December 2024 arXiv papers — page 77

Karen Yeats

The causal set theory d'Alembertian has rational coefficients for which alternating expressions are known. Here, a combinatorial interpretation of these numbers is given.

Manuel Egner, Matteo Fael, Alexander Lenz, Maria Laura Piscopo

We update the Standard Model (SM) predictions for the lifetimes of the $B^+$, $B_d$ and $B_s$ mesons within the heavy quark expansion (HQE), including the recently determined NNLO-QCD corrections to non-leptonic decays of the free $b$-quark. In addition, we update the HQE predictions for the lifetime ratios $\tau (B^+)/\tau (B_d)$ and $\tau (B_s)/\tau (B_d)$

Xiaodong Zhang

The concept of partial structure R1 (pR1) is a generalization of the concept of single atom R1 (sR1) (Zhang & Donahue, 2024). The hypothesis is that the deepest hole of a pR1 map determines the orientation and location of a missing fragment. In current implementation, the calculation is divided into two steps. The first step is to detect possible orientation

Seoha Song, Junhyun Lee, Hyeonmok Ko

Despite the great success of large language models (LLMs), efficiently controlling the length of the output sequence still remains a challenge. In this paper, we propose Hansel, an efficient framework for length control in LLMs without affecting its generation ability. Hansel utilizes periodically outputted hidden special tokens to keep track of the remainin

Rishabh B. Mishra, Sherjeel M. Khan, Sohail F. Shaikh, Aftab M. Hussain

Capacitive pressure sensors have several advantages in areas such as robotics, automation, aerospace, biomedical and consumer electronics. We present mathematical modelling, finite element analysis (FEA), fabrication and experimental characterization of ultra-low cost and paper-based, touch-mode, flexible capacitive pressure sensor element using Do-It-Yourse

Maksymilian Środa, Ken Inayoshi, Hiroshi Shinaoka, Philipp Werner

One of the challenges in diagrammatic simulations of nonequilibrium phenomena in lattice models is the large memory demand for storing momentum-dependent two-time correlation functions. This problem can be overcome with the recently introduced quantics tensor train (QTT) representation of multivariable functions. Here, we demonstrate nonequilibrium Green's f

Semih Cayci

In this work, we establish non-asymptotic convergence bounds for the Gauss-Newton method in training neural networks with smooth activations. In the underparameterized regime, the Gauss-Newton gradient flow in parameter space induces a Riemannian gradient flow on a low-dimensional embedded submanifold of the function space. Using tools from Riemannian optimi

Samaa Elnagar, Kweku-Muata Osei-Bryson

Deep Learning (DL) modeling has been a recent topic of interest. With the accelerating need to embed Deep Learning Networks (DLNs) to the Internet of Things (IoT) applications, many DL optimization techniques were developed to enable applying DL to IoTs. However, despite the plethora of DL optimization techniques, there is always a trade-off between accuracy

Xiaodong Zhang

This paper demonstrates how pre-knowledge of a crystal structure, including the constituent fragments, the atomic connectivity, and the approximate bond lengths, etc., can be utilized in the partial-structure R1 (pR1) and the single-atom R1 (sR1) calculations. It has been shown that taking advantage of pre-known information the calculations can proceed in an

Eivind Bøhn, Sølve Eidnes, Kjell Rune Jonassen

Wastewater treatment plants are increasingly recognized as promising candidates for machine learning applications, due to their societal importance and high availability of data. However, their varied designs, operational conditions, and influent characteristics hinder straightforward automation. In this study, we use data from a pilot reactor at the Veas tr

Tommaso Rizzo, Sebastiano Strangio, Alessandro Catania, Giuseppe Iannaccone

In analog neuromorphic chips, designers can embed computing primitives in the intrinsic physical properties of devices and circuits, heavily reducing device count and energy consumption, and enabling high parallelism, because all devices are computing simultaneously. Neural network parameters can be stored in local analog non-volatile memories (NVMs), saving

Yang Yang, Yi-Hsuan Liu, Rafael M. Fernandes, Gia-Wei Chern

We study the phase ordering dynamics of the classical antiferromagnetic $J_1$-$J_2$ (nearest-neighbor and next-nearest-neighbor couplings) Heisenberg model on the square lattice in the strong frustration regime ($J_2/J_1 > 1/2$). While thermal fluctuations preclude any long-range magnetic order at finite temperatures, the system exhibits a long-range spin-dr

Alexander Luce, Rasoul Alaee, Aimi Abass

Micro-LEDs ($\mu$LEDs) are poised to transform AR/VR, display, and optical communication technologies, but they are currently hindered by low light extraction efficiency and non-directional emission. Our study introduces an innovative approach using a descending index multilayer anti-reflection coating combined with a horn collimator structure atop the $\mu$

Santosh K. Das, Prabhakar Palni, Amal Sarkar, Vineet Kumar Agotiya

The hot and dense QCD matter, known as the Quark-Gluon Plasma (QGP), is explored through heavy-ion collision experiments at the LHC and RHIC. Jets and heavy flavors, produced from the initial hard scattering, are used as hard probes to study the properties of the QGP. Recent experimental observations on jet quenching and heavy-flavor suppression have strengt

Samaa Elnagar, Kweku-Muata Osei-Bryson

Ideas generation is a core activity for innovation in organizations. The creativity of the generated ideas depends not only on the knowledge retrieved from the organizations' knowledge bases, but also on the external knowledge retrieved from other resources. Unfortunately, organizations often cannot efficiently utilize the knowledge in the knowledge bases du

Matěj Kudrna

We attempt to describe the moduli space of boundary states in the SU(2)$_k$ WZW model by constructing marginally deformed solutions in open string field theory in the level truncation approximation. In contrast with other approaches to marginal deformations, our solutions exhibit a $g$-function different from that of the background (typically lower). Thus, o

Christian J. Kirkham, William J. Handley, Jiacong Zhu, Kaan Artuc

Due to the large dynamic ranges involved with separating the cosmological 21-cm signal from the Cosmic Dawn from galactic foregrounds, a well-calibrated instrument is essential to avoid biases from instrumental systematics. In this paper we present three methods for calibrating a global 21-cm cosmology experiment using the noise wave parameter formalisation

Holly P. Preece, A. Vigna-Gómez, A. S. Rajamuthukumar, P. Vynatheya

We investigate how the multiplicity of binary, triple and quadruple star systems changes as the systems evolve from the zero-age main-sequence to the Hubble time. We find the change in multiplicity fractions over time for each data set, identify the number of changes to the orbital configuration and the dominant underlying physical mechanism responsible for

Daniela Pinto, João Vitorino, Eva Maia, Ivone Amorim

High-quality datasets are critical for training machine learning models, as inconsistencies in feature generation can hinder the accuracy and reliability of threat detection. For this reason, ensuring the quality of the data in network intrusion detection datasets is important. A key component of this is using reliable tools to generate the flows and feature

Ronald Schnitzer, Lennart Kilian, Simon Roessner, Konstantinos Theodorou

Artificial Intelligence (AI) has emerged as a key technology, driving advancements across a range of applications. Its integration into modern autonomous systems requires assuring safety. However, the challenge of assuring safety in systems that incorporate AI components is substantial. The lack of concrete specifications, and also the complexity of both the

Federico Baldo, Simon Ferreira, Charles K. Assaad

Traditional causal discovery methods often depend on strong, untestable assumptions, making them unreliable in real-world applications. In this context, Large Language Models (LLMs) have emerged as a promising alternative for extracting causal knowledge from text-based metadata, effectively consolidating domain expertise. However, LLMs are prone to hallucina

Jean-Pierre Gazeau, Célestin Habonimana, Romain Murenzi, Aidan Zlotak

The Weyl-Heisenberg symmetries originate from translation invariances of various manifolds viewed as phase spaces, e.g. Euclidean plane, semi-discrete cylinder, torus, in the two-dimensional case, and higher-dimensional generalisations. In this review we describe, on an elementary level, how this symmetry emerges through displacement operators and standard F

Tong Chen, Shuya Yang, Junyi Wang, Long Bai

Surgical video generation can enhance medical education and research, but existing methods lack fine-grained motion control and realism. We introduce SurgSora, a framework that generates high-fidelity, motion-controllable surgical videos from a single input frame and user-specified motion cues. Unlike prior approaches that treat objects indiscriminately or r

Sarah Khalifeh, Ken R. Duffy, Muriel Medard

Long, powerful soft detection forward error correction codes are typically constructed by concatenation of shorter component codes that are decoded through iterative Soft-Input Soft-Output (SISO) procedures. The current gold-standard is Low Density Parity Check (LDPC) codes, which are built from weak single parity check component codes that are capable of pr

Lê Vǎn Thành

This paper presents an exposition of Rio's proof of the strong law of large numbers and extends his method to random fields. In addition to considering the rate of convergence in the Marcinkiewicz--Zygmund strong law of large numbers, we go a step further by establishing (i) the Hsu--Robbins--Erd\"{o}s--Spitzer--Baum--Katz theorem, (ii) the Feller weak law o

Haotong Lin, Sida Peng, Jingxiao Chen, Songyou Peng

Prompts play a critical role in unleashing the power of language and vision foundation models for specific tasks. For the first time, we introduce prompting into depth foundation models, creating a new paradigm for metric depth estimation termed Prompt Depth Anything. Specifically, we use a low-cost LiDAR as the prompt to guide the Depth Anything model for a

Juan Maldacena

The sphere partition function is one of the simplest euclidean gravity computations. It is usually interpreted as count of states. However, the one loop gravity correction contains a dimension dependent phase factor, $i^{D+2}$, which seems confusing for such an interpretation. We show that, after including an observer, this phase gets mostly cancelled for th

Jordan Slessor, Dezheng Kong, Xiaofen Tang, Zheng En Than

Federated learning (FL) is a machine learning methodology that involves the collaborative training of a global model across multiple decentralized clients in a privacy-preserving way. Several FL methods are introduced to tackle communication inefficiencies but do not address how to sample participating clients in each round effectively and in a privacy-prese

Valeria Banica, Luis Vega

In the last three decades there has been an intense activity on the exploration of turbulent phenomena of dispersive equations, as for instance the growth of Sobolev norms since the work of Bourgain in the 90s. In general the 1D cubic Schr\"odinger equation has been left aside because of its complete integrability. In a series of papers of the last six years

Rudolf Tange

Let k be an algebraically closed field of characteristic p>0, let G=GL_n be the general linear group over k, let P be a parabolic subgroup of G, and let u_P be the Lie algebra of its unipotent radical. We show that the Kumar-Lauritzen-Thomsen splitting of the cotangent bundle Gx^Pu_P of G/P has top degree (p-1)\dim(G/P). The component of that degree is there

Samuel Falcon, Jaime Leon

Evaluating teachers' skills is crucial for enhancing education quality and student outcomes. Teacher discourse, significantly influencing student performance, is a key component. However, coding this discourse can be laborious. This study addresses this issue by introducing a new methodology for optimising the assessment of teacher discourse. The research co

N. M. Jiménez Cruz, Ameek Malhotra, Gianmassimo Tasinato, Ivonne Zavala

High-precision astrometry offers a promising approach to detect low-frequency gravitational waves, complementing pulsar timing array (PTA) observations. We explore the response of astrometric measurements to a stochastic gravitational wave background (SGWB) in synergy with PTA data. Analytical, covariant expressions for this response are derived, accounting

Xin Wang, Boyan Gao, Yi Dai, Lei Cao

Stress is a pervasive global health issue that can lead to severe mental health problems. Early detection offers timely intervention and prevention of stress-related disorders. The current early detection models perform "black box" inference suffering from limited explainability and trust which blocks the real-world clinical application. Thanks to the genera

Majid Zarharan, Maryam Hashemi, Malika Behroozrazegh, Sauleh Eetemadi

We introduce FarExStance, a new dataset for explainable stance detection in Farsi. Each instance in this dataset contains a claim, the stance of an article or social media post towards that claim, and an extractive explanation which provides evidence for the stance label. We compare the performance of a fine-tuned multilingual RoBERTa model to several large

Chengzhi Huang, Jian Chen, Liping Tang

This paper proposes a new backtracking strategy based on the FISTA accelerated algorithm for multiobjective optimization problems. The strategy focuses on solving the problem of Lipschitz constant being unknown. It allows estimate parameter updates non-increasingly. Furthermore, the proposed strategy effectively avoids the limitation in convergence proofs ar

Cong Wei, Yujie Zhong, Haoxian Tan, Yingsen Zeng

Boosted by Multi-modal Large Language Models (MLLMs), text-guided universal segmentation models for the image and video domains have made rapid progress recently. However, these methods are often developed separately for specific domains, overlooking the similarities in task settings and solutions across these two areas. In this paper, we define the union of

Manu Gond, Emin Zerman, Sebastian Knorr, Mårten Sjöström

Recent advancements in view synthesis have significantly enhanced immersive experiences across various computer graphics and multimedia applications, including telepresence and entertainment. By enabling the generation of new perspectives from a single input view, view synthesis allows users to better perceive and interact with their environment. However, ma

Manuel Rispler, Davide Vodola, Markus Müller, Seyong Kim

We map the decoding problem of the surface code under depolarizing and syndrome noise to a disordered spin model, which we call the random coupled-plaquette gauge model (RCPGM). By coupling X- and Z-syndrome volumes, this model allows us to optimally account for genuine Y-errors in the surface code in a setting with noisy measurements. Using Parallel Temperi

Hallmann Ó. Gestsson, Charlie Nation, Jacob S. Higgins, Gregory S. Engel

Two-dimensional optical spectroscopy experiments have shown that exciton transfer pathways in the Fenna-Matthews-Olson (FMO) photosynthetic complex differ drastically under reduced and oxidised conditions, suggesting a functional role for collective vibronic mechanisms that may be active in the reduced form but attenuated in the oxidised state. Higgins et al

Sanghyoup Gu, Ratnesh Kumar

Recent advances in deep learning have provided new data-driven ways of controller design to replace the traditional manual synthesis and certification approaches. Employing neural network (NN) as controllers however, presents its own challenge: that of certifying stability due to their inherent complex nonlinearity, and while NN controllers have demonstrated

Panagiotis D. Grontas, Anastasios Tsiamis, John Lygeros

We consider finite Markov decision processes (MDPs) with convex constraints and known dynamics. In principle, this problem is amenable to off-the-shelf convex optimization solvers, but typically this approach suffers from poor scalability. In this work, we develop a first-order algorithm, based on the Douglas-Rachford splitting, that allows us to decompose t

Daniel Baranowski, Maximilian Delto, Kirill Melnikov, Andrey Pikelner

Recently, we have presented the result for the zero-jettiness soft function at next-to-next-to-next-to-leading order (N3LO) in perturbative QCD [arXiv:2409.11042], without providing technical details of the calculation. The goal of this paper is to describe the most important element of that computation, the triple real-emission contribution. We present a de

Igor Benek-Lins, Dean Fountas, Jonathan Discenza, Saurabh Maiti

Nonanalytic features are interesting in physics as they carry valuable information about the physical properties of the system. These properties manifest themselves in observables containing a one- or two-particle spectral function. In this work, we use a stationary-point analysis to deduce the nonanalytic features of spectral functions that appear while com

Benjamin Biewald, Bastien Cozian, Laurent Dubus, William Zappa

Weather conditions associated with low electricity production from renewable energy sources (RES) can result in challenging 'dunkelflaute' events, where 'dunkel' means dark and 'flaute' refers to low windspeeds. In a power system relying significantly on RES, such events can pose a risk for maintaining resource adequacy, i.e. the balance between generation a

Katarzyna Kobalczyk, Claudio Fanconi, Hao Sun, Mihaela van der Schaar

As large language models (LLMs) become increasingly embedded in everyday applications, ensuring their alignment with the diverse preferences of individual users has become a critical challenge. Currently deployed approaches typically assume homogeneous user objectives and rely on single-objective fine-tuning. However, human preferences are inherently heterog

Anilatmaja Aryasomayajula, Debasish Sadhukhan

In this article, we derive estimates of Teichm\"uller modular forms, and associated invariants. Let $\mathcal{M}_{g}$ denote the moduli space of compact hyperbolic Riemann surfaces of genus $g\geq 2$, and let $\overline{M}_{g}$ be the Deligne-Mumford compactification of $\mathcal{M}_{g}$, and we denote its boundary by $\partial\mathcal{M}_{g}$. Let $\pi:\mat

Raz Lotan, Sharon Shoham

Liveness properties are traditionally proven using a ranking function that maps system states to some well-founded set. Carrying out such proofs in first-order logic enables automation by SMT solvers. However, reasoning about many natural ranking functions is beyond reach of existing solvers. To address this, we introduce the notion of implicit rankings - fi

Erik Rosenberg, Steven Gratton, Anthony Challinor

Motivated by observed discrepancies between the Atacama Cosmology Telescope Data Release 4 (ACT DR4) and Planck 2018 cosmic microwave background (CMB) anisotropy power spectra, particularly in the cross-correlation of temperature and E-mode polarization, we investigate challenges that may be encountered in the comparison of satellite and ground-based CMB dat

Jun Hu, Bryan Hooi, Bingsheng He, Yinwei Wei

Multimodal recommendation systems can learn users' preferences from existing user-item interactions as well as the semantics of multimodal data associated with items. Many existing methods model this through a multimodal user-item graph, approaching multimodal recommendation as a graph learning task. Graph Neural Networks (GNNs) have shown promising performa

John M. Hanna, Hugues Talbot, Irene E. Vignon-Clementel

In machine learning and statistical modeling, the mean square or absolute error is commonly used as an error metric, also called a "loss function." While effective in reducing the average error, this approach may fail to address localized outliers, leading to significant inaccuracies in regions with sharp gradients or discontinuities. This issue is particula

Alice Cortinovis, Daniel Kressner

We derive a new adaptive leverage score sampling strategy for solving the Column Subset Selection Problem (CSSP). The resulting algorithm, called Adaptive Randomized Pivoting, can be viewed as a randomization of Osinsky's recently proposed deterministic algorithm for CSSP. It guarantees, in expectation, an approximation error that matches the optimal existen

Thierry Bodineau, Bernard Derrida

In this paper, we study the stationary states of diffusive dynamics driven out of equilibrium by reservoirs. For a small forcing, the system remains close to equilibrium and the large deviation functional of the density can be computed perturbatively by using the macroscopic fluctuation theory. This applies to general domains in $\mathbb{R}^d$ and diffusive

Foivos Alimisis, Bart Vandereycken

We make a full landscape analysis of the (generally non-convex) orthogonal Procrustes problem. This problem is equivalent to computing the polar factor of a square matrix. We reveal a convexity-like structure, which explains the already established tractability of the problem and show that gradient descent in the orthogonal group computes the polar factor of

Candace Ross, Melissa Hall, Adriana Romero Soriano, Adina Williams

Language models are increasingly being incorporated as components in larger AI systems for various purposes, from prompt optimization to automatic evaluation. In this work, we analyze the construct validity of four recent, commonly used methods for measuring text-to-image consistency - CLIPScore, TIFA, VPEval, and DSG - which rely on language models and/or V

Julyan H. E. Cartwright

The history of the discovery of nuclear fission and how science fiction had anticipated it.

Mingyang Zhang, Jing Liu, Ganggui Ding, Xinyi Yu

Lately, the practice of utilizing task-specific fine-tuning has been implemented to improve the performance of large language models (LLM) in subsequent tasks. Through the integration of diverse LLMs, the overall competency of LLMs is significantly boosted. Nevertheless, traditional ensemble methods are notably memory-intensive, necessitating the simultaneou

Zaynab Batool Reza, Abdul Rafay Syed, Omer Iqbal, Ethel Mensah

In an era where digital security is crucial, efficient processing of security-related inquiries through supply chain security questionnaires is imperative. This paper introduces a novel approach using Natural Language Processing (NLP) and Retrieval-Augmented Generation (RAG) to automate these responses. We developed QuestSecure, a system that interprets dive

Ido Siovitz, Anna-Maria E. Glück, Yannick Deller, Alexander Schmutz

Far from equilibrium, universal dynamics prevails in many different situations, from pattern coarsening to turbulence. A central longstanding problem concerns the development of a theory of coarsening that rests on the microscopic properties of the system and allows identifying the interaction mechanisms underlying a possible overarching universality class o

Pratikkumar Raje, Eric Parish, Jean-Pierre Hickey, Paola Cinnella

Hypersonic flow conditions pose exceptional challenges for Reynolds-Averaged Navier-Stokes (RANS) turbulence modeling. Critical phenomena include compressibility effects, shock/turbulent boundary layer interactions, turbulence-chemistry interaction in thermo-chemical non-equilibrium, and ablation-induced surface roughness and blowing effects. This comprehens

Jialiang Hu, Jing Ye, Yuhao Chen, Zhixing Mei

The propagation of disturbances in the solar atmosphere is inherently three dimensional (3D), yet comprehensive studies on the spatial structure and dynamics of 3D wavefronts are scarce. Here we conduct high resolution 3D numerical simulations to investigate filament eruptions, focusing particularly on the 3D structure and genesis of EUV waves. Our results d

Xiaobao Wei, Peng Chen, Ming Lu, Hui Chen

Rendering photorealistic head avatars from arbitrary viewpoints is crucial for various applications like virtual reality. Although previous methods based on Neural Radiance Fields (NeRF) can achieve impressive results, they lack fidelity and efficiency. Recent methods using 3D Gaussian Splatting (3DGS) have improved rendering quality and real-time performanc

Kayoung Ban, Myeonghun Park, Raymundo Ramos

We develop a machine learning algorithm to turn around stratification in Monte Carlo sampling. We use a different way to divide the domain space of the integrand, based on the height of the function being sampled, similar to what is done in Lebesgue integration. This means that isocontours of the function define regions that can have any shape depending on t

A. Semakin, J. Ahokas, O. Hanski, V. Dvornichenko

We present a design and performance tests of an intense source of cold hydrogen atoms for loading large magnetic traps. Our source is based on a cryogenic dissociator of molecular hydrogen at 0.6 K followed by a series of thermal accommodators at 0.5, 0.2 and 0.13 K with inner surfaces covered by a superfluid helium film. All components are thermally anchore

Jing-Rong Wang, Chang-Jin Zhang

An anisotropic flat band fermion system with a novel dispersion that is linear along one direction and cubic along another is proposed in Phys. Rev. X. 13, 021012 (2023). We study the effects of Coulomb interaction in this fermion system by renormalization group theory and Dyson-Schwinger gap equation. We perform renormalizaton group analysis and find that f

PandaX Collaboration, Shu Zhang, Zihao Bo, Wei Chen

We report the search for neutrinoless double-beta decay of $^{136}$Xe from the PandaX-4T experiment with a 3.7-tonne natural xenon target. The data reconstruction and the background modeling are optimized in the MeV energy region. A blind analysis is performed with data from the commissioning run and the first science run. No significant excess of signal ove

Michael Abia

Every human with a functioning vestibular system is capable of feeling motion sickness, but some are more vulnerable than others. Based on the leading theories explaining this condition, vulnerability should be predicted by a person's years of real-life experience before using a VR device and years of VR experience after. A questionnaire was filled out on su

Ipsita Mandal

We investigate the behaviour of the linear-response coefficients, when in-plane electric field ($\mathbf E$) or/and temperature gradient ($\nabla_{\mathbf r} T$) is/are applied on a two-dimensional semimetal harbouring anisotropic Dirac cones. The anisotropy is caused by (1) differing Fermi velocities along the two mutually perpendicular momentum axes, and (

Z. Hasan, M. Imran, A. A. Usmani, Z. A. Khan

To establish credibility for the use of the Slater determinant harmonic oscillator (SDHO) density in predicting root-mean-square proton and neutron radii for Be, B, C, N, O, and F isotopes [M. Imran et al., {\color{blue}Phys. Rev. C {\bf 110}, 014623 (2024)}], in this work we propose to study charge-changing and interaction cross sections for the said isotop

Louie Corpe, Thomas Chehab, Andreas Goudelis

This note describes the validation of material allowing the reinterpretation of an ATLAS search for decays of pair-produced neutral long-lived particles decaying in the hadronic part of the calorimeter, or at the edge of the electromagnetic calorimeter, using the full Run-2 ATLAS dataset. This reinterpretation material includes an efficiency map linking trut

Svante Janson, Tiffany Y. Y. Lo

We study the number $X^{(n)}$ of vertices that can be reached from the last added vertex $n$ via a directed path (the descendants) in the standard preferential attachment graph. In this model, vertices are sequentially added, each born with outdegree $m\ge 2$; the endpoint of each outgoing edge is chosen among previously added vertices with probability propo

Anji Dong, The Nguyen, Alexandru Zaharescu

In light of Kim's conjecture on regular polytopes of dimension four, which is a generalization of Waring's problem, we establish asymptotic formulas for representing any sufficiently large integer as a sum of numbers in the form of those regular 4-polytopes. Moreover, we are able to obtain a more general result of the asymptotics for any degree-four polynomi

Purba Mukherjee, Anjan A. Sen

Here we explore certain subtle features imprinted in data from the completed Sloan Digital Sky Survey IV (SDSS-IV) extended Baryon Oscillation Spectroscopic Survey (eBOSS) as a combined probe for the background and perturbed Universe. We reconstruct the baryon Acoustic Oscillation (BAO) and Redshift Space Distortion (RSD) observables as functions of redshift

Edwin Lock, Benjamin Patrick Evans, Eleonora Kreacic, Sujay Bhatt

We propose a decentralized market model in which agents can negotiate bilateral contracts. This builds on a similar, but centralized, model of trading networks introduced by Hatfield et al. in 2013. Prior work has established that fully-substitutable preferences guarantee the existence of competitive equilibria which can be centrally computed. Our motivation

Wen Chang

It is demonstrated that any almost-tilting module over a gentle algebra is indeed partial-tilting, meaning it can be completed as a tilting module. Furthermore, such a module has at most $2n$ possible complements, thereby confirming a (modified) conjecture of Happel for the case of gentle algebras. Additionally, for any $n\geq 3$ and $1\leq m \leq n-2$, ther

F. Delgado, H. Maugendre

We consider a finite analytic morphism $\varphi =(f,g)$ defined from a complex analytic normal surface $(Z,z)$ to ${\mathbb C}^2$. We describe the topology of the image by $\varphi$ of a reduced curve on $(Z,z)$ by means of iterated pencils defined recursively for each branch of the curve from the initial one $\langle f,g \rangle$. This result generalizes th

Kaidi Wang, Zhiguo Ding, Robert Schober

In this letter, a non-orthogonal multiple access (NOMA) assisted downlink pinching-antenna system is investigated, where multiple pinching antennas can be activated at pre-configured positions along a dielectric waveguide to serve users via NOMA. In particular, the objective of this letter is to study at what locations and how many pinching antennas should b

Massimo Giovannini

The timeline of the expansion rate ultimately defines the interplay between high energy physics, astrophysics and cosmology. The guiding theme of this topical review is provided by the scrutiny of the early history of the space-time curvature through the diffuse backgrounds of gravitational radiation that are sensitive to all the stages of the evolution of t

Sen Yan, David J. O'Connor, Xiaojun Wang, Noel E. O'Connor

Urban pollution poses serious health risks, particularly in relation to traffic-related air pollution, which remains a major concern in many cities. Vehicle emissions contribute to respiratory and cardiovascular issues, especially for vulnerable and exposed road users like pedestrians and cyclists. Therefore, accurate air quality monitoring with high spatial

Amedeo Pachera, Mattia Palmiotto, Angela Bonifati, Andrea Mauri

Graphs are expressive abstractions representing more effectively relationships in data and enabling data science tasks. They are also a widely adopted paradigm in causal inference focusing on causal directed acyclic graphs. Causal DAGs (Directed Acyclic Graphs) are manually curated by domain experts, but they are never validated, stored and integrated as dat

Stefano M. Nicoletti, E. Moritz Hahn, Mattia Fumagalli, Giancarlo Guizzardi

When considering risky events or actions, we must not downplay the role of involved objects: a charged battery in our phone averts the risk of being stranded in the desert after a flat tyre, and a functional firewall mitigates the risk of a hacker intruding the network. The Common Ontology of Value and Risk (COVER) highlights how the role of objects and thei

Ruijie He, Xiangcun Meng, Zhenxin Lei, Huahui Yan

Different types of hot subdwarfs may have different origins, which will cause them to present different radial velocity (RV) variability properties. Only 6$\pm$4% of our single-lined He-rich hot subdwarfs that only show spectroscopic features of hot subdwarfs are found to be RV variable, which is lower than the fraction of single-lined He-poor sdB stars (31$

Martin Kurečka, Václav Nevyhoštěný, Petr Novotný, Vít Unčovský

Constrained Markov decision processes (CMDPs), in which the agent optimizes expected payoffs while keeping the expected cost below a given threshold, are the leading framework for safe sequential decision making under stochastic uncertainty. Among algorithms for planning and learning in CMDPs, methods based on Monte Carlo tree search (MCTS) have particular i

Gunindra Krishna Mahanta, Subhashis Roy, Sagar Godambe, Bitan Ghosal

Recent observations by the Large High Altitude Air Shower Observatory (LHAASO) detected Ultra High Energy (UHE) photons in the range 100 TeV to 1.4 PeV from twelve sources including Crab nebula. The detection of these photons demands the presence of at least PeV energy particle in the source. It is important to understand particle acceleration and radiation

Lorenzo Basile, Maria Grazia Berni, Antonio Celani

Airborne Wind Energy (AWE) is an emerging technology designed to harness the power of high-altitude winds, offering a solution to several limitations of conventional wind turbines. AWE is based on flying devices (usually gliders or kites) that, tethered to a ground station and driven by the wind, convert its mechanical energy into electrical energy by means

Hyunjin Seo, Kyusung Seo, Joonhyung Park, Eunho Yang

Recent advancements in graph neural networks (GNNs) have highlighted the critical need of calibrating model predictions, with neighborhood prediction similarity recognized as a pivotal component. Existing studies suggest that nodes with analogous neighborhood prediction similarity often exhibit similar calibration characteristics. Building on this insight, r

Alessia Gentili, Rainer Klages, Giorgio Volpe

Living organisms have developed advanced motion strategies for efficient space exploration, serving as inspiration for the movements of microrobots. These real-life strategies often involve anomalous dynamics displaying random movement patterns that deviate from Brownian motion. Despite their biological inspiration, autonomous stochastic navigation strategie

Arce Domingo-Relloso, Yuchen Zhang, Ziqing Wang, Astrid M Suchy-Dicey

Not accounting for competing events in survival analysis can lead to biased estimates, as individuals who die from other causes do not have the opportunity to develop the event of interest. Formal definitions and considerations for causal effects in the presence of competing risks have been published, but not for the mediation analysis setting. We propose, f

Joongwon Kim, Anirudh Goyal, Aston Zhang, Bo Xiong

Preference learning is a widely adopted post-training technique that aligns large language models (LLMs) to human preferences and improves specific downstream task capabilities. In this work we systematically investigate how specific attributes of preference datasets affect the alignment and downstream performance of LLMs in instruction-following tasks. We u

LHCb collaboration, R. Aaij, A. S. W. Abdelmotteleb, C. Abellan Beteta

A search for $CP$ violation in $\Lambda_b^0\rightarrow pK^-$ and $\Lambda_b^0\rightarrow p\pi^-$ decays is presented using the full Run 1 and Run 2 data samples of $pp$ collisions collected with the LHCb detector, corresponding to an integrated luminosity of 9 $\mathrm{fb}^{-1}$ at center-of-mass energies of 7, 8, and 13 TeV. For the Run 2 data sample, the $

Aaron Van Poecke, Tobias Sebastian Finn, Ruoke Meng, Joris Van den Bergh

Current postprocessing techniques often require separate models for each lead time and disregard possible inter-ensemble relationships by either correcting each member separately or by employing distributional approaches. In this work, we tackle these shortcomings with an innovative, fast and accurate Transformer which postprocesses each ensemble member indi

Tobias Barthel, Drew Heard, Beren Sanders, Changhan Zou

We introduce a notion of stratification for rigidly-compactly generated tensor-triangulated categories relative to the homological spectrum and develop the fundamental features of this theory. In particular, we demonstrate that it exhibits excellent descent properties. In conjunction with Balmer's Nerves of Steel conjecture, we conclude that stratification a

Xing Wang, Cheng Zhang

On smooth compact manifolds with smooth boundary, we first establish the sharp lower bounds for the restrictions of harmonic functions in terms of their frequency functions, by using a combination of microlocal analysis and frequency function techniques by Almgren and Garofalo-Lin. The lower bounds can be saturated by Steklov eigenfunctions on Euclidean ball

Habib Ammari, Silvio Barandun, Alexander Uhlmann

We elucidate the different mechanisms of wave localisation in disordered finite systems of subwavelength resonators, where the disorder is in the spatial arrangement of the resonators. To do so, we employ the capacitance matrix formalism and develop a variety of tools to understand localisation in this setting. Namely, we adapt the Thouless criterion of loca

Philipp Binfet, Janis Adamek, Nils Schlüter, Moritz Schulze Darup

Cooperative control is crucial for the effective operation of dynamical multi-agent systems. Especially for distributed control schemes, it is essential to exchange data between the agents. This becomes a privacy threat if the data is sensitive. Encrypted control has shown the potential to address this risk and ensure confidentiality. However, existing appro

Eleni Sgouritsa, Virginia Aglietti, Yee Whye Teh, Arnaud Doucet

The reasoning abilities of Large Language Models (LLMs) are attracting increasing attention. In this work, we focus on causal reasoning and address the task of establishing causal relationships based on correlation information, a highly challenging problem on which several LLMs have shown poor performance. We introduce a prompting strategy for this problem t

Hosho Katsura, Chihiro Matsui, Chiara Paletta, Balázs Pozsgay

We consider spin chain models with local Hamiltonians that display weak ergodicity breaking. In these models, the majority of the eigenstates are thermal, but there is a distinguished subspace of the Hilbert space in which ergodicity is broken. We achieve such a weak breaking by embedding selected integrable models into larger Hilbert spaces of otherwise cha

Jan Stock, Till Schmidt, André Xhonneux, Dirk Müller

District heating (DH) systems play a pivotal role in decarbonizing the building sector's heat supply. While innovative low-exergy DH and cooling systems are increasingly adopted in new developments, the transformation of existing DH systems remains critical, as many still depend on fossil-based heating plants. Achieving a sustainable heat supply necessitates

Jinghan He, Kuan Zhu, Haiyun Guo, Junfeng Fang

Large vision-language models (LVLMs) have made substantial progress in integrating large language models (LLMs) with visual inputs, enabling advanced multimodal reasoning. Despite their success, a persistent challenge is hallucination-where generated text fails to accurately reflect visual content-undermining both accuracy and reliability. Existing methods f

Mathias Neufang, Emma Pajak, Damien van de Berg, Ye Seol Lee

Optimization plays an important role in chemical engineering, impacting cost-effectiveness, resource utilization, product quality, and process sustainability metrics. This chapter broadly focuses on data-driven optimization, particularly, on model-based derivative-free techniques, also known as surrogate-based optimization. The chapter introduces readers to

Ethan Baron, Idan Tankel, Peter Tu, Guy Ben-Yosef

In this study, we define and tackle zero shot "real" classification by description, a novel task that evaluates the ability of Vision-Language Models (VLMs) like CLIP to classify objects based solely on descriptive attributes, excluding object class names. This approach highlights the current limitations of VLMs in understanding intricate object descriptions