March 2023 arXiv papers — page 111

Simon Blouin, Huaqing Mao, Falk Herwig, Pavel Denissenkov

We present the first 3D hydrodynamics simulations of the excitation and propagation of internal gravity waves (IGWs) in the radiative interiors of low-mass stars on the red giant branch (RGB). We use the PPMstar explicit gas dynamics code to simulate a portion of the convective envelope and all the radiative zone down to the hydrogen-burning shell of a 1.2$M

Yuji Kodama, Yuancheng Xie

This paper concerns the solutions of the full Kostant-Toda (f-KT) hierarchy in the Hessenberg form and their reductions to the $\ell$-banded Kostant-Toda ($\ell$-KT) hierarchy. We also study the f-KT hierarchy and the corresponding $\ell$-KT hierarchy on simple Lie algebras of type $A, B$ and $G$ based on root space reductions with proper Chevalley systems.

S. Ta Ho, V. Nam Do

This study investigates optical activity and quantum transport in twisted bilayer graphene (TBG) systems, demonstrating that the former results from spatial dispersion effects. The transfer matrix method is used to solve the propagation of electromagnetic waves through two graphene layers that act as the coupling surfaces of a dielectric slab. The resulting

Matthew J. Smith, Rachael V. Phillips, Miguel Angel Luque-Fernandez, Camille Maringe

The Targeted Maximum Likelihood Estimation (TMLE) statistical data analysis framework integrates machine learning, statistical theory, and statistical inference to provide a least biased, efficient and robust strategy for estimation and inference of a variety of statistical and causal parameters. We describe and evaluate the epidemiological applications that

Arman Taghavi-Chabert

We introduce a generalisation of Fefferman's conformal circle bundle over a contact Cauchy-Riemann three-manifold. These can be viewed as exact `perturbations' of Fefferman's structure by a semi-basic one-form, which encodes additional data on the CR three-manifold. We find conditions on the Weyl tensor and the Bach tensor for a Lorentzian conformal four-man

Cong Cao, Huanjing Yue, Xin Liu, Jingyu Yang

Capturing high dynamic range (HDR) images (videos) is attractive because it can reveal the details in both dark and bright regions. Since the mainstream screens only support low dynamic range (LDR) content, tone mapping algorithm is required to compress the dynamic range of HDR images (videos). Although image tone mapping has been widely explored, video tone

Ali Reza Pedram, Takashi Tanaka

This paper explores minimum sensing navigation of robots in environments cluttered with obstacles. The general objective is to find a path plan to a goal region that requires minimal sensing effort. In [1], the information-geometric RRT* (IG-RRT*) algorithm was proposed to efficiently find such a path. However, like any stochastic sampling-based planner, the

Zhuocheng Xie, Dimitri Chauraud, Erik Bitzek, Sandra Korte-Kerzel

Activation of non-basal slip is essential in improving the deformability of hexagonal crystals. However, the mechanism of non-basal slip remains largely unknown, especially for complex intermetallics such as Laves phases. In this work, the prismatic slip systems of C14 Laves crystals and possible metastable states along the slip paths are assessed using atom

Gordon Blower, Simon J. A. Malham

We prove that each member of the non-commutative nonlinear Schrodinger and modified Korteweg--de Vries hierarchy is a Fredholm Grassmannian flow, and for the given linear dispersion relation and corresponding equivalencing group of Fredholm transformations, is unique in the class of odd-polynomial partial differential fields. Thus each member is linearisable

J. L. Rodríguez-Sánchez, J. Cugnon, J. -C. David, J. Hirtz

Experimental studies of nuclear fission induced by fusion, transfer, spallation, fragmentation, and electromagnetic reactions in combination with state-of-the-art calculations are successful to investigate the nuclear dissipation mechanism in normal nuclear matter, containing only nucleons. The dissipation mechanism has been widely studied by the use of many

Mikhail Tokman, Maria Erukhimova, Yongrui Wang, Alexey Belyanin

Many-body Coulomb interactions drastically modify the optical response of highly doped semiconductor quantum wells leading to a merger of all intersubband transition resonances into one sharp peak at the frequency substantially higher than all single-particle transition frequencies. Starting from standard density matrix equations for the gas of pairwise inte

Zhongwen Zhang, Yuri Boykov

We propose "collision cross-entropy" as a robust alternative to Shannon's cross-entropy (CE) loss when class labels are represented by soft categorical distributions y. In general, soft labels can naturally represent ambiguous targets in classification. They are particularly relevant for self-labeled clustering methods, where latent pseudo-labels are jointly

Mrigank Raman, Pratyush Maini, J. Zico Kolter, Zachary C. Lipton

In recent years, NLP practitioners have converged on the following practice: (i) import an off-the-shelf pretrained (masked) language model; (ii) append a multilayer perceptron atop the CLS token's hidden representation (with randomly initialized weights); and (iii) fine-tune the entire model on a downstream task (MLP-FT). This procedure has produced massive

Michael M. Fausnaugh, Rahul Jayaraman, Roland Vanderspek, George R. Ricker

We present the TESS light curve of GRB 230307A. We find two distinct components: a bright, prompt optical component at the time of the Fermi observation that peaked at TESS magnitude 14.49 (averaged over 200 seconds), followed by a gradual rise and fall over 0.5 days, likely associated with the afterglow, that peaked at 17.65 mag. The prompt component is obs

Brad Koplitz, Jared Johnson, Benjamin F. Williams, Mariangelly Diaz-Rodriguez

Using resolved optical stellar photometry from the Panchromatic Hubble Andromeda Treasury Triangulum Extended Region (PHATTER) survey, we measured the star formation history (SFH) near the position of 85 supernova remnants (SNRs) in M33. We constrained the progenitor masses for 60 of these SNRs, finding the remaining 25 remnants had no local SF in the last 5

David Fan, Deyu Yang, Xinyu Li, Vimal Bhat

Contrastive learning has recently narrowed the gap between self-supervised and supervised methods in image and video domain. State-of-the-art video contrastive learning methods such as CVRL and $\rho$-MoCo spatiotemporally augment two clips from the same video as positives. By only sampling positive clips locally from a single video, these methods neglect ot

Ron Kerman, Rama Rawat, Rajesh K. Singh

We study inequalities of the form \begin{equation*} \rho ( \lvert \hat{f} \rvert) \leq C \sigma(f) < \infty, \end{equation*} with $f \in L_{1}(\mathbb{R}^n)$, the Lebesgue-integrable functions on $\mathbb{R}^n$ and \begin{equation*} \hat{f}(\xi) := \int_{\mathbb{R}^n} f(x) \, e^{- 2 \pi i \xi \cdot x} dx, \ \ \ \xi \in \mathbb{R}^n. \end{equation*} The funct

Zhenning Cai, Manuel Torrilhon, Siyao Yang

We develop the steady-state regularized 13-moment equations in the linear regime for rarefied gas dynamics with general collision models. For small Knudsen numbers, the model is accurate up to the super-Burnett order, and the resulting system of moment equations is shown to have a symmetric structure. We also propose Onsager boundary conditions for the momen

Justin McKeown

Our research addresses the question: What are the conditions of the UK's cyber threat landscape? In addressing this we focus on detectable, known and therefore potentially preventable cyber threats, specifically those that are identifiable by the types of malicious scanning activities they exhibit. We have chosen this approach for two reasons. First, as is e

Omar Salem, Emanuele Giacomini, Leonardo Brizi, Luca Di Giammarino

Most commercially available Light Detection and Ranging (LiDAR)s measure the distances along a 2D section of the environment by sequentially sampling the free range along directions centered at the sensor's origin. When the sensor moves during the acquisition, the measured ranges are affected by a phenomenon known as "skewing", which appears as a distortion

Vikrant Malik, Gourab Ghatak, Abhishek K. Gupta, Sanket S. Kalamkar

Wireless communications aided by reconfigurable intelligent surfaces (RISs) is a promising way to improve the coverage for cellular users. The controlled reflection of signals from RISs is especially useful in mm-wave/THz networks when the direct link between a cellular user and its serving base station (BS) is weak or unavailable due to blockages. However,

Luca Pegolotti, Martin R. Pfaller, Natalia L. Rubio, Ke Ding

Reduced-order models based on physics are a popular choice in cardiovascular modeling due to their efficiency, but they may experience reduced accuracy when working with anatomies that contain numerous junctions or pathological conditions. We develop one-dimensional reduced-order models that simulate blood flow dynamics using a graph neural network trained o

Ehsan Haghighat, David Santillán

We propose a phase-field model of shear fractures using the deviatoric stress decomposition (DSD). This choice allows us to use general three-dimensional Mohr-Coulomb's (MC) failure function for formulating the relations and evaluating peak and residual stresses. We apply the model to a few benchmark problems of shear fracture and strain localization and rep

Jiahui Fu, Yilun Du, Kurran Singh, Joshua B. Tenenbaum

We present NeuSE, a novel Neural SE(3)-Equivariant Embedding for objects, and illustrate how it supports object SLAM for consistent spatial understanding with long-term scene changes. NeuSE is a set of latent object embeddings created from partial object observations. It serves as a compact point cloud surrogate for complete object models, encoding full shap

Oleg Makarenkov, Josean Albelo-Cortes

A recent result by Gudoshnikov et al [SIAM J. Control Optim. 2022] ensures finite-time convergence of the stress-vector of an elastoplastic lattice spring model under assumption that the vector $g'(t)$ of the applied displacement controlled-loading lies strictly inside the normal cone to the associated polyhedral set (that depends on mechanical parameters of

Raphael Bennett-Tennenhaus, Johanne Haugland, Mads Hustad Sandøy, Amit Shah

Building on previous work, we study the splitting of idempotents in the category of extensions $\mathbb{E}\operatorname{-Ext}(\mathcal{C})$ associated to a pair $(\mathcal{C},\mathbb{E})$ of an additive category and a biadditive functor to the category of abelian groups. In particular, we show that idempotents split in $\mathbb{E}\operatorname{-Ext}(\mathcal

Brandon Silva, Miguel Contreras, Tezcan Ozrazgat Baslanti, Yuanfang Ren

Acute brain dysfunctions (ABD), which include coma and delirium, are prevalent in the ICU, especially among older patients. The current approach in manual assessment of ABD by care providers may be sporadic and subjective. Hence, there exists a need for a data-driven robust system automating the assessment and prediction of ABD. In this work, we develop a ma

Timothée Goubault de Brugière, Simon Martiel

We focus on the depth optimization of CNOT circuits on hardwares with limited connectivity. We adapt the algorithm from Kutin et al. that implements any $n$-qubit CNOT circuit in depth at most $5n$ on a Linear Nearest Neighbour (LNN) architecture. Our proposal is a block version of Kutin et al.'s algorithm that is scalable with the number of interactions ava

Mayukh R. Gangopadhyay, M. Sami, Mohit K. Sharma

We study the late-time cosmological tensions using the low-redshift background and redshift-space distortion data by employing a machine learning (ML) technique. By comparing the generated observables with the standard cosmological scenario, our findings indicate support for the phantom nature of dark energy, which ultimately leads to a reduction in the exis

M. Zmeškal, L. Thulliez, E. Dumonteil

Whether it is for shielding applications or for safety criticality studies, numerically solving the neutron transport equation with a good accuracy requires to precisely estimate the Doppler broadened elastic scattering kernel in the thermal and epithermal energy range of neutrons travelling in a free gas. In Geant4, low energy neutrons are transported using

Antonio Amariti, Davide Morgante, Antoine Pasternak, Simone Rota

We classify the global one-form symmetries for non-Lagrangian $\mathcal{N}=3$ SCFTs that arise by the action of $S$-fold projections on D3-branes. Such a classification is dictated, on a generic point of the Coulomb branch, by probing the charge spectrum of $(p, q)$-strings in the brane setup. The charge lattice of lines is then obtained by finding the ones

Carl Johan Peter Johansson

We study very weak solutions to scalar Euler-Lagrange equations associated with quadratic convex functionals. We investigate whether $W^{1,1}$ solutions are necessarily $W^{1,2}_{\operatorname{loc}}$, which would make the theories by De Giorgi-Nash and Schauder applicable. We answer this question positively for a suitable class of functionals. This is an ext

Johan Appelgren, Michiel Lambrechts, Nienke van der Marel

Surveys of star-forming regions reveal that the dust mass of protoplanetary discs decreases by several orders of magnitude on a timescale of a few million years. This decrease in the mass budget of solids is likely due to the gas-drag-induced radial drift of mm-sized solids, called pebbles. However, quantifying the evolution of this dust component in young s

Samuel Epstein

We extend algorithmic conservation inequalities to probability measures. The amount of self information of a probability measure cannot increase when submitted to randomized processing. This includes (potentially non-computable) measures over finite sequences, infinite sequences, and $T_0$, second countable topologies. One example is the convolution of signa

Nicole Venner

While geometry with transcendental curves, like the Quadratrix of Hippias and the Spiral of Archimedes, played a significant role in our modern developments of geometry and algebra. The investigation has fallen off in the modern era despite advancements in algebraic tooling. This paper gives a description of the fields using modern techniques such as Galois

Anh Nguyen, Nikos Karampatziakis, Weizhu Chen

Most language models (LMs) are trained and applied in an autoregressive left-to-right fashion, assuming that the next token only depends on the preceding ones. However, this assumption ignores the potential benefits of using the full sequence information during training, and the possibility of having context from both sides during inference. In this paper, w

Luis Darmendrail, Alice Gasparini, Andreas Müller

The present text provides a short, non-technical account of some historical and educational background and, based on this, of the rationale of the use of mobile devices in physics education.

Artem Burdanov, Samantha Hasler, Julien de Wit

Small Solar System bodies are pristine remnants of Solar System formation, which provide valuable insights for planetary science and astronomy. Their discovery and cataloging also have strong practical implications to life on Earth as the nearest asteroids could pose a serious impact threat. Concurrently with dedicated observational projects, searches for sm

Olumide Ebenezer Ojo, Hoang Thang Ta, Alexander Gelbukh, Hiram Calvo

The use of transfer learning methods is largely responsible for the present breakthrough in Natural Learning Processing (NLP) tasks across multiple domains. In order to solve the problem of sentiment detection, we examined the performance of four different types of well-known state-of-the-art transformer models for text classification. Models such as Bidirec

Chien-Hung Lin, Fiona J. Burnell

We study condensation of abelian bosons in string-net models, by constructing a family of Hamiltonians that can be tuned through any such transition. We show that these Hamiltonians admit two exactly solvable, string-net limits: one deep in the uncondensed phase, described by an initial, uncondensed string net Hamiltonian, and one deep in the condensed phase

Mark de Berg, Andrés López Martínez, Frits Spieksma

Recently, many studies have been devoted to finding diverse solutions in classical combinatorial problems, such as Vertex Cover (Baste et al., IJCAI'20), Matching (Fomin et al., ISAAC'20) and Spanning Tree (Hanaka et al., AAAI'21). We initiate the algorithmic study of $k$-Diverse Minimum s-t Cuts which, given a directed graph $G = (V, E)$, two specified vert

Zihui Lin, Dagang Li

Efficiency of Battery Energy Storage Systems (BESSs) is increasingly critical as renewable energy generation becomes more prevalent on the grid. Therefore, it is necessary to study the energy efficiency of lithium-ion batteries, which are typically used in BESSs. The purpose of this study is to propose the State of Efficiency (SOE) as a measure of how effici

Martino Borello, Ferdinando Zullo

The main purpose of this paper is to further study the structure, parameters and constructions of the recently introduced minimal codes in the sum-rank metric. These objects form a bridge between the classical minimal codes in the Hamming metric, the subject of intense research over the past three decades partly because of their cryptographic properties, and

Huiming Zhang, Haoyu Wei, Guang Cheng

In non-asymptotic learning, variance-type parameters of sub-Gaussian distributions are of paramount importance. However, directly estimating these parameters using the empirical moment generating function (MGF) is infeasible. To address this, we suggest using the sub-Gaussian intrinsic moment norm [Buldygin and Kozachenko (2000), Theorem 1.3] achieved by max

David G. Felton, David A. Hague

This paper describes a gradient-descent based optimization algorithm for synthesizing Constant Envelope Orthogonal Frequency Division Multiplexing (CE-OFDM) waveforms with low Auto-Correlation Function (ACF) sidelobes in a specified region of time-delays. The algorithm optimizes the Generalized Integrated Sidelobe Level (GISL) which controls the mainlobe and

César Barilla, Duarte Gonçalves

We study a model in which two players with opposing interests try to alter a status quo through instability-generating actions. We show that instability can be used to secure longer-term durable changes, even if it is costly to generate and does not generate short-term gains. In equilibrium, instability generated by a player decreases when the status quo fav

Bo He, Jun Wang, Jielin Qiu, Trung Bui

The goal of multimodal summarization is to extract the most important information from different modalities to form output summaries. Unlike the unimodal summarization, the multimodal summarization task explicitly leverages cross-modal information to help generate more reliable and high-quality summaries. However, existing methods fail to leverage the tempor

Sehrish Malik, Moeen Ali Naqvi, Leon Moonen

There is an increasing need to assess the correct behavior of self-adaptive and self-healing systems due to their adoption in critical and highly dynamic environments. However, there is a lack of systematic evaluation methods for self-adaptive and self-healing systems. We proposed CHESS, a novel approach to address this gap by evaluating self-adaptive and se

E. Barrelet

Recent progresses of electronics, essentially due to its miniaturization, are opening new fields that were just dreamed of, notably in astronomy. At start in paragraph 3, we introduce the time variation of images expressing the dual nature of the optical signal (ZO) and we expose several useful applications where the optical signal variations are not faster

Yuqing Du, Ksenia Konyushkova, Misha Denil, Akhil Raju

Detecting successful behaviour is crucial for training intelligent agents. As such, generalisable reward models are a prerequisite for agents that can learn to generalise their behaviour. In this work we focus on developing robust success detectors that leverage large, pretrained vision-language models (Flamingo, Alayrac et al. (2022)) and human reward annot

Jaouad Mourtada

We study sequential probability assignment in the Gaussian setting, where the goal is to predict, or equivalently compress, a sequence of real-valued observations almost as well as the best Gaussian distribution with mean constrained to a given subset of $\mathbb{R}^n$. First, in the case of a convex constraint set $K$, we express the hardness of the predict

Michalis Chatzittofi, Ramin Golestanian, Jaime Agudo-Canalejo

A system of two enzymes mechanically coupled to each other in a viscous medium was recently studied, and conditions for obtaining synchronization and an enhanced average rate of the thermally-activated catalytic reactions of the enzymes were identified. The transition to synchronization occurred as the result of a global bifurcation in the underlying dynamic

Ali Menati, Yuting Cai, Rayan El Helou, Chao Tian

Proof-of-work computation used in cryptocurrencies has witnessed significant growth in the U.S. and many other regions around the world. One of the most significant bottlenecks for the scalable deployment of such computation is its energy demand. On the other hand, the electric energy system is increasing the need for flexibility for energy balancing and anc

Xuansheng Wu, Kaixiong Zhou, Mingchen Sun, Xin Wang

The recent "pre-train, prompt, predict training" paradigm has gained popularity as a way to learn generalizable models with limited labeled data. The approach involves using a pre-trained model and a prompting function that applies a template to input samples, adding indicative context and reformulating target tasks as the pre-training task. However, the des

Nitzan Bitton-Guetta, Yonatan Bitton, Jack Hessel, Ludwig Schmidt

Weird, unusual, and uncanny images pique the curiosity of observers because they challenge commonsense. For example, an image released during the 2022 world cup depicts the famous soccer stars Lionel Messi and Cristiano Ronaldo playing chess, which playfully violates our expectation that their competition should occur on the football field. Humans can easily

Slava G. Turyshev, Viktor T. Toth

We investigate the general relativistic phase of an electromagnetic wave as it propagates in the gravitational field of the Earth, which is modeled as an isolated, weakly aspherical gravitating body. We introduce coordinate systems to describe light propagation in the Earth's vicinity along with the relevant coordinate transformations, and discuss the transf

Zhuoran Yu, Yin Li, Yong Jae Lee

Recent state-of-the-art methods in imbalanced semi-supervised learning (SSL) rely on confidence-based pseudo-labeling with consistency regularization. To obtain high-quality pseudo-labels, a high confidence threshold is typically adopted. However, it has been shown that softmax-based confidence scores in deep networks can be arbitrarily high for samples far

Sara Fraschini, Gabriele Loli, Andrea Moiola, Giancarlo Sangalli

We study space--time isogeometric discretizations of the linear acoustic wave equation that use splines of arbitrary degree p, both in space and time. We propose a space--time variational formulation that is obtained by adding a non-consistent penalty term of order 2p+2 to the bilinear form coming from integration by parts. This formulation, when discretized

Qianli Ma, Xiaojian Bai, Erxi Feng, Guannan Zhang

We present a new lite python-based program, CrysFieldExplorer, for fast optimizing crystal electric field (CEF) parameters to fit experimental data. The main novelty of CrysFieldExplorer is the development of a unique loss function, referred to as the Spectrum-Characteristic Loss ($L_{\text{Spectrum}}$), which is defined based on the characteristic polynomia

Afagh Mehri Shervedani, Siyu Li, Natawut Monaikul, Bahareh Abbasi

Robot assistants for older adults and people with disabilities need to interact with their users in collaborative tasks. The core component of these systems is an interaction manager whose job is to observe and assess the task, and infer the state of the human and their intent to choose the best course of action for the robot. Due to the sparseness of the da

Shuxian Wang, Yubo Zhang, Sarah K. McGill, Julian G. Rosenman

Reconstructing a 3D surface from colonoscopy video is challenging due to illumination and reflectivity variation in the video frame that can cause defective shape predictions. Aiming to overcome this challenge, we utilize the characteristics of surface normal vectors and develop a two-step neural framework that significantly improves the colonoscopy reconstr

Matthew Jin, Syed Shahriar, Michele Tufano, Xin Shi

Software development life cycle is profoundly influenced by bugs: their introduction, identification, and eventual resolution account for a significant portion of software cost. This has motivated software engineering researchers and practitioners to propose different approaches for automating the identification and repair of software defects. Large language

Antoine Rignon-Bret

I study the balance law equation of surface charges in the presence of background fields. The construction allows a unified description of Noether's theorem for both global and local symmetries. From the balance law associated with some of these symmetries, I will discuss generalizations of Wald's Noether entropy formula and general entropy balance laws on n

Arne Schobert, Jan Berges, Erik G. C. P. van Loon, Michael A. Sentef

The interplay of electronic and nuclear degrees of freedom presents an outstanding problem in condensed matter physics and chemistry. Computational challenges arise especially for large systems, long time scales, in nonequilibrium, or in systems with strong correlations. In this work, we show how downfolding approaches facilitate complexity reduction on the

Milad Bader, Robert G. Clapp, Kurt T. Nihei, Biondo Biondi

Distributed acoustic sensing (DAS) fibers have enabled various geophysical applications in unconventional reservoirs. Combined with perforation shots, a DAS fiber can record valuable guided waves that propagate in the reservoir formation and carry information about its properties. However, the representation of perforation shots as seismic sources, needed to

Yang Liu, Xia Chen, Wei-min Yang

In this paper, we propose a novel approach for tackling the obstacles of empirical likelihood in the face of massive data, which is called split sample mean empirical likelihood (SSMEL), our approach provides a unique perspective for solving big data problems. We show that the SSMEL estimator has the same estimation efficiency as the empirical likelihood est

Svitlana Bilun, Maria Hrechko, Olena Myshnova, Alexandr Prishlyak

We describe all possible structures of discrete vector field (discrete Morse functions) with minimal number of critical cells on the regular CW-complex for the 2-disk (1 cell), the 2-sphere (2 cells), the cylinder (2 cells) and Mobius band (2 cells).

Amandine Brunetto, Sascha Hornauer, Stella X. Yu, Fabien Moutarde

Vision research showed remarkable success in understanding our world, propelled by datasets of images and videos. Sensor data from radar, LiDAR and cameras supports research in robotics and autonomous driving for at least a decade. However, while visual sensors may fail in some conditions, sound has recently shown potential to complement sensor data. Simulat

Christian Barth, Tobias Klausmann, Andreas Bayha, Matthias Freund

The MTP is an emerging standard for the software integration of process modules into a control system. The core concept of MTP is to separate process plants into autonomous modules called PEA which offer easy to use high level services. Applying the MTP concept leads to a modular production which can easily be adapted by adding, subtracting, or exchanging in

Andrea Benvenuti, Gabriele Loli, Giancarlo Sangalli, Thomas Takacs

We present an isogeometric mortar method for the discretization of the biharmonic equation posed on multi-patch domains. We assume only $C^0$-conformity at interfaces and employs a mortar approach to weakly enforce $C^1$-continuity across patch interfaces. Discrete inf-sup stability is ensured by selecting a Lagrange multiplier space consisting of splines of

Hong Li, Dongjin Oh, Mingu Kang, He Zhao

The kagome superconductor family AV3Sb5 (A=Cs, K, Rb) emerged as an exciting platform to study exotic Fermi surface instabilities. Here we use spectroscopic-imaging scanning tunneling microscopy (SI-STM) and angle-resolved photoemission spectroscopy (ARPES) to reveal how the surprising cascade of higher and lower-dimensional density waves in CsV3Sb5 is intim

S. S. Baturin

We present an analytical study on the effects of the transverse plasma gradient in the blowout regime of a plasma wakefield accelerator (PWA). The analysis departs from a simple ballistic model of plasma electrons that allows us to derive a complete analytic solution for the pseudopotential and, consequently, for the wakefield. We demonstrate that the transv

N. V. Krylov

In this paper we present an approach to proving parabolic Aleksandrov estimates with mixed norms for stochastic integrals with singular ``moderated'' drift.

Fadi Sun, Jinwu Ye

The order from quantum disorder (OFQD) phenomenon was first discovered in quantum spin systems in geometric frustrated lattice. Similar phenomenon was also discovered in interacting bosonic systems or quantum spin systems with spin-orbit coupling in a bipartite lattice. Here we show that the OFQD also leads to a topological phase transition. We demonstrate t

Amit Samadder, Arnab Chattopadhyay, Sabyasachi Bhattacharya

In freshwater ecosystems, aquatic insects that ontogenetically shift their habitat from aquatic to terrestrial play vital roles as prey subsidies that move nutrients and energy from aquatic to terrestrial food webs. As a result, these subsidies negatively affect alternative terrestrial prey by enhancing predator density. However, these aquatic insects can al

Sanjay Deshpande, Jakub Szefer

ChatGPT has recently gathered attention from the general public and academia as a tool that is able to generate plausible and human-sounding text answers to various questions. One potential use, or abuse, of ChatGPT is in answering various questions or even generating whole essays and research papers in an academic or classroom setting. While recent works ha

Fraser Binns

Heegaard Floer homology and knot Floer homology are powerful invariants of 3-manifolds and links respectively. L-space knots are knots which admit Dehn surgeries to 3-manifolds with Heegaard Floer homology of minimal rank. In this paper we study almost L-space knots, which are knots admitting large Dehn surgeries to 3-manifolds with Heegaard Floer homology o

Younan Mou, Sicong Liu

The underwater propagation environment for visible light signals is affected by complex factors such as absorption, shadowing, and reflection, making it very challengeable to achieve effective underwater visible light communication (UVLC) channel estimation. It is difficult for the UVLC channel to be sparse represented in the time and frequency domains, whic

Sahil Girhepuje, Anmol Goel, Gokul S Krishnan, Shreya Goyal

Recent advances and applications of language technology and artificial intelligence have enabled much success across multiple domains like law, medical and mental health. AI-based Language Models, like Judgement Prediction, have recently been proposed for the legal sector. However, these models are strife with encoded social biases picked up from the trainin

Ramiro Cayuso, Pau Figueras, Tiago França, Luis Lehner

The majority of extensions to General Relativity display mathematical pathologies (higher derivatives, character change in equations that can be classified within PDE theory, and even unclassifiable ones) that cause severe difficulties to study them, especially in dynamical regimes. We present here an approach that enables their consistent treatment and extr

Amedeo Roberto Esposito, Marco Mondelli

We propose a novel approach to concentration for non-independent random variables. The main idea is to ``pretend'' that the random variables are independent and pay a multiplicative price measuring how far they are from actually being independent. This price is encapsulated in the Hellinger integral between the joint and the product of the marginals, which i

Muhammad Aufaristama

This study aimed to examine the correlation between the stock prices of two major Indonesian holding companies, MNC Group and Elang Mahkota Teknologi (Emtek) Group, and their respective subsidiaries as case studies. The data for the analysis were collected from 2013 to 2022, and Spearman correlation was used to determine the strength and direction of the rel

Bhaskar Joshi, Dhruv Kapur, Harikumar Kandath

Deep Reinforcement Learning is quickly becoming a popular method for training autonomous Unmanned Aerial Vehicles (UAVs). Our work analyzes the effects of measurement uncertainty on the performance of Deep Reinforcement Learning (DRL) based waypoint navigation and obstacle avoidance for UAVs. Measurement uncertainty originates from noise in the sensors used

Shreya Chowdhary, Anna Kawakami, Mary L. Gray, Jina Suh

Sensing technologies deployed in the workplace can unobtrusively collect detailed data about individual activities and group interactions that are otherwise difficult to capture. A hopeful application of these technologies is that they can help businesses and workers optimize productivity and wellbeing. However, given the workplace's inherent and structural

Filippo Fabiani, Andrea Simonetto

Modern socio-technical systems typically consist of many interconnected users and competing service providers, where notions like market equilibrium are tightly connected to the ``evolution'' of the network of users. In this paper, we model the users' dynamics as a linear dynamical system, and the service providers as agents taking part to a generalized Nash

Weixiong Lin, Ziheng Zhao, Xiaoman Zhang, Chaoyi Wu

Foundation models trained on large-scale dataset gain a recent surge in CV and NLP. In contrast, development in biomedical domain lags far behind due to data scarcity. To address this issue, we build and release PMC-OA, a biomedical dataset with 1.6M image-caption pairs collected from PubMedCentral's OpenAccess subset, which is 8 times larger than before. PM

Yangqiuting Li, Chandralekha Singh

Students' self-efficacy, interest, and perceived recognition from others have been shown to be very important for the development of their identity in a given field, which is a critical predictor of students' career decisions. Prior research suggests that students' self-efficacy and interest play an important role in their performance and persistence in STEM

A. C. Khunt, V. O. Thomas, P. C. Vinodkumar

We investigate the effect of density perturbations and local anisotropy on the stability of stellar matter structures in general relativity using the concept of cracking. Adopting a core-envelope model of a super-dense star, we examine the properties and stability conditions by introducing anisotropic pressure to the envelope region. Furthermore, we propose

Andrea Gebek, Maarten Baes, Benedikt Diemer, W. J. G. de Blok

Cold gas in galaxies provides a crucial test to evaluate the realism of cosmological hydrodynamical simulations. To extract the atomic and molecular hydrogen properties of the simulated galaxy population, postprocessing methods taking the local UV field into account are required. We improve upon previous studies by calculating realistic UV fields with the du

Mihir Dharmadhikari, Kostas Alexis

This paper contributes a novel strategy for semantics-aware autonomous exploration and inspection path planning. Attuned to the fact that environments that need to be explored often involve a sparse set of semantic entities of particular interest, the proposed method offers volumetric exploration combined with two new planning behaviors that together ensure

Alexei Yu. Uteshev, Elizaveta A. Kalinina, Marina V. Goncharova

We treat the problem of the Frobenius distance evaluation from a given matrix $ A \in \mathbb R^{n\times n} $ with distinct eigenvalues to the manifold of matrices with multiple eigenvalues. On restricting considerations to the rank $ 1 $ real perturbation matrices, we prove that the distance in question equals $ \sqrt{z_{\ast}} $ where $ z_{\ast} $ is a pos

Francisco J. Mendez, Miguel A. Mendez, Nicola Sciarra, Antonio Pasculli

The Sand Hypoplastic (SH) constitutive law by von Wolffersdorff (1996) is a interesting hypoplastic model for soil mechanics. This model includes eight parameters, usually calibrated using the oedometric (OE) and the drained isotropically consolidated triaxial tests (CD). However, previous studies show that the SH model calibration in the CD test has conflic

Rajam Elancheliyan, Edouard Chauveau, Domenico Truzzolillo

We explore the impact of three water-soluble polyelectrolytes (PEs) on the flow of concentrated suspensions of poly(N-isopropylacrylamide) (PNIPAm) microgels with thermoresponsive anionic charge density. By progressively adding the PEs to a jammed suspension of swollen microgels, we show that the rheology of the mixtures is remarkably influenced by the sign

Alicia Palmisano-Kyle, Artemis Spyrou, Paul DeYoung, Panagiotis Gastis

One of the biggest questions in nuclear astrophysics is understanding where the elements come from and how they are made. This work focuses on the p process, a nucleosynthesis process that consists of a series of photodisintegration reactions responsible for producing stable isotopes on the proton-rich side of stability. These nuclei, known as the p nuclei,

Valdemar Melin, Edwin Langmann

We present an exact closed-form expression for the propagator of the Calogero model, i.e., for the integral kernel of the time evolution operator of the quantum many-body system on the real line with an external harmonic potential and inverse-square two-body interactions. This expression is obtained by combining two results: first, a simple formula relating

Fatemeh Hadadi, Joshua H. Dawes, Donghwan Shin, Domenico Bianculli

With the increasing complexity and scope of software systems, their dependability is crucial. The analysis of log data recorded during system execution can enable engineers to automatically predict failures at run time. Several Machine Learning (ML) techniques, including traditional ML and Deep Learning (DL), have been proposed to automate such tasks. Howeve

Jonas Ellert, Paweł Gawrychowski, Garance Gourdel

Squares (fragments of the form $xx$, for some string $x$) are arguably the most natural type of repetition in strings. The basic algorithmic question concerning squares is to check if a given string of length $n$ is square-free, that is, does not contain a fragment of such form. Main and Lorentz [J. Algorithms 1984] designed an $\mathcal{O}(n\log n)$ time al

Chengkai Zhu, Chenghong Zhu, Xin Wang

Quantum Internet relies on quantum entanglement as a fundamental resource for secure and efficient quantum communication, reshaping data transmission. In this context, entanglement distillation emerges as a crucial process that plays a pivotal role in realizing the full potential of the quantum internet. Nevertheless, it remains challenging to accurately est

Florian Damerow, Yuda Li, Tim Puphal, Benedict Flade

This work addresses the task of risk evaluation in traffic scenarios with limited observability due to restricted sensorial coverage. Here, we concentrate on intersection scenarios that are difficult to access visually. To identify the area of sight, we employ ray casting on a local dynamic map providing geometrical information and road infrastructure. Based

Sheng Shen, Zhewei Yao, Chunyuan Li, Trevor Darrell

The field of natural language processing (NLP) has made significant strides in recent years, particularly in the development of large-scale vision-language models (VLMs). These models aim to bridge the gap between text and visual information, enabling a more comprehensive understanding of multimedia data. However, as these models become larger and more compl