December 2024 arXiv papers — page 172

Huiyu Li, Ao Ma

The bottleneck in enhanced sampling lies in finding collective variables (CVs) that can effectively accelerate protein conformational changes. True reaction coordinates (tRCs) that can predict the committor are considered the optimal CVs, but identifying them requires unbiased natural reactive trajectories, which, paradoxically, depend on effective enhanced

Trine Kay Quady, Sonja Bumann, Eric Neuscamman

We present an approach for augmenting Gaussian atomic orbitals with correct nuclear cusps. Like the atomic orbital basis set itself, and unlike previous cusp corrections, this approach is independent of the many-body method used to prepare wave functions for quantum Monte Carlo. Once the basis set and molecular geometry are specified, the cusp-corrected atom

Sebastian Nagies, Kevin T. Geier, Javed Akram, Dimitrios Bantounas

Quantum annealing aims at solving optimization problems of practical relevance using quantum-computing hardware. Problems of interest are typically formulated in terms of quadratic unconstrained binary optimization (QUBO) Hamiltonians. However, many optimization problems are much more naturally formulated in terms of polynomial unconstrained binary optimizat

Christian de Ronde, Raimundo Fernández Mouján, César Massri

In this work we present an invariant-objective formalization of multi screen-entanglement grounded on Tensorial Quantum Mechanics (TQM) [12]. This new tensorial formulation of the theory of quanta -- basically, an extension of Heisenberg's matrix mechanics -- allows not only to escape the many problems present in the current account of multi-partite entangle

Dirk Erhard, Tertuliano Franco, Tiecheng Xu

In \cite{fgn1}, the hydrodynamic limit in the diffusive scaling of the symmetric simple exclusion process with a finite number of slow bonds of strength $n^{-\beta}$ has been studied. Here $n$ is the scaling parameter and $\beta>0$ is fixed. As shown in \cite{fgn1}, when $\beta>1$, such a limit is given by the heat equation with Neumann boundary conditions.

Aaron Lattanzi, Ann Almgren, Eliot Quon, Mahesh Natarajan

High performance computing (HPC) architectures have undergone rapid development in recent years. As a result, established software suites face an ever increasing challenge to remain performant on and portable across modern systems. Many of the widely adopted atmospheric modeling codes cannot fully (or in some cases, at all) leverage the acceleration provided

Alexandra Ramôa, Luis Paulo Santos

We present BAE, a problem-tailored and noise-aware Bayesian algorithm for quantum amplitude estimation. In a fault tolerant scenario, BAE is capable of saturating the Heisenberg limit; if device noise is present, BAE can dynamically characterize it and self-adapt. We further propose aBAE, an annealed variant of BAE drawing on methods from statistical inferen

John Marcotte, Sandipan Mishra, John T. Wen

Robotic wire arc additive manufacturing has been widely adopted due to its high deposition rates and large print volume relative to other metal additive manufacturing processes. For complex geometries, printing with variable height within layers offer the advantage of producing overhangs without the need for support material or geometric decomposition. This

Jerry Jun-Yan Zhang, Nicolas Lodieu, Eduardo L. Martín, María Rosa Zapatero Osorio

The coldest metal-poor population made of T and Y dwarfs are archaeological tracers of our Galaxy because they are very old and have kept the pristine material. The optical properties of these objects are important to characterise their atmospheric properties. We aim at characterising further the optical properties of ultracool metal-poor population with dee

Chiyu Max Jiang, Yijing Bai, Andre Cornman, Christopher Davis

Realistic and interactive scene simulation is a key prerequisite for autonomous vehicle (AV) development. In this work, we present SceneDiffuser, a scene-level diffusion prior designed for traffic simulation. It offers a unified framework that addresses two key stages of simulation: scene initialization, which involves generating initial traffic layouts, and

Yujin Taguchi, Yusuke Shibuya, Yusuke Hiki, Takashi Morikura

Bayesian optimization is efficient even with a small amount of data and is used in engineering and in science, including biology and chemistry. In Bayesian optimization, a parameterized model with an uncertainty is fitted to explain the experimental data, and then the model suggests parameters that would most likely improve the results. Batch Bayesian optimi

Yi-Feng Zhao, Antia S. Botana

Signatures of superconductivity under pressure have recently been reported in the bilayer La$_3$Ni$_2$O$_7$ and trilayer La$_4$Ni$_3$O$_{10}$ Ruddlesden-Popper (RP) nickelates with general chemical formula La$_{n+1}$Ni$_n$O$_{3n+1}$ ($n=$ number of perovskite layers along the $c$-axis). The emergence of superconductivity is always concomitant with a structur

Joris Raeymaekers, Paolo Rossi, Canberk Sanli

Type B superconformal quantum mechanical sigma models are of physical interest as they arise in the description of D-brane bound states forming an AdS$_2$ throat. In this work we discuss the applicability of localization methods to compute the superconformal index in these theories, despite the fact that their target spaces are generically singular. Similar

Anthony Bonato, Caleb Jones, Trent G. Marbach, Teddy Mishura

Lazy burning is a recently introduced variation of burning where only one set of vertices is chosen to burn in the first round. In hypergraphs, lazy burning spreads when all but one vertex in a hyperedge is burned. The lazy burning number is the minimum number of initially burned vertices that eventually burns all vertices. We give several equivalent charact

Abinand Gopal, Nico Gubernari

We derive a generalisation of the Boyd-Grinstein-Lebed (BGL) parametrization. Most form factors (FFs) in $b$-hadron decays exhibit additional branch cuts -- namely subthreshold and anomalous branch cuts -- beyond the ``standard'' unitarity cut. These additional cuts cannot be adequately accounted for by the BGL parametrization. For instance, these cuts arise

Agnese Bissi, Nicola Dondi, Alessandro Piazza, Tomas Reis

We analytically determine the large central charge asymptotic expansion of the Virasoro conformal blocks entering in four-point functions with external degenerate operators on a sphere in $2d$ CFTs, and study its resurgence properties as a function of the conformal cross-ratio $z$. We focus on the cases of four heavy $(2,1)$ degenerate operators, and two $(2

Sarah Healy, Shunsaku Horiuchi, Chris Ashall

The red supergiant (RSG) problem, which describes the apparent lack of high-luminosity progenitors detected in Type II supernova (SN) pre-images, has been a contentious topic for two decades. We re-assess this problem using a new RSG population of the Milky Way supplemented with RSGs from other galaxies in the Local Group. In particular, we quantify the unce

Ilian T. Iliev, Azizah R. Hosein, Jens Chluba, Luke Conaboy

The thermal Sunyaev-Zel'dovich (tSZ) effect arises from inverse Compton scattering of low energy photons onto thermal electrons, proportional to the integrated electron pressure, and is usually observed from galaxy clusters. However, we can expect that the Epoch of Reionization (EoR) also contributes to this signal, but that contribution has not been previou

Yuanhui Huang, Amonnut Thammatadatrakoon, Wenzhao Zheng, Yunpeng Zhang

3D semantic occupancy prediction is an important task for robust vision-centric autonomous driving, which predicts fine-grained geometry and semantics of the surrounding scene. Most existing methods leverage dense grid-based scene representations, overlooking the spatial sparsity of the driving scenes. Although 3D semantic Gaussian serves as an object-centri

Rong Li, Shijie Li, Lingdong Kong, Xulei Yang

3D Visual Grounding (3DVG) aims to locate objects in 3D scenes based on textual descriptions, essential for applications like augmented reality and robotics. Traditional 3DVG approaches rely on annotated 3D datasets and predefined object categories, limiting scalability and adaptability. To overcome these limitations, we introduce SeeGround, a zero-shot 3DVG

Weihua Xie, Michael Kolodrubetz, Vadim Oganesyan, Daniel P. Arovas

Time crystals are systems that spontaneously break time-translation symmetry, exhibiting repeating patterns in time. Recent work has shown that non-Hermitian Floquet systems can host a time crystalline phase with quasi-long-range order. In this work, we investigate the effect of introducing a non-integrable interaction term into this non-Hermitian time cryst

Emiel Claasen, Mehregan Doroudiani

We calculate the four-graviton scattering amplitude in Type II superstring theory at one loop up to seventh order in the low-energy expansion through the recently developed iterated integral formalism of Modular Graph Functions (MGFs). The machinery of the novel method allows us to propose a general form of the amplitude, which suggests that the expansion is

Yuqi Wu, Wenzhao Zheng, Sicheng Zuo, Yuanhui Huang

3D occupancy prediction provides a comprehensive description of the surrounding scenes and has become an essential task for 3D perception. Most existing methods focus on offline perception from one or a few views and cannot be applied to embodied agents that demand to gradually perceive the scene through progressive embodied exploration. In this paper, we fo

Andrey Podlazov, Vadim Makarov

Electoral fraud often manifests itself as statistical anomalies in election results, yet its extent can rarely be reliably confirmed by other evidence. Here we report the complete results of municipal elections in the town of Vlasikha near Moscow, where we observe both statistical irregularities in the vote-counting transcripts and forensic evidence of tampe

Jia-Long Wang, Shi-Jie Hu, Xue-Feng Zhang

Although both experimental observations and numerical simulations have reached a consensus that the stripe phase is intertwined with superconductivity in cuprates, the microscopic mechanism behind $d$-wave pairing in the presence of stripes remains unclear. Using the effective theory of quantum colored strings, we derive the wavefunction in Fock space. Our r

Yassine Ouali, Adrian Bulat, Alexandros Xenos, Anestis Zaganidis

Contrastively-trained Vision-Language Models (VLMs) like CLIP have become the de facto approach for discriminative vision-language representation learning. However, these models have limited language understanding, often exhibiting a "bag of words" behavior. At the same time, Large Vision-Language Models (LVLMs), which combine vision encoders with LLMs, have

Anaïs Halin, Sébastien Piérard, Anthony Cioppa, Marc Van Droogenbroeck

Properly understanding the performances of classifiers is essential in various scenarios. However, the literature often relies only on one or two standard scores to compare classifiers, which fails to capture the nuances of application-specific requirements. The Tile is a recently introduced visualization tool organizing an infinity of ranking scores into a

D. Billington, E. Riordan, C. Cafolla-Ward, J. Wilson

Magnetization noise measurements on the spin ice Dy${}_2$Ti${}_2$O${}_7$ have revealed a remarkable `pink noise' power spectrum $S(f,T)$ below 4 K, including evidence of magnetic monopole excitations diffusing in a fractal landscape. However, at higher temperatures, the reported values of the anomalous exponent $b(T)$ describing the high frequency tail of $S

Abhishek Baruah, Prabwal Phukon

We study the thermodynamics of $4D$ dyonic AdS black hole in the Kaniadakis statistics framework using the Restricted Phase Space (RPST) formalism. This framework provides a non-extensive extension of classical statistical mechanics, drawing inspiration from relativistic symmetries and presenting a fresh perspective on black hole thermodynamics. Our study an

Joe G Greener

The next generation of force fields for molecular dynamics will be developed using a wealth of data. Training systematically with experimental data remains a challenge, however, especially for machine learning potentials. Differentiable molecular simulation calculates gradients of observables with respect to parameters through molecular dynamics trajectories

Rajesh Maiti, Debarati Chatterjee

Recent studies based on the relativistic mean field (RMF) model found certain nuclear empirical parameters, in particular the nucleon effective mass, to be strongly correlated with observable properties of Neutron Stars (NSs), such as the frequencies of $f-$mode oscillations. This shows the potential to constrain the values of effective mass from future obse

Severin Bochem, Victor J. B. Jung, Arpan Prasad, Francesco Conti

Contextual Artificial Intelligence (AI) based on emerging Transformer models is predicted to drive the next technology revolution in interactive wearable devices such as new-generation smart glasses. By coupling numerous sensors with small, low-power Micro-Controller Units (MCUs), these devices will enable on-device intelligence and sensor control. A major b

Matteo Scerbo, Sebastian J. Schlecht, Randall Ali, Lauri Savioja

Modeling late reverberation in real-time interactive applications is a challenging task when multiple sound sources and listeners are present in the same environment. This is especially problematic when the environment is geometrically complex and/or features uneven energy absorption (e.g. coupled volumes), because in such cases the late reverberation is dep

Yongkang Li, Tianheng Cheng, Bin Feng, Wenyu Liu

Recent open-vocabulary segmentation methods adopt mask generators to predict segmentation masks and leverage pre-trained vision-language models, e.g., CLIP, to classify these masks via mask pooling. Although these approaches show promising results, it is counterintuitive that accurate masks often fail to yield accurate classification results through pooling

L. Ben Ltaief, K. Sishodia, J. D. Asmussen, A. R. Abid

Irradiation of condensed matter with ionizing radiation generally causes direct photoionization as well as secondary processes that often dominate the ionization dynamics. Here, large helium (He) nanodroplets with radius >40 nm doped with lithium (Li) atoms are irradiated with extreme ultraviolet (XUV) photons of energy >44.4 eV and indirect ionization of th

ATLAS Collaboration

The ATLAS experiment at the Large Hadron Collider explores the use of modern neural networks for a multi-dimensional calibration of its calorimeter signal defined by clusters of topologically connected cells (topo-clusters). The Bayesian neural network (BNN) approach not only yields a continuous and smooth calibration function that improves performance relat

Haoran Su, Joseph Y. J. Chow

Emergency response times are critical in densely populated urban environments like New York City (NYC), where traffic congestion significantly impedes emergency vehicle (EMV) mobility. This study introduces an intersection-aware emergency medical service (EMS) accessibility model to evaluate and improve EMV travel times across NYC. Integrating intersection d

Edoardo Cetin, Ahmed Touati, Yann Ollivier

The forward-backward representation (FB) is a recently proposed framework (Touati et al., 2023; Touati & Ollivier, 2021) to train behavior foundation models (BFMs) that aim at providing zero-shot efficient policies for any new task specified in a given reinforcement learning (RL) environment, without training for each new task. Here we address two core limit

Luke Swaby, Matthew Stewart, Daniel Harrold, Chris Willis

Intelligent autonomous agents hold much potential for the domain of cyber-security. However, due to many state-of-the-art approaches relying on uninterpretable black-box models, there is growing demand for methods that offer stakeholders clear and actionable insights into their latent beliefs and motivations. To address this, we evaluate Theory of Mind (ToM)

Jaan Aru

Artificial intelligence (AI) systems capable of generating creative outputs are reshaping our understanding of creativity. This shift presents an opportunity for creativity researchers to reevaluate the key components of the creative process. In particular, the advanced capabilities of AI underscore the importance of studying the internal processes of creati

Julian Le Clainche

We show that if $H$ is a Hopf algebra with bijective antipode and $B \subset A$ is a faithfully flat $H$-Galois extension, then $A$ is homologically smooth if $H$ and $B$ are.

Nina Verheijen

Ensuring compliance of norms and policies when working on administrative law cases can be difficult to manage for government organisations. Automating this process could save a lot of time, effort and ensure compliance. Prior research resulted in a method to formalize sources of norms. These can be turned into executable specifications using the domain-speci

Giorgio Mangioni, Alessandro Sisto

We prove that most Artin groups of large and hyperbolic type are Hopfian, meaning that every self-epimorphism is an isomorphism. The class covered by our result is generic, in the sense of Goldsborough-Vaskou. Moreover, assuming the residual finiteness of certain hyperbolic groups with an explicit presentation, we get that all large and hyperbolic type Artin

Wenting Zhao, Alexander M. Rush, Tanya Goyal

Open community-driven platforms like Chatbot Arena that collect user preference data from site visitors have gained a reputation as one of the most trustworthy publicly available benchmarks for LLM performance. While now standard, it is tricky to implement effective guardrails to collect high-quality annotations from humans. In this paper, we demonstrate tha

Sourav Mondal, Julia Netz, David Hunger, Simon Suhr

Magnetic relaxation in coordination compounds is largely dominated by the interaction of the spin with phonons. Large zero-field splitting and exchange coupling values have been empirically found to strongly suppress spin relaxation and have been used as the main guideline for designing new molecular compounds. Although a comprehensive understanding of spin-

Yinghe Qi, Yaxing Li, Filippo Coletti

The dynamics of small-scale structures in free-surface turbulence is crucial to large-scale phenomena in natural and industrial environments. Here we conduct experiments on the quasi-flat free surface of a zero-mean-flow turbulent water tank over the Reynolds number range $Re_{\lambda} = 207\textrm{--}312$. By seeding microscopic floating particles at high c

M. Pula, S. Sharma, J. Gautreau, Sajilesh K. P.

The experimental study of magnetism on the Shastry-Sutherland lattice has been ongoing for more than two decades, following the discovery of the first Shastry-Sutherland lattice materials SrCu$_2$(BO$_3$)$_2$. However, the study of Shastry-Sutherland systems is often complicated by the requirements of high magnetic fields ($>$~20~T SrCu$_2$(BO$_3$)$_2$) or t

A. Lira-Barria, J. N. Harvey, T. Konings, R. Baeyens

Exoplanet atmospheric modeling is advancing from chemically diverse one-dimensional (1D) models to three-dimensional (3D) global circulation models (GCMs), which are crucial for interpreting observations from facilities like the James Webb Space Telescope (JWST) and Extremely Large Telescope (ELT). However, maintaining chemical diversity in models, especiall

Oscar Key, Luka Ribar, Alberto Cattaneo, Luke Hudlass-Galley

We present an evaluation of bucketed approximate top-$k$ algorithms. Computing top-$k$ exactly suffers from limited parallelism, because the $k$ largest values must be aggregated along the vector, thus is not well suited to computation on highly-parallel machine learning accelerators. By relaxing the requirement that the top-$k$ is exact, bucketed algorithms

CMS Collaboration

A search for heavy neutral gauge bosons (Z') decaying into a pair of tau leptons is performed in proton-proton collisions at $\sqrt{s}$ = 13 TeV at the CERN LHC. The data were collected with the CMS detector and correspond to an integrated luminosity of 138 fb$^{-1}$. The observations are found to be in agreement with the expectation from standard model proc

Nicholas P. Ballering, L. Ilsedore Cleeves, Ryan D. Boyden, Mark J. McCaughrean

We examine images of the protoplanetary disk 114--426 with JWST/NIRCam in 12 bands. This large disk is oriented edge-on with a dark midplane flanked by lobes of scattered light. The outer edges of the midplane are seen in silhouette against the Orion Nebula, providing a unique opportunity to study planet-forming material in absorption. We discover a dip in t

Md. Khairul Islam, Judy Fox

Interpreting complex time series forecasting models is challenging due to the temporal dependencies between time steps and the dynamic relevance of input features over time. Existing interpretation methods are limited by focusing mostly on classification tasks, evaluating using custom baseline models instead of the latest time series models, using simple syn

F. Rabec, G. Chauveau, G. Brochier, S. Nascimbene

The motion of a quantum system subjected to an external force often defeats our classical intuition. A celebrated example is the dynamics of a single particle in a periodic potential, which undergoes Bloch oscillations under the action of a constant force. Surprisingly, Bloch-like oscillations can also occur in one-dimensional quantum fluids without requirin

Riccardo Milocco, Fabian Jansen, Diego Garlaschelli

Lying at the interface between Network Science and Machine Learning, node embedding algorithms take a graph as input and encode its structure onto output vectors that represent nodes in an abstract geometric space, enabling various vector-based downstream tasks such as network modelling, data compression, link prediction, and community detection. Two apparen

Dayoung Gong, Suha Kwak, Minsu Cho

Temporal action segmentation and long-term action anticipation are two popular vision tasks for the temporal analysis of actions in videos. Despite apparent relevance and potential complementarity, these two problems have been investigated as separate and distinct tasks. In this work, we tackle these two problems, action segmentation and action anticipation,

Fang Qin, Rui Chen

We investigate a three-dimensional (3D) topological phase resembling a Weyl semimetal, modulated by a periodic potential and engineered through Floquet dynamics. This system is constructed by stacking two-dimensional Chern insulators and hosts Weyl-like points defined in the parameter space $(k_x, k_y, z)$, distinct from conventional Weyl points in momentum

Vandan Mujadia, Dipti Misra Sharma

This paper focuses on developing translation models and related applications for 36 Indian languages, including Assamese, Awadhi, Bengali, Bhojpuri, Braj, Bodo, Dogri, English, Konkani, Gondi, Gujarati, Hindi, Hinglish, Ho, Kannada, Kangri, Kashmiri (Arabic and Devanagari), Khasi, Mizo, Magahi, Maithili, Malayalam, Marathi, Manipuri (Bengali and Meitei), Nep

Iman Kazemian, Bahar Cavdar, Murat Yildirim

Advancements in sensor technology offer significant insights into vehicle conditions, unlocking new venues to enhance fleet operations. While current vehicle health management models provide accurate predictions of vehicle failures, they often fail to integrate these forecasts into operational decision-making, limiting their practical impact. This paper addr

Ismail Lotfi, Marwa Qaraqe, Ali Ghrayeb, Dusit Niyato

The vehicular Metaverse represents an emerging paradigm that merges vehicular communications with virtual environments, integrating real-world data to enhance in-vehicle services. However, this integration faces critical security challenges, particularly in the data collection layer where malicious sensing IoT (SIoT) devices can compromise service quality th

Miaosen Zhang, Qi Dai, Yifan Yang, Jianmin Bao

LMMs have shown impressive visual understanding capabilities, with the potential to be applied in agents, which demand strong reasoning and planning abilities. Nevertheless, existing benchmarks mostly assess their reasoning abilities in language part, where the chain-of-thought is entirely composed of text.We consider the scenario where visual signals are co

Ivan Kostyuk, Benedetta Ciardi

Aims: We investigate the impact of galaxy mergers on the Lyman Continuum (LyC) radiation escape, fesc, from high-redshift galaxies. Methods: We post-process ~ 6e5 galaxies (redshift 5.2 < z < 10) extracted from the TNG50 cosmological simulation using a physically motivated analytic model for LyC escape. Results: Galaxies that have not experienced a merger fo

Alexandre Chénier, Bosco d'Aligny, Félix Pellerin, Paul-Édouard Blanchard

The quantization of transport and its resilience to backscattering are key features for leveraging topological matter in applications that demand stringent noise mitigation, such as metrology and quantum information processing. Due to the bosonic nature of light, engineering such robust, ``one-way'' channels in synthetic photonic systems imposes the implemen

I. M. Fradkin, A. V. Nikulin, N. S. Solodovchenko, D. S. Filonov

Dense lattices of photonic crystals can serve as artificial materials, with light propagation in these structures described by effective material parameters that surpass the capabilities of natural materials. In this study, we introduce a metamaterial that supports quadrupole magnetization, a characteristic rarely observed in existing structures. We experime

Songkai Xue, Yuekai Sun

Performative prediction aims to model scenarios where predictive outcomes subsequently influence the very systems they target. The pursuit of a performative optimum (PO) -- minimizing performative risk -- is generally reliant on modeling of the distribution map, which characterizes how a deployed ML model alters the data distribution. Unfortunately, inevitab

V. A. Emelyanov, D. Robertz

The concept of a particle is ambiguous in quantum field theory. It is generally agreed that particles depend not only on spacetime, but also on coordinates used to parametrise spacetime points. One of us has in contrast proposed a coordinate-frame-independent model of quantum particles within the framework of quantum field theory in curved spacetime. The aim

Soumya Ranjan Padhi, Sanchayan Banerjee, Tanay Nag, Tapan Mishra

The spectra of particles in disordered lattices can either be completely extended or localized or can be intermediate which hosts both the localized and extended states separated from each other. In this work, however, we show that in the case of a one dimensional lattice with long-range hopping and non-Hermitian quasiperiodic onsite potential, the localized

Zhouyingcheng Liao, Mingyuan Zhang, Wenjia Wang, Lei Yang

While motion generation has made substantial progress, its practical application remains constrained by dataset diversity and scale, limiting its ability to handle out-of-distribution scenarios. To address this, we propose a simple and effective baseline, RMD, which enhances the generalization of motion generation through retrieval-augmented techniques. Unli

Jiaan Wang, Fandong Meng, Yingxue Zhang, Jie Zhou

Retrieval-augmented generation (RAG) introduces additional information to enhance large language models (LLMs). In machine translation (MT), previous work typically retrieves in-context examples from paired MT corpora, or domain-specific knowledge from knowledge graphs, to enhance MT models. However, a large amount of world knowledge is organized in unstruct

Ke Sun, Huan Yu

Lane change decision-making is challenging due to complex vehicle-vehicle and vehicle-infrastructure interactions. Existing lane-change control methods often rely on vehicles with some level of autonomy, limiting their applicability at low penetration rates of automated vehicles. To address this issue, we propose a lane-change regulation framework based on m

Simone Cannarozzo, Stephan Pomp, Andreas Solders, Ali Al-Adili

The origin of the large angular momenta observed for fission fragments is still a question under discussion. To address this, we study isomeric yield ratios (IYR), i.e. the relative population of two or more long-lived metastable states with different spins, of fission products. We report on IYR of 17 isotopes produced in the 28 MeV $\alpha$-induced fission

George Webber, Yuya Mizuno, Oliver D. Howes, Alexander Hammers

Medical image reconstruction with pre-trained score-based generative models (SGMs) has advantages over other existing state-of-the-art deep-learned reconstruction methods, including improved resilience to different scanner setups and advanced image distribution modeling. SGM-based reconstruction has recently been applied to simulated positron emission tomogr

Alexander K. Hartmann

An introduction to numerical large-deviation sampling is provided. First, direct biasing with a known distribution is explained. As simple example, the Bernoulli experiment is used throughout the text. Next, Markov chain Monte Carlo (MCMC) simulations are introduced. In particular, the Metropolis-Hastings algorithm is explained. As first implementation of MC

Saheli Hazra, Sudip Das, Rohit Choudhary, Arindam Das

Applying pseudo labeling techniques has been found to be advantageous in semi-supervised 3D object detection (SSOD) in Bird's-Eye-View (BEV) for autonomous driving, particularly where labeled data is limited. In the literature, Exponential Moving Average (EMA) has been used for adjustments of the weights of teacher network by the student network. However, th

Christian Brennecke, Adrien Schertzer

We consider $N$ i.i.d. Ising spins with mean $m\in (-1,1)$ whose interactions are described by a Sherrington-Kirkpatrick Hamiltonian with a quartic correction. This model was recently introduced by Bolthausen in \cite{Bolt2} as a toy model to understand whether a second moment argument can be used to derive the replica symmetric formula in the full high temp

Thomas Williams, Christopher B. Prior, David MacTaggart

The accurate forecasting of solar flares is considered a key goal within the solar physics and space weather communities. There is significant potential for flare prediction to be improved by incorporating topological fluxes of magnetogram datasets, without the need to invoke three-dimensional magnetic field extrapolations. Topological quantities such as mag

Shilpa Garg, Ashok Kumar Pathak, Aditya Maheshwari

Traditionally, fractional counting processes, such as the fractional Poisson process, etc. have been defined using fractional differential and integral operators. Recently, Laskin (2024) introduced a generalized fractional counting process (FCP) by changing the probability mass function (pmf) of the time fractional Poisson process using the generalized three

M. Madurga, Z. Y. Xu, 1 R. Grzywacz, M. R. Mumpower

Using the time-of-flight technique, we measured the beta-delayed neutron emission of $^{132}$Cd. From our large-scale shell model (LSSM) calculation using the N$^3$LO interaction [Z.Y. Xu et al., Phys. Rev. Lett. 131, 022501 (2023)], we suggest the decay is dominated by the transformation of a neutron in the $g_{7/2}$ orbital, deep below the Fermi surface, i

Alexander Chervov, Anton Vakhrushev, Sergei Fironov, Loredana Martignetti

Predicting protein properties, functions and localizations are important tasks in bioinformatics. Recent progress in machine learning offers an opportunities for improving existing methods. We developed a new approach called ProtBoost, which relies on the strength of pretrained protein language models, the new Py-Boost gradient boosting method and Graph Neur

Junfeng Wu, Yi Jiang, Chuofan Ma, Yuliang Liu

We present Liquid, an auto-regressive generation paradigm that seamlessly integrates visual comprehension and generation by tokenizing images into discrete codes and learning these code embeddings alongside text tokens within a shared feature space for both vision and language. Unlike previous multimodal large language model (MLLM), Liquid achieves this inte

J. Kriewald, E. Pinsard, A. M. Teixeira

Within the context of heavy neutral lepton (HNL) extensions of the Standard Model, we compute the cross-sections for $\mu^+ e^-\to \ell_\alpha^+\ell_\beta^-$ scattering, as well as several angular observables. In particular, we investigate the future sensitivity of a $\mu$TRISTAN collider in discovering such charged lepton flavour violating processes and the

D. Sicilia, L. Malavolta, G. Scandariato, L. Fossati

The neutral sodium resonance doublet (Na i D) has been detected in the upper atmosphere of several close-in gas giants, through high-resolution transmission spectroscopy. We aim to investigate whether its variability is linked to the planets' properties, the data quality, or the accuracy of the system parameters used. Using the public code SLOPpy, we extract

N. Ryde, G. Nandakumar, M. Schultheis, G. Kordopatis

A chemical characterization of the Galactic Center is essential for understanding its formation and structural evolution. Trends of alpha-elements, such as Mg, Si, and Ca, serve as powerful diagnostic tools, offering insights into star-formation rates and gas-infall history. However, high extinction has previously hindered such studies. In this study, we pre

Tomáš Hale, Brayden R. Hull, David Kubizňák, Robert B. Mann

In their seminal 1992 paper, Ba\~{n}ados, Teitelboim and Zanelli (BTZ) proposed a simple charged generalization of what is now known as the spinning BTZ black hole, the proposal being that a rotating metric can be supported by a `static vector' potential. While with such an ansatz the Einstein equations are satisfied, and the corresponding energy-momentum te

Thomas Budzinski, Alice Contat

The Karp--Sipser algorithm consists in removing recursively the leaves as well their unique neighbours and all isolated vertices of a given graph. The remaining graph obtained when there is no leaf left is called the Karp--Sipser core. When the underlying graph is the classical sparse Erd\H{o}s--R\'enyi random graph $ \mathrm{G}[n, \lambda/n]$, it is known t

Mirco Theile, Lukas Dirnberger, Raphael Trumpp, Marco Caccamo

Deep reinforcement learning (DRL) has had success across various domains, but applying it to environments with constraints remains challenging due to poor sample efficiency and slow convergence. Recent literature explored incorporating model knowledge to mitigate these problems, particularly through the use of models that assess the feasibility of proposed a

Lucas Böttcher, Mason A. Porter

Quantum walks on networks are a paradigmatic model in quantum information theory. Quantum-walk algorithms have been developed for various applications, including spatial-search problems, element-distinctness problems, and node centrality analysis. Unlike their classical counterparts, the evolution of quantum walks is unitary, so they do not converge to a sta

George Webber, Yuya Mizuno, Oliver D. Howes, Alexander Hammers

Large high-quality medical image datasets are difficult to acquire but necessary for many deep learning applications. For positron emission tomography (PET), reconstructed image quality is limited by inherent Poisson noise. We propose a novel method for synthesising diverse and realistic pseudo-PET images with improved signal-to-noise ratio. We also show how

James Queeney, Xiaoyi Cai, Alexander Schperberg, Radu Corcodel

The reliable deployment of deep reinforcement learning in real-world settings requires the ability to generalize across a variety of conditions, including both in-distribution scenarios seen during training as well as novel out-of-distribution scenarios. In this work, we present a framework for dynamics generalization in deep reinforcement learning that unif

Jacob Mercer

$N$-Brownian bees is a branching-selection particle system in $\mathbb{R}^d$ in which $N$ particles behave as independent binary branching Brownian motions, and where at each branching event, we remove the particle furthest from the origin. We study a variant in which $d=1$ and particles have an additional drift $\mu\in\mathbb{R}$. We show that there is a cr

Simone Fratini, Ivan Duchemin, Arnaud Ralko, Sergio Ciuchi

Metals hosting strong electronic interactions, including high-temperature superconductors, behave in ways that do not conform to normal Fermi liquid theory. To pinpoint the microscopic origin of this strange metal behavior, here we reexamine the d.c. and frequency-dependent conductivity of the two-dimensional t-J model taking advantage of recent improvements

Federico Tosi, Alessandro Mura, Francesca Zambon

A recent paper (ref. 1) used infrared images of Io acquired by the Juno/JIRAM instrument to derive a latitudinal dependence of the spectral radiance and conclude that such latitudinal dependence is consistent with a magma ocean model. We challenge their conclusions, and we draw attention to some potential issues with their analysis. In this letter, we will u

Antoine Prouff

We prove a general version of Egorov's theorem for evolution propagators in the Euclidean space, in the Weyl--H\"ormander framework of metrics on the phase space. Mild assumptions on the Hamiltonian allow for a wide range of applications that we describe in the paper, including Schr\"odinger, wave and transport evolutions. We also quantify an Ehrenfest time

George Webber, Yuya Mizuno, Oliver D. Howes, Alexander Hammers

Score-based generative models (SGMs) have recently shown promising results for image reconstruction on simulated positron emission tomography (PET) datasets. In this work we have developed and implemented practical methodology for 3D image reconstruction with SGMs, and perform (to our knowledge) the first SGM-based reconstruction of real fully 3D PET data. W

Fredrik Carlsson, Fangyu Liu, Daniel Ward, Murathan Kurfali

This paper introduces the counter-intuitive generalization results of overfitting pre-trained large language models (LLMs) on very small datasets. In the setting of open-ended text generation, it is well-documented that LLMs tend to generate repetitive and dull sequences, a phenomenon that is especially apparent when generating using greedy decoding. This is

David Linteau, Gabriel Pescia, Jannes Nys, Giuseppe Carleo

We study the zero-temperature phase diagram of two-dimensional helium-4 using neural quantum states. Our variational description allows us to address liquid and solid phases using the same functional form as well as exploring possible melting scenarios, for instance via an intermediate hexatic phase. Notably, this is achieved by performing fixed pressure var

Bo Tong, Bokai Lai, Yiyi Zhou, Gen Luo

Despite a big leap forward in capability, multimodal large language models (MLLMs) tend to behave like a sloth in practical use, i.e., slow response and large latency. Recent efforts are devoted to building tiny MLLMs for better efficiency, but the plethora of visual tokens still used limit their actual speedup. In this paper, we propose a powerful and fast

Mohammad Hussein Yoosefian Nooshabadi, Rifat Sipahi, Laurent Lessard

We study a stealthy range-sensor placement problem where a set of range sensors are to be placed with respect to targets to effectively localize them while maintaining a degree of stealthiness from the targets. This is an open and challenging problem since two competing objectives must be balanced: (a) optimally placing the sensors to maximize their ability

Chaojun Xiao, Jie Cai, Weilin Zhao, Guoyang Zeng

Large Language Models (LLMs) have emerged as a milestone in artificial intelligence, and their performance can improve as the model size increases. However, this scaling brings great challenges to training and inference efficiency, particularly for deploying LLMs in resource-constrained environments, and the scaling trend is becoming increasingly unsustainab

Bo Ji, Angela Yao

Standard single-image super-resolution (SR) upsamples and restores entire images. Yet several real-world applications require higher resolutions only in specific regions, such as license plates or faces, making the super-resolution of the entire image, along with the associated memory and computational cost, unnecessary. We propose a novel task, called Local

Shotaro Yamasaki, Tomonori Totani

The production mechanism of fast radio bursts (FRBs)--mysterious, bright, millisecond-duration radio flashes from cosmological distances--remains unknown. Understanding potential correlations between burst occurrence times and various burst properties may offer important clues about their origins. Among these properties, the spectral peak frequency of an ind