achieve an optimal solution. Another major drawback is that it is sensitive to noise and

Chaopeng Tan, Kaidi Yang

Although Connected Vehicles (CVs) have demonstrated tremendous potential to enhance traffic operations, they can impose privacy risks on individual travelers, e.g., leaking sensitive information about their frequently visited places, routing behavior, etc. Despite the large body of literature that devises various algorithms to exploit CV information, researc

Caihao Qiu, Marco Salvalaglio, David J. Srolovitz, Jian Han

Interface migration in microstructures is mediated by the motion of line defects with step and dislocation character, i.e., disconnections. We propose a continuum model for arbitrarily-curved grain boundaries or heterophase interfaces accounting for disconnections' role in grain rotation. Numerical simulations show that their densities evolve as grain size a

Fan Wu, Jian-shen Hu, Lianyi He

In this work, the dressed molecules theory is used to describe the two-dimensional quantum anomaly of breathing mode in the recent experimental system\cite{Holten2018,Peppler2018}. With the aid of a beyond mean-field, Gaussian pair fluctuation theory, we employ the dressed molecules states to characterize the axial excited states and the Feshbach molecular s

Chongzhou Fang, Ning Miao, Han Wang, Jiacheng Zhou

In recent decades, due to the emerging requirements of computation acceleration, cloud FPGAs have become popular in public clouds. Major cloud service providers, e.g. AWS and Microsoft Azure have provided FPGA computing resources in their infrastructure and have enabled users to design and deploy their own accelerators on these FPGAs. Multi-tenancy FPGAs, wh

Cory McCartan, Tyler Simko, Kosuke Imai

The U.S. Census Bureau collects and publishes detailed demographic data about Americans which are heavily used by researchers and policymakers. The Bureau has recently adopted the framework of differential privacy in an effort to improve confidentiality of individual census responses. A key output of this privacy protection system is the Noisy Measurement Fi

Eray Güven, Olfa Ben Yahia, Güneş Karabulut Kurt

The advancement in satellite networks (SatNets) offers vast opportunities for large-scale connectivity and flexibility. The intersection of inter-satellite communication (ISC) and the developments in rate-hungry enhanced mobile broadband (eMBB) services have resulted in potential ultra-dense deployments such as mega-constellations. The integration of inter-s

Zhao Hu, Yunge Xu, Nian Li, Xiangyong Zeng

In this paper, we construct a large family of projective linear codes over ${\mathbb F}_{q}$ from the general simplicial complexes of ${\mathbb F}_{q}^m$ via the defining-set construction, which generalizes the results of [IEEE Trans. Inf. Theory 66(11):6762-6773, 2020]. The parameters and weight distribution of this class of codes are completely determined.

Chuheng Zhang, Yitong Duan, Xiaoyu Chen, Jianyu Chen

Optimized trade execution is to sell (or buy) a given amount of assets in a given time with the lowest possible trading cost. Recently, reinforcement learning (RL) has been applied to optimized trade execution to learn smarter policies from market data. However, we find that many existing RL methods exhibit considerable overfitting which prevents them from r

Gopi Krishna Jha, Anthony Thomas, Nilesh Jain, Sameh Gobriel

Deep learning-based recommendation systems (e.g., DLRMs) are widely used AI models to provide high-quality personalized recommendations. Training data used for modern recommendation systems commonly includes categorical features taking on tens-of-millions of possible distinct values. These categorical tokens are typically assigned learned vector representati

Zhichao Wang, Liumeng Xue, Qiuqiang Kong, Lei Xie

Zero-shot voice conversion (VC) converts source speech into the voice of any desired speaker using only one utterance of the speaker without requiring additional model updates. Typical methods use a speaker representation from a pre-trained speaker verification (SV) model or learn speaker representation during VC training to achieve zero-shot VC. However, ex

Viktoriia Savchuk, Ruizheng Wang, Lyle Small, Anatoliy Pinchuk

Photothermal conversion efficiency ({\eta}) plays a crucial role in selecting suitable gold nanoparticles for photothermal therapeutic applications. The photothermal efficiency depends on the material used for the nanoparticles as well as their various parameters such as size and shape. By maximizing the light-to-heat conversion efficiency ({\eta}), one can

Long Chen, Yuchen Li, Chao Huang, Yang Xing

Interest in autonomous driving (AD) and intelligent vehicles (IVs) is growing at a rapid pace due to the convenience, safety, and economic benefits. Although a number of surveys have reviewed research achievements in this field, they are still limited in specific tasks and lack systematic summaries and research directions in the future. Our work is divided i

Shangkun Wang, Adam P. Generale, Surya R. Kalidindi, V. Roshan Joseph

Space-filling designs are commonly used in computer experiments to fill the space of inputs so that the input-output relationship can be accurately estimated. However, in certain applications such as inverse design or feature-based modeling, the aim is to fill the response or feature space. In this article, we propose a new experimental design framework that

Donatella Danielli, Alaa Haj Ali, Arshak Petrosyan

In this paper, we consider the obstacle problem for the fractional Laplace operator $(-\Delta)^s$ in the Euclidian space $\mathbb{R}^n$ in the case where $1<s<2$. As first observed in \cite{Y}, the problem can be extended to the upper half-space $\mathbb{R}_+^{n+1}$ to obtain a thin obstacle problem for the weighted biLaplace operator $\Delta^2_b U$, where $

Waseet Kazmi

The goal of this paper is to show the following result: For every integer $n\geq 2$ there is a countable orderable group such that its space of orders is countable and has Cantor-Bendixson rank $n$. We show this by explicitly constructing a family of orderable groups with the desired properties.

Jongwan Lee, Marissa McDonald, Nikiwe Mhlanga, Jeon Woong Kang

Dynabeads are superparamagnetic particles used for immunomagnetic purification of cells and biomolecules. Post-capture, however, target identification relies on tedious culturing, fluorescence staining and/or target amplification. Raman spectroscopy presents a rapid detection alternative, but current implementations target cells themselves with weak Raman si

Chandra Sekhar Sanaboina, Harish Bommidi

Nowadays, people use electricity in all aspects of their lives so that electricity consumption increases gradually. There can be wastage of electricity due to various reasons, such as human negligence, daylighting, etc. Hence, conservation of energy is the need of the day. This paper deals with the fabrication of an "Automated Power Conservation System (APCS

Hikaru Hoshino, T. John Koo, Yun-Chung Chu, Yoshihiko Susuki

Flexibility provided by Combined Heat and Power (CHP) units in district heating networks is an important means to cope with increasing penetration of intermittent renewable energy resources, and various methods have been proposed to exploit thermal storage tanks installed in these networks. This paper studies a novel problem motivated by an example of distri

Bjoern Bringmann, Sky Cao

We consider the stochastic Yang-Mills heat equation on the two-dimensional torus. Using regularity structures, Chandra, Chevyrev, Hairer, and Shen previously proved both the local well-posedness and gauge-covariance of this model. In this article, we revisit their results using para-controlled calculus. One of the main ingredients is a new coordinate-invaria

Naoya Ando, Masaaki Umehara

In this paper, we prove several fundamental properties on umbilics of a space-like or time-like surface in the Lorentz-Minkowski space $L^3$. In particular, we show that the local behavior of the curvature line flows of the germ of a space-like surface in $L^3$ is essentially the same as that of a surface in Euclidean space. As a consequence, for each positi

Hikaru Hoshino, Eiko Furutani

Nondepolarizing neuromuscular blocking drugs (NDNBs) are clinically used to produce muscle relaxation during general anesthesia. This paper explores a suitable model structure to simultaneously describe in vivo and in vitro effects of three clinically used NDNBs, cisatracurium, vecuronium, and rocuronium. In particular, it is discussed how to reconcile an ap

Holger F. Hofmann

Quantum mechanics describes the relation between different measurement contexts in terms of superpositions of the potential measurement outcomes. This relation between measurement contexts makes it impossible to determine context independent realities. Here, I illustrate the problem using three path interferences that implement the three box paradox and show

Aniket Anand, Michalis Kallitsis, Jackson Sippe, Alberto Dainotti

Aggressive network scanners, i.e., ones with immoderate and persistent behaviors, ubiquitously search the Internet to identify insecure and publicly accessible hosts. These scanners generally lie within two main categories; i) benign research-oriented probers; ii) nefarious actors that forage for vulnerable victims and host exploitation. However, the origins

Joanna Boyland, William Gasarch, Nathan Hurtig, Robert Rust

Ramsey's theorem states that for all finite colorings of an infinite set, there exists an infinite homogeneous subset. What if we seek a homogeneous subset that is also order-equivalent to the original set? Let $S$ be a linearly ordered set and $a \in N$. The big Ramsey degree of $a$ in $S$, denoted $T(a,S)$, is the least integer $t$ such that, for any finit

Xiaohu Zheng, Qiangqiang Gu, Yiyuan Liu1, Bingbing Tong

Fermi arcs on Weyl semimetals exhibit many exotic quantum phenomena. Usually considered on atomically-flat surfaces with approximate translation symmetry, Fermi arcs are rooted in peculiar topology of bulk Bloch bands of three-dimensional (3D) crystals. The fundamental question of whether a 1D Fermi arc can be probed remains unanswered. Such answer could sig

M. M. Hedman, I. Regan, T. Becker, S. M. Brooks

The zeta ring is the innermost component of the Uranian ring system. It is of scientific interest because its morphology changed significantly between the Voyager 2 encounter in 1986 and subsequent Earth-based observations around 2007. It is also of practical interest because some Uranus mission concepts have the spacecraft pass through the inner flank of th

Takehito Nakano, Shun Kontani, Masatoshi Hiraishi, Kaito Mita

We have performed a powder neutron diffraction study on CsO$_2$, where the unpaired electron with $s=1/2$ in the $\pi^*$ orbital of the O$_2^-$ ion is responsible for the magnetism. The magnetic reflections 0 $\frac{1}{2}$ 0 and 0 $\frac{1}{2}$ 1 were observed below the N\'{e}el temperature of about 10 K. An antiferromagnetic structure with a propagation vec

William R. Tavares, Sidney S. Avancini, Ricardo L. S. Farias

In this work we study the influence of external electric field and temperature on the chiral phase transition of Quantum Chromodynamics. We use the two-flavor Linear Sigma Model coupled with quarks (LSMq) in a thermal and electrized medium to evaluate the effective quark mass and the Schwinger pair production. To this end, we apply one-loop correction to the

Zhelong Jiang, Gang Chen, Ruixiu Qiao, Pengcheng Feng

The ground state search of the Ising model can be used to solve many combinatorial optimization problems. Under the current computer architecture, an Ising ground state search algorithm suitable for hardware computing is necessary for solving practical problems. Inspired by the potential energy conversion of springs, we propose a point convolutional neural n

Zhiwei Shan, Xinping Yi, Han Yu, Chung-Shou Liao

The state-of-the-art coding schemes for topological interference management (TIM) problems are usually handcrafted for specific families of network topologies, relying critically on experts' domain knowledge. This inevitably restricts the potential wider applications to wireless communication systems, due to the limited generalizability. This work makes the

Lili Yu, Dániel Simig, Colin Flaherty, Armen Aghajanyan

Autoregressive transformers are spectacular models for short sequences but scale poorly to long sequences such as high-resolution images, podcasts, code, or books. We proposed Megabyte, a multi-scale decoder architecture that enables end-to-end differentiable modeling of sequences of over one million bytes. Megabyte segments sequences into patches and uses a

Zhuoran Li, Zhen Gao, Tuan Li

This paper investigates the sensing of user's uplink channel and location in terahertz extra-large reconfigurable intelligent surface (XL-RIS) systems, where the unique hybrid far-near field effect and the beam squint effect caused by the XL array aperture as well as the XL bandwidth are overcome. Specifically, we first propose a joint channel and location s

Jeevan Chandra

We interpret appropriate families of Euclidean wormhole solutions of AdS$_3$ gravity in individual 2d CFTs as replica wormholes described by branching around the time-symmetric apparent horizons of black holes sourced by the backreaction of heavy point particles. These wormholes help describe a rich formalism to coarse grain pure states in 2d CFTs dual to th

Qingpeng Zhao, Yuanyang Zhu, Zichuan Liu, Zhi Wang

In cooperative multi-agent reinforcement learning (MARL), the environmental stochasticity and uncertainties will increase exponentially when the number of agents increases, which puts hard pressure on how to come up with a compact latent representation from partial observation for boosting value decomposition. To tackle these issues, we propose a simple yet

Zelalem Arega Worku, David W. Zingg

High-order Hadamard-form entropy stable multidimensional summation-by-parts discretizations of the Euler and compressible Navier-Stokes equations are considerably more expensive than the standard divergence-form discretization. In search of a more efficient entropy stable scheme, we extend the entropy-split method for implementation on unstructured grids and

Haiqi Liu, C. L. Philip Chen, Xinrong Gong, Tong Zhang

Recognizing novel sub-categories with scarce samples is an essential and challenging research topic in computer vision. Existing literature addresses this challenge by employing local-based representation approaches, which may not sufficiently facilitate meaningful object-specific semantic understanding, leading to a reliance on apparent background correlati

Amine Ouazad, Matthew E. Kahn

Climate change poses new risks for real estate assets. Given that the majority of home buyers use a loan to pay for their homes and the majority of these loans are purchased by the Government Sponsored Enterprises (GSEs), it is important to understand how rising natural disaster risk affects the mortgage finance market. The climate securitization hypothesis

Alexandr Polyanskii, Rinat Sadykov

The unraveled ball of radius $r$ centered at a vertex $v$ in a weighted graph $G$ is the ball of radius $r$ centered at $v$ in the universal cover of $G$. We present a general bound on the maximum spectral radius of unraveled balls of fixed radius in a weighted graph. The weighted degree of a vertex in a weighted graph is the sum of weights of edges incident

L. Elliott, A. Levine, J. D. Mitchell

In this short note, we show that the number of monogenic submonoids of the full transformation monoid of degree $n$ for $n > 0$, equals the sum of the number of cyclic subgroups of the symmetric groups on $1$ to $n$ points. We also prove an analogous statement for monogenic subsemigroups of the finite full transformation monoids, as well as monogenic inverse

Frank Neumann, Carsten Witt

Pareto optimization using evolutionary multi-objective algorithms has been widely applied to solve constrained submodular optimization problems. A crucial factor determining the runtime of the used evolutionary algorithms to obtain good approximations is the population size of the algorithms which grows with the number of trade-offs that the algorithms encou

Emerson de Melo, Jhone Caldeira

Let $A$ be a non-metacyclic finite group. Suppose that $A$ acts coprimely on a finite group $G$ in such a manner that $C_G(a)$ is nilpotent for any $a\in A^{\#}$. In the present paper we investigate some conditions on $A$ which imply that $G$ is nilpotent with ``bounded'' nilpotency class. More precisely, we generalize known results on action of $q$-groups a

Bhanu Prakash Voutharoja, Lei Wang, Luping Zhou

Automatic radiology report generation is challenging as medical images or reports are usually similar to each other due to the common content of anatomy. This makes a model hard to capture the uniqueness of individual images and is prone to producing undesired generic or mismatched reports. This situation calls for learning more discriminative features that

Valerio Bertacchi

We report the recent measurements performed using the data sample collected from 2019 to 2022 by the Belle~II experiment~\cite{Belle2:TDR} at $\Upsilon(4S)$ resonance, corresponding to an integrated luminosity of $362~\mathrm{fb}^{-1}$. We present the measurement of $B$ lifetime and mixing frequency, the time dependent CP-violation analyses using the $B^0\to

Yanqiu Wang, Chen Zhang, Z. Y. Chen, Bin Liang

Spatial symmetries appearing in both real and momentum space are of fundamental significance to crystals. However, in the conventional framework, every space group in real space, either symmorphic or nonsymmorphic, corresponds to a symmorphic dual in momentum space. Our experiment breaks the framework by showing that in a 2D acoustic crystal with chess-board

Kepler Domurat-Sousa, Cameron M. Poe, Maya S. McDaniel, Eric Spieglan

Two major challenges in time-of-flight positron emission tomography (TOF-PET) are low spatial resolution and high radioactive dose to the patient, both of which result from limitations in detection technology rather than fundamental physics. A new type of TOF-PET detector employing low-atomic number (low-Z) scintillation media and large-area, high-resolution

Akram Alishahi, Linh Truong, Melissa Zhang

We use involutive Heegaard Floer homology to extend the Ozsv\'ath-Szab\'o branched double cover spectral sequence relating a version of Khovanov homology and the Heegaard Floer homology of branched double covers. Our main tools are Lipshitz, Ozsv\'ath, and Thurston's reconstruction of the Ozsv\'ath-Szab\'o spectral sequence using bordered Floer homology and

Gabriel Khan

We study curve-shortening flow for twisted curves in $\mathbb{R}^3$ (i.e., curves with nowhere vanishing curvature $\kappa$ and torsion $\tau$) and define a notion of torsion-curvature entropy. Using this functional, we show that either the curve develops an inflection point or the eventual singularity is highly irregular (and likely impossible). In particul

Max W. Shen, Emmanuel Bengio, Ehsan Hajiramezanali, Andreas Loukas

Generative flow networks (GFlowNets) are a family of algorithms that learn a generative policy to sample discrete objects $x$ with non-negative reward $R(x)$. Learning objectives guarantee the GFlowNet samples $x$ from the target distribution $p^*(x) \propto R(x)$ when loss is globally minimized over all states or trajectories, but it is unclear how well the

Javier Alejandro Chávez-Domínguez

The classical Mazur map is a uniform homeomorphism between the unit spheres of $L_p$ spaces, and the version for noncommutative $L_p$ spaces has the same property. Odell and Schlumprecht used two types of generalized Mazur maps to prove that the unit sphere of a Banach space $X$ with an unconditional basis is uniformly homeomorphic to the unit sphere of a Hi

Deven Santosh Shah, Gosuddin Kamaruddin Siddiqi, Shiying He, Radhika Bansal

Local news has become increasingly important in the news industry due to its various benefits. It offers local audiences information that helps them participate in their communities and interests. It also serves as a reliable source of factual reporting that can prevent misinformation. Moreover, it can influence national audiences as some local stories may h

Jinglun Cai, Monica Sunkara, Xilai Li, Anshu Bhatia

Masked Language Models (MLMs) have proven to be effective for second-pass rescoring in Automatic Speech Recognition (ASR) systems. In this work, we propose Masked Audio Text Encoder (MATE), a multi-modal masked language model rescorer which incorporates acoustic representations into the input space of MLM. We adopt contrastive learning for effectively aligni

Shakti N. Wadekar, Eugenio Culurciello

Vision-Transformers (ViTs) and Convolutional neural networks (CNNs) are widely used Deep Neural Networks (DNNs) for classification task. These model architectures are dependent on the number of classes in the dataset it was trained on. Any change in number of classes leads to change (partial or full) in the model's architecture. This work addresses the quest

S. Criscuoli, L. Bertello, D. P. Choudhary, M. DeLand

Long-term high-cadence measurements of stellar spectral variability are fundamental to better understand stellar atmospheric properties and stellar magnetism. These, in turn, are fundamental for the detectability of exoplanets as well as the characterization of their atmospheres and habitability. The Sun, viewed as a star via disk-integrated observations, of

Mengge Li, Shuaijie Qian, Chao Zhou

The classical mean-variance portfolio selection problem induces time-inconsistent (precommited) strategies (see Zhou and Li (2000)). To overcome this time-inconsistency, Basak and Chabakauri (2010) introduce the game theoretical approach and look for (sub-game perfect Nash) equilibrium strategies, which is solved from the corresponding partial differential e

Leslie Greengard, Shidong Jiang, Manas Rachh, Jun Wang

We present a new version of the fast Gauss transform (FGT) for discrete and continuous sources. Classical Hermite expansions are avoided entirely, making use only of the plane-wave representation of the Gaussian kernel and a new hierarchical merging scheme. For continuous source distributions sampled on adaptive tensor-product grids, we exploit the separable

Ya-Chun Liang, Clifford Stein, Hao-Ting Wei

The modern network aims to prioritize critical traffic over non-critical traffic and effectively manage traffic flow. This necessitates proper buffer management to prevent the loss of crucial traffic while minimizing the impact on non-critical traffic. Therefore, the algorithm's objective is to control which packets to transmit and which to discard at each s

Fernando Zhapa-Camacho, Robert Hoehndorf

Generating vector representations (embeddings) of OWL ontologies is a growing task due to its applications in predicting missing facts and knowledge-enhanced learning in fields such as bioinformatics. The underlying semantics of OWL ontologies are expressed using Description Logics (DLs). Initial approaches to generate embeddings relied on constructing a gra

Pengfei Zhang, Hui Tan, Jianmin Yuan, Yongqiang Li

Orbital degree of freedom plays an important role for understanding quantum many-body phenomena. In this work, we study an experimentally related setup with ultracold bosons loaded into hybridized bands of two-dimensional hexagonal optical lattices. We find that the system supports various quantum many-body phases at zero temperature, including chiral superf

Agnes Barsi, Suvendu Chandan Nayak, Sasmita Parida, Raj Mani Shukla

This paper presents an autoencoder-based neural network architecture to compress histopathological images while retaining the denser and more meaningful representation of the original images. Current research into improving compression algorithms is focused on methods allowing lower compression rates for Regions of Interest (ROI-based approaches). Neural net

Gabriele Mondello, Dmitri Panov

We analyse local features of the spaces of representations of the fundamental group of a punctured surface in $\mathrm{SU}_2$ equipped with a decoration, namely a choice of a logarithm of the representation at peripheral loops. Such decorated representations naturally arise as monodromies of spherical surfaces with conical points. Among other things, in this

Fabian Menezes, Adriana Valio, Yuri Netto, Alexandre Araújo

The Sun and other solar-type stars have magnetic fields that permeate their interior and surface, extends through the interplanetary medium, and is the main driver of stellar activity. Stellar magnetic activity affects physical processes and conditions of the interplanetary medium and orbiting planets. Coronal mass ejections (CMEs) are the most impacting of

V. Almendros-Abad, K. Mužić, H. Bouy, A. Bayo

We aim at characterizing the low-mass (sub)stellar population of the central portion (2.4 pc$^2$) of the $\sim$2 Myr old cluster NGC 2244 using near infrared spectroscopy. By studying this cluster, characterized by a low stellar density and numerous OB stars, we aim at exploring the effect that OB stars may have on the production of BDs. We obtain near infra

Soham Parikh, Quaizar Vohra, Prashil Tumbade, Mitul Tiwari

Conversational NLU providers often need to scale to thousands of intent-classification models where new customers often face the cold-start problem. Scaling to so many customers puts a constraint on storage space as well. In this paper, we explore four different zero and few-shot intent classification approaches with this low-resource constraint: 1) domain a

Ittai Rubinstein, Roni Con

The {\em binary deletion channel} with deletion probability $d$ ($\text{BDC}_d$) is a random channel that deletes each bit of the input message i.i.d with probability $d$. It has been studied extensively as a canonical example of a channel with synchronization errors. Perhaps the most important question regarding the BDC is determining its capacity. Mitzenma

Liam Quinn, Gang Xu, Yiqing Xu, Zongda Li

We experimentally demonstrate an all-optical random number generator based on spontaneous symmetry breaking in a coherently-driven Kerr resonator. Random bit sequences are generated by repeatedly tuning a control parameter across a symmetry-breaking bifurcation that enacts random selection between two possible steady-states of the system. Experiments are per

Nathan Hughes, Yun Chang, Siyi Hu, Rajat Talak

3D spatial perception is the problem of building and maintaining an actionable and persistent representation of the environment in real-time using sensor data and prior knowledge. Despite the fast-paced progress in robot perception, most existing methods either build purely geometric maps (as in traditional SLAM) or flat metric-semantic maps that do not scal

Jonas Schuett, Noemi Dreksler, Markus Anderljung, David McCaffary

A number of leading AI companies, including OpenAI, Google DeepMind, and Anthropic, have the stated goal of building artificial general intelligence (AGI) - AI systems that achieve or exceed human performance across a wide range of cognitive tasks. In pursuing this goal, they may develop and deploy AI systems that pose particularly significant risks. While t

Polina Tsvilodub, Michael Franke, Robert D. Hawkins, Noah D. Goodman

When faced with a polar question, speakers often provide overinformative answers going beyond a simple "yes" or "no". But what principles guide the selection of additional information? In this paper, we provide experimental evidence from two studies suggesting that overinformativeness in human answering is driven by considerations of relevance to the questio

Elisa Davoli, Rita Ferreira, Irene Fonseca, José A. Iglesias

Due to their ability to handle discontinuous images while having a well-understood behavior, regularizations with total variation (TV) and total generalized variation (TGV) are some of the best-known methods in image denoising. However, like other variational models including a fidelity term, they crucially depend on the choice of their tuning parameters. A

Didier Bresch, Pierre-Emmanuel Jabin, Fei Wang

This paper concerns the existence of global weak solutions {\it \`a la Leray} for compressible Navier--Stokes--Fourier systems with periodic boundary conditions and the truncated virial pressure law which is assumed to be thermodynamically unstable. More precisely, the main novelty is that the pressure law is not assumed to be monotone with respect to the de

Román Cárdenas, Patricia Arroba, José L. Risco-Martín

The Internet of Things is transforming our society, providing new services that improve the quality of life and resource management. These applications are based on ubiquitous networks of multiple distributed devices, with limited computing resources and power, capable of collecting and storing data from heterogeneous sources in real-time. To avoid network s

Sriharsha Kocherla, Zhixin Song, Fatima Ezahra Chrit, Bryan Gard

Fluid flow simulations marshal our most powerful computational resources. In many cases, even this is not enough. Quantum computers provide an opportunity to speed up traditional algorithms for flow simulations. We show that lattice-based mesoscale numerical methods can be executed as efficient quantum algorithms due to their statistical features. This appro

Haolan Liu, Zixuan Wang, Jishen Zhao

Autonomous vehicles (AVs) are envisioned to revolutionize our life by providing safe, relaxing, and convenient ground transportation. The computing systems in such vehicles are required to interpret various sensor data and generate responses to the environment in a timely manner to ensure driving safety. However, such timing-related safety requirements are l

Pedro Jimenez, Luis Chacon, Mario Merino

An electrostatic, implicit particle-in-cell (PIC) model for collisionless, fully magnetized, paraxial plasma expansions in a magnetic nozzle is introduced with exact charge, energy, and magnetic moment conservation properties. The approach is adaptive in configuration space by the use of mapped meshes, and exploits the strict conservation of the magnetic mom

Fethi Ali Cheddad

This field case study aims to address the challenge of accurately predicting petrophysical properties in heterogeneous reservoir formations, which can significantly impact reservoir performance predictions. The study employed three machine learning algorithms, namely Artificial Neural Network (ANN), Random Forest Classifier (RFC), and Support Vector Machine

Silvio Mandelli, Marcus Henninger, Maximilian Bauhofer, Thorsten Wild

As the research community starts to address the* key features of 6G cellular standards, one of the agreed bridge topics to be studied already in 5G advanced releases is Integrated Sensing and Communication (ISAC). The first efforts of the research community are focusing on ISAC enablers, fundamental limits, and first demonstrators, that show that the time ha

Arshia M. Jacob, Karl M. Menten, Friedrich Wyrowski, Olli Sipilä

While the abundance of elemental deuterium is relatively low (D/H ~ a few 1E-5), orders of magnitude higher D/H abundance ratios have been found for many interstellar molecules, enhanced by deuterium fractionation. In cold molecular clouds (T < 20K) deuterium fractionation is driven by the H2D+ ion, whereas at higher temperatures (T > 20-30K) gas-phase deute

Nischal Ashok Kumar, Wanyong Feng, Jaewook Lee, Hunter McNichols

In this paper, we take a preliminary step towards solving the problem of causal discovery in knowledge tracing, i.e., finding the underlying causal relationship among different skills from real-world student response data. This problem is important since it can potentially help us understand the causal relationship between different skills without extensive

Elahe Vedadi, Yasaman Keshtkarjahromi, Hulya Seferoglu

Multi-party computation (MPC) is promising for designing privacy-preserving machine learning algorithms at edge networks. An emerging approach is coded-MPC (CMPC), which advocates the use of coded computation to improve the performance of MPC in terms of the required number of workers involved in computations. The current approach for designing CMPC algorith

Arseny Moskvichev, Victor Vikram Odouard, Melanie Mitchell

The abilities to form and abstract concepts is key to human intelligence, but such abilities remain lacking in state-of-the-art AI systems. There has been substantial research on conceptual abstraction in AI, particularly using idealized domains such as Raven's Progressive Matrices and Bongard problems, but even when AI systems succeed on such problems, the

Ghurumuruhan Ganesan

The hull of a linear code (i.e., a finite field vector space)~\({\mathcal C}\) is defined to be the vector space formed by the intersection of~\({\mathcal C}\) with its dual~\({\mathcal C}^{\perp}.\) Constructing vector spaces with a specified hull dimension has important applications and it is therefore of interest to study minimum distance properties of su

Zachary Sweger

The STAR experiment at Brookhaven National Laboratory has completed data taking for the second phase of the beam energy scan (BES-II) program, including in a fixed-target (FXT) mode. The BES-II program has collected high-statistics data on Au+Au collisions in the high baryon-density region of the QCD phase diagram. Together those data cover a wide range of p

Sepideh Neshatfar, Abram Magner, Salimeh Yasaei Sekeh

Graph summarization is the problem of producing smaller graph representations of an input graph dataset, in such a way that the smaller compressed graphs capture relevant structural information for downstream tasks. There is a recent graph summarization method that formulates an optimal transport-based framework that allows prior information about node, edge

Ghurumuruhan Ganesan

The Eulerian extension number of any graph~\(H\) (i.e. the minimum number of edges needed to be added to make~\(H\) Eulerian) is at least~\(t(H),\) half the number of odd degree vertices of~\(H.\) In this paper we consider an inhomogenous random graph~\(G\) whose edge probabilities need not all be the same and use an iterative probabilistic method to obtain

Divya K. Bilolikar, Aishwarya More, Aella Gong, Joseph Janssen

Predictions are a central part of water resources research. Historically, physically-based models have been preferred; however, they have largely failed at modeling hydrological processes at a catchment scale and there are some important prediction problems that cannot be modeled physically. As such, machine learning (ML) models have been seen as a valid alt

Jaewook Lee, Andrew Lan

In second language vocabulary learning, existing works have primarily focused on either the learning interface or scheduling personalized retrieval practices to maximize memory retention. However, the learning content, i.e., the information presented on flashcards, has mostly remained constant. Keyword mnemonic is a notable learning strategy that relates new

Yeshwanth Venkatesha, Youngeun Kim, Hyoungseob Park, Priyadarshini Panda

Federated Learning (FL) is a privacy-preserving distributed machine learning approach geared towards applications in edge devices. However, the problem of designing custom neural architectures in federated environments is not tackled from the perspective of overall system efficiency. In this paper, we propose DC-NAS -- a divide-and-conquer approach that perf

Ghurumuruhan Ganesan

Consider~\(n\) nodes~\(\{X_i\}_{1 \leq i \leq n}\) independently distributed in the unit square~\(S,\) each according to a distribution~\(f\) and let~\(K_n\) be the complete graph formed by joining each pair of nodes by a straight line segment. For every edge~\(e\) in~\(K_n\) we associate a weight~\(w(e)\) that may depend on the \emph{individual locations} o

Dalila Rivero Jerez, Claudio Pessoa, Gustavo de França, Raul Celistrino Teixeira

We report on the observation of a novel manifestation of saturation-induced optical bistability in a resonantly pumped optical ring cavity interacting strongly with a cloud of atoms via a narrow atomic transition. The bistability emerges, above a critical pump rate, as an additional peak in the cavity's normal mode spectrum close to atomic resonance. This th

Jayneel Parekh, Sanjeel Parekh, Pavlo Mozharovskyi, Gaël Richard

This paper tackles two major problem settings for interpretability of audio processing networks, post-hoc and by-design interpretation. For post-hoc interpretation, we aim to interpret decisions of a network in terms of high-level audio objects that are also listenable for the end-user. This is extended to present an inherently interpretable model with high

Mathias Seuret, Janne van der Loop, Nikolaus Weichselbaumer, Martin Mayr

In this paper, we investigate the usage of fine-grained font recognition on OCR for books printed from the 15th to the 18th century. We used a newly created dataset for OCR of early printed books for which fonts are labeled with bounding boxes. We know not only the font group used for each character, but the locations of font changes as well. In books of thi

Tao Jiang, Foad Sohrabi, Wei Yu

Beam alignment is an important task for millimeter-wave (mmWave) communication, because constructing aligned narrow beams both at the transmitter (Tx) and the receiver (Rx) is crucial in terms of compensating the significant path loss in very high-frequency bands. However, beam alignment is also a highly nontrivial task because large antenna arrays typically

Ricardo J. C. Rosado, Adriano Cherchiglia, Marcos Sampaio, Brigitte Hiller

We employ implicit regularization (IReg) in quark-antiquark decays of the Z, or of a scalar (CP-even or odd) boson at NLO, and compare with dimensional schemes to reveal subtleties involving infrared divergence cancellation and $\gamma_5$-matrix issues. Besides the absence of evanescent fields in IReg, such as $\epsilon$-scalars required in certain schemes t

Raúl Vargas, Lenny A. Romero, Song Zhang, Andres G. Marrugo

This Letter presents a novel structured light system model that effectively considers local lens distortion by pixel-wise rational functions. We leverage the stereo method for initial calibration and then estimate the rational model for each pixel. Our proposed model can achieve high measurement accuracy within and outside the calibration volume, demonstrati

Nkeh Oma Nfor, Michael Nana Jipdi

We consider the Davydov model of {\alpha}-helix protein chain with both exciton-exciton and excitonphonon couplings and investigate on the evolution of elliptic solitons. In the discrete regime of the adiabatic limit, we analytically and numerically show that modulational instability induces the self-localization of energy in the {\alpha}-helix protein chain

David Gonzalez, Dino Rossegger

We study possible Scott sentence complexities of linear orderings using two approaches. First, we investigate the effect of the Friedman-Stanley embedding on Scott sentence complexity and show that it only preserves $\Pi^{\mathrm{in}}_{\alpha}$ complexities. We then take a more direct approach and exhibit linear orderings of all Scott complexities except $\S

Hanuš Seiner, Petr Sedlák, Miroslav Frost, Petr Šittner

Irreversible plastic forming of B19$^\prime$ martensite of the NiTi shape memory alloy is discussed within the framework of continuum mechanics. It is suggested that the main mechanism arises from coupling between martensite reorientation and coordinated $[100](001)_{\rm M}$ dislocation slip. A heuristic model is proposed, showing that the ${(20\bar{1})_{\rm

Argyrios Deligkas, Eduard Eiben, Gregory Gutin, Philip R. Neary

We investigate the difficulty of finding economically efficient solutions to coordination problems on graphs. Our work focuses on two forms of coordination problem: pure-coordination games and anti-coordination games. We consider three objectives in the context of simple binary-action polymatrix games: (i) maximizing welfare, (ii) maximizing potential, and (