March 2024 arXiv papers — page 122

Jan Kalinowski, Wojciech Kotlarski

The Minimal R-symmetric Supersymmetric Standard Model (MRSSM) is a well motivated BSM model which can accommodate the observed 125 GeV Higgs boson in agreement with electroweak precision observables, in particular with the $W$ boson mass and $T$ parameter. In the 2016 paper we showed that the SM-like 125 GeV Higgs state can be also realised as the second-to-

Keller Blackwell, Mary Wootters

A Homomorphic Secret Sharing (HSS) scheme is a secret-sharing scheme that shares a secret $x$ among $s$ servers, and additionally allows an output client to reconstruct some function $f(x)$ using information that can be locally computed by each server. A key parameter in HSS schemes is download rate, which quantifies how much information the output client ne

Fatima Tuz Zohora, Vedant Karia, Nicholas Soures, Dhireesha Kudithipudi

Edge devices operating in dynamic environments critically need the ability to continually learn without catastrophic forgetting. The strict resource constraints in these devices pose a major challenge to achieve this, as continual learning entails memory and computational overhead. Crossbar architectures using memristor devices offer energy efficiency throug

Oliver Müller, Marcel S. Pawlowski, Yves Revaz, Aku Venhola

Dwarf galaxies in groups of galaxies provide excellent test cases for models of structure formation. This led to a so-called small-scale crisis, including the famous missing satellite and too-big-to-fail problems. It was suggested that these two problems are solved by the introduction of baryonic physics in cosmological simulations. We test for the nearby gr

Zilin Si, Gu Zhang, Qingwei Ben, Branden Romero

We introduce DIFFTACTILE, a physics-based differentiable tactile simulation system designed to enhance robotic manipulation with dense and physically accurate tactile feedback. In contrast to prior tactile simulators which primarily focus on manipulating rigid bodies and often rely on simplified approximations to model stress and deformations of materials in

Ruiyi Wang, Haofei Yu, Wenxin Zhang, Zhengyang Qi

Humans learn social skills through both imitation and social interaction. This social learning process is largely understudied by existing research on building language agents. Motivated by this gap, we propose an interactive learning method, SOTOPIA-$\pi$, improving the social intelligence of language agents. This method leverages behavior cloning and self-

Gesa Sarnighausen, Anne Wald, Alexander Meaney

Reconstructing a dynamic object with affine motion in computerized tomography (CT) leads to motion artifacts if the motion is not taken into account. In most cases, the actual motion is neither known nor can be determined easily. As a consequence, the respective model that describes CT is incomplete. The iterative RESESOP-Kaczmarz method can - under certain

Andreas Rückriegel, Dmytro Tarasevych, Peter Kopietz

We use the spin functional renormalization group to investigate the $J_1$-$J_2$ quantum Heisenberg model on a square lattice. By incorporating sum rules associated with the fixed length of the spin operators as well as the nontrivial quantum dynamics implied by the spin algebra, we are able to compute the ground state phase diagram for arbitrary spin $S$, in

Atuhurra Jesse, N'guessan Yves-Roland Douha, Pabitra Lenka

The detection of diseases within plants has attracted a lot of attention from computer vision enthusiasts. Despite the progress made to detect diseases in many plants, there remains a research gap to train image classifiers to detect the cacao swollen shoot virus disease or CSSVD for short, pertinent to cacao plants. This gap has mainly been due to the unava

Satish Kumar, Nguyen Ba An, Anirban Pathak

The existing notion of the shared entangled state-assisted remote preparation of unitary operator (equivalently the existing notion of quantum remote control) using local operation and classical communication is generalized to a scenario where under the control of a supervisor two users can jointly implement arbitrary unitaries (one unknown unitary operation

Mohit Kumar, Fabio Semperlotti

The geometric phase provides important mathematical insights to understand the fundamental nature and evolution of the dynamic response in a wide spectrum of systems ranging from quantum to classical mechanics. While the concept of geometric phase, which is an additional phase factor occurring in dynamical systems, holds the same meaning across different fie

Pamela C. Guruciaga, Takafumi Ichikawa, Steffen Plunder, Takashi Hiiragi

Topological defects determine the collective properties of anisotropic materials. How their configurations are controlled is not well understood however, especially in 3D. In living matter moreover, 2D defects have been linked to biological functions, but the role of 3D polar defects is unclear. Combining computational and experimental approaches, we investi

Gregorio Chinni, Makhlouf Derridj

We study the microlocal regularity of the analytic/Gevrey vectors for the following class of second order partial differential equations \begin{align*} P(x,D) = \sum_{\ell,j=1}^{n} a_{\ell,j}(x) D_{\ell} D_{j} + \sum_{\ell=1}^{n} i b_{\ell}(x) D_{\ell} +c(x), \end{align*} where $a_{\ell,j}(x) = a_{j,\ell}(x)$, $b_{\ell}(x)$, $\ell,j \in \lbrace 1,\dots,\, n\

Ann Aisling, Renee Saraka, Nicolas J. Alvarez

This work focuses on inferring the molecular state of the polymer chain required to induce elasto-capillary stress relaxation and the accurate measure of the polymer relaxation time in uniaxial stretching of dilute polymer solutions. This work is facilitated by the discovery that constant velocity applied at early times leads to initial constant extension ra

Pantelis Andreou, Stavros Konstantinidis, Taylor J. Smith

We build on recent research on polynomial randomized approximation (PRAX) algorithms for the hard problems of NFA universality and NFA equivalence. Loosely speaking, PRAX algorithms use sampling of infinite domains within any desired accuracy $\delta$. In the spirit of experimental mathematics, we extend the concept of PRAX algorithms to be applicable to the

Andrew S. Darmawan

Information obtained from noise characterization of a quantum device can be used in classical decoding algorithms to improve the performance of quantum error-correcting codes. Focusing on the surface code under local (i.e. single-qubit) noise, we present a simple method to determine the maximum extent to which adapting a surface-code decoder to a noise featu

Guillermo Lara, Harald P. Pfeiffer, Nikolas A. Wittek, Nils L. Vu

One of the most promising avenues to perform numerical evolutions in theories beyond General Relativity is the fixing-the-equations approach, a proposal in which new ``driver'' equations are added to the evolution equations in a way that allows for stable numerical evolutions. In this direction, we extend the numerical relativity code SpECTRE to evolve a ``f

Anthony H. Minter, Felix J. Lockman, S. A. Balashev, H. Alyson Ford

We have used the Green Bank Telescope (GBT) to search for the OH molecule at several locations in the Smith Cloud, one of the most prominent of the high-velocity clouds that surround the Milky Way. Five positions with a high HI column density were selected as targets for individual pointings, along with a square degree around a molecular cloud detected with

Davide Guidobene, Guido Cera

The Maximum Common Subgraph (MCS) problem plays a crucial role across various domains, bridging theoretical exploration and practical applications in fields like bioinformatics and social network analysis. Despite its wide applicability, MCS is notoriously challenging and is classified as an NP-Complete (NPC) problem. This study introduces new heuristics aim

Sebastiaan Maes, Raghav Malhotra

We study a setting where an analyst has access to purely aggregate information about the consumption choices of a heterogenous population of individuals. We show that observing the statistical moments of market demand allows the analyst to test aggregate data for rationality. Interestingly, just the mean and variance of demand carry observable restrictions.

Xiang-Yu Li, Hailong Wang, TC Chakraborty, Armin Sorooshian

Aerosol-cloud interactions (ACI) pose the largest uncertainty for climate projections. Among many challenges of understanding ACI, the question of whether ACI is deterministic or stochastic has not been explicitly formulated and asked. Here we attempt to answer this question by predicting cloud droplet number concentration Nc from aerosol number concentratio

Yagmur Yigit, William J Buchanan, Madjid G Tehrani, Leandros Maglaras

Over the last decade, Artificial Intelligence (AI) has become increasingly popular, especially with the use of chatbots such as ChatGPT, Gemini, and DALL-E. With this rise, large language models (LLMs) and Generative AI (GenAI) have also become more prevalent in everyday use. These advancements strengthen cybersecurity's defensive posture and open up new att

Paraskevas Pegios, Manxi Lin, Nina Weng, Morten Bo Søndergaard Svendsen

Obstetric ultrasound image quality is crucial for accurate diagnosis and monitoring of fetal health. However, acquiring high-quality standard planes is difficult, influenced by the sonographer's expertise and factors like the maternal BMI or fetus dynamics. In this work, we explore diffusion-based counterfactual explainable AI to generate realistic, high-qua

Heejune Sheen, Siyu Chen, Tianhao Wang, Harrison H. Zhou

We study gradient flow on the exponential loss for a classification problem with a one-layer softmax attention model, where the key and query weight matrices are trained separately. Under a separability assumption on the data, we show that when gradient flow achieves the minimal loss value, it further implicitly minimizes the nuclear norm of the product of t

Eleonora Fiorellino, Peter Abraham, Agnes Kospal, Maria Kun

Context. Gaia18cjb is one of the Gaia-alerted eruptive young star candidates which has been experiencing a slow and strong brightening during the last 13 years, similar to some FU Orionis-type objects. Aims. The aim of this work is to derive the young stellar nature of Gaia18cjb, determine its physical and accretion properties to classify its variability. Me

Charu Goel, Bruce Reznick

In this paper we study the cones corresponding to sums of squares of $n$-ary $d$-ic forms with at most $k$ terms. We show that these are strictly nested as $k$ increases, leading to the usual sum of squares cone. We also discuss the duals of these cones. For $n \ge 3$, we construct indefinite irreducible $n$-ary $d$-ic forms with exactly $k$ terms for $2 \le

Luca V. Iliesiu, Adam Levine, Henry W. Lin, Henry Maxfield

What is the bulk Hilbert space of quantum gravity? In this paper, we resolve this problem in 2d JT gravity, both with and without matter, providing an explicit definition of a non-perturbative Hilbert space specified in terms of metric variables. The states are wavefunctions of the length and matter state, but with a non-trivial and highly degenerate inner p

Daniel Kovac, Jan Mucha, Jon Alvarez Justo, Jiri Mekyska

This article explores the latest Convolutional Neural Networks (CNNs) for cloud detection aboard hyperspectral satellites. The performance of the latest 1D CNN (1D-Justo-LiuNet) and two recent 2D CNNs (nnU-net and 2D-Justo-UNet-Simple) for cloud segmentation and classification is assessed. Evaluation criteria include precision and computational efficiency fo

Shangding Gu, Alois Knoll, Ming Jin

The development of Large Language Models (LLMs) often confronts challenges stemming from the heavy reliance on human annotators in the reinforcement learning with human feedback (RLHF) framework, or the frequent and costly external queries tied to the self-instruct paradigm. In this work, we pivot to Reinforcement Learning (RL) -- but with a twist. Diverging

Soumen Basu, Mayuna Gupta, Chetan Madan, Pankaj Gupta

In recent years, automated Gallbladder Cancer (GBC) detection has gained the attention of researchers. Current state-of-the-art (SOTA) methodologies relying on ultrasound sonography (US) images exhibit limited generalization, emphasizing the need for transformative approaches. We observe that individual US frames may lack sufficient information to capture di

Rik van Noord, Taja Kuzman, Peter Rupnik, Nikola Ljubešić

Large, curated, web-crawled corpora play a vital role in training language models (LMs). They form the lion's share of the training data in virtually all recent LMs, such as the well-known GPT, LLaMA and XLM-RoBERTa models. However, despite this importance, relatively little attention has been given to the quality of these corpora. In this paper, we compare

Michael H. Seymour, Siddharth Sule

The Single Instruction, Multiple Thread (SIMT) paradigm of GPU programming does not support the branching nature of a parton shower algorithm by definition. However, modern GPUs are designed to schedule threads with diverging processes independently, allowing them to handle such branches. With regular thread synchronisation and careful treatment of the indiv

Federica Milinanni, Pierre Nyquist

In this paper, we prove large deviation principles for the empirical measures associated with the Independent Metropolis Hastings (IMH) sampler and the Metropolis-adjusted Langevin Algorithm (MALA). These are the first large deviation results for empirical measures of Markov chains arising from specific Metropolis-Hastings methods on a continuous state space

Jakob Roth, Martin Reinecke, Gordian Edenhofer

JAX is widely used in machine learning and scientific computing, the latter of which often relies on existing high-performance code that we would ideally like to incorporate into JAX. Reimplementing the existing code in JAX is often impractical and the existing interface in JAX for binding custom code either limits the user to a single Jacobian product or re

Michael Herty, Chiara Segala, Giuseppe Visconti

Residual deep neural networks are formulated as interacting particle systems leading to a description through neural differential equations, and, in the case of large input data, through mean-field neural networks. The mean-field description allows also the recast of the training processes as a controllability problem for the solution to the mean-field dynam

Tiange Xiang, Yixiao Zhang, Yongyi Lu, Alan Yuille

Radiography imaging protocols focus on particular body regions, therefore producing images of great similarity and yielding recurrent anatomical structures across patients. Exploiting this structured information could potentially ease the detection of anomalies from radiography images. To this end, we propose a Simple Space-Aware Memory Matrix for In-paintin

Ben Athiwaratkun, Shiqi Wang, Mingyue Shang, Yuchen Tian

Generative models, widely utilized in various applications, can often struggle with prompts corresponding to partial tokens. This struggle stems from tokenization, where partial tokens fall out of distribution during inference, leading to incorrect or nonsensical outputs. This paper examines a technique to alleviate the tokenization artifact on text completi

Xiangchun Chen, Jiannong Cao, Zhixuan Liang, Yuvraj Sahni

Collaborative edge computing (CEC) has emerged as a promising paradigm, enabling edge nodes to collaborate and execute microservices from end devices. Microservice offloading, a fundamentally important problem, decides when and where microservices are executed upon the arrival of services. However, the dynamic nature of the real-world CEC environment often l

Ayan Ghosh, Souvik Chakraborty, Ranit Dutta, Adhip Agarwala

Recent experiments on magic-angle twisted bilayer graphene (MATBLG) have revealed the formation of flatbands, suggesting that correlation effects are likely to dominate in this system. Yet, a global transport measurement showing distinct signatures of strong correlations like local moments arising from the flatbands is missing. Utilizing thermopower as a sen

Zan Ahmad, Minglang Yin, Yashil Sukurdeep, Noam Rotenberg

Morphological variations in the left atrial appendage (LAA) are associated with different levels of ischemic stroke risk for patients with atrial fibrillation (AF). Studying LAA morphology can elucidate mechanisms behind this association and lead to the development of advanced stroke risk stratification tools. However, current categorical descriptions of LAA

Aliénor Rivière, Stéphane Perrard

Bubbles drive gas and chemical transfers in various industrial and geophysical contexts, in which flows are typically turbulent. As gas and chemical transfers are bubble size dependent, their quantification requires a prediction of bubble breakup. The most common idea, introduced by Kolmogorov and Hinze, is to consider a sharp limit between breaking and non

Laura J. Johnston, Takoua Jendoubi

In the context of higher education's evolving dynamics post-COVID-19, this paper assesses the impact of new pedagogical incentives implemented in a first-year undergraduate computing module at University College London. We employ a mixed methods approach, combining learning analytics with qualitative data, to evaluate the effectiveness of these incentives on

Laciel Alonso-Llanes, Angel Garcimartín, Iker Zuriguel

We present experimental results on the single file motion of a group of robots interacting with each other through position sensors. We successfully replicate the fundamental diagram typical of these systems, with a transition from free flow to congested traffic as the density of the system increases. In the latter scenario we also observe the characteristic

Wanyun Li, Pinxue Guo, Xinyu Zhou, Lingyi Hong

Contemporary Video Object Segmentation (VOS) approaches typically consist stages of feature extraction, matching, memory management, and multiple objects aggregation. Recent advanced models either employ a discrete modeling for these components in a sequential manner, or optimize a combined pipeline through substructure aggregation. However, these existing e

Han Yan, Xian Chen, Jinhai Zhang, Fan Zhang

The recent increasing interest in detecting gravitational waves (GWs) by lunar seismic measurement urges us to have a clear understanding of the response of the moon to passing GWs. In this paper, we clarify the relationship between two seemly different response functions which have been derived previously using two different methods, one taking the field-th

Qian Ding, Jie Yang, Yang Luo, Chunbo Luo

This commentary dedicates to envision what role THz is going to play in the coming human-centric 6G era. Three distinct THz network types including outdoor, indoor, and body area networks are discussed, with an emphasis on their capabilities in human body detection. Synthesizing these networks will unlock a bunch of fascinating applications across industrial

Roozbeh Qorbanian, Nils Löhndorf, David Wozabal

Corporate renewable power purchase agreements (PPAs) are long-term contracts that enable companies to source renewable energy without having to develop and operate their own capacities. Typically, producers and consumers agree on a fixed per-unit price at which power is purchased. The value of the PPA to the buyer depends on the so called capture price defin

Manish Kr. Singh, A. K. Bera, Ajay Kumar, S. M. Yusuf

We investigate crystal structure and magnetic properties including spin relaxation and magnetocaloric effect in NASICON type NaFe$_2$PO$_4$(SO$_4$)$_2$ sample. The Rietveld refinement of x-ray and neutron diffraction patterns show a rhombohedral crystal structure with the R$\bar{3}$c space group. The core-level spectra confirm the desired oxidation state of

Petri P. Karenlampi

This paper investigates the financial economics of simple periodic systems. Well-established financial procedures appear to be complicated, and lead to partially biased results. Probability theory is applied, and the focus is on the finances of simple periodic growth processes, in the absence of intermediate divestments. The expected value of the profit rate

Noah Graham, Herbert Weigel

We compute the renormalized one-loop quantum corrections to the energy density $T_{00}(x)$ and pressure $T_{11}(x)$ for solitons in the $1+1$ dimensional scalar sine-Gordon and kink models. We show how precise implementation of counterterms in dimensional regularization resolves previously identified discrepancies between the integral of $T_{00}(x)$ and the

Robert A. Lawrence

The MBD model of the van der Waals interaction is extended to also consider magnetic interactions, and it is demonstrated how this can be made to reproduce the Heisenberg Hamiltonian. It is found that this leads to a weak coupling between the charge dipole waves that are the basis for the electric-only van der Waals interaction and the spin-dipole waves (mag

Ben Athiwaratkun, Sujan Kumar Gonugondla, Sanjay Krishna Gouda, Haifeng Qian

This study introduces bifurcated attention, a method designed to enhance language model inference in shared-context batch decoding scenarios. Our approach addresses the challenge of redundant memory IO costs, a critical factor contributing to latency in high batch sizes and extended context lengths. Bifurcated attention achieves this by strategically dividin

Gabriela Barenboim, Pyungwon Ko, Wan-il Park

The details of the minimal cosmological standard model (MCSM) proposed in [arXiv:2403.05390.] are discussed. The model is based on the scale-symmetry and the global Peccei-Quinn(PQ) symmetry with a key assumption that the latter is broken only in the gravity sector in a scale-invariant manner. We show that the model provides a quite simple unified framework

Laura Zarraoa, Romain Veyron, Tomas Lamich, Lorena C. Bianchet

Using a single neutral 87Rb atom held in an optical trap, and "quantum jump" detection of single-photon-initiated state changes, we demonstrate a single-photon quantum jump photodetector (QJPD) with intrinsically narrow bandwidth and strong rejection of out-of-band photons, of interest for detecting weak optical signals in the presence of a strong broadband

Gautham Govind Anil, Pascal Esser, Debarghya Ghoshdastidar

Contrastive learning is a paradigm for learning representations from unlabelled data that has been highly successful for image and text data. Several recent works have examined contrastive losses to claim that contrastive models effectively learn spectral embeddings, while few works show relations between (wide) contrastive models and kernel principal compon

Sanjiv Kumar Bariwal, Rajesh Kumar

This article aims to provide approximate solutions for the non-linear collision-induced breakage equation using two different semi-analytical schemes, i.e., variational iteration method (VIM) and optimized decomposition method (ODM). The study also includes the detailed convergence analysis and error estimation for ODM in the case of product collisional ($K(

Cecile Andersen, Christopher J. Lustri, Scott W. McCue, Philippe H. Trinh

We study self-similar viscous fingering for the case of divergent flow within a wedge-shaped Hele-Shaw cell. Previous authors have conjectured the existence of a countably-infinite number of selected solutions, each distinguished by a different value of the relative finger angle. Interestingly, the associated solution branches have been posited to merge and

Michael Kastner, Philip Osterholz, Christian Gross

We introduce a protocol that gives access to out-of-time-ordered correlation functions in many-body quantum systems. Unlike other such protocols, our proposal, which can be applied to arbitrary initial states, neither requires ancilla degrees of freedom to the quantum system of interest, nor has the need for randomized measurements. Nontrivial experimental c

Markus Mayer, Ashutosh Dash, Gabriele Inghirami, Hannah Elfner

We present BHAC-QGP, a new numerical code to simulate the evolution of matter created in heavy-ion collisions. BHAC-QGP is based on the Black Hole Accretion Code (BHAC), which has been designed to model astrophysical processes through the solution of the equations of general-relativistic magnetohydrodynamics. Like the mother code, BHAC-QGP uses Adaptive Mesh

Markus Mayer, Ashutosh Dash, Gabriele Inghirami, Hannah Elfner

We present BHAC-QGP, a new numerical code to simulate the evolution of matter created in heavy-ion collisions in the presence of electromagnetic fields. It is derived from the Black Hole Accretion Code (BHAC), which has been designed to model astrophysical processes in a general-relativistic magnetohydrodynamical description. As the original Black Hole Accre

Gianluca Basso, Alessandro Codenotti, Andrea Vaccaro

The space of chains on a compact connected space encodes all the different ways of continuously growing out of a point until exhausting the space. A chain is \emph{generic} if its orbit under the action of the underlying homeomorphism group is comeager. In this paper we show that a large family of topological spaces do not have a generic chain: in addition t

Mar Bastero-Gil, Teresa Huertas-Roldan, Daniel Santos

The discrepancies in different measurements of the lifetime of isolated neutrons could be resolved by considering an extra neutron decay channel into dark matter, with a branching ratio of the order of $O(1$\%). Although the decay channel into a dark fermion $\chi$ plus visible matter has been already experimentally excluded, a dark decay with either a scala

Xiaopeng Xia

We establish a theorem concerning the commuting scheme in characteristic p. As a significant application of this theorem, we derive an explicit lower bound for the characteristic p, ensuring the validity of the higher-dimensional Chevalley restriction theorem for classical groups.

Erlend Frayling, Jake Lever, Graham McDonald

The challenge of accessing historical patient data for clinical research, while adhering to privacy regulations, is a significant obstacle in medical science. An innovative approach to circumvent this issue involves utilising synthetic medical records that mirror real patient data without compromising individual privacy. The creation of these synthetic datas

Maryam Tavassoli Sabzevari, Matteo Esposito, Arif Ali Khan, Davide Taibi

Quantum Computing (QC) is transitioning from theoretical frameworks to an indispensable powerhouse of computational capability, resulting in extensive adoption across both industrial and academic domains. QC presents exceptional advantages, including unparalleled processing speed and the potential to solve complex problems beyond the capabilities of classica

Tzvi Diskin, Ami Wiesel

We consider the use of deep learning for covariance estimation. We propose to globally learn a neural network that will then be applied locally at inference time. Leveraging recent advancements in self-supervised foundational models, we train the network without any labeling by simply masking different samples and learning to predict their covariance given t

F. Becattini, D. Roselli

We present a study of energy density and pressure of a free real scalar quantum field after its decoupling from a thermal bath in the spatially flat Friedman-Lema\^itre-Robertson-Walker space-time by solving the Klein-Gordon equation both analytically and numerically for different predetermined scale factor functions $a(t)$. The energy density and pressure,

Vikash Sharma, Sitaram Ramakrishnan, S. S. Jayakrishnan, Surya Rohith Kotla

Charge density wave (CDW) systems are proposed to exhibit application potential for electronic and optoelectronic devices. Therefore, identifying new materials that exhibit a CDW state at room temperature is crucial for the development of CDW-based devices. Here, we present a non-layered tetragonal polymorph of Gd2Os3Si5, which exhibits a CDW state at room t

Wayne M Lawton, August K. Tsikh

This paper has three aims. First, for $n \geq 1$ we construct a family of real-rooted trigonometric polynomial maps $P : \mathbb C^n \mapsto \mathbb C^n$ whose divisors are Fourier Quasicrystals (FQ). For $n = 1$ these divisors include the first nontrivial FQ with positive integer coefficients constructed by Kurasov and Sarnak [47, and for $n > 1$ they overl

Charles Hovine, Alexander Bertrand

A wireless sensor network often relies on a fusion center to process the data collected by each of its sensing nodes. Such an approach relies on the continuous transmission of raw data to the fusion center, which typically has a major impact on the sensors' battery life. To address this issue in the particular context of spatial filtering and signal fusion p

Joe Gilkes, Mark Storr, Reinhard J. Maurer, Scott Habershon

Predicting the degradation processes of molecules over long timescales is a key aspect of industrial materials design. However, it is made computationally challenging by the need to construct large networks of chemical reactions that are relevant to the experimental conditions that kinetic models must mirror, with every reaction requiring accurate kinetic da

Alon Hillel-Tuch, Aspen Olmstead

Programming errors, defective hardware components (such as hard disk spindle defects), and environmental hazards can lead to invalid memory operations. In addition, less predictable forms of environmental stress, such as radiation, thermal influence, and energy fluctuations, can induce hardware faults. Sometimes, a soft error can occur instead of a complete

J. Hätinen, A. Ronzani, R. P. Loreto, E. Mykkänen

Replacing the bulky cryoliquid-based cooling stages of cryoenabled instruments by chip-scale refrigeration is envisioned to disruptively reduce the system size similar to microprocessors did for computers. Electronic refrigerators based on superconducting tunnel junctions have been anticipated to provide a solution, but reaching the necessary above the 1-K o

Heitor R. Guimarães, Arthur Pimentel, Anderson R. Avila, Mehdi Rezagholizadeh

Self-supervised speech representation learning enables the extraction of meaningful features from raw waveforms. These features can then be efficiently used across multiple downstream tasks. However, two significant issues arise when considering the deployment of such methods ``in-the-wild": (i) Their large size, which can be prohibitive for edge application

David Shulman, Assaf Israeli, Yael Botnaro, Ori Margalit

We present an innovative approach leveraging Physics-Guided Neural Networks (PGNNs) for enhancing agricultural quality assessments. Central to our methodology is the application of physics-guided inverse regression, a technique that significantly improves the model's ability to precisely predict quality metrics of crops. This approach directly addresses the

Paul Ardis, Arjuna Flenner

Deep Neural Networks (DNNs) do not inherently compute or exhibit empirically-justified task confidence. In mission critical applications, it is important to both understand associated DNN reasoning and its supporting evidence. In this paper, we propose a novel Bayesian approach to extract explanations, justifications, and uncertainty estimates from DNNs. Our

Jianan Jiang, Xinglin Li, Weiren Yu, Di Wu

In the realm of fashion design, sketches serve as the canvas for expressing an artist's distinctive drawing style and creative vision, capturing intricate details like stroke variations and texture nuances. The advent of sketch-to-image cross-modal translation technology has notably aided designers. However, existing methods often compromise these sketch det

Wentao Jiang, Yige Zhang, Shaozhong Zheng, Si Liu

This survey presents a comprehensive analysis of data augmentation techniques in human-centric vision tasks, a first of its kind in the field. It delves into a wide range of research areas including person ReID, human parsing, human pose estimation, and pedestrian detection, addressing the significant challenges posed by overfitting and limited training data

Liang Chen, Yong Zhang, Yibing Song, Zhen Zhang

Learning domain-invariant semantic representations is crucial for achieving domain generalization (DG), where a model is required to perform well on unseen target domains. One critical challenge is that standard training often results in entangled semantic and domain-specific features. Previous works suggest formulating the problem from a causal perspective

Sajad Faramarzi, Sepideh Javadi, Farshad Zeinali, Hosein Zarini

Mounting a reconfigurable intelligent surface (RIS) on an unmanned aerial vehicle (UAV) holds promise for improving traditional terrestrial network performance. Unlike conventional methods deploying passive RIS on UAVs, this study delves into the efficacy of an aerial active RIS (AARIS). Specifically, the downlink transmission of an AARIS network is investig

Letizia Branca, Giovanni Catino, Davide Dameno, Paolo Mastrolia

We provide optimal pinching results on closed Einstein manifolds with positive Yamabe invariant in any dimension, extending the optimal bound for the scalar curvature due to Gursky and LeBrun in dimension four. We also improve the known bounds of the Yamabe invariant \emph{via} the $L^{\frac{n}{2}}$-norm of the Weyl tensor for low-dimensional Einstein manifo

Phan Viet Hoa Vu, Arnaud Breloy, Frédéric Brigui, Yajing Yan

Interferometric phase linking (IPL) has become a prominent technique for processing images of areas containing distributed scaterrers in SAR interferometry. Traditionally, IPL consists in estimating consistent phase differences between all pairs of SAR images in a time series from the sample covariance matrix of pixel patches on a sliding window. This paper

Pallavi Dani, Timothy Riley

For all integers $p>q>0$ and $k >0$, and all non-elementary torsion-free hyperbolic groups $H$, we construct a hyperbolic group $G$ in which $H$ is a subgroup, such that the distortion function of $H$ in $G$ grows like $\exp^k(n^{p/q})$. Here, $\exp^k$ denotes the $k$-fold-iterated exponential function.

Junsoo Park, Zhigang Wu, John W. Lawson

We demonstrate an efficient and accurate, general-purpose first-principles blueprint for calculating anharmonic vibrational free energy and predicting structural phase transition temperatures of solids. Thermodynamic integration is performed without molecular dynamics using only interatomic force constants to model analogues of the true potential and generat

Thomas Hamori, Changhui Tan

We present a new family of second-order traffic flow models, extending the Aw-Rascle-Zhang (ARZ) model to incorporate nonlocal interactions. Our model includes a specific nonlocal Arrhenius-type look-ahead slowdown factor. We establish both local and global well-posedness theories for these nonlocal ARZ models. In contrast to the local ARZ model, where gener

Yi-Ming Ding, Jun-Song Sun, Nvsen Ma, Gaopei Pan

Efficient and accurate algorithm for partition function, free energy and thermal entropy calculations is of great significance in statistical physics and quantum many-body physics. Here we present an unbiased but low-technical-barrier algorithm within the quantum Monte Carlo framework, which has exceptionally high accuracy and no systemic error. Compared wit

Thomas Übellacker

AcademiaOS is a first attempt to automate grounded theory development in qualitative research with large language models. Using recent large language models' language understanding, generation, and reasoning capabilities, AcademiaOS codes curated qualitative raw data such as interview transcripts and develops themes and dimensions to further develop a ground

K. G. Scheuer, R. G. DeCorby

We used an optomechanical sensor to study the ultrasound generated by manually operated piezoelectric spark igniters. These low-energy sparks produce short-duration acoustic shock-wave pulses, with sub-microsecond rise times and frequency content extending well beyond 2 MHz in air. The same source-receiver combination was then used to demonstrate broadband c

Mengkun She, Felix Seegräber, David Nakath, Kevin Köser

In this paper, we present a complete refractive Structure-from-Motion (RSfM) framework for underwater 3D reconstruction using refractive camera setups (for both, flat- and dome-port underwater housings). Despite notable achievements in refractive multi-view geometry over the past decade, a robust, complete and publicly available solution for such tasks is no

Yi Zhou, Hui Zhang, Jiaqian Yu, Yifan Yang

Vectorized High-Definition (HD) map construction requires predictions of the category and point coordinates of map elements (e.g. road boundary, lane divider, pedestrian crossing, etc.). State-of-the-art methods are mainly based on point-level representation learning for regressing accurate point coordinates. However, this pipeline has limitations in obtaini

Vishwali Mhasawade, Rumi Chunara

Transported mediation effects provide an avenue to understand how upstream interventions (such as improved neighborhood conditions like green spaces) would work differently when applied to different populations as a result of factors that mediate the effects. However, when mediators are missing in the population where the effect is to be transported, these e

Niels Linnemann, Chris Smeenk, Mark Robert Baker

The self-interaction spin-2 approach to general relativity (GR) has been extremely influential in the particle physics community. Leaving no doubt regarding its heuristic value, we argue that a view of the metric field of GR as nothing but a stand-in for a self-coupling field in flat spacetime runs into a dilemma: either the view is physically incomplete in

Zhi-Hai Liu, Chuanchang Zeng, H. Q. Xu

Recently, exciting progress has been made in using the superconducting nanowires coupled to gate-defined quantum dots (QDs) to mimic the Kiteav chain and realize the Majorana-bound states via a poor man's route. The essential ingredient is to balance the interdot elastic-cotunneling (ECT) and crossed Andreev reflection (CAR). As theoretically proposed, this

Daniele Calandriello, Daniel Guo, Remi Munos, Mark Rowland

Ensuring alignment of language models' outputs with human preferences is critical to guarantee a useful, safe, and pleasant user experience. Thus, human alignment has been extensively studied recently and several methods such as Reinforcement Learning from Human Feedback (RLHF), Direct Policy Optimisation (DPO) and Sequence Likelihood Calibration (SLiC) have

Jacopo Salvalaggio, Lina Castiblanco, Jorge Noreña, Emiliano Sefusatti

We provide an analytical description of the galaxy bispectrum covariance and the power spectrum-bispectrum cross-covariance in redshift space that captures the dominant non-Gaussian contributions. The Gaussian prediction for the variance of the halo bispectrum monopole significantly underestimates numerical estimates particularly for squeezed triangles, that

Elkin A. Santos, Maximilian A. Weissflog, Thomas Pertsch, Frank Setzpfandt

We develop a fully vectorial and non-paraxial formalism to describe spontaneous parametric down-conversion in nonlinear thin films. The formalism is capable of treating slabs with a sub-wavelength thickness, describe the associated Fabry-P\'erot effects, and even treat absorptive nonlinear materials. With this formalism, we perform an in-depth study of the d

Zhuang Liu, Kaiming He

We revisit the "dataset classification" experiment suggested by Torralba & Efros (2011) a decade ago, in the new era with large-scale, diverse, and hopefully less biased datasets as well as more capable neural network architectures. Surprisingly, we observe that modern neural networks can achieve excellent accuracy in classifying which dataset an image is fr

Guy P Nason, James L. Wei

This article combines wavelet analysis techniques with machine learning methods for univariate time series forecasting, focusing on three main contributions. Firstly, we consider the use of Daubechies wavelets with different numbers of vanishing moments as input features to both non-temporal and temporal forecasting methods, by selecting these numbers during

Nan Jiang, Zhiyuan Zhang, Hongjie Li, Xiaoxuan Ma

Confronting the challenges of data scarcity and advanced motion synthesis in human-scene interaction modeling, we introduce the TRUMANS dataset alongside a novel HSI motion synthesis method. TRUMANS stands as the most comprehensive motion-captured HSI dataset currently available, encompassing over 15 hours of human interactions across 100 indoor scenes. It i

Mathias Barreto, Olivier Marchal, Julyan Arbel

This paper establishes the optimal sub-Gaussian variance proxy for truncated Gaussian and truncated exponential random variables. The proofs rely on first characterizing the optimal variance proxy as the unique solution to a set of two equations and then observing that for these two truncated distributions, one may find explicit solutions to this set of equa