October 2024 arXiv papers — page 2

Pranav Singh Chib, Pravendra Singh

Pedestrian trajectory prediction is crucial for several applications such as robotics and self-driving vehicles. Significant progress has been made in the past decade thanks to the availability of pedestrian trajectory datasets, which enable trajectory prediction methods to learn from pedestrians' past movements and predict future trajectories. However, thes

John Wu, David Wu, Jimeng Sun

Medical coding, the translation of unstructured clinical text into standardized medical codes, is a crucial but time-consuming healthcare practice. Though large language models (LLM) could automate the coding process and improve the efficiency of such tasks, interpretability remains paramount for maintaining patient trust. Current efforts in interpretability

Kien X. Nguyen, Fengchun Qiao, Arthur Trembanis, Xi Peng

A major obstacle to the advancements of machine learning models in marine science, particularly in sonar imagery analysis, is the scarcity of AI-ready datasets. While there have been efforts to make AI-ready sonar image dataset publicly available, they suffer from limitations in terms of environment setting and scale. To bridge this gap, we introduce Seafloo

Qianqian Wang, Wei Wang, Yuqi Fang, Hong-Jun Li

Brain networks/graphs derived from resting-state functional MRI (fMRI) help study underlying pathophysiology of neurocognitive disorders by measuring neuronal activities in the brain. Some studies utilize learning-based methods for brain network analysis, but typically suffer from low model generalizability caused by scarce labeled fMRI data. As a notable se

Mujin Cheon, Jay H. Lee, Dong-Yeun Koh, Calvin Tsay

To avoid myopic behavior, multi-step lookahead Bayesian optimization (BO) algorithms consider the sequential nature of BO and have demonstrated promising results in recent years. However, owing to the curse of dimensionality, most of these methods make significant approximations or suffer scalability issues. This paper presents a novel reinforcement learning

Shuzhe Yang, Guido Masella, Vase Moeini, Amar Bellahsene

A key requirement for quantum technologies based on atoms, ions, and molecules, is the ability to realize precise phase- and amplitude-controlled quantum operations via coherent laser pulses. However, for generating pulses on the sub-microsecond timescale, the characteristics of the optical and electronic components can introduce unwanted distortions that ha

Ibne Hassan, Aman Mujahid, Abdullah Al Hasib, Andalib Rahman Shagoto

Countries in South Asia experience many catastrophic flooding events regularly. Through image classification, it is possible to expedite search and rescue initiatives by classifying flood zones, including houses and humans. We create a new dataset collecting aerial imagery of flooding events across South Asian countries. For the classification, we propose a

Yue Fu, Han Bin, Tony Zhou, Marx Wang

As generative AI (GenAI) increasingly permeates design workflows, its impact on design outcomes and designers' creative capabilities warrants investigation. We conducted a within-subjects experiment where we asked participants to design advertisements both with and without GenAI support. Our results show that expert evaluators rated GenAI-supported designs a

Álvaro Álvarez-Domínguez, Elizabeth Winstanley

Unlike a classical charged bosonic field, a classical charged fermion field on a static charged black hole does not exhibit superradiant scattering. We demonstrate that the quantum analogue of this classical process is however present. We construct a vacuum state for the fermion field which has no incoming particles from past null infinity, but which contain

Anshika Anshika, Jiaxing Li, Debdas Ghosh, Xiangxiong Zhang

This work presents a new three-operator splitting method to handle monotone inclusion and convex optimization problems. The proposed splitting serves as another natural extension of the Douglas-Rachford splitting technique to problems involving three operators. For solving a composite convex minimization of a sum of three functions, its formula resembles but

Thomas Grandits, Karli Gillette, Gernot Plank, Simone Pezzuto

Digital twins for cardiac electrophysiology are an enabling technology for precision cardiology. Current forward models are advanced enough to simulate the cardiac electric activity under different pathophysiological conditions and accurately replicate clinical signals like torso electrocardiograms (ECGs). In this work, we address the challenge of matching s

Mohammad Farazi, Yalin Wang

Utilizing patch-based transformers for unstructured geometric data such as polygon meshes presents significant challenges, primarily due to the absence of a canonical ordering and variations in input sizes. Prior approaches to handling 3D meshes and point clouds have either relied on computationally intensive node-level tokens for large objects or resorted t

Weiqin Yang, Jiawei Chen, Xin Xin, Sheng Zhou

Softmax Loss (SL) is widely applied in recommender systems (RS) and has demonstrated effectiveness. This work analyzes SL from a pairwise perspective, revealing two significant limitations: 1) the relationship between SL and conventional ranking metrics like DCG is not sufficiently tight; 2) SL is highly sensitive to false negative instances. Our analysis in

Shekhar Priyadarshi, Rinis Ferizaj, Oliver Kieler, Alexander Fernandez Scarioni

We demonstrate a cryogenic electro-optic sampling (EOS) setup that allows for the measurement of microwave signals at arbitrary positions on a cryogenic chip-scale device. We use a Josephson Arbitrary Waveform Synthesizer (JAWS) to generate quantum-accurate voltage signals and measure them with the EOS setup, allowing for the calibration of its response, yie

Kacper Wyrwal, Andreas Krause, Viacheslav Borovitskiy

We propose practical deep Gaussian process models on Riemannian manifolds, similar in spirit to residual neural networks. With manifold-to-manifold hidden layers and an arbitrary last layer, they can model manifold- and scalar-valued functions, as well as vector fields. We target data inherently supported on manifolds, which is too complex for shallow Gaussi

Bernat Espigule, David Juher, Joan Saldaña

For a complex parameter $c$ outside the unit disk and an integer $n\ge2$, we examine the $n$-ary collinear fractal $E(c,n)$, defined as the attractor of the iterated function system $\{\mbox{$f_k \colon \mathbb{C} \longrightarrow \mathbb{C}$}\}_{k=1}^n$, where $f_k(z):=1+n-2k+c^{-1}z$. We investigate some topological features of the connectedness locus $\mat

Catalina Rus-Casas, Carlos Gilabert-Torres, Juan Ignacio Fernandez-Carrasco

HEMS optimization for RTPVs with BESS: impact on costs and temporal resolution

Marcelo Eduardo Marques Ribas, Heloisa Benedet Mendes, Luiz Eduardo Soares de Oliveira, Luiz Antonio Zanlorensi

In smart cities, it is common practice to define a maximum length of stay for a given parking space to increase the space's rotativity and discourage the usage of individual transportation solutions. However, automatically determining individual car dwell times from images faces challenges, such as images collected from low-resolution cameras, lighting varia

Eric E. Handy-Cardenas, Yuanhang Zhu, Kenneth S. Breuer

The energy harvesting performance of a pair of oscillating hydrofoil turbines in tandem configuration is experimentally studied to determine the optimal kinematics of the array. By characterizing interactions between the wake produced by the leading foil and the trailing foil, the kinematic configuration required to maximize array power extraction is determi

Elliot Meyerson, Olivier Francon, Darren Sargent, Babak Hodjat

Solving societal problems on a global scale requires the collection and processing of ideas and methods from diverse sets of international experts. As the number and diversity of human experts increase, so does the likelihood that elements in this collective knowledge can be combined and refined to discover novel and better solutions. However, it is difficul

Jayson G. Cosme, Phatthamon Kongkhambut, Anton Bölian, Richelle Jade L. Tuquero

Using a quantum gas setup consisting of a Bose-Einstein condensate strongly coupled to a high-finesse optical cavity by a transverse pump laser, we experimentally observe an instability of a dissipative continuous time crystal (CTC) towards a time crystalline state exhibiting two prominent oscillation frequencies. Applying a mean-field approximation model an

Haritz Puerto, Martin Gubri, Sangdoo Yun, Seong Joon Oh

Membership inference attacks (MIA) attempt to verify the membership of a given data sample in the training set for a model. MIA has become relevant in recent years, following the rapid development of large language models (LLM). Many are concerned about the usage of copyrighted materials for training them and call for methods for detecting such usage. Howeve

Antonio Almudévar, Romain Serizel, Alfonso Ortega

Classification is a pivotal task in deep learning not only because of its intrinsic importance, but also for providing embeddings with desirable properties in other tasks. To optimize these properties, a wide variety of loss functions have been proposed that attempt to minimize the intra-class distance and maximize the inter-class distance in the embeddings

Pake Melland, Rodica Curtu, Zahra Aminzare

In this work, we investigate the spike-adding mechanism in a class of three-dimensional fast-slow systems with three distinct timescales, inspired by the FitzHugh-Nagumo (FHN) model driven by periodic input. First, we numerically generate a spike-adding diagram for the FHN model by varying the frequency and amplitude of the input, revealing that as the frequ

Nursena Köprücü, Destiny Okpekpe, Antonio Orvieto

Transformers have become dominant in large-scale deep learning tasks across various domains, including text, 2D and 3D vision. However, the quadratic complexity of their attention mechanism limits their efficiency as the sequence length increases, particularly in high-resolution 3D data such as point clouds. Recently, state space models (SSMs) like Mamba hav

Christopher Richardson, Roshan Sharma, Neeraj Gaur, Parisa Haghani

Zero-shot domain adaptation for dialogue state tracking (DST) remains a challenging problem in task-oriented dialogue (TOD) systems, where models must generalize to target domains unseen at training time. Current large language model approaches for zero-shot domain adaptation rely on prompting to introduce knowledge pertaining to the target domains. However,

Michael Köhler-Bussmeier, Lorenzo Capra

In this contribution we extend the concept of a Petri net morphism to Elementary Object Systems (EOS). EOS are a nets-within-nets formalism, i.e. we allow the tokens of a Petri net to be Petri nets again. This nested structure has the consequence that even systems defined by very small Petri nets have a quite huge reachability graph. In this contribution we

Hernan Rincon, Segev BenZvi, Kelly Douglass, Dahlia Veyrat

We present three separate void catalogs created using a volume-limited sample of the DESI Year 1 Bright Galaxy Survey. We use the algorithms VoidFinder and V2 to construct void catalogs out to a redshift of z=0.24. We obtain 1,461 interior voids with VoidFinder, 420 with V2 using REVOLVER pruning, and 295 with V2 using VIDE pruning. Comparing our catalog wit

Charles Westphal, Stephen Hailes, Mirco Musolesi

Pruning has emerged as the primary approach used to limit the resource requirements of large neural networks (NNs). Since the proposal of the lottery ticket hypothesis, researchers have focused either on pruning at initialization or after training. However, recent theoretical findings have shown that the sample efficiency of robust pruned models is proportio

Chunyan Mu, Muhammad Najib, Nir Oren

Responsibility plays a key role in the development and deployment of trustworthy autonomous systems. In this paper, we focus on the problem of strategic reasoning in probabilistic multi-agent systems with responsibility-aware agents. We introduce the logic PATL+R, a variant of Probabilistic Alternating-time Temporal Logic. The novelty of PATL+R lies in its i

Chunyan Mu, Nir Oren

We introduce a family of quantitative measures of responsibility in multi-agent planning, building upon the concepts of causal responsibility proposed by Parker et al.~[ParkerGL23]. These concepts are formalised within a variant of probabilistic alternating-time temporal logic. Unlike existing approaches, our framework ascribes responsibility to agents for a

Rang Liu, A. Lee Swindlehurst, Ming Li

This paper presents a novel parametric scattering model (PSM) for sensing extended targets in integrated sensing and communication (ISAC) systems. The PSM addresses the limitations of traditional models by efficiently capturing the target's angular characteristics through a compact set of key parameters, including the central angle and angular spread, enabli

Chen Zhao, Xuan Wang, Tong Zhang, Saqib Javed

3D Gaussian Splatting (3DGS) has demonstrated remarkable effectiveness in novel view synthesis (NVS). However, 3DGS tends to overfit when trained with sparse views, limiting its generalization to novel viewpoints. In this paper, we address this overfitting issue by introducing Self-Ensembling Gaussian Splatting (SE-GS). We achieve self-ensembling by incorpor

Marco Morik, Ali Hashemi, Klaus-Robert Müller, Stefan Haufe

Reconstructing brain sources is a fundamental challenge in neuroscience, crucial for understanding brain function and dysfunction. Electroencephalography (EEG) signals have a high temporal resolution. However, identifying the correct spatial location of brain sources from these signals remains difficult due to the ill-posed structure of the problem. Traditio

Tong Niu, Shafiq Joty, Ye Liu, Caiming Xiong

Accurate document retrieval is crucial for the success of retrieval-augmented generation (RAG) applications, including open-domain question answering and code completion. While large language models (LLMs) have been employed as dense encoders or listwise rerankers in RAG systems, they often struggle with reasoning-intensive tasks because they lack nuanced an

Lars Becker, Polona Durcik, Fred Yu-Hsiang Lin

We classify all trilinear singular Brascamp-Lieb forms, completing the classification in the two dimensional case by Demeter and Thiele in arXiv:0803.1268. We use known results in the representation theory of finite dimensional algebras, namely the classification of indecomposable representations of the four subspace quiver. Our classification lays out a roa

Sanaz Mahmoodi Takaghaj

The recent success of Vision Transformers has generated significant interest in attention mechanisms and transformer architectures. Although existing methods have proposed spiking self-attention mechanisms compatible with spiking neural networks, they often face challenges in effective deployment on current neuromorphic platforms. This paper introduces a nov

Pantelis I. Kaplanoglou, Konstantinos Diamantaras

Our proposed framework attempts to break the trade-off between performance and explainability by introducing an explainable-by-design convolutional neural network (CNN) based on the lateral inhibition mechanism. The ExplaiNet model consists of the predictor, that is a high-accuracy CNN with residual or dense skip connections, and the explainer probabilistic

Sokbae Lee

Joel L. Horowitz has made profound contributions to many areas in econometrics and statistics. These include bootstrap methods, semiparametric and nonparametric estimation, specification testing, nonparametric instrumental variables estimation, high-dimensional models, functional data analysis, and shape restrictions, among others. Originally trained as a ph

Ricardo Valadas, Maximilian Stölzle, Jingyue Liu, Cosimo Della Santina

Obtaining dynamic models of continuum soft robots is central to the analysis and control of soft robots, and researchers have devoted much attention to the challenge of proposing both data-driven and first-principle solutions. Both avenues have, however, shown their limitations; the former lacks structure and performs poorly outside training data, while the

Sapphira Akins, Hans Mertens, Frances Zhu

In missions constrained by finite resources, efficient data collection is critical. Informative path planning, driven by automated decision-making, optimizes exploration by reducing the costs associated with accurate characterization of a target in an environment. Previous implementations of active learning did not consider the action cost for regression pro

Krishna Teja Chitty-Venkata, Siddhisanket Raskar, Bharat Kale, Farah Ferdaus

Large Language Models (LLMs) have propelled groundbreaking advancements across several domains and are commonly used for text generation applications. However, the computational demands of these complex models pose significant challenges, requiring efficient hardware acceleration. Benchmarking the performance of LLMs across diverse hardware platforms is cruc

Nada Bouchekout, Abdelkrim Boukabou, Morad Grimes

Orthogonal Frequency Division Multiplexing (OFDM) is a popular modulation technique for transmitting digital data over wireless radio channels, including medical images due to its high transmission capacity, low interference, bandwidth efficiency, and scalability. However, the security of medical images is a major concern, and combining OFDM with encryption

Sam Reifenstein, Timothée Leleu

In this note we study an iterative belief propagation (IBP) algorithm and demonstrate it's ability to solve sparse combinatorial optimization problems. Similar to simulated annealing (SA), our IBP algorithm attempts to sample from the Boltzmann distribution of the objective function but also uses belief propagation (BP) to improve convergence.

Lijie Ding, Chi-Huan Tung, Zhiqiang Cao, Zekun Ye

Ladder polymers, known for their rigid, ladder-like structures, exhibit exceptional thermal stability and mechanical strength, positioning them as candidates for advanced applications. However, accurately determining their structure from solution scattering remains a challenge. Their chain conformation is largely governed by the intrinsic orientational prope

Benjamin Cookson, Soroush Ebadian, Nisarg Shah

Fairness and efficiency have become the pillars of modern fair division research, but prior work on achieving both simultaneously is largely limited to the unconstrained setting. We study fair and efficient allocations of indivisible goods under additive valuations and various types of allocation feasibility constraints, and demonstrate the unreasonable effe

Tang Li, Mengmeng Ma, Xi Peng

Large pretrained foundation models demonstrate exceptional performance and, in some high-stakes applications, even surpass human experts. However, most of these models are currently evaluated primarily on prediction accuracy, overlooking the validity of the rationales behind their accurate predictions. For the safe deployment of foundation models, there is a

John Whitington

We describe a hidden surface removal algorithm for two-dimensional layered scenes built from arbitrary primitives, particularly suited to interaction and animation in rich scenes (for example, in illustration). The method makes use of a set-based raster representation to implement a front-to-back rendering model which analyses and dramatically reduces the am

Luca Quaglia, ALICE collaboration

The ALICE experiment at the CERN Large Hadron Collider (LHC) is a multi-purpose particle detector, mainly focused on the study of quark-gluon plasma (QGP) in heavy-ion collisions. In the forward rapidity region, 2.5 $<$ y $<$ 4, ALICE is equipped with a muon spectrometer (MS), which allows to study quarkonia and open heavy-flavor particles, both key probes t

Masahiro Ono

Advancements in cytometry technologies have led to a remarkable increase in the number of markers that can be analyzed simultaneously, presenting significant challenges in data analysis. Traditional approaches, such as dimensional reduction techniques and computational clustering, although popular, often face reproducibility challenges due to their heavy rel

David Schneider, Simon Reiß, Marco Kugler, Alexander Jaus

Exploring the intricate dynamics between muscular and skeletal structures is pivotal for understanding human motion. This domain presents substantial challenges, primarily attributed to the intensive resources required for acquiring ground truth muscle activation data, resulting in a scarcity of datasets. In this work, we address this issue by establishing M

Alessandro Perotti, Caterina Stoppato

We provide a classification of Fueter-regular quaternionic functions $f$ in terms of the degree of complex linearity of their real differentials $df$. Quaternionic imaginary units define orthogonal almost-complex structures on the tangent bundle of the quaternionic space by left or right multiplication. Every map of two complex variables that is holomorphic

Thomas Crasson, Yacine Nabet, Mathias Lécuyer

Deep learning time-series models are often used to make forecasts that inform downstream decisions. Since these decisions can differ from those in the training set, there is an implicit requirement that time-series models will generalize outside of their training distribution. Despite this core requirement, time-series models are typically trained and evalua

M. Magnaterra, A. Sandberg, H. Schilling, P. Warzanowski

The insulating mixed-valent Ir$^{+3.66}$ compound Ba$_4$NbIr$_3$O$_{12}$ hosts two holes per Ir$_3$O$_{12}$ trimer unit. We address the electronic structure via resonant inelastic x-ray scattering (RIXS) at the Ir $L_3$ edge and exact diagonalization. The holes occupy quasimolecular orbitals that are delocalized over a trimer. This gives rise to a rich intra

Berkay Kaplan

We present Globalping, an open-source, community-driven platform for scalable, real-time global network measurements. It democratizes access to network diagnostics by offering every user, including non-technicals, technicals, and companies, the ability to perform ping, traceroute, and DNS lookups from a globally distributed network of user-hosted probes usin

Paolo Gidoni, Alessandro Margheri

We study the existence of a global periodic attractor for the reduced dynamics of a discrete toy model for rectilinear crawling locomotion, corresponding to a limit cycle in the shape and velocity variables. The body of the crawler consists of a chain of point masses, joined by active elastic links and subject to smooth friction forces, so that the dynamics

M. Ozdogan, T. Iken, D. Cakir, N. Oncel

In this study, we employed lateral force microscopy, a specialized technique within atomic force microscopy, to explore the nanoscale frictional properties of 2D SnSe and SnSe$_2$ layers, and we compared these findings with graphene, a commonly used solid lubricant. Our results revealed that SnSe and SnSe2 layers exhibited superior lubricative performance co

Zirui Zhang, Wei Hao, Aroon Sankoh, William Lin

Recent advances in AI-generated voices have intensified the challenge of detecting deepfake audio, posing risks for scams and the spread of disinformation. To tackle this issue, we establish the largest public voice dataset to date, named DeepFakeVox-HQ, comprising 1.3 million samples, including 270,000 high-quality deepfake samples from 14 diverse sources.

Mimi Dai

We consider the electron magnetohydrodynamics (MHD) in the context where the 3D magnetic field depends only on the two horizontal plane variables. In particular, the magnetic field takes the form $B=\nabla\times (a\vec e_z)+b\vec e_z$ with $a=a(x,y)$ and $b=b(x,y)$. Initial data $(a_0,b_0)$ is constructed in the Sobolev space $H^\beta \times H^{\beta-1}$ wit

Marc Lanctot, Kate Larson, Michael Kaisers, Quentin Berthet

Driving progress of AI models and agents requires comparing their performance on standardized benchmarks; for general agents, individual performances must be aggregated across a potentially wide variety of different tasks. In this paper, we describe a novel ranking scheme inspired by social choice frameworks, called Soft Condorcet Optimization (SCO), to comp

Sylvain Cordier, Karl Thibault, Marie-Luc Arpin, Ben Amor

With the demonstrated ability to perform calculations in seconds that would take classical supercomputers thousands of years, quantum computers namely hold the promise of radically advancing sustainable IT. However, quantum computers face challenges due to the inherent noise in physical qubits, necessitating error correction for reliable operation in solving

Shankara Narayanan Krishna, Khushraj Madnani, Agnipratim Nag, Paritosh Pandya

Metric Temporal Logic (MTL) and Timed Propositional Temporal Logic (TPTL) extend Linear Temporal Logic (LTL) for real-time constraints, with MTL using time-bounded modalities and TPTL employing freeze quantifiers. Satisfiability for both is generally undecidable; however, MTL becomes decidable under certain non-punctual and partially-punctual restrictions. P

I. I. Zinchenko, S. -Y. Liu, Y. -N. Su

We present observations of the high-mass star-forming region S255IR, which harbors the $\sim$20 M$_\odot$ protostar NIRS3, where a disk-mediated accretion burst was recorded several years ago, with the angular resolution of $\sim$15 mas, which corresponds to $\sim$25 au and is almost an order of magnitude better than in the previous studies of this object. T

Abhishek Balakrishna, Igor Kukavica, Boris Muha, Amjad Tuffaha

We address a moving boundary problem that consists of a system of equations modeling an inviscid fluid interacting with a two-dimensional nonlinear Koiter plate at the boundary. We derive a priori estimates needed to prove the local-in-time existence of solutions. We use the Arbitrary Lagrange Euler (ALE) coordinates to fix the domain and obtain careful esti

Nishanth Basava

As cancer cases continue to rise, with a 2023 study from Zhejiang and Harvard predicting a 31 percent increase in cases and a 21 percent increase in deaths by 2030, the need to find more effective treatments for cancer is greater than ever before. Traditional approaches to treating cancer, such as chemotherapy, often kill healthy cells because of their lack

Altera. AL, Andrew Ahn, Nic Becker, Stephanie Carroll

AI agents have been evaluated in isolation or within small groups, where interactions remain limited in scope and complexity. Large-scale simulations involving many autonomous agents -- reflecting the full spectrum of civilizational processes -- have yet to be explored. Here, we demonstrate how 10 - 1000+ AI agents behave and progress within agent societies.

Brendan Leigh Ross, Hamidreza Kamkari, Tongzi Wu, Rasa Hosseinzadeh

As deep generative models have progressed, recent work has shown them to be capable of memorizing and reproducing training datapoints when deployed. These findings call into question the usability of generative models, especially in light of the legal and privacy risks brought about by memorization. To better understand this phenomenon, we propose the manifo

Wang Du-Yi, Liang Guo, Liu Guangwu, Zhang Kun

Derivative-free optimization (DFO) is vital in solving complex optimization problems where only noisy function evaluations are available through an oracle. Within this domain, DFO via finite difference (FD) approximation has emerged as a powerful method. Two classical approaches are the Kiefer-Wolfowitz (KW) and simultaneous perturbation stochastic approxima

Chloé Gaudu

The Pierre Auger Observatory has revealed a significant challenge in air shower physics: a discrepancy between the simulated and observed muon content in cosmic-ray interactions, known as the 'Muon Puzzle'. This issue stems from a lack of understanding of high-energy hadronic interactions. Current state-of-the-art hadronic interaction models fall short, unde

Viviana Alejandra Diaz, Leandro Martin Salomone, Marcela Zuccalli

Lagrangian Neural Networks (LNNs) are a powerful tool for addressing physical systems, particularly those governed by conservation laws. LNNs can parametrize the Lagrangian of a system to predict trajectories with nearly conserved energy. These techniques have proven effective in unconstrained systems as well as those with holonomic constraints. In this work

Ashwin De Silva, Rahul Ramesh, Rubing Yang, Siyu Yu

In real-world applications, the distribution of the data, and our goals, evolve over time. The prevailing theoretical framework for studying machine learning, namely probably approximately correct (PAC) learning, largely ignores time. As a consequence, existing strategies to address the dynamic nature of data and goals exhibit poor real-world performance. Th

Erwin T. Lau, Daisuke Nagai, Arya Farahi, Tomoaki Ishiyama

We present the Baryon Pasted (BP) X-ray and thermal Sunyaev-Zel'dovich (tSZ) maps derived from the half-sky Uchuu Lightcone simulation. These BP-Uchuu maps are constructed using more than $75$ million dark matter halos with masses $M_{500c} \geq 10^{13} M_\odot$ within the redshift range $0 \leq z \leq 2$. A distinctive feature of our BP-Uchuu Lightcone maps

Alexander Davydov, Franck Djeumou, Marcus Greiff, Makoto Suminaka

Combining data-driven models that adapt online and model predictive control (MPC) has enabled effective control of nonlinear systems. However, when deployed on unstable systems, online adaptation may not be fast enough to ensure reliable simultaneous learning and control. For example, a controller on a vehicle executing highly dynamic maneuvers--such as drif

Roberto Correa da Silveira, Fernando E. Serna, Bruno El-Bennich

We calculate the quark self-energy correction in light-cone gauge motivated by distribution amplitudes whose definition implies a Wilson line. The latter serves to preserve the gauge invariance of the hadronic amplitudes and becomes trivial in light-cone gauge. Therefore, the calculation of the distribution amplitudes simplifies significantly provided that w

Philip A. LeMaitre, T. Rick Perche, Marius Krumm, Hans J. Briegel

We present an explicit construction of a relativistic quantum computing architecture using a variational quantum circuit approach that is shown to allow for universal quantum computing. The variational quantum circuit consists of tunable single-qubit rotations and entangling gates that are implemented successively. The single qubit rotations are parameterize

Andrew J. Larkoski

Identification of boosted, hadronically-decaying top quarks is a problem of central importance for physics goals of the Large Hadron Collider. We present a theoretical analysis of top quark tagging, establishing zeroth-order, minimal assumptions that should be satisfied by any purported top-tagged jet, like existence of three hard subjets, a bottom-tagged su

Larissa Tevlin, Thomas Berlok, Christoph Pfrommer, Rosie Y. Talbot

Observations of Faraday rotation and synchrotron emission in galaxy clusters imply large-scale magnetic fields with $\mu\mathrm{G}$ strengths possibly extending back to $z=4$. Non-radiative cosmological simulations of galaxy clusters show a comparably slow magnetic field growth that only saturates at late times. We include galaxy formation physics and find a

Elizabeth Taylor, David Maltby, Omar Almaini, Michael Merrifield

High-velocity outflows are ubiquitous in star-forming galaxies at cosmic noon, but are not as common in passive galaxies at the same epoch. Using optical spectra of galaxies selected from the UKIDSS Ultra Deep Survey (UDS) at z > 1, we perform a stacking analysis to investigate the transition in outflow properties along a quenching time sequence. To do this,

Pablo Sesma

We present a functional method to perform complete one-instanton calculations of the axion potential. This is done for an $SU(N)$ gauge theory with a matter content in any representation of the gauge group. This type of computation requires the expression of the fermion zero modes of the theory. We construct them for all representations of $SU(2)$, which ser

Diego Calderón, Jorge Cuadra, Christopher M. P. Russell, Andreas Burkert

The reported discovery of a cold (~10$^4$ K) disc-like structure within 0.005 pc around the super-massive black hole at the centre of the Milky Way, Sgr A*, has challenged our understanding of the gas dynamics and thermodynamic state of the plasma in its immediate vicinity. State-of-the-art simulations do not agree on whether or not such a disc can be a prod

Dylan Shearsby, Jiaqi Wu, Dekun Yang, Bo Peng

Benefiting from improved stability due to stronger interlayer van der Waals interactions, few-layer fullerene networks are experimentally more accessible compared to monolayer polymeric C$_{60}$. However, there is a lack of systematic theoretical studies on the material properties of few-layer C$_{60}$ networks. Here, we compare the structural, electronic an

Matteo Boschini, Nicholas Loutrel, Davide Gerosa, Giulia Fumagalli

While the orbital eccentricity is a key feature of the gravitational two-body problem, providing an unambiguous definition in general relativity poses significant challenges. Despite such foundational issue, the eccentricity of binary black holes has important implications in gravitational-wave astronomy. We present a novel approach to consistently define th

V. Cirigliano, M. Dawid, W. Dekens, S. Reddy

We identify a new class of three-nucleon forces that arises in the low-energy effective theory of nuclear interactions including pions. We estimate their contribution to the energy of neutron and nuclear matter and find that it can be as important as the leading-order three-nucleon forces previously considered in the literature. The magnitude of this force i

Maximilian Ruhdorfer, Ennio Salvioni, Andrea Wulzer

We survey the opportunities offered by the detection of the forward muons that accompany the creation of neutral effective vector bosons at a muon collider, in different kinematic regimes. Vectors with relatively low energy produce the Higgs boson and the extended muon angular coverage enables studies of the Higgs properties, such as the measurement of the i

Mauricio Lopez-Hernandez, Josue De-Santiago

The discrepancy between the Hubble constant $H_0$ values derived from early-time and late-time measurements, reaching up to $4\sigma$, represents the most serious challenge in modern cosmology and astrophysics. In this work, we investigate if a similar tension exists between only late time measurements at different redshifts. We use the latest public dataset

Adriano Cherchiglia, Leonardo J. Ferreira Leite

Recent efforts, both theoretical and experimental, have increasingly focused on the scalar potential of the Standard Model, with a highlight on the trilinear Higgs coupling. This parameter has long been recognized for its potential to test Beyond-Standard-Model (BSM) theories and its significance in understanding early cosmological dynamics. In order to broa

Rafał Masełek, Mihoko M. Nojiri, Kazuki Sakurai

The system of light electroweakinos and heavy squarks gives rise to one of the most challenging signatures to detect at the LHC. It consists of missing transverse energy recoiled against a few hadronic jets originating either from QCD radiation or squark decays. The analysis generally suffers from the large irreducible Z + jets $(Z \to \nu \bar \nu)$ backgro

I. Veronesi, I. Bartalucci, E. Rasia, S. Molendi

X-ray observations of galaxy clusters are routinely used to derive radial distributions of ICM thermdynamical properties such as density and temperature. However, observations allow us to access quantities projected on the celestial sphere only, so that an assumption on the 3D distribution of the ICM is necessary. Usually, spherical geometry is assumed. The

Ragadeepika Pucha, S. Juneau, Arjun Dey, M. Siudek

Using early data from the Dark Energy Spectroscopic Instrument (DESI) survey, we search for AGN signatures in 410,757 line-emitting galaxies. By employing the BPT emission-line ratio diagnostic diagram, we identify AGN in 75,928/296,261 ($\approx$25.6%) high-mass ($\log (M_{\star}/\rm M_{\odot}) >$ 9.5) and 2,444/114,496 ($\approx$2.1%) dwarf ($\log (M_{\sta

Pilar Coloma, Enrique Fernández-Martínez, Jacobo López-Pavón, Xabier Marcano

We analyze how neutrino oscillation and coherent elastic neutrino-nucleus scattering data impact the global SMEFT fit. We first review the mapping between the SMEFT parameters and the so-called NSI framework, commonly considered in the neutrino literature. We also present a detailed discussion of how the measurements for the normalization of neutrino fluxes

Jordan Flitter, Sarah Libanore, Ely D. Kovetz

The cosmological 21-cm signal is sourced from hyperfine transitions in neutral hydrogen atoms. Yet, although the abundance of hydrogen atoms follows the baryon density field, semi-numerical codes that simulate the 21-cm signal simplify their treatment as if all the matter in the Universe was in the form of collisionless cold dark matter (CDM). This is usuall

Kareem El-Badry, Casey Lam, Berry Holl, Jean-Louis Halbwachs

Astrometry from Gaia DR3 has produced a sample of $\sim$170,000 Keplerian orbital solutions, with many more anticipated in the next few years. These data have enormous potential to constrain the population of binary stars, giant planets, and compact objects in the Solar neighborhood. But in order to use the published orbit catalogs for statistical inference,

Kayla E. O'Donnell, Tracy R. Slatyer

A number of new balloon or space-based $\gamma$-ray observatories have been proposed to close a "MeV gap" in sensitivity to $\gamma$ rays in the MeV-GeV energy band. One aspect of the science case for these instruments is their ability to constrain or discover decaying or annihilating dark matter. In this work, we forecast the sensitivity of these instrument

A. Le Reste, M. J. Hayes, J. M. Cannon, J. Melinder

The Lyman-$\alpha$ (Lya) line of hydrogen is a well-known tracer of galaxies at high-z. However, the connection between Lya observables and galaxy properties has not fully been established, limiting the use of the line to probe the physics of galaxies. Here, we derive global neutral hydrogen gas (HI) properties of nearby Lya-emitting galaxies to assess the i

Ranit Das, Thorben Finke, Marie Hein, Gregor Kasieczka

Resonant anomaly detection methods have great potential for enhancing the sensitivity of traditional bump hunt searches. A key component of these methods is a high quality background template used to produce an anomaly score. Using the LHC Olympics R&D dataset, we demonstrate that this background template can also be repurposed to directly estimate the backg

Carlos Miró, Miguel Nebot, Daniel Queiroz

In models with extended scalar sectors consisting of multiple Higgs doublets that trigger spontaneous electroweak symmetry breaking, it might be expected that the abundance of dimensionful quadratic couplings in the scalar potential could allow for a regime where, apart from the would-be Goldstone bosons and a neutral Higgs-like state, all new scalars have m

Wenzhe Tong, Tzu-Yuan Lin, Jonathan Mi, Yicheng Jiang

Tensegrity robots, characterized by a synergistic assembly of rigid rods and elastic cables, form robust structures that are resistant to impacts. However, this design introduces complexities in kinematics and dynamics, complicating control and state estimation. This work presents a novel proprioceptive state estimator for tensegrity robots. The estimator in

John Ellis, Malcolm Fairbairn, Juan Urrutia, Ville Vaskonen

We present a new semi-analytical model for the evolution of galaxies and supermassive black holes (SMBHs) that is based on the extended Press-Schechter formalism and phenomenological modelling of star formation. The model yields BH mass-stellar mass relations that reproduce both the JWST and pre-JWST observations. If the efficiency for BH mergers is high the

Junxuan Li, Chen Cao, Gabriel Schwartz, Rawal Khirodkar

We present a new approach to creating photorealistic and relightable head avatars from a phone scan with unknown illumination. The reconstructed avatars can be animated and relit in real time with the global illumination of diverse environments. Unlike existing approaches that estimate parametric reflectance parameters via inverse rendering, our approach dir

Sebastian Ament, Elizabeth Santorella, David Eriksson, Ben Letham

Gaussian processes (GPs) are non-parametric probabilistic regression models that are popular due to their flexibility, data efficiency, and well-calibrated uncertainty estimates. However, standard GP models assume homoskedastic Gaussian noise, while many real-world applications are subject to non-Gaussian corruptions. Variants of GPs that are more robust to