July 2023 arXiv papers — page 83

W Kent Tobiska

We face unprecedented resource stresses in the 21st Century such as global climate disruptions, freshwater scarcity, expanding energy demands, and the threat of global pandemics. Historically, societies have relieved resource stress by increasing trade, innovating technologically, expanding territorially, regulating, redistributing, making alliances, creatin

Yining Lu, Haoping Yu, Daniel Khashabi

Augmenting large language models (LLM) to use external tools enhances their performance across a variety of tasks. However, prior works over-rely on task-specific demonstration of tool use that limits their generalizability and computational cost due to making many calls to large-scale LLMs. We introduce GEAR, a computationally efficient query-tool grounding

César França

This article explores the transformative impact of artificial intelligence (AI) on scientific research. It highlights ten ways in which AI is revolutionizing the work of scientists, including powerful referencing tools, improved understanding of research problems, enhanced research question generation, optimized research design, stub data generation, data tr

Lily Xu, Esther Rolf, Sara Beery, Joseph R. Bennett

In this white paper, we synthesize key points made during presentations and discussions from the AI-Assisted Decision Making for Conservation workshop, hosted by the Center for Research on Computation and Society at Harvard University on October 20-21, 2022. We identify key open research questions in resource allocation, planning, and interventions for biodi

Shuli Jones, Isabella Pedraza Pineros, Daniel Hajas, Jonathan Zong

Customization is crucial for making visualizations accessible to blind and low-vision (BLV) people with widely-varying needs. But what makes for usable or useful customization? We identify four design goals for how BLV people should be able to customize screen-reader-accessible visualizations: presence, or what content is included; verbosity, or how concisel

Amir Azarmehr, Soheil Behnezhad, Mohammad Roghani

We study maximum matchings in fully dynamic graphs, which are graphs that undergo both edge insertions and deletions. Our focus is on algorithms that estimate the size of maximum matching after each update while spending a small time. An important question studied extensively is the best approximation achievable via algorithms that only spend $\text{poly}(\l

Alvin Wan, Hanxiang Hao, Kaushik Patnaik, Yueyang Xu

As neural networks grow in size and complexity, inference speeds decline. To combat this, one of the most effective compression techniques -- channel pruning -- removes channels from weights. However, for multi-branch segments of a model, channel removal can introduce inference-time memory copies. In turn, these copies increase inference latency -- so much s

Ema F. S. Valente, Alexandre C. M. Correia

Close-in planets undergo strong tidal interactions with the parent star that modify their spins and orbits. In the two-body problem, the final stage for tidal evolution is the synchronisation of the rotation and orbital periods, and the alignment of the planet spin axis with the normal to the orbit (zero planet obliquity). The orbital eccentricity is also da

Lukas Karoly, David C. Latimer

Thermal relics with masses in the GeV to TeV range remain possible candidates for the Universe's dark matter (DM). These neutral particles are often assumed to have vanishing electric and magnetic dipole moments so that they do not interact with single real photons, but the anapole moment can still be nonzero, permitting interactions with single virtual phot

Hamed Amini, Maxim Bichuch, Zachary Feinstein

Prediction markets allow traders to bet on potential future outcomes. These markets exist for weather, political, sports, and economic forecasting. Within this work we consider a decentralized framework for prediction markets using automated market makers (AMMs). Specifically, we construct a liquidity-based AMM structure for prediction markets that, under re

Gang Chen

When to solve math problems, most language models take a sampling strategy to predict next word according conditional probabilities. In the math reasoning step, it may generate wrong answer. Considering math problems are deterministic, we propose a mixed policy exploration approach to solve math problems with reinforcement learning. In peculiar, we propose a

Felipe M. Dias, Marcelo A. F. Toledo, Diego A. C. Cardenas, Douglas A. Almeida

Photoplethysmography (PPG) is a non-invasive technology that measures changes in blood volume in the microvascular bed of tissue. It is commonly used in medical devices such as pulse oximeters and wrist worn heart rate monitors to monitor cardiovascular hemodynamics. PPG allows for the assessment of parameters (e.g., heart rate, pulse waveform, and periphera

Kazuki Watanabe, Clovis Eberhart, Kazuyuki Asada, Ichiro Hasuo

We present a compositional model checking algorithm for Markov decision processes, in which they are composed in the categorical graphical language of string diagrams. The algorithm computes optimal expected rewards. Our theoretical development of the algorithm is supported by category theory, while what we call decomposition equalities for expected rewards

Rigoberto Zelada

In "IP-sets and polynomial recurrence", Bergelson, Furstenberg, and McCutcheon established the following far reaching extension of Khintchine's recurrence theorem: For any invertible probability preserving system $(X,\mathcal A,\mu,T)$, any non-constant polynomial $p\in\mathbb Z[x]$ with $p(0)=0$, any $A\in\mathcal A$, and any $\epsilon>0$, the set $$R_\epsi

Kumar Ashutosh, Santhosh Kumar Ramakrishnan, Triantafyllos Afouras, Kristen Grauman

Procedural activity understanding requires perceiving human actions in terms of a broader task, where multiple keysteps are performed in sequence across a long video to reach a final goal state -- such as the steps of a recipe or a DIY fix-it task. Prior work largely treats keystep recognition in isolation of this broader structure, or else rigidly confines

Ningshan Wang, Reza Hamrah, Amit K. Sanyal, Mark N. Glauser

This article presents an extended state observer for vehicle modeled as a rigid body in three-dimensional translational and rotational motions. The extended state observer is applicable to a rotorcraft aerial vehicle with a fixed plane of rotors, modeled as an under-actuated system on the tangent bundle of the six-dimensional Lie group of rigid body motions,

Pablo Sala, Yizhi You, Johannes Hauschild, Olexei Motrunich

We investigate the effect that spatially modulated continuous conserved quantities can have on quantum ground states. We do so by introducing a family of one-dimensional local quantum rotor and bosonic models which conserve finite Fourier momenta of the particle number, but not the particle number itself. These correspond to generalizations of the standard B

Tung Nguyen, Jeffrey Uhlmann

In this paper we discuss pre- and post-processing methods to induce desired consistency and/or invariance properties in blackbox systems, e.g., AI-based. We demonstrate our approach in the context of blackbox SVD-based matrix-completion methods commonly used in recommender system (RS) applications. We provide empirical results showing that enforcement of uni

Alex Hubers, J. Garrett Morris

We present a novel approach to generic programming over extensible data types. Row types capture the structure of records and variants, and can be used to express record and variant subtyping, record extension, and modular composition of case branches. We extend row typing to capture generic programming over rows themselves, capturing patterns including lift

U. Pudasaini, G. V. Eremeev, S. Cheban

The mechanical vulnerability of the Nb3Sn-coated cavities is identified as one of the significant technical hurdles toward deploying them in practical accelerator applications in the not-so-distant future. It is crucial to characterize the material's mechanical properties in ways to address such vulnerability. Nanoindentation is a widely used technique for m

Alexander Rawlings, Matias Mannerkoski, Peter H. Johansson, Thorsten Naab

We study supermassive black hole (SMBH) binary eccentricity of equal-mass galaxy mergers in $N$-body simulations with the KETJU code, which combines the GAGDET-4 fast multipole gravity solver with accurate regularised integration and post-Newtonian corrections around SMBHs. In simulations with realistic, high eccentricity galactic merger orbits, the hard bin

Marina Huerta, Guido van der Velde

In this article, we extend our study on a new class of modular Hamiltonians on an interval attached to the origin on the semi-infinite line, introduced in a recent work dedicated to scalar fields. Here, we shift our attention to fermions and similarly to the scalar case, we investigate the modular Hamiltonians of theories which are obtained through dimension

C. Martínez-Strasser, M. A. J. Herrera, A. García-Etxarri, G. Palumbo

The spectral properties of a non-Hermitian quasi-1D lattice in two of the possible dimerization configurations are investigated. Specifically, it focuses on a non-Hermitian diamond chain that presents a zero-energy flat band. The flat band originates from wave interference and results in eigenstates with a finite contribution only on two sites of the unit ce

Phil Van-Lane, Joshua S. Speagle, Stephanie Douglas

Stellar ages are critical building blocks of evolutionary models, but challenging to measure for low mass main sequence stars. An unexplored solution in this regime is the application of probabilistic machine learning methods to gyrochronology, a stellar dating technique that is uniquely well suited for these stars. While accurate analytical gyrochronologica

Sukrit Ranjan, Edward W. Schwieterman, Michaela Leung, Chester E. Harman

Efforts to spectrally characterize the atmospheric compositions of temperate terrestrial exoplanets orbiting M-dwarf stars with the James Webb Space Telescope (JWST) are now underway. Key molecular targets of such searches include O$_2$ and CO, which are potential indicators of life. Recently, it was proposed that CO$_2$ photolysis generates abundant ($\gtrs

Daniel Arean, David Garcia-Fariña, Karl Landsteiner

Quasinormal modes and frequencies are the eigenvectors and eigenvalues of a non-Hermitian differential operator. They hold crucial significance in the physics of black holes. The analysis of quasinormal modes of black holes in asymptotically Anti-de Sitter geometries plays also a key role in the study of strongly coupled quantum many-body systems via gauge/g

Jun-Qing Cheng, Shuai Yin, Dao-Xin Yao

Local constraint in the lattice gauge theory provides an exotic mechanism that facilitates the disorder-free localization. However, the understanding of nonequilibrium dynamics in the non-Hermitian lattice gauge model remains limited. Here, we investigate the quench dynamics in a system of spinless fermions with nonreciprocal hopping in the $\mathbb{Z}_2$ ga

S. Damsted, A. Finoguenov, N. Clerc, I. Davalgaite

The use of galaxy clusters as cosmological probes relies on a detailed understanding of their properties. We aim to update the spectroscopic cluster identification of CODEX by running the spectroscopic group finder on the follow-up spectroscopy results and connecting the dynamical state of clusters to their scaling relations. We implemented a reproducible sp

Ilya Lukin, Andrii Sotnikov, Alexander Kruchkov

We investigate superfluidity of bosons in gapped topological bands and discover a new phase that has no counterparts in the previous literature. This phase is characterized by a highly unconventional modulation of the order parameter, breaking the crystallographic symmetry, and for which the condensation momentum is neither zero nor any other high-symmetry v

Daniel F. Litim, Nahzaan Riyaz, Emmanuel Stamou, Tom Steudtner

We investigate a family of four-dimensional quantum field theories with weakly interacting ultraviolet fixed points up to four loop order in perturbation theory. Key new ingredients are the three loop gauge contributions to quartic scalar beta functions, which we compute in the $\overline{\text{MS}}$ scheme for a template $SU(N_c)$ gauge theory coupled to $N

S. Ganguly, S. Walch, D. Seifried, S. D. Clarke

To what extent magnetic fields affect how molecular clouds (MCs) fragment and create dense structures is an open question. We present a numerical study of cloud fragmentation using the SILCC-Zoom simulations. These simulations follow the self-consistent formation of MCs in a few hundred parsec sized region of a stratified galactic disc; and include magnetic

Jason Aebischer, Matteo Fael, Javier Fuentes-Martín, Anders Eller Thomsen

In recent years, theoretical and phenomenological studies with effective field theories have become a trending and prolific line of research in the field of high-energy physics. In order to discuss present and future prospects concerning automated tools in this field, the SMEFT-Tools 2022 workshop was held at the University of Zurich from 14th-16th September

Prantik Sarmah

The recent supernova, SN 2023ixf, one of the closest observed type II SNe has revealed the presence of a dense circumstellar material (CSM). Interaction of the SN ejecta with this dense CSM might create high energy protons of PeV energies through shock acceleration. These accelerated protons then colliding with the CSM (inelastic $pp$ collision) can produce

Albert Bosma

The concept of dark matter in the Universe and its components has been discussed in the 1930s by several authors, and in particular by Oort (1932) and Zwicky (1933). However, it is only in the 1970s that the existence of dark matter was considered convincing, thanks in part to observations of the rotation curves of galaxies. This dark matter should be presen

Vijay Pal Singh, Juan Polo, Ludwig Mathey, Luigi Amico

We study driven atomic Josephson junctions realized by coupling two two-dimensional atomic clouds with a tunneling barrier. By moving the barrier at a constant velocity, dc and ac Josephson regimes are characterized by a zero and nonzero atomic density difference across the junction, respectively. Here, we monitor the dynamics resulting in the system when, i

Chi-Ming Chang, Xiaoyang Shen

We investigate a large class of $\mathcal{N} = (2, 2)$ supersymmetric field theories in two dimensions, which contains the Murugan-Stanford-Witten model, and can be naturally regarded as a disordered generalization of the two-dimensional Landau-Ginzburg models. We analyze the two and four-point functions of chiral superfields, and extract from them the centr

Noah Kaufmann, Ivan Rojkov, Florentin Reiter

Characterization of quantum devices generates insights into their sources of disturbances. State-of-the-art characterization protocols often focus on incoherent noise and eliminate coherent errors when using Pauli or Clifford twirling techniques. This approach biases the structure of the effective noise and adds a circuit and sampling overhead. We motivate t

Christoph F. Uhlemann, Mianqi Wang

We discuss entanglement entropies in 4d interface CFTs based on 4d $\mathcal N=4$ SYM coupled to 3d $\mathcal N=4$ degrees of freedom localized on an interface. Focusing on the entanglement between the two half spaces to either side of the interface, we show that applying the Ryu-Takayanagi prescription in general leads to multiple natural entanglement entro

José Antonio Marín Guzmán, Paul Erker, Simone Gasparinetti, Marcus Huber

Controlled quantum machines have matured significantly. A natural next step is to increasingly grant them autonomy, freeing them from time-dependent external control. For example, autonomy could pare down the classical control wires that heat and decohere quantum circuits; and an autonomous quantum refrigerator recently reset a superconducting qubit to near

Kristen B. W. McQuinn, Yao-Yuan Mao, Erik J. Tollerud, Roger E. Cohen

We report the discovery of two ultra-faint dwarf galaxies, Leo M and Leo K, that lie outside the halo of the Milky Way. Using Hubble Space Telescope imaging of the resolved stars, we create color-magnitude diagrams reaching the old main sequence turn-off of each system and (i) fit for structural parameters of the galaxies; (ii) measure their distances using

Harry Levine, Arbel Haim, Jimmy S. C. Hung, Nasser Alidoust

Quantum error correction with erasure qubits promises significant advantages over standard error correction due to favorable thresholds for erasure errors. To realize this advantage in practice requires a qubit for which nearly all errors are such erasure errors, and the ability to check for erasure errors without dephasing the qubit. We demonstrate that a "

Euclid Collaboration, L. Leuzzi, M. Meneghetti, G. Angora

Forthcoming imaging surveys will potentially increase the number of known galaxy-scale strong lenses by several orders of magnitude. For this to happen, images of tens of millions of galaxies will have to be inspected to identify potential candidates. In this context, deep learning techniques are particularly suitable for the finding patterns in large data s

David Vartanyan, Adam Burrows

We present in this paper a public data release of an unprecedentedly-large set of core-collapse supernova (CCSN) neutrino emission models, comprising one hundred detailed 2D-axisymmetric radiation-hydrodynamic simulations evolved out to as late as ~5 seconds post-bounce and spanning a extensive range of massive-star progenitors. The motivation for this paper

Tyler Gorda, Risto Paatelainen, Saga Säppi, Kaapo Seppänen

Accurately understanding the equation of state (EOS) of high-density, zero-temperature quark matter plays an essential role in constraining the behavior of dense strongly interacting matter inside the cores of neutron stars. In this Letter, we study the weak-coupling expansion of the EOS of cold quark matter and derive the complete, gauge-invariant contribut

Gongjun Choi, Jacob Leedom

The QCD axion can be be formulated in a dual description as a massive 2-form field. In this picture, the QCD axion quality problem translates into the question if there are additional 3-forms coupled to the axion other than the QCD 3-form that emerges at low energy. If such forms exist, the quality problem can be resolved via the introduction of other massiv

Li Gan

We obtain an explicit formula to enumerate closed random walks on a cubic lattice with a specified length and 3D algebraic area. The 3D algebraic area is defined as the sum of algebraic areas obtained from the walk's projection onto the three Cartesian planes. This enumeration formula can be mapped onto the cluster coefficients of three types of particles th

Erik S. Sørensen, Hae-Young Kee

Finding the Kitaev spin liquid in candidate materials involves understanding the entire phase diagram, including other allowed interactions. One of these interactions, called the Gamma ($\Gamma$) interaction, causes magnetic frustration and its interplay with the Kitaev ($K$) interaction is crucial to comprehend Kitaev materials. Due to the complexity of the

Emily C. Cunningham, Jason A. S. Hunt, Adrian M. Price-Whelan, Kathryn V. Johnston

The stellar stream connected to the Sagittarius (Sgr) dwarf galaxy is the most massive tidal stream that has been mapped in the Galaxy, and is the dominant contributor to the outer stellar halo of the Milky Way. We present metallicity maps of the Sgr stream, using 34,240 red giant branch stars with inferred metallicities from Gaia BP/RP spectra. This sample

T. Pantev, E. Sharpe

It was recently argued by Nguyen-Tanizaki-Unsal that two-dimensional pure Yang-Mills theory is equivalent to (decomposes into) a disjoint union of (invertible) quantum field theories, known as universes. In this paper we compare this decomposition to the Gross-Taylor expansion of two-dimensional pure SU(N) Yang-Mills theory in the large N limit as the string

Jung-Tsung Li, John F. Beacom, Spencer Griffith, Annika H. G. Peter

The Sun is a bright gamma-ray source due to hadronic cosmic-ray interactions with solar gas. While it is known that incoming cosmic rays must generally first be reflected by solar magnetic fields to produce outgoing gamma rays, theoretical models have yet to reproduce the observed spectra. We introduce a simplified model of the solar magnetic fields that cap

Haochang Jiang, Yu Wang, Chris W. Ormel, Sebastiaan Krijt

Protoplanetary disks, the birthplaces of planets, commonly feature bright rings and dark gaps in both continuum and line emission maps. Accreting planets are interacting with the disk, not only through gravity, but also by changing the local irradiation and elemental abundances, which are essential ingredients for disk chemistry. We propose that giant planet

Soumik Mukhopadhyay, Matthew Gwilliam, Vatsal Agarwal, Namitha Padmanabhan

While many unsupervised learning models focus on one family of tasks, either generative or discriminative, we explore the possibility of a unified representation learner: a model which uses a single pre-training stage to address both families of tasks simultaneously. We identify diffusion models as a prime candidate. Diffusion models have risen to prominence

Lichang Chen, Shiyang Li, Jun Yan, Hai Wang

Large language models (LLMs) strengthen instruction-following capability through instruction-finetuning (IFT) on supervised instruction/response data. However, widely used IFT datasets (e.g., Alpaca's 52k data) surprisingly contain many low-quality instances with incorrect or irrelevant responses, which are misleading and detrimental to IFT. In this paper, w

Vít Růžička, Gonzalo Mateo-García, Chris Bridges, Chris Brunskill

Artificial intelligence onboard satellites has the potential to reduce data transmission requirements, enable real-time decision-making and collaboration within constellations. This study deploys a lightweight foundational model called RaVAEn on D-Orbit's ION SCV004 satellite. RaVAEn is a variational auto-encoder (VAE) that generates compressed latent vector

Jinghao Wang, Zhengyu Wen, Xiangtai Li, Zujin Guo

Panoptic Scene Graph (PSG) is a challenging task in Scene Graph Generation (SGG) that aims to create a more comprehensive scene graph representation using panoptic segmentation instead of boxes. Compared to SGG, PSG has several challenging problems: pixel-level segment outputs and full relationship exploration (It also considers thing and stuff relation). Th

Quan Dao, Hao Phung, Binh Nguyen, Anh Tran

Flow matching is a recent framework to train generative models that exhibits impressive empirical performance while being relatively easier to train compared with diffusion-based models. Despite its advantageous properties, prior methods still face the challenges of expensive computing and a large number of function evaluations of off-the-shelf solvers in th

Tomas Andrade, Juan Trenado, Simone Albanesi, Rossella Gamba

Dynamical captures of black holes may take place in dense stellar media due to the emission of gravitational radiation during a close passage. Detection of such events requires detailed modelling, since their phenomenology qualitatively differs from that of quasi-circular binaries. Very few models can deliver such waveforms, and none includes information fro

Steven Rosenberg

In this note, we compute the reproducing kernel for the RKHS of functions on $\mathbb{R}^n$ in a sufficiently high Sobolev norm.

Yiran Wang, Min Shi, Jiaqi Li, Chaoyi Hong

Video depth estimation aims to infer temporally consistent depth. One approach is to finetune a single-image model on each video with geometry constraints, which proves inefficient and lacks robustness. An alternative is learning to enforce consistency from data, which requires well-designed models and sufficient video depth data. To address both challenges,

David Conlon, Sam Mattheus, Dhruv Mubayi, Jacques Verstraëte

Building on recent work of Mattheus and Verstra\"ete, we establish a general connection between Ramsey numbers of the form $r(F,t)$ for $F$ a fixed graph and a variant of the Zarankiewicz problem asking for the maximum number of 1s in an $m$ by $n$ $0/1$-matrix that does not have any matrix from a fixed finite family $\mathcal{L}(F)$ derived from $F$ as a su

Vic De Ridder, Bappaditya Dey, Sandip Halder, Bartel Van Waeyenberge

With continuous progression of Moore's Law, integrated circuit (IC) device complexity is also increasing. Scanning Electron Microscope (SEM) image based extensive defect inspection and accurate metrology extraction are two main challenges in advanced node (2 nm and beyond) technology. Deep learning (DL) algorithm based computer vision approaches gained popul

M. Vivienne Liu, Patrick M. Reed, David Gold, Garret Quist

The emergence of microgrids (MGs) has provided a promising solution for decarbonizing and decentralizing the power grid, mitigating the challenges posed by climate change. However, MG operations often involve considering multiple objectives that represent the interests of different stakeholders, leading to potentially complex conflicts. To tackle this issue,

Tri Dao

Scaling Transformers to longer sequence lengths has been a major problem in the last several years, promising to improve performance in language modeling and high-resolution image understanding, as well as to unlock new applications in code, audio, and video generation. The attention layer is the main bottleneck in scaling to longer sequences, as its runtime

Akanimoh Adeleye

Robotic Exploration has evolved rapidly in the past two decades as new and more complex techniques have been created to explore unknown regions efficiently. Exciting advancements in exploration, autonomous navigation, and sensor technology have created opportunities for robots to be utilized in new environments and for new objectives ranging from mapping of

Shunyu Yao, Howard Chen, Austin W. Hanjie, Runzhe Yang

Text generation under constraints have seen increasing interests in natural language processing, especially with the rapidly improving capabilities of large language models. However, existing benchmarks for constrained generation usually focus on fixed constraint types (e.g.,generate a sentence containing certain words) that have proved to be easy for state-

Shilong Wu, Jun Du, Maokui He, Shutong Niu

Most neural speaker diarization systems rely on sufficient manual training data labels, which are hard to collect under real-world scenarios. This paper proposes a semi-supervised speaker diarization system to utilize large-scale multi-channel training data by generating pseudo-labels for unlabeled data. Furthermore, we introduce cross-channel attention into

Lukas Knobel, Tengda Han, Yuki M. Asano

While recent supervised methods for reference-based object counting continue to improve the performance on benchmark datasets, they have to rely on small datasets due to the cost associated with manually annotating dozens of objects in images. We propose UnCounTR, a model that can learn this task without requiring any manual annotations. To this end, we cons

Jia-Heng Jin, Zu-Cheng Chen, Zhu Yi, Zhi-Qiang You

The stochastic signal detected by pulsar timing arrays (PTAs) has raised great interest in understanding its physical origin. Assuming the signal is a cosmological gravitational-wave background produced by overly large primordial curvature perturbations, we investigate the sound speed resonance effect with an oscillatory behavior using the combined PTA data

Joshua Wolff Anderson, Cyril Rakovski

This article explains the usage of R package CausalModels, which is publicly available on the Comprehensive R Archive Network. While packages are available for sufficiently estimating causal effects, there lacks a package that provides a collection of structural models using the conventional statistical approach developed by Hernan and Robins (2020). CausalM

Robert C. Garrett, Trevor Harris, Zhuo Wang, Bo Li

Climate model evaluation plays a crucial role in ensuring the accuracy of climatological predictions. However, existing statistical evaluation methods often overlook time misalignment of events in a system's evolution, which can lead to a failure in identifying specific model deficiencies. This issue is particularly relevant for climate variables that involv

Ning Bao, Gavin S. Hartnett

The problem of decomposing an arbitrary Clifford element into a sequence of Clifford gates is known as Clifford synthesis. Drawing inspiration from similarities between this and the famous Rubik's Cube problem, we develop a machine learning approach for Clifford synthesis based on learning an approximation to the distance to the identity. This approach is pr

F. T. B. Pérez, J. M. Matera

The maximum-entropy principle (Max-Ent) is a valuable and extensively used tool in statistical mechanics and quantum information theory. It provides a method for inferring the state of a system by utilizing a reduced set of parameters associated with measurable quantities. However, the computational cost of employing Max-Ent projections in simulations of qua

Maciej Baczmanski, Mateusz Wasala, Tomasz Kryjak

Perception and control systems for autonomous vehicles are an active area of scientific and industrial research. These solutions should be characterised by high efficiency in recognising obstacles and other environmental elements in different road conditions, real-time capability, and energy efficiency. Achieving such functionality requires an appropriate al

Matthis Kruse, Michael Backes, Marco Patrignani

To ensure that secure applications do not leak their secrets, they are required to uphold several security properties such as spatial and temporal memory safety as well as cryptographic constant time. Existing work shows how to enforce these properties individually, in an architecture-independent way, by using secure compiler passes that each focus on an ind

Sabyasachi Basu, Manuj Mukherjee

Storage codes on graphs are an instance of \emph{codes with locality}, which are used in distributed storage schemes to provide local repairability. Specifically, the nodes of the graph correspond to storage servers, and the neighbourhood of each server constitute the set of servers it can query to repair its stored data in the event of a failure. A storage

Yanda Chen, Ruiqi Zhong, Narutatsu Ri, Chen Zhao

Large language models (LLMs) are trained to imitate humans to explain human decisions. However, do LLMs explain themselves? Can they help humans build mental models of how LLMs process different inputs? To answer these questions, we propose to evaluate $\textbf{counterfactual simulatability}$ of natural language explanations: whether an explanation can enabl

Gabriele Bogo, Yingkun Li, Markus Schwagenscheidt

In this paper, we study the Laurent coefficients of meromorphic modular forms at CM points by giving two approaches of computing them. The first is a generalization of the method of Rodriguez-Villegas and Zagier, which expresses the Laurent coefficients as constant terms of a family of polynomials obtained through recursion. The second applies to meromorphic

Liangyu Zha, Junlin Zhou, Liyao Li, Rui Wang

Tables are prevalent in real-world databases, requiring significant time and effort for humans to analyze and manipulate. The advancements in large language models (LLMs) have made it possible to interact with tables using natural language input, bringing this capability closer to reality. In this paper, we present TableGPT, a unified fine-tuned framework th

Fan Fan, Georgia Martinez, Thomas Desilvio, John Shin

Batch effects (BEs) refer to systematic technical differences in data collection unrelated to biological variations whose noise is shown to negatively impact machine learning (ML) model generalizability. Here we release CohortFinder, an open-source tool aimed at mitigating BEs via data-driven cohort partitioning. We demonstrate CohortFinder improves ML model

Arunabh Srivastava, Sennur Ulukus

We consider a gossip network consisting of a source generating updates and $n$ nodes connected in a two-dimensional square grid. The source keeps updates of a process, that might be generated or observed, and shares them with the grid network. The nodes in the grid network communicate with their neighbors and disseminate these version updates using a push-st

Mo Houtti, Isaac Johnson, Morten Warncke-Wang, Loren Terveen

Peer production platforms like Wikipedia commonly suffer from content gaps. Prior research suggests recommender systems can help solve this problem, by guiding editors towards underrepresented topics. However, it remains unclear whether this approach would result in less relevant recommendations, leading to reduced overall engagement with recommended items.

Alex Day, Ioannis Karamouzas

Despite recent advancements in human-robot interaction (HRI), there is still limited knowledge about how humans interact and behave in the presence of small service indoor robots and, subsequently, about the human-centered behavior of such robots. This also raises concerns about the applicability of current trajectory prediction methods to indoor HRI setting

Satori Tsuzuki, Eri Itoh, Katsuhiro Nishinari

Recently, we presented a new numerical scheme for vortex lattice formation in a rotating Bose-Einstein condensate (BEC) using smoothed particle hydrodynamics (SPH) with an explicit time-integrating scheme; our SPH scheme could reproduce the vortex lattices and their formation processes in rotating quasi-two-dimensional (2D) BECs trapped in a 2D harmonic pote

Prosenjit Paul, Sudhaker Upadhyay, Dharm Veer Singh

We investigate Einstein--Gauss--Bonnet--Maxwell massive gravity in $4D$ AdS background and find an exact black hole solution. The horizon structure of the black holes studied. Treating the cosmological constant as pressure and Gauss-Bonnet coupling parameters, and massive gravity parameters as variables, we drive the first law of black hole thermodynamics. T

Andrea Bianchi, Andreas Stavrou

For a compact orientable surface $\Sigma_{g,1}$ of genus $g$ with one boundary component and for an odd prime number $p$, we study the homology of the unordered configuration spaces $C_\bullet(\Sigma_{g,1}):=\coprod_{n\ge0}C_n(\Sigma_{g,1})$ with coefficients in $\mathbb{F}_p$. We describe $H_*(C_\bullet(\Sigma_{g,1});\mathbb{F}_p)$ as a bigraded module over

Gerardo Altamirano-Gomez, Carlos Gershenson

Since their first applications, Convolutional Neural Networks (CNNs) have solved problems that have advanced the state-of-the-art in several domains. CNNs represent information using real numbers. Despite encouraging results, theoretical analysis shows that representations such as hyper-complex numbers can achieve richer representational capacities than real

V\'{\I}tor Paiva, Paulo J. Silva, Orlando Oliveira

The temperature dependence of the Landau gauge ghost propagator is investigated in pure SU(3) Yang-Mills theory with lattice QCD simulations. Its behavior around the confined-deconfined phase transition temperature, $T_c \sim 270$ MeV, is investigated. The simulations show that in the deconfined phase, the ghost propagator is enhanced for small momenta, $\le

Guillaume Aubian

The aim of this thesis is to investigate how the structure of a digraph affects its dichromatic number and to extend various results on undirected colouring to digraphs. In the first part of this thesis, we examine how the dichromatic number interacts with other metrics. First, we consider the degree, which is the maximum number of neighbours of a vertex. In

Christine E. Collins, Stuart A. Sim, Luke. J. Shingles, Sabrina Gronow

The double detonation is a widely discussed mechanism to explain Type Ia supernovae from explosions of sub-Chandrasekhar mass white dwarfs. In this scenario, a helium detonation is ignited in a surface helium shell on a carbon/oxygen white dwarf, which leads to a secondary carbon detonation. Explosion simulations predict high abundances of unburnt helium in

Adam Rycerz, Maciej Fidrysiak, Danuta Goc-Jagło

Magnetic flux piercing a carbon nanotube induce periodic gap oscillations which represent the Aharonov-Bohm effect at nanoscale. Here we point out, by analyzing numerically the anisotropic Hubbard model on a honeycomb lattice, that similar oscillations should be observable when uniaxial strain is applied to a nanotube. In both cases, a vector potential (magn

N. V. Brilliantov, W. Otieno, S. A. Matveev, A. P. Smirnov

We report surprising steady oscillations in aggregation-fragmentation processes. Oscillating solutions are observed for the class of aggregation kernels $K_{i,j} = i^{\nu}j^{\mu} + j^{\nu}i^{\mu}$ homogeneous in masses $i$ and $j$ of merging clusters and fragmentation kernels, $F_{ij}=\lambda K_{ij}$, with parameter $\lambda$ quantifying the intensity of the

Kelsey Lieberman, James Diffenderfer, Charles Godfrey, Bhavya Kailkhura

Recent advances in neural image compression (NIC) have produced models that are starting to outperform classic codecs. While this has led to growing excitement about using NIC in real-world applications, the successful adoption of any machine learning system in the wild requires it to generalize (and be robust) to unseen distribution shifts at deployment. Un

N. T. Nguyen-Dang, G. Pühlhofer, M. Sasaki, A. Bamba

We report the results obtained from XMM-Newton observations of the TeV-detected supernova remnant (SNR) HESS J1534-571. We focus on the nature of the cosmic-ray particle content in the SNR, which is revealed by its $\gamma$-ray emission. No signatures of X-ray synchrotron emission were detected from the SNR. This is consistent with earlier results obtained w

Hongyu Gong, Ning Dong, Sravya Popuri, Vedanuj Goswami

Speech-to-speech translation (S2ST) enables spoken communication between people talking in different languages. Despite a few studies on multilingual S2ST, their focus is the multilinguality on the source side, i.e., the translation from multiple source languages to one target language. We present the first work on multilingual S2ST supporting multiple targe

Linda Knüver, Mareike Fischer, Marc Hellmuth, Kristina Wicke

Phylogenetic networks play an important role in evolutionary biology as, other than phylogenetic trees, they can be used to accommodate reticulate evolutionary events such as horizontal gene transfer and hybridization. Recent research has provided a lot of progress concerning the reconstruction of such networks from data as well as insight into their graph t

D. Turrini, F. Marzari, D. Polychroni, R. Claudi

Observational data from space and ground-based campaigns reveal that the 10-30 Ma old V1298 Tau star hosts a compact and massive system of four planets. Mass estimates for the two outer giant planets point to unexpectedly high densities for their young ages. We investigate the formation of these two outermost giant planets, V1298 Tau b and e, and the present

Aalok Gangopadhyay, Paras Gupta, Tarun Sharma, Prajwal Singh

We introduce the problem of knot-based inverse perceptual art. Given multiple target images and their corresponding viewing configurations, the objective is to find a 3D knot-based tubular structure whose appearance resembles the target images when viewed from the specified viewing configurations. To solve this problem, we first design a differentiable rende

Flavio Salvati, Wilfred Salmon, Crispin H. W. Barnes, David R. M. Arvidsson-Shukur

In quantum metrology, information about unknown parameters $\mathbf{\theta} = (\theta_1,\ldots,\theta_M)$ is accessed by measuring probe states $\hat{\rho}_{\mathbf{\theta}}$. In experimental settings where copies of $\hat{\rho}_{\mathbf{\theta}}$ can be produced rapidly (e.g., in optics), the information-extraction bottleneck can stem from high post-process

Daniel Matter, Elizaveta Kuznetsova, Victoria Vziatysheva, Ilaria Vitulano

Russian propaganda outlet RT (formerly, Russia Today) produces content in seven languages. There is ample evidence that RT's communication techniques differ for different language audiences. In this article, we offer the first comprehensive analysis of RT's multi-lingual article collection, analyzing all 2.4 million articles available on the online platform

Jingyu Song, Onur Bagoren, Katherine A. Skinner

For underwater vehicles, robotic applications have the added difficulty of operating in highly unstructured and dynamic environments. Environmental effects impact not only the dynamics and controls of the robot but also the perception and sensing modalities. Acoustic sensors, which inherently use mechanically vibrated signals for measuring range or velocity,