October 2024 arXiv papers — page 95

Sung-Wook Lee, Xuhui Kang, Yen-Ling Kuo

Recently, diffusion policy has shown impressive results in handling multi-modal tasks in robotic manipulation. However, it has fundamental limitations in out-of-distribution failures that persist due to compounding errors and its limited capability to extrapolate. One way to address these limitations is robot-gated DAgger, an interactive imitation learning w

K. Shibin, G. Auzinger, H. Bakhshiansohi, A. Dabrowski

The high-luminosity upgrade of the LHC (HL-LHC) brings unprecedented requirements for precision bunch-by-bunch luminosity measurement and beam-induced background monitoring in real time. A key component of the CMS Beam Radiation Instrumentation and Luminosity detector system is a stand-alone luminometer, the Fast Beam Condition Monitor (FBCM), which is able

Jayoon Jang, Guenther Walther

The paper considers the problem of detecting and localizing changepoints in a sequence of independent observations. We propose to evaluate a local test statistic on a triplet of time points, for each such triplet in a particular collection. This collection is sparse enough so that the results of the local tests can simply be combined with a weighted Bonferro

Yunhao Yang, Neel P. Bhatt, William Ward, Zichao Hu

Large language models possess impressive capabilities in generating programs (e.g., Python) from natural language descriptions to execute robotic tasks. However, these generated programs often contain errors that violate externally given task specifications. Without an effective method to verify their correctness, the reliable deployment of language models i

Yanlin Qu, Ravi Kant, Yan Chen, Brendan Kitts

Bid shading has become a standard practice in the digital advertising industry, in which most auctions for advertising (ad) opportunities are now of first price type. Given an ad opportunity, performing bid shading requires estimating not only the value of the opportunity but also the distribution of the highest bid from competitors (i.e. the competitive lan

Jixun K. Ding, Emily Z. Zhang, Wen O. Wang, Tessa Cookmeyer

In light of recent experimental data indicating a substantial thermal Hall effect in square lattice antiferromagnetic Mott insulators, we investigate whether a simple Mott insulator can sustain a finite thermal Hall effect. We verify that the answer is "no" if one performs calculations within a spin-only low-energy effective spin model with non-interacting m

João Vitor da Silva, Disson dos Prazeres, Gleydson Ricarte, Ginaldo Sá

In this manuscript, we obtain sharp and improved regularity estimates for weak solutions of weighted quasilinear elliptic models of Hardy-H\'{e}non-type, featuring an explicit regularity exponent depending only on universal parameters. Our approach is based on geometric tangential methods and uses a refined oscillation mechanism, compactness, and scaling tec

Edward Chernysh

We establish Struwe-type decompositions of Palais-Smale sequences for a class of critical $p$-Laplace equations of the Caffarelli-Kohn-Nirenberg type in a bounded domain $\Omega\subset\mathbb{R}^n$, $n\ge2$, containing the origin. In doing so, we highlight important differences introduced by the weights and require new rescaling laws to account for this new

Filippo Iulianelli, Sung Kim, Joshua Sussan, Aaron D. Lauda

We propose a framework for topological quantum computation using newly discovered non-semisimple analogs of topological quantum field theories in 2+1 dimensions. These enhanced theories offer more powerful models for quantum computation. The conventional theory of Ising anyons, which is believed to describe excitations in the $\nu = 5/2$ fractional quantum H

Jean-Luc Vay, Justin Ray Angus, Olga Shapoval, Remi Lehe

The Particle-In-Cell (PIC) and Monte Carlo Collisions (MCC) methods are workhorses of many numerical simulations of physical systems. Recently, it was pointed out that, while the two methods can be exactly - or nearly - energy-conserving independently, combining the two is leading to anomalous numerical heating. This paper reviews the standard explicit PIC-M

Stephen Prochaska, Julie Vera, Douglas Lew Tan, Kate Starbird

Informal sensemaking surrounding U.S. election processes has been fraught in recent years, due to the inherent uncertainty of elections, the complexity of election processes in the U.S., and to disinformation. Based on insights from qualitative analysis of election rumors spreading online in 2020 and 2022, we introduce the concept of manipulated sensemaking

Iman Mohamed Attia

Parametric quantile regression is illustrated for the one parameter new unit Rayleigh distribution called Median Based Unit Rayleigh distribution (MBUR) distribution. The estimation process using re-parameterized maximum likelihood function is highlighted with real dataset example. The inference and goodness of fit is also explored.

Pierre-Antoine Bernard, Abderahmane Bouziane, Samuel Pellerin, Simone Têtu

A unified algebraic interpretation of both finite families of orthogonal polynomials and biorthogonal rational functions of $q$-Hahn type is provided. The approach relies on the meta $q$-Hahn algebra and its finite-dimensional bidiagonal representations. The functions of $q$-Hahn type are identified as overlaps (up to global factors) between bases solving or

Chengzhang Li, Ali Kanj, Jiheng Jing, Gaurav Bahl

Rotating magnets have recently emerged as an efficient method for producing ultra-low frequency signals for through-earth and through-seawater communications. Magneto-mechanical resonator (MMR) arrays, which are magnetized torsional rotors with a restoring torque, are a promising implementation of this idea that use resonance to enhance the magnetic signal g

Qizan Chen, Dinesh Sundaravadivelu Devarajan, Arash Nikoubashman, Michael P. Howard

Nanoparticle gels have attracted considerable attention due to their highly tunable properties. One strategy for producing nanoparticle gels involves using strong local attractions between polymeric molecules, such as DNA hybridization or dynamic covalent chemistry, to form percolated nanoparticle networks. These molecules can be used in two distinct roles:

Wanyu Du, Song Feng, James Gung, Lijia Sun

Developing language model-based dialogue agents requires effective data to train models that can follow specific task logic. However, most existing data simulation methods focus on increasing diversity in language, topics, or dialogue acts at the utterance level, largely neglecting a critical aspect of task logic diversity at the dialogue level. This paper p

Guangji Bai, Yijiang Li, Zilinghan Li, Liang Zhao

Large Language Models (LLMs) achieve state-of-the-art performance but are challenging to deploy due to their high computational and storage demands. Pruning can reduce model size, yet existing methods assume public access to calibration data, which is impractical for privacy-sensitive applications. To address the challenge of pruning LLMs in privacy-preservi

Fama Ngom, Huaxi Zhang, Lei Zhang, Karen Godary-Dejean

Semantic navigation enables robots to understand their environments beyond basic geometry, allowing them to reason about objects, their functions, and their interrelationships. In semantic robotic navigation, creating accurate and semantically enriched maps is fundamental. Planning based on semantic maps not only enhances the robot's planning efficiency and

Dhruv Bhagtani, Andy McC. Hogg, Ryan M. Holmes, Navid C. Constantinou

The North Atlantic Oscillation (NAO) is a leading mode of atmospheric variability, affecting the North Atlantic Ocean on sub-seasonal to multi-decadal timescales. The NAO changes the atmospheric forcing at the ocean's surface, including winds and surface buoyancy fluxes, both of which are known to impact large-scale gyre circulation. However, the relative ro

Xin Li, Benedetta Flebus

Non-reciprocal propagation of energy and information is fundamental to a wide range of quantum technology applications. In this work, we explore the quantum many-body dynamics of a qubit ensemble coupled to a shared bath that mediates coherent and dissipative inter-qubit interactions with both symmetric and anti-symmetric components. We find that the interpl

James Greenberg, Brendan M. Heffernan, William F. McGrew, Keisuke Nose

Optical-based terahertz sources are important for many burgeoning scientific and technological applications. Among such applications is precision spectroscopy of molecules, which exhibit rotational transitions at terahertz frequencies. Stemming from precision spectroscopy is frequency discrimination and stabilization of terahertz sources. Because many molecu

Armen Tokadjian, Renyu Hu, Mario Damiano

The chemical makeup of Earth's atmosphere during the Archean (4 Ga-2.5 Ga) and Proterozoic eon (2.5 Ga-0.5 Ga) contrast considerably with the present-day: the Archean was rich in carbon dioxide and methane and the Proterozoic had potentially higher amounts of nitrous oxide. CO$_2$ and CH$_4$ in an Archean Earth analog may be a compelling biosignature because

Levi T. Kaganowich, Deepak C. Akiwate, Trevor J. Cox, Olga Umnova

Transformation Acoustics emerged in the mid-2000s, initiating a new paradigm of metamaterial designs. One of the most compelling designs, the invisibility cloak, holds promise for stealth and noise reduction applications in aviation. However, adapting this design to meet the demands of realistic conditions has proven challenging. This work focusses on the de

Marco Billo', Luca Griguolo, Alessandro Testa

We study the 1/2 BPS circular Wilson loop in four-dimensional SU(N), $N = 2$ SYM theories with massless hypermultiplets and non-vanishing $\beta$-function. Using super-symmetric localization on $S_4$ , we map the path-integral associated with this observable onto an interacting matrix model. Despite the breaking of conformal symmetry at the quantum level, we

Harley Katz, Alexander P. Ji, O. Grace Telford, Peter Senchyna

The first few cycles of JWST have identified an overabundance of UV-bright galaxies and a general excess of UV luminosity density at $z\gtrsim10$ compared to expectations from most (pre-JWST) theoretical models. Moreover, some of the brightest high-redshift spectroscopically confirmed galaxies exhibit peculiar chemical abundance patterns, most notably extrem

Subhajit Kar, Rajorshi Bhattacharya, Ramkrishna Das, Ylva Pihlström

We develop a robust Machine Learning classifier model utilizing the eXtreme-Gradient Boosting (XGB) algorithm for improved classification of Galactic Wolf-Rayet (WR) stars based on Infrared (IR) colors and positional attributes. For our study, we choose an extensive dataset of 6555 stellar objects (from 2MASS and AllWISE data releases) lying in the Milky Way

Juraj Fulir, Natascha Jeziorski, Lovro Bosnar, Hans Hagen

The use of machine learning (ML) methods for development of robust and flexible visual inspection system has shown promising. However their performance is highly dependent on the amount and diversity of training data. This is often restricted not only due to costs but also due to a wide variety of defects and product surfaces which occur with varying frequen

Bruno Guindani, Davide Gadioli, Roberto Rocco, Danilo Ardagna

Virtual screening applications are highly parameterized to optimize the balance between quality and execution performance. While output quality is critical, the entire screening process must be completed within a reasonable time. In fact, a slight reduction in output accuracy may be acceptable when dealing with large datasets. Finding the optimal quality-thr

Yizhan Shu, John M. Mulvey

This article explores dynamic factor allocation by analyzing the cyclical performance of factors through regime analysis. The authors focus on a U.S. equity investment universe comprising seven long-only indices representing the market and six style factors: value, size, momentum, quality, low volatility, and growth. Their approach integrates factor-specific

Yann-Edwin Keta, Silke Henkes

In two-dimensional tissues, such as developing germ layers, pair-wise forces (or active stresses) arise from the contractile activity of the cytoskeleton, with dissipation provided by the three-dimensional surroundings. We show analytically how these pair-wise stochastic forces, unlike the particle-wise independent fluctuating forces usually considered in ac

Adel Javanmard, Jingwei Ji, Renyuan Xu

We study the dynamic pricing problem faced by a broker seeking to learn prices for a large number of credit market securities, such as corporate bonds, government bonds, loans, and other credit-related securities. A major challenge in pricing these securities stems from their infrequent trading and the lack of transparency in over-the-counter (OTC) markets,

Felix Ivander, Lachlan P. Lindoy, Joonho Lee

We present our response to the commentary piece by Makri {\it et al.} [arXiv:2410.08239], which raises critiques of our work [Nat. Commun. 15, 8087 (2024)]. In our paper, we considered various settings of open-quantum system dynamics, including non-commuting, non-diagonalizable system-bath coupling, and bosonic/spin/fermionic baths. For these, we showed a di

Marc A. Tunnell, Zachary J. DeBruine, Erin Carrier

Determining the appropriate rank in Non-negative Matrix Factorization (NMF) is a critical challenge that often requires extensive parameter tuning and domain-specific knowledge. Traditional methods for rank determination focus on identifying a single optimal rank, which may not capture the complex structure inherent in real-world datasets. In this study, we

Marco Nurisso, Pierrick Leroy, Francesco Vaccarino

Studying the interplay between the geometry of the loss landscape and the optimization trajectories of simple neural networks is a fundamental step for understanding their behavior in more complex settings. This paper reveals the presence of topological obstruction in the loss landscape of shallow ReLU neural networks trained using gradient flow. We discuss

Arpan Mahara, Md Rezaul Karim Khan, Naphtali D. Rishe, Wenjia Wang

Road Extraction is a sub-domain of Remote Sensing applications; it is a subject of extensive and ongoing research. The procedure of automatically extracting roads from satellite imagery encounters significant challenges due to the multi-scale and diverse structures of roads; improvement in this field is needed. The DeepLab series, known for its proficiency i

Prasita Mukherjee, Benjamin Delaware

We present \synver{}, a novel synthesis and verification framework for C programs, that deploys a Large Language Model (LLM) to search for a candidate program that satisfies the given specification. Our key idea is to impose syntactic and semantic biases on programs generated by LLMs, such that the synthesized program is more amenable to automated verificati

Joe Skeens, Johnathan York, Leonid Petrov, Kyle Herrity

We have been developing a novel interferometer formed directly between a Very Long Baseline Interferometry (VLBI) radio telescope and a Global Navigation Satellite Systems (GNSS) antenna/receiver. This interferometer is enabled by the High Rate Tracking Receiver (HRTR), a high-performance GNSS software-defined receiver that records baseband data similar to a

Sayeda Sanzida Ferdous Ruhi, Fokrun Nahar, Adnan Ferdous Ashrafi

Computer-aided diagnosis (CAD) is today considered a vital tool in the field of biological image categorization, segmentation, and other related tasks. The current breakthrough in computer vision algorithms and deep learning approaches has substantially enhanced the effectiveness and precision of apps built to recognize and locate regions of interest inside

Ambesh Pratik Singh, Michael Mitchell, Will Henshon, Addison Hartman

The ability to prepare molecular ions in selected quantum states enables studies in areas such as chemistry, metrology, spectroscopy, quantum information, and precision measurements. Here, we demonstrate $(2+1)$ resonance-enhanced multiphoton ionization (REMPI) of oxygen, both in a molecular beam and in an ion trap. The two-photon transition in the REMPI spe

Ben Biedermann, Fahima Gibrel

This report introduces the Grant Maturity Index (GMI), a novel evaluative framework designed to assess the maturity and operational effectiveness of Web3 grant programs. As Web3 continues to develop, the decentralized nature of these programs brings both opportunities and challenges, particularly when it comes to governance, transparency, and community engag

Heidy Khlaaf, Sarah Myers West, Meredith Whittaker

Discussions regarding the dual use of foundation models and the risks they pose have overwhelmingly focused on a narrow set of use cases and national security directives-in particular, how AI may enable the efficient construction of a class of systems referred to as CBRN: chemical, biological, radiological and nuclear weapons. The overwhelming focus on these

Otilia A. Ducu, Ana E. Dumitriu, Adam Jinaru, Romain Kukla

The Type-II Seesaw Model provides an attractive scenario to account for Majorana-neutrino masses. Its extended Higgs sector, if sufficiently light, can have a rich and distinctive phenomenology at the LHC while yielding automatically an essentially Standard-Model-Higgs-like state. Several phenomenological studies have been devoted to the scalar sector of thi

Norita Kawanaka, Rohta Takahashi

We propose a brand-new formalism for the propagation of relativistic cosmic ray (CR) particles. The propagation of CRs has often been described using the diffusion approximation, which has the drawback that the propagation speed of CRs near the source exceeds the speed of light. By applying the analytic solution of the time-dependent distribution function of

Kristen M. Parzuchowski, Michael D. Mazurek, Charles H. Camp, Martin J. Stevens

We introduce a toluene-filled fiber platform for two-photon absorption measurements. By confining both the light and molecular sample inside the 5 $\mu$m hollow core of the fiber, we increase the distance over which the nonlinear light-matter interaction occurs. With only a 7.3 nL excitation volume, we measure classical two-photon absorption (C2PA) at an ave

Zedian Shao, Hongbin Liu, Jaden Mu, Neil Zhenqiang Gong

Prompt injection attack, where an attacker injects a prompt into the original one, aiming to make an Large Language Model (LLM) follow the injected prompt to perform an attacker-chosen task, represent a critical security threat. Existing attacks primarily focus on crafting these injections at inference time, treating the LLM itself as a static target. Our ex

Lechen Zhang, Tolga Ergen, Lajanugen Logeswaran, Moontae Lee

Large Language Models (LLMs) have shown impressive capabilities in many scenarios, but their performance depends, in part, on the choice of prompt. Past research has focused on optimizing prompts specific to a task. However, much less attention has been given to optimizing the general instructions included in a prompt, known as a system prompt. To address th

Zhi Liu, Nikhil Garg

We consider government service allocation -- how the government allocates resources (e.g., maintenance of public infrastructure) over time. It is important to make these decisions efficiently and equitably -- though these desiderata may conflict. In particular, we consider the design of Service Level Agreements (SLA) in city government operations: promises t

Jesse Held, Henry Maxfield

We study de Sitter JT gravity in the canonical formulation to illustrate constructions of Hilbert spaces in quantum gravity, which is challenging due to the Hamiltonian constraints. The key ideas include representing states as "invariants" (solutions to the Wheeler-DeWitt equation) or dual "co-invariants" (equivalence classes under gauge transformations), de

Chuanzhong Li, A. Mironov, A. Yu. Orlov

The goal of this note is to study integrable properties of a generating function of the HOMFLY-PT invariants of the Hopf link colored with different representations. We demonstrate that such a generating function is a $\tau$-function of the KP hierarchy. Furthermore, this Hopf generating function in the case of composite representations, which is a generatin

Rodrigo Maier

In this paper we examine the relativistic Einstein rings assuming a nonminimal coupling between gravitation and electromagnetism in a Reissner-Norstr\"om background. Starting from a general action of a nonminimal coupled electrodynamics we show that an unstable effective photon sphere may be obtained in the regime of eikonal approximation. Restricting oursel

Mansoor Ali Teevno, Gilberto Ochoa-Ruiz, Sharib Ali

While recent advances in deep learning (DL) for surgical scene segmentation have yielded promising results on single-center and single-imaging modality data, these methods usually do not generalize well to unseen distributions or modalities. Even though human experts can identify visual appearances, DL methods often fail to do so when data samples do not fol

Johannes M. A. Lechner, Pietro Marabotti, Lei Shi, Thomas Pichler

Carbyne, an infinite linear chain of carbon atoms, is the truly one-dimensional allotrope of carbon. While ideal carbyne and its fundamental properties have remained elusive, carbyne-like materials like carbyne chains confined inside carbon nanotubes are available for study. Here, we probe the longitudinal optical phonon (C-mode) of confined carbyne chains b

Dietmar Bisch, Julio Cáceres

Subfactors of the hyperfinite II$_1$ factor with ''exotic'' properties can be constructed from nondegenerate commuting squares of multi-matrix algebras. We show that the subfactor planar algebra of these commuting square subfactors necessarily embeds into Jones' graph planar algebra associated to one of the inclusion graphs in the commuting square. This lead

Tushar, Sawan Kumar, Souvik Chakraborty

Constitutive modelling is crucial for engineering design and simulations to accurately describe material behavior. However, traditional phenomenological models often struggle to capture the complexities of real materials under varying stress conditions due to their fixed forms and limited parameters. While recent advances in deep learning have addressed some

Paschal C. Amusuo, Parth V. Patil, Owen Cochell, Taylor Le Lievre

Formal verification provides mathematical guarantees that a software is correct. Design-level verification tools ensure software specifications are correct, but they do not expose defects in actual implementations. For this purpose, engineers use code-level tools. However, such tools struggle to scale to large software. The process of "Unit Proofing" mitigat

Eric Elmoznino, Thomas Jiralerspong, Yoshua Bengio, Guillaume Lajoie

Compositionality is believed to be fundamental to intelligence. In humans, it underlies the structure of thought, language, and higher-level reasoning. In AI, compositional representations can enable a powerful form of out-of-distribution generalization, in which a model systematically adapts to novel combinations of known concepts. However, while we have st

Fangyuan Lin

This paper revisits the classical notion of unicity distance from an enlightening perspective grounded in information theory, specifically by framing the encryption process as a noisy transmission channel. Using results from reliable communication theory, we derive a simple information-theoretic proof of the same unicity distance formula as in Shannon's clas

Raviraj Joshi, Kanishk Singla, Anusha Kamath, Raunak Kalani

Multilingual LLMs support a variety of languages; however, their performance is suboptimal for low-resource languages. In this work, we emphasize the importance of continued pre-training of multilingual LLMs and the use of translation-based synthetic pre-training corpora for improving LLMs in low-resource languages. We conduct our study in the context of the

Steven Triplett, Simon Minami, Rakesh Verma

In the modern age an enormous amount of communication occurs online, and it is difficult to know when something written is genuine or deceitful. There are many reasons for someone to deceive online (e.g., monetary gain, political gain) and detecting this behavior without any physical interaction is a difficult task. Additionally, deception occurs in several

Jarett LeVan, Scott D. Baalrud

Intrinsic bulk viscosity of the one-component plasma (OCP) is computed and analyzed using equilibrium molecular dynamics simulations and the Green-Kubo formalism. It is found that bulk viscosity exhibits a maximum at $\Gamma \approx 1$, corresponding to the condition that the average kinetic energy of particles equals the potential energy at the average inte

Victoria Lin, Louis-Philippe Morency, Eli Ben-Michael

As language technologies become widespread, it is important to understand how changes in language affect reader perceptions and behaviors. These relationships may be formalized as the isolated causal effect of some focal language-encoded intervention (e.g., factual inaccuracies) on an external outcome (e.g., readers' beliefs). In this paper, we introduce a f

Kausalya Mahadevan, Michelle C. Yuen, David T. Farrell, Conor J. Walsh

Curved elastic shells can be fabricated through molding or by harnessing residual stresses. These shells often exhibit snap-through behavior and multistability when loaded. We present a unique way of fabricating curved elastic shells that exhibit multistability and snap-through behavior, weft-knitting. The knitting process introduces internal stresses into t

Carrie Clark, Richard S. Laugesen

A shape optimization program is developed for the ratio of Riesz capacities $\text{Cap}_q(K)/\text{Cap}_p(K)$, where $K$ ranges over compact sets in $\mathbb{R}^n$. In different regions of the $pq$-parameter plane, maximality is conjectured for the ball, the vertices of a regular simplex, or the endpoints of an interval. These cases are separated by a symmet

Praveen Kumar Pandian Shanmuganathan, Vatsal Sivaratri

Arrhythmias are irregularities in the hearts electrical system which cause rapid and irregular heartbeats. These heart conditions affect over 33 million people globally and significantly increase the risk of severe complications, including stroke, heart failure, and sudden death. Modern screening and treatment approaches, like 12 lead ECG tests and analyzing

Shirly Stephen, Mitchell Faulk, Krzysztof Janowicz, Colby Fisher

Geospatial Knowledge Graphs (GeoKGs) have become integral to the growing field of Geospatial Artificial Intelligence. Initiatives like the U.S. National Science Foundation's Open Knowledge Network program aim to create an ecosystem of nation-scale, cross-disciplinary GeoKGs that provide AI-ready geospatial data aligned with FAIR principles. However, building

Yusen Xie, Zhenmin Huang, Jin Wu, Jun Ma

In this paper, we introduce GS-LIVM, a real-time photo-realistic LiDAR-Inertial-Visual mapping framework with Gaussian Splatting tailored for outdoor scenes. Compared to existing methods based on Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS), our approach enables real-time photo-realistic mapping while ensuring high-quality image rendering i

Hong Jun Jeon, Benjamin Van Roy

The staggering feats of AI systems have brought to attention the topic of AI Alignment: aligning a "superintelligent" AI agent's actions with humanity's interests. Many existing frameworks/algorithms in alignment study the problem on a myopic horizon or study learning from human feedback in isolation, relying on the contrived assumption that the agent has al

Artem Basalaev, Jan-Niklas Feldhusen, Oliver Gerberding

We investigate seismic motion propagation through a passively isolated mechanical system, using Wiener filters and convolutional neural networks with time-dilation layers. The goal of this study was to explore the capabilities of neural networks and Wiener filters in characterizing a mechanical system from the measurements. The mechanical system used is a te

Ruoyu Zhang, Lihui Wang, Kun Tang, Jingwen Xu

Currently, cortical surface registration techniques based on classical methods have been well developed. However, a key issue with classical methods is that for each pair of images to be registered, it is necessary to search for the optimal transformation in the deformation space according to a specific optimization algorithm until the similarity measure fun

Yongda Zhu, Stacey Alberts, Jianwei Lyu, Jane Morrison

The topology of reionization and the environments where galaxies efficiently produce ionizing photons are key open questions. For the first time, we investigate the trend between ionizing photon production efficiency, $\xi_{\rm ion}$, and galaxy overdensity, $\log(1+\delta)$. We analyze the ionizing properties of 79 galaxies between $1.0 < z < 5.2$ using JWS

Taiyi Wang, Zhihao Wu, Jianheng Liu, Jianye Hao

On-device control agents, especially on mobile devices, are responsible for operating mobile devices to fulfill users' requests, enabling seamless and intuitive interactions. Integrating Multimodal Large Language Models (MLLMs) into these agents enhances their ability to understand and execute complex commands, thereby improving user experience. However, fin

Bingcong Li, Liang Zhang, Niao He

Sharpness-aware minimization (SAM) improves generalization of various deep learning tasks. Motivated by popular architectures such as LoRA, we explore the implicit regularization of SAM for scale-invariant problems involving two groups of variables. Instead of focusing on commonly used sharpness, this work introduces a concept termed balancedness, defined as

Yiqi Xie, Adrian Ka-Wai Chung, Thomas P. Sotiriou, Nicolás Yunes

Massive scalar fields are promising candidates to address many unresolved problems in fundamental physics. We report the first model-agnostic Bayesian search of massive scalar fields in LIGO/Virgo/KAGRA gravitational-wave data. We find no evidence for such fields and place the most stringent upper limits on their coupling for scalar masses $\lesssim 2\times1

Agnese Grison, Irene Mendez Guerra, Alexander Kenneth Clarke, Silvia Muceli

The decomposition of high-density surface electromyography (HD-sEMG) signals into motor unit discharge patterns has become a powerful tool for investigating the neural control of movement, providing insights into motor neuron recruitment and discharge behavior. However, current algorithms, while very effective under certain conditions, face significant chall

Rujiao Yan, Linda Schubert, Alexander Kamm, Matthias Komar

This paper describes a method to detect generic dynamic objects for automated driving. First, a LiDAR-based dynamic grid is generated online. Second, a deep learning-based detector is trained on the dynamic grid to infer the presence of dynamic objects of any type, which is a prerequisite for safe automated vehicles in arbitrary, edge-case scenarios. The Rot

Dhairya R. Vyas, Julio M. Ottino, Richard M. Lueptow, Paul B. Umbanhowar

The Discrete Element Method is widely employed for simulating granular flows, but conventional integration techniques may produce unphysical results for simulations with static friction when particle size ratios exceed $R \approx 3$. These inaccuracies arise because some variables in the velocity-Verlet algorithm are calculated at the half-timestep, while ot

Giacomo Jarc, Shahla Yasmin Mathengattil, Angela Montanaro, Enrico Maria Rigoni

The use of optical cavities on resonance with material excitations allows controlling light-matter interaction in both the regimes of weak and strong coupling. We study here the multimode vibrational coupling of low energy phonons in the charge-density-wave material 1T-TaS$_{2}$ across its insulator-to-metal phase transition. For this purpose, we embed 1T-Ta

Cameron Franc, Geoffrey Mason

An overview of the authors' ideas about the process of completing a $p$-adically normed space in the setting of vertex operator algebras. We focus in particular on the $p$-adic Heisenberg VOA and its connections with $p$-adic modular forms.

William Walden, Pavlo Kuchmiichuk, Alexander Martin, Chihsheng Jin

Event-keyed summarization (EKS) requires summarizing a specific event described in a document given the document text and an event representation extracted from it. In this work, we extend EKS to the cross-document setting (CDEKS), in which summaries must synthesize information from accounts of the same event as given by multiple sources. We introduce SEAMUS

Jorge Alfaro

We extend the $\tmop{Sim}(2)$ invariant infrared regularization of Very Special Relativity models, that we have proposed recently, to include $\gamma_5$ Dirac matrix. Then, we solve the Very Special Relativity Schwinger model, find the chiral anomaly, and clarify its meaning in the new context. In addition, we show that the triangle anomaly in four space-tim

Jonghyun Hwang, Mariana Altomare, Howard A. Stone

We report a surface instability observed during the extrusion of extremely soft elastic solids in confined geometries. Due to their unique rheological properties, these soft solids can migrate through narrow gaps by continuously everting the bulk material. The extrusion front spontaneously buckles in the direction transverse to the flow, resulting in a furro

Eli Goldin, Tomoyuki Morimae, Saachi Mutreja, Takashi Yamakawa

We construct a unitary oracle relative to which $\mathbf{BQP}=\mathbf{QCMA}$ but quantum-computation-classical-communication (QCCC) commitments and QCCC multiparty non-interactive key exchange exist. We also construct a unitary oracle relative to which $\mathbf{BQP}=\mathbf{QMA}$, but quantum lightning (a stronger variant of quantum money) exists. This exten

Hai-Jun Li, Wei Chao, Xiu-Hui Tan, Yu-Feng Zhou

The extragalactic background light (EBL) plays a crucial role in the propagation of high-energy particles throughout the Universe. In this work, we explore the impact of the EBL absorption effect on photon to axionlike particle (ALP) conversions from the very-high-energy gamma-ray spectral irregularities. For our purpose, we select four BL Lac blazars: Marka

Yuki Ohnishi, Jordan Awan

Differential privacy is the leading mathematical framework for privacy protection, providing a probabilistic guarantee that safeguards individuals' private information when publishing statistics from a dataset. This guarantee is achieved by applying a randomized algorithm to the original data, which introduces unique challenges in data analysis by distorting

Takashi Furuya, Anastasis Kratsios

Neural operator (NO) architectures learn nonlinear maps between infinite-dimensional function spaces and are widely used to accelerate simulation and enable data-driven model discovery. While universality results ensure expressivity, they do not address \emph{complexity}: for broad operator classes described only through regularity (e.g.\ uniform continuity

Simone Bombari, Marco Mondelli

Differentially private gradient descent (DP-GD) is a popular algorithm to train deep learning models with provable guarantees on the privacy of the training data. In the last decade, the problem of understanding its performance cost with respect to standard GD has received remarkable attention from the research community, which formally derived upper bounds

Mandy C. Chen, Hsiao-Wen Chen, Michael Rauch, Andrey Vayner

In this Letter, we investigate the turbulence and energy injection in the extended nebulae surrounding two luminous obscured quasars, WISEA J100211.29$+$013706.7 ($z=1.5933$) and SDSS J165202.64$+$172852.3 ($z=2.9489$). Utilizing high-resolution data from the NIRSpec IFU onboard the James Webb Space Telescope, we analyze the velocity fields of line-emitting

Moein N. Ivaki, Teemu Ojanen, Ali G. Moghaddam

Monitored quantum circuits offer great perspectives for exploring the interplay of quantum information and complex quantum dynamics. These systems could realize the extensively studied entanglement and purification phase transitions, as well as a rich variety of symmetry-protected and ordered non-equilibrium phases. The central question regarding such phases

Elynn Chen, Yuefeng Han, Jiayu Li

Tensor classification is gaining importance across fields, yet handling partially observed data remains challenging. In this paper, we introduce a novel approach to tensor classification with incomplete data, framed within high-dimensional tensor linear discriminant analysis. Specifically, we consider a high-dimensional tensor predictor with missing observat

Kevin Marimbu, Eve J. Lee

Do sub-Neptunes assemble close to where we see them or do they form full-fledged farther away from their host star then migrate inwards? We explore this question using the distribution of measured orbital periods, one of the most fundamental observable parameters. Under disk-induced migration, planet occurrence rate is expected to decrease towards shorter or

Sebastián Arriagada-Neira, Rodrigo Herrera-Camus, Vicente Villanueva, Natascha M. Förster Schreiber

We present deep ($\sim$ 20 hr), high-angular resolution Atacama Large Millimeter/submillimeter Array (ALMA) observations of the $\rm CO ~ (4-3)$ and $\rm [CI] ~ (1-0)$ transitions, along with the rest-frame 630 $\mu$m dust continuum, in BX610 --a massive, main-sequence galaxy at the peak epoch of cosmic star formation $(z = 2.21)$. Combined with deep Very La

Henrik T. Kaarbø, Henning G. Hugdal, Sol H. Jacobsen

We show strong coupling between antiferromagnetic magnons and microwave cavity photons at both high and externally controllable magnon frequencies. Using the fully quantum mechanical path-integral method, we study an antiferromagnetic insulator (AFM) interfaced with a topological insulator (TI), taking Bi$_2$Se$_3$--MnSe as a representative example. We show

Luis Pedro García-Pintos, Mrunmay Sahasrabudhe, Christian Arenz

We derive lower bounds on the time needed for a quantum annealer to prepare the ground state of a target Hamiltonian. These bounds do not depend on the annealing schedule and can take the local structure of the Hamiltonian into account. Consequently, the bounds are computable without knowledge of the annealer's dynamics and, in certain cases, scale with the

Kishalay De, Morgan MacLeod, Jacob E. Jencson, Elizabeth Lovegrove

When a massive star reaches the end of its lifetime, its core collapses and releases neutrinos that drive a shock into the outer layers (stellar envelope). A sufficiently strong shock ejects the envelope, producing a supernova. If the shock fails to eject it, the envelope is predicted to fall back onto the collapsing core, producing a stellar-mass black hole

Brandt A. L. Gaches, Jonathan C. Tan, Anna L. Rosen, Rolf Kuiper

How embedded, actively accreting low-mass protostars accrete their mass is still greatly debated. Observations are now piecing together the puzzle of embedded protostellar accretion, in particular with new facilities in the near-infrared. However, high-resolution theoretical models are still lacking, with a stark paucity of detailed simulations of these earl

J. M. Alarcón, E. Lope-Oter, J. A. Oller

We study the equation of state (EoS) of a neutron star (NS) accounting for new advances. In the low energy density, $n\leq 0.1 n_s$, with $n_s$ the saturation density, we use a new pure neutron matter EoS that is regulator independent and expressed directly in terms of experimental nucleon-nucleon scattering data. In the highest-density domain our EoS's are

Zehao Jin, Mario Pasquato, Benjamin L. Davis, Andrea V. Macciò

We present the first steps toward applying causal representation learning to astronomy. Following up on previous work that introduced causal discovery to the field for the first time, here we solve a long standing conundrum by identifying the direction of the causal relation between supermassive black hole (SMBH) mass and their host galaxy properties. This l

David Cyncynates, Zachary J. Weiner

Ultralight dark photon dark matter features distinctive cosmological and astrophysical signatures and is also supported by a burgeoning direct-detection program searching for its kinetic mixing with the ordinary photon over a wide mass range. Dark photons, however, cannot necessarily constitute the dark matter in all of this parameter space. In minimal model

William M. Baker, Seunghwan Lim, Francesco D'Eugenio, Roberto Maiolino

We use NIRSpec/MSA spectroscopy and NIRCam imaging to study a sample of 18 massive ($\log\; M_{*}/M_{\odot} \gt 10\;$dex), central quiescent galaxies at $2\leq z \leq 5$ in the GOODS fields, to investigate their number density, star-formation histories, quenching timescales, and incidence of AGN. The data depth reaches $\log M_*/M_\odot \approx 9\;$dex, yet

Scott Lucchini

The formation of the Magellanic Stream has puzzled astronomers for decades. In this review, we outline the history of our understanding of the Magellanic System highlighting key observations that have revolutionized thinking of its evolution. We also walk through the major models and theoretical advances that have led to our current paradigm - (1) the LMC an