October 2023 arXiv papers — page 140

O. R. Dehghan

In this paper, three topics in bipolar fuzzy soft hypervector spaces are investigated. At first, four equivalent conditions to definition of a bipolar fuzzy soft hypervector space are presented, from different point of views. Then some new bipolar fuzzy soft hypervector spaces are constructed, using the notions of subhyperspace, level subset, generated subhy

Ofek Eliyahu

In this paper we will discuss optimal lower and upper density of non-parallel cylinder packings in $R^{3}$ and similar problems. The main result of the paper is a proof of the conjecture of K. Kuperberg for upper density (existence of a non-parallel cylinder packing with upper density ${\pi}/{\sqrt{12}}$). Moreover, we prove that for every $\varepsilon > 0$

Jun Liu, Yongxin Yao, Vladimir Antropov, Kai-Ming Ho

We applied the ab initio spin-polarized Correlation Matrix Renormalization Theory (CMRT) to the ferromagnetic state of the bulk BCC iron. We showed that it was capable of reproducing the equilibrium physical properties and the pressure-volume curve in good comparison with experiments. We then focused on the analysis of its local electronic correlations. By e

Alexis Olsho, Charlotte Zimmerman, Suzanne White Brahmia

Covariational reasoning--considering how changes in one quantity affect another, related quantity--is a foundation of quantitative modeling in physics. Understanding quantitative models is a learning objective of introductory physics instruction at the college level. Prior work suggests that covariational reasoning in physics contexts differs in important wa

Valderrama Jonatan, Aguilar-Alonso Igor

In the era of digital transformation, customer service is of paramount importance to the success of organizations, and to meet the growing demand for immediate responses and personalized assistance 24 hours a day, chatbots have become a promising tool to solve these problems. Currently, there are many companies that need to provide these solutions to their c

Kasturi Bhattacharjee, Rashmi Gangadharaiah

Aspect-based sentiment analysis (ABSA) is a widely studied topic, most often trained through supervision from human annotations of opinionated texts. These fine-grained annotations include identifying aspects towards which a user expresses their sentiment, and their associated polarities (aspect-based sentiments). Such fine-grained annotations can be expensi

Benedikt Dornauer, Wolfgang Vigl, Michael Felderer

The success of a web application is closely linked to its performance, which positively impacts user satisfaction and contributes to energy-saving efforts. Among the various optimization techniques, one specific subject focuses on improving the utilization of web fonts. This study investigates the impact of different font formats on client-side resource cons

A. Alvarado, T. Capistrán, I. Torres, J. R. SacahuÍ

HAWC is a ground-based observatory consisting of 300 water Cherenkov detectors, which observes the extensive air showers induced by cosmic rays from some TeV to a few PeV and, in particular, gamma rays from 300 GeV to more than 100 TeV. One of the crucial features required for a detector of extensive air showers is the estimation of the primary energy of the

Calin Martin

We consider here time-dependent three-dimensional stratified geophysical water flows of finite depth over a variable bottom with a free surface and an interface (separating two layers of constant and different densities). Under the assumption that the vorticity vectors in the two layers are constant, we prove that bounded solutions to the three-dimensional w

Stephen Moskal, Sam Laney, Erik Hemberg, Una-May O'Reilly

In this paper, we explore the potential of Large Language Models (LLMs) to reason about threats, generate information about tools, and automate cyber campaigns. We begin with a manual exploration of LLMs in supporting specific threat-related actions and decisions. We proceed by automating the decision process in a cyber campaign. We present prompt engineerin

Mohsen Heidari, Mobasshir A Naved, Zahra Honjani, Wenbo Xie

Gradient-based optimizers have been proposed for training variational quantum circuits in settings such as quantum neural networks (QNNs). The task of gradient estimation, however, has proven to be challenging, primarily due to distinctive quantum features such as state collapse and measurement incompatibility. Conventional techniques, such as the parameter-

Christopher Chong, Brian Kim, Evelyn Wallace, Chiara Daraio

We demonstrate the existence of wavenumber bandgap (q-gap) breathers in a time-periodic phononic lattice. These breathers are localized in time and periodic in space, and are the counterparts to the classical breathers found in spatially-periodic systems. We derive an exact condition for modulation instability that leads to the opening of wavenumber bandgaps

Kaleb Ben Naveed, Devansh Agrawal, Christopher Vermillion, Dimitra Panagou

Planning informative trajectories while considering the spatial distribution of the information over the environment, as well as constraints such as the robot's limited battery capacity, makes the long-time horizon persistent coverage problem complex. Ergodic search methods consider the spatial distribution of environmental information while optimizing robot

Balázs Havasi-Tóth, Eszter Fehér

We propose a geometrically motivated mathematical model which reveals the key features of coastal and fluvial fragment shape evolution from the earliest stages of the abrasion. Our \textit{collisional polygon model} governs the evolution through an ordinary differential equation (ODE) that determines the rounding rate of initially sharp corners in the functi

Matthew Nice, Matthew Bunting, George Gunter, William Barbour

This work demonstrates a new capability in roadway control: Speed-adaptive, infrastructure-linked connected and automated vehicles. We develop and deploy a lightly modified vehicle that is able to dynamically adjust the vehicle speed in response to posted variable speed limit messages generated by the infrastructure using LTE connectivity. This work describe

Charuta Pethe, Bach Pham, Felix D Childress, Yunting Yin

Recent advances in text-to-speech have made it possible to generate natural-sounding audio from text. However, audiobook narrations involve dramatic vocalizations and intonations by the reader, with greater reliance on emotions, dialogues, and descriptions in the narrative. Using our dataset of 93 aligned book-audiobook pairs, we present improved models for

Andreas Schanz, Florian List, Oliver Hahn

In recent years, deep learning models have been successfully employed for augmenting low-resolution cosmological simulations with small-scale information, a task known as "super-resolution". So far, these cosmological super-resolution models have relied on generative adversarial networks (GANs), which can achieve highly realistic results, but suffer from var

Changseok Kim, Jong-Hak Woo, Rongxin Luo, Aeree Chung

We present spatially resolved gas kinematics, ionization, and energetics of 11 type 1 and 5 type 2 active galactic nuclei (AGNs) with strong ionized gas outflows at z $<0.3$ using Gemini Multi-Object Spectrograph Integral Field Unit (GMOS-IFU) data. We find a strongly blueshifted region in [OIII] velocity maps, representing an approaching cone in biconical o

Eldar Kurtic, Denis Kuznedelev, Elias Frantar, Michael Goin

We consider the problem of accurate sparse fine-tuning of large language models (LLMs), that is, fine-tuning pretrained LLMs on specialized tasks, while inducing sparsity in their weights. On the accuracy side, we observe that standard loss-based fine-tuning may fail to recover accuracy, especially at high sparsities. To address this, we perform a detailed s

Panagiotis Papastamoulis, Fotios Milienos

Estimating model parameters of a general family of cure models is always a challenging task mainly due to flatness and multimodality of the likelihood function. In this work, we propose a fully Bayesian approach in order to overcome these issues. Posterior inference is carried out by constructing a Metropolis-coupled Markov chain Monte Carlo (MCMC) sampler,

Medet Nursultanov, Lauri Oksanen, Leo Tzou

We consider an inverse problem for a non-linear hyperbolic equation. We show that conformal structure of a Lorentzian manifold can be determined by the source-to-solution map evaluated along a single timelike curve. We use the microlocal analysis of non-linear wave interaction.

Gerrit Behner, Kristof Moors, Yong Zhang, Michael Schleenvoigt

We present low-temperature magnetoconductance measurements on Bi$_{1.5}$Sb$_{0.5}$Te$_{1.8}$Se$_{1.2}$ kinks with ribbon-shaped legs. The conductance displays a clear dependence on the in-plane magnetic field orientation. The conductance modulation is consistent with orbital effect-driven trapping of the topological surface states on different side facets of

Qianli Shen, Wai Hoh Tang, Zhun Deng, Apostolos Psaros

Standard approaches for uncertainty quantification in deep learning and physics-informed learning have persistent limitations. Indicatively, strong assumptions regarding the data likelihood are required, the performance highly depends on the selection of priors, and the posterior can be sampled only approximately, which leads to poor approximations because o

Georg Feulner, Mona Bukenberger, Stefan Petri

The instability with respect to global glaciation is a fundamental property of the climate system caused by the positive ice-albedo feedback. The atmospheric carbon dioxide concentration at which this Snowball bifurcation occurs changes through Earth's history because of the slowly increasing solar luminosity. Quantifying this critical CO$_2$ level is not on

Hang Zhang, Yuhao Zhang, Xiangru Xu

The increasing prevalence of neural networks in safety-critical control systems underscores the imperative need for rigorous methods to ensure the reliability and safety of these systems. This work introduces a novel approach employing hybrid zonotopes to compute the over-approximation of backward reachable sets for neural feedback systems with nonlinear pla

Isam Al-Darabsah

The logistic equation has many applications and is used frequently in different fields, such as biology, medicine, and economics. In this paper, we study the stability of a single-species logistic model with a general distribution delay kernel and an inflow of nutritional resources at a constant rate. In particular, we provide precise conditions for the line

Katherine Goldman

In this note we connect the language of Bessis's Garisde categories with Salvetti's metrical-hemisphere complexes in order to find new examples of weak Garside groups. As our main example, we show that the fundamental group of the (appropriately defined) complexified complement of a pseudohyperplane arrangement is a weak Garside group. As a consequence of th

Pengyue Hou, Xingyu Li

The recent success of SimCSE has greatly advanced state-of-the-art sentence representations. However, the original formulation of SimCSE does not fully exploit the potential of hard negative samples in contrastive learning. This study introduces an unsupervised contrastive learning framework that combines SimCSE with hard negative mining, aiming to enhance t

Abhishek Majhi

Maxwell's verbal statement of Coulomb's experimental verification of his hypothesis, concerning force between two electrified bodies, is suggestive of a modification of the respective computable expression on logical grounds. This modification is in tandem with the completeness condition for a physical theory, that was stated by Einstein, Podolsky and Rosen

Ryohei Kobayashi, Guanyu Zhu

We consider the $\mathbb{Z}_2$ toric code, surface code and Floquet code defined on a non-orientable surface, which can be considered as families of codes extending Shor's 9-qubit code. We investigate the fault-tolerant logical gates of the $\mathbb{Z}_2$ toric code in this setup, which corresponds to $e\leftrightarrow m$ exchanging symmetry of the underlyin

Lakshmi Arunachalam, Fahim Mohammad, Vrushabh H. Sanghavi

In this paper, we explore how transfer learning, coupled with Intel Xeon, specifically 4th Gen Intel Xeon scalable processor, defies the conventional belief that training is primarily GPU-dependent. We present a case study where we achieved near state-of-the-art accuracy for image classification on a publicly available Image Classification TensorFlow dataset

Aaron Dines, Matthew Ellis, Jochen Blumberger

Coupled trajectory mixed quantum classical (CTMQC) dynamics is a rigorous approach to trajectory-based non-adiabatic dynamics, which has recently seen an improvement to energy conservation via the introduction of the CTMQC-E algorithm. Despite this, the method's two key quantities distinguishing it from Ehrenfest dynamics, the modified Born-Oppenheimer momen

Robert Witteck, John. F. Geisz, Emily. L. Warren, William. E. McMahon

In this work, we investigate how a varying spectral irradiance and top cell bandgap affect the energy yield of 2T and 4T perovskite//silicon tandem solar cells under outdoor operating conditions. For the comparison, we first validate an optoelectronic model employing a 1-year outdoor data set for a 4T mechanical stacked GaAs on Silicon tandem device. We then

Atish Kumar Dipongkor, Kevin Moran

Often, the first step in managing bug reports is related to triaging a bug to the appropriate developer who is best suited to understand, localize, and fix the target bug. Additionally, assigning a given bug to a particular part of a software project can help to expedite the fixing process. However, despite the importance of these activities, they are quite

Nima Shiri Harzevili, Hung Viet Pham, Song Wang

In this work, we set out to conduct the first ground-truth empirical evaluation of state-of-the-art DL fuzzers. Specifically, we first manually created an extensive DL bug benchmark dataset, which includes 627 real-world DL bugs from TensorFlow and PyTorch libraries reported by users between 2020 and 2022. Then we run three state-of-the-art DL fuzzers, i.e.,

Van-Dung Nguyen, Ling Wu, Françoise Remacle, Ludovic Noels

We propose a framework to solve non-linear and history-dependent mechanical problems based on a hybrid classical computer -- quantum annealer approach. Quantum Computers are anticipated to solve particular operations exponentially faster. The available possible operations are however not as versatile as with a classical computer. However, quantum annealers (

H. Rueda, F. Bradascio, J. A. Barrio, J. Biteau

NectarCAM is a Cherenkov camera that will be installed on Medium-Sized Telescopes of the northern array of the Cherenkov Telescope Array Observatory (CTAO). It is composed of 265 modules, each of which includes 7 photo-multiplier tubes, a Front-End Board and a camera trigger system for data collection. The first NectarCAM unit is currently being integrated a

Andriy Smolyanyuk, Igor I. Mazin, Laura Garcia-Gassull, Roser Valentí

Altermagnetism is a topic that has lately been gaining attention and the RuO$_2$ compound is among one of the most studied altermagnetic candidates. However, the survey of available literature on RuO$_2$ properties suggests that there is no consensus about the magnetism of this material. By performing density functional theory calculations, we show that the

Dylan J. Kline, Michael P. Hennessey, David K. Amondson, Steve Lin

In this work, we detail a novel application of inverse design and advanced manufacturing to rapidly develop and experimentally validate modifications to a shaped charge jet analogue. The shaped charge jet analogue comprises a conical copper liner, high explosive (HE), and silicone buffer. We apply a genetic algorithm to determine an optimal buffer design tha

Görkay Aydemir, Weidi Xie, Fatma Güney

Unsupervised multi-object segmentation has shown impressive results on images by utilizing powerful semantics learned from self-supervised pretraining. An additional modality such as depth or motion is often used to facilitate the segmentation in video sequences. However, the performance improvements observed in synthetic sequences, which rely on the robustn

Anbang Yang, Ge Jin, Junjie Huang, Yao Wang

Real-time visual localization often utilizes online computing, for which query images or videos are transmitted to remote servers for visual place recognition (VPR). However, limited network bandwidth necessitates image-quality reduction and thus the degradation of global image descriptors, reducing VPR accuracy. We address this issue at the descriptor extra

Brian Batell, Akshay Ghalsasi, Matthew Low, Mudit Rai

We study the prospects for probing the Nnaturalness solution to the electroweak hierarchy problem with future gravitational wave observatories. Nnaturalness, in its simplest incarnation, predicts $N$ copies of the Standard Model with varying Higgs mass parameters. We show that in certain parameter regions the scalar reheaton transfers a substantial energy de

Piero Esposito, Parmida Atighehchian, Anastasis Germanidis, Deepti Ghadiyaram

State-of-the-art generative text-to-image models are known to exhibit social biases and over-represent certain groups like people of perceived lighter skin tones and men in their outcomes. In this work, we propose a method to mitigate such biases and ensure that the outcomes are fair across different groups of people. We do this by finetuning text-to-image m

Fan Yang, Wenxuan Zhou, Zuxin Liu, Ding Zhao

Safe Reinforcement Learning (RL) plays an important role in applying RL algorithms to safety-critical real-world applications, addressing the trade-off between maximizing rewards and adhering to safety constraints. This work introduces a novel approach that combines RL with trajectory optimization to manage this trade-off effectively. Our approach embeds saf

Tetsuya Takabatake, Keisuke Yano

This paper studies a specific class of statistical divergences for spectral densities of time series: the spectral $\alpha$-R\'{e}nyi divergences, which include the Itakura-Saito divergence as a limiting case. The aim of this paper is to highlight both information-theoretic and statistical properties of spectral $\alpha$-R\'{e}nyi divergences. We reveal the

Ryusuke Jinno, Jun'ya Kume, Masaki Yamada

We discuss the unique phenomenology of first-order phase transitions catalyzed by primordial black holes (BHs). If the number of BHs within one Hubble volume is smaller than unity at the time of bubble nucleation, each bubble catalyzed around them can expand to the Hubble size, and the universe is eventually filled with true vacuum much after nucleation. Thi

Jason Young, Alexandra Pope, Anna Sajina, Lin Yan

We present JWST/MIRI/MRS observations of an infrared luminous disk galaxy, FLS1, at z=0.54. With a lookback time of 5 Gyr, FLS1 is chronologically at the midpoint between the peak epoch of star formation and the present day. The MRS data provide maps of the atomic fine structure lines [Ar II]6.99 micron, [Ar III]8.99 micron, [Ne II]12.81 micron, and [Ne III]

Sumanta Chakraborty, Anupam Mazumdar, Ritapriya Pradhan

In general relativity, the use of conformal transformation is ubiquitous and leads to two different frames of reference, known as the Jordan and the Einstein frames. Typically, the transformation from the Jordan frame to the Einstein frame involves introducing an additional scalar degree of freedom, often already present in the theory. We will show that at t

Ritik Sharma, Mahavir Sharma

We investigate the steady spherically symmetric accretion in the combined potential of a central black hole and a dark matter halo. For the halo, we consider a Hernquist and an NFW potential and calculate the critical points of the flow. We find that the trans-sonic solution to the centre is not possible without a black hole, whereas two types of trans-sonic

Erik Buhmann, Cedric Ewen, Gregor Kasieczka, Vinicius Mikuni

Physics beyond the Standard Model that is resonant in one or more dimensions has been a longstanding focus of countless searches at colliders and beyond. Recently, many new strategies for resonant anomaly detection have been developed, where sideband information can be used in conjunction with modern machine learning, in order to generate synthetic datasets

Massimo Dotti, Fabio Rigamonti, Stefano Rinaldi, Walter Del Pozzo

We present a new observational test to identify massive black hole binaries in large multi-epoch spectroscopical catalogues and to probe the real nature of already proposed binary candidates. The test is tailored for binaries with separations large enough to allow each black hole to retain its own broad line region. In this limit the fast AGN variability typ

Ivano Basile, Arun Debray, Matilda Delgado, Miguel Montero

The three tachyon-free non-supersymmetric string theories in ten dimensions provide a handle on quantum gravity away from the supersymmetric lamppost. However, they have not been shown to be fully consistent; although local anomalies cancel due to versions of the Green-Schwarz mechanism, there could be global anomalies, not cancelled by the Green-Schwarz mec

Filippo Camilloni, Gianluca Grignani, Troels Harmark, Marta Orselli

Binary-black-hole (BBH) mergers can take place close to a supermassive black hole (SMBH) while being in a bound orbit around the SMBH. In this paper, we study such bound triple systems and show that including the strong gravity effects of describing the SMBH with a Kerr metric can significantly modify the dynamics, as compared to a Newtonian point particle d

Hansel Gordillo-Ruiz, Cesar Morales-Mena, Saul Ramos-Sanchez

Dark matter and cosmic inflation represent two of the major puzzles in cosmology. They are typically addressed by introducing separate fields with independent dynamics. On the other hand, extra dimensions might play an important role for observable physics. We introduce a five-dimensional model called dark inflaxion that includes an axion-like particle, whos

Johannes Lang, Michael Buchhold, Sebastian Diehl

A field theory approach for the nonequilibrium relaxation dynamics in open systems at late times is developed. In the absence of conservation laws, all excitations are subject to dissipation. Nevertheless, ordered stationary states satisfy Goldstone's theorem. It implies a vanishing damping rate at small momenta, which in turn allows for competition between

Wen-Han Kao, Gábor B. Halász, Natalia B. Perkins

We study the dynamical response of vacancy-induced quasiparticle excitations in the site-diluted Kitaev spin liquid with a magnetic field. Due to the flux-binding effect and the emergence of dangling Majorana fermions around each spin vacancy, the low-energy physics is governed by a set of vacancy-induced quasi-zero-energy modes. These localized modes result

Jindra Gensior, Timothy A. Davis, Martin Bureau, J. M. Diederik Kruijssen

Observations indicate that the central gas discs are smoother in early-type galaxies than their late-type counterparts, while recent simulations predict that the dynamical suppression of star formation in spheroid-dominated galaxies is preceded by the suppression of fragmentation of their interstellar media. The mass surface density power spectrum is a power

Nils Quetschlich, Lukas Burgholzer, Robert Wille

Fueled by recent accomplishments in quantum computing hardware and software, an increasing number of problems from various application domains are being explored as potential use cases for this new technology. Similarly to classical computing, realizing an application on a particular quantum device requires the corresponding (quantum) circuit to be compiled

Brian McPeak, Marco Venuti, Alessandro Vichi

Dispersion relations let us leverage the analytic structure of scattering amplitudes to derive constraints such as bounds on EFT coefficients. An important input is the large-energy behavior of the amplitude. In this paper, we systematically study how different large-energy behavior affects EFT bounds for the $2 \to 2$ amplitude of complex scalars coupled to

Erica J. Nelson, Gabriel Brammer, Clara Gimenez-Arteaga, Pascal A. Oesch

With the remarkable sensitivity and resolution of JWST in the infrared, measuring rest-optical kinematics of galaxies at $z>5$ has become possible for the first time. This study pilots a new method for measuring galaxy dynamics for highly multiplexed, unbiased samples by combining FRESCO NIRCam grism spectroscopy and JADES medium-band imaging. Here we presen

Stuart Yi-Thomas, Brayden Ware, Jay D. Sau, Christopher David White

In ergodic quantum spin chains, locally conserved quantities such as energy or particle number generically evolve according to hydrodynamic equations as they relax to equilibrium. We investigate the complexity of simulating hydrodynamics at infinite temperature with multiple methods: time evolving block decimation (TEBD), TEBD with density matrix truncation

Giulia Santucci, Claudia Del P. Lagos, Katherine E. Harborne, Aaron Ludlow

The merger history of a galaxy is thought to be one of the major factors determining its internal dynamics, with galaxies having undergone different types or mergers (e.g. dry, minor or major mergers) predicted to show different dynamical properties. We study the instantaneous orbital distribution of galaxies in the Eagle simulation, colouring the orbits of

Huiqiang Jiang, Qianhui Wu, Xufang Luo, Dongsheng Li

In long context scenarios, large language models (LLMs) face three main challenges: higher computational cost, performance reduction, and position bias. Research indicates that LLM performance hinges on the density and position of key information in the input prompt. Inspired by these findings, we propose LongLLMLingua for prompt compression towards improvin

Tengda Han, Max Bain, Arsha Nagrani, Gül Varol

Audio Description (AD) is the task of generating descriptions of visual content, at suitable time intervals, for the benefit of visually impaired audiences. For movies, this presents notable challenges -- AD must occur only during existing pauses in dialogue, should refer to characters by name, and ought to aid understanding of the storyline as a whole. To t

Eric Zelikman, Wanjing Anya Ma, Jasmine E. Tran, Diyi Yang

Developing an educational test can be expensive and time-consuming, as each item must be written by experts and then evaluated by collecting hundreds of student responses. Moreover, many tests require multiple distinct sets of questions administered throughout the school year to closely monitor students' progress, known as parallel tests. In this study, we f

Guanqi Zhan, Chuanxia Zheng, Weidi Xie, Andrew Zisserman

Our objective in this paper is to probe large vision models to determine to what extent they 'understand' different physical properties of the 3D scene depicted in an image. To this end, we make the following contributions: (i) We introduce a general and lightweight protocol to evaluate whether features of an off-the-shelf large vision model encode a number

Kaustuv Mukherji, Devendra Parkar, Lahari Pokala, Dyuman Aditya

Recent advances in reinforcement learning (RL) have shown much promise across a variety of applications. However, issues such as scalability, explainability, and Markovian assumptions limit its applicability in certain domains. We observe that many of these shortcomings emanate from the simulator as opposed to the RL training algorithms themselves. As such,

Kai Niklas Hansmann, Franziska Dommermuth, Wolfgang Elsäßer, Reinhold Walser

We investigate the intra-waveguide statistics manipulation of broadband light by combining semiconductor quantum dot physics with quantum optics. By cooling a quantum dot superluminescent diode to liquid nitrogen temperature of $77K$, Blazek et al. [Phys. Rev. A 84, 63840 (2011)] have demonstrated a temperature-dependent reduction of the second-order intensi

Kristian Blom, Kevin Song, Etienne Vouga, Aljaž Godec

Many non-equilibrium, active processes are observed at a coarse-grained level, where different microscopic configurations are projected onto the same observable state. Such "lumped" observables display memory, and in many cases the irreversible character of the underlying microscopic dynamics becomes blurred, e.g., when the projection hides dissipative cycle

Brendan Pankovich, Alex Neville, Angus Kan, Srikrishna Omkar

Fault-tolerant quantum computation can be achieved by creating constant-sized, entangled resource states and performing entangling measurements on subsets of their qubits. Linear optical quantum computers can be designed based on this approach, even though entangling operations at the qubit level are non-deterministic in this platform. Probabilistic generati

Aisha S. Almannaei, Daisuke Kawata, Junichi Baba, Jason A. S. Hunt

A simple one-dimensional axisymmetric disc model is applied to the kinematics of OB stars near the Sun obtained from Gaia DR3 catalogue. The model determines the 'local centrifugal speed' $V_\mathrm{c}(R_{0})$ - defined as the circular velocity in the Galactocentric rest frame, where the star would move in a near-circular orbit if the potential is axisymmetr

Yiheng Xu, Hongjin Su, Chen Xing, Boyu Mi

We introduce Lemur and Lemur-Chat, openly accessible language models optimized for both natural language and coding capabilities to serve as the backbone of versatile language agents. The evolution from language chat models to functional language agents demands that models not only master human interaction, reasoning, and planning but also ensure grounding i

Kitinan Pongsangangan, Pedro Cosme, Emanuele Di Salvo, Lars Fritz

One of the hallmark properties of fluids is their shear viscosity which is, among other things, responsible for parabolic flow profiles through narrow channels. In recent years, there has been a growing number of observations of said flow profiles in electronic transport measurements in a variety of material systems, most notably in graphene. In this paper,

Vikash Kumar, Rutav Shah, Gaoyue Zhou, Vincent Moens

We present RoboHive, a comprehensive software platform and ecosystem for research in the field of Robot Learning and Embodied Artificial Intelligence. Our platform encompasses a diverse range of pre-existing and novel environments, including dexterous manipulation with the Shadow Hand, whole-arm manipulation tasks with Franka and Fetch robots, quadruped loco

Erik Jones, Hamid Palangi, Clarisse Simões, Varun Chandrasekaran

Large language models (LLMs) frequently hallucinate on abstractive summarization tasks such as document-based question-answering, meeting summarization, and clinical report generation, even though all necessary information is included in context. However, optimizing LLMs to hallucinate less on these tasks is challenging, as hallucination is hard to efficient

Endre Csóka, András Pongrácz

We consider the problem of finding a large clique in an Erd\H{o}s--R\'enyi random graph where we are allowed unbounded computational time but can only query a limited number of edges. Recall that the largest clique in $G \sim G(n,1/2)$ has size roughly $2\log_{2} n$. Let $\alpha_{\star}(\delta,\ell)$ be the supremum over $\alpha$ such that there exists an al

Albert Q. Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford

We introduce Mistral 7B v0.1, a 7-billion-parameter language model engineered for superior performance and efficiency. Mistral 7B outperforms Llama 2 13B across all evaluated benchmarks, and Llama 1 34B in reasoning, mathematics, and code generation. Our model leverages grouped-query attention (GQA) for faster inference, coupled with sliding window attention

Samuel Marks, Max Tegmark

Large Language Models (LLMs) have impressive capabilities, but are prone to outputting falsehoods. Recent work has developed techniques for inferring whether a LLM is telling the truth by training probes on the LLM's internal activations. However, this line of work is controversial, with some authors pointing out failures of these probes to generalize in bas

Mouïn Ben Ammar, Nacim Belkhir, Sebastian Popescu, Antoine Manzanera

Detecting out-of-distribution (OOD) data is a critical challenge in machine learning due to model overconfidence, often without awareness of their epistemological limits. We hypothesize that ``neural collapse'', a phenomenon affecting in-distribution data for models trained beyond loss convergence, also influences OOD data. To benefit from this interplay, we

Ho-Chun Herbert Chang, Sunny Fang

The 2024 Taiwanese Presidential Election is not just a critical geopolitical event, it also engages with long-standing debate in politics regarding the factors that lead to the rise of new political parties and candidates. In 2021, the Economist called Taiwan "the most dangerous place on earth" due to its critical role in a fragile supply chain. Additionally

Stephanie Wenxin Liu, Michael Fischer, Paul D. Yoo, Tobias Ritschel

Bounding volumes are an established concept in computer graphics and vision tasks but have seen little change since their early inception. In this work, we study the use of neural networks as bounding volumes. Our key observation is that bounding, which so far has primarily been considered a problem of computational geometry, can be redefined as a problem of

Dmitrii Zakharov

We show that there exists an absolute constant $c_0<1$ such that for all $n \ge 2$, any measurable set $A \subset S^{n-1}$ of density at least $c_0$ contains $n$ pairwise orthogonal vectors. The result is sharp up to the value of the constant $c_0$. Moreover, we show that for all $2\le k \le n$ a set $A$ avoiding $k$ pairwise orthogonal vectors has measure a

Naomi Gendler, Nate MacFadden, Liam McAllister, Jakob Moritz

We enumerate topologically-inequivalent compact Calabi-Yau threefold hypersurfaces. By computing arithmetic and algebraic invariants and the Gopakumar-Vafa invariants of curves, we prove that the number of distinct simply connected Calabi-Yau threefold hypersurfaces resulting from triangulations of four-dimensional reflexive polytopes is 4, 27, 183, 1,184 an

Aminabi Thekkoth, S. Sahayanathan, Zahir Shah, Vaidehi S. Paliya

The long term broadband spectral study of Flat Spectrum Radio Quasars during different flux states has the potential to infer the emission mechanisms and the cause of spectral variations. To scrutinize this, we performed a detailed broadband spectral analysis of 3C 279 using simultaneous Swift-XRT/UVOT and Fermi-LAT observations spanning from August 2008 to

M. T. Mak, N. J. Mayne, D. E. Sergeev, J. Manners

We present results from 3D simulations of the Archean Earth including a prescribed (non-interactive) spherical haze generated through a 1D photochemical model. Our simulations suggest that a thin haze layer, formed when CH4/CO2 = 0.1, leads to global warming of ~10.6 K due to the change of water vapour and cloud feedback, compared to the simulation without a

Mriganka Basu Roy Chowdhury, Pietro Caputo, Shirshendu Ganguly

Gibbsian line ensembles are families of Brownian lines arising in many natural contexts such as the level curves of three dimensional Ising interfaces, the solid-on-solid model, multi-layered polynuclear growth etc. An important example is a class of non-intersecting Brownian lines above a hard wall, which are subject to geometrically growing area tilt poten

N. Meyer-Vernet, A. Lecacheux, M. Moncuquet, K. Issautier

The quasi-thermal motion of plasma particles produces electrostatic fluctuations, whose voltage power spectrum induced on electric antennas reveals plasma properties. In weakly magnetised plasmas, the main feature of the spectrum is a line at the plasma frequency -- proportional to the square root of the electron density -- whose global shape can reveal the

John X. Morris, Volodymyr Kuleshov, Vitaly Shmatikov, Alexander M. Rush

How much private information do text embeddings reveal about the original text? We investigate the problem of embedding \textit{inversion}, reconstructing the full text represented in dense text embeddings. We frame the problem as controlled generation: generating text that, when reembedded, is close to a fixed point in latent space. We find that although a

Herve Zwirn

A classification of different interpretations of the quantum formalism is examined and the concept of perspectival interpretation is presented. A perspectival interpretation implies that the truth is relative to the observer. The degree to which QBism and Convivial Solipsism are perspectival is examined and Convivial Solipsim is shown to be perspectival at a

A. Gkini, R. Lunnan, S. Schulze, L. Dessart

SN\,2020zbf is a hydrogen-poor superluminous supernova (SLSN) at $z = 0.1947$ that shows conspicuous \ion{C}{II} features at early times, in contrast to the majority of H-poor SLSNe. Its peak magnitude is $M_{\rm g}$ = $-21.2$~mag and its rise time ($\lesssim 26.4$ days from first light) places SN\,2020zbf among the fastest rising type I SLSNe. We used spect

Aurélien Alfonsi, Nerea Vadillo

This paper develops a coupled model for day-ahead electricity prices and average daily temperature which allows to model quanto weather and energy derivatives. These products have gained on popularity as they enable to hedge against both volumetric and price risks. Electricity day-ahead prices and average daily temperatures are modelled through non homogeneo

Ashay Burungale, Kâzım Büyükboduk, Antonio Lei

Let $E/\mathbb{Q}$ an elliptic curve with good supersingular reduction at a prime $p\geq 5$, and $K$ an imaginary quadratic field such that the root number of $E$ over $K$ equals $-1$. When $p$ splits in $K$, Castella and Wan formulated the plus/minus Heegner point main conjectures for $E$ along the anticyclotomic $\mathbb{Z}_p$-extension of $K$, and proved

Serge N. Gavrilov, Ekaterina V. Shishkina

The fundamental solution describing non-stationary elastic wave scattering on an isotopic defect in a one-dimensional harmonic chain is obtained in an asymptotic form. The chain is subjected to unit impulse point loading applied to a particle far enough from the defect. The solution is a large time asymptotics at a moving point of observation, and it is in e

Vijay Kumar, Dibyendu Roy

We study spectral correlations in many-body quantum mixtures of fermions, bosons, and qubits with periodically kicked spreading and mixing of species. We take two types of mixing, namely, Jaynes-Cummings and Rabi, respectively, satisfying and breaking the conservation of a total number of species. We analytically derive the generating Hamiltonians whose spec

Oleg Kargaltsev, Jeremy Hare, Igor Volkov, Alexander Lange

The Nuclear Spectroscopic Array (NuSTAR) observed the gamma-ray binary LS 5039 for a second time in order to check for the presence of a periodic signal candidate found in the data from the previous NuSTAR observation. We do not detect the candidate signal in the vicinity of its previously reported frequency, assuming the same orbital ephemeris as in our pre

Shin-ichiro Seki

We introduce finite multiple zeta values of general level and discuss the relationship between the non-zeroness of these values and regular or non-Wieferich primes. Because it's challenging to prove the infinitude of these types of primes, we suggest tackling several related problems more promptly.

Brian Knaeble, Julian Chan

This article introduces a new condition based on odds ratios for sensitivity analysis. The analysis involves the average effect of a treatment or exposure on a response or outcome with estimates adjusted for and conditional on a single, unmeasured, dichotomous covariate. Results of statistical simulations are displayed to show that the odds ratio condition i

Choloong Hahn, Junho Chang, Zhiping Jiang

We propose the world first longitudinal gOSNR estimation by using correlation template method at Rx, without any monitoring devices located in the middle of the link. The proposed method is experimentally demonstrated in a 12-span link with commercial transceiver.

Chengyang Shao

This paper provides a toolbox of para-differential calculus on compact Lie groups. The toolbox is based on representation theory of compact Lie groups and contains exact formulas of symbolic calculus. Para-differential operators are constructed in a global, coordinate-free manner, giving lower order terms in symbolic calculus a clear form. The toolbox helps

Tanmoy Bhattacharya, Shailesh Chandrasekharan, Rajan Gupta, Thomas R. Richardson

Qubit regularization provides a rich framework to explore quantum field theories. The freedom to choose how the important symmetries of the theory are embedded in the qubit regularization scheme allows us to construct new lattice models with rich phase diagrams. Some of the phases can contain topological terms which lead to critical phases. In this work we i