July 2023 arXiv papers — page 145

Takao Inoué

In 1986, Flagg and Friedman \cite{ff} gave an elegant alternative proof of the faithfulness of G\"{o}del (or Rasiowa-Sikorski) translation $(\cdot)^\Box$ of Heyting arithmetic $\bf HA$ to Shapiro's epistemic arithmetic $\bf EA$. In \S 2, we shall prove the faithfulness of $(\cdot)^\Box$ without using stability, by introducing another translation from an epis

Ondřej Kreml, Václav Mácha, Šárka Nečasová, Srđan Trifunović

In this paper, we study a nonlinear fluid-structure interaction problem between a viscoelastic beam and a compressible viscous fluid. The beam is immersed in the fluid which fills a two-dimensional rectangular domain with periodic boundary conditions. Under the effect of periodic forces acting on the beam and the fluid, at least one time-periodic weak soluti

Dmitrii Konnov, Andrey Muraviev, Sergey Vasilyev, Konstantin Vodopyanov

Ultrabroadband electro-optic sampling with few-cycle optical pulses is known to be an extremely sensitive technique to detect electric field amplitudes. By combining this method with dual-comb spectroscopy and with a new class of ultrafast lasers, we perform high-resolution (<10 MHz, 0.0003 wavenumbers) spectroscopic measurements across the whole frequency r

C. Reichhardt, C. J. O. Reichhardt

We investigate the topological defect populations for superconducting vortices and magnetic skyrmions on random pinning substrates under driving amplitudes that are swept at different rates or suddenly quenched. When the substrate pinning is sufficiently strong, the system exhibits a nonequilibrium phase transition at a critical drive into a more topological

Parisa Ramezani, Alva Kosasih, Amna Irshad, Emil Björnson

In this article, we present our vision for how extremely large aperture arrays (ELAAs), equipped with hundreds or thousands of antennas, can play a major role in future 6G networks by enabling a remarkable increase in data rates through spatial multiplexing of a massive number of data streams to both a single user and many simultaneous users. Specifically, w

Djuna Croon, Patrick J. Fox, Roni Harnik, Simon Knapen

We present the results of a survey meant to assess the opinion of the high-energy physics theory (HET) community on the January 7th postdoc acceptance deadline - specifically, whether there is a preference to shift the deadline to later in January or February. This survey, which served for information-gathering purpose only, is part of a community conversati

Yongrae Jo, Seongyun Lee, Aiden SJ Lee, Hyunji Lee

Dense video captioning, a task of localizing meaningful moments and generating relevant captions for videos, often requires a large, expensive corpus of annotated video segments paired with text. In an effort to minimize the annotation cost, we propose ZeroTA, a novel method for dense video captioning in a zero-shot manner. Our method does not require any vi

C. Walsh, M. Charlebois, P. Sémon, A. -M. S. Tremblay

Doping a Mott insulator gives rise to unconventional superconducting correlations. Here we address the interplay between d-wave superconductivity and Mott physics using the two-dimensional Hubbard model with cellular dynamical mean-field theory on a $2\times2$ plaquette. Our approach is to study superconducting correlations from the perspective of a cluster

Marshall Davey, Charles Puelz, Simone Rossi, Margaret Anne Smith

Cardiac fluid dynamics fundamentally involves interactions between complex blood flows and the structural deformations of the muscular heart walls and the thin, flexible valve leaflets. There has been longstanding scientific, engineering, and medical interest in creating mathematical models of the heart that capture, explain, and predict these fluid-structur

Na Zhang

Deep learning has received increasing interests in face recognition recently. Large quantities of deep learning methods have been proposed to handle various problems appeared in face recognition. Quite a lot deep methods claimed that they have gained or even surpassed human-level face verification performance in certain databases. As we know, face image qual

Giovanni M. Mirouh, David D. Hendriks, Sophie Dykes, Maxwell Moe

Binary stars evolve into chemically-peculiar objects and are a major driver of the Galactic enrichment of heavy elements. During their evolution they undergo interactions, including tides, that circularize orbits and synchronize stellar spins, impacting both individual systems and stellar populations. Using Zahn's tidal theory and MESA main-sequence model gr

Krish Jhurani, Pranav Gunhal

This research addresses the influence of Primordial Black Holes (PBHs) on cosmic reionization, using a robust semi-analytical model. This model encapsulates cosmological theory, PBH physics, and radiative transfer, with a lognormal PBH mass function as the fulcrum, with the mean PBH mass set at 30 solar masses and the fraction of dark matter in PBHs ($f_{PBH

Maksym Luz, Mikhail Moklyachuk

We deal with the problem of optimal estimation of the linear functionals constructed from the missed values of a continuous time stochastic process $\xi(t)$ with periodically stationary increments at points $t\in[0;(N+1)T]$ based on observations of this process with periodically stationary noise. To solve the problem, a sequence of stochastic functions $ \{\

Anton M. Zeitlin

The relation between special connections on the projective line, called Miura opers, and the spectra of integrable models of Gaudin type provides an important example of the geometric Langlands correspondence. The possible generalization of that correspondence to simple Lie superalgebras is much less studied. Recently some progress has been made in understan

Diane M. Salim, Blakesley Burkhart, David Sondak

Advancing our understanding of astrophysical turbulence is bottlenecked by the limited resolution of numerical simulations that may not fully sample scales in the inertial range. Machine learning (ML) techniques have demonstrated promise in up-scaling resolution in both image analysis and numerical simulations (i.e., superresolution). Here we employ and furt

Andrii Babii, Ryan T. Ball, Eric Ghysels, Jonas Striaukas

The paper uses structured machine learning regressions for nowcasting with panel data consisting of series sampled at different frequencies. Motivated by the problem of predicting corporate earnings for a large cross-section of firms with macroeconomic, financial, and news time series sampled at different frequencies, we focus on the sparse-group LASSO regul

Julia Lust, Alexandru P. Condurache

Deep neural networks tend to make overconfident predictions and often require additional detectors for misclassifications, particularly for safety-critical applications. Existing detection methods usually only focus on adversarial attacks or out-of-distribution samples as reasons for false predictions. However, generalization errors occur due to diverse reas

Pascal Van Hentenryck, Kevin Dalmeijer

This article is a short introduction to AI4OPT, the NSF AI Institute for Advances in Optimization. AI4OPT fuses AI and Optimization, inspired by end-use cases in supply chains, energy systems, chip design and manufacturing, and sustainable food systems. AI4OPT also applies its "teaching the teachers" philosophy to provide longitudinal educational pathways in

Rebekah Hounsell, Dan Scolnic, Dillon Brout, Benjamin Rose

We motivate the cosmological science case of measuring Type Ia supernovae with the Nancy Grace Roman Space Telescope as part of the High Latitude Time Domain Survey. We discuss previously stated requirements for the science, and a baseline survey strategy. We discuss the various areas that must still be optimized and point to the other white papers that cons

Jiaying Zhou, Yong Ye, Alex Arenas, Sergio Gómez

The spontaneous emergence of ordered structures, known as Turing patterns, in complex networks is a phenomenon that holds potential applications across diverse scientific fields, including biology, chemistry, and physics. Here, we present a novel delayed fractional-order susceptible-infected-recovered-susceptible (SIRS) reaction-diffusion model functioning o

Qun Yang, Jiewen Xiao, Iñigo Robredo, Maia G. Vergniory

The interplay between chirality and topology nurtures many exotic electronic properties. For instance, topological chiral semimetals display multifold chiral fermions that manifest nontrivial topological charge and spin texture. They are an ideal playground for exploring chirality-driven exotic physical phenomena. In this work, we reveal a monopole-like orbi

David B. McCoy, Alan E. Hubbard, Mark van der Laan, Alejandro Schuler

Mediation analysis in causal inference typically concentrates on one binary exposure, using deterministic interventions to split the average treatment effect into direct and indirect effects through a single mediator. Yet, real-world exposure scenarios often involve multiple continuous exposures impacting health outcomes through varied mediation pathways, wh

Huwan Peng, Scott Davidson, Richard Shi, Shuaiwen Leon Song

Large language models (LLMs) such as OpenAI's ChatGPT and Google's Gemini have demonstrated unprecedented capabilities of autoregressive AI models across multiple tasks triggering disruptive technology innovations around the world. However, as models continue to grow the cost to serve these models also continues to grow threatening the democratization of LLM

Tobias Dehling, Ali Sunyaev

The rising diffusion of information systems (IS) throughout society poses an increasingly serious threat to privacy as a social value. One approach to alleviating this threat is to establish transparency of information privacy practices (TIPP) so that consumers can better understand how their information is processed. However, the design of transparency arti

Nic Roberts, Noushin Raeisi Kheirabadi, Michail-Antisthenis Tsompanas, Alessandro Chiolerio

Colloid-based computing devices offer remarkable fault tolerance and adaptability to varying environmental conditions due to their amorphous structure. An intriguing observation is that a colloidal suspension of ZnO nanoparticles in DMSO exhibits reconfiguration when exposed to electrical stimulation and produces spikes of electrical potential in response. T

Tengchan Zeng, Aidin Ferdowsi, Omid Semiari, Walid Saad

Connected and autonomous vehicles (CAVs) can reduce human errors in traffic accidents, increase road efficiency, and execute various tasks ranging from delivery to smart city surveillance. Reaping these benefits requires CAVs to autonomously navigate to target destinations. To this end, each CAV's navigation controller must leverage the information collected

Zihe Ye, Yu Sun

Picking up multiple objects at once is a grasping skill that makes a human worker efficient in many domains. This paper presents a system to pick a requested number of objects by only picking once (OPO). The proposed Only-Pick-Once System (OPOS) contains several graph-based algorithms that convert the layout of objects into a graph, cluster nodes in the grap

Jackson Dean, Nick Cheney

Real-world problems are often comprised of many objectives and require solutions that carefully trade-off between them. Current approaches to many-objective optimization often require challenging assumptions, like knowledge of the importance/difficulty of objectives in a weighted-sum single-objective paradigm, or enormous populations to overcome the curse of

Leonardo V. Bastos, Rushikesh S. Ambekar, Chandra S. Tiwary, Douglas S. Galvao

We carried out fully atomistic reactive molecular dynamics simulations to study the mechanical behavior of six newly proposed hybrid schwarzite-based structures (interlocked petal-schwarzites). Schwarzites are carbon crystalline nanostructures with negative Gaussian curvature created by mapping a TPMS (Triply Periodic Minimal Surface) with carbon rings conta

Lysea Haggie, Thor Besier, Angus McMorland

Computational models of cortical activity provide insight into the mechanisms of higher-order processing in the human brain including planning, perception and the control of movement. Activity in the cortex is ongoing even in the absence of sensory input or discernible movements and is thought to be linked to the topology of cortical circuitry. However, the

Thomas Walker, Varun Anand, Pavlos Andreadis

Semantic segmentation for spherical data is a challenging problem in machine learning since conventional planar approaches require projecting the spherical image to the Euclidean plane. Representing the signal on a fundamentally different topology introduces edges and distortions which impact network performance. Recently, graph-based approaches have bypasse

Dingshan Deng, Maxime Ruaud, Uma Gorti, Ilaria Pascucci

CO is one of the most abundant molecules in protoplanetary disks, and optically thin emission from its isotopologues has been detected in many of them. However, several past works have argued that reproducing the relatively low emission of CO isotopologues requires a very low disk mass or significant CO depletion. Here, we present a Python code, DiskMINT, wh

Jonathan N. Blakely, Shawn D. Pethel, Kurt Jacobs

Electromagnetic remote sensing technologies such as radar can be mislead by targets that generate spoof pulses. Typically, a would-be spoofer must make measurements to characterize a received pulse in order to design a convincing spoof pulse. The precision of such measurements are ultimately limited by quantum noise. Here we introduce a model of electromagne

Krish Jhurani

This study investigates the complex dynamics of AGN (Active Galactic Nucleus) jet-cloud interactions, particularly focusing on the impact of non-thermal particle acceleration on the resulting radiative signatures. We utilize advanced computational simulations, tracking changes in jet properties and emissions over a span of 0.2 Myr (millions of years). The re

Habib Alizadeh

We prove a new Hamiltonian extension and consequently a fragmentation result in dimension $4$ for the symplectic manifold $\mathbb{D}^{2}\times \mathbb{D}^{2}$. Polterovich and Shelukhin have recently constructed a family of functionals on the space of time dependent Hamiltonian functions on $S^{2}(1) \times S^{2}(a)$ for certain rational $0 < a < 1$, called

Simon Guist, Jan Schneider, Hao Ma, Le Chen

Operating robots precisely and at high speeds has been a long-standing goal of robotics research. Balancing these competing demands is key to enabling the seamless collaboration of robots and humans and increasing task performance. However, traditional motor-driven systems often fall short in this balancing act. Due to their rigid and often heavy design exac

Krish Jhurani, Tyler McMaken

This paper examines the issue of the existence and nature of time-like geodesics in asymptotically flat spacetimes and proposes a novel generalized topological criterion for the existence of time-like geodesics. Its validity is proved using theorems such as the Jordan-Brouwer Separation Theorem, the Raychaudhuri Equation, and key elements of Differential Geo

Rebecca Bourn, William Q. Erickson

We prove a conjecture of Bourn and Willenbring (2020) regarding the palindromicity and unimodality of a certain family of polynomials $N_n(t)$. These recursively defined polynomials arise as the numerators of generating functions in the context of the discrete one-dimensional earth mover's distance (EMD). The key to our proof is showing that the defining rec

Matteo Straccamore, Vittorio Loreto, Pietro Gravino

Cities and metropolitan areas are major drivers of creativity and innovation in all possible sectors: scientific, technological, social, artistic, etc. The critical concentration and proximity of diverse mindsets and opportunities, supported by efficient infrastructures, enable new technologies and ideas to emerge, thrive, and trigger further innovation. Tho

James Jackson, Robin Mitra, Niels Hagenbuch, Sarah McGough

Our capacity to process large complex data sources is ever-increasing, providing us with new, important applied research questions to address, such as how to handle missing values in large-scale databases. Mitra et al. (2023) noted the phenomenon of Structured Missingness (SM), which is where missingness has an underlying structure. Existing taxonomies for d

Joseph Cho, Katrin Leschke, Yuta Ogata

We express Darboux transformations of discrete polarised curves as parallel sections of discrete connections in the quaternionic formalism. This immediately leads to the linearisation of the monodromy of the transformation. We also consider the integrable reduction to the case of discrete bicycle correspondence. Applying our method to the case of discrete ci

Léopold Van Brandt, Jean-Charles Delvenne

An extension of fluctuation-dissipation theorem is used to derive a "speed limit" theorem for nonlinear electronic devices. This speed limit provides a lower bound on the dissipation that is incurred when transferring a given amount of electric charge in a certain amount of time with a certain noise level (average variance of the current). This bound, which

Miriam Baglioni, Andrea Mannocci, Gina Pavone, Michele De Bonis

The full exploitation of scholarly repositories is pivotal in modern Open Science, and scholarly repository registries are kingpins in enabling researchers and research infrastructures to list and search for suitable repositories. However, since multiple registries exist, repository managers are keen on registering multiple times the repositories they manage

Maria Gillespie, Sean T. Griffin

We prove that $\omega \Delta'_{e_{k}}e_n|_{t=0}$, the symmetric function in the Delta Conjecture at $t=0$, is a skewing operator applied to a Hall-Littlewood polynomial, and generalize this formula to the Frobenius series of all $\Delta$-Springer modules. We use this to give an explicit Schur expansion in terms of the Lascoux-Sch\"utzenberger cocharge statis

Anuj S. Vora, Ankur A. Kulkarni

We consider a setting of non-cooperative communication where a receiver wants to recover randomly generated sequences of symbols that are observed by a strategic sender. The sender aims to maximize an average utility that may not align with the recovery criterion of the receiver, whereby the signals it sends may not be truthful. The rate of communication is

Christiane Gresse von Wangenheim, Gustavo Dirschnabel

Advances in mobile applications providing image classification enabled by Deep Learning require innovative User Experience solutions in order to assure their adequate use by users. To aid the design process, usability heuristics are typically customized for a specific kind of application. Therefore, based on a literature review and analyzing existing mobile

R. E. Kastner, Andreas Schlatter

A restricted form of Landauer's Principle, independent of computational considerations, is shown to hold for thermal systems by reference to the joint entropy associated with conjugate observables. It is shown that the source of the compensating entropy for irreversible physical processes is due to the ontological uncertainty attending values of such mutuall

M. Zubair, P. Vasilopoulos, M. Tahir

We investigate the electronic dispersion and transport properties of graphene/WSe$_{2}$ heterostructures in the presence of a proximity-induced spin-orbit coupling $\lambda_{v}$, sublattice potential $\Delta$, and an off-resonant circularly polarized light of frequency $\Omega$ that renormalizes $\Delta$ to $\bar{\Delta}_{\eta p} = \Delta +\eta p \Delta_{\Om

Christopher McClurg, Ali Ayub, Harsh Tyagi, Sarah M. Rajtmajer

For real-world applications, robots will need to continually learn in their environments through limited interactions with their users. Toward this, previous works in few-shot class incremental learning (FSCIL) and active class selection (ACS) have achieved promising results but were tested in constrained setups. Therefore, in this paper, we combine ideas fr

Alexandrea K. Ramnarine

The massive collection of user posts across social media platforms is primarily untapped for artificial intelligence (AI) use cases based on the sheer volume and velocity of textual data. Natural language processing (NLP) is a subfield of AI that leverages bodies of documents, known as corpora, to train computers in human-like language understanding. Using a

Christina Psaroudaki, Gil Refael

We explore the statistical properties of energy transfer in ensembles of doubly-driven Random- Matrix Floquet Hamiltonians, based on universal symmetry arguments. The energy pumping efficiency distribution P(E) is associated with the Hamiltonian parameter ensemble and the eigenvalue statistics of the Floquet operator. For specific Hamiltonian ensembles, P(E)

Alfred Schreiber

Starting from the representation of a function $f(x,y)$ as a formal power series with Taylor coefficients $f_{m,n}$, we establish a formal series for the implicit function $y=y(x)$ such that $f(x,y)=0$ and the coefficients of the series for $y$ depend exclusively on the $f_{m,n}$. The solution to this problem provided here relies on using partial Bell polyno

Daniel Garces, Stephanie Gil

Large events such as conferences, concerts and sports games, often cause surges in demand for ride services that are not captured in average demand patterns, posing unique challenges for routing algorithms. We propose a learning framework for an autonomous fleet of taxis that leverages event data from the internet to predict demand surges and generate cooper

M. Zubair, P. Vasilopoulos, M. Tahir

We investigate the electronic dispersion and transport properties of graphene/WSe$_{2}$ heterostructures in the presence of a proximity induced spin-orbit coupling (SOC) using a low-energy Hamiltonian, with different types of symmetry breaking terms, obtained from a four-band, first and second nearest-neighbour tight-binding (TB) one. The competition between

Hugo Åström, Susi Lehtola

Strong magnetic fields such as those found on white dwarfs have significant effects on the electronic structure of atoms and molecules. However, the vast majority of molecular studies in the literature in such fields are carried out with Gaussian basis sets designed for zero field, leading to large basis set truncation errors [Lehtola et al, Mol. Phys. 2020,

Gabriele Bruni, Javier Moldón, Enrico Piconcelli, Francesca Panessa

In the past years, the results obtained by the WISSH quasar project provided a novel general picture on the distinctive multi-band properties of hyper-luminous ($L_{bol}>10^{47}$ erg/s) quasars at high redshift (z$\sim$2-4), unveiling interesting relations among active galactic nuclei, winds and interstellar medium, in these powerful sources at cosmic noon.

Paulin de Schoulepnikoff, Oriel Kiss, Sofia Vallecorsa, Giuseppe Carleo

Entanglement forging based variational algorithms leverage the bi-partition of quantum systems for addressing ground state problems. The primary limitation of these approaches lies in the exponential summation required over the numerous potential basis states, or bitstrings, when performing the Schmidt decomposition of the whole system. To overcome this chal

Sean Meyn

Q-learning has become an important part of the reinforcement learning toolkit since its introduction in the dissertation of Chris Watkins in the 1980s. The purpose of this paper is in part a tutorial on stochastic approximation and Q-learning, providing details regarding the INFORMS APS inaugural Applied Probability Trust Plenary Lecture, presented in Nancy

Jade M. Almeida, Giovanna A. Castro, João A. Machado-Neto, Tiago A. Almeida

Acute Myeloid Leukemia (AML) is one of the most aggressive types of hematological neoplasm. To support the specialists' decision about the appropriate therapy, patients with AML receive a prognostic of outcomes according to their cytogenetic and molecular characteristics, often divided into three risk categories: favorable, intermediate, and adverse. However

Jake A. Miller, Edward M. Cackett, Michael R. Goad, Keith Horne

Continuum reverberation mapping probes the sizescale of the optical continuum-emitting region in active galactic nuclei (AGN). Through 3 years of multiwavelength photometric monitoring in the optical with robotic observatories, we perform continuum reverberation mapping on Mrk~876. All wavebands show large amplitude variability and are well correlated. Slow

Lorenzo Carfagna, Giovanni Manzini

In this paper we extend to two-dimensional data two recently introduced one-dimensional compressibility measures: the $\gamma$ measure defined in terms of the smallest string attractor, and the $\delta$ measure defined in terms of the number of distinct substrings of the input string. Concretely, we introduce the two-dimensional measures $\gamma_{2D}$ and $\

Luciano Del Corro, Allie Del Giorno, Sahaj Agarwal, Bin Yu

Autoregressive large language models (LLMs) have made remarkable progress in various natural language generation tasks. However, they incur high computation cost and latency resulting from the autoregressive token-by-token generation. To address this issue, several approaches have been proposed to reduce computational cost using early-exit strategies. These

Jacqueline Harding

Transitive proxy voting (or "liquid democracy") is a novel form of collective decision making, often framed as an attractive hybrid of direct and representative democracy. Although the ideas behind liquid democracy have garnered widespread support, there have been relatively few attempts to model it formally. This paper makes three main contributions. First,

Jiaqi Wang, Tianyi Li, Anni Wang, Xiaoze Liu

Hosting database services on cloud systems has become a common practice. This has led to the increasing volume of database workloads, which provides the opportunity for pattern analysis. Discovering workload patterns from a business logic perspective is conducive to better understanding the trends and characteristics of the database system. However, existing

Yeganeh Gharedaghi, Gene Cheung, Xianming Liu

Images captured in poorly lit conditions are often corrupted by acquisition noise. Leveraging recent advances in graph-based regularization, we propose a fast Retinex-based restoration scheme that denoises and contrast-enhances an image. Specifically, by Retinex theory we first assume that each image pixel is a multiplication of its reflectance and illuminat

James D. Brunner, Laverne A. Gallegos-Graves, Marie E. Kroeger

Microbial communities assemble through a complex set of interactions between microbes and their environment, and the resulting metabolic impact on the host ecosystem can be profound. Microbial activity is known to impact human health, plant growth, water quality, and soil carbon storage which has lead to the development of many approaches and products meant

Irene Wang, Prashant J. Nair, Divya Mahajan

Federated Learning (FL) allows machine learning models to train locally on individual mobile devices, synchronizing model updates via a shared server. This approach safeguards user privacy; however, it also generates a heterogeneous training environment due to the varying performance capabilities across devices. As a result, straggler devices with lower perf

Maryam Mansoury, Vram Mughnetsyan, Aram Manaselyan, Albert Kirakosyan

In this paper a comparative study of the electronic and magnetic properties of quasi-two-dimensional electrons in an artificial graphene-like superlattice composed of circular and elliptical quantum dots is presented. A complete orthonormal set of basis wave functions, which has previously been constructed in the frame of the Coulomb gauge for the vector pot

Dorothee Knees, Sebastian Owczarek, Patrizio Neff

We derive a global higher regularity result for weak solutions of the linear relaxed micromorphic model on smooth domains. The governing equations consist of a linear elliptic system of partial differential equations that is coupled with a system of Maxwell-type. The result is obtained by combining a Helmholtz decomposition argument with regularity results f

Colin Bellinger, Mark Crowley, Isaac Tamblyn

Reinforcement learning (RL) has been shown to learn sophisticated control policies for complex tasks including games, robotics, heating and cooling systems and text generation. The action-perception cycle in RL, however, generally assumes that a measurement of the state of the environment is available at each time step without a cost. In applications such as

J. Kim, A. López-García, V. A. Prokhorov

We begin our analysis with the study of two collections of lattice paths in the plane, denoted $\mathcal{D}_{[n,i,j]}$ and $\mathcal{P}_{[n,i,j]}$. These paths consist of sequences of $n$ steps, where each step allows movement in three directions: upward (with a maximum displacement of $q$ units), rightward (exactly one unit), or downward (with a maximum dis

K. E. Harborne, A. Serene, E. J. A. Davies, C. Derkenne

In this work, we present a methodology and a corresponding code-base for constructing mock integral field spectrograph (IFS) observations of simulated galaxies in a consistent and reproducible way. Such methods are necessary to improve the collaboration and comparison of observation and theory results, and accelerate our understanding of how the kinematics o

Miguel Campercholi, Diego Castaño, Gonzalo Zigarán

The Chinese Remainder Theorem for the integers says that every system of congruence equations is solvable as long as the system satisfies an obvious necessary condition. This statement can be generalized in a natural way to arbitrary algebraic structures using the language of Universal Algebra. In this context, an algebra is a structure of a first-order lang

Ruiqi Lyu, Roni Rosenfeld, Bryan Wilder

Epidemic surveillance is a challenging task, especially when crucial data is fragmented across institutions and data custodians are unable or unwilling to share it. This study aims to explore the feasibility of a simple federated surveillance approach. The idea is to conduct hypothesis tests for a rise in counts behind each custodian's firewall and then comb

Yuwei Bao, Barrett Martin Lattimer, Joyce Chai

Human language acquisition is an efficient, supervised, and continual process. In this work, we took inspiration from how human babies acquire their first language, and developed a computational process for word acquisition through comparative learning. Motivated by cognitive findings, we generated a small dataset that enables the computation models to compa

Yarin Ozery, Asaf Nadler, Asaf Shabtai

Data exfiltration over the DNS protocol and its detection have been researched extensively in recent years. Prior studies focused on offline detection methods, which although capable of detecting attacks, allow a large amount of data to be exfiltrated before the attack is detected and dealt with. In this paper, we introduce Information-based Heavy Hitters (i

Anemarie DeYoung, Anna Hayes

We examine the effect of realistic pinhole geometries used in neutron imaging on the size of the image produced in the image plane. Using a simple inverted parabola shape for the neutron source, an MCNP simulation shows that the area of the images are only very slightly changed by pinhole blurring. Using a metric, A80, which is the area of the image that con

Francisco Correa, Andreas Fring, Octavio Quintana

We propose generalizations of Calogero models that exhibit invariance with respect to the infinite Weyl groups of affine, hyperbolic, and Lorentzian types. Our approach involves deriving closed analytic formulas for the action of the associated Coxeter elements of infinite order acting on arbitrary roots within their respective root spaces. These formulas ar

Claes Wohlin, Marcos Kalinowski, Katia Romero Felizardo, Emilia Mendes

Background: A good search strategy is essential for a successful systematic literature study. Historically, database searches have been the norm, which has later been complemented with snowball searches. Our conjecture is that we can perform even better searches if combining the two search approaches, referred to as a hybrid search strategy. Objective: Our m

Alberto Barchielli, Reinhard Werner

In the case of a quantum-classical hybrid system with a finite number of degrees of freedom, the problem of characterizing the most general dynamical semigroup is solved, under the restriction of being quasi-free. This is a generalization of a Gaussian dynamics, and it is defined by the property of sending (hybrid) Weyl operators into Weyl operators in the H

Tomer Ezra, Tamar Garbuz

We study the measure of order-competitive ratio introduced by Ezra et al. [2023] for online algorithms in Bayesian combinatorial settings. In our setting, a decision-maker observes a sequence of elements that are associated with stochastic rewards that are drawn from known priors, but revealed one by one in an online fashion. The decision-maker needs to deci

Jiaqi Xu, Cheng Luo, Weicheng Xie, Linlin Shen

Verbal and non-verbal human reaction generation is a challenging task, as different reactions could be appropriate for responding to the same behaviour. This paper proposes the first multiple and multimodal (verbal and nonverbal) appropriate human reaction generation framework that can generate appropriate and realistic human-style reactions (displayed in th

Pradeep Kushwaha, Jai Sukhatme, Ravi S. Nanjundiah

Arabian Sea Mid-Tropospheric Cyclones (MTCs), which are responsible for extreme rainfall events in western India, often coincide with monsoon low-pressure systems (LPS) over the Bay of Bengal. However, the role of Bay of Bengal LPSs in the formation of Arabian Sea MTCs remains unclear. This study utilizes the Weather Research and Forecasting Model (WRF) to i

Hao Wan, Geunwoong Jeon, Weiyue Xin, Gregory M. Grason

The morphologies of two-dimensional (2D) crystals, nucleated, grown, and integrated within 2D elastic fluids, for instance in giant vesicle membranes, are dictated by an interplay of mechanics, permeability, and thermal contraction. Mitigation of solid strain drives formation of crystals with developable shapes (e.g. planar or cylindrical) that expel Gaussia

Rikpratik Sengupta, Shounak Ghosh, M. Kalam

In this paper we have explored the possibility of constructing a traversable wormhole on the Shtanov-Sahni braneworld with a timelike extra dimension. We find that the Weyl curvature singularity at the throat of the wormhole can be removed with physical matter satisfying the NEC $\rho+p \geq 0$, even in the absence of any effective $\Lambda$-term or any type

Mario Becerra, Peter Goos

Many food products involve mixtures of ingredients, where the mixtures can be expressed as combinations of ingredient proportions. In many cases, the quality and the consumer preference may also depend on the way in which the mixtures are processed. The processing is generally defined by the settings of one or more process variables. Experimental designs stu

Lucas Vogels, Reza Mohammadi, Marit Schoonhoven, S. Ilker Birbil

Gaussian graphical models provide a powerful framework to reveal the conditional dependency structure between multivariate variables. The process of uncovering the conditional dependency network is known as structure learning. Bayesian methods can measure the uncertainty of conditional relationships and include prior information. However, frequentist methods

Rikpratik Sengupta, Anirban Chanda, B C Paul, M Kalam

In this paper, we attempt to explore the possibility of a obtaining a viable emergent universe scenario supported by a type of fluid known as the extended Chaplygin gas, which extends a modification to the equation of state of the well known modified Chaplygin gas by considering additional higher order barotropic fluid terms. We consider quadratic modificati

Nico Schliwa, Alice-Agnes Gabriel

Dynamic rupture simulations generate synthetic waveforms that account for non-linear source, path, and site complexity. Here, we analyze millions of spatially dense waveforms from 3D dynamic rupture simulations in a novel way to illuminate the spectral fingerprints of earthquake physics. We define a Brune-type equivalent near-field corner frequency ($f_c$) t

Ligesh Theeyancheri, Subhasish Chaki, Tapomoy Bhattacharjee, Rajarshi Chakrabarti

To understand the dynamical and conformational properties of deformable active agents in porous media, we computationally investigate the dynamics of linear chains and rings made of active Brownian monomers. In porous media, flexible linear chains and rings always migrate smoothly and undergo activity-induced swelling. However, semiflexible linear chains tho

Shuyang Cai, Wanyun Cui

ChatGPT brings revolutionary social value but also raises concerns about the misuse of AI-generated text. Consequently, an important question is how to detect whether texts are generated by ChatGPT or by human. Existing detectors are built upon the assumption that there are distributional gaps between human-generated and AI-generated text. These gaps are typ

Sébastien Lachapelle, Divyat Mahajan, Ioannis Mitliagkas, Simon Lacoste-Julien

We tackle the problems of latent variables identification and ``out-of-support'' image generation in representation learning. We show that both are possible for a class of decoders that we call additive, which are reminiscent of decoders used for object-centric representation learning (OCRL) and well suited for images that can be decomposed as a sum of objec

Mohamed A. Serry, Sean D. Peterson, Jun Liu

In this paper, we investigate the Euler-Bernoulli fourth-order boundary value problem (BVP) $w^{(4)}=f(x,w)$, $x\in \intcc{a,b}$, with specified values of $w$ and $w''$ at the end points, where the behaviour of the right-hand side $f$ is motivated by biomechanical, electromechanical, and structural applications incorporating contact forces. In particular, we

A. R. Moura

Over the years, the Self-Consistent Harmonic Approximation (SCHA) has been successfully utilized to determine the transition temperature of many different magnetic models, particularly the Berezinskii-Thouless-Kosterlitz transition in two-dimensional ferromagnets. More recently, the SCHA has found application in describing ferromagnetic samples in spintronic

Harbir Antil, David Sayre

This paper explores the application of event-based cameras in the domains of image segmentation and motion estimation. These cameras offer a groundbreaking technology by capturing visual information as a continuous stream of asynchronous events, departing from the conventional frame-based image acquisition. We introduce a Generalized Nash Equilibrium based f

Renjie Luo, Tanner J. Legvold, Liyang Chen, Henry Navarro

The spin Seebeck effect (SSE) is sensitive to thermally driven magnetic excitations in magnetic insulators. Vanadium dioxide in its insulating low temperature phase is expected to lack magnetic degrees of freedom, as vanadium atoms are thought to form singlets upon dimerization of the vanadium chains. Instead, we find a paramagnetic SSE response in VO2 films

Joshua Foo, Magdalena Zych

Recent results in relativistic quantum information and quantum thermodynamics have independently shown that in the quantum regime, a system may fail to thermalise when subject to quantum-controlled application of the same, single thermalisation channel. For example, an accelerating system with fixed proper acceleration is known to thermalise to an accelerati

Mar Císcar-Monsalvatje, Alejandro Ibarra, Jérôme Vandecasteele

There is currently no evidence for a baryon asymmetry in our Universe. Instead, cosmological observations have only demonstrated the existence of a quark-antiquark asymmetry, which does not necessarily imply a baryon asymmetric Universe, since the baryon number of the dark sector particles is unknown. In this paper we discuss a framework where the total bary

Sunjae Kwon, Xun Wang, Weisong Liu, Emily Druhl

Opioid related aberrant behaviors (ORABs) present novel risk factors for opioid overdose. This paper introduces a novel biomedical natural language processing benchmark dataset named ODD, for ORAB Detection Dataset. ODD is an expert-annotated dataset designed to identify ORABs from patients' EHR notes and classify them into nine categories; 1) Confirmed Aber

Salvatore Spina

Computerization has created a digital ecological niche where humans live in a state of interconnection that modifies their Epigenetics. Within this hyper-datafied virtual space, the logged-in agent enhances their intellectual and rational abilities, giving rise to a new cognitive entity. Humans are evolving towards a new anthropological status that shifts th