March 2024 arXiv papers — page 177

Robert Baumgarth

On a smooth (not necessarily compact) manifold $M$ equipped with a $\sf C^1$-family of complete Riemannian metrics $g(t)$ and a $\sf C^{1,\infty}$-family of vector fields $Z(t)$ both indexed by the real interval $[0,T)$ where $T \in (0,\infty]$, we prove moment estimates, exponential integrability, concentration inequalities and exit times estimates for diff

Tijana Zrnic, Emmanuel J. Candès

Inspired by the concept of active learning, we propose active inference$\unicode{x2013}$a methodology for statistical inference with machine-learning-assisted data collection. Assuming a budget on the number of labels that can be collected, the methodology uses a machine learning model to identify which data points would be most beneficial to label, thus eff

Francis Halzen, Paolo Lipari

We describe the pioneering contributions of Thomas K. Gaisser to the birth and development of particle astrophysics, a new field of research at the intersection of cosmic ray physics, astronomy, astrophysics, and particle physics that has emerged in the last few decades. We will especially focus on his studies of natural beams of neutrinos: those generated b

Patrick Esser, Sumith Kulal, Andreas Blattmann, Rahim Entezari

Diffusion models create data from noise by inverting the forward paths of data towards noise and have emerged as a powerful generative modeling technique for high-dimensional, perceptual data such as images and videos. Rectified flow is a recent generative model formulation that connects data and noise in a straight line. Despite its better theoretical prope

Elchanan Mossel, Anirudh Sridhar

Suppose that a cascade (e.g., an epidemic) spreads on an unknown graph, and only the infection times of vertices are observed. What can be learned about the graph from the infection times caused by multiple distinct cascades? Most of the literature on this topic focuses on the task of recovering the entire graph, which requires $\Omega ( \log n)$ cascades fo

Chandan Kumar

While photon catalyzed two mode squeezed vacuum state has been considered in context of quantum teleportation, similar studies have not been yet conducted for photon catalyzed two-mode squeezed thermal (TMST) state. This can be attributed to challenges involved in the evaluation of teleportation fidelity for photon catalyzed TMST state. In this article, we c

Savitha Sam Abraham, Marjan Alirezaie, Luc De Raedt

The integration of learning and reasoning is high on the research agenda in AI. Nevertheless, there is only a little attention to use existing background knowledge for reasoning about partially observed scenes to answer questions about the scene. Yet, we as humans use such knowledge frequently to infer plausible answers to visual questions (by eliminating al

Francesco Perciavalle, Davide Rossini, Juan Polo, Oliver Morsch

Quantum simulation of many-body quantum systems using Rydberg-atom platforms has become of extreme interest in the last years. The possibility to realize spin Hamiltonians and the accurate control at the single atom level paved the way for the study of quantum phases of matter and dynamics. Here, we propose a quantum optimal control protocol to engineer curr

Keyu Zeng, Ziqiang Wang

Flat bands are intriguing platforms for correlated and topological physics. Various methods have been developed to create flat bands utilizing lattice geometry, but the investigation of orbital symmetry in multiorbital materials is a new area of focus. Here, we introduce a site symmetry based approach to emerging multiorbital 2D and 3D flat bands on the kago

Gabriel Khan, Soumyajit Saha, Malik Tuerkoen

We study the Dirichlet problem for the weighted Schr\"odinger operator \[-\Delta u +Vu = \lambda \rho u,\] where $\rho$ is a positive weighting function and $V$ is a potential. Such equations appear naturally in conformal geometry and in the composite membrane problem. Our primary goal is to establish concavity estimates for the principle eigenfunction with

Xiu-Zhe Luo, Di Luo, Roger G. Melko

In this paper, we present a general framework for quantum many-body simulations called the operator learning renormalization group (OLRG). Inspired by machine learning perspectives, OLRG is a generalization of Wilson's numerical renormalization group and White's density matrix renormalization group, which recursively builds a simulatable system to approximat

Marek Jacewicz, Iaroslava Profatilova, Piotr Szaniawski, Inna Popov

In order to investigate the dependence of conditioning and field-holding on temperature, three pairs of copper electrodes underwent high voltage conditioning with direct current (DC) pulses while kept at a single temperature, unique for each set (300~K, 30~K and 10~K), until saturation field for each set was found. The sets conditioned at cold showed a signi

Sébastien Labbé

We consider a new family $(\mathcal{T}_n)_{n\geq1}$ of aperiodic sets of Wang tiles and we describe the dynamical properties of the set $\Omega_n$ of valid configurations $\mathbb{Z}^2\to\mathcal{T}_n$. The tiles can be defined as the different instances of a square-shaped computer chip whose inputs and outputs are 3-dimensional integer vectors. The family i

Luis Sanchez, Luis Muñoz, Jose Antonio Galache, Pablo Sotres

This paper describes the deployment and experimentation architecture of the Internet of Things experimentation facility being deployed at Santander city. The facility is implemented within the SmartSantander project, one of the projects of the Future Internet Research and Experimentation initiative of the European Commission and represents a unique in the wo

Anderson Cardoso, João Marcos do Ó, Diego Ferraz

We analyse Hamiltonian-type systems of second-order elliptic PDE invariant under a non-compact group and, consequently, involve a lack of compactness of the Sobolev embedding. We show that the loss of compactness can be compensated by using a concentration-compactness principle via weak profile decomposition for bounded Palais-Smale sequences in Banach space

Hossein Aboutalebi, Hwanjun Song, Yusheng Xie, Arshit Gupta

Development of multimodal interactive systems is hindered by the lack of rich, multimodal (text, images) conversational data, which is needed in large quantities for LLMs. Previous approaches augment textual dialogues with retrieved images, posing privacy, diversity, and quality constraints. In this work, we introduce Multimodal Augmented Generative Images D

Yang He, Pinhan Zhao, Xinyu Wang, Yuepeng Wang

The task of SQL query equivalence checking is important in various real-world applications (including query rewriting and automated grading) that involve complex queries with integrity constraints; yet, state-of-the-art techniques are very limited in their capability of reasoning about complex features (e.g., those that involve sorting, case statement, rich

Masahiro Kato

This study investigates the estimation and the statistical inference about Conditional Average Treatment Effects (CATEs), which have garnered attention as a metric representing individualized causal effects. In our data-generating process, we assume linear models for the outcomes associated with binary treatments and define the CATE as a difference between t

Hannah Übler, Francesco D'Eugenio, Michele Perna, Santiago Arribas

We present rest-frame optical data of the z~4 sub-millimeter galaxy GN20 obtained with JWST/NIRSpec in integral field spectroscopy (IFS) mode. The H$\alpha$ emission is asymmetric and clumpy and extends over a projected distance of more than 15 kpc. To first order, the large-scale ionised gas kinematics are consistent with a turbulent ($\sigma\sim90$ km/s),

Alex Cowan, Sam Frengley, Kimball Martin

Explicit models of families of genus 2 curves with multiplication by $\sqrt D$ are known for $D= 2, 3, 5$. We obtain generic models for genus 2 curves over $\mathbb Q$ with real multiplication in 12 new cases, including all fundamental discriminants $D < 40$. A key step in our proof is to develop an algorithm for minimisation of conic bundles fibred over $\m

Ruizhuo Song, Beiming Yuan

This paper introduces innovative frameworks for visual abstract reasoning, aiming to boost deep learning model performance. It emphasizes the importance of separating abstract concept and reasoning feature extraction processes. The effectiveness of the Cross-Feature Network (CFN) and its enhanced version, Triple-CFN, validates this approach. Challenges in vi

Zazil Santizo Huerta, Melissa Keranen, Vladimir Tonchev

A maximal arc of degree k in a finite projective plane P of order q = ks is a set of (q-s+1)k points that meets every line of P in either k or 0 points. The collection of the nonempty intersections of a maximal arc with the lines of P is a resolvable Steiner 2-((q-s+1)k, k, 1) design. Necessary and sufficient conditions for a resolvable Steiner 2- design to

Rafaela Martelo, Kimia Ahmadiyehyazdi, Ruo-Qian Wang

Real-time flood forecasting is vital for effective emergency responses, but bridging the gap between complex numerical models and practical decision-making remains challenging. Decision-makers often rely on experts, while the public struggles to interpret flood risk information. To address this, we developed a customized AI Assistant powered by GPT-4. This t

Akari Asai, Zexuan Zhong, Danqi Chen, Pang Wei Koh

Parametric language models (LMs), which are trained on vast amounts of web data, exhibit remarkable flexibility and capability. However, they still face practical challenges such as hallucinations, difficulty in adapting to new data distributions, and a lack of verifiability. In this position paper, we advocate for retrieval-augmented LMs to replace parametr

Weihao Tan, Wentao Zhang, Xinrun Xu, Haochong Xia

Despite the success in specific scenarios, existing foundation agents still struggle to generalize across various virtual scenarios, mainly due to the dramatically different encapsulations of environments with manually designed observation and action spaces. To handle this issue, we propose the General Computer Control (GCC) setting to restrict foundation ag

Cassidy Laidlaw, Shivam Singhal, Anca Dragan

Because it is difficult to precisely specify complex objectives, reinforcement learning policies are often optimized using proxy reward functions that only approximate the true goal. However, optimizing proxy rewards frequently leads to reward hacking: the optimized reward function ceases to be a good proxy and the resulting policy performs poorly with respe

I. S. Yeremenko, M. A. Dmytruk, A. A. Semenov

Gaussian boson sampling (GBS) is a model of nonuniversal quantum computation that claims to demonstrate quantum supremacy with current technologies. This model entails sampling photocounting events from a multimode Gaussian state at the outputs of a linear interferometer. In this scheme, collision events -- those with more than one photon for each mode -- ar

Angeliki Giannou, Liu Yang, Tianhao Wang, Dimitris Papailiopoulos

Transformer-based models have demonstrated remarkable in-context learning capabilities, prompting extensive research into its underlying mechanisms. Recent studies have suggested that Transformers can implement first-order optimization algorithms for in-context learning and even second order ones for the case of linear regression. In this work, we study whet

R. S. O. Dias, M. Martarelli, P. Chiariotti

This paper aims at introducing a methodology to compute stable coupled state-space models for dynamic substructuring applications by introducing two novel approaches targeted to accomplish this task: a) a procedure to impose Newtons's second law without relying on the use of undamped RCMs (residual compensation modes) and b) a novel approach to impose stabil

Seungjae Lee, Yibin Wang, Haritheja Etukuru, H. Jin Kim

Generative modeling of complex behaviors from labeled datasets has been a longstanding problem in decision making. Unlike language or image generation, decision making requires modeling actions - continuous-valued vectors that are multimodal in their distribution, potentially drawn from uncurated sources, where generation errors can compound in sequential pr

Trang H. Tran, Quoc Tran-Dinh, Lam M. Nguyen

The Stochastic Gradient Descent method (SGD) and its stochastic variants have become methods of choice for solving finite-sum optimization problems arising from machine learning and data science thanks to their ability to handle large-scale applications and big datasets. In the last decades, researchers have made substantial effort to study the theoretical p

Lin Chen, Ling-Yan Hung, Yikun Jiang, Bing-Xin Lao

We demonstrate that, by utilizing the boundary conformal field theory (BCFT) operator algebra of the Liouville CFT, one can express its path-integral on any Riemann surface as a three dimensional path-integral with appropriate boundary conditions, generalising the recipe for rational CFTs \cite{Hung:2019bnq, Brehm:2021wev, Chen:2022wvy, Cheng:2023kxh}. This

Daniel López Garcia, Nicolas Martinez Alba

The Marsden-Weinstein-Meyer symplectic reduction has an analogous version for cosymplectic manifolds. In this paper we extend this cosymplectic reduction to the context of groupoids. Moreover, we prove how in the case of an algebroid associated to a cosymplectic groupoid, the integration commutes with the reduction (analogously to what happens in Poisson geo

C. O. Obasi, M. Gomez, D. Minniti, L. D. Baravalle

In this study, we search for Globular Clusters (GCs) in the inner halo of the Circinus galaxy using a combination of observational data. Our dataset includes observations from the VISTA Variables in the V\'ia L\'actea Extended Survey (VVVX), optical data from Gaia Release 3 (DR3), and observations from the Dark Energy Camera (DECam). These multiple data sour

Michael Katz, Junkyu Lee, Shirin Sohrabi

The growing utilization of planning tools in practical scenarios has sparked an interest in generating multiple high-quality plans. Consequently, a range of computational problems under the general umbrella of top-quality planning were introduced over a short time period, each with its own definition. In this work, we show that the existing definitions can b

Darmindra Arumugam, Jun-Hee Park, Brook Feyissa, Jack Bush

Spaceborne radar remote sensing of the earth system is essential to study natural and man-made changes in the ecosystem, water and energy cycles, weather and air quality, sea level, and surface dynamics. A major challenge with current approaches is the lack of broad spectrum tunability due to narrow band microwave electronics, that limit systems to specific

Fangchen Liu, Kuan Fang, Pieter Abbeel, Sergey Levine

Open-world generalization requires robotic systems to have a profound understanding of the physical world and the user command to solve diverse and complex tasks. While the recent advancement in vision-language models (VLMs) has offered unprecedented opportunities to solve open-world problems, how to leverage their capabilities to control robots remains a gr

Ruizhuo Song, Beiming Yuan

Visual abstract reasoning problems pose significant challenges to the perception and cognition abilities of artificial intelligence algorithms, demanding deeper pattern recognition and inductive reasoning beyond mere identification of explicit image features. Research advancements in this field often provide insights and technical support for other similar d

Liangzhou Wang, Kaiwen Zhu, Fengming Zhu, Xinghu Yao

Reaching consensus is key to multi-agent coordination. To accomplish a cooperative task, agents need to coherently select optimal joint actions to maximize the team reward. However, current cooperative multi-agent reinforcement learning (MARL) methods usually do not explicitly take consensus into consideration, which may cause miscoordination problem. In thi

C. T. Donnan, R. J. McLure, J. S. Dunlop, D. J. McLeod

We present a new determination of the evolving galaxy UV luminosity function (LF) over the redshift range $8.5<z<15.5$ using a combination of several major Cycle-1 JWST imaging programmes - PRIMER, JADES and NGDEEP. This multi-field approach yields a total of $\simeq370$ sq. arcmin of JWST/NIRCam imaging, reaching (5-$\sigma$) depths of $\simeq30$ AB mag in

Peng Qi, Zehong Yan, Wynne Hsu, Mong Li Lee

Misinformation is a prevalent societal issue due to its potential high risks. Out-of-context (OOC) misinformation, where authentic images are repurposed with false text, is one of the easiest and most effective ways to mislead audiences. Current methods focus on assessing image-text consistency but lack convincing explanations for their judgments, which is e

Honglie Ning, Kyoung Hun Oh, Yifan Su, Alexander von Hoegen

The kagome superconductor CsV$_3$Sb$_5$ hosts a variety of charge density wave (CDW) phases, which play a fundamental role in the formation of other exotic electronic instabilities. However, identifying the precise structure of these CDW phases and their intricate relationships remain the subject of intense debate, due to the lack of static probes that can d

Andrei Pătraşcu, Cristian Rusu, Paul Irofti

Sparsifying transforms became in the last decades widely known tools for finding structured sparse representations of signals in certain transform domains. Despite the popularity of classical transforms such as DCT and Wavelet, learning optimal transforms that guarantee good representations of data into the sparse domain has been recently analyzed in a serie

Arda Uzunoglu, Abdalfatah Rashid Safa, Gözde Gül Şahin

Recently, there has been growing interest within the community regarding whether large language models are capable of planning or executing plans. However, most prior studies use LLMs to generate high-level plans for simplified scenarios lacking linguistic complexity and domain diversity, limiting analysis of their planning abilities. These setups constrain

Benjamin Fernando, Pierrick Mialle, G öram Ekstr öm, Constantinos Charalambous

We conduct a thorough analysis of seismic and acoustic data from the so-called `Interstellar Meteor' which entered the Earth's atmosphere off the coast of Papua New Guinea on 2014-01-08. We conclude that both previously-reported seismic signals are spurious -- one has characteristics suggesting a local vehicular-traffic based origin; whilst the other is stat

Philip David Kennedy, Abhirup Banerjee, Armin Köhl, Detlef Stammer

4D-variational data assimilation is applied to the Lorenz '63 model to introduce a new method for parameter estimation in chaotic climate models. The approach aims to optimise an Earth system model (ESM), for which no adjoint exists, by utilising the adjoint of a different, potentially simpler ESM. This relies on the synchronisation of the model to observed

Yaqian Qi, Yuan Feng, Xiangxiang Wang, Hanzhe Li

To enable large-scale and efficient deployment of artificial intelligence (AI), the combination of AI and edge computing has spawned Edge Intelligence, which leverages the computing and communication capabilities of end devices and edge servers to process data closer to where it is generated. A key technology for edge intelligence is the privacy-protecting m

Anna Valette, Guillaume Valette

We establish that if a submanifold $M$ of $\mathbb{R}^n$ is definable in some o-minimal structure then any definable submanifold $N\subset \mathbb{R}^n$ which is $\mathscr{C}^\infty$ diffeomorphic to $M$, with a diffeomorphism $h:N\to M$ that is sufficiently close to the identity, must be $\mathscr{C}^\infty$ definably diffeomorphic to $M$. The definable dif

Chenglei Si, Yanzhe Zhang, Ryan Li, Zhengyuan Yang

Generative AI has made rapid advancements in recent years, achieving unprecedented capabilities in multimodal understanding and code generation. This can enable a new paradigm of front-end development in which multimodal large language models (MLLMs) directly convert visual designs into code implementations. In this work, we construct Design2Code - the first

Gunjan Auti, Soumyadeep Paul, Wei-Lun Hsu, Shohei Chiashi

The phase transition of confined fluids in mesoporous materials deviates from that of bulk fluids due to the interactions with the surrounding heterogeneous structure. For example, adsorbed fluids in metal-organic-frameworks (MOFs) have atypical phase characteristics such as capillary condensation and higher-order phase transitions due to a strong heterogene

M. Bousder, H. Ez-Zahraouy

This letter presents a novel model that characterizes the curvature of space-time, influenced by a massive gauge field in the early universe. This curvature can lead to a multitude of observations, including the Hubble tension issue and the isotropic stochastic gravitational-wave background. We introduce, for the first time, the concept of gauge field Hopfio

Kangning Cui, Zishan Shao, Gregory Larsen, Victor Pauca

Palms play an outsized role in tropical forests and are important resources for humans and wildlife. A central question in tropical ecosystems is understanding palm distribution and abundance. However, accurately identifying and localizing palms in geospatial imagery presents significant challenges due to dense vegetation, overlapping canopies, and variable

Sasan Rahmanian, Hamza Mouharrar, Amin Alibakhshi, Zeeshan Iqbal

Solitons, arising from nonlinear wave-matter interactions, stand out for their intrinsic stability during wave propagation and exceptional spectral characteristics. Their applications span diverse physical systems, including telecommunications, atomic clocks, and precise measurements. In recent years, significant strides have been made in developing cavity-o

Suvendu Barik, Alexander. S. Garkun, Vladimir Gritsev

We explore the algebraic structure of a particular ansatz of Yang Baxter Equation which is inspired from the Bethe Ansatz treatment of the ASEP spin-model. Various classes of Hamiltonian density arriving from two types of R-Matrices are found which also appear as solutions of constant YBE. We identify the idempotent and nilpotent categories of such constant

Christian Kuehn, Sebastian Throm

Non-local reaction-diffusion partial differential equations (PDEs) involving the fractional Laplacian have arisen in a wide variety of applications. One common tool to analyse the dynamics of classical local PDEs near instability is to derive local amplitude/modulation approximations, which provide local normal forms classifying a wide variety of pattern-for

Yushen Lin, Kaidi Wang, Zhiguo Ding

This study explores the benefits of integrating the novel clustered federated learning (CFL) approach with non-orthogonal multiple access (NOMA) under non-independent and identically distributed (non-IID) datasets, where multiple devices participate in the aggregation with time limitations and a finite number of sub-channels. A detailed theoretical analysis

Yuyi Wang, Aiqiang Zhang, Yiyang Wu, Benda Xu

Photomultiplier tubes (PMTs) are widely deployed at neutrino and dark matter experiments for photon counting. When multiple photons hit a PMT consecutively, their photo-electron (PE) pulses pile up to hinder the precise measurements of the count and timings. We introduce Fast Stochastic Matching Pursuit (FSMP) to analyze the PMT signal waveforms into individ

Szu-Chao Chen, Alina Mreńca-Kolasińska, Ming-Hao Liu

Bernal-stacked bilayer graphene (BLG) provides an ideal basis for gate-controlled, and free of etching, electronic devices. Theoretical modeling of realistic devices is an essential part of research, however, simulations of large-scale BLG devices continue to be extremely challenging. Micrometer-sized systems are predominantly beyond the reach of the commonl

Haining Pan, Nayantara Mudur, Will Taranto, Maria Tikhanovskaya

Large language models (LLMs) have demonstrated an unprecedented ability to perform complex tasks in multiple domains, including mathematical and scientific reasoning. We demonstrate that with carefully designed prompts, LLMs can accurately carry out key calculations in research papers in theoretical physics. We focus on a broadly used approximation method in

Jonathan Wurtz, Stefan Sack, Sheng-Tao Wang

Combinatorial optimization is a challenging problem applicable in a wide range of fields from logistics to finance. Recently, quantum computing has been used to attempt to solve these problems using a range of algorithms, including parameterized quantum circuits, adiabatic protocols, and quantum annealing. These solutions typically have several challenges: 1

Andrei Gruzinov, Mehrdad Mirbabayi

It has been claimed that matter effects cause an asymmetry in the density of relic neutrinos versus antineutrinos near the surface of Earth, of order $O(G_F^{1/2})\sim 10^{-4}$, with the vertical extent $\sim 10$m. We argue that the effect is of order $O(G_F)\sim 10^{-8}$, with the vertical extent $\sim 1$mm.

Benoît Corsini, Victor Dubach, Valentin Féray

Binary search trees (BST) are a popular type of data structure when dealing with ordered data. Indeed, they enable one to access and modify data efficiently, with their height corresponding to the worst retrieval time. From a probabilistic point of view, binary search trees associated with data arriving in a uniform random order are well understood, but less

Armani Rodriguez, Yagna Kaasaragadda, Silvija Kokalj-Filipovic

Next-generation cellular concepts rely on the processing of large quantities of radio-frequency (RF) samples. This includes Radio Access Networks (RAN) connecting the cellular front-end based on software defined radios (SDRs) and a framework for the AI processing of spectrum-related data. The RF data collected by the dense RAN radio units and spectrum sensor

Yichang Xu, Ming Yin, Minghong Fang, Neil Zhenqiang Gong

Recent studies have revealed that federated learning (FL), once considered secure due to clients not sharing their private data with the server, is vulnerable to attacks such as client-side training data distribution inference, where a malicious client can recreate the victim's data. While various countermeasures exist, they are not practical, often assuming

Andrea De Domenico, Ali Farjami, Krishna Manoorkar, Alessandra Palmigiano

We further develop the algebraic approach to input/output logic initiated in \cite{wollic22}, where subordination algebras and a family of their generalizations were proposed as a semantic environment of various input/output logics. In particular, we consider precontact algebras as a suitable algebraic environment for negative permission, and we characterize

Giovanni Maria Tomaselli, Thomas F. M. Spieksma, Gianfranco Bertone

Rotating black holes can produce superradiant clouds of ultralight bosons. When the black hole is part of a binary system, its cloud can undergo resonances and ionization. These processes leave a distinct signature on the gravitational waveform that depends on the cloud's properties. To determine the state of the cloud by the time the system enters the band

Franco Giovenzana, Luca Giovenzana, Michele Graffeo, Paolo Lella

We investigate some aspects of the geometry of two classical generalisations of the Hilbert schemes of points. Precisely, we show that parity conjecture for $\text{Quot}_r^d\mathbb{A}^3$ already fails for $d=8$ and $r=2$ and that lots of the elementary components of the nested Hilbert schemes of points on smooth quasi-projective varieties of dimension at lea

Yuxin Guo, Shijie Ma, Hu Su, Zhiqing Wang

Audio-Visual Source Localization (AVSL) aims to locate sounding objects within video frames given the paired audio clips. Existing methods predominantly rely on self-supervised contrastive learning of audio-visual correspondence. Without any bounding-box annotations, they struggle to achieve precise localization, especially for small objects, and suffer from

Amanda Matson, Leonid Kagan, Claude-Michel Brauner, Gregory Sivashinsky

In this paper we consider a classical model of gasless combustion in a one dimensional formulation under the assumption of ignition temperature kinetics. We study the propagation of flame fronts in this model when the initial distribution of the solid fuel is a spatially periodic function that varies on a large scale. It is shown that in certain parametric r

Nicolò Alessandro Girardini, Arkadiusz Stopczynski, Olga Baranov, Cornelia Betsch

One of the most important tools available to limit the spread and impact of infectious diseases is vaccination. It is therefore important to understand what factors determine people's vaccination decisions. To this end, previous behavioural research made use of, (i) controlled but often abstract or hypothetical studies (e.g., vignettes) or, (ii) realistic bu

Alexander Baikalov, Daline Tho, Kevin Liu, Stefan Bartzsch

Background: Scintillation dosimetry has promising qualities for ultra-high dose rate (UHDR) radiotherapy (RT), but no system has shown compatibility with mean dose rates ($\bar{DR}$) above 100 Gy/s and doses per pulse ($D_p$) exceeding 1.5 Gy typical of UHDR (FLASH)-RT. The aim of this study was to characterize a novel scintillator dosimetry system with the

Hitesh Golchha, Sahil Yerawar, Dhruvesh Patel, Soham Dan

Real-world sequential decision making is characterized by sparse rewards and large decision spaces, posing significant difficulty for experiential learning systems like $\textit{tabula rasa}$ reinforcement learning (RL) agents. Large Language Models (LLMs), with a wealth of world knowledge, can help RL agents learn quickly and adapt to distribution shifts. I

Miguel Sánchez Sánchez, Israel Díaz, José González, Tobias Stauber

We address the precise determination of the phase diagram of magic angle twisted bilayer graphene under hydrostatic pressure within a self-consistent Hartree-Fock method in real space, including all the remote bands of the system. We further present a novel algorithm that maps the full real-space density matrix to a $4\times4$ density matrix based on a $SU(4

Valerio Buttinelli

We study the positivity of an Ulrich vector bundle defined with respect to a globally generated ample line bundle. First we prove a generalization of a Lopez theorem on the first Chern class and the bigness of an Ulrich bundle. Then, under some additional assumptions on the polarization, we give a description of its augmented base locus, which consequently l

Juliette Murris, Tristan Amadei, Tristan Kirscher, Antoine Klein

Heart failure (HF) contributes to circa 200,000 annual hospitalizations in France. With the increasing age of HF patients, elucidating the specific causes of inpatient mortality became a public health problematic. We introduce a novel methodological framework designed to identify prevalent health trajectories and investigate their impact on death. The initia

Derong Xu, Vasiliy S. Morozov, David Sagan, Yue Hao

Beam-beam interactions pose substantial challenges in the design and operation of circular colliders, significantly affecting their performance. In particular, the weak-strong simulation approach is pivotal for investigating single-particle dynamics during the collider design phase. This paper evaluates the limitations of existing models in weak-strong simul

Dimitrios Katsinis, Georgios Pastras, Nikolaos Tetradis

We study the entanglement entropy within a spherical region for a free scalar field in a squeezed state in 3+1 dimensions. We show that, even for small squeezing, a volume term appears, whose coefficient is essentially independent of the field mass. This is in line with Page's argument that the entanglement entropy in an arbitrary quantum state is proportion

Beata Kocel-Cynk, Wiesław Pawłucki, Anna Valette

We prove that, for any closed semialgebraic subset $W$ of $\mathbb{R}^n$ and for any positive integer $p$, there exists a Nash function $f:\mathbb{R}^n\setminus W\longrightarrow (0, \infty)$ which is equivalent to the distance function from $W$ and at the same time it is $\Lambda_p$-regular in the sense that $|D^\alpha f(x)|\leq C d(x, W)^{1- |\alpha|}$, for

Tingke Shen, Surabhi S Nath, Aenne Brielmann, Peter Dayan

The complexity of visual stimuli plays an important role in many cognitive phenomena, including attention, engagement, memorability, time perception and aesthetic evaluation. Despite its importance, complexity is poorly understood and ironically, previous models of image complexity have been quite complex. There have been many attempts to find handcrafted fe

Guilhem P. Baeza, Florent Dalmas, Fabien Dutertre, Jean-Charles Majesté

Following a previous work evidencing that short poly-propylene glycol (PPG) chains incorporated to crude SBR-silica nanocomposites act as filler-network softeners without changing their structure, we propose in the present letter to examine more operative cross-linked materials. We first evidence that the adsorption of PPG onto silica deactivates progressive

Luan Hoang

We study the three-dimensional Navier-Stokes equations in a periodic domain with the force decaying in time. Although the force has a certain coherent decay, as time tends to infinity, it can be too complicated for the previous theory of asymptotic expansions to be applicable. To deal with this issue, we systematically develop a new theory of asymptotic expa

Jeffrey P. Morgenthaler, Carl A. Schmidt, Marissa F. Vogt, Nicholas M. Schneider

We present first results derived from the largest collection of contemporaneously recorded Jovian sodium nebula and Io plasma torus (IPT) in [S II] 673.1 nm images assembled to date. The data were recorded by the Planetary Science Institute's Io Input/Output observatory (IoIO) and provide important context to Io geologic and atmospheric studies as well as th

Zahra Ansarilari, Merve Bodur, Amer Shalaby

We investigate the stochastic transfer synchronization problem, which seeks to synchronize the timetables of different routes in a transit network to reduce transfer waiting times, delay times, and unnecessary in-vehicle times. We present a sophisticated two-stage stochastic mixed-integer programming model that takes into account variability in passenger wal

Kaiyan Zhang, Jianyu Wang, Ermo Hua, Biqing Qi

With the advancement of language models (LMs), their exposure to private data is increasingly inevitable, and their deployment (especially for smaller ones) on personal devices, such as PCs and smartphones, has become a prevailing trend. In contexts laden with user information, enabling models to both safeguard user privacy and execute commands efficiently e

Hong-Jian He, Yu-Chen Wang, Jiaming Zheng

For dark matter (DM) direct detections, the kinematic effects such as those of the inelastic scattering can play important role in light DM searches. The light DM detection is generally difficult because of its small recoil energy. But the recoil energy of the exothermic inelastic DM scattering could exceed the detection threshold due to the contribution fro

Victor Doroshenko

Absorption of light is one of the main selection effects limiting our ability to detect celestial sources, and ultimately, appearance of the sky across most of the electromagnetic spectrum. Recent advances in quantity and quality of available observational data and analysis methods led to major improvements in resolution, depth and fidelity of 3D dust distri

Maeshal Hijazi, Payman Dehghanian

Maintaining the privacy of power system data is essential for protecting sensitive information and ensuring the operation security of critical infrastructure. Therefore, the adoption of centralized deep learning (DL) transient stability assessment (TSA) frameworks can introduce risks to electric utilities. This is because these frameworks make utility data s

Sylvia Adscheid, Benjamin Magnelli, Daizhong Liu, Frank Bertoldi

Galaxy submillimetre number counts are a fundamental measurement in our understanding of galaxy evolution models. Most early measurements are obtained via single-dish telescopes with substantial source confusion, whereas recent interferometric observations are limited to small areas. We used a large database of ALMA continuum observations to accurately measu

Adam G. Rennie, Craig M. Buttar, Yanyan Gao, Ricardo González López

Following the Phase-II upgrade during Long Shutdown (LS3), the LHC aims to reach a peak instantaneous luminosity of $7.5\times 10^{34}$cm$^{-2}$s$^{-1}$, which corresponds to an average of around 200 inelastic proton-proton collisions per beam-crossing (every 25 ns). To cope with these conditions, the ATLAS Inner Detector will be replaced by a new all-silico

Run Hou, Shouvik Sur, Lucas K. Wagner, Andriy H. Nevidomskyy

Despite much theoretical work, developing a comprehensive ab initio model for twisted bilayer graphene (TBG) has proven challenging due to the inherent trade-off between accurately describing the band structure and incorporating the interactions within the Hamiltonian, particularly given the topological obstruction -- so-called fragile topology -- to the des

Yannan He, Garvita Tiwari, Tolga Birdal, Jan Eric Lenssen

Faithfully modeling the space of articulations is a crucial task that allows recovery and generation of realistic poses, and remains a notorious challenge. To this end, we introduce Neural Riemannian Distance Fields (NRDFs), data-driven priors modeling the space of plausible articulations, represented as the zero-level-set of a neural field in a high-dimensi

Flor Miriam Plaza-del-Arco, Amanda Cercas Curry, Alba Curry, Gavin Abercrombie

Large language models (LLMs) reflect societal norms and biases, especially about gender. While societal biases and stereotypes have been extensively researched in various NLP applications, there is a surprising gap for emotion analysis. However, emotion and gender are closely linked in societal discourse. E.g., women are often thought of as more empathetic,

Robert Mendel, Tobias Rueckert, Dirk Wilhelm, Daniel Rueckert

Real-time computational speed and a high degree of precision are requirements for computer-assisted interventions. Applying a segmentation network to a medical video processing task can introduce significant inter-frame prediction noise. Existing approaches can reduce inconsistencies by including temporal information but often impose requirements on the arch

Leonardo Santilli, Richard J. Szabo

We undertake a detailed study of the gaugings of two-dimensional Yang-Mills theory by its intrinsic charge conjugation 0-form and centre 1-form global symmetries, elucidating their higher algebraic and geometric structures, as well as the meaning of dual lower form symmetries. Our derivations of orbifold gauge theories make use of a combination of standard c

Vinicius Zampronio, Alejandro Mendoza-Coto, Tommaso Macrì, Fabio Cinti

The effects of frustration on extended supersolid states is a largely unexplored subject in the realm of cold-atom systems. In this work, we explore the impact of quasicrystalline lattices on the supersolid phases of dipolar bosons. Our findings reveal that weak quasicrystalline lattices can induce a variety of modulated phases, merging the inherent solid pa

Ryan Van Haren, David Lederman

Thin films of the topological insulator (BiSb)$_2$Te$_3$ oriented along the [0001] direction were grown via molecular beam epitaxy on substrates of Al$_2$O$_3$ (0001) and MgF$_2$ (110) single crystals, as well as on an epitaxial thin film of the antiferromagnetic insulator MnF$_2$ (110). Magnetoconductivity measurements of these samples showed close proximit

Juan Casado-Díaz

The present paper is devoted to study the asymptotic behavior of a sequence of linear elliptic equations with a varying drift term, whose coefficients are just bounded in $L^N(\Omega)$, with $N$ the dimension of the space. It is known that there exists a unique solution for each of these problems in the Sobolev space $H^1_0(\Omega)$. However, because the ope

Simon Krogmann, Pascal Lenzner, Alexander Skopalik, Marc Uetz

We consider competitive facility location as a two-stage multi-agent system with two types of clients. For a given host graph with weighted clients on the vertices, first facility agents strategically select vertices for opening their facilities. Then, the clients strategically select which of the opened facilities in their neighborhood to patronize. Facilit

Jacopo Vialetto, Shivaprakash N. Ramakrishna, Lucio Isa, Marco Laurati

Particle surface chemistry and internal softness are two fundamental parameters in governing the mechanical properties of dense colloidal suspensions, dictating structure and flow, therefore of interest from materials fabrication to processing. Here, we modulate softness by tuning the crosslinker content of poly(N-isopropylacrylamide) microgels, and we adjus

Elijah Mullens, Catherine Zucker, Claire E. Murray, Rowan Smith

We leverage recent numerical simulations of highly resolved star-forming regions in a Milky Way-like Galaxy to explore the nature of extended gaseous envelopes around molecular clouds. We extract a sample of two dozen star-forming clouds from the feedback-dominated suite of the "Cloud Factory'' simulations. With the goal of exploring the 3D thermal and chemi