December 2023 arXiv papers — page 124

Gyeong-Geon Lee, Lehong Shi, Ehsan Latif, Yizhu Gao

This paper presents a comprehensive examination of how multimodal artificial intelligence (AI) approaches are paving the way towards the realization of Artificial General Intelligence (AGI) in educational contexts. It scrutinizes the evolution and integration of AI in educational systems, emphasizing the crucial role of multimodality, which encompasses audit

Antoine Marot, David Rousseau, Zhen, Xu

The conclusion of an AI challenge is not the end of its lifecycle; ensuring a long-lasting impact requires meticulous post-challenge activities. The long-lasting impact also needs to be organised. This chapter covers the various activities after the challenge is formally finished. This work identifies target audiences for post-challenge initiatives and outli

Jerome A. Orosz

We present photodynamical models of four eclipsing binary systems that are members of higher-order multiple systems. We provide some radial velocities measurements and use recent TESS data for three of the systems. KIC 7668648 consists of an eclipsing binary (P=27.8 d) with late-type stars that has a low-mass star on a roughly coplanar outer orbit (P=208 d).

Piotr Miłkowski, Konrad Karanowski, Patryk Wielopolski, Jan Kocoń

Designing predictive models for subjective problems in natural language processing (NLP) remains challenging. This is mainly due to its non-deterministic nature and different perceptions of the content by different humans. It may be solved by Personalized Natural Language Processing (PNLP), where the model exploits additional information about the reader to

S. Pinto, R. C. de Lamare

This work studies multiple-antenna wireless communication systems based on super-resolution arrays (SRAs). We consider the uplink of a multiple-antenna system in which users communicate with a multiple-antenna base station equipped with SRAs. In particular, we develop linear minimum mean-square error (MMSE) receive filters along with linear and successive in

Shawn Im, Jacob Andreas, Yilun Zhou

One of the motivations for explainable AI is to allow humans to make better and more informed decisions regarding the use and deployment of AI models. But careful evaluations are needed to assess whether this expectation has been fulfilled. Current evaluations mainly focus on algorithmic properties of explanations, and those that involve human subjects often

D. Navarro-Gironés, E. Gaztañaga, M. Crocce, A. Wittje

We present photometric redshifts (photo-$z$) for the deep wide fields of the Physics of the Accelerating Universe Survey (PAUS), covering an area of $\sim$50 deg$^{2}$, for $\sim$1.8 million objects up to $i_{\textrm{AB}}<23$. The PAUS deep wide fields overlap with the W1 and W3 fields from CFHTLenS and the G09 field from KiDS/GAMA. Photo-$z$ are estimated u

Minye Wu, Zehao Wang, Georgios Kouros, Tinne Tuytelaars

Neural Radiance Fields (NeRF) revolutionize the realm of visual media by providing photorealistic Free-Viewpoint Video (FVV) experiences, offering viewers unparalleled immersion and interactivity. However, the technology's significant storage requirements and the computational complexity involved in generation and rendering currently limit its broader applic

Chang Liu, Manjul Sharma, Keith Julien, Edgar Knobloch

This work studies two-dimensional fixed-flux Rayleigh-B\'enard convection with periodic boundary conditions in both horizontal and vertical directions and analyzes its dynamics using numerical continuation, secondary instability analysis and direct numerical simulation. The fixed-flux constraint leads to time-independent elevator modes with a well-defined am

Muhammad Marwan Muhammad Fuad

Time series classification (TSC) is the most import task in time series mining as it has several applications in medicine, meteorology, finance cyber security, and many others. With the ever increasing size of time series datasets, several traditional TSC methods are no longer efficient enough to perform this task on such very large datasets. Yet, most recen

Pavel N. Krivitsky, Pietro Coletti, Niel Hens

This rejoinder responds to discussions by of Caimo, Niezink, and Schweinberger and Fritz of ''A Tale of Two Datasets: Representativeness and Generalisability of Inference for Samples of Networks'' by Krivitsky, Coletti, and Hens, all published in the Journal of the American Statistical Association in 2023.

Ethan Carragher, Kenn Shern Goh, Wei Su, Martin White

We perform the first convergent Bayesian global fits of 4D Composite Higgs Models with partially-composite third generation quarks and leptons based on the minimal $SO(5) \rightarrow SO(4)$ symmetry breaking pattern. We consider two models with the $\tau$ lepton and its associated neutrino in different representations of $SO(5)$. Fitting each model with a wi

Luigi De Masi, Carlo Gasparetto

We generalize a result by Alberti, showing that, if a first-order linear differential operator $\mathcal{A}$ belongs to a certain class, then any $L^1$ function is the absolutely continuous part of a measure $\mu$ satisfying $\mathcal{A}\mu=0$. When $\mathcal{A}$ is scalar valued, we provide a necessary and sufficient condition for the above property to hold

Rojaina Mahmoud, Mona Mamdouh, Omneya Attallah, Ahmad Al-Kabbany

In this research, we are concerned with the applicability of virtual reality-based attention training as a tool for stress management. Mental stress is a worldwide challenge that is still far from being fully managed. This has maintained a remarkable research attention on developing and validating tools for detecting and managing stress. Technology-based too

Soya Park, Hari Subramonyam, Chinmay Kulkarni

Many AI systems focus solely on providing solutions or explaining outcomes. However, complex tasks like research and strategic thinking often benefit from a more comprehensive approach to augmenting the thinking process rather than passively getting information. We introduce the concept of "Thinking Assistant", a new genre of assistants that help users impro

Shubham R. Jathar, Manas Kar, Jesse Railo

We study the injectivity of the matrix attenuated and nonabelian ray transforms on compact surfaces with boundary for nontrapping $\lambda$-geodesic flows and the general linear group of invertible complex matrices. We generalize the loop group factorization argument of Paternain and Salo to reduce to the setting of the unitary group when $\lambda$ has the v

Yash Kumar Atri, Vikram Goyal, Tanmoy Chakraborty

Abstractive text summarization is surging with the number of training samples to cater to the needs of the deep learning models. These models tend to exploit the training data representations to attain superior performance by improving the quantitative element of the resultant summary. However, increasing the size of the training set may not always be the id

Karlo Koledić, Luka Petrović, Ivan Petrović, Ivan Marković

Learning-based monocular depth estimation leverages geometric priors present in the training data to enable metric depth perception from a single image, a traditionally ill-posed problem. However, these priors are often specific to a particular domain, leading to limited generalization performance on unseen data. Apart from the well studied environmental dom

Ayşegül Tümer, Daniel R. Wik, Gerrit Schellenberger, Eric D. Miller

Observations of galaxy cluster mergers provide insights on the particle acceleration and heating mechanisms taking place within the intracluster medium. Mergers form shocks that propagate through the plasma, which result in shock/cold fronts in the X-ray, and radio halos and/or relics in the radio regime. The connection between these tracers and the mechanis

Lawrence Frolov, Samuel E. Leigh, A. Shadi Tahvildar-Zadeh

A Lorentz-covariant system of wave equations is formulated for a quantum-mechanical three-body system in one space dimension, comprised of one photon and two identical massive spin one-half Dirac particles, which can be thought of as two electrons (or alternatively, two positrons). Manifest covariance is achieved using Dirac's formalism of multi-time wave fu

Giovanni Poli, Elena Fountzilas, Apostolia-Maria Tsimeridou, Peter Müller

We develop a non-parametric Bayesian prior for a family of random probability measures by extending the Polya tree ($PT$) prior to a joint prior for a set of probability measures $G_1,\dots,G_n$, suitable for meta-analysis with event time outcomes. In the application to meta-analysis $G_i$ is the event time distribution specific to study $i$. The proposed mo

Luca Wolf, Tobias Buck

State-of-the-art galaxy formation simulations generate data within weeks or months. Their results consist of a random sub-sample of possible galaxies with a fixed number of stars. We propose a ML based method, GalacticFlow, that generalizes such results. We use normalizing flows to learn the extended distribution function of galaxies conditioned on global ga

Zhipeng Bao, Yijun Li, Krishna Kumar Singh, Yu-Xiong Wang

Despite recent significant strides achieved by diffusion-based Text-to-Image (T2I) models, current systems are still less capable of ensuring decent compositional generation aligned with text prompts, particularly for the multi-object generation. This work illuminates the fundamental reasons for such misalignment, pinpointing issues related to low attention

Ufuk Çakır, Tobias Buck

We introduce the GAMMA (Galactic Attributes of Mass, Metallicity, and Age) dataset, a comprehensive collection of galaxy data tailored for Machine Learning applications. This dataset offers detailed 2D maps and 3D cubes of 11 727 galaxies, capturing essential attributes: stellar age, metallicity, and mass. Together with the dataset, we publish our code to ex

Immanuel Sulzer, Tobias Buck

In astrophysics, solving complex chemical reaction networks is essential but computationally demanding due to the high dimensionality and stiffness of the ODE systems. Traditional approaches for reducing computational load are often specialized to specific chemical networks and require expert knowledge. This paper introduces a machine learning-based solution

Anders Rantzer

This is a draft paper originally posted on Arxiv as a documentation of a plenary lecture at CDC2023. The core material has been accepted for publication at L4DC 2024. Certainty equivalence adaptive controllers are analysed using a ``data-driven Riccati equation'', corresponding to the model-free Bellman equation used in Q-learning. The equation depends quadr

Kevin M. Dempsey

We develop principal branches for three key square root functions and for the inverse trigonometric and inverse hyperbolic functions. The three square root branches are integral to defining the inverse function branches, their derivatives, and their antiderivatives. Complex analysis is used to turn the definitions of the principal branches into concrete expr

Isabel Colaço, Ignacio Ojeda

Let $\Bbbk$ be an arbitrary field and let $b > 1, n > 1$ and $a$ be three positive integers. In this paper we explicitly describe a minimal $S-$graded free resolution of the semigroup algebra $\Bbbk[S]$ when $S$ is a generalized repunit numerical semigroup, that is, when $S$ is the submonoid of $\mathbb{N}$ generated by $\{a_1, a_2, \ldots, a_n\}$ where $a_1

Yichen You

Let $\mathcal{A}$ be a set of mutually coprime positive integers, satisfying \begin{align*} \sum\limits_{a\in\mathcal{A}}\frac{1}{a} = \infty. \end{align*} Define the (possibly non-multiplicative) "Liouville-like" functions \begin{align*} \lambda_{\mathcal{A}}(n) = (-1)^{\#\{a:a|n, a \in \mathcal{A}\}} \text{ or } (-1)^{\#\{a:a^\nu\parallel n, a \in \mathcal

Germán E. López, Chumin Wang

In contrast to most microscopic theories of superconductivity based on the reciprocal space, the Bogoliubov-de Gennes (BdG) formalism provides a real-space alternative for addressing inhomogeneous systems. In this article, we study the superconducting states in correlated single-walled carbon nanotubes (SWNTs) with curvature and spin-orbit corrections, as we

Yuanda Wang, Qiben Yan, Nikolay Ivanov, Xun Chen

The emergence of Artificial Intelligence (AI)-driven audio attacks has revealed new security vulnerabilities in voice control systems. While researchers have introduced a multitude of attack strategies targeting voice control systems (VCS), the continual advancements of VCS have diminished the impact of many such attacks. Recognizing this dynamic landscape,

Kruthi Krishna, Aditya Vijaykumar, Apratim Ganguly, Colm Talbot

We describe an implementation of the relative binning technique to speed up parameter estimation of gravitational-wave signals. We first give a pedagogical overview of relative binning, discussing also the expressions for the likelihood marginalized over phase and distance. Then, we describe the details of the code in \texttt{Bilby}, an open-source software

Subhadip Kumar

Artificial Intelligence for IT Operations (AIOps) is a rapidly growing field that applies artificial intelligence and machine learning to automate and optimize IT operations. AIOps vendors provide services that ingest end-to-end logs, traces, and metrics to offer a full stack observability of IT systems. However, these data sources may contain sensitive info

Teja Begari, Thomas J. Maccarone

AM CVn systems are a rare type of cataclysmic variable star consisting of a w hite dwarf accreting material from a low-mass, hydrogen-poor donor star. These helium-rich systems usually have orbital periods that are less than 65 minutes an d are predicted to be sources of gravitational waves. We have analyzed the catalogued X-ray data from the Chandra, XMM-Ne

H. Kalantarova, L. Klinger, E. Rabkin

We use the sixth order linear parabolic equation \begin{equation} \frac{\partial y}{\partial t}=B\left(\alpha\frac{\partial^{6}y}{\partial x^{6}}-\frac{\partial^{4}y}{\partial x^{4}}\right),\ x\in \mathbb{R}_{+},\ t>0,\nonumber \end{equation} proposed by Rabkin and describing the evolution of a solid surface covered with a thin, inert and fully elastic passi

Adekunle Ajibode, Dong Yunwei, Yang Hongji

Issue resolution and bug-fixing processes are essential in the development of machine-learning libraries, similar to software development, to ensure well-optimized functions. Understanding the issue resolution and bug-fixing process of machine-learning libraries can help developers identify areas for improvement and optimize their strategies for issue resolu

Kapil Panda

Public finances are one of the fundamental mechanisms of economic governance that refer to the financial activities and decisions made by government entities to fund public services, projects, and operations through assets. In today's globalized landscape, even subtle shifts in one nation's public debt landscape can have significant impacts on that of intern

Andy Wanna, Samuel Coward, Theo Drane, George A. Constantinides

Multiplier circuits account for significant resource usage in datapath-dominated circuit designs, and RTL designers continue to build bespoke hand-crafted multiplication arrays for their particular application. The construction of an optimized multiplier presents trade-offs between pre-processing to generate a smaller array and array reduction. A data struct

Enrique Artal Bartolo

We study the existence of some irreducible projective plane curves of degree~$8$ with some prescribed topological type of singularities in the algebraic and symplectic worlds.

Ruiyu Wang, Matthew Choi

Lexical Semantic Change Detection stands out as one of the few areas where Large Language Models (LLMs) have not been extensively involved. Traditional methods like PPMI, and SGNS remain prevalent in research, alongside newer BERT-based approaches. Despite the comprehensive coverage of various natural language processing domains by LLMs, there is a notable s

Saswat Padhi, Elizabeth Polgreen, Mukund Raghothaman, Andrew Reynolds

The classical formulation of the program-synthesis problem is to find a program that meets a correctness specification given as a logical formula. Syntax-guided synthesis (SyGuS) is a standardized format for specifying the correctness specification with a syntactic template that constrains the space of allowed implementations. The input to SyGuS consists of

Carsten Dominik, Cornelis Dullemond

Context. A leading paradigm in planet formation is currently the streaming instability and pebble accretion scenario. For this scenario, dust must grow into sizes in a specific regime of Stokes numbers in order to make these processes viable and sufficiently effective. The dust growth models currently in use do not implement some of the growth barriers sugge

B. Kazarnovskii

For systems of equations with an infinite set of roots, one can sometimes obtain Kushnirenko-Bernstein-Khovanskii type theorem if replace the number of roots by their asymptotic density. We consider systems of entire functions with exponential growth in the space $\mathbb C^n$, and calculate the asymptotic distribution of their common zeros in terms of the g

S. Mohammad Ahmadi, Nahid Ahmadi, Mehdi Shokri

In this work, we explore the prospect of generalizing the constant-roll condition in canonical inflationary model to non-canonical models. To find a natural generalization, we focus on three manifestations of this condition and construct constant-roll models corresponding to each manifestation. These models are not equivalent but reduce to the familiar const

Shujian Chen, Kiyoshi Igusa

In 2017, Igusa and Todorov gave a bijection between signed exceptional sequences and ordered partial clusters. In this paper, we show that every term in an exceptional sequence is either relatively projective or relatively injective or both and we refine this bijection to one between projectively signed exceptional sequences and ordered partial positive clus

Ozan Candogan, Feiyu Han, Haihao Lu

Regressivity in property taxation, or the disproportionate overassessment of lower-valued properties compared to higher-valued ones, results in an unfair taxation burden for Americans living in poverty. To address regressivity and enhance both the accuracy and fairness of property assessments, we introduce a scalable property valuation model called the $K$-s

Javier Tirado-Garín, Javier Civera

Estimating the relative camera pose from $n \geq 5$ correspondences between two calibrated views is a fundamental task in computer vision. This process typically involves two stages: 1) estimating the essential matrix between the views, and 2) disambiguating among the four candidate relative poses that satisfy the epipolar geometry. In this paper, we demonst

Christos Plachouras, Pablo Alonso-Jiménez, Dmitry Bogdanov

Music Information Retrieval (MIR) research is increasingly leveraging representation learning to obtain more compact, powerful music audio representations for various downstream MIR tasks. However, current representation evaluation methods are fragmented due to discrepancies in audio and label preprocessing, downstream model and metric implementations, data

Yohann De Castro, Sébastien Gadat, Clément Marteau

This paper presents a novel algorithm that leverages Stochastic Gradient Descent strategies in conjunction with Random Features to augment the scalability of Conic Particle Gradient Descent (CPGD) specifically tailored for solving sparse optimization problems on measures. By formulating the CPGD steps within a variational framework, we provide rigorous mathe

Tisa Biswas

We study the interactions of Axion-Like Particles (ALPs) with the Standard Model particles, aiming to probe their phenomenology via non-resonant searches at the LHC. These interactions are mediated by higher dimensional effective operators within two possible frameworks of linearly and non-linearly realised electroweak symmetry breaking. We consider the ALPs

Isaac Sheidlower, Reuben Aronson, Elaine Short

It is crucial that users are empowered to use the functionalities of a robot to creatively solve problems on the fly. A user who has access to a Reinforcement Learning (RL) based robot may want to use the robot's autonomy and their knowledge of its behavior to complete new tasks. One way is for the user to take control of some of the robot's action space thr

Zening Duan, Anqi Shao, Yicheng Hu, Heysung Lee

While researchers often study message features like moral content in text, such as party manifestos and social media, their quantification remains a challenge. Conventional human coding struggles with scalability and intercoder reliability. While dictionary-based methods are cost-effective and computationally efficient, they often lack contextual sensitivity

Sokhna Diarra Mbacke, Omar Rivasplata

Diffusion models are one of the most important families of deep generative models. In this note, we derive a quantitative upper bound on the Wasserstein distance between the data-generating distribution and the distribution learned by a diffusion model. Unlike previous works in this field, our result does not make assumptions on the learned score function. M

Srijeet Halder, Kereshmeh Afsari, Alireza Shojaei

This article explores natural interaction modalities for human-cyber-physical systems (CPS) interaction in construction. CPS has been applied in construction for many purposes with the promise of improving the safety and productivity of construction operations. However, there is little research on human-CPS interaction in construction. This study proposes tw

Auguste Gezalyan, Soo Kim, Carlos Lopez, Daniel Skora

The Hilbert metric is a distance function defined for points lying within the interior of a convex body. It arises in the analysis and processing of convex bodies, machine learning, and quantum information theory. In this paper, we show how to adapt the Euclidean Delaunay triangulation to the Hilbert geometry defined by a convex polygon in the plane. We anal

Xiaoyang Chen, Junjie Zhao, Siyuan Liu, Sahar Ahmad

Cortical surface reconstruction (CSR) from MRI is key to investigating brain structure and function. While recent deep learning approaches have significantly improved the speed of CSR, a substantial amount of runtime is still needed to map the cortex to a topologically-correct spherical manifold to facilitate downstream geometric analyses. Moreover, this map

Gregory Faletto

To address the bias of the canonical two-way fixed effects estimator for difference-in-differences under staggered adoptions, Wooldridge (2021) proposed the extended two-way fixed effects estimator, which adds many parameters. However, this reduces efficiency. Restricting some of these parameters to be equal (for example, subsequent treatment effects within

Aditya Chetan, Guandao Yang, Zichen Wang, Steve Marschner

Neural fields have become widely used in various fields, from shape representation to neural rendering, and for solving partial differential equations (PDEs). With the advent of hybrid neural field representations like Instant NGP that leverage small MLPs and explicit representations, these models train quickly and can fit large scenes. Yet in many applicati

D. Trotta, A. Larosa, G. Nicolaou, T. S. Horbury

The Parker Solar Probe (PSP) and Solar Orbiter (SolO) missions opened a new observational window in the inner heliosphere, which is finally accessible to direct measurements. On September 05, 2022, a coronal mass ejection (CME)-driven interplanetary (IP) shock has been observed as close as 0.07 au by PSP. The CME then reached SolO, which was well radially-al

Maxence Faldor, Félix Chalumeau, Manon Flageat, Antoine Cully

A hallmark of intelligence is the ability to exhibit a wide range of effective behaviors. Inspired by this principle, Quality-Diversity algorithms, such as MAP-Elites, are evolutionary methods designed to generate a set of diverse and high-fitness solutions. However, as a genetic algorithm, MAP-Elites relies on random mutations, which can become inefficient

Cordula Reisch, Sandra Nickel, Hans-Michael Tautenhahn

The paper presents an approach for overcoming modeling problems of typical life science applications with partly unknown mechanisms and lacking quantitative data: A model family of reaction diffusion equations is built up on a mesoscopic scale and uses classes of feasible functions for reaction and taxis terms. The classes are found by translating biological

Sal Wanying Fu, Daniel R. Weisz, Else Starkenburg, Nicolas Martin

We measure the metallicities of 374 red giant branch (RGB) stars in the isolated, quenched dwarf galaxy Tucana using Hubble Space Telescope (HST) narrow-band (F395N) Calcium H & K (CaHK) imaging. Our sample is a factor of $\sim7$ larger than what is published. Our main findings are: (i) A global metallicity distribution function (MDF) with $\langle \mbox{[Fe

Pierre-Luc Parent, Margarida Carvalho, Miguel F. Anjos, Ribal Atallah

With the increasing effects of climate change, the urgency to step away from fossil fuels is greater than ever before. Electric vehicles (EVs) are one way to diminish these effects, but their widespread adoption is often limited by the insufficient availability of charging stations. In this work, our goal is to expand the infrastructure of EV charging statio

Peter West, Ronan Le Bras, Taylor Sorensen, Bill Yuchen Lin

We present NovaCOMET, an open commonsense knowledge model, that combines the best aspects of knowledge and general task models. Compared to previous knowledge models, NovaCOMET allows open-format relations enabling direct application to reasoning tasks; compared to general task models like Flan-T5, it explicitly centers knowledge, enabling superior performan

Luke McDermott, Jason Weitz, Dmitri Demler, Daniel Cummings

We develop an automated pipeline to streamline neural architecture codesign for fast, real-time Bragg peak analysis in high-energy diffraction microscopy. Traditional approaches, notably pseudo-Voigt fitting, demand significant computational resources, prompting interest in deep learning models for more efficient solutions. Our method employs neural architec

Roger J. A. Laeven, Mitja Stadje

This paper axiomatizes, in a two-stage setup, a new theory for decision under risk and ambiguity. The axiomatized preference relation $\succeq$ on the space $\tilde{V}$ of random variables induces an ambiguity index $c$ on the space $\Delta$ of probabilities, a probability weighting function $\psi$, generating the measure $\nu_{\psi}$ by transforming an obje

Ashish Dhiman, Yibei Hu

We apply a physics-informed deep-learning approach the PINN approach to the Black-Scholes equation for pricing American and European options. We test our approach on both simulated as well as real market data, compare it to analytical/numerical benchmarks. Our model is able to accurately capture the price behaviour on simulation data, while also exhibiting r

Joel Frank, Franziska Herbert, Jonas Ricker, Lea Schönherr

AI-generated media has become a threat to our digital society as we know it. These forgeries can be created automatically and on a large scale based on publicly available technology. Recognizing this challenge, academics and practitioners have proposed a multitude of automatic detection strategies to detect such artificial media. However, in contrast to thes

Augusto Santos, Diogo Rente, Rui Seabra, José M. F. Moura

This paper considers learning the hidden causal network of a linear networked dynamical system (NDS) from the time series data at some of its nodes -- partial observability. The dynamics of the NDS are driven by colored noise that generates spurious associations across pairs of nodes, rendering the problem much harder. To address the challenge of noise corre

Michael Kupper, Max Nendel, Alessandro Sgarabottolo

In this paper, we study convex risk measures with weak optimal transport penalties. In a first step, we show that these risk measures allow for an explicit representation via a nonlinear transform of the loss function. In a second step, we discuss computational aspects related to the nonlinear transform as well as approximations of the risk measures using, f

Oussama Messai, Abdelouahid Bentamou, Abbass Zein-Eddine, Yann Gavet

Deep learning-based quality assessments have significantly enhanced perceptual multimedia quality assessment, however it is still in the early stages for 3D visual data such as 3D point clouds (PCs). Due to the high volume of 3D-PCs, such quantities are frequently compressed for transmission and viewing, which may affect perceived quality. Therefore, we prop

Kenan Morani

This paper extends our previous method for COVID-19 diagnosis, proposing an enhanced solution for detecting COVID-19 from computed tomography (CT) images. To decrease model misclassifications, two key steps of image processing were employed. Firstly, the uppermost and lowermost slices were removed, preserving sixty percent of each patient's slices. Secondly,

Devin Gonier, Adrian Adduci, Cassidy LoCascio

AI Alignment research seeks to align human and AI goals to ensure independent actions by a machine are always ethical. This paper argues empathy is necessary for this task, despite being often neglected in favor of more deductive approaches. We offer an inside-out approach that grounds morality within the context of the brain as a basis for algorithmically u

Marco Gortan, Lorenzo Testa, Giorgio Fagiolo, Francesco Lamperti

Although high-resolution gridded climate variables are provided by multiple sources, the need for country and region-specific climate data weighted by indicators of economic activity is becoming increasingly common in environmental and economic research. We process available information from different climate data sources to provide spatially aggregated data

Parameshwar R. Pasnoori, Yicheng Tang, Junhyun Lee, J. H. Pixley

In this work we argue that the antiferromagnetic spin $\frac{1}{2}$ XXZ chain in the gapped phase with boundary magnetic fields hosts fractional spin $\frac{1}{4}$ at its edges. Using a combination of Bethe ansatz and the density matrix renormalization group we show that these fractional spins are sharp quantum observables in both the ground and the first ex

Hao Li, Brandon Bennett

Text classification is an important topic in the field of natural language processing. It has been preliminarily applied in information retrieval, digital library, automatic abstracting, text filtering, word semantic discrimination and many other fields. The aim of this research is to use a variety of algorithms to test the ability to identify offensive post

Nikola Spasić

Chung, Graham and Wilson defined a set of graphs $\mathcal{H}$ to be forcing, if any sequence of graphs $\{G_n\}_{n \geq 0}$ with $|G_n| = n$ must be quasirandom, whenever $hom(H, G_n)= (p^{|E(H)|}+o(1))n^{|V(H)|}$ for every $H \in \mathcal{H}$ and some constant $p \in (0, 1)$. Answering a question of Horn, attributed to Graham, a forcing set of three graphs

Deepak Alapatt, Aditya Murali, Vinkle Srivastav, Pietro Mascagni

Consensus amongst researchers and industry points to a lack of large, representative annotated datasets as the biggest obstacle to progress in the field of surgical data science. Advances in Self-Supervised Learning (SSL) represent a solution, reducing the dependence on large labeled datasets by providing task-agnostic initializations. However, the robustnes

Mohammad Habibur Rahaman, Chang-Min Lee, Mustafa Atabey Buyukkaya, Samuel Harper

Photonic crystal nanobeam cavities are valued for their small mode volume, CMOS compatibility, and high coupling efficiency crucial features for various low-power photonic applications and quantum information processing. However, despite their potential, nanobeam cavities often suffer from low quality factors due to fabrication imperfections that create surf

Rui Ye, Yaxin Du, Zhenyang Ni, Siheng Chen

In federated learning (FL), data heterogeneity is one key bottleneck that causes model divergence and limits performance. Addressing this, existing methods often regard data heterogeneity as an inherent property and propose to mitigate its adverse effects by correcting models. In this paper, we seek to break this inherent property by generating data to compl

Yilber Fabian Bautista, Giulio Bonelli, Cristoforo Iossa, Alessandro Tanzini

We present a novel study of Kerr Compton amplitudes in a partial wave basis in terms of the Nekrasov-Shatashvili (NS) function of the confluent Heun equation (CHE). Remarkably, NS-functions enjoy analytic properties and symmetries that are naturally inherited by the Compton amplitudes. Based on this, we characterize the analytic dependence of the Compton pha

Brandon Theodorou, Shrusti Jain, Cao Xiao, Jimeng Sun

Generative models can produce synthetic patient records for analytical tasks when real data is unavailable or limited. However, current methods struggle with adhering to domain-specific knowledge and removing invalid data. We present ConSequence, an effective approach to integrating domain knowledge into sequential generative neural network outputs. Our rule

Nimesh Agrawal, Anuj Kumar Sirohi, Jayadeva, Sandeep Kumar

Ensuring fairness in Recommendation Systems (RSs) across demographic groups is critical due to the increased integration of RSs in applications such as personalized healthcare, finance, and e-commerce. Graph-based RSs play a crucial role in capturing intricate higher-order interactions among entities. However, integrating these graph models into the Federate

Lauren McGough, Helena Casademunt, Miloš Nikolić, Mariela D. Petkova

In a developing embryo, information about the position of cells is encoded in the concentrations of "morphogen" molecules. In the fruit fly, the local concentrations of just a handful of proteins encoded by the gap genes are sufficient to specify position with a precision comparable to the spacing between cells along the anterior--posterior axis. This matche

Kshitij Deshpande, Varad Mashalkar, Kaustubh Mhaisekar, Amaan Naikwadi

With the advent of the pandemic, the use of video conferencing platforms as a means of communication has greatly increased and with it, so have the remote opportunities. The deaf and dumb have traditionally faced several issues in communication, but now the effect is felt more severely. This paper proposes an all-encompassing video conferencing utility that

Naghmeh Shafiee Roudbari, Charalambos Poullis, Zachary Patterson, Ursula Eicker

The hydrometric prediction of water quantity is useful for a variety of applications, including water management, flood forecasting, and flood control. However, the task is difficult due to the dynamic nature and limited data of water systems. Highly interconnected water systems can significantly affect hydrometric forecasting. Consequently, it is crucial to

Sagbo Marcel Zodji

We prove existence of a unique global-in-time weak solutions of the Navier-Stokes equations that govern the motion of a compressible viscous fluid with density-dependent viscosity in two-dimensional space. The initial velocity belongs to the Sobolev space $H^1(\mathbb{R}^2)$, and the initial fluid density is $\alpha$-H\"older continuous on both sides of a $\

Josh Gardner, Zoran Popovic, Ludwig Schmidt

Robustness to distribution shift has become a growing concern for text and image models as they transition from research subjects to deployment in the real world. However, high-quality benchmarks for distribution shift in tabular machine learning tasks are still lacking despite the widespread real-world use of tabular data and differences in the models used

Søren Gammelgaard

Consider a Kleinian singularity $ \mathbb{C}^2/\Gamma $, where $ \Gamma $ is a finite subgroup of $ SL_2(\mathbb{C}) $. In this paper, we construct moduli spaces of framed sheaves on a projective Deligne-Mumford stack compactifying the singularity, and we show that these moduli spaces are quasiprojective schemes.

Hollan Haule, Ian Piper, Patricia Jones, Chen Qin

Artifacts are a common problem in physiological time series collected from intensive care units (ICU) and other settings. They affect the quality and reliability of clinical research and patient care. Manual annotation of artifacts is costly and time-consuming, rendering it impractical. Automated methods are desired. Here, we propose a novel fully unsupervis

Sergei N. Yurchenko, Wojciech Szajna, Rafał Hakalla, Mikhail Semenov

New ExoMol line lists AloHa for AlH and AlD are presented improving the previous line lists WYLLoT (Yurchenko et al., MNRAS 479, 1401 (2018)). The revision is motivated by the recent experimental measurements and astrophysical findings involving the highly excited rotational states of AlH in its $A\,^{1}\Pi-{X}\,^{1}\Sigma^{+}$ system. A new high-resolution

Yizhuang Liu

We show that as a result of causality-constrained coordinate space analyticity, the Drell-Yan-shape transverse-momentum dependent soft factor in the exponential regulator allows Euclidean-type parametric representations without cuts, to all orders in perturbation theory. Moreover, it is identical to another soft factor defined with a single time-ordering tha

Kohitij Kar, James J DiCarlo

Visual object recognition -- the behavioral ability to rapidly and accurately categorize many visually encountered objects -- is core to primate cognition. This behavioral capability is algorithmically impressive because of the myriad identity-preserving viewpoints and scenes that dramatically change the visual image produced by the same object. Until recent

Jiaxi Li, Xiongjie Chen, Yunpeng Li

Differentiable particle filters are an emerging class of sequential Bayesian inference techniques that use neural networks to construct components in state space models. Existing approaches are mostly based on offline supervised training strategies. This leads to the delay of the model deployment and the obtained filters are susceptible to distribution shift

Peiyi Cui

Let F be a non-archimedean local field with residual characteristic p, and k an algebraically closed field with characteristic l, where l different from p. Let Rep_k(SL_n(F)) be the category of smooth k-representations of SL_n(F). In this work, we establish the block decomposition of Rep_k(SL_n(F)) under the condition that p does not divide the order of the

Zelong Liu, Alexander Zhou, Arnold Yang, Alara Yilmaz

Deep learning in medical imaging often requires large-scale, high-quality data or initiation with suitably pre-trained weights. However, medical datasets are limited by data availability, domain-specific knowledge, and privacy concerns, and the creation of large and diverse radiologic databases like RadImageNet is highly resource-intensive. To address these

Keerthi Chacko, Midhun T. Augustine, S. Janardhanan, Deepak U. Patil

This paper studies the optimal control problem for discrete-time nonlinear systems and an approximate dynamic programming-based Model Predictive Control (MPC) scheme is proposed for minimizing a quadratic performance measure. In the proposed approach, the value function is approximated as a quadratic function for which the parametric matrix is computed using

Jochen Heinloth, Xucheng Zhang

The moment measure conjecture of Bialynicki-Birula and Sommese gives a combinatorial characterization of all open substacks of a global quotient stack for a torus action on a normal projective variety that admit a proper good moduli space, in other words it characterizes the invariant open subvarieties that admit a proper quotient. In this article we prove t

Sheref Nasereldin, Kayll Lake

We present the results of an analysis of three maximal extensions of the Vaidya metric in Israel coordinates, a spherically symmetric solution to the Einstein field equations for the energy momentum tensor of pure radiation in the high-frequency approximation. This metric is necessary for various applications, such as describing the exterior geometry of a ra

Khanh Doan, Quyen Tran, Tung Lam Tran, Tuan Nguyen

Mitigating catastrophic forgetting is a key hurdle in continual learning. Deep Generative Replay (GR) provides techniques focused on generating samples from prior tasks to enhance the model's memory capabilities using generative AI models ranging from Generative Adversarial Networks (GANs) to the more recent Diffusion Models (DMs). A major issue is the deter