October 2023 arXiv papers — page 48

Alokendu Mazumder, Tirthajit Baruah, Bhartendu Kumar, Rishab Sharma

The autoencoder is an unsupervised learning paradigm that aims to create a compact latent representation of data by minimizing the reconstruction loss. However, it tends to overlook the fact that most data (images) are embedded in a lower-dimensional space, which is crucial for effective data representation. To address this limitation, we propose a novel app

Chenghao Xiao, Yizhi Li, G Thomas Hudson, Chenghua Lin

In recent years, contrastive learning (CL) has been extensively utilized to recover sentence and document-level encoding capability from pre-trained language models. In this work, we question the length generalizability of CL-based models, i.e., their vulnerability towards length-induced semantic shift. We verify not only that length vulnerability is a signi

ABS Thakur, Jai Sukhatme, Nili Harnik

The tropical overturning circulation is examined in a moist aquaplanet general circulation model forced using a non-interactive sea surface temperature (SST) distribution that varies between a present-day Earth-like profile and one that is globally uniform. A traditional Hadley Cell (HC)-like flow is observed in all experiments along with the poleward transp

Zhuolin Yang, Zain Sarwar, Iris Hwang, Ronik Bhaskar

Virtual Reality (VR) has gained popularity by providing immersive and interactive experiences without geographical limitations. It also provides a sense of personal privacy through physical separation. In this paper, we show that despite assumptions of enhanced privacy, VR is unable to shield its users from side-channel attacks that steal private information

Filip Rezabek, Kilian Glas, Richard von Seck, Achraf Aroua

The recent developments and research in distributed ledger technologies and blockchain have contributed to the increasing adoption of distributed systems. To collect relevant insights into systems' behavior, we observe many evaluation frameworks focusing mainly on the system under test throughput. However, these frameworks often need more comprehensiveness a

Gennaro Notomista, Mario Selvaggio, Francesca Pagano, María Santos

The ability of executing multiple tasks simultaneously is an important feature of redundant robotic systems. As a matter of fact, complex behaviors can often be obtained as a result of the execution of several tasks. Moreover, in safety-critical applications, tasks designed to ensure the safety of the robot and its surroundings have to be executed along with

Louis Saddier, Ambre Palotai, Matheo Aksil, Michel Tsamados

When particles of a few tens of microns are spread on the surface of water, they aggregate under the action of capillary forces and form a thin floating membrane, a particle raft. In a tank with a raft made of graphite powder, we generate in the laboratory gravity surface waves, whose wavelength {about 17 cm} is very large compared to the thickness of the ra

Sibo Cheng, Che Liu, Yike Guo, Rossella Arcucci

Variational Data Assimilation (DA) has been broadly used in engineering problems for field reconstruction and prediction by performing a weighted combination of multiple sources of noisy data. In recent years, the integration of deep learning (DL) techniques in DA has shown promise in improving the efficiency and accuracy in high-dimensional dynamical system

Howard Yanxon, Eric Roberts, Hannah Parraga, James Weng

Scientific researchers frequently use the in situ synchrotron high-energy powder X-ray diffraction (XRD) technique to examine the crystallographic structures of materials in functional devices such as rechargeable battery materials. We propose a method for identifying artifacts in experimental XRD images. The proposed method uses deep learning convolutional

Alaul Islam, Tingying He, Anastasia Bezerianos, Bongshin Lee

We present a systematic review and design space for visualizations on smartwatches and the context in which these visualizations are displayed--smartwatch faces. A smartwatch face is the main smartwatch screen that wearers see when checking the time. Smartwatch faces are small data dashboards that present a variety of data to wearers in a compact form. Yet,

Sophie Morel

These are the notes of a course on Shimura varieties that I gave at the 2022 IHES summer school on the Langlands program. Lecture 1 gives an introduction to Shimura varieties over the complex numbers (defined here as a special type of locally symmetric spaces) and to the general theory of canonical models; it also discusses in more detail the example of the

Md. Arid Hasan, Firoj Alam, Anika Anjum, Shudipta Das

We present an overview of the BLP Sentiment Shared Task, organized as part of the inaugural BLP 2023 workshop, co-located with EMNLP 2023. The task is defined as the detection of sentiment in a given piece of social media text. This task attracted interest from 71 participants, among whom 29 and 30 teams submitted systems during the development and evaluatio

Vladimir M. Subbotin, Michael V. Subotin

Solid organ transplantation has by now become a common medical procedure. Owing to the introduction of new immunosuppressive drugs, the allograft loss due to acute rejection has been reduced significantly over time. Tragically, the number of donor organs lost due to allograft vasculopathy (AV), generally named chronic vascular rejection or chronic rejection,

Xiangyi Meng, Onur Varol, Albert-László Barabási

References, the mechanism scientists rely on to signal previous knowledge, lately have turned into widely used and misused measures of scientific impact. Yet, when a discovery becomes common knowledge, citations suffer from obliteration by incorporation. This leads to the concept of hidden citation, representing a clear textual credit to a discovery without

Brenden R. Ortiz, Paul M. Sarte, Ganesh Pokharel, Miles J. Knudston

Pyrochlore materials are characterized by their hallmark network of corner-sharing rare-earth tetrahedra, which can produce a wide array of complex magnetic ground states. Ferromagnetic Ising pyrochlores often obey the "two-in-two-out" spin ice rules, which can lead to a highly-degenerate spin structure. Large moment systems, such as Ho$_2$Ti$_2$O$_7$ and Dy

Felix Brandt, Matthias Hieber, Arnab Roy

This paper provides a framework to strong time periodic solutions of quasilinear evolution equations. The novelty of this approach is that zero is allowed to be a spectral value of the underlying linearized operator. This approach is then applied to time periodic problems associated to the Navier-Stokes equations, generalized Newtonian fluids, quasilinear re

Eric David-Bosne, Ângelo Costa, Benedikt Bergmann, João Guilherme Correia

Electron Emission Channeling (EC) is a powerful technique for the investigation of the lattice location of radioactive isotopes implanted into single crystals. After implantation the isotopes occupy certain lattice locations in the crystal, which can in some cases be altered by annealing. Upon decay, the emission of a charged particle, typically a beta, may

Alexander V. Savin, Margarita Kovaleva

Carbon nanotubes are nano-objects with quite anisotropic properties, for example the mechanical properties in longitudinal and radial directions differ significantly. This feature of the carbon nanotubes yields many interesting phenomena investigated in last decades. One of them is the ability to form both hollow and collapsed states if the radius of the nan

Zhenzhen Liu, Chao Wan, Varsha Kishore, Jin Peng Zhou

Abstractive summarization aims at generating natural language summaries of a source document that are succinct while preserving the important elements. Despite recent advances, neural text summarization models are known to be susceptible to hallucinating (or more correctly confabulating), that is to produce summaries with details that are not grounded in the

Md Mostafijur Rahman, Radu Marculescu

In recent years, medical image segmentation has become an important application in the field of computer-aided diagnosis. In this paper, we are the first to propose a new graph convolution-based decoder namely, Cascaded Graph Convolutional Attention Decoder (G-CASCADE), for 2D medical image segmentation. G-CASCADE progressively refines multi-stage feature ma

Ioannis Petrides, Jonathan B. Curtis, Marie Wesson, Amir Yacoby

Reciprocal and nonreciprocal effects in dielectric and magnetic materials provide crucial information about the microscopic properties of electrons. However, experimentally distinguishing the two has proven to be challenging, especially when the associated effects are extremely small. To this end, we propose a contact-less detection using a cross-cavity devi

Shuai Zhang, Hongkang Li, Meng Wang, Miao Liu

This paper provides a theoretical understanding of Deep Q-Network (DQN) with the $\varepsilon$-greedy exploration in deep reinforcement learning. Despite the tremendous empirical achievement of the DQN, its theoretical characterization remains underexplored. First, the exploration strategy is either impractical or ignored in the existing analysis. Second, in

Sophia Yi, Shu Yan Lau, Kent Yagi, Phil Arras

Accreting binary white dwarf systems are among the sources expected to emanate gravitational waves that the Laser Interferometer Space Antenna (LISA) will detect. We investigate how accurately the binary parameters may be measured from LISA observations. We complement previous studies by performing our parameter estimation on binaries containing a low-mass d

Hao Li, Xiangxiong Zhang

For solving two-dimensional incompressible flow in the vorticity form by the fourth-order compact finite difference scheme and explicit strong stability preserving (SSP) temporal discretizations, we show that the simple bound-preserving limiter in [Li H., Xie S., Zhang X., SIAM J. Numer. Anal., 56 (2018)]. can enforce the strict bounds of the vorticity, if t

Hao Li, Shusen Xie, Xiangxiong Zhang

We show that the classical fourth order accurate compact finite difference scheme with high order strong stability preserving time discretizations for convection diffusion problems satisfies a weak monotonicity property, which implies that a simple limiter can enforce the bound-preserving property without losing conservation and high order accuracy. Higher o

Brandon Robinson, Philippe Bisaillon, Jodi D. Edwards, Tetyana Kendzerska

We consider state and parameter estimation for compartmental models having both time-varying and time-invariant parameters. Though the described Bayesian computational framework is general, we look at a specific application to the susceptible-infectious-removed (SIR) model which describes a basic mechanism for the spread of infectious diseases through a syst

Ryan Jacobs, Philip E. Goins, Dane Morgan

Materials discovery and design typically proceeds through iterative evaluation (both experimental and computational) to obtain data, generally targeting improvement of one or more properties under one or more constraints (e.g., time or budget). However, there can be great variation in the quality and cost of different data, and when they are mixed together i

Yash Kant, Aliaksandr Siarohin, Michael Vasilkovsky, Riza Alp Guler

We present a method for generating consistent novel views from a single source image. Our approach focuses on maximizing the reuse of visible pixels from the source image. To achieve this, we use a monocular depth estimator that transfers visible pixels from the source view to the target view. Starting from a pre-trained 2D inpainting diffusion model, we tra

Sumyeong Ahn, Sihyeon Kim, Jongwoo Ko, Se-Young Yun

The presence of noisy labels in a training dataset can significantly impact the performance of machine learning models. To tackle this issue, researchers have explored methods for Learning with Noisy Labels to identify clean samples and reduce the influence of noisy labels. However, constraining the influence of a certain portion of the training dataset can

Huawen Xu, Timothy C. H. Liew, Yuri G. Rubo

The quantum theory of polariton condensation in a trapped state reveals a second-order phase transition evidenced by spontaneous polarization parity breaking in sub-spaces of fixed polariton occupation numbers. The emission spectra of a polariton condensate demonstrate the coexistence of a symmetry-conserving condensate state with {linear} polarization and t

Mohannad Shehadeh, Frank R. Kschischang, Alvin Y. Sukmadji

We introduce a natural generalization of staircase codes in which each bit is protected by arbitrarily many component codewords rather than two. This enables powerful energy-efficient FEC based on iterative decoding of Hamming components.

Bhavya Chopra, Ananya Singha, Anna Fariha, Sumit Gulwani

Large Language Models (LLMs) are being increasingly employed in data science for tasks like data preprocessing and analytics. However, data scientists encounter substantial obstacles when conversing with LLM-powered chatbots and acting on their suggestions and answers. We conducted a mixed-methods study, including contextual observations, semi-structured int

Endre Csaki, Antonia Foldes

A tribute to the life and work of Pal Revesz. The Hungarian mathematical community lost one of his leading members, when Pal Revesz passed away on 14 of November 2022.

Mohamed Masoud, Pratyush Reddy, Farfalla Hu, Sergey Plis

Performing volumetric image processing directly within the browser, particularly with medical data, presents unprecedented challenges compared to conventional backend tools. These challenges arise from limitations inherent in browser environments, such as constrained computational resources and the availability of frontend machine learning libraries. Consequ

Nico Schiavone, Jingyi Wang, Shuangzhi Li, Roger Zemp

Active Learning (AL) and Few Shot Learning (FSL) are two label-efficient methods which have achieved excellent results recently. However, most prior arts in both learning paradigms fail to explore the wealth of the vast unlabelled data. In this study, we address this issue in the scenario where the annotation budget is very limited, yet a large amount of unl

Yingjia Lin, Shilin Huang, Kenneth R. Brown

For quantum error correction codes the required number of measurement rounds typically increases with the code distance when measurements are faulty. Single-shot error correction allows for an error threshold with only one round of noisy syndrome measurements regardless of the code size. Here we implement single-shot check operators for toric codes. The sing

David W. Kanaar, J. P. Kestner

A major current challenge in solid-state quantum computing is to scale qubit arrays to a larger number of qubits. This is hampered by the complexity of the control wiring for the large number of independently tunable interqubit couplings within these arrays. One approach to simplifying the problem is to use a qubit array with fixed Ising ($ZZ$) interactions.

G. J. Bean, N. D. Drummond, J. Ruostekoski

We analyze the scattering of light from dipolar emitters whose disordered positions exhibit correlations induced by static, long-range dipole-dipole interactions. The quantum-mechanical position correlations are calculated for zero temperature bosonic atoms or molecules using variational and diffusion quantum Monte Carlo methods. For stationary atoms in dens

Jinfeng Zhong, Elsa Negre

Feature attribution is a fundamental task in both machine learning and data analysis, which involves determining the contribution of individual features or variables to a model's output. This process helps identify the most important features for predicting an outcome. The history of feature attribution methods can be traced back to General Additive Models (

András I. Stipsicz, Zoltán Szabó

In this paper we study smooth structures on closed oriented 4-manifolds with fundamental group Z_2 and definite intersection form. We construct infinitely many irreducible, smooth, oriented, closed, definite four-manifolds with fundamental group Z_2 and second Betti number 1 and 2. As an application, we prove that when the second Betti number of a definite f

Hana K. Warner, Jeffrey Holzgrafe, Beatriz Yankelevich, David Barton

Quantum communications technologies require a network of quantum processors connected with low loss and low noise communication channels capable of distributing entangled states. Superconducting microwave qubits operating in cryogenic environments have emerged as promising candidates for quantum processor nodes. However, scaling these systems is challenging

Leonardo Petrini

Artificial intelligence, particularly the subfield of machine learning, has seen a paradigm shift towards data-driven models that learn from and adapt to data. This has resulted in unprecedented advancements in various domains such as natural language processing and computer vision, largely attributed to deep learning, a special class of machine learning mod

Mustafa Jarrar, Muhammad Abdul-Mageed, Mohammed Khalilia, Bashar Talafha

We present WojoodNER-2023, the first Arabic Named Entity Recognition (NER) Shared Task. The primary focus of WojoodNER-2023 is on Arabic NER, offering novel NER datasets (i.e., Wojood) and the definition of subtasks designed to facilitate meaningful comparisons between different NER approaches. WojoodNER-2023 encompassed two Subtasks: FlatNER and NestedNER.

Taekeun Yoo, Young-Ho Yoo, Suk-Joo Byun, A-Ram You

Nanobubble-related technologies have been confirmed to be useful in various fields such as climate change and the environment as well as water-based industries such as water purification, crops, horticulture, medical care, bio, and sterilization. However, a method of mass production in real time enough to apply nano-bubbles to the industry has not yet been d

Jakub Šebesta, Oscar Grånäs

The use of ultrashort laser pulses to investigate the response of materials on femtosecond time-scales enables detailed tracking of charge, spin and lattice degrees of freedom. When pushing the limits of the experimental resolution, connection to theoretical modeling becomes increasingly important in order to infer causality relations. Weyl-semimetals is par

Enrique G. Neyra, Laureano A. Bulus Rossini, Fabián Videla, Lorena Rebón

The concept of superbandwidth refers to the fact that a band-limited signal can exhibit, locally, an increase of its bandwidth, i.e., an effective bandwidth greater than that predicted by its Fourier transform. In this work, we study the propagation of superbandwidth laser pulses in a dispersive medium, characterized by the group velocity dispersion. In part

Augusto Seben da Rosa, Frederico Santos de Oliveira, Anderson da Silva Soares, Arnaldo Candido Junior

Computer vision in general presented several advances such as training optimizations, new architectures (pure attention, efficient block, vision language models, generative models, among others). This have improved performance in several tasks such as classification, and others. However, the majority of these models focus on modifications that are taking dis

Wookje Han, Jinsol Park, Kyungjae Lee

Information-seeking questions in long-form question answering (LFQA) often prove misleading due to ambiguity or false presupposition in the question. While many existing approaches handle misleading questions, they are tailored to limited questions, which are insufficient in a real-world setting with unpredictable input characteristics. In this work, we prop

Zheyuan Zhang, Shane Storks, Fengyuan Hu, Sungryull Sohn

Pre-trained language models (PLMs) have shown impressive performance in various language tasks. However, they are prone to spurious correlations, and often generate illusory information. In real-world applications, PLMs should justify decisions with formalized, coherent reasoning chains, but this challenge remains under-explored. Cognitive psychology theoriz

Alejandro Lozano, Scott L Fleming, Chia-Chun Chiang, Nigam Shah

The quickly-expanding nature of published medical literature makes it challenging for clinicians and researchers to keep up with and summarize recent, relevant findings in a timely manner. While several closed-source summarization tools based on large language models (LLMs) now exist, rigorous and systematic evaluations of their outputs are lacking. Furtherm

J. F. Zhu, H. L. Ding, H. K. Li, J. W. Han

The primary accelerator of DCLS (Dalian Coherent Light Source) operates at a repetition rate of 20 Hz now, and the beam is divided at the end of the linear accelera-tor through Kicker to make two 10 Hz beamlines work simultaneously. For the simultaneous emission FEL of two beamlines, the beam energy of the two beamlines is required to be controlled independe

Rupak Majumdar, V. R. Sathiyanarayana

We study the recursion-theoretic complexity of Positive Almost-Sure Termination ($\mathsf{PAST}$) in an imperative programming language with rational variables, bounded nondeterministic choice, and discrete probabilistic choice. A program terminates positive almost-surely if, for every scheduler, the program terminates almost-surely and the expected runtime

Raul Cruz-Oliver, Luis Monzon, Edgar Ramirez-Laboreo, Jose-Manuel Rodriguez-Fortun

This paper proposes a model-based optimization method for the production of automotive seals in an extrusion process. The high production throughput, coupled with quality constraints and the inherent uncertainty of the process, encourages the search for operating conditions that minimize nonconformities. The main uncertainties arise from the process variabil

José A. Carrillo, Jingwei Hu, Samuel Q. Van Fleet

The multispecies Landau collision operator describes the two-particle, small scattering angle or grazing collisions in a plasma made up of different species of particles such as electrons and ions. Recently, a structure preserving deterministic particle method arXiv:1910.03080 has been developed for the single species spatially homogeneous Landau equation. T

William Timkey, Tal Linzen

Two of the central factors believed to underpin human sentence processing difficulty are expectations and retrieval from working memory. A recent attempt to create a unified cognitive model integrating these two factors relied on the parallels between the self-attention mechanism of transformer language models and cue-based retrieval theories of working memo

Jinfeng Zhong, Elsa Negre

Knowledge graphs contain rich semantic relationships related to items and incorporating such semantic relationships into recommender systems helps to explore the latent connections of items, thus improving the accuracy of prediction and enhancing the explainability of recommendations. However, such explainability is not adapted to users' contexts, which can

Reda Alami, Mohammed Mahfoud, Mastane Achab

In a typical stochastic multi-armed bandit problem, the objective is often to maximize the expected sum of rewards over some time horizon $T$. While the choice of a strategy that accomplishes that is optimal with no additional information, it is no longer the case when provided additional environment-specific knowledge. In particular, in areas of high volati

Fernando Marcos, Rodrigo Tamaki, Mateo Cámara, Virginia Yagüe

Dependence on raw materials, especially in the mining sector, is a key part of today's economy. Aggregates are vital, being the second most used raw material after water. Digitally transforming this sector is key to optimizing operations. However, supervision and maintenance (predictive and corrective) are challenges little explored in this sector, due to th

Caixin Wang, Jie Zhang, Matthew A. Wilson, Ralph Etienne-Cummings

Accurately capturing dynamic scenes with wide-ranging motion and light intensity is crucial for many vision applications. However, acquiring high-speed high dynamic range (HDR) video is challenging because the camera's frame rate restricts its dynamic range. Existing methods sacrifice speed to acquire multi-exposure frames. Yet, misaligned motion in these fr

Shaotong Zhu, Michael Wan, Sai Kumar Reddy Manne, Emily Zimmerman

Non-nutritive sucking (NNS), which refers to the act of sucking on a pacifier, finger, or similar object without nutrient intake, plays a crucial role in assessing healthy early development. In the case of preterm infants, NNS behavior is a key component in determining their readiness for feeding. In older infants, the characteristics of NNS behavior offer v

Liu Cao, Yahia Shabara, Parisa Cheraghi

The transformative enhancements of fifth-generation (5G) mobile devices bring about new challenges to achieve better uplink (UL) performance. Particularly, in codebook-based transmission, the wide-band (WB) precoding and the legacy UL codebook may become main bottlenecks for higher efficient data transmission. In this paper, we investigate the codebook-based

Hyeongseong Lee, Udit Paul, Arpit Gupta, Elizabeth Belding

Crowdsourced speedtest measurements are an important tool for studying internet performance from the end user perspective. Nevertheless, despite the accuracy of individual measurements, simplistic aggregation of these data points is problematic due to their intrinsic sampling bias. In this work, we utilize a dataset of nearly 1 million individual Ookla Speed

Chenghao Yang, Allyson Ettinger

Understanding sentence meanings and updating information states appropriately across time -- what we call "situational understanding" (SU) -- is a critical ability for human-like AI agents. SU is essential in particular for chat models, such as ChatGPT, to enable consistent, coherent, and effective dialogue between humans and AI. Previous works have identifi

Panagiotis Bountzioukas, Georgios Kikas, Christoforos Tsiolakis, Dimitrios Stoupis

AcubeSAT is an open-source CubeSat mission aiming to explore the effects of microgravity and radiation on eukaryotic cells using a compact microfluidic LoC platform. It is developed by SpaceDot, a volunteer, interdisciplinary student team at the Aristotle University of Thessaloniki and supported by the "Fly Your Satellite! 3" program of the ESA Education Off

L. Coraggio, N. Itaco, G. De Gregorio, A. Gargano

For the first time, we approach in this work the problem of the renormalization of the Gamow-Teller decay operator for nuclear shell-model calculations by way of many-body perturbation theory, starting from a nuclear Hamiltonian and electroweak currents derived consistently by way of the chiral perturbation theory. These are the inputs we need to construct m

Aditya Shankar Narayanan, Dheeraj Vagavolu, Nancy A Day, Meiyappan Nagappan

Diversity with respect to ethnicity and gender has been studied in open-source and industrial settings for software development. Publication avenues such as academic conferences and journals contribute to the growing technology industry. However, there have been very few diversity-related studies conducted in the context of academia. In this paper, we study

Panfeng Cao, Ye Wang, Qiang Zhang, Zaiqiao Meng

Key information extraction (KIE) from scanned documents has gained increasing attention because of its applications in various domains. Although promising results have been achieved by some recent KIE approaches, they are usually built based on discriminative models, which lack the ability to handle optical character recognition (OCR) errors and require labo

M. A. Khamsi, Osvaldo Mendez

We establish the existence and uniqueness of the solution to the Dirichlet problem for the variable exponent $p$-Laplacian on a bounded, smooth domain $\Omega \subset {\mathbb R}^n$, where the boundary datum belongs to $W^{1,p}(\Omega)$. Our analysis considers a continuous and bounded exponent $p$ satisfying $1<\inf\limits_{x\in \Omega}p(x)$ and $\sup\limits

Vladimir Koltchinskii, Minghao Li

Let ${\mathcal P}$ be a family of probability measures on a measurable space $(S,{\mathcal A}).$ Given a Banach space $E,$ a functional $f:E\mapsto {\mathbb R}$ and a mapping $\theta: {\mathcal P}\mapsto E,$ our goal is to estimate $f(\theta(P))$ based on i.i.d. observations $X_1,\dots, X_n\sim P, P\in {\mathcal P}.$ In particular, if ${\mathcal P}=\{P_{\the

Florian Leben, Edison Leguizamón, Carsten Trunk, Monika Winklmeier

The limit point and limit circle classification of real Sturm-Liouville problems by H. Weyl more than 100 years ago was extended by A.R. Sims around 60 years ago to the case when the coefficients are complex. Here, the main result is a collection of various criteria which allow us to decide to which class of Sims' scheme a given Sturm-Liouville problem with

AbdelRahim Elmadany, El Moatez Billah Nagoudi, Muhammad Abdul-Mageed

Understanding Arabic text and generating human-like responses is a challenging endeavor. While many researchers have proposed models and solutions for individual problems, there is an acute shortage of a comprehensive Arabic natural language generation toolkit that is capable of handling a wide range of tasks. In this work, we present a novel Arabic text-to-

Ladislav Hlavatý, Ivo Petr

Poisson-Lie T-duality/plurality was recently generalized to Jacobi-Lie T-plurality formulated in terms of Double Field Theory and based on Leibniz algebras given by structure coefficients $f_{ab}{}^{c},f_{c}{}^{ab},$ and $Z_a,Z^a$. We investigate three- and four-dimensional sigma models corresponding to six-dimensional Leibniz algebras with $f_b{}^{ba}\neq 0

Yifan Tang, M. Rahmani Dehaghani, Pouyan Sajadi, Shahriar Bakrani Balani

This paper aims to propose an online two-stage thermal history prediction method, which could be integrated into a metal AM process for performance control. Based on the similarity of temperature curves (curve segments of a temperature profile of one point) between any two successive layers, the first stage of the proposed method designs a layer-to-layer pre

Bradley W. Hindman, J. R. Fuentes

Recent measurements of Jupiter's gravitational field (by Juno) and seismology of Saturn's rings (by Cassini) strongly suggest that both planets have a stably-stratified core that still possesses a primordial gradient in the concentration of heavy elements. The existence of such a "diffusely" stratified core has been a surprise as it was long expected that th

Jianming Huang, Xun Su, Zhongxi Fang, Hiroyuki Kasai

In machine learning, Optimal Transport (OT) theory is extensively utilized to compare probability distributions across various applications, such as graph data represented by node distributions and image data represented by pixel distributions. In practical scenarios, it is often necessary to solve multiple OT problems. Traditionally, these problems are trea

Esteban M. Rangel, S. John Pennycook, Adrian Pope, Nicholas Frontiere

The first generation of exascale systems will include a variety of machine architectures, featuring GPUs from multiple vendors. As a result, many developers are interested in adopting portable programming models to avoid maintaining multiple versions of their code. It is necessary to document experiences with such programming models to assist developers in u

Alexander Bogatskiy, Timothy Hoffman, Jan T. Offermann

As particle accelerators increase their collision rates, and deep learning solutions prove their viability, there is a growing need for lightweight and fast neural network architectures for low-latency tasks such as triggering. We examine the potential of one recent Lorentz- and permutation-symmetric architecture, PELICAN, and present its instances with as f

Robert Kerschner, Rakesh John Amala Arokia Nathan, Rafal Mantiuk, Oliver Bimber

Both humans and computational methods struggle to discriminate the depths of objects hidden beneath foliage. However, such discrimination becomes feasible when we combine computational optical synthetic aperture sensing with the human ability to fuse stereoscopic images. For object identification tasks, as required in search and rescue, wildlife observation,

Ondřej Kobza, Jan Čuhel, Tommaso Gargiani, David Herel

We present our SocialBot -- Alquist~5.0 -- developed for the Alexa Prize SocialBot Grand Challenge~5. Building upon previous versions of our system, we introduce the NRG Barista and outline several innovative approaches for integrating Barista into our SocialBot, improving the overall conversational experience. Additionally, we extend our SocialBot to suppor

Guoqi Yan

We compute the $RO(D_{2p})$-graded cohomology of a point with constant coefficient $\underline{\mathbb{Z}}$ together with its Green functor structure. Here $D_{2p}$ is the dihedral group with $p$ an odd prime. This result extends the additive computation of Kriz-Lu.

Muhammad Abdul-Mageed, AbdelRahim Elmadany, Chiyu Zhang, El Moatez Billah Nagoudi

We describe the findings of the fourth Nuanced Arabic Dialect Identification Shared Task (NADI 2023). The objective of NADI is to help advance state-of-the-art Arabic NLP by creating opportunities for teams of researchers to collaboratively compete under standardized conditions. It does so with a focus on Arabic dialects, offering novel datasets and defining

Georgios Valogiannis, Sihan Yuan, Cora Dvorkin

We perform a reanalysis of the BOSS CMASS DR12 galaxy dataset using a simulation-based emulator for the Wavelet Scattering Transform (WST) coefficients. Moving beyond our previous works, which laid the foundation for the first galaxy clustering application of this estimator, we construct a neural net-based emulator for the cosmological dependence of the WST

Yaoyao Liu, Yingying Li, Bernt Schiele, Qianru Sun

Not forgetting old class knowledge is a key challenge for class-incremental learning (CIL) when the model continuously adapts to new classes. A common technique to address this is knowledge distillation (KD), which penalizes prediction inconsistencies between old and new models. Such prediction is made with almost new class data, as old class data is extreme

A. P. Santos, Ishan Srivastava, Leonardo E. Silbert, Jeremy B. Lechman

Granular matter takes many paths to pack. Gentle compression, compaction or repetitive tapping can happen in natural and industrial processes. The path influences the packing microstructure, and thus macroscale properties, particularly for frictional grains. We perform discrete element modeling simulations to construct packings of frictional spheres implemen

Ryan Chipwanya

The stock market has been established since the 13th century, but in the current epoch of time, it is substantially more practicable to anticipate the stock market than it was at any other point in time due to the tools and data that are available for both traditional and algorithmic trading. There are many different machine learning models that can do time-

James K Ruffle, Henry Watkins, Robert J Gray, Harpreet Hyare

The architecture of the brain is too complex to be intuitively surveyable without the use of compressed representations that project its variation into a compact, navigable space. The task is especially challenging with high-dimensional data, such as gene expression, where the joint complexity of anatomical and transcriptional patterns demands maximum compre

Mehmet Gok, Mehmet Tekerek, Hamza Aydemir

Different methods are used for a mobile robot to go to a specific target location. These methods work in different ways for online and offline scenarios. In the offline scenario, an environment map is created once, and fixed path planning is made on this map to reach the target. Path planning algorithms such as A* and RRT (Rapidly-Exploring Random Tree) are

Gregory Holste, Yiliang Zhou, Song Wang, Ajay Jaiswal

Many real-world image recognition problems, such as diagnostic medical imaging exams, are "long-tailed" $\unicode{x2013}$ there are a few common findings followed by many more relatively rare conditions. In chest radiography, diagnosis is both a long-tailed and multi-label problem, as patients often present with multiple findings simultaneously. While resear

Saiteja Utpala, Sara Hooker, Pin Yu Chen

Numerous studies have highlighted the privacy risks associated with pretrained large language models. In contrast, our research offers a unique perspective by demonstrating that pretrained large language models can effectively contribute to privacy preservation. We propose a locally differentially private mechanism called DP-Prompt, which leverages the power

Cari Cesarotti, Rikab Gambhir

As the field examines a future muon collider as a possible successor to the LHC, we must consider how to fully utilize not only the high-energy particle collisions, but also any lower-energy staging facilities necessary in the R&D process. An economical and efficient possibility is to use the accelerated muon beam from either the full experiment or from cool

Youshan Zhang, Jialu Li

Achieving high-performance audio denoising is still a challenging task in real-world applications. Existing time-frequency methods often ignore the quality of generated frequency domain images. This paper converts the audio denoising problem into an image generation task. We first develop a complex image generation SwinTransformer network to capture more inf

Sander Schulhoff, Jeremy Pinto, Anaum Khan, Louis-François Bouchard

Large Language Models (LLMs) are deployed in interactive contexts with direct user engagement, such as chatbots and writing assistants. These deployments are vulnerable to prompt injection and jailbreaking (collectively, prompt hacking), in which models are manipulated to ignore their original instructions and follow potentially malicious ones. Although wide

Samuel Y W Low, Simone D'Amico

Precise relative navigation is a critical enabler for distributed satellites to achieve new mission objectives impossible for a monolithic spacecraft. Carrier phase differential GPS (CDGPS) with integer ambiguity resolution (IAR) is a promising means of achieving cm-level accuracy for high-precision Rendezvous, Proximity-Operations and Docking (RPOD), In-Spa

Matteo Scandi, Paolo Abiuso, Dario De Santis, Jacopo Surace

Statistical quantifiers are generically required to contract under physical evolutions, following the intuition that information should be lost under noisy transformations. This principle is very relevant in statistics, and it even allows to derive uniqueness results based on it: by imposing their contractivity under any physical maps, the Chentsov-Petz theo

Daniel Pérez Herrera, Zheng Chen, Erik G. Larsson

This work centers on the communication aspects of decentralized learning over wireless networks, using consensus-based decentralized stochastic gradient descent (D-SGD). Considering the actual communication cost or delay caused by in-network information exchange in an iterative process, our goal is to achieve fast convergence of the algorithm measured by imp

Ziqin Chen, Yongqiang Wang

Distributed online learning has been proven extremely effective in solving large-scale machine learning problems over streaming data. However, information sharing between learners in distributed learning also raises concerns about the potential leakage of individual learners' sensitive data. To mitigate this risk, differential privacy, which is widely regard

Stephen R. Kane

Exoplanets have been detected around stars at various stages of their lives, ranging from young stars emerging from formation, to latter stages of evolution, including white dwarfs and neutron stars. Post main sequence stellar evolution can result in dramatic, and occasionally traumatic, alterations to the planetary system architecture, such as tidal disrupt

Lakshmikar R. Polamreddy, Youshan Zhang

The majority of road accidents occur because of human errors, including distraction, recklessness, and drunken driving. One of the effective ways to overcome this dangerous situation is by implementing self-driving technologies in vehicles. In this paper, we focus on building an efficient deep-learning model for self-driving cars. We propose a new and effect

J. F. Zhu, H. L. Ding, H. K. Li, J. W. Han

In FEL (Free-Electron Laser) accelerators, LLRF (Low-Level Radiofrequency) systems usually deploy feedback or feedforward algorithms requiring precise microwave measurement. The slow drift of the clock allocation network of LLRF significantly impacts the measured microwave phase, thereby affecting the stability of the closed-loop operation. The reference tra

Cassandra Tong Ye, Jiashu Han, Kunzan Liu, Anastasios Angelopoulos

Scanning microscopy systems, such as confocal and multiphoton microscopy, are powerful imaging tools for probing deep into biological tissue. However, scanning systems have an inherent trade-off between acquisition time, field of view, phototoxicity, and image quality, often resulting in noisy measurements when fast, large field of view, and/or gentle imagin