March 2024 arXiv papers — page 130

Stephen R. Kane, Paul K. Byrne

A major focus of the planetary science and astrobiology community is the understanding of planetary habitability, including the myriad factors that control the evolution and sustainability of temperate surface environments such as that of Earth. The few substantial terrestrial planetary atmospheres within the Solar System serve as a critical resource in stud

J. A. Garbutt, S. G. Parsons, O. Toloza, B. T. Gänsicke

White dwarfs with a F, G or K type companion represent the last common ancestor for a plethora of exotic systems throughout the galaxy, though to this point very few of them have been fully characterised in terms of orbital period and component masses, despite the fact several thousand have been identified. Gaia data release 3 has examined many hundreds of t

Ryan Plestid, Bei Zhou

Charged leptons produced by high-energy and ultrahigh-energy neutrinos have a substantial probability of emitting prompt internal bremsstrahlung $\nu_\ell + N \rightarrow \ell + X + \gamma$. This can have important consequences for neutrino detection. We discuss observable consequences at high- and ultrahigh-energy neutrino telescopes and the Large Hadron Co

Luc Bazinet, Stefan Pelletier, Björn Benneke, Ricardo Salinas

Studying and understanding the physical and chemical processes that govern hot Jupiters gives us insights on the formation of these giant planets. Having a constraint on the molecular composition of their atmosphere can help us pinpoint their evolution timeline. Namely, the metal enrichment and carbon-to-oxygen ratio can give us information about where in th

D. J. Turner, P. A. Giles, A. K. Romer, J. Pilling

We describe features of the X-ray: Generate and Analyse (XGA) open-source software package that have been developed to facilitate automated hydrostatic mass ($M_{\rm hydro}$) measurements from XMM X-ray observations of clusters of galaxies. This includes describing how XGA measures global, and radial, X-ray properties of galaxy clusters. We then demonstrate

Neil Christensen

SPINAS is a C++ package created for the implementation and numerical computation of phase-space points of constructive amplitudes in particle physics. This package contains a suite of classes and methods for handling particles, propagators, spinor products, and processes. SPINAS is structured to offer straightforward usability while ensuring maximum efficien

Adrien Kuntz, Enrico Barausse

Scalar-tensor theories have a long history as possible phenomenological alternatives to General Relativity, but are known to potentially produce deviations from the (strong) equivalence principle in systems involving self-gravitating objects, as a result of the presence of an additional gravitational scalar field besides the tensor modes of General Relativit

Giorgio Franceschelli, Mirco Musolesi

The Overfitted Brain hypothesis suggests dreams happen to allow generalization in the human brain. Here, we ask if the same is true for reinforcement learning agents as well. Given limited experience in a real environment, we use imagination-based reinforcement learning to train a policy on dream-like episodes, where non-imaginative, predicted trajectories a

Neil Christensen

We present a complete set of 4-point amplitudes in the constructive Standard Model at tree level. Any 4-point amplitude can be obtained from the results presented here by a suitable choice of masses, a permutation of the particles (by crossing symmetry), and a reversal of the momenta of the outgoing particles. We have validated all of these amplitudes by com

Neil Christensen

We find a complete set of 4-point vertices in the Constructive Standard Model (CSM). This set is smaller than in Feynman diagrams as the CSM does not need or allow any additional 4-point vertices (or "contact" terms) beyond what is present in Feynman diagrams and, furthermore, it does $\textit{not}$ need or allow a 4-point vertex for $Z Z \bar{W} W$, $W W \b

Roark Habegger, Ka Wai Ho, Ka Ho Yuen, Ellen G. Zweibel

While cosmic rays $(E\gtrsim 1\,\mathrm{GeV})$ are well coupled to a galaxy's interstellar medium (ISM) at scales of $L>100\,\mathrm{pc}$, adjusting stratification and driving outflows, their impact on small scales is less clear. Based on calculations of the cosmic ray diffusion coefficient from observations of the grammage in the Milky Way, cosmic rays have

Kaylee M. de Soto, Ashley Villar, Edo Berger, Sebastian Gomez

Photometric classifications of supernova (SN) light curves have become necessary to utilize the full potential of large samples of observations obtained from wide-field photometric surveys, such as the Zwicky Transient Facility (ZTF) and the Vera C. Rubin Observatory. Here, we present a photometric classifier for SN light curves that does not rely on redshif

Lei Zhu, Fangyun Wei, Yanye Lu

In this work, we investigate the potential of a large language model (LLM) to directly comprehend visual signals without the necessity of fine-tuning on multi-modal datasets. The foundational concept of our method views an image as a linguistic entity, and translates it to a set of discrete words derived from the LLM's vocabulary. To achieve this, we present

Zhihuan Dong, Adarsh S. Patri, T. Senthil

Recent experiments showing an integer quantum anomalous Hall effect in pentalayer rhombohedral graphene have been interpreted in terms of a valley-polarized interaction-induced Chern band. The resulting many-body state can be viewed as an Anomalous Hall Crystal (AHC), with a further coupling to a weak moir\'e potential. We explain the origin of the Chern ban

Fangyun Wei, Xi Chen, Lin Luo

Despite their sophisticated capabilities, large language models (LLMs) encounter a major hurdle in effective assessment. This paper first revisits the prevalent evaluation method-multiple choice question answering (MCQA), which allows for straightforward accuracy measurement. Through a comprehensive evaluation of 24 models across 11 benchmarks, we highlight

Konstantin Karchev, Matthew Grayling, Benjamin M. Boyd, Roberto Trotta

We present the first fully simulation-based hierarchical analysis of the light curves of a population of low-redshift type Ia supernovae (SNae Ia). Our hardware-accelerated forward model, released in the Python package slicsim, includes stochastic variations of each SN's spectral flux distribution (based on the pre-trained BayeSN model), extinction from dust

Aadhithya Iyer, Zhuoran Peng, Yinlong Dai, Irmak Guzey

Open-sourced, user-friendly tools form the bedrock of scientific advancement across disciplines. The widespread adoption of data-driven learning has led to remarkable progress in multi-fingered dexterity, bimanual manipulation, and applications ranging from logistics to home robotics. However, existing data collection platforms are often proprietary, costly,

Naman Jain, King Han, Alex Gu, Wen-Ding Li

Large Language Models (LLMs) applied to code-related applications have emerged as a prominent field, attracting significant interest from both academia and industry. However, as new and improved LLMs are developed, existing evaluation benchmarks (e.g., HumanEval, MBPP) are no longer sufficient for assessing their capabilities. In this work, we propose LiveCo

Shivin Dass, Wensi Ai, Yuqian Jiang, Samik Singh

A critical bottleneck limiting imitation learning in robotics is the lack of data. This problem is more severe in mobile manipulation, where collecting demonstrations is harder than in stationary manipulation due to the lack of available and easy-to-use teleoperation interfaces. In this work, we demonstrate TeleMoMa, a general and modular interface for whole

Yuhan Yi, Guanglin Zhang, Hai Jiang

For users requesting popular contents from content providers, edge caching can alleviate backhaul pressure and enhance the quality of experience of users. Recently there is also a growing concern about content freshness that is quantified by age of information (AoI). Therefore, AoI-aware online caching algorithms are required, which is challenging because th

Anuj Pasricha, Alessandro Roncone

In this work, we introduce LazyBoE, a multi-query method for kinodynamic motion planning with forward propagation. This algorithm allows for the simultaneous exploration of a robot's state and control spaces, thereby enabling a wider suite of dynamic tasks in real-world applications. Our contributions are three-fold: i) a method for discretizing the state an

Sanjeev Kumar, Vikram Tripathi

We investigate the fragility of a topologically ordered state, namely, the ground state of a weakly Zeeman perturbed honeycomb Kitaev model to environment induced decoherence effects mimicked by random local projective measurements. Our findings show the nonabelian Ising topological order, as quantified by a tripartite mutual information (the topological ent

Qibing Ren, Chang Gao, Jing Shao, Junchi Yan

The rapid advancement of Large Language Models (LLMs) has brought about remarkable generative capabilities but also raised concerns about their potential misuse. While strategies like supervised fine-tuning and reinforcement learning from human feedback have enhanced their safety, these methods primarily focus on natural languages, which may not generalize t

Yuxing Ren, Lixuan Tai, Kaicheng Pan, Yueyun Chen

Quasi van der Waals Epitaxy (qvdWE) has been realized for decades at the interfaces between 3D and 2D materials or van der Waals materials. The growth of magnetic topological insulators (MTI) Cr: (BixSb1-x)2Te3 (CBST) on GaAs (111) substrates for Quantum Anomalous Hall Effect (QAH) is actually one of the examples of qvdWE, which is not well noticed despite t

Barney Bramham, Abror Pirnapasov

For a symplectic isotopy on the two-dimensional disc we show that the classical spectral invariants of Viterbo [20] can be extended in a meaningful way to {\it non-compactly} supported Hamiltonians. We establish some basic properties of these extended invariants and as an application we show that Hutchings' inequality in [8] between the Calabi invariant and

Ben L. Titzer, Elizabeth Gilbert, Bradley Wei Jie Teo, Yash Anand

A key strength of managed runtimes over hardware is the ability to gain detailed insight into the dynamic execution of programs with instrumentation. Analyses such as code coverage, execution frequency, tracing, and debugging, are all made easier in a virtual setting. As a portable, low-level bytecode, WebAssembly offers inexpensive in-process sandboxing wit

Dmitriy Kunisky

We study when low coordinate degree functions (LCDF) -- linear combinations of functions depending on small subsets of entries of a vector -- can hypothesis test between high-dimensional probability measures. These functions are a generalization, proposed in Hopkins' 2018 thesis but seldom studied since, of low degree polynomials (LDP), a class widely used i

Mikhail Kareev, Xiaoran Liu, Michael Terilli, Fangdi Wen

The synthesis of stoichiometric and epitaxial pyrochlore iridate thin films presents significant challenges yet is critical for unlocking experimental access to novel topological and magnetic states. Towards this goal, we unveil an in-situ two-stage growth mechanism that facilitates the synthesis of high-quality oriented pyrochlore iridate thin films. The gr

Shihao Zhao, Shaozhe Hao, Bojia Zi, Huaizhe Xu

Text-to-image generation has made significant advancements with the introduction of text-to-image diffusion models. These models typically consist of a language model that interprets user prompts and a vision model that generates corresponding images. As language and vision models continue to progress in their respective domains, there is a great potential i

Barbara Fantechi, Andrea T. Ricolfi

Let $X$ be a variety. In this survey, we study (decompositions of) the motivic class, in the Grothendieck ring of stacks, of the stack $\mathscr{C}oh^n(X)$ of $0$-dimensional coherent sheaves of length $n$ on $X$. To do so, we review the construction of the support map $\mathscr{C}oh^n(X) \to \mathrm{Sym}^n(X)$ to the symmetric product and we prove that, for

Linshan Qiu, Zhonggen Li, Xiangyu Ke, Lu Chen

Counting (p,q)-bicliques in bipartite graphs poses a foundational challenge with broad applications, from densest subgraph discovery in algorithmic research to personalized content recommendation in practical scenarios. Despite its significance, current leading (p,q)-biclique counting algorithms fall short, particularly when faced with larger graph sizes and

Sierra Wyllie, Ilia Shumailov, Nicolas Papernot

Model-induced distribution shifts (MIDS) occur as previous model outputs pollute new model training sets over generations of models. This is known as model collapse in the case of generative models, and performative prediction or unfairness feedback loops for supervised models. When a model induces a distribution shift, it also encodes its mistakes, biases,

Walid El Maouaki, Taoufik Said, Mohamed Bennai

This study addresses the urgent need for improved prostate cancer detection methods by harnessing the power of advanced technological solutions. We introduce the application of Quantum Support Vector Machine (QSVM) to this critical healthcare challenge, showcasing an enhancement in diagnostic performance over the classical Support Vector Machine (SVM) approa

Mikhail V. Bondarko

We study $t$-structures (on triangulated categories) that are closely related to weight structures. A $t$-structure couple $t=(C_{t\le 0},C_{t\ge 0})$ is said to be adjacent to a weight structure $w=(C_{w\le 0}, C_{w\ge 0})$ if $C_{t\ge 0}=C_{w\ge 0}$. For a category $C$ that satisfies the Brown representability property we prove that $t$ that is adjacent to

Emanuel Ben-Baruch, Adam Botach, Igor Kviatkovsky, Manoj Aggarwal

With the increasing size of datasets used for training neural networks, data pruning becomes an attractive field of research. However, most current data pruning algorithms are limited in their ability to preserve accuracy compared to models trained on the full data, especially in high pruning regimes. In this paper we explore the application of data pruning

Rahul K. Gupta, Daniel K. Molzahn

In PV-rich power distribution systems, over-voltage issues are often addressed by curtailing excess generation from PV plants (in addition to reactive power control), raising fairness concerns. Existing fairness-aware control schemes tackle this problem by incorporating fairness objectives into the cost function. However, such schemes result in increased ove

Young Ho Kim

Singular fourth-order Abreu equations have been used to approximate minimizers of convex functionals subject to a convexity constraint in dimensions higher than or equal to two. For Abreu type equations, they often exhibit different solvability phenomena in dimension one and dimensions at least two. We prove the analogues of these results for the variational

Yoga Esa Wibowo, Cristian Cioflan, Thorir Mar Ingolfsson, Michael Hersche

Few-Shot Class-Incremental Learning (FSCIL) enables machine learning systems to expand their inference capabilities to new classes using only a few labeled examples, without forgetting the previously learned classes. Classical backpropagation-based learning and its variants are often unsuitable for battery-powered, memory-constrained systems at the extreme e

Yanzhao Guo, John P. Hadden, Federico Gorrini, Giulio Coccia

Quantum emitters, such as the negatively charged nitrogen-vacancy center in diamond, are attractive for quantum technologies such as nano-sensing, quantum information processing, and as a non-classical light source. However, it is still challenging to position individual emitters in photonic structures whilst preserving the spin coherence properties of the d

Harish G. Naik, Jan Polster, Raj Shekhar, Tamás Horváth

We formulate an XAI-based model improvement approach for Graph Neural Networks (GNNs) for node classification, called Explanation Enhanced Graph Learning (EEGL). The goal is to improve predictive performance of GNN using explanations. EEGL is an iterative self-improving algorithm, which starts with a learned "vanilla" GNN, and repeatedly uses frequent subgra

R. A. Konoplya, A. Zhidenko

Recently, Bueno, Cano, and Hennigar [arXiv:2403.04827] proposed a generic approach for incorporating an infinite tower of higher-curvature corrections into the Einstein theory. In this study, we compute quasinormal modes for certain regular D-dimensional black holes resulting from this infinite series of higher-curvature corrections, specifically focusing on

Andrey Morozov

In this paper we discuss the principles of measuring topological charge or representation traveling in the set of anyons. We describe the procedure and analyze how it works for the different values of parameters of the theory. We also show how it can be modified to be more effective for different levels of Chern-Simons theory.

Kuangyin Deng, Ran Cheng

We propose a mechanism of topology-induced symmetry breaking, where certain local symmetry preserved by the Hamiltonian is explicitly broken in the eigenmodes of excitation due to nontrivial real-space topology. We demonstrate this phenomenon by studying magnonic excitations on a M\"obius strip comprising two antiferromagnetically coupled spin chains. Even w

Florian Sammüller, Silas Robitschko, Sophie Hermann, Matthias Schmidt

We present a scheme for investigating arbitrary thermal observables in spatially inhomogeneous equilibrium many-body systems. Extending the grand canonical ensemble yields any given observable as an explicit hyper-density functional. Associated local fluctuation profiles follow from an exact hyper-Ornstein-Zernike equation. While the local compressibility an

Gregory P. Smith, Chenhui Zhu, Mikhail Zernenkov, Guillaume Freychet

We report the observation of a Twist Grain Boundary (TGB) phase of DNA, a striking motif of three dimensional (3D) equilibrium self-assembly of the DNA tetramer 5'-GTAC-3', a base sequence that is self-complimentary, pairing to form 4-base long, blunt-end Watson/Crick (WC) duplexes. Hydrophobic blunt ends and liquid crystal ordering enable these short duplex

Tuhin Subhra De, Pranjal Singh, Alok Patel

In the context of developing nations like India, traditional business to business (B2B) commerce heavily relies on the establishment of robust relationships, trust, and credit arrangements between buyers and sellers. Consequently, ecommerce enterprises frequently. Established in 2016 with a vision to revolutionize trade in India through technology, Udaan is

Chaoyi Zhu, Jiayi Tang, Juan F. Pérez, Marten van Dijk

Synthetic data from generative models emerges as the privacy-preserving data sharing solution. Such a synthetic data set shall resemble the original data without revealing identifiable private information. Till date, the prior focus on limited types of tabular synthesizers and a small number of privacy attacks, particularly on Generative Adversarial Networks

Mercy Ranjit, Gopinath Ganapathy, Shaury Srivastav, Tanuja Ganu

Small Language Models (SLMs) have shown remarkable performance in general domain language understanding, reasoning and coding tasks, but their capabilities in the medical domain, particularly concerning radiology text, is less explored. In this study, we investigate the application of SLMs for general radiology knowledge specifically question answering relat

Romain Petrides, David Tewodrose

We set up a new framework to study critical points of functionals defined as combinations of eigenvalues of operators with respect to a given set of parameters: Riemannian metrics, potentials, etc. Our setting builds upon Clarke's differentiation theory to provide a novel understanding of critical metrics. In particular, we unify and refine previous research

Mohammad Charara, Zak Kujala, Sungyon Lee, Stefano Gonella

Motion control of droplets has generated much attention for its applications to microfluidics, where precise control of small fluid volumes is an imperative requirement. Mechanical vibrations have been shown to be effective at inducing controllable depinning, and activation of different drop motion regimes. However, existing vibration-based strategies involv

Haokun Lin, Haoli Bai, Zhili Liu, Lu Hou

Vision-language pre-trained models have achieved impressive performance on various downstream tasks. However, their large model sizes hinder their utilization on platforms with limited computational resources. We find that directly using smaller pre-trained models and applying magnitude-based pruning on CLIP models leads to inflexibility and inferior perform

Guibo Luo, Hanwen Zhang, Xiuling Wang, Mingzhi Chen

Privacy-preserving computing is crucial for multi-center machine learning in many applications such as healthcare and finance. In this paper a Multi-center Privacy Computing framework with Predictions Aggregation (MPCPA) based on denoising diffusion probabilistic model (DDPM) is proposed, in which conditional diffusion model training, DDPM data generation, a

An Aloysius Wang, Zimo Zhao, Yifei Ma, Yuxi Cai

Optical Skyrmions have many important properties that make them ideal units for high-density data applications, including the ability to carry digital information through a discrete topological number and the independence of spatially varying polarization to other dimensions. More importantly, the topological nature of the optical Skyrmion heuristically sugg

Helmut Satz

In a prime number decomposition of integers in a given set, the occurrence frequencies of prime numbers are shown to satisfy a general forms of Zipf's law.

Bram Evert, Zoe Gonzalez Izquierdo, James Sud, Hong-Ye Hu

Theoretically understanding and experimentally characterizing and modifying the underlying Hamiltonian of a quantum system is of utmost importance in achieving high-fidelity quantum gates for quantum computing. In this work, we explore the use of dynamical decoupling (DD) in characterizing and suppressing undesired two-qubit couplings as well as the underlyi

Lilan Karunaratne, Nilanjan Ray Chaudhuri, Amirthagunaraj Yogarathnam, Meng Yue

Time-varying phasor-based analysis of subsynchronous oscillations (SSOs) involving grid-following converters (GFLCs) and its benchmarking with electromagnetic transient (EMT) models have so far been restricted to highly simplified grid models with constant voltage sources behind series R-L circuits. In this paper, modeling adequacy of bulk power systems with

Sahar Moradizeyveh, Mehnaz Tabassum, Sidong Liu, Robert Ahadizad Newport

Eye-gaze tracking research offers significant promise in enhancing various healthcare-related tasks, above all in medical image analysis and interpretation. Eye tracking, a technology that monitors and records the movement of the eyes, provides valuable insights into human visual attention patterns. This technology can transform how healthcare professionals

A. Ali, M. Barone, S. Brentjes, D. Britzger

The armed invasion of Ukraine by the Russian Federation has adversely affected the relations between Russia and Western countries. Among other aspects, it has put scientific cooperation and collaboration into question and changed the scientific landscape significantly. Cooperation between some Western institutions and their Russian and Belarusian partners we

William Xie, Maria Valentini, Jensen Lavering, Nikolaus Correll

Large language models (LLMs) can provide rich physical descriptions of most worldly objects, allowing robots to achieve more informed and capable grasping. We leverage LLMs' common sense physical reasoning and code-writing abilities to infer an object's physical characteristics$\unicode{x2013}$mass $m$, friction coefficient $\mu$, and spring constant $k$$\un

Rohit Konda, Vikas Chandan, Jesse Crossno, Blake Pollard

The ubiquity and energy needs of industrial refrigeration has prompted several research studies investigating various control opportunities for reducing energy demand. This work focuses on one such opportunity, termed compressor sequencing, which entails intelligently selecting the operational state of the compressors to service the required refrigeration lo

Matthis Lehmkuehler, Wei Qian, Wendelin Werner

The critical two-dimensional Brownian loop-soup is an infinite collection of non-interacting Brownian loops in a planar domain that possesses some combinatorial features related to the notion of indistinguishability of bosons. The properly renormalized occupation time field of this collection of loops is known to be distributed like the properly defined squa

Ignacy Kaliszewski

We investigate a class of polyhedral convex cones, with $R^k_+$ (the nonegative orthant in $\mathbb{R}^k$) as a special case. We start with the observation that for convex cones contained in $\mathbb{R}^k$, the respective cone efficiency is inconsistent with the Pareto efficiency, the latter being deeply rooted in economics, the decision theory, and the mult

Lucas Vogel, Thomas Springer, Matthias Wählisch

Over the last 30 years, the World Wide Web has changed significantly. In this paper, we argue that common practices to prepare web pages for delivery conflict with many efforts to present content with minimal latency, one fundamental goal that pushed changes in the WWW. To bolster our arguments, we revisit reasons that led to changes of HTTP and compare them

Behnam Pourhassan, Hoda Farahani, Farideh Kazemian, İzzet Sakallı

In this paper, we use the holographic principle to obtain a modified metric of black holes that reproduces the exponentially corrected entropy. The exponential correction of the black hole entropy comes from non-perturbative corrections. It interprets as a quantum effect which affects black hole thermodynamics especially in the infinitesimal scales. Hence, i

Preetam Prabhu Srikar Dammu, Himanshu Naidu, Mouly Dewan, YoungMin Kim

In the midst of widespread misinformation and disinformation through social media and the proliferation of AI-generated texts, it has become increasingly difficult for people to validate and trust information they encounter. Many fact-checking approaches and tools have been developed, but they often lack appropriate explainability or granularity to be useful

Simone Cerreia-Vioglio, Fabio Maccheroni, Massimo Marinacci, Luigi Montrucchio

This paper presents a general study of one-dimensional differentiability for functionals defined on convex domains that are not necessarily open. The local approximation is carried out using affine functionals, as opposed to linear functionals typically employed in standard Gateaux differentiability. This affine notion of differentiability naturally arises i

Akshayveer Akshayveer, Federico C Buroni, Roderick Melnik, Luis Rodriguez-Tembleque

This study presents a computational framework to investigate and predict the complicated multiphase properties of eco-friendly lead-free piezoelectric materials, which are crucial for sustainable technological progress. Although their electromechanical properties vary by phase, lead-free piezoelectric materials show a considerable thermo-electromechanical re

Jianchen Wang, Zhouhong Gu, Xiaoxuan Zhu, Lin Zhang

Large Language Models have revolutionized numerous tasks with their remarkable efficacy. However, editing these models, crucial for rectifying outdated or erroneous information, often leads to a complex issue known as the ripple effect in the hidden space. While difficult to detect, this effect can significantly impede the efficacy of model editing tasks and

Nicolas Venkovic, Paul Mycek, Olivier Le Maître

A preconditioning strategy is proposed for the iterative solve of large numbers of linear systems with parameter-dependent matrix and right-hand side which arise during the computation of solution statistics of stochastic elliptic partial differential equations with random and spatially variable coefficients sampled by Monte Carlo. Building on the assumption

Areefa Khatoon, Abdur Raheem, Asma Afreen

This paper discusses a multi-term time-fractional delay differential equation in a real Hilbert space. An iterative scheme for a multi-term time-fractional differential equation is established using Rothe's method. The method of semi-discretization is extended to this kind of time fractional problem with delay in the case that the time delay parameter $\nu >

Jacob A. Johnson, Matthew J. Heaton, William F. Christensen, Lynsie R. Warr

Autoencoders are powerful machine learning models used to compress information from multiple data sources. However, autoencoders, like all artificial neural networks, are often unidentifiable and uninterpretable. This research focuses on creating an identifiable and interpretable autoencoder that can be used to meld and combine climate data products. The pro

Dirk Beyer, Po-Chun Chien, Nian-Ze Lee

Software model checking is a challenging problem, and generating relevant invariants is a key factor in proving the safety properties of a program. Program invariants can be obtained by various approaches, including lightweight procedures based on data-flow analysis and intensive techniques using Craig interpolation. Although data-flow analysis runs efficien

Hong-Sheng Huang, Yu-Lei Fu, Han-Yu Lin

In this work, we introduce a strong Designated Verifier Signature (DVS) scheme that incorporates a message recovery mechanism inspired by the concept of the Universal Designated Verifier Signature (UDVS) scheme. It is worth noting that Saeednia's strong designated verifier signature scheme fails to guarantee the privacy of the signature, making it unsuitable

Ian Rouse, David Power, Julia Subbotina, Vladimir Lobaskin

The corona of a nanoparticle immersed in a biological fluid is of key importance to its eventual fate and bioactivity in the environment or inside live tissues. It is critical to have insight into both the underlying bionano interactions and the corona composition to ensure biocompatibility of novel engineered nanomaterials. A prediction of these properties

Iman Islam, Esther Puyol-Antón, Bram Ruijsink, Andrew J. Reader

Echocardiography (echo) is the first imaging modality used when assessing cardiac function. The measurement of functional biomarkers from echo relies upon the segmentation of cardiac structures and deep learning models have been proposed to automate the segmentation process. However, in order to translate these tools to widespread clinical use it is importan

Rabiah Alnashwan, Prosanta Gope, Benjamin Dowling

Introducing Small Cell Networks (SCN) has significantly improved wireless link quality, spectrum efficiency and network capacity, which has been viewed as one of the key technologies in the fifth-generation (5G) mobile network. However, this technology increases the frequency of handover (HO) procedures caused by the dense deployment of cells in the network

Sainbayar Sukhbaatar, Olga Golovneva, Vasu Sharma, Hu Xu

We investigate efficient methods for training Large Language Models (LLMs) to possess capabilities in multiple specialized domains, such as coding, math reasoning and world knowledge. Our method, named Branch-Train-MiX (BTX), starts from a seed model, which is branched to train experts in embarrassingly parallel fashion with high throughput and reduced commu

Abdul Fatir Ansari, Lorenzo Stella, Caner Turkmen, Xiyuan Zhang

We introduce Chronos, a simple yet effective framework for pretrained probabilistic time series models. Chronos tokenizes time series values using scaling and quantization into a fixed vocabulary and trains existing transformer-based language model architectures on these tokenized time series via the cross-entropy loss. We pretrained Chronos models based on

Edem Kossi Akakpo, Marc Haelterman, Francois Leo, Pedro Parra-Rivas

We analyze the implication of tristability on localization phenomena in one-dimensional extended dissipative systems. In this context, localized states appear due to the interaction and locking of front waves connecting different extended states. In the tristable regime investigated here two extended uniform states coexist with one periodic Turing pattern. T

Liang Kong, Zhi-Hao Zhang, Jiaheng Zhao, Hao Zheng

We develop a unified mathematical theory of defect condensations for topological orders in all dimensions based on higher categories, higher algebras and higher representations. A k-codimensional topological defect $A$ in an n+1D (potentially anomalous) topological order $C^{n+1}$ is condensable if it is equipped with the structure of a condensable $E_k$-alg

S. M. Thomas, S. Seo, T. Asaba, F. Ronning

CeRh$_6$Ge$_4$ is unusual in that its ferromagnetic transition can be suppressed continuously to zero temperature, i.e., to a ferromagnetic quantum-critical point (QCP), through the application of modest hydrostatic pressure. This discovery has raised the possibility that the ferromagnetic QCP may be of the Kondo-breakdown type characterized by a jump in Fer

Eduardo M. G. Vila, Eric C. Kerrigan, Paul Bruce

We propose a novel early-terminating mesh refinement strategy using an integrated residual method to solve dynamic feasibility problems. As a generalization of direct collocation, the integrated residual method is used to approximate an infinite-dimensional problem into a sequence of finite-dimensional optimization subproblems. Each subproblem in the sequenc

Karin Baur, Raquel Coelho Simoes

In this paper, we give a geometric construction of string algebras and of their module categories. Our approach uses dissections of punctured Riemann surfaces with extra data at marked points, called labels. As an application, we give a classification of support tau-tilting modules in terms of arcs in such a tiled surface. In the case when the string algebra

Ao Chen, Xin Zhang

Acoustic wave modulation plays a pivotal role in various applications, including sound-field reconstruction, wireless communication, and particle manipulation, among others. However, current acoustic metamaterial and metasurface designs typically focus on controlling either reflection or transmission waves, often overlooking the coupling between amplitude an

Zhengxuan Wu, Atticus Geiger, Aryaman Arora, Jing Huang

Interventions on model-internal states are fundamental operations in many areas of AI, including model editing, steering, robustness, and interpretability. To facilitate such research, we introduce $\textbf{pyvene}$, an open-source Python library that supports customizable interventions on a range of different PyTorch modules. $\textbf{pyvene}$ supports comp

Anna-Katharina Wickert, Michael Schlichtig, Marvin Vogel, Lukas Winter

Context: Static analyses are well-established to aid in understanding bugs or vulnerabilities during the development process or in large-scale studies. A low false-positive rate is essential for the adaption in practice and for precise results of empirical studies. Unfortunately, static analyses tend to report where a vulnerability manifests rather than the

Kunhao Liu, Fangneng Zhan, Muyu Xu, Christian Theobalt

We introduce StyleGaussian, a novel 3D style transfer technique that allows instant transfer of any image's style to a 3D scene at 10 frames per second (fps). Leveraging 3D Gaussian Splatting (3DGS), StyleGaussian achieves style transfer without compromising its real-time rendering ability and multi-view consistency. It achieves instant style transfer with t

Tongyao Zhu, Qian Liu, Liang Pang, Zhengbao Jiang

Recent developments in Language Models (LMs) have shown their effectiveness in NLP tasks, particularly in knowledge-intensive tasks. However, the mechanisms underlying knowledge storage and memory access within their parameters remain elusive. In this paper, we investigate whether a generative LM (e.g., GPT-2) is able to access its memory sequentially or ran

Bing Ma, Ling Chen, De-Jin Wu, Marc Pulupa

Solar and interplanetary radio bursts can reflect the existence and motion of energetic electrons and are therefore a kind of vital phenomenon in solar activities. The present study reported a solar radio burst (SRB) event observed by Parker Solar Probe (PSP) in its 8th orbital encounter phase, and it lasted about 20 hours in a frequency range of 0.5-15 MHz,

Filippo Quattrocchi

We prove the convergence of a modified Jordan--Kinderlehrer--Otto scheme to a solution to the Fokker--Planck equation in $\Omega \Subset \mathbb R^d$ with general -- strictly positive and temporally constant -- Dirichlet boundary conditions. We work under mild assumptions on the domain, the drift, and the initial datum. In the special case where $\Omega$ is

Cristian Cioflan, Lukas Cavigelli, Luca Benini

Keyword spotting systems for always-on TinyML-constrained applications require on-site tuning to boost the accuracy of offline trained classifiers when deployed in unseen inference conditions. Adapting to the speech peculiarities of target users requires many in-domain samples, often unavailable in real-world scenarios. Furthermore, current on-device learnin

Charlotte Fairman, Hannah R. Wakeford, Ryan J. MacDonald

Exoplanet transmission spectra provide rich information about the chemical composition, clouds and temperature structure of exoplanet atmospheres. Most exoplanet transmission spectra only span infrared wavelengths ($\gtrsim$ 1 $\rm{\mu m}$), which can preclude crucial atmospheric information from shorter wavelengths. Here, we explore how retrieved atmospheri

Ivo M. Baltruschat, Parvaneh Janbakhshi, Matthias Lenga

This work addresses the Brain Magnetic Resonance Image Synthesis for Tumor Segmentation (BraSyn) challenge, which was hosted as part of the Brain Tumor Segmentation (BraTS) challenge in 2023. In this challenge, researchers are invited to synthesize a missing magnetic resonance image sequence, given other available sequences, to facilitate tumor segmentation

Simon Finster, Patrick Loiseau, Simon Mauras, Mathieu Molina

We initiate the study of how auction design affects the division of surplus among buyers. We propose a parsimonious measure for equity and apply it to the family of standard auctions for homogeneous goods. Our surplus-equitable mechanism is efficient, Bayesian-Nash incentive compatible, and achieves surplus parity among winners ex-post. The uniform-price auc

Le Luo, Bingrong Xu, Qingyong Zhang, Cheng Lian

By using unsupervised domain adaptation (UDA), knowledge can be transferred from a label-rich source domain to a target domain that contains relevant information but lacks labels. Many existing UDA algorithms suffer from directly using raw images as input, resulting in models that overly focus on redundant information and exhibit poor generalization capabili

Miguel Fuentes, Brett Mullins, Ryan McKenna, Gerome Miklau

Mechanisms for generating differentially private synthetic data based on marginals and graphical models have been successful in a wide range of settings. However, one limitation of these methods is their inability to incorporate public data. Initializing a data generating model by pre-training on public data has shown to improve the quality of synthetic data

K. Abe, C. Bronner, Y. Hayato, K. Hiraide

The first loading of gadolinium (Gd) into Super-Kamiokande in 2020 was successful, and the neutron capture efficiency on Gd reached 50\%. To further increase the Gd neutron capture efficiency to 75\%, 26.1 tons of $\rm Gd_2(\rm SO_4)_3\cdot \rm 8H_2O$ was additionally loaded into Super-Kamiokande (SK) from May 31 to July 4, 2022. As the amount of loaded $\rm

Riccardo Rende, Sebastian Goldt, Federico Becca, Luciano Loris Viteritti

Recent progress in the design and optimization of neural-network quantum states (NQSs) has made them an effective method to investigate ground-state properties of quantum many-body systems. In contrast to the standard approach of training a separate NQS from scratch at every point of the phase diagram, we demonstrate that the optimization of a NQS at a highl

Hanxu Hu, Simon Yu, Pinzhen Chen, Edoardo M. Ponti

Despite the success of existing instruction-tuned models, we find that they usually struggle to respond to queries with multiple instructions. This impairs their performance in complex problems whose solution consists of multiple intermediate tasks. Thus, we contend that part of the fine-tuning data mixture should be sequential--containing a chain of interre

Xavier Ros-Oton, Marvin Weidner

In this article we establish for the first time the $C^s$ boundary regularity of solutions to nonlocal elliptic equations with kernels $K(y)\asymp |y|^{-n-2s}$. This was known to hold only when $K$ is homogeneous, and it is quite surprising that it holds for general inhomogeneous kernels, too. As an application of our results, we also establish the optimal $