October 2023 arXiv papers — page 149

Chul Chung, Suk-Jin Yoon, Seunghyun Park, Seunghyeon An

It is well established that the Hubble residuals of type Ia supernovae (SNe Ia) show the luminosity step with respect to their host galaxy stellar masses. This `mass-step' is taken as an additional correction factor for the SN Ia luminosity standardization. Here we investigate the root cause of the mass-step and propose that the bimodal nature of the host $a

Yuren Cong, Mengmeng Xu, Christian Simon, Shoufa Chen

Text-to-video editing aims to edit the visual appearance of a source video conditional on textual prompts. A major challenge in this task is to ensure that all frames in the edited video are visually consistent. Most recent works apply advanced text-to-image diffusion models to this task by inflating 2D spatial attention in the U-Net into spatio-temporal att

Hurum Maksora Tohfa, Simeon Bird, Ming-Feng Ho, Mahdi Qezlou

We make forecasts for the constraining power of the 1D Wavelet Scattering Transform (WST) when used with a Lyman-$\alpha$ forest cosmology survey. Using mock simulations and a Fisher matrix, we show that there is considerable cosmological information in the scattering transform coefficients not captured by the flux power spectrum. We estimate mock covariance

Jordan Lekeufack, Anastasios N. Angelopoulos, Andrea Bajcsy, Michael I. Jordan

We introduce Conformal Decision Theory, a framework for producing safe autonomous decisions despite imperfect machine learning predictions. Examples of such decisions are ubiquitous, from robot planning algorithms that rely on pedestrian predictions, to calibrating autonomous manufacturing to exhibit high throughput and low error, to the choice of trusting a

Peter S. Park, Max Tegmark

AI companies are attempting to create AI systems that outperform humans at most economically valuable work. Current AI models are already automating away the livelihoods of some artists, actors, and writers. But there is infighting between those who prioritize current harms and future harms. We construct a game-theoretic model of conflict to study the causes

Duy-Kien Nguyen, Martin R. Oswald, Cees G. M. Snoek

The ability to detect objects in images at varying scales has played a pivotal role in the design of modern object detectors. Despite considerable progress in removing hand-crafted components and simplifying the architecture with transformers, multi-scale feature maps and pyramid designs remain a key factor for their empirical success. In this paper, we show

Chung-Ming Chien, Mingjiamei Zhang, Ju-Chieh Chou, Karen Livescu

Recent work on speech representation models jointly pre-trained with text has demonstrated the potential of improving speech representations by encoding speech and text in a shared space. In this paper, we leverage such shared representations to address the persistent challenge of limited data availability in spoken language understanding tasks. By employing

Ziming Liu, Ziqian Zhong, Max Tegmark

We attribute grokking, the phenomenon where generalization is much delayed after memorization, to compression. To do so, we define linear mapping number (LMN) to measure network complexity, which is a generalized version of linear region number for ReLU networks. LMN can nicely characterize neural network compression before generalization. Although the $L_2$

Donglai Xiang, Fabian Prada, Zhe Cao, Kaiwen Guo

Clothing is an important part of human appearance but challenging to model in photorealistic avatars. In this work we present avatars with dynamically moving loose clothing that can be faithfully driven by sparse RGB-D inputs as well as body and face motion. We propose a Neural Iterative Closest Point (N-ICP) algorithm that can efficiently track the coarse g

Yossi Gandelsman, Alexei A. Efros, Jacob Steinhardt

We investigate the CLIP image encoder by analyzing how individual model components affect the final representation. We decompose the image representation as a sum across individual image patches, model layers, and attention heads, and use CLIP's text representation to interpret the summands. Interpreting the attention heads, we characterize each head's role

Baian Chen, Chang Shu, Ehsan Shareghi, Nigel Collier

Recent efforts have augmented language models (LMs) with external tools or environments, leading to the development of language agents that can reason and act. However, most of these agents rely on few-shot prompting techniques with off-the-shelf LMs. In this paper, we investigate and argue for the overlooked direction of fine-tuning LMs to obtain language a

Neel Jain, Ping-yeh Chiang, Yuxin Wen, John Kirchenbauer

We show that language model finetuning can be improved, sometimes dramatically, with a simple augmentation. NEFTune adds noise to the embedding vectors during training. Standard finetuning of LLaMA-2-7B using Alpaca achieves 29.79% on AlpacaEval, which rises to 64.69% using noisy embeddings. NEFTune also improves over strong baselines on modern instruction d

Vo Tien Phong, Eugene J. Mele

It is well-known that macroscopically-normalizable zero-energy wavefunctions of spin-$\frac{1}{2}$ particles in a two-dimensional inhomogeneous magnetic field are spin-polarized and exactly calculable with degeneracy equaling the number of flux quanta linking the whole system. Extending this argument to massless Dirac fermions subjected to magnetic fields th

Chethan Krishnan, Vyshnav Mohan

Opinion is divided about the nature of state dependence in the black hole interior. Some argue that it is a necessary feature, while others argue it is a bug. In this paper, we consider the extended half-sided modular translation $U(s_0)$ (with $s_0 > 0$) of Leutheusser and Liu that takes us inside the horizon. We note that we can use this operator to constr

Arghyadeep Barat, Prabuchandran. K. J, Shalabh Bhatnagar

In this paper, we consider the problem of finding an optimal energy management policy for a network of sensor nodes capable of harvesting their own energy and sharing it with other nodes in the network. We formulate this problem in the discounted cost Markov decision process framework and obtain good energy-sharing policies using the Deep Deterministic Polic

Zhiqing Sun, Yikang Shen, Hongxin Zhang, Qinhong Zhou

Supervised Fine-Tuning (SFT) on response demonstrations combined with Reinforcement Learning from Human Feedback (RLHF) constitutes a powerful paradigm for aligning LLM-based AI agents. However, a significant limitation of such an approach is its dependency on high-quality human annotations, making its application to intricate tasks challenging due to diffic

Aditi Chandra, Andrei Constantin, Kit Fraser-Taliente, Thomas R. Harvey

The diffeomorphism class of simply-connected smooth Calabi-Yau threefolds with torsion-free cohomology is determined via certain basic topological invariants: the Hodge numbers, the triple intersection form, and the second Chern class. In the present paper, we shed some light on this classification by placing bounds on the number of diffeomorphism classes pr

Donghao Qiao, Farhana Zulkernine

Autonomous Vehicles (AVs) use multiple sensors to gather information about their surroundings. By sharing sensor data between Connected Autonomous Vehicles (CAVs), the safety and reliability of these vehicles can be improved through a concept known as cooperative perception. However, recent approaches in cooperative perception only share single sensor inform

Sourov Roy, Pratick Sarkar, Subhadip Sau, Soumitra SenGupta

In this study, we examine the phenomenon of photon axion conversion occurring in the spacetime surrounding a black hole. Specifically, we focus on the potential existence of a magnetic field around the supermassive black hole M87*, which could facilitate the conversion of photons into axions in close proximity to the photon sphere. While photons traverse thr

Juan J. Jiménez

In this article, we study the stochastic wave equation in spatial dimensions $d \le 2$ with multiplicative L\'evy noise that can have infinite $p$-th moments. Using the past light-cone property of the wave equation, we prove the existence and uniqueness of a solution, considering only the $p$-integrability of the L\'evy measure $\nu$ for the region correspon

Mikuláš Matoušek, Katarzyna Pernal, Fabijan Pavošević, Libor Veis

In this work we integrate the variational quantum eigensolver (VQE) with the adiabatic connection (AC) method for efficient simulations of chemical problems on near-term quantum computers. Orbital optimized VQE methods are employed to capture the strong correlation within an active space and classical AC corrections recover the dynamical correlation effects

Zuxin Liu, Jesse Zhang, Kavosh Asadi, Yao Liu

The full potential of large pretrained models remains largely untapped in control domains like robotics. This is mainly because of the scarcity of data and the computational challenges associated with training or fine-tuning these large models for such applications. Prior work mainly emphasizes either effective pretraining of large models for decision-making

Ethan Lau, Vaibhav Srivastava, Shaunak D. Bopardikar

We examine how a human-robot interaction (HRI) system may be designed when input-output data from previous experiments are available. In particular, we consider how to select an optimal impedance in the assistance design for a cooperative manipulation task with a new operator. Due to the variability between individuals, the design parameters that best suit o

Tara Abrishami, Eli Berger, Maria Chudnovsky, Shira Zerbib

Sivaraman conjectured that if $G$ is a graph with no induced even cycle then there exist sets $X_1, X_2 \subseteq V(G)$ satisfying $V(G) = X_1 \cup X_2$ such that the induced graphs $G[X_1]$ and $G[X_2]$ are both chordal. We prove this conjecture in the special case where $G$ contains no sector wheel, namely, a pair $(H, w)$ where $H$ is an induced cycle of

David Shlivko

We use the Trans-Planckian Censorship Conjecture (TCC) to constrain the decay constants $f$ characterizing a set of N identical axion-like fields with cosine potentials, improving upon the precision of other Swampland conjectures and existing string-theoretic arguments. We find that consistency with the TCC requires any such set of axion-like fields to satis

Johannes Bausch, Andrew W Senior, Francisco J H Heras, Thomas Edlich

Quantum error-correction is a prerequisite for reliable quantum computation. Towards this goal, we present a recurrent, transformer-based neural network which learns to decode the surface code, the leading quantum error-correction code. Our decoder outperforms state-of-the-art algorithmic decoders on real-world data from Google's Sycamore quantum processor f

Lizhang Chen, Bo Liu, Kaizhao Liang, Qiang Liu

Lion (Evolved Sign Momentum), a new optimizer discovered through program search, has shown promising results in training large AI models. It performs comparably or favorably to AdamW but with greater memory efficiency. As we can expect from the results of a random search program, Lion incorporates elements from several existing algorithms, including signed m

Jason Iaconis, Sonika Johri

Image-based data is a popular arena for testing quantum machine learning algorithms. A crucial factor in realizing quantum advantage for these applications is the ability to efficiently represent images as quantum states. Here we present a novel method for creating quantum states that approximately encode images as amplitudes, based on recently proposed tech

Ilyoun Na, Marc Vila, Sinéad M. Griffin

The possibility of selecting magnetic space groups by orienting the magnetization direction or tuning magnetic orders offers a vast playground for engineering symmetry protected topological phases in magnetic materials. In this work, we study how selective tuning of symmetry and magnetism can influence and control the resulting topology in a 2D magnetic syst

N. E. Khokhlov, Ia. A. Filatov, A. M. Kalashnikova

We analytically discuss and micromagnetically prove the ways to tune the spatial asymmetry of the initial phase, amplitude, and wavevectors of magnetostatic waves driven by ultrafast laser excitation. We consider that the optical pulse heats a thin ferromagnetic metallic film and abruptly decreases the saturation magnetization and the parameter of uniaxial a

Songfu Cai, Xuanyu Cao

We consider a wireless networked control system (WNCS) with multiple controllers and multiple attackers. The dynamic interaction between the controllers and the attackers is modeled as a linear quadratic (LQ) zero-sum difference game with multiplicative randomness induced by the the multiple-input and multiple-output (MIMO) wireless fading. The existence of

Ioannis Avgerinos, Ioannis Mourtos, Stavros Vatikiotis, Georgios Zois

Logic-Based Benders Decomposition (LBBD) and its Branch-and-Cut variant, namely Branch-and-Check, enjoy an extensive applicability on a broad variety of problems, including scheduling. Although LBBD offers problem-specific cuts to impose tighter dual bounds, its application to resource-constrained scheduling remains less explored. Given a position-based Mixe

Quan Huu Do, Binh T. Nguyen, Lam Si Tung Ho

Existing generalization bounds for deep neural networks require data to be independent and identically distributed (iid). This assumption may not hold in real-life applications such as evolutionary biology, infectious disease epidemiology, and stock price prediction. This work establishes a generalization bound of feed-forward neural networks for non-station

Alexei Lisitsa, Zipei Nie, Alexei Vernitski

We demonstrate how a generic automated theorem prover can be applied to establish the non-orderability of groups. Our approach incorporates various tools such as positive cones, torsions, generalised torsions and cofinal elements.

Ayush Moharana, K. G. Hełminiak, F. Marcadon, T. Pawar

Eclipse timing variations (ETV) have been a successful tool for detecting circumbinary companions to eclipsing binaries (EB). While TESS and Kepler have been prolific for ETV searches, they sometimes can be limited by time and sky coverage which can be addressed by specialised ground-based ETV surveys. We present the initial results from the Solaris photomet

Timo Aukusti Laine

We investigate the Navier-Stokes turbulence driven by a stochastic random Gaussian force. Using a field-theoretic approach, we uncover an anomaly that brings hidden structure to the theory. The anomaly is generated by a non-self-adjoint operator of the Jacobian and it follows the symmetries of the stochastic Navier-Stokes equation. We calculate the anomaly a

Chaohan Cui, Jack Postlewaite, Babak N. Saif, Linran Fan

Achieving the ultimate Holevo limit of optical communication capacity requires a joint-detection receiver which makes a collective quantum measurement over multiple modulated symbols. Such superadditivity -- a higher communication rate than that achievable by symbol-by-symbol optical detection -- is a special case of the well-known nonlocality without entang

Sevdzhan Hakkaev, Atanas G. Stefanov

We study the periodic Schr\"odinger-Korteweg de Vries system. We describe the two-parametetric family of $2T$ periodic traveling waves of dnoidal type. The main objective of the paper is to establish their spectral stability with respect to co-periodic perturbations. In the limit $T\to \infty$, we recover the results of Albert-Angulo, for the stability of th

Steven R. Cranmer, Rohit Chhiber, Chris R. Gilly, Iver H. Cairns

The solar wind is the extension of the Sun's hot and ionized corona, and it exists in a state of continuous expansion into interplanetary space. The radial distance at which the wind's outflow speed exceeds the phase speed of Alfvenic and fast-mode magnetohydrodynamic (MHD) waves is called the Alfven radius. In one-dimensional models, this is a singular poin

Utkarsh Oggy Sarawgi, John Berkowitz, Vineet Garg, Arnav Kundu

Streaming neural network models for fast frame-wise responses to various speech and sensory signals are widely adopted on resource-constrained platforms. Hence, increasing the learning capacity of such streaming models (i.e., by adding more parameters) to improve the predictive power may not be viable for real-world tasks. In this work, we propose a new loss

Zhiyu Huang, Peter Karkus, Boris Ivanovic, Yuxiao Chen

Motion prediction and cost evaluation are vital components in the decision-making system of autonomous vehicles. However, existing methods often ignore the importance of cost learning and treat them as separate modules. In this study, we employ a tree-structured policy planner and propose a differentiable joint training framework for both ego-conditioned pre

Xinbo Wu, Lav R. Varshney

The Transformer architecture has become prominent in developing large causal language models. However, mechanisms to explain its capabilities are not well understood. Focused on the training process, here we establish a meta-learning view of the Transformer architecture when trained for the causal language modeling task, by explicating an inner optimization

Estelle Basor, Rebecca Morrison

Solutions to most nonlinear ordinary differential equations (ODEs) rely on numerical solvers, but this gives little insight into the nature of the trajectories and is relatively expensive to compute. In this paper, we derive analytic solutions to a class of nonlinear, homogeneous ODEs with linear and quadratic terms on the right-hand side. We formulate a pow

C. Zhang, D. Storey, P. San Miguel Claveria, Z. Nie

High repetition rates and efficient energy transfer to the accelerating beam are important for a future linear collider based on the beam-driven plasma wakefield acceleration scheme (PWFA-LC). This paper reports the first results from the Plasma Wakefield Acceleration Collaboration (E300) that are beginning to address both of these issues using the recently

Erica Weng, Kenta Mukoya, Deva Ramanan, Kris Kitani

Autonomous vehicles (AVs) require comprehensive and reliable pedestrian trajectory data to ensure safe operation. However, obtaining data of safety-critical scenarios such as jaywalking and near-collisions, or uncommon agents such as children, disabled pedestrians, and vulnerable road users poses logistical and ethical challenges. This paper evaluates a Virt

Francesco Dalla Serra, Chaoyang Wang, Fani Deligianni, Jeffrey Dalton

Radiology reports are detailed text descriptions of the content of medical scans. Each report describes the presence/absence and location of relevant clinical findings, commonly including comparison with prior exams of the same patient to describe how they evolved. Radiology reporting is a time-consuming process, and scan results are often subject to delays.

Aswin Balasubramanian, Jacques Distler, Ron Donagi, Carlos Perez-Pardavila

Motivated by their appearance as Coulomb branch geometries of Class S theories, we study the image of the local Hitchin map in tame Hitchin systems of type-D with residue in a special nilpotent orbit $\mathcal{O}_H$. We describe two important features which distinguish it from the type A case studied in arXiv:2008.01020. The first feature, which we term even

Prabin Bhandari, Antonios Anastasopoulos, Dieter Pfoser

Despite the impressive performance of Large Language Models (LLM) for various natural language processing tasks, little is known about their comprehension of geographic data and related ability to facilitate informed geospatial decision-making. This paper investigates the extent of geospatial knowledge, awareness, and reasoning abilities encoded within such

Daniel A. Messenger, Joshua W. Burby, David M. Bortz

The Weak-form Sparse Identification of Nonlinear Dynamics algorithm (WSINDy) has been demonstrated to offer coarse-graining capabilities in the context of interacting particle systems (https://doi.org/10.1016/j.physd.2022.133406). In this work we extend this capability to the problem of coarse-graining Hamiltonian dynamics which possess approximate symmetrie

Archit Gupta, Chong Yock Eng, Deon Lim Meng Wee, Rashna Analia Ahmed

A single event upset (SEU) is a critical soft error that occurs in semiconductor devices on exposure to ionising particles from space environments. SEUs cause bit flips in the memory component of semiconductors. This creates a multitude of safety hazards as stored information becomes less reliable. Currently, SEUs are only detected several hours after their

Pablo Rodríguez-Gil, Jesús M. Corral-Santana, Nancy Elías-Rosa, Boris T. Gänsicke

We present the first reliable determination of the orbital period of the recurrent nova V2487 Oph (Nova Oph 1998). We derived a value of $0.753 \pm 0.016$ d ($18.1 \pm 0.4$ h) from the radial velocity curve of the intense He II $\lambda$4686 emission line as detected in time-series X-shooter spectra. The orbital period is significantly shorter than earlier c

Kayla Matteucci, Shahar Avin, Fazl Barez, Seán Ó hÉigeartaigh

Concerns around future dangers from advanced AI often centre on systems hypothesised to have intrinsic characteristics such as agent-like behaviour, strategic awareness, and long-range planning. We label this cluster of characteristics as "Property X". Most present AI systems are low in "Property X"; however, in the absence of deliberate steering, current re

Suman Bhowmick, Megan Fritz, Rebecca Lee Smith

West Nile virus (WNV) is prevalent in the United States but it shows considerable divergence in transmission patterns and spatio-temporal intensity.It is to be noted that the mechanism that drives the transmission potential of WNV is described by the abilities of host species to maintain and disseminate the pathogens pertinent with different eco-epidemiologi

Arne Jensen, Hynek Kovarik

We obtain asymptotic resolvent expansions at the threshold of the essential spectrum for magnetic Schr\"odinger and Pauli operators in dimension three. These operators are treated as perturbations of the Laplace operator in $L^2(\mathbb{R}^3)$ and $L^2(\mathbb{R}^3;\mathbb{C}^2)$, respectively. The main novelty of our approach is to show that the relative pe

Zhili Liu, Kai Chen, Yifan Zhang, Jianhua Han

Text-to-image (T2I) diffusion models often inadvertently generate unwanted concepts such as watermarks and unsafe images. These concepts, termed as the "implicit concepts", could be unintentionally learned during training and then be generated uncontrollably during inference. Existing removal methods still struggle to eliminate implicit concepts primarily du

Kaiwen Zhou, Kwonjoon Lee, Teruhisa Misu, Xin Eric Wang

In our work, we explore the synergistic capabilities of pre-trained vision-and-language models (VLMs) and large language models (LLMs) on visual commonsense reasoning (VCR) problems. We find that VLMs and LLMs-based decision pipelines are good at different kinds of VCR problems. Pre-trained VLMs exhibit strong performance for problems involving understanding

Marcin Korecki, Damian Dailisan, Cesare Carissimo

The development of ethical AI systems is currently geared toward setting objective functions that align with human objectives. However, finding such functions remains a research challenge, while in RL, setting rewards by hand is a fairly standard approach. We present a methodology for dynamic value alignment, where the values that are to be aligned with are

Rajesh K. Malla, Andreas Weichselbaum, Tzu-Chieh Wei, Robert M. Konik

We present a protocol for detecting multipartite entanglement in itinerant many-body electronic systems using single particle Green's functions. To achieve this, we first establish a connection between the quantum Fisher information (QFI) and single particle Green's functions by constructing a set of witness operators built out of single electron creation an

Insu Han, Rajesh Jayaram, Amin Karbasi, Vahab Mirrokni

We present an approximate attention mechanism named HyperAttention to address the computational challenges posed by the growing complexity of long contexts used in Large Language Models (LLMs). Recent work suggests that in the worst-case scenario, quadratic time is necessary unless the entries of the attention matrix are bounded or the matrix has low stable

Daniel Casanueva-Morato, Alvaro Ayuso-Martinez, Juan P. Dominguez-Morales, Angel Jimenez-Fernandez

The brain has computational capabilities that surpass those of modern systems, being able to solve complex problems efficiently in a simple way. Neuromorphic engineering aims to mimic biology in order to develop new systems capable of incorporating such capabilities. Bio-inspired learning systems continue to be a challenge that must be solved, and much work

Li Li, You Qin, Wei Ji, Yuxiao Zhou

Panoptic Scene Graph Generation (PSG) involves the detection of objects and the prediction of their corresponding relationships (predicates). However, the presence of biased predicate annotations poses a significant challenge for PSG models, as it hinders their ability to establish a clear decision boundary among different predicates. This issue substantiall

Bingzhi Zhang, Peng Xu, Xiaohui Chen, Quntao Zhuang

Deep generative models are key-enabling technology to computer vision, text generation, and large language models. Denoising diffusion probabilistic models (DDPMs) have recently gained much attention due to their ability to generate diverse and high-quality samples in many computer vision tasks, as well as to incorporate flexible model architectures and a re

Neil C. Janwani, Ersin Daş, Thomas Touma, Skylar X. Wei

Ensuring robot safety in complex environments is a difficult task due to actuation limits, such as torque bounds. This paper presents a safety-critical control framework that leverages learning-based switching between multiple backup controllers to formally guarantee safety under bounded control inputs while satisfying driver intention. By leveraging backup

LHCb collaboration, R. Aaij, A. S. W. Abdelmotteleb, C. Abellan Beteta

The identification of helium nuclei at LHCb is achieved using a method based on measurements of ionisation losses in the silicon sensors and timing measurements in the Outer Tracker drift tubes. The background from photon conversions is reduced using the RICH detectors and an isolation requirement. The method is developed using $pp$ collision data at $\sqrt{

Guangzhi Sun, Wenyi Yu, Changli Tang, Xianzhao Chen

Audio-visual large language models (LLM) have drawn significant attention, yet the fine-grained combination of both input streams is rather under-explored, which is challenging but necessary for LLMs to understand general video inputs. To this end, a fine-grained audio-visual joint representation (FAVOR) learning framework for multimodal LLMs is proposed in

Archiki Prasad, Elias Stengel-Eskin, Mohit Bansal

An increasing number of vision-language tasks can be handled with little to no training, i.e., in a zero and few-shot manner, by marrying large language models (LLMs) to vision encoders, resulting in large vision-language models (LVLMs). While this has huge upsides, such as not requiring training data or custom architectures, how an input is presented to an

Saeed Ullah Khan, Javlon Rayimbaev, Furkat Sarikulov, Ozodbek Abdurakhmonov

One of the most important and actual issues in relativistic astrophysics is testing gravity theories and obtaining constraint values for the parameters of black holes using observational data. In this research, we aimed to explore the optical features of a Kerr--Newman black hole model in the presence of a quintessential field, which may be a candidate for a

Marco Robalo

In this short note we record the fact that the set of multiplicative HKR natural equivalences defined simultaneously for all derived schemes, functorialy splitting the HKR-filtration and rendering the circle action compatible with the de Rham differential, is, via Cartier duality, in a natural bijection with the set of filtered formal exponential maps $ \wid

Jingliang Duan, Wenxuan Wang, Liming Xiao, Jiaxin Gao

Reinforcement learning (RL) has shown remarkable success in solving complex decision-making and control tasks. However, many model-free RL algorithms experience performance degradation due to inaccurate value estimation, particularly the overestimation of Q-values, which can lead to suboptimal policies. To address this issue, we previously proposed the Distr

Zonghai Yao, Benjamin J Schloss, Sai P. Selvaraj

Recent work has shown the promise of learning with human feedback paradigms to produce human-determined high-quality text. Existing works use human feedback to train large language models (LLMs) in general domain abstractive summarization and have obtained summary quality exceeding traditional likelihood training. In this paper, we focus on a less explored f

Hamdy Arkoub, Swarit Dwivedi, Adri C. T. van Duin, Miaomiao Jin

The interface between NiCr alloys and FLiNaK molten salt exhibits complex corrosion behavior, mainly driven by intricate chemical interactions involving Cr and F$\mathrm{^-}$ ions. Understanding these dynamic reactions is crucial for developing effective corrosion mitigation strategies to ensure the long-term durability of Ni-based alloy components in molten

Yaohua Li, Chenyang Wang, Yuanjiang Tang, Yong-Chun Liu

Time crystal is a class of non-equilibrium phases with broken time-translational symmetry. Here we demonstrate the time crystal in a single-mode nonlinear cavity. The time crystal originates from the self-oscillation induced by a linear gain and is stabilized by a nonlinear damping. We show in the time crystal phase there are sharp dissipative gap closing an

Szeyi Chan, Jiachen Li, Bingsheng Yao, Amama Mahmood

The rapid advancement of Large Language Models (LLMs) has created numerous potentials for integration with conversational assistants (CAs) assisting people in their daily tasks, particularly due to their extensive flexibility. However, users' real-world experiences interacting with these assistants remain unexplored. In this research, we chose cooking, a com

Claudio Ternullo

Joel Hamkins has described his multiverse position as being one of `higher-order realism -- Platonism about universes', whereby one takes models of set theory to be actually existing objects (vis-\`a-vis `first-order realism', which takes only sets to be actually existing objects). My goal in this paper is to make sense of the view in the very context of Ham

Rodolfo Carobene, Alessandro Candido, Javier Serrano, Alvaro Orgaz-Fuertes

We present Qibosoq, an open-source server-side software package designed for radio frequency system on chip (RFSoC) for executing arbitrary pulse sequences and algorithms on self-hosted quantum processing units using only open-source software. Qibosoq connects the RFSoC firmware provided by Qick, a Quantum Instrumentation Control Kit, with Qibo, a quantum co

Guangchen Lan, Han Wang, James Anderson, Christopher Brinton

Federated reinforcement learning (FedRL) enables agents to collaboratively train a global policy without sharing their individual data. However, high communication overhead remains a critical bottleneck, particularly for natural policy gradient (NPG) methods, which are second-order. To address this issue, we propose the FedNPG-ADMM framework, which leverages

S. Belliard, R. A. Pimenta, N. A. Slavnov

We consider a rational six vertex model on a rectangular lattice with boundary conditions that generalize the usual domain wall type. We find that the partition function of the inhomogeneous version of this model is given by a modified Izergin determinant. The proofs are based on the quantum inverse scattering method and its representation theory together wi

Andres-David Suarez-Gomez, Andres A. Hernandez Ortega

This article presents a comprehensive review of control algorithms used in mobile robotics, a field in constant evolution. Mobile robotics has seen significant advances in recent years, driven by the demand for applications in various sectors, such as industrial automation, space exploration, and medical care. The review focuses on control algorithms that ad

Yu Qiao

Recent research on the fundamentals of statistical mechanics has led to an interesting discovery [1-3]: With locally nonchaotic barriers, as Boltzmann's H-theorem is inapplicable, there exist nontrivial non-thermodynamic systems that can produce useful work by absorbing heat from a single thermal reservoir without any other effect, thereby breaking the bound

Erika Palmerio, Anwesha Maharana, Benjamin J. Lynch, Camilla Scolini

We present observations and modeling results of the propagation and impact at Earth of a high-latitude, extended filament channel eruption that commenced on 2015 July 9. The coronal mass ejection (CME) that resulted from the filament eruption was associated with a moderate disturbance at Earth. This event could be classified as a so-called "problem storm" be

Ziwei Chai, Tianjie Zhang, Liang Wu, Kaiqiao Han

The advancement of Large Language Models (LLMs) has remarkably pushed the boundaries towards artificial general intelligence (AGI), with their exceptional ability on understanding diverse types of information, including but not limited to images and audio. Despite this progress, a critical gap remains in empowering LLMs to proficiently understand and reason

Jie Wang, Jacopo Surace, Irénée Frérot, Benoît Legat

A ubiquitous problem in quantum physics is to understand the ground-state properties of many-body systems. Confronted with the fact that exact diagonalisation quickly becomes impossible when increasing the system size, variational approaches are typically employed as a scalable alternative: energy is minimised over a subset of all possible states and then di

Indranil Biswas, Alessandro Ghigi, Carolina Tamborini

There are two canonical projective structures on any compact Riemann surface of genus at least two: one coming from the uniformization theorem, and the other from Hodge theory. They produce two (different) families of projective structures over the moduli space $M_g$ of compact Riemann surfaces. A recent work of Biswas, Favale, Pirola, and Torelli shows that

Yixuan He, Gesine Reinert, David Wipf, Mihai Cucuringu

The angular synchronization problem aims to accurately estimate (up to a constant additive phase) a set of unknown angles $\theta_1, \dots, \theta_n\in[0, 2\pi)$ from $m$ noisy measurements of their offsets $\theta_i-\theta_j \;\mbox{mod} \; 2\pi.$ Applications include, for example, sensor network localization, phase retrieval, and distributed clock synchron

Joan Bagaria, Claudio Ternullo

We deal with the complex issue of whether large cardinals are intrinsically justified principles of set theory (we call this the Intrinsicness Issue). In order to do this, we review, in a systematic fashion, (1.) the abstract principles that have been formulated to motivate them, as well as (2.) their mathematical expressions, and assess the justifiability o

Adekunle Adefabi, Somtobe Olisah, Callistus Obunadike, Oluwatosin Oyetubo

Road accidents have significant economic and societal costs, with a small number of severe accidents accounting for a large portion of these costs. Predicting accident severity can help in the proactive approach to road safety by identifying potential unsafe road conditions and taking well-informed actions to reduce the number of severe accidents. This study

George Osipov, Marcin Pilipczuk, Magnus Wahlström

The input in the Minimum-Cost Constraint Satisfaction Problem (MinCSP) over the Point Algebra contains a set of variables, a collection of constraints of the form $x < y$, $x = y$, $x \leq y$ and $x \neq y$, and a budget $k$. The goal is to check whether it is possible to assign rational values to the variables while breaking constraints of total cost at mos

R. Donagi, M. Wijnholt

While M- and F-theory compactifications describe a much larger class of vacua than perturbative string compactifications, they typically need singularities to generate non-abelian gauge fields and charged matter. The physical explanation involves M2-branes wrapped on vanishing cycles. Here we seek an alternative explanation that could address outstanding iss

Keyang Ye, Hongzhi Wu, Xin Tong, Kun Zhou

We present the first real-time method for inserting a rigid virtual object into a neural radiance field, which produces realistic lighting and shadowing effects, as well as allows interactive manipulation of the object. By exploiting the rich information about lighting and geometry in a NeRF, our method overcomes several challenges of object insertion in aug

Richard Cobos, Aditya S. Khair, Ory Schnitzer

The squirmer is a popular model to analyse the fluid mechanics of a self-propelled object, such as a micro-organism. We demonstrate that some fore-aft symmetric squirmers can spontaneously self-propel above a critical Reynolds number. Specifically, we numerically study the effects of inertia on spherical squirmers characterised by an axially and fore-aft sym

Yuchen Yang, Linyida Zhang

Audiovisual (AV) archives are invaluable for holistically preserving the past. Unlike other forms, AV archives can be difficult to explore. This is not only because of its complex modality and sheer volume but also the lack of appropriate interfaces beyond keyword search. The recent rise in text-to-video retrieval tasks in computer science opens the gate to

Adam M. Ritchey

We present an analysis of high-resolution far-ultraviolet archival spectra obtained with the Space Telescope Imaging Spectrograph on the Hubble Space Telescope of the star HD 75309, which probes high-velocity shocked gas in the Vela supernova remnant (SNR). We examine high-velocity features from intrinsically strong absorption lines of O I, Si II, Si II*, C

Sebastian G. Gruber, Florian Buettner

Generative models, like large language models, are becoming increasingly relevant in our daily lives, yet a theoretical framework to assess their generalization behavior and uncertainty does not exist. Particularly, the problem of uncertainty estimation is commonly solved in an ad-hoc and task-dependent manner. For example, natural language approaches cannot

Rafael Aoude, Kays Haddad, Carlo Heissenberg, Andreas Helset

Starting with on-shell amplitudes compatible with the scattering of Kerr black holes, we produce the gravitational waveform and memory effect including spin at their leading post-Minkowskian orders to all orders in the spins of both scattering objects. For the memory effect, we present results at next-to-leading order as well, finding a closed form for all s

Jia-Shu Niu, Hui-Fang Xue

People can diagnose the interiors of stars by sensing their pulsations. Pulsation modes, which are determined by the internal state and structure of a star, are typically considered stable over short timescales. These independent pulsation modes have been used in asteroseismology to reconstruct the interior structure of stars. Here, we report the discovery o

Zaza N. Osmanov, Swadesh M. Mahajan

Based on the recently demonstrated resonant wave-wave process, it is shown that electrons can be accelerated to ultra-relativistic energies in the magnetospheres of radio pulsars. The energization occurs via the resonant interaction of the electron wave (described by a Klein-Gordon (KG) equation) moving in unison with an intense electromagnetic (EM) wave; th

Cheng Tan, Jue Wang, Zhangyang Gao, Siyuan Li

Spatio-temporal predictive learning plays a crucial role in self-supervised learning, with wide-ranging applications across a diverse range of fields. Previous approaches for temporal modeling fall into two categories: recurrent-based and recurrent-free methods. The former, while meticulously processing frames one by one, neglect short-term spatio-temporal i

Siva Mythili Gonuguntla, Douglas Singleton

It is well known that a magnetic monopole-electric charge system carries an angular momentum in its electromagnetic fields. Here we show that in the Dirac string formulation of magnetic charge the monopole-electric charge system also carries a momentum in its electromagnetic fields. This overlooked field momentum arises from the Coulomb electric field of the

Mojtaba Moradi-Sepahvand, Turaj Amraee

In this paper, a dynamic (i.e. multi-year) hybrid model is presented for Transmission Expansion Planning (TEP) utilizing the High Voltage Alternating Current (HVAC) and multiterminal Voltage Sourced Converter (VSC)-based High Voltage Direct Current (HVDC) alternatives. In addition to new HVAC and HVDC lines, the possibility of converting existing HVAC transm

Giovanni Armando, Paolo Panci, Joachim Weiss, Robert Ziegler

We discuss the leptonic ALP portal as a simple scenario that connects observed discrepancies in anomalous magnetic moments to the Dark Matter relic abundance. In this framework an axion-like particle in the multi-MeV range couples to SM leptons and a DM fermion, with mass above the ALP mass but below a GeV. The ALP contributes to $(g-2)_\mu$ and $(g-2)_e$ do