May 2024 arXiv papers — page 82

Marcelo M. Alves, Willian G. G. Velasco

In this work we introduce partial and global actions of inverse categories on posets in two variants, fibred actions and actions by symmetries. We study in detail actions of an inverse category $\mathcal{C}$ on specific subposets of the poset of finite subsets of $\mathcal{C}$, the Bernoulli actions. We show that to each fibred action of an inverse category

Kiranjyot Gill

Gravitational wave (GW) astronomy truly began with the detection of merging compact binaries. The next breakthrough lies in detecting GWs from core-collapse supernovae (CCSNe), particularly the GW linear memory -- a phenomenon arising from aspherical matter ejection and anisotropic neutrino emission during stellar collapse. In this Letter, we examine the fea

S. I. Kruglov

We study Einstein's gravity coupled to nonlinear electrodynamics with two parameters in Anti-de Sitter spacetime. Magnetically charged black holes in an extended phase space is investigated. We obtain the mass and metric functions, their asymptotic and corrections to the Reissner--Nordstr\"{o}m metric function when the cosmological constant vanishes. The fir

Neisarg Dave, Daniel Kifer, C. Lee Giles, Ankur Mali

Prompting techniques have significantly enhanced the capabilities of Large Language Models (LLMs) across various complex tasks, including reasoning, planning, and solving math word problems. However, most research has predominantly focused on language-based reasoning and word problems, often overlooking the potential of LLMs in handling symbol-based calculat

Mikhail M. Ivanov, Michael W. Toomey, Naim Göksel Karaçaylı

We present an effective field theory (EFT) approach to extract fundamental cosmological parameters from the Lyman-alpha forest flux fluctuations as an alternative to the standard simulation-based techniques. As a first application, we re-analyze the publicly available one-dimensional Lyman-alpha flux power spectrum data from the Sloan Digital Sky Survey. Our

Holger R. Roth, Daniel J. Beutel, Yan Cheng, Javier Fernandez Marques

Several open-source systems, such as Flower and NVIDIA FLARE, have been developed in recent years while focusing on different aspects of federated learning (FL). Flower is dedicated to implementing a cohesive approach to FL, analytics, and evaluation. Over time, Flower has cultivated extensive strategies and algorithms tailored for FL application development

A. Ihyadn, S. Merselmiz, D. Mezzane, L. Bih

Lead-free Ba0.85Ca0.15Zr0.10Ti0.90O3 (BCZT) ceramics with different BaO-Na2O-Nb2O5-WO3-P2O5 (BNNWP) glass content, forming (1-x)BCZT-xBNNWP lead-free ceramics (abbreviated as BCZTx; x=0, 2, 4, 6, and 8wt%) were synthesized using the conventional solid-state processing route. The XRD investigation shows the coexistence of tetragonal and orthorhombic phases in

Rong Gao, Xin Liu, Bohao Xing, Zitong Yu

In this work, we focus on a special group of human body language -- the micro-gesture (MG), which differs from the range of ordinary illustrative gestures in that they are not intentional behaviors performed to convey information to others, but rather unintentional behaviors driven by inner feelings. This characteristic introduces two novel challenges regard

Amutheezan Sivagnanam, Ava Pettet, Hunter Lee, Ayan Mukhopadhyay

An emergency responder management (ERM) system dispatches responders, such as ambulances, when it receives requests for medical aid. ERM systems can also proactively reposition responders between predesignated waiting locations to cover any gaps that arise due to the prior dispatch of responders or significant changes in the distribution of anticipated reque

Abraham George, Yibo Chen, Atharva Dikshit, Peter Pak

In robotic manipulation, tactile sensors are indispensable, especially when dealing with soft objects, objects of varying dimensions, or those out of the robot's direct line of sight. Traditional tactile sensors often grapple with challenges related to cost and durability. To address these issues, our study introduces a novel approach to visuo-tactile sensin

Garrett Tanzer, Gustaf Ahdritz, Luke Melas-Kyriazi

Chatbots built upon language models have exploded in popularity, but they have largely been limited to synchronous, turn-by-turn dialogues. In this paper we present a simple yet general method to simulate real-time interactive conversations using pretrained text-only language models, by modeling timed diarized transcripts and decoding them with causal reject

Nawfal Guefrachi, Hakim Ghazzai, Ahmad Alsharoa

The 3D object detection capabilities in urban environments have been enormously improved by recent developments in Light Detection and Range (LiDAR) technology. This paper presents a novel framework that transforms the detection and analysis of 3D objects in traffic scenarios by utilizing the power of elevated LiDAR sensors. We are presenting our methodology

Emilie Hertig, Kevin Wolz, Toshiya Namikawa, Antón Baleato Lizancos

The Simons Observatory (SO), a next-generation ground-based CMB experiment in its final stages of construction, will target primordial $B$-modes with unprecedented sensitivity to set tight bounds on the amplitude of inflationary gravitational waves. Aiming to infer the tensor-to-scalar ratio $r$ with precision $\sigma(r=0) \leq 0.003$, SO will rely on powerf

Giulia Maniccia, Giovanni Montani

A precise interpretation of the Universe wave function is forbidden in the spirit of the Copenhagen School since a precise notion of measure operation cannot be satisfactorily defined. Here we propose a Bohmian interpretation of the isotropic Universe quantum dynamics, in which the Hamilton-Jacobi equation is restated by including quantum corrections, which

Lujia Zhong, Shuo Huang, Jiaxin Yue, Jianwei Zhang

The emergence of tau PET imaging over the last decade has enabled Alzheimer's disease (AD) researchers to examine tau pathology in vivo and more effectively characterize the disease trajectories of AD. Current tau PET analysis methods, however, typically perform inferences on large cortical ROIs and are limited in the detection of localized tau pathology tha

Hooman Davoudiasl

We consider a long-range force, mediated by an ultralight scalar, which can give rise to violation of baryon number. This would lead to very different lifetimes for nucleons in different astrophysical environments. Possible signals of this scenario include a flux of O(10 MeV) solar neutrinos or anomalous heating of old neutron stars; we find the latter to yi

Zhanchao Huang, Wenjun Hong, Hua Su

The recognition of sea ice is of great significance for reflecting climate change and ensuring the safety of ship navigation. Recently, many deep learning based methods have been proposed and applied to segment and recognize sea ice regions. However, the diverse scales of sea ice areas, the zigzag and fine edge contours, and the difficulty in distinguishing

David Kremer, Victor Villar, Hanhee Paik, Ivan Duran

This paper demonstrates the integration of Reinforcement Learning (RL) into quantum transpiling workflows, significantly enhancing the synthesis and routing of quantum circuits. By employing RL, we achieve near-optimal synthesis of Linear Function, Clifford, and Permutation circuits, up to 9, 11 and 65 qubits respectively, while being compatible with native

Andrew Marmon, Grant Schindler, José Lezama, Dan Kondratyuk

We extend multimodal transformers to include 3D camera motion as a conditioning signal for the task of video generation. Generative video models are becoming increasingly powerful, thus focusing research efforts on methods of controlling the output of such models. We propose to add virtual 3D camera controls to generative video methods by conditioning genera

Hugues Thomas, Yao-Hung Hubert Tsai, Timothy D. Barfoot, Jian Zhang

In the field of deep point cloud understanding, KPConv is a unique architecture that uses kernel points to locate convolutional weights in space, instead of relying on Multi-Layer Perceptron (MLP) encodings. While it initially achieved success, it has since been surpassed by recent MLP networks that employ updated designs and training strategies. Building up

Victor Kolev, Rafael Rafailov, Kyle Hatch, Jiajun Wu

We tackle the problem of policy learning from expert demonstrations without a reward function. A central challenge in this space is that these policies fail upon deployment due to issues of distributional shift, environment stochasticity, or compounding errors. Adversarial imitation learning alleviates this issue but requires additional on-policy training sa

Philip Kaaret, Andrea Prestwich

Luminous, compact emission-line galaxies (LCGs) are the most abundant class of confirmed Lyman continuum (LyC) emitters. An optical integral field study of the nearby LCG NGC 2366 reveals an outflow originating at the star cluster 'knot B' thought to clear a channel via mechanical feedback that enables LyC escape. We observed NGC 2366 with the Chandra X-ray

Djalel Benbouzid, Christiane Plociennik, Laura Lucaj, Mihai Maftei

The growing adoption and deployment of Machine Learning (ML) systems came with its share of ethical incidents and societal concerns. It also unveiled the necessity to properly audit these systems in light of ethical principles. For such a novel type of algorithmic auditing to become standard practice, two main prerequisites need to be available: A lifecycle

Haoteng Tang, Guodong Liu, Siyuan Dai, Kai Ye

The MRI-derived brain network serves as a pivotal instrument in elucidating both the structural and functional aspects of the brain, encompassing the ramifications of diseases and developmental processes. However, prevailing methodologies, often focusing on synchronous BOLD signals from functional MRI (fMRI), may not capture directional influences among brai

E. G. Timoshenko

The thesis is devoted to the problem of colour confinement in the non-Abelian Yang-Mills theory (gluon part of Quantum Chromodynamics). A generalisation of the 3-dimensional Fock-Schwinger gauge is proposed where the Gauss law constraint is exactly solvable. This simplifies the theory in a finite domain and incorporates the variables at the boundary into the

Jon Lafuente-Bartolome, Chao Lian, Feliciano Giustino

Halide perovskites emerged as a revolutionary family of high-quality semiconductors for solar energy harvesting and energy-efficient lighting. There is mounting evidence that the exceptional optoelectronic properties of these materials could stem from unconventional electron-phonon couplings, and it has been suggested that the formation of polarons and self-

Sandra Zilker, Sven Weinzierl, Mathias Kraus, Patrick Zschech

Proactive analysis of patient pathways helps healthcare providers anticipate treatment-related risks, identify outcomes, and allocate resources. Machine learning (ML) can leverage a patient's complete health history to make informed decisions about future events. However, previous work has mostly relied on so-called black-box models, which are unintelligible

Mihai Christodorescu, Ryan Craven, Soheil Feizi, Neil Gong

The rise of Generative AI (GenAI) brings about transformative potential across sectors, but its dual-use nature also amplifies risks. Governments globally are grappling with the challenge of regulating GenAI, balancing innovation against safety. China, the United States (US), and the European Union (EU) are at the forefront with initiatives like the Manageme

Ingela Alger, José Ignacio Rivero-Wildemauwe

We conduct a laboratory experiment using framing to assess the willingness to ``sell a lemon'', i.e., to undertake an action that benefits self but hurts the other (the ``buyer''). We seek to disentangle the role of other-regarding preferences and (Kantian) moral concerns, and to test if it matters whether the decision is described in neutral terms or as a m

N. Anakidze, N. Areshidze, L. -E. Persson, G. Tephnadze

In this paper we present and prove some new results concerning approximation properties of $T$ means with respect to the Vilenkin system in Lebesgue spaces and Lipschitz classes for any $1\leq p<\infty$. As applications, we obtain extension of some known approximation inequalities.

Claudio Di Sipio, Riccardo Rubei, Juri Di Rocco, Davide Di Ruscio

Software engineering (SE) activities have been revolutionized by the advent of pre-trained models (PTMs), defined as large machine learning (ML) models that can be fine-tuned to perform specific SE tasks. However, users with limited expertise may need help to select the appropriate model for their current task. To tackle the issue, the Hugging Face (HF) plat

Zehra İşbilir, Bahar Doğan Yazıcı, Murat Tosun

In this study, we introduce the generalized Tribonacci hyperbolic spinors and properties of this new special numbers system by the generalized Tribonacci numbers, which are one of the most general form of the third-order recurrence sequences, generalized Tribonacci quaternions, and hyperbolic spinors, which have quite an importance and framework from mathema

Kierra J. Weatherhead, Jeroen M. Stil, Michael Rugel, Wendy M. Peters

The radio transient ASKAP J173608.2-321735, at the position (l,b)= (356.0872,-0.0390), was serendipitously observed by The HI/OH/Recombination Line Survey of the Galactic Center (THOR-GC) at three epochs in March 2020, April 2020 and February 2021. The source was detected only on 2020 April 11 with flux density 20.6 +/- 1.1 mJy at 1.23 GHz and in-band spectr

Heather L Cihak, Zachary P Kilpatrick

Localized persistent neural activity can encode delayed estimates of continuous variables. Common experiments require that subjects store and report the feature value (e.g., orientation) of a particular cue (e.g., oriented bar on a screen) after a delay. Visualizing recorded activity of neurons along their feature tuning reveals activity bumps whose centers

Krishna Prasad Varadarajan Srinivasan, Prasanth Gumpena, Madhusudhana Yattapu, Vishal H. Brahmbhatt

In the domain of large language models (LLMs), arXiv:2305.16938 showed that few-shot full-model fine-tuning -- namely Vanilla Fine Tuning (FT) and Pattern-Based Fine Tuning (PBFT) --, and In-Context Learning (ICL) generalize similarly on Out-Of-Domain (OOD) datasets, but vary in terms of task adaptation. However, they both pose challenges, especially in term

Melissa Adrian, Daniel Sanz-Alonso, Rebecca Willett

Modern data-driven surrogate models for weather forecasting provide accurate short-term predictions but inaccurate and nonphysical long-term forecasts. This paper investigates online weather prediction using machine learning surrogates supplemented with partial and noisy observations. We empirically demonstrate and theoretically justify that, despite the lon

Yuelyu Ji, Zhuochun Li, Rui Meng, Sonish Sivarajkumar

This paper introduces the RAG-RLRC-LaySum framework, designed to make complex biomedical research understandable to laymen through advanced Natural Language Processing (NLP) techniques. Our Retrieval Augmented Generation (RAG) solution, enhanced by a reranking method, utilizes multiple knowledge sources to ensure the precision and pertinence of lay summaries

Xinyu Zhang, Abdeslam Boularias

Learning a single universal policy that can perform a diverse set of manipulation tasks is a promising new direction in robotics. However, existing techniques are limited to learning policies that can only perform tasks that are encountered during training, and require a large number of demonstrations to learn new tasks. Humans, on the other hand, often can

Laura Dietz

This resource paper addresses the challenge of evaluating Information Retrieval (IR) systems in the era of autoregressive Large Language Models (LLMs). Traditional methods relying on passage-level judgments are no longer effective due to the diversity of responses generated by LLM-based systems. We provide a workbench to explore several alternative evaluatio

Vadim E. Levit, Eugen Mandrescu

Let $\alpha(G)$ denote the cardinality of a maximum independent set, while $\mu(G)$ be the size of a maximum matching in $G=\left( V,E\right) $. It is known that if $\alpha(G)+\mu(G)=\left\vert V\right\vert $, then $G$ is a K\"{o}nig-Egerv\'{a}ry graph. The critical difference $d(G)$ is $\max\{d(I):I\in\mathrm{Ind}(G)\}$, where $\mathrm{Ind}(G)$\ denotes the

Nikolaos Pantelaios, Alexandros Kapravelos

Evasion techniques allow malicious code to never be observed. This impacts significantly the detection capabilities of tools that rely on either dynamic or static analysis, as they never get to process the malicious code. The dynamic nature of JavaScript, where code is often injected dynamically, makes evasions particularly effective. Yet, we lack tools that

Darlington Hove, Farai J. Mhlanga, Rafał M. Łochowski, Phumlani L. Zondi

In this note, we define the numbers of level crossings by a c{\`a}dl{\`a}g (RCLL) real function $x: [0,+\infty) \rightarrow R$ and, in analogy to the work of Bertoin and Yor [BY14] we prove that for $x$ with locally finite total variation these numbers are densities of relevant occupation measures associated with $x$. Next, depending on the regularity of $x$

Biplob Biswas, Rajiv Ramnath

Expansion-enhanced sparse lexical representation improves information retrieval (IR) by minimizing vocabulary mismatch problems during lexical matching. In this paper, we explore the potential of jointly learning dense semantic representation and combining it with the lexical one for ranking candidate information. We present a hybrid information retrieval me

Thomas Alfroy, Thomas Holterbach, Thomas Krenc, KC Claffy

While the increasing number of Vantage Points (VPs) in RIPE RIS and RouteViews improves our understanding of the Internet, the quadratically increasing volume of collected data poses a challenge to the scientific and operational use of the data. The design and implementation of BGP and BGP data collection systems lead to data archives with enormous redundanc

Maoyang Xiang, T. Hui Teo

The rapid evolution of large multimodal models (LMMs) has significantly impacted modern teaching and learning, especially in computer engineering. While LMMs offer extensive opportunities for enhancing learning, they also risk undermining traditional teaching methods and fostering excessive reliance on automated solutions. To counter this, we have developed

Ying Choon Wu, Christopher Maymon, Jonathon Paden, Weichen Liu

The proliferation and refinement of affordable virtual reality (VR) technologies and wearable sensors have opened new frontiers in cognitive and behavioral neuroscience. This chapter offers a broad overview of VR for anyone interested in leveraging it as a research tool. In the first section, it examines the fundamental functionalities of VR and outlines imp

Jianming Tong, Anirudh Itagi, Prasanth Chatarasi, Tushar Krishna

The inference of ML models composed of diverse structures, types, and sizes boils down to the execution of different dataflows (i.e. different tiling, ordering, parallelism, and shapes). Using the optimal dataflow for every layer of workload can reduce latency by up to two orders of magnitude over a suboptimal dataflow. Unfortunately, reconfiguring hardware

Michiel Kenis, Vladimir Dvorkin, Tim Schittekatte, Kenneth Bruninx

Policy makers are formulating offshore energy infrastructure plans, including wind turbines, electrolyzers, and HVDC transmission lines. An effective market design is crucial to guide cost-efficient investments and dispatch decisions. This paper jointly studies the impact of offshore market design choices on the investment in offshore electrolyzers and HVDC

Irina Jurenka, Markus Kunesch, Kevin R. McKee, Daniel Gillick

A major challenge facing the world is the provision of equitable and universal access to quality education. Recent advances in generative AI (gen AI) have created excitement about the potential of new technologies to offer a personal tutor for every learner and a teaching assistant for every teacher. The full extent of this dream, however, has not yet materi

Manuel Ballester, Jaromir Kaspar, Francesc Massanes, Srutarshi Banerjee

Photon-counting detectors based on CZT are essential in nuclear medical imaging, particularly for SPECT applications. Although CZT detectors are known for their precise energy resolution, defects within the CZT crystals significantly impact their performance. These defects result in inhomogeneous material properties throughout the bulk of the detector. The p

V. M. Maslov

Prompt fission neutron spectra produced up to 20 MeV. Simultaneous analysis of measured and calculated data for 233U(n, F), 235U(n, F) and 239Pu(n, F) maintains stronger justification for the predicted PFNS of 233U(n, F). For the latter the reliable measured PFNS data are available at Eth only. Pre-fission neutron spectra influence the partitioning of fissio

Yicheng Wang, Mark Cusick, Mohamed Laila, Kate Puech

Automatic speech recognition (ASR) techniques have become powerful tools, enhancing efficiency in law enforcement scenarios. To ensure fairness for demographic groups in different acoustic environments, ASR engines must be tested across a variety of speakers in realistic settings. However, describing the fairness discrepancies between models with confidence

Pratik Suchde

Similar to the notion of h-adaptivity, where the discretization resolution is adaptively changed, I propose the notion of model adaptivity, where the underlying model (the governing equations) is adaptively changed in space and time. Specifically, this work introduces a hybrid and adaptive coupling of a 3D bulk fluid flow model with a 2D thin film flow model

M. Hernández-Sánchez, G. Tapia-Labra, J. A. Mendez-Bermudez

Here we introduce the non-Hermitian diluted banded random matrix (nHdBRM) ensemble as the set of $N\times N$ real non-symmetric matrices whose entries are independent Gaussian random variables with zero mean and variance one if $|i-j|<b$ and zero otherwise, moreover off-diagonal matrix elements within the bandwidth $b$ are randomly set to zero such that the

P. Mai, B. Cohen-Stead, T. A. Maier, S. Johnston

The high-temperature superconducting cuprates host unidirectional spin- and charge-density-wave orders that can intertwine with superconductivity in non-trivial ways. While the charge components of these stripes have now been observed in nearly all cuprate families, their detailed evolution with doping varies across different materials and at high and low te

Zehra İşbilir, Işıl Arda Kösal, Murat Tosun

In this study, we intend to bring together Padovan and Perrin number sequences, which are one of the most popular third-order recurrence sequences, and hyperbolic spinors, which are used in several disciplines from physics to mathematics, with the help of the split quaternions. This paper especially improves the relationship between hyperbolic spinors both a

Vladimir Nechaev, Sergey Kosyakov

Currently, the development of Foreign Accent Conversion (FAC) models utilizes deep neural network architectures, as well as ensembles of neural networks for speech recognition and speech generation. The use of these models is limited by architectural features, which does not allow flexible changes in the timbre of the generated speech and requires the accumu

Michele Simoncelli, Daniele Fournier, Massimiliano Marangolo, Etienne Balan

The thermal conductivities of crystals and glasses vary strongly and with opposite trends upon heating, decreasing in crystals and increasing in glasses. Here, we show--both with first-principles predictions based on the Wigner transport equation and with thermoreflectance experiments--that the dominant transport mechanisms of crystals (particle-like propaga

Nicola Bariletto, Khai Nguyen, Nhat Ho

We develop an analytical synthesis that bridges data-driven Distributionally Robust Optimization (DRO) and Economic Decision Theory under Ambiguity (DTA). By reinterpreting standard regularization and DRO techniques as data-driven counterparts of ambiguity-averse decision models, we provide a unified framework that clarifies their intrinsic connections. Buil

N. A. Carella

Let $p$ be a large odd prime, let $x=\log p)(\log\log p)^{3+\varepsilon}$ and let $q\ll\log\log p$ be an integer, where $\varepsilon>0$ is a small number. This note proves the existence of small prime quadratic residues and small prime quadratic nonresidues in the arithmetic progression $a+qm\ll x$, with relatively prime $1\leq a<q$, unconditionally. The sam

Baptiste Gault, Aparna Saksena, Xavier Sauvage, Paul Bagot

As hydrogen is touted as a key player in the decarbonization of modern society, it is critical to enable quantitative H analysis at high spatial resolution, if possible at the atomic scale. Indeed, H has a known deleterious impact on the mechanical properties (strength, ductility, toughness) of most materials that can hinder their use as part of the infrastr

Nienke Nijkamp, June Sallou, Niels van der Heijden, Luís Cruz

Integrating Artificial Intelligence (AI) into software systems has significantly enhanced their capabilities while escalating energy demands. Ensemble learning, combining predictions from multiple models to form a single prediction, intensifies this problem due to cumulative energy consumption. This paper presents a novel approach to model selection that add

Brandon T. Shapiro, David I. Spivak

We show that the construction due to Leinster and Weber of a generalized Lawvere theory for a familially representable monad on a (co)presheaf category, and the associated ``nerve'' functor from monad algebras to (co)presheaves, have an elegant categorical description in the double category $\mathbb{C}\mathbf{at}^{\#}$ of categories, cofunctors, familial fun

Talgar Bayan, Richard Banach

This paper presents a decentralised autonomous organisation (DAO) model that uses non-fungible tokens (NFTs) for identity management and privacy-preserving interactions within a Punishment not Reward (PnR) blockchain mechanism. The proposed model introduces a dual NFT architecture deployed on Layer 2 networks: Membership NFTs (\(NFT_{auth}\)) for authenticat

Louis Leconte, Lisa Bedin, Van Minh Nguyen, Eric Moulines

We introduce ReALLM, a novel approach for compression and memory-efficient adaptation of pre-trained language models that encompasses most of the post-training quantization and fine-tuning methods for a budget of <4 bits. Pre-trained matrices are decomposed into a high-precision low-rank component and a vector-quantized latent representation (using an autoen

Xinyi Zhang, Pamela J. Wisniewski, Jin-hee Cho, Lifu Huang

Cybergrooming emerges as a growing threat to adolescent safety and mental health. One way to combat cybergrooming is to leverage predictive artificial intelligence (AI) to detect predatory behaviors in social media. However, these methods can encounter challenges like false positives and negative implications such as privacy concerns. Another complementary s

Ruiyu Han, Cynthia Rush, Johannes Wiesel

Optimal transport and the Wasserstein distance $\mathcal{W}_p$ have recently seen a number of applications in the fields of statistics, machine learning, data science, and the physical sciences. These applications are however severely restricted by the curse of dimensionality, meaning that the number of data points needed to estimate these problems accuratel

Shiji Huang, Lei Ye, Min Chen, Wenhai Luo

A thorough understanding of the interaction between the target agent and surrounding agents is a prerequisite for accurate trajectory prediction. Although many methods have been explored, they assign correlation coefficients to surrounding agents in a purely learning-based manner. In this study, we present ASPILin, which manually selects interacting agents a

Soveny Solís, Vicente Vergara

Osgood functions in the source term are used to produce results for non-existence of local solutions into the framework of non-Gaussian diffusion equations. The critical exponent for non-existence of local solutions is found to depend on the fractional derivative, the non-Gaussian diffusion and the non-linear term. The instantaneous blow-up phenomenon is stu

Denjoe O'Connor, Sanjaye Ramgoolam

We study the thermodynamic properties of the simplest gauged permutation invariant matrix quantum mechanical system of oscillators, for general matrix size $N$. In the canonical ensemble, the model has a transition at a temperature $T$ given by $x = e^{ -1/ T } \sim x_c=e^{-1/T_c}=\frac{\log N}{N}$, characterised by a sharp peak in the specific heat capacity

Yifan Chen, Bamdad Hosseini, Houman Owhadi, Andrew M Stuart

The article presents a systematic study of the problem of conditioning a Gaussian random variable $\xi$ on nonlinear observations of the form $F \circ \phi(\xi)$ where $\phi: \mathcal{X} \to \mathbb{R}^N$ is a bounded linear operator and $F$ is nonlinear. Such problems arise in the context of Bayesian inference and recent machine learning-inspired PDE solver

Krittapas Chanchaiworawit, Vicki Sarajedini

We present the results from a study of ~9,600 Broad-Line selected AGN with host galaxies detected from the Sloan Digital Sky Survey Data Release 17 (SDSS DR17). We compute ensemble variability statistics based on the comparison of the original SDSS photometric data with spectrophotometric measurements obtained days to decades later in the Sloan g-, r-, and i

Gaoxiang Li, Yu Zhuang

Physical Unclonable Functions (PUFs) are emerging as promising security primitives for IoT devices, providing device fingerprints based on physical characteristics. Despite their strengths, PUFs are vulnerable to machine learning (ML) attacks, including conventional and reliability-based attacks. Conventional ML attacks have been effective in revealing vulne

Yu Zhuang, Gaoxiang Li

Lightweight authentication is essential for resource-constrained Internet-of-Things (IoT). Implementable with low resource and operable with low power, Physical Unclonable Functions (PUFs) have the potential as hardware primitives for implementing lightweight authentication protocols. The arbiter PUF (APUF) is probably the most lightweight strong PUF capable

Kaijie Yang, Yunzhe Liu, Frank Schindler, Chao-Xing Liu

The emergence of topologically non-trivial flat bands in moir\'e materials provides an opportunity to explore the interplay between topological physics and correlation effects, leading to the recent experimental realization of interacting topological phases, e.g. fractional Chern insulators. In this work, we propose a mechanism of band inversion induced by b

Xuhan Huang, Qingning Shen, Yan Hu, Anningzhe Gao

Large Language Models (LLMs) have demonstrated strong performance across various natural language processing tasks, yet their proficiency in mathematical reasoning remains a key challenge. Addressing the gap between natural and mathematical language requires advanced reasoning capabilities, approaching those of Artificial General Intelligence (AGI). However,

Harm Derksen, Peter Ivanov, Chin Ho Lee, Emanuele Viola

We prove several new results about bounded uniform and small-bias distributions. A main message is that, small-bias, even perturbed with noise, does not fool several classes of tests better than bounded uniformity. We prove this for threshold tests, small-space algorithms, and small-depth circuits. In particular, we obtain small-bias distributions that 1) ac

Vinay A. Juvekar, Arun K. Singh

In this work, we analyse the stick-slip motion of a soft elastomeric block on a smooth, hard surface under the application of shear, which is induced by a puller moving at a steady velocity. The frictional stress is generated by make-break of bonds between the pendent chains of the elastomeric block and bonding sites on the hard surface. Relation between vel

M. Babcinschi, F. Cruz, N. Duarte, S. Santos

Robots have been successfully deployed in both traditional and novel manufacturing processes. However, they are still difficult to program by non-experts, which limits their accessibility to a wider range of potential users. Programming robots requires expertise in both robotics and the specific manufacturing process in which they are applied. Robot programs

Soumyadip Ghosh, Yingdong Lu, Tomasz Nowicki

We study convergence rates of Hamiltonian Monte Carlo (HMC) algorithms with leapfrog integration under mild conditions on stochastic gradient oracle for the target distribution (SGHMC). Our method extends standard HMC by allowing the use of general auxiliary distributions, which is achieved by a novel procedure of Alternating Directions. The convergence anal

Konstantinos Koutrolikos

We present a non-geometric derivation of $\mathcal{N}$=1 Super Yang-Mills by focusing on the consistency of interactions that extend the free vector supermultiplet rather than assuming gauge invariance under extended symmetries. By utilizing a superspace first-order description, the theory is given in closed form as a third-order polynomial which includes a

László Székelyhidi

In this paper we show that spectral analysis implies spectral synthesis for arbitrary varieties on locally compact Abelian groups, which have no discrete subgroup of infinite torsion free rank..

John Graf, Naihuan Jing

We introduce a Pfaffian formula that extends Schur's $Q$-functions $Q_\lambda$ to be indexed by compositions $\lambda$ with negative parts. This formula makes the Pfaffian construction more consistent with other constructions, such as the Young tableau and Vertex Operator constructions. With this construction, we develop a proof technique involving decomposi

Michael Lu, Matin Aghaei, Anant Raj, Sharan Vaswani

We consider (stochastic) softmax policy gradient (PG) methods for bandits and tabular Markov decision processes (MDPs). While the PG objective is non-concave, recent research has used the objective's smoothness and gradient domination properties to achieve convergence to an optimal policy. However, these theoretical results require setting the algorithm para

Tong Zeng, Daniel Acuna

Datasets are critical for scientific research, playing an important role in replication, reproducibility, and efficiency. Researchers have recently shown that datasets are becoming more important for science to function properly, even serving as artifacts of study themselves. However, citing datasets is not a common or standard practice in spite of recent ef

Xuezhang Chen, Wei Wei

We establish the global $C^2$-estimates for the modified $\sigma_2$ curvature equation with prescribed boundary mean curvature, and particularly, the local boundary $C^2$ estimates on three-manifolds.

Kyle R. Tregoning, Jeff J. Andrews, Marcel A. Agüeros, Phillip A. Cargile

The application of clustering algorithms to the Gaia astrometric catalog has revolutionized our census of stellar populations in the Milky Way, including the discovery of many new, dispersed structures. We focus on one such structure, Theia 456 (COIN-Gaia-13), a loosely bound collection of ~320 stars spanning ~120 pc that has previously been shown to exhibit

Mrinalini Mulukutla, A. Nicole Person, Sven Voigt, Lindsey Kuettner

Algorithmic materials discovery is a multi-disciplinary domain that integrates insights from specialists in alloy design, synthesis, characterization, experimental methodologies, computational modeling, and optimization. Central to this effort is a robust data management system paired with an interactive work platform. This platform should empower users to n

Raghuveer Thirukovalluru, Yukun Huang, Bhuwan Dhingra

Recent work has aimed to improve LLM generations by filtering out hallucinations, thereby improving the precision of the information in responses. Correctness of a long-form response, however, also depends on the recall of multiple pieces of information relevant to the question. In this paper, we introduce Atomic Self-Consistency (ASC), a technique for impro

A. Norman Redlich

A recursive tree planner (RTP) is designed to function as a pure planner without policies at one extreme and run a pure greedy policy at the other. In between, the RTP exploits policies to improve planning performance and improve zero-shot transfer from one class of planning problem to another. Policies are learned through imitation of the planner. These are

Markus Stocker, Lauren Snyder, Matthew Anfuso, Oliver Ludwig

Literature is the primary expression of scientific knowledge and an important source of research data. However, scientific knowledge expressed in narrative text documents is not inherently machine reusable. To facilitate knowledge reuse, e.g. for synthesis research, scientific knowledge must be extracted from articles and organized into databases post-public

Jerry W. Xuan, Chih-Chun Hsu, Luke Finnerty, Jason J. Wang

Using Keck Planet Imager and Characterizer (KPIC) high-resolution ($R$~35000) spectroscopy from 2.29-2.49 $\mu$m, we present uniform atmospheric retrievals for eight young substellar companions with masses of ~10-30 $M_\textrm{Jup}$, orbital separations spanning ~50-360 au, and $T_\textrm{eff}$ between ~1500-2600 K. We find that all companions have solar C/O

Sara Sarto, Marcella Cornia, Lorenzo Baraldi, Alessandro Nicolosi

The objective of image captioning models is to bridge the gap between the visual and linguistic modalities by generating natural language descriptions that accurately reflect the content of input images. In recent years, researchers have leveraged deep learning-based models and made advances in the extraction of visual features and the design of multimodal c

Eugeny Babichev, Keisuke Izumi, Karim Noui, Norihiro Tanahashi

We study new classes of metric transformations in the context of scalar-tensor theories, which involve both higher derivatives of the scalar field and derivatives of the metric itself. In general, such transformations are not invertible as they involve derivatives of the metric, which typically leads to instability due to Ostrogradsky ghosts. We show, howeve

Evan C. Morris, Jason J. Wang, Chih-Chun Hsu, Jean-Baptiste Ruffio

We used the Keck Planet Imager and Characterizer (KPIC) to obtain high-resolution (R$\sim$35,000) K-band spectra of kappa Andromedae b, a planetary-mass companion orbiting the B9V star, kappa Andromedae A. We characterized its spin, radial velocity, and bulk atmospheric parameters through use of a forward modeling framework to jointly fit planetary spectra a

Jennifer A. Burt, Matthew J. Hooton, Eric E. Mamajek, Oscar Barragán

We present an updated characterization of the TOI-1685 planetary system, which consists of a P$_{\rm{b}}$ = 0.69\,day USP super-Earth planet orbiting a nearby ($d$ = 37.6\,pc) M2.5V star (TIC 28900646, 2MASS J04342248+4302148). This planet was previously featured in two contemporaneous discovery papers, but the best-fit planet mass, radius, and bulk density

Akshara Viswanathan, Zhen Yuan, Anke Ardern-Arentsen, Else Starkenburg

The Pristine-\textit{Gaia} synthetic catalogue provides reliable photometric metallicities for $\sim$30 million FGK stars using the Pristine survey model and Gaia XP spectra. We perform the first low-to-medium-resolution spectroscopic follow-up of bright (G<15) and distant (up to 35 kpc) very and extremely metal-poor (V/EMP, [Fe/H]<-2.5) red giant branch sta

Carlo Nipoti, Cristina Caprioglio, Cecilia Bacchini

The local gravitational instability of rotating discs is believed to be an important mechanism in different astrophysical processes, including the formation of gas and stellar clumps in galaxies. We aim to study in three dimensions the local gravitational instability of two-component thick discs. We take as starting point a recently proposed analytic three-d

Zhiyuan Song, Naveen A. Reddy, Yuguang Chen, Alice E. Shapley

We present Keck Cosmic Web Imager IFU observations around extended Ly$\alpha$ halos of 27 typical star-forming galaxies with redshifts $2.0 < z < 3.2$ drawn from the MOSFIRE Deep Evolution Field survey. We examine the average Ly$\alpha$ surface-brightness profiles in bins of star-formation rate (SFR), stellar mass ($M_*$), age, stellar continuum reddening, S

Timon Thomas, Christoph Pfrommer, Rüdiger Pakmor

Galactic outflows influence the evolution of galaxies not only by expelling gas from their disks but also by injecting energy into the circumgalactic medium (CGM). This alters or even prevents the inflow of fresh gas onto the disk and thus reduces the star formation rate. Supernovae (SNe) are the engines of galactic winds as they release thermal and kinetic