September 2024 arXiv papers

Levi Burner, Cornelia Fermüller, Yiannis Aloimonos

Imagine sitting at your desk, looking at objects on it. You do not know their exact distances from your eye in meters, but you can immediately reach out and touch them. Instead of an externally defined unit, your sense of distance is tied to your action's embodiment. In contrast, conventional robotics relies on precise calibration to external units, with

Ricardo A. Podestá, Denis E. Videla

In this work we consider the class of Cayley graphs known as generalized Paley graphs (GP-graphs for short) given by $Γ(k,q) = Cay(\mathbb{F}_q, \{x^k : x\in \mathbb{F}_q^* \})$, where $\mathbb{F}_q$ is a finite field with $q$ elements, both in the directed and undirected case. Hence $q=p^m$ with $p$ prime, $m\in \mathbb{N}$ and one can assume that $k\mid q-

Y. F. Adans, A. R. Aguirre, J. F. Gomes, G. V. Lobo

A systematic construction for supersymmetric negative graded (non-local) flows for mKdV and KdV based on $sl(2,1)$ with a principal gradation is proposed in this paper. We show that smKdV and sKdV can be mapped onto each other through a gauge super Miura transformation, together with an additional condition for the negative flows, which ensure the supersymme

Grigorios Pavliotis, Renato Spacek, Gabriel Stoltz, Urbain Vaes

We propose a method utilizing physics-informed neural networks (PINNs) to solve Poisson equations that serve as control variates in the computation of transport coefficients via fluctuation formulas, such as the Green--Kubo and generalized Einstein-like formulas. By leveraging approximate solutions to the Poisson equation constructed through neural networks,

Rohaifa Khaldi, Domingo Alcaraz-Segura, Ignacio Sánchez-Herrera, Javier Martinez-Lopez

Social media images provide valuable insights for modeling, mapping, and understanding human interactions with natural and cultural heritage. However, categorizing these images into semantically meaningful groups remains highly complex due to the vast diversity and heterogeneity of their visual content as they contain an open-world human and nature elements.

Amina Kobenova, Cyan DeVeaux, Samyak Parajuli, Andrzej Banburski-Fahey

Generative artificial intelligence has shown promise in prompting virtual worlds into existence, yet little attention has been given to understanding how this process unfolds as social interaction. We present Social Conjurer, a framework for AI-augmented dynamic 3D scene co-creation, where multiple users collaboratively build and modify virtual worlds in rea

Zida Wu, Ankur Mehta

A crucial challenge in decentralized systems is state estimation in the presence of unknown inputs, particularly within heterogeneous sensor networks with dynamic topologies. While numerous consensus algorithms have been introduced, they often require extensive information exchange or multiple communication iterations to ensure estimation accuracy. This pape

Qiyu Sha, Daniel Murnane, Max Fieg, Shelley Tong

Analysis of data from particle physics experiments traditionally sacrifices some sensitivity to new particles for the sake of practical computability, effectively ignoring some potentially striking signatures. However, recent advances in ML-based tracking allow for new inroads into previously inaccessible territory, such as reconstruction of tracks which do

Geoffrey Lovelace, Kyle C. Nelli, Nils Deppe, Nils L. Vu

Binary black holes are the most abundant source of gravitational-wave observations. Gravitational-wave observatories in the next decade will require tremendous increases in the accuracy of numerical waveforms modeling binary black holes, compared to today's state of the art. One approach to achieving the required accuracy is using spectral-type methods t

Chiara Coviello, Maria Luisa Chiofalo, Dario Grasso, Stefano Liberati

Phonons in Bose-Einstein condensates propagate as massless scalar particles on top of an emergent acoustic metric. This hydrodynamics/gravity analogy can be exploited to realize acoustic black holes, featuring an event horizon that traps phonons. We show that by an appropriate external potential, gravitational wave-like perturbations of the acoustic metric c

Kun Yuan, Vinkle Srivastav, Nassir Navab, Nicolas Padoy

Surgical video-language pretraining (VLP) faces unique challenges due to the knowledge domain gap and the scarcity of multi-modal data. This study aims to bridge the gap by addressing issues regarding textual information loss in surgical lecture videos and the spatial-temporal challenges of surgical VLP. We propose a hierarchical knowledge augmentation appro

Kejia Ren, Gaotian Wang, Andrew S. Morgan, Lydia E. Kavraki

Nonprehensile actions such as pushing are crucial for addressing multi-object rearrangement problems. Many traditional methods generate robot-centric actions, which differ from intuitive human strategies and are typically inefficient. To this end, we adopt an object-centric planning paradigm and propose a unified framework for addressing a range of large-sca

Vinayak Arannil, Neha Narwal, Sourav Sanjukta Bhabesh, Sai Nikhil Thirandas

Large Language Models (LLMs) have shown remarkable ability to generalize effectively across numerous industry domains while executing a range of tasks. Many of these competencies are obtained from the data utilized during the pre-training phase of the Language Models (LMs). However, these models exhibit limitations when tasked with performing in specialized

Qingyuan Yang, Gregory S Elsaesser, Marcus Van Lier-Walqui, Trude Eidhammer

We present a new additive method, nicknamed sage for Simplified Additive Gaussian processes Emulator, to emulate climate model Perturbed Parameter Ensembles (PPEs). It estimates the value of a climate model output as the sum of additive terms. Each additive term is the mean of a Gaussian Process, and corresponds to the impact of a parameter or parameter grou

Lancelot Da Costa, Tomáš Gavenčiak, David Hyland, Mandana Samiei

This paper offers a roadmap for the development of scalable aligned artificial intelligence (AI) from first principle descriptions of natural intelligence. In brief, a possible path toward scalable aligned AI rests upon enabling artificial agents to learn a good model of the world that includes a good model of our preferences. For this, the main objective is

Qianwen Xing, Chang Yu, Sining Huang, Qi Zheng

In contemporary economic society, credit scores are crucial for every participant. A robust credit evaluation system is essential for the profitability of core businesses such as credit cards, loans, and investments for commercial banks and the financial sector. This paper combines high-performance models like XGBoost and LightGBM, already widely used in mod

Weitai Kang, Haifeng Huang, Yuzhang Shang, Mubarak Shah

Recent advancements in 3D Large Language Models (3DLLMs) have highlighted their potential in building general-purpose agents in the 3D real world, yet challenges remain due to the lack of high-quality robust instruction-following data, leading to limited discriminative power and generalization of 3DLLMs. In this paper, we introduce Robin3D, a powerful 3DLLM

Cristián Peña, Christina Wang, Si Xie, Adolf Bornheim

We present the first detailed study of an 8-channel $2\times2$ mm$^{2}$ WSi superconducting microwire single photon detector (SMSPD) array exposed to 120 GeV proton beam and 8 GeV electron and pion beam at the Fermilab Test Beam Facility. The SMSPD detection efficiency was measured for the first time for protons, electrons, and pions, enabled by the use of a

Weiliang Qi, Jiahao Cao, Darsh Poddar, Sophia Li

With the rapid development and widespread use of advanced network systems, software vulnerabilities pose a significant threat to secure communications and networking. Learning-based vulnerability detection systems, particularly those leveraging pre-trained language models, have demonstrated significant potential in promptly identifying vulnerabilities in com

Benjamin Baily, Amichai Lampert

Let $f$ be a homogeneous polynomial over a field. For many fields, including number fields and function fields, we prove that the strength of $f$ is bounded above by a constant multiple of the Birch rank of $f.$ The constant depends only on the degree of $f$ and the absolute transcendence degree of the field. This is the first linear bound obtained for forms

Rahul Somasundaram, Isak Svensson, Soumi De, Andrew E. Deneris

Understanding the interactions between nucleons in dense matter is an important challenge in theoretical physics. Effective field theories have emerged as the dominant approach to address this problem at low energies, with many successful applications to the structure of nuclei and the properties of dense nucleonic matter. However, how far into the interior

Martin Kittel, Wolf-Peter Schill

Variable renewable energy droughts, so called Dunkelflaute events, emerge as a challenge for climate-neutral energy systems based on variable renewables. Here we characterize European drought events for on- and offshore wind power, solar photovoltaics, and renewable technology portfolios, using 38 historic weather years and an advanced identification method.

Ashley R. Bemis, Christine D. Wilson, Piyush Sharda, Ian D. Roberts

We model emissivities of the HCN and CO $J=1-0$ transitions using measured properties of clouds found in normal star forming galaxies and more extreme systems. These models are compared with observations of HCN and CO $J=1-0$ transitions. We combine these model emissivities with predictions of gravoturbulent models of star formation, explore the impact of ex

Nicolò D'Anna, Jamie Bragg, Elizabeth Skoropata, Nazareth Ortiz Hernández

Fabrication of semiconductor heterostructures is now so precise that metrology has become a key challenge for progress in science and applications. It is now relatively straightforward to characterize classic III-V and group IV heterostructures consisting of slabs of different semiconductor alloys with thicknesses of $\sim$5 nm and greater using sophisticate

Evgeny Saveliev, Tim Schubert, Thomas Pouplin, Vasilis Kosmoliaptsis

Despite its significant promise and continuous technical advances, real-world applications of artificial intelligence (AI) remain limited. We attribute this to the "domain expert-AI-conundrum": while domain experts, such as clinician scientists, should be able to build predictive models such as risk scores, they face substantial barriers in accessing

Denise Hung, Brian C. Lemaux, Olga Cucciati, Ben Forrest

The Charting Cluster Construction with VUDS and ORELSE (C3VO) survey is an ongoing imaging and spectroscopic campaign aiming to map out the growth of structure up to $z\sim5$ and was born from the combination of the Visible Multi-Object Spectrograph Ultra Deep Survey and the Observations of Redshift Evolution in Large-Scale Environments (ORELSE) survey. As w

Francisco Ricardo Torres Arvizu, Adrian Ortega, Hernán Larralde

We study the spectrum, eigenstates and transport properties of a simple $\mathcal{P}\mathcal{T}$-symmetric model consisting in a finite, complex, square well potential with a delta potential at the origin. We show that as the strength of the delta potential increases, the system exhibits exceptional points accompanied by an accumulation of density associated

Martin A. Achondo, Jehanzeb H. Chaudhry, Christopher D. Cooper

Physics-informed neural networks (PINN) is a machine learning (ML)-based method to solve partial differential equations that has gained great popularity due to the fast development of ML libraries in the last few years. The Poisson-Boltzmann equation (PBE) is widely used to model mean-field electrostatics in molecular systems, and in this work we present a d

Qiang Ye

Accelerated training algorithms, such as adaptive learning rates (or preconditioning) and various normalization methods, are widely used but not fully understood. When regularization is introduced, standard optimizers like adaptive learning rates may not perform effectively. This raises the need for alternative regularization approaches such as AdamW and the

Kamil Nalikowski, Valera Veryazov, Kjeld Beeks, Thorsten Schumm

Building on recent advances of the embedded cluster approach combined with multiconfigurational theory, this work investigates the electronic states in thorium-doped CaF2 crystals. Th:CaF2 is currently establishing as a promising material for solid-state nuclear clocks, which utilize the laser-accessible isomeric state in thorium-229. By comparing simulated

Arturo Rodriguez, Piyush Kumar, Cesar Diaz-Caraveo, Richard O. Adansi

In this aerothermal study, we performed a two-dimensional steady-state Computational Fluid Dynamics (CFD) and heat conduction simulation at Mach 6. The key to our methodology was a one-way coupling between CFD surface temperature as a boundary condition and the calculation of the heat transfer flux and temperatures inside the solid stainless-steel body of a

Mang Hei Gordon Lee, Enrico Pajer, Mathieu Giroux, Holmfridur S. Hannesdottir

By directly probing the initial conditions of our universe, cosmological surveys offer us a unique observational handle on quantum field theory in curved spacetime with dynamical gravity and might even allow us to glean information about a full theory of quantum gravity. Here we report on recent progress to study the natural observables in the problem, namel

Md Rejwanur Rahman, Adrian Rodriguez-Marek, Nina Stark, Grace Massey

The geotechnical evaluation of seabed sediments is important for engineering projects and naval applications, offering valuable insights into sediment properties, behavior, and strength. Obtaining high-quality seabed samples can be a challenging task, making in situ testing an essential part of site characterization. Free-fall penetrometers (FFPs) are robust

David Grangier, Simin Fan, Skyler Seto, Pierre Ablin

Specialist language models (LMs) focus on a specific task or domain on which they often outperform generalist LMs of the same size. However, the specialist data needed to pretrain these models is only available in limited amount for most tasks. In this work, we build specialist models from large generalist training sets instead. We propose a novel method, Cl

Cesar Ayala, Gorazd Cvetic

We construct a QCD coupling ${\mathcal{A}}(Q^2)$ in the Effective Charge (ECH) scheme of the canonical part $d(Q^2)$ of the (inelastic) polarised Bjorken Sum Rule (BSR) ${\overline Γ}_1^{\rm p-n}(Q^2)$. In the perturbative domain, the coupling ${\mathcal{A}}(Q^2)$ practically coincides with the perturbative coupling $a(Q^2)$ [$\equiv α_s(Q^2)/π$] in the four

Stanislav Minsker, Yinan Shen

Is there a natural way to order data in dimension greater than one? The approach based on the notion of data depth, often associated with John Tukey, is among the most popular. Tukey's depth has found applications in robust statistics, graph theory, and the study of elections and social choice. We present improved performance guarantees for empirical Tuk

Chenhao Fang, Derek Larson, Shitong Zhu, Sophie Zeng

This paper presents new methods that have the potential to improve privacy process efficiency with LLM and RAG. To reduce hallucination, we continually pre-train the base LLM model with a privacy-specific knowledge base and then augment it with a semantic RAG layer. Our evaluations demonstrate that this approach enhances the model performance (as much as dou

Vedant Vohra

Economists often estimate causal effects of policies on multiple outcomes and summarize them into scalar measures of cost-effectiveness or welfare, such as the Marginal Value of Public Funds (MVPF). In many settings, microdata underlying these estimates are unavailable, leaving researchers with only published estimates and their standard errors. We develop t

Yejin Lee, Anna Sun, Basil Hosmer, Bilge Acun

Generative artificial intelligence (AI) technology is revolutionizing the computing industry. Not only its applications have broadened to various sectors but also poses new system design and optimization opportunities. The technology is capable of understanding and responding in multiple modalities. However, the advanced capability currently comes with signi

Dimitris Diamantidis, Takis Konstantopoulos, Linglong Yuan

We consider two independent Erdős-Rényi random graphs, with possibly different parameters, and study two isomorphism problems, a graph embedding problem and a common subgraph problem. Under certain conditions on the graph parameters we show a sharp asymptotic phase transition as the graph sizes tend to infinity. This extends known results for the case of uni

Pierre Monmarché, Renato Spacek, Gabriel Stoltz

In molecular dynamics, transport coefficients measure the sensitivity of the invariant probability measure of the stochastic dynamics at hand with respect to some perturbation. They are typically computed using either the linear response of nonequilibrium dynamics, or the Green--Kubo formula. The estimators for both approaches have large variances, which mot

Éric Colin de Verdière, Petr Hliněný

The basic (and traditional) crossing number problem is to determine the minimum number of crossings in a topological drawing of an input graph in the plane. We develop a unified framework yielding fixed-parameter tractable (FPT) algorithms for many generalized crossing number problems. Our framework takes the following form. We fix a surface S and a class D

Tuan Anh Nguyen

We prove that multilevel Picard approximations are capable of approximating solutions of semilinear heat equations in $L^{p}$-sense, ${p}\in [2,\infty)$, in the case of gradient-dependent, Lipschitz-continuous nonlinearities, in the sense that the computational effort of the multilevel Picard approximations grow at most polynomially in both the dimension $d$

Clement Yung

We introduce the notion of a weak A2 space (or wA2-space), which generalises spaces satisfying Todorčević's axioms A1-A4 and countable vector spaces. We show that in any Polish weak A2 space, analytic sets are Kastanas Ramsey, and discuss the relationship between Kastanas Ramsey sets and sets in the projective hierarchy. We also show that in all spaces s

Aroosa Ijaz, C. Huerta Alderete, Frédéric Sauvage, Lukasz Cincio

Quantum sensing is one of the most promising applications for quantum technologies. However, reaching the ultimate sensitivities enabled by the laws of quantum mechanics can be a challenging task in realistic scenarios where noise is present. While several strategies have been proposed to deal with the detrimental effects of noise, these come at the cost of

Si Cheng, Haocong Cheng, Suzy Su, Lu Ming

Deaf and Hard-of-Hearing (DHH) learners face unique challenges in video-based learning due to the complex interplay between visual and auditory information in videos. Traditional approaches to making video content accessible primarily focus on captioning, but these solutions often neglect the cognitive demands of processing both visual and textual informatio

Yohsuke Enomoto, Atsushi Taruya, Satoshi Tanaka, Takahiro Nishimichi

Using the number of apocenter passages $p$ and the radial action $J_r$ of each particle, we characterize the phase-space structure within the multi-stream regions of cold and warm dark matter halos in cosmological $N$-body simulations. Building on previous work by Enomoto et al. (2024), we analyze the radial density profiles of particles classified by $p$ an

Yifan Hou, Buse Giledereli, Yilei Tu, Mrinmaya Sachan

Visual language is a system of communication that conveys information through symbols, shapes, and spatial arrangements. Diagrams are a typical example of a visual language depicting complex concepts and their relationships in the form of an image. The symbolic nature of diagrams presents significant challenges for building models capable of understanding th

Daeun Joyce Chung, Muya Guoji, Nina Mindel, Alexis Malkin

Empowering people who are blind or visually impaired (BVI) to enhance their orientation and mobility skills is critical to equalizing their access to social and economic opportunities. To manage this crucial challenge, we employed a novel design process based on a large-scale, longitudinal, community-based structure. Across three annual programs we engaged w

The Anh Bui, Xuan Thinh Duong, Konstantin Merz

We consider the ordinary or fractional Laplacian plus a homogeneous, scaling-critical drift term. This operator is non-symmetric but homogeneous, and generates scales of $L^p$-Sobolev spaces which we compare with the ordinary homogeneous Sobolev spaces. Unlike in previous studies concerning Hardy operators, i.e., ordinary or fractional Laplacians plus scalin

Ettore Zaffaroni, Guido Haefeli

CuboDAQ is a custom data acquisition system to read out SiPM-based detectors. It features electronic boards to digitize the SiPMs signal, an FPGA-based system-on-module board, the connectivity to transmit the data to a central server, and all the software necessary to operate them. The front-end is based on the TOFPET2 ASIC, produced by PETsys, connected to

Alessio Pomponio, Tatsuya Watanabe

This paper is devoted to the study of the nonlinear scalar field equation with a point interaction at the origin in dimensions two and three. By applying the mountain pass theorem and the technique of adding one dimensional space, we prove the existence of a nontrivial singular solution for a wide class of nonlinearities. We also establish the Pohozaev ident

Hossein Amiri, Will Kohn, Shiyang Ruan, Joon-Seok Kim

We demonstrate the Patterns of Life Simulation to create realistic simulations of human mobility in a city. This simulation has recently been used to generate massive amounts of trajectory and check-in data. Our demonstration focuses on using the simulation twofold: (1) using the graphical user interface (GUI), and (2) running the simulation headless by disa

Jin Koda, Francoise Combes, Monica Rubio, Morten Andersen

The extended ultraviolet (XUV) disks of nearby galaxies show ongoing massive star formation, but their parental molecular clouds remain mostly undetected despite searches in CO(1-0) and CO(2-1). The recent detection of 23 clouds in the higher excitation transition CO(3-2) within the XUV disk of M83 requires an explanation. We test the hypothesis: the clouds

Kush Dubey

Few-shot learning benchmarks are critical for evaluating modern NLP techniques. It is possible, however, that benchmarks favor methods which easily make use of unlabeled text, because researchers can use unlabeled text from the test set to pretrain their models. Given the dearth of research on this potential problem, we run experiments to quantify the bias c

Holley Friedlander, Elena Fuchs, Piper Harris, Catherine Hsu

Inspired by a question of Sarnak, we introduce the notion of a prime component in an Apollonian circle packing: a maximal tangency-connected subset having all prime curvatures. We also consider thickened prime components, which are augmented by all circles immediately tangent to the prime component. In both cases, we ask about the curvatures which appear. We

Bingsheng Yao, Yao Du, Yue Fu, Xuhai Xu

Advanced AI technologies are increasingly integrated into clinical domains to advance patient care. The design and development of clinical AI technologies necessitate seamless collaboration between clinical and technical experts. However, such interdisciplinary teams are often unsuccessful, with a lack of systematic analysis of collaboration barriers and cop

Tyler Ingebrand, Adam J. Thorpe, Somdatta Goswami, Krishna Kumar

We present Basis-to-Basis (B2B) operator learning, a novel approach for learning operators on Hilbert spaces of functions based on the foundational ideas of function encoders. We decompose the task of learning operators into two parts: learning sets of basis functions for both the input and output spaces and learning a potentially nonlinear mapping between t

John H. Palmieri

We prove that every quasi-elementary sub-Hopf algebra of the polynomial part of the odd primary Steenrod algebra must lie in a certain sub-Hopf algebra called $D$.

Weiting Tan, Hirofumi Inaguma, Ning Dong, Paden Tomasello

Fusing speech into pre-trained language model (SpeechLM) usually suffers from inefficient encoding of long-form speech and catastrophic forgetting of pre-trained text modality. We propose SSR-Connector (Segmented Speech Representation Connector) for better modality fusion. Leveraging speech-text alignments, our approach segments and compresses speech feature

Hongyu Chen, Weiming Zeng, Chengcheng Chen, Luhui Cai

In the fields of affective computing (AC) and brain-machine interface (BMI), the analysis of physiological and behavioral signals to discern individual emotional states has emerged as a critical research frontier. While deep learning-based approaches have made notable strides in EEG emotion recognition, particularly in feature extraction and pattern recognit

Isaac Rehg

Context lengths of Large Language Models (LLMs) have exploded in recent years, with 128k-token context becoming a standard and million-token context becoming a reality. Efficiently supporting long-context inference remains challenging as the memory that must be allocated in key-value (KV) cache for a generation scales with its context length, limiting the nu

R. Aliannejadi, Z. Haghani

We investigate the physical properties of quark stars with two different equations of state in generalized hybrid metric-Palatini gravity. This theory corresponds to a bi-scalar gravitational theory in which there are two non-minimally-coupled scalar fields. The field equations of the metric tensor and scalar fields are derived by varying the action with res

Bingzhen Geng, Yang Liu, Hongfu Wan

Systemic risk is receiving increasing attention in the insurance industry. In this paper, we propose a multi-dimensional Lévy process-based renewal risk model with heterogeneous insurance claims, where every dimension indicates a business line of an insurer. We use the systemic expected shortfall (SES) and marginal expected shortfall (MES) defined with a Val

Haiyan Zhao, Heng Zhao, Bo Shen, Ali Payani

Probing learned concepts in large language models (LLMs) is crucial for understanding how semantic knowledge is encoded internally. Training linear classifiers on probing tasks is a principle approach to denote the vector of a certain concept in the representation space. However, the single vector identified for a concept varies with both data and training,

Stephen Miner, Yoshiki Takashima, Simeng Han, Sam Kouteili

Benchmarks are critical for measuring Large Language Model (LLM) reasoning capabilities. Some benchmarks have even become the de facto indicator of such capabilities. However, as LLM reasoning capabilities improve, existing widely-used benchmarks such as GSM8K marginally encapsulate model reasoning differentials - most state-of-the-art models for example ach

Qiushuo Hou, Sangwoo Park, Matteo Zecchin, Yunlong Cai

In modern wireless network architectures, such as Open Radio Access Network (O-RAN), the operation of the radio access network (RAN) is managed by applications, or apps for short, deployed at intelligent controllers. These apps are selected from a given catalog based on current contextual information. For instance, a scheduling app may be selected on the bas

Taeho Ryu, Alison Sills, Ruediger Pakmor, Selma de Mink

Blue straggler stars in stellar clusters are a subset of stars that are bluer and appear younger than other cluster members, seemingly straggling behind in their evolution. They offer a unique opportunity to understand the stellar dynamics and populations within their hosts. In the collisional formation scenario, a persistent challenge is the excessive angul

Jacinda Terkel

Let $A\subseteq \mathbb{Z}_p^2$ be a set of size $2p+1$ for prime $p\geq 5$. In this paper, we prove that $A\hat{+}A=\{a_1+a_2\mid a_1,a_2\in A, a_1\neq a_2\}$ has cardinality at least $4p$. This result is the first advancement in over two decades on a variant of the Erdős-Heilbronn problem studied by Eliahou and Kervaire.

Jesus Enrique Achire Quispe, Eduardo Ramos, Pedro Luiz Ramos

The Rician distribution, a well-known statistical distribution frequently encountered in fields like magnetic resonance imaging and wireless communications, is particularly useful for describing many real phenomena such as signal process data. In this paper, we introduce objective Bayesian inference for the Rician distribution parameters, specifically the Je

Erin O'Neil, Sarah Tymochko

In light of the increase in frequency of extreme heat events, there is a critical need to develop tools to identify geographic locations that are at risk of heat-related mortality. This paper aims to identify locations by assessing holes in cooling-center coverage using persistent homology (PH), a method from topological data analysis (TDA). Persistent homol

Ankit Kumar

Reduction of lattice thermal conductivity ($κ_L$) is one of the most effective ways of improving thermoelectric properties. However extraction of $κ_L$ from the total measured thermal conductivity can be misleading if Lorenz ($L$) number is not estimated correctly. The $κ_L$ is obtained using Wiedemann-Franz law which estimates electronic part of thermal con

Nathan Rutherford, Chanda Prescod-Weinstein, Anna Watts

It is possible for asymmetric dark matter (ADM) to accumulate in neutron star interiors and affect their global properties. Considering the effects of this accumulation, neutron star mass-radius measurements can deliver new insights into the cold dense matter equation of state (EoS). In this paper, we employ Bayesian parameter estimation using real and synth

Ramandeep Singh Arora, Navnath Daundkar, Soumen Sarkar

Let $X$ be a $G$-space. In this paper, we introduce the notion of sectional category with respect to $G$. As a result, we obtain $G$-homotopy invariants: the LS category with respect to $G$, the sequential topological complexity with respect to $G$ (which is same as the weak sequential equivariant topological complexity $\mathrm{TC}_{k,G}^w(X)$ in the sense

Chengyan Xie, Ilaria Pascucci, Dingshan Deng, Naman S. Bajaj

We present JWST/MIRI observations of T~Cha, a highly variable ($ΔV \sim$3-5\,mag) accreting Sun-like star surrounded by a disk with a large ($\sim 15$\,au) dust gap. We find that the JWST mid-infrared spectrum is signiticantly different from the {\it Spitzer} spectrum obtained 17 years before, where the emission at short wavelengths ($5-10 μm$) has decreased

Ji Liu, Jiaxiang Ren, Ruoming Jin, Zijie Zhang

As a promising paradigm to collaboratively train models with decentralized data, Federated Learning (FL) can be exploited to fine-tune Large Language Models (LLMs). While LLMs correspond to huge size, the scale of the training data significantly increases, which leads to tremendous amounts of computation and communication costs. The training data is generall

Maciej Krzywda, Szymon Łukasik, Amir Gandomi H

The present study covers an approach to neural architecture search (NAS) using Cartesian genetic programming (CGP) for the design and optimization of Convolutional Neural Networks (CNNs). In designing artificial neural networks, one crucial aspect of the innovative approach is suggesting a novel neural architecture. Currently used architectures have mostly b

Adi Armoni, Ricardo Stuardo

We consider SQCD for $N_{f}<N_{c}$. We use a brane configuration in type IIA string theory to propose a Seiberg dual. The brane configuration that realises the magnetic theory contains $N_{f}$ branes and $N_{c}-N_{f}$ \textit{anti}-branes, leading to an $SU(N_{c}-N_{f})$ gauge theory with $N_{f}$ flavours. The potential between the branes triggers a RG flow

Alp Sahin, Nicolas Kozachuk, Rick S. Blum, Subhrajit Bhattacharya

Resonance is a well-known phenomenon that happens in systems with second order dynamics. In this paper we address the fundamental question of making a network robust to signal being periodically pumped into it at or near a resonant frequency by an adversarial agent with the aim of saturating the network with the signal. Towards this goal, we develop the noti

Mary Andrews, Smitha S, Sudheesh K Kattumannil

We develop a relative cumulative residual information measure (RCRI) that aims to quantify the divergence between two survival functions. The dynamic relative cumulative residual information (DRCRI) measure is also introduced. We establish some characterization results under the assumption of the proportional hazards model. Additionally, we obtained the non-

Tim-Eric Rathjen, Stefanie Walch, Thorsten Naab, Pierre Nürnberger

We present magnetohydrodynamic simulations of star formation in the multiphase interstellar medium to quantify the impact of non-ionising far-ultraviolet (FUV) radiation within the \textsc{Silcc Project} simulation framework. Our study incorporates the radiative transfer of ionising radiation and self-consistent modelling of variable FUV radiation from star

Clémence Chanavat, Amar Hadzihasanovic

We show that diagrammatic sets, a topologically sound alternative to polygraphs and strict $ω$-categories, admit an internal notion of equivalence in the sense of coinductive weak invertibility. We prove that equivalences have the expected properties: they include all degenerate cells, are closed under 2-out-of-3, and satisfy an appropriate version of the &#

Levi Cai, Kevin Chang, Yogesh Girdhar

Controlling AUVs can be challenging because of the effect of complex non-linear hydrodynamic forces acting on the robot, which are significant in water and cannot be ignored. The problem is exacerbated for small AUVs for which the dynamics can change significantly with payload changes and deployments under different hydrodynamic conditions. The common approa

Alan Papalia, Yulun Tian, David M. Rosen, Jonathan P. How

This paper presents an overview of the Burer-Monteiro method (BM), a technique that has been applied to solve robot perception problems to certifiable optimality in real-time. BM is often used to solve semidefinite programming relaxations, which can be used to perform global optimization for non-convex perception problems. Specifically, BM leverages the low-

Charlie Sire, Josselin Garnier, Cédric Durantin, Baptiste Kerleguer

Numerical simulations are widely used to predict the behavior of physical systems, with Bayesian approaches being particularly well suited for this purpose. However, experimental observations are necessary to calibrate certain simulator parameters for the prediction. In this work, we use a multi-output simulator to predict all its outputs, including those th

Hector Gisbert, Gudrun Hiller, Dominik Suelmann

We perform a global analysis of $|Δc| = |Δu| = 1$ transitions using recent data on $D^0 \to μ^+μ^-$, $D^+ \to π^+\,μ^+μ^-$, $Λ_c \to p\,μ^+μ^-$, and $D^0 \to π^+π^-\,μ^+μ^-$ decays, and work out constraints on new physics Wilson coefficients $\mathcal{C}_{7,9,10}^{(\prime)}$. While results are consistent with the standard model, we find sizeable room for new

Manuel Cavieres, Julio Chanamé, Camila Navarrete, Yasna Ordenes-Briceño

The infall of the Large Magellanic Cloud (LMC) into the Milky Way&#39;s halo impacts the distribution of stars and dark matter in our Galaxy. Mapping the observational consequences of this encounter can inform us about the properties of both galaxies, details of their interaction, and possibly distinguish between different dark matter models. N-body simulati

Molly Kaplan, Donald Marolf, Xuyang Yu, Ying Zhao

Quantum theories of gravity are generally expected to have some degree of non-locality, with familiar local physics emerging only in a particular limit. Perturbative quantum gravity around backgrounds with isometries and compact Cauchy slices provides an interesting laboratory in which this emergence can be explored. In this context, the remaining isometries

Alice E. Shapley, Ryan L. Sanders, Michael W. Topping, Naveen A. Reddy

We present the properties of a massive, large, dusty, metal-rich, star-forming galaxy at z_spec=6.73. GOODSN-100182 was observed with JWST/NIRSpec as part of the AURORA survey, and is also covered by public multi-wavelength HST and JWST imaging. While the large stellar mass of GOODSN-100182 (~10^10 M_sun) was indicated prior to JWST, NIRCam rest-optical imag

Vedran Brdar, Leonardo J. Ferreira Leite, George A. Parker, Xun-Jie Xu

We scrutinize the potential of upcoming ultra-near reactor neutrino experiments to detect radiative corrections in the elastic neutrino-electron scattering channel, focusing on the JUNO-TAO and CLOUD detectors, which employ advanced scintillator detection technologies. Previous reactor experiments have already constrained the electron neutrino charge radius,

Kevin N. Hainline, Roberto Maiolino, Ignas Juodzbalis, Jan Scholtz

Recently, a large number of compact sources at $z > 4$ with blue UV slopes and extremely red rest-frame optical slopes have been found in James Webb Space Telescope (JWST) extragalactic surveys. As a subsample of these sources, commonly called ``little red dots&#39;&#39; (LRDs), have been spectroscopically observed to host a broad-line active galactic nucleu

Zhiyuan Ji, Mauro Giavalisco

We study the evolution of stellar kinematics of a sample of 952 massive quiescent galaxies with $M_*>10^{10.5}M_\odot$ at $0.6<z<1$. Utilizing spatially integrated spectroscopy from the LEGA-C survey, we focus on the relationship between the observed integrated stellar velocity dispersion ($σ^\prime_{star}$) and the morphological axial ratio ($q$), and its v

Christopher Hopp, David Wittman

We present a novel, model-independent technique for fitting the cross-component of weak lensing shear, $γ_\times$, along a line of sight by combining kinematic and photometric measurements of a single lensed galaxy. Rather than relying on parametric models, we fit for the shear parameter that best transforms the velocity field to restore its underlying symme

Michele Cicoli, Christopher Hughes, Ahmed Rakin Kamal, Francesco Marino

We study a new framework for brane-antibrane inflation where moduli stabilisation relies purely on perturbative corrections to the effective action. This guarantees that the model does not suffer from the eta-problem. The inflationary potential has two contributions: the tension of an antibrane at the tip of a warped throat, and its Coulomb interaction with

Debasish Borah, Nayan Das, Indrajit Saha

Dark matter (DM), in spite of being stable or long-lived on cosmological scales, can decay in the early Universe due to finite-temperature effects. In particular, a first order phase transition (FOPT) in the early Universe can provide a finite window for such decay, guaranteeing DM stability at lower temperatures, consistent with observations. The FOPT can l

Anya E. Nugent, Alexander P. Ji, Wen-fai Fong, Hilay Shah

Neutron star (NS) mergers are currently the only observed source of $r$-process production in the Universe. Yet, it is unclear how much $r$-process mass from these mergers is incorporated into star-forming gas to enrich stars. This is crucial to consider as all other $r$-process mass estimates in the Universe beyond Earth are based on stellar $r$-process abu

Federico Castellani, Carlos Nunez

In this paper, we present a series of solutions of type IIB Supergravity, obtained through the TsT deformation of a seed background that is holographically dual to ${\cal N}=4$ Super Yang-Mills theory compactified on a circle (with a twist). We explore various holographic observables, focusing on disentangling the contributions of confinement, marginal defor

Marilena Loverde, Zachary J. Weiner

Cosmological data probe massive neutrinos via their effects on the geometry of the Universe and the growth of structure, both of which are degenerate with the late-time expansion history. We clarify the nature of these degeneracies and the individual roles of both probes in neutrino mass inference. Geometry is strongly sensitive to neutrino masses: within $Λ

Zheng Zhou, Yin-Chen He

The quest to discover new 3D CFTs has been intriguing for physicists. For this purpose, fuzzy sphere reguarlisation that studies interacting quantum systems defined on the lowest Landau level on a sphere has emerged as a powerful tool. In this paper, we discover a series of new CFTs with global symmetry $\mathrm{Sp}(N)$ in the fuzzy sphere models that are cl

Jonathan D. Kroth, Kirill Tuchin

We study electromagnetic radiation by electrically charged fermions embedded in a rotating medium in an external magnetic field. We compute the dependence of the radiation intensity on the angular velocity $Ω$ of the rotating medium for fermion polarizations along and opposite the magnetic field direction in intense subcritical fields. The polarization depen