October 2024 arXiv papers — page 77

Anup Biswas

In this article we prove the Pohozaev identity for the semilinear Dirichlet problem of the form $-\Delta u + a(-\Delta)^s u = f(u)$ in $\Omega$, and $u=0$ in $\Omega^c$, where $a$ is a non-negative constant and $\Omega$ is a bounded $C^2$ domain. We also establish similar identity for systems of equations. As applications of this identity, we deduce a unique

Huck Bennett, Alexander Golovnev, Noah Stephens-Davidowitz

In this note, we present examples showing that several natural ways of constructing lattices from error-correcting codes do not in general yield a correspondence between minimum-weight non-zero codewords and shortest non-zero lattice vectors. From these examples, we conclude that the main results in two works of Vl\u{a}du\c{t} (Moscow J. Comb. Number Th., 20

Ram Mohan Rao Kadiyala

With increasing usage of generative models for text generation and widespread use of machine generated texts in various domains, being able to distinguish between human written and machine generated texts is a significant challenge. While existing models and proprietary systems focus on identifying whether given text is entirely human written or entirely mac

Janghoon Ock, Radheesh Sharma Meda, Tirtha Vinchurkar, Yayati Jadhav

Adsorption energy is a key reactivity descriptor in catalysis. Determining adsorption energy requires evaluating numerous adsorbate-catalyst configurations, making it computationally intensive. Current methods rely on exhaustive sampling, which does not guarantee the identification of the global minimum energy. To address this, we introduce Adsorb-Agent, a L

Bao Q. Tran, Viet Nguyen, Anh Tran, Toan Tran

Diffusion models have demonstrated remarkable capabilities in image synthesis, but their recently proven vulnerability to Membership Inference Attacks (MIAs) poses a critical privacy concern. This paper introduces two novel and efficient approaches (DualMD and DistillMD) to protect diffusion models against MIAs while maintaining high utility. Both methods ar

Arpan Das, Pier Marzocca, Oleg Levinski

This paper introduces the Parsimonious Dynamic Mode Decomposition (parsDMD), a novel algorithm designed to automatically select an optimally sparse subset of dynamic modes for both spatiotemporal and purely temporal data. By incorporating time-delay embedding and leveraging Orthogonal Matching Pursuit (OMP), parsDMD ensures robustness against noise and effec

Thanh Le-Cong, Bach Le, Toby Murray

In this paper, we first show that increases in beam size, even for small-sized LLMs (1B-7B params), require extensive GPU usage, leading to up to 80% of recurring crashes due to memory overloads in LLM-based APR. Seemingly simple solutions to reduce memory consumption are (1) to quantize LLM models, i.e., converting the weights of an LLM from high-precision

Emily J. Ahern, Sarah K. Scholten, Clayton Locke, Nicolas Bourbeau-Hebert

Rubidium two-photon frequency standards are emerging as powerful contenders for compact, durable devices with exceptional stability. The field has focused on single-color excitation to date. Here we demonstrate the key advantages of a two-color excitation of a two-photon optical frequency standard based on the $5S_{1/2}\,{\rightarrow}\,5D_{5/2}$ transition o

Kimiya Saadat, Richard Zhao

Playing two-player games using reinforcement learning and self-play can be challenging due to the complexity of two-player environments and the possible instability in the training process. We propose that a reinforcement learning algorithm can train more efficiently and achieve improved performance in a two-player game if it leverages the knowledge from the

Andrea Chiesa, Ulisse Stefanelli

We investigate the evolution of a two-phase viscoelastic material at finite strains. The phase evolution is assumed to be irreversible: One phase accretes in time in its normal direction, at the expense of the other. Mechanical response depends on the phase. At the same time, growth is influenced by the mechanical state at the boundary of the accreting phase

Lin Lin, Shiyin Shen, Hassen M. Yesuf, Ye-Wei Mao

We analyzed the global and resolved properties of approximately 1,240 nearby star-forming galaxies from the MaNGA survey, comparing compact and extended galaxies -- those with smaller and larger radii ($R_{\rm e}$), respectively -- at a fixed stellar mass ($M_{\ast}$). Compact galaxies typically exhibit lower HI gas fractions, higher dust extinction, higher

Jun Geng, Zhongwei Shen

This paper studies the Neumann boundary value problems for the Stokes equations in a convex domain in $\mathbb{R}^d$. We obtain nontangential-maximal-function estimates in $L^p$ and $W^{1, p}$ estimates for $p$ in certain ranges depending on $d$. These ranges are larger than the known ranges for Lipschitz domains. The proof relies on a $W^{2, 2}$ estimate fo

Bikai Gao, Masayasu Harada

We present a novel unified approach to describe the dense symmetric nuclear matter by combining the quarkyonic matter framework with the parity doublet model. This integration allows for a consistent treatment of the transition from hadronic to quark degrees of freedom while incorporating chiral symmetry restoration effects. Our model introduces a chiral inv

Congcong Chen, Jinhua Cui, Jiliang Zhang

Modern processor advancements have introduced security risks, particularly in the form of microarchitectural timing attacks. High-profile attacks such as Meltdown and Spectre have revealed critical flaws, compromising the entire system's security. Recent black-box automated methods have demonstrated their advantages in identifying these vulnerabilities on va

Norbert Dzadz, Maciej Romaniuk

The precise and large dataset concerning catastrophic events is very important for insurers. To improve the quality of such data three methods based on the bootstrap, bootknife, and GAN algorithms are proposed. Using numerical experiments and real-life data, simulated outputs for these approaches are compared based on the mean squared (MSE) and mean absolute

Pai Zhu, Jacob W. Bartel, Dhruuv Agarwal, Kurt Partridge

We propose GE2E-KWS -- a generalized end-to-end training and evaluation framework for customized keyword spotting. Specifically, enrollment utterances are separated and grouped by keywords from the training batch and their embedding centroids are compared to all other test utterance embeddings to compute the loss. This simulates runtime enrollment and verifi

Saumya Gupta, Dimitris Samaras, Chao Chen

Diffusion models excel at creating visually impressive images but often struggle to generate images with a specified topology. The Betti number, which represents the number of structures in an image, is a fundamental measure in topology. Yet, diffusion models fail to satisfy even this basic constraint. This limitation restricts their utility in applications

Iwan Williams, Tim Bayne

This paper addresses the question of whether large language model-powered chatbots are capable of assertion. According to what we call the Thesis of Chatbot Assertion (TCA), chatbots are the kinds of things that can assert, and at least some of the output produced by current-generation chatbots qualifies as assertion. We provide some motivation for TCA, argu

Axiu Mao, Meilu Zhu, Zhaojin Guo, Zheng He

Deep learning techniques are dominating automated animal activity recognition (AAR) tasks with wearable sensors due to their high performance on large-scale labelled data. However, current deep learning-based AAR models are trained solely on datasets of individual animal species, constraining their applicability in practice and performing poorly when trainin

Abdurrahman Naufal, Yusei Koyama, Chiara D'Eugenio, Helmut Dannerbauer

We report the HST WFC3 G141 grism slitless spectroscopy observation of the core region of the Spiderweb protocluster at $z=2.16$. We analyzed the spectra of all objects in a $\sim 2 \times 2 \text{ arcmin}^2$ field of view and identified 40 protocluster members, recovering 19 previously identified H$\alpha$-emitters in addition to revealing 21 new members. T

Xiaoyi Han, Yanfei Wu, Nan Pu, Zunlei Feng

An effective Fire and Smoke Detection (FSD) and analysis system is of paramount importance due to the destructive potential of fire disasters. However, many existing FSD methods directly employ generic object detection techniques without considering the transparency of fire and smoke, which leads to imprecise localization and reduces detection performance. T

Ali Al-Bayaty, Marek Perkowski

A Boolean-Phase swapping gate is introduced for quantum generality and cost-effectiveness, which is termed the "p-SWAP gate", where p is a customizable phase difference for a set of swapped qubits and 0 <= p <= ${\pm \pi}$ radians. The generality of the p-SWAP gate is proposed for quantum Phase oracles requiring a desirable p for a set of swapped qubits, as

Ryosuke Sonoda, Ramya Srinivasan

Large language models (LLMs) such as ChatGPT, GPT-4, Claude-3, and Llama are being integrated across a variety of industries. Despite this rapid proliferation, experts are calling for caution in the interpretation and adoption of LLMs, owing to numerous associated ethical concerns. Research has also uncovered shortcomings in LLMs' reasoning and logical abili

Kazuki Nakazawa, Henry F. Legg, Jelena Klinovaja, Daniel Loss

Spin-orbit interaction (SOI) is a crucial ingredient for many potential applications of quantum devices, such as the use of semiconductor nanostructures for quantum computing. It is known that nonlinear conductivities are sensitive to the strength and type of SOI, however, many calculations of nonlinear transport coefficients are based on the semiclassical B

Mengdi Zhang, Kai Kiat Goh, Peixin Zhang, Jun Sun

Despite the success of Large Language Models (LLMs) across various fields, their potential to generate untruthful, biased and harmful responses poses significant risks, particularly in critical applications. This highlights the urgent need for systematic methods to detect and prevent such misbehavior. While existing approaches target specific issues such as

Yufei Feng, Xianyong Bai, Sifan Guo, Hui Tian

The spatial-temporal evolution of coronal plasma parameters of the solar outer atmosphere at global scales, derived from solar full-disk imaging spectroscopic observation in the extreme-ultraviolet band, is critical for understanding and forecasting solar eruptions. We propose a multi-slits extreme ultraviolet imaging spectrograph for global coronal diagnost

Seongchan Lee, Suman Cha, Ilmun Kim

We study the problem of conditional two-sample testing, which aims to determine whether two populations have the same distribution after accounting for confounding factors. This problem commonly arises in various applications, such as domain adaptation and algorithmic fairness, where comparing two groups is essential while controlling for confounding variabl

Langxuan Chen, Pengfei Zhang

Remarkable breakthroughs in quantum science and technology are demanding for more efficient methods in analyzing quantum many-body states. A significant challenge in this field is to verify whether a quantum state prepared by quantum devices in the lab accurately matches the desired target pure state. Recent advancements in randomized measurement techniques

Tobias Weinberg, Kowe Kadoma, Ricardo E. Gonzalez Penuela, Stephanie Valencia

People with disabilities that affect their speech may use speech-generating devices (SGD), commonly referred to as Augmentative and Alternative Communication (AAC) technology. This technology enables practical conversation; however, delivering expressive and timely comments remains challenging. This paper explores how to extend AAC technology to support a su

Aitaro Yamamoto, Hiroyuki Otomo, Hiroki Ouchi, Shohei Higashiyama

Previous studies on sequence-based extraction of human movement trajectories have an issue of inadequate trajectory representation. Specifically, a pair of locations may not be lined up in a sequence especially when one location includes the other geographically. In this study, we propose a graph representation that retains information on the geographic hier

Guilherme Christmann, Ying-Sheng Luo, Hanjaya Mandala, Wei-Chao Chen

Reinforcement learning (RL) policies are prone to high-frequency oscillations, especially undesirable when deploying to hardware in the real-world. In this paper, we identify, categorize, and compare methods from the literature that aim to mitigate high-frequency oscillations in deep RL. We define two broad classes: loss regularization and architectural meth

Xiaoyi Han, Nan Pu, Zunlei Feng, Yijun Bei

The current irregularities in existing public Fire and Smoke Detection (FSD) datasets have become a bottleneck in the advancement of FSD technology. Upon in-depth analysis, we identify the core issue as the lack of standardized dataset construction, uniform evaluation systems, and clear performance benchmarks. To address this issue and drive innovation in FS

Michael Reitz, Arghadip Koner, Joel Yuen-Zhou

We introduce a theoretical framework that allows for the systematic and efficient description of the ultrafast nonlinear response of molecular polaritons, i.e., hybrid light-matter states, in the collective regime of large numbers of molecules $\mathcal N$ coupled to the cavity photon mode. Our approach is based on a semiclassical, mean-field evolution of th

Ante Prodan, Jo-An Occhipinti, Rehez Ahlip, Goran Ujdur

This paper explores the nuanced landscape of generative AI (genAI), particularly focusing on neural network-based models like Large Language Models (LLMs). While genAI garners both optimistic enthusiasm and sceptical criticism, this work seeks to provide a balanced examination of its capabilities, limitations, and the profound impact it may have on societal

Nail Khusnutdinov, Natalia Emelianova

This paper gives a brief overview of the polarization tensor approach to the Casimir effect. The fundamental principles of this approach are discussed, along with its various applications to both three-dimensional and two-dimensional systems, with a focus on its implications for graphene.

Benjamin Osafo Agyare

In this study, we present a comprehensive evaluation of the Two-Block Gibbs (2BG) sampler as a robust alternative to the traditional Three-Block Gibbs (3BG) sampler in Bayesian shrinkage models. Through extensive simulation studies, we demonstrate that the 2BG sampler exhibits superior computational efficiency and faster convergence rates, particularly in hi

Boyu Ning, Weidong Mei, Lipeng Zhu, Zhi Chen

The codebook-based analog beamforming is appealing for future terahertz (THz) communications since it can generate high-gain directional beams with low-cost phase shifters via low-complexity beam training. However, conventional beamforming codebook design based on array response vectors for narrowband communications may suffer from severe performance loss in

Mohammad Hossein Samaei, Faryad Darabi Sahneh, Caterina Scoglio

Predicting the spread of processes across complex multi-layered networks has long challenged researchers due to the intricate interplay between network structure and propagation dynamics. Each layer of these networks possesses unique characteristics, further complicating analysis. To authors' knowledge, a comprehensive framework capable of simulating various

Tian-Zi Niu, Zhen-Duo Chen, Xin Luo, Xin-Shun Xu

Conventional approaches for video captioning leverage a variety of offline-extracted features to generate captions. Despite the availability of various offline-feature-extractors that offer diverse information from different perspectives, they have several limitations due to fixed parameters. Concretely, these extractors are solely pre-trained on image/video

Sudarshan Harithas, Srinath Sridhar

This paper introduces MotionGlot, a model that can generate motion across multiple embodiments with different action dimensions, such as quadruped robots and human bodies. By leveraging the well-established training procedures commonly used in large language models (LLMs), we introduce an instruction-tuning template specifically designed for motionrelated ta

Caleb J. Grimms, Robert D. Nevels

In this paper the finite-difference time-domain general vector auxiliary differential equation method [Greene, J. H. and A. Taflove, Opt. Express 14, 8305 (2006)], nonlinear polarization vector, the nonlinear electric dipole moment per unit volume, is extended to include anisotropy, in nonlinear isotropic media at optical frequencies. The theory is presented

Xiaochen Chen, Guohui Guan, Zongxia Liang

This paper investigates portfolio selection within a continuous-time financial market with regime-switching and beliefs-dependent utilities. The market coefficients and the investor's utility function both depend on the market regime, which is modeled by an observable finite-state continuous-time Markov chain. The optimization problem is formulated by aggreg

Xuwen Chen, Jiahao Wu, Zhifei Zhang

For a system of $N$ Fermions of spin $1/2$, with its interaction potential of scattering length $a$, the classical Huang-Yang formula states that the energy density $e(\rho)$ is of the form \begin{equation*} e(\rho)=\frac{3}{5}(3\pi^2)^{\frac{2}{3}}\rho^{\frac{5}{3}}+2\pi a\rho^2 +\frac{12}{35}(11-2\ln2)3^{\frac{1}{3}}\pi^{\frac{2}{3}}a^2\rho^{\frac{7}{3}} +

Gregory J. Galloway, Eric Ling

We establish a new CMC (constant mean curvature) existence result for cosmological spacetimes, i.e., globally hyperbolic spacetimes with compact Cauchy surfaces satisfying the strong energy condition. If the spacetime contains an expanding Cauchy surface and is future timelike geodesically complete, then the spacetime contains a CMC Cauchy surface. This resu

Linkang Du, Xuanru Zhou, Min Chen, Chusong Zhang

As the implementation of machine learning (ML) systems becomes more widespread, especially with the introduction of larger ML models, we perceive a spring demand for massive data. However, it inevitably causes infringement and misuse problems with the data, such as using unauthorized online artworks or face images to train ML models. To address this problem,

Dirk Douwes-Schultz, Alexandra M. Schmidt, Laís Picinini Freitas, Marilia Sá Carvalho

Univariate zero-inflated models are increasingly being used to account for excess zeros in spatio-temporal infectious disease counts. However, the multivariate case is challenging due to the need to account for correlations across space, time and disease in both the count and zero-inflated components of the model. We are interested in comparing the transmiss

Miles Angelo P. Sodejana

In this paper, the weak gravitational lensing phenomenon for a recently proposed rotating regular black hole with an asymptotically Minkowski core characterized by a sub-Planckian curvature was investigated. Using the Gauss-Bonnet Theorem, the deflection of light in the weak limit was computed by taking the black hole as a lens at a finite distance from both

Dayoung Lee, Jung Keun Ann, Seung-il Nam

We employ an effective Lagrangian approach to investigate double $\phi$ production in the $\bar{p}p$ reaction near the threshold and describe a notable violation of the Okubo-Zweig-Iizuka (OZI) rule in this reaction process through hadronic degrees of freedom, using the currently available theoretical and experimental information. The ground-state nucleon an

M. Dion, S. Ghotb, G. Hardy, P. Fournier

We report on a systematic study of the field dependence of the Hall resistivity $\rho _{yx} (B)$ as a function of doping in thin films of electron-doped superconducting cuprate Pr$_{2-x}$Ce$_{x}$CuO$_{4 \pm \delta}$ (PCCO). Across the studied doping range from $x = 0.125$ to $x = 0.20$, we observe a nonlinear dependence of $\rho_{yx}$ with $B$. The leading $

Jonathan Light, Yue Wu, Yiyou Sun, Wenchao Yu

We frame code generation as a black-box optimization problem within the code space and demonstrate how optimization-inspired techniques can enhance inference scaling. Based on this perspective, we propose SCATTERED FOREST SEARCH (SFS), a novel approach that improves solution diversity and better exploits feedback during evolutionary search. Our theoretical a

Ruixin Lia, Guoxu Zhaoa, Dylan Richard Muir, Yuya Ling

Analyzing electroencephalogram (EEG) signals to detect the epileptic seizure status of a subject presents a challenge to existing technologies aimed at providing timely and efficient diagnosis. In this study, we aimed to detect interictal and ictal periods of epileptic seizures using a spiking neural network (SNN). Our proposed approach provides an online an

Jiyu Tian, Mingchu Li, Zumin Wang, Liming Chen

Log anomaly detection (LAD) is essential to ensure safe and stable operation of software systems. Although current LAD methods exhibit significant potential in addressing challenges posed by unstable log events and temporal sequence patterns, their limitations in detection efficiency and generalization ability present a formidable challenge when dealing with

Yijiashun Qi, Shuzhang Cai, Zunduo Zhao, Jiaming Li

As Artificial Intelligence (AI) has developed rapidly over the past few decades, the new generation of AI, Large Language Models (LLMs) trained on massive datasets, has achieved ground-breaking performance in many applications. Further progress has been made in multimodal LLMs, with many datasets created to evaluate LLMs with vision abilities. However, none

Lijun Bo, Yijie Huang, Kaixin Yan, Xiang Yu

This paper studies an optimal consumption problem with both relaxed benchmark tracking and consumption drawdown constraint, leading to a stochastic control problem with dynamic state-control constraints. In our relaxed tracking formulation, it is assumed that the fund manager can strategically inject capital to the fund account such that the total capital pr

Ruben A. Martinez-Avendaño

If a real-valued function is continuous on a real interval and it takes on two different values, then it will also take any value in between those two, by the Intermediate Value Theorem. It is not immediately clear what would be a natural generalization for functions whose domain and range are in higher-dimensional Euclidean spaces. In this article, we analy

Qi-Yi Wu, Chen Zhang, Bai-Zhuo Li, Hao Liu

The quasiparticle relaxation dynamics of the iron-based superconductor CsCa$_2$Fe$_4$As$_4$F$_2$ ($T_c$ $\sim$ 29 K) were investigated using ultrafast optical spectroscopy. A pseudogap ($\Delta_{PG}$ $\approx$ 3.3 meV) was observed to open below $T^{\ast}$ $\approx$ 60 K, prior to the emergence of a superconducting gap ($\Delta$ $\approx$ 6.6 $\pm$ 0.4 meV).

Megan Shabram, Ryan McClelland, John Wu, Hamsa Shwetha Venkataram

Here we present several use cases for using Generative AI (Gen AI) to improve systems engineering and cognitive knowledge management related to the future of astronomy from a culmination of working meetings and presentations as part of the Gen AI Task Group for the NASA Habitable Worlds Observatory (HWO) Science and Technology Architecture Review Team (START

Zhexuan Liu, Rong Ma, Yiqiao Zhong

Visualizing high-dimensional data is essential for understanding biomedical data and deep learning models. Neighbor embedding methods, such as t-SNE and UMAP, are widely used but can introduce misleading visual artifacts. We find that the manifold learning interpretations from many prior works are inaccurate and that the misuse stems from a lack of data-inde

Geunsu Choi

We study two types of approximations of Lipschitz maps with derivatives of maximal slopes on Banach spaces. First, we characterize the Radon-Nikod\'ym property in terms of strongly norm attaining Lipschitz maps and maximal derivative attaining Lipschitz maps, which complements the characterization presented in \cite{CCM}. It is shown in particular that if ev

Junyu Luo, Yiyang Gu, Xiao Luo, Wei Ju

Source-free domain adaptation is a crucial machine learning topic, as it contains numerous applications in the real world, particularly with respect to data privacy. Existing approaches predominantly focus on Euclidean data, such as images and videos, while the exploration of non-Euclidean graph data remains scarce. Recent graph neural network (GNN) approach

Alice Kate Li, Thales C. Silva, M. Ani Hsieh

In this letter, we address the task of adaptive sampling to model vector fields. When modeling environmental phenomena with a robot, gathering high resolution information can be resource intensive. Actively gathering data and modeling flows with the data is a more efficient alternative. However, in such scenarios, data is often sparse and thus requires flow

Quanyu Tang, Yinchen Liu, Wei Wang

For a given simple graph \( G \), the \( p \)-energy of \( G \), denoted by \( \mathcal{E}_p(G) \), is defined as the sum of the \( p \)-th power of the absolute values of the eigenvalues of its adjacency matrix. Let \( S_n \) denote the star graph with one internal node and \( n-1 \) leaves. Nikiforov conjectured that for \( 1 < p < 2 \), the connected grap

Yiqian Huang, Shiqi Zhang, Laks V. S. Lakshmanan, Wenqing Lin

Influence maximization (IM) is a classic problem that aims to identify a small group of critical individuals, known as seeds, who can influence the largest number of users in a social network through word-of-mouth. This problem finds important applications including viral marketing, infection detection, and misinformation containment. The conventional IM pro

Aoran Xiao, Weihao Xuan, Junjue Wang, Jiaxing Huang

Remote Sensing (RS) is a crucial technology for observing, monitoring, and interpreting our planet, with broad applications across geoscience, economics, humanitarian fields, etc. While artificial intelligence (AI), particularly deep learning, has achieved significant advances in RS, unique challenges persist in developing more intelligent RS systems, includ

Jingchao Zhang, Chunsheng Nie, Jinsheng Cai, Shucheng Pan

An implicit coupling framework between hypersonic nonequilibrium flows and material thermal response is proposed for the numerical simulation of ablative thermal protection materials during its flight trajectory. Charring ablative materials, when subjected to aerodynamic heating from hypersonic flows, undergo complex processes such as ablation and pyrolysis,

Ian Gemp, Andreas Haupt, Luke Marris, Siqi Liu

Behavioral diversity, expert imitation, fairness, safety goals and others give rise to preferences in sequential decision making domains that do not decompose additively across time. We introduce the class of convex Markov games that allow general convex preferences over occupancy measures. Despite infinite time horizon and strictly higher generality than Ma

Rafael Grossi

There has been some recent interest in applying the techniques of Algebraic Quantum Field Theory (AQFT) to entanglement problems in perturbative QFT. In particular, the Hilbert space independence of this formulation makes it particularly interesting in the context of curved spacetimes and the emphasis on the algebra of observables makes the treatment of Bell

Hao Hu, Shanli Ye

In this paper, we calculate the exact value of the norm of the Hilbert matrix operator $\mathcal{H}$ from the logarithmically weighted Korenblum space $H^\infty_{\alpha,\log}$ into Korenblum space $H^\infty_\alpha$, and from the Hardy space $H^\infty$ to the classical Bloch space $\mathcal{B}$. Furthermore, we compute the precise value of the norm on the log

Prafulla Kumar Choubey, Xin Su, Man Luo, Xiangyu Peng

Document-level knowledge graph (KG) construction faces a fundamental scaling challenge: existing methods either rely on expensive large language models (LLMs), making them economically nonviable for large-scale corpora, or employ smaller models that produce incomplete and inconsistent graphs. We find that this limitation stems not from model capabilities but

Joseph Carolan, Alexander Poremba, Mark Zhandry

Indifferentiability is a popular cryptographic paradigm for analyzing the security of ideal objects -- both in a classical as well as in a quantum world. It is typically stated in the form of a composable and simulation-based definition, and captures what it means for a construction (e.g., a cryptographic hash function) to be ``as good as'' an ideal object (

Christopher West, Alexander Heger, Benoit Cote, Lev Serxner

We study the sensitivity of presupernova evolution and supernova nucleosynthesis yields of massive stars to variations of the initial composition. We use the solar abundances from Lodders (2009), and compute two different initial stellar compositions: i) scaled solar abundances, and ii) the isotopic galactic chemical history model (GCH) developed by West and

Haoyu Wang, Renyuan Ma, Gonzalo Mateos, Luana Ruiz

Graph neural networks (GNNs) achieve strong performance on graph learning tasks, but training on large-scale networks remains computationally challenging. Transferability results show that GNNs with fixed weights can generalize from smaller graphs to larger ones drawn from the same family, motivating the use of sampled subgraphs to boost training efficiency.

Andrew Kan, Christopher Kan, Zaid Nabulsi

The rise of social media and short-form video (SFV) has facilitated a breeding ground for misinformation. With the emergence of large language models, significant research has gone into curbing this misinformation problem with automatic false claim detection for text. Unfortunately, the automatic detection of misinformation in SFV is a more complex problem t

Alvin Zhu, Yusuke Tanaka, Fadi Rafeedi, Dennis Hong

This paper presents a novel approach through the design and implementation of Cycloidal Quasi-Direct Drive actuators for legged robotics. The cycloidal gear mechanism, with its inherent high torque density and mechanical robustness, offers significant advantages over conventional designs. By integrating cycloidal gears into the Quasi-Direct Drive framework,

Christian Aarset

The \emph{sensor placement problem} for stochastic linear inverse problems consists of determining the optimal manner in which sensors can be employed to collect data. Specifically, one wishes to place a limited number of sensors over a large number of candidate locations, quantifying and optimising over the effect this data collection strategy has on the so

Hongcheng Ding, Fuzhen Hu, Ruiting Deng, Xuanze Zhao

Sentiment analysis has become increasingly important for assessing public opinion and informing decision-making. Large language models (LLMs) have revolutionized this field by capturing nuanced language patterns. However, adapting LLMs to domain-specific sentiment analysis tasks remains challenging due to computational constraints and the need for optimal fi

Wenxuan Zhu, Hua Bai, Lei Han, Feng Pan

Quantum anomalous Hall (QAH) effect provides dissipationless chiral channels for spin transport, expected as an outstanding candidate in future low-power quantum computation. The spin-splitting band structure is vital for obtaining QAH effect in topological systems, with ferromagnetism indispensable to manipulate the Chern number. Herein, we challenge this w

Pietro Bernardelle, Gianluca Demartini

Aligning the output of Large Language Models (LLMs) with human preferences (e.g., by means of reinforcement learning with human feedback, or RLHF) is essential for ensuring their effectiveness in real-world scenarios. Despite significant advancements in LLM alignment techniques, the impact of different type of preference data on model performance has yet to

M. T. Franciscovich, J. McClung, K. Germaschewski, C. S. Ng

In this paper, we present three-dimensional (3D) Particle-In-Cell (PIC) simulations to study the stability of 2D Bernstein-Greene-Kruskal (BGK) modes in a magnetized plasma with a finite background magnetic field. The simulations were performed using the Plasma Simulation Code (PSC) [Germaschewski et al., J. of Comp. Phys. 318, 305 (2016)], as in our recent

Kyoung-Seog Lee, Anatoly Libgober, Nikolai Saveliev

In the early days of the Floer theory, Atiyah asked if there is a Milnor fiber description of the Floer homology of the links of singularities. We answer this question for the Brieskorn-Hamm complete intersection singularities. The resulting combinatorial formulas lead to an independent proof of the equality of the Casson invariants in the Donaldson and Seib

Hubert C. George, Mateusz T. Mądzik, Eric M. Henry, Andrew J. Wagner

Intels efforts to build a practical quantum computer are focused on developing a scalable spin-qubit platform leveraging industrial high-volume semiconductor manufacturing expertise and 300 mm fabrication infrastructure. Here, we provide an overview of the design, fabrication, and demonstration of a new customized quantum test chip, which contains 12-quantum

Shrey Vishen, Jatin Sarabu, Saurav Kumar, Chinmay Bharathulwar

We present a novel method for diffusion-guided frameworks for view-consistent super-resolution (SR) in neural rendering. Our approach leverages existing 2D SR models in conjunction with advanced techniques such as Variational Score Distilling (VSD) and a LoRA fine-tuning helper, with spatial training to significantly boost the quality and consistency of upsc

Jesse Phitidis, Alison Q. O&#39;Neil, William N. Whiteley, Beatrice Alex

Cerebrovascular diseases (CVD) can lead to stroke and dementia. Stroke is the second leading cause of death world wide and dementia incidence is increasing by the year. There are several markers of CVD that are visible on brain imaging, including: white matter hyperintensities (WMH), acute and chronic ischaemic stroke lesions (ISL), lacunes, enlarged perivas

Yu Zhang, Yijia Wu, Jie Liu, X. C. Xie

Non-Abelian braiding is a key property of Majorana zero modes (MZMs) that can be utilized for topological quantum computation. However, the presence of trivial Andreev bound states (ABSs) in topological superconductors can hinder the non-Abelian braiding of MZMs. We systematically investigate the braiding properties of ABSs induced by various inhomogeneous p

Constantin Kleinbeck, Hannah Schieber, Klaus Engel, Ralf Gutjahr

In medical image visualization, path tracing of volumetric medical data like CT scans produces lifelike three-dimensional visualizations. Immersive VR displays can further enhance the understanding of complex anatomies. Going beyond the diagnostic quality of traditional 2D slices, they enable interactive 3D evaluation of anatomies, supporting medical educati

BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson

Studies are performed of the Cabibbo-favored decay $Λ_{c}^{+}\toΛK_{S}^{0}K^+$ and the singly Cabibbo-suppressed decay $Λ_{c}^{+}\toΛK_{S}^{0}π^+$, based on a sample of $e^{+}e^{-}$ collision data, corresponding to an integrated luminosity of 4.5 fb$^{-1}$, accumulated at center-of-mass energies between $4599.53$ MeV and $4698.82$ MeV with the BESIII detecto

Wenxiang Zhao, Juntao Wu, Zhaoyi Meng

Due to the vast array of Android applications, their multifarious functions and intricate behavioral semantics, attackers can adopt various tactics to conceal their genuine attack intentions within legitimate functions. However, numerous learning-based methods suffer from a limitation in mining behavioral semantic information, thus impeding the accuracy and

Zhipeng Zhao, Bowen Li, Yi Du, Taimeng Fu

Motion prediction is critical for autonomous off-road driving, however, it presents significantly more challenges than on-road driving because of the complex interaction between the vehicle and the terrain. Traditional physics-based approaches encounter difficulties in accurately modeling dynamic systems and external disturbance. In contrast, data-driven neu

Toyomu Matsuda, Willem van Zuijlen

We construct random Schrödinger operators, called Anderson Hamiltonians, with Dirichlet and Neumann boundary conditions for a fairly general class of singular random potentials on bounded domains. Furthermore, we construct the integrated density of states of these Anderson Hamiltonians, and we relate the Lifschitz tails (the asymptotics of the left tails of

Marc-Andre Wessner, Federico Villani, Sofia Papa, Kirill Keller

Accurate characterization of ferroelectric properties in polymer piezoelectrics is critical for optimizing the performance of flexible and wearable ultrasound transducers, such as screen-printed PVDF devices. Standard charge measurement techniques, like the Sawyer-Tower circuit, often fall short when applied to ferroelectric polymers due to low-frequency lea

Gangcheng Yuan, Jared H. Cole, Alison M. Funston

Localized surface plasmons (LSPs) are collective oscillations of free electrons in metal nanoparticles that confine electromagnetic waves into subwavelength regions, making them an ideal platform for light-matter coupling. To design and understand plasmonic structures, numerical computations of Maxwell's equations are commonly used. However, obtaining physic

Zhiyu Xue, Haohan Wang, Yao Qin, Ramtin Pedarsani

Adversarial training is the most effective method to obtain adversarial robustness for deep neural networks by directly involving adversarial samples in the training procedure. To obtain an accurate and robust model, the weighted-average method is applied to optimize standard loss and adversarial loss simultaneously. In this paper, we argue that the weighted

Be'eri Greenfeld, Sarah Mathison, Aditya Saini, Scott Wynn

We compute the truncated point schemes of subalgebras of Fomin-Kirillov algebras associated with certain graphs. While Fomin-Kirillov algebras do not admit any truncated point modules, we prove a tight bound on the degrees of truncated point modules over generalized Fomin-Kirillov algebras associated with trees.

Samhita Pal, Subhashis Ghoshal

We consider a novel Bayesian approach to estimation, uncertainty quantification, and variable selection for a high-dimensional linear regression model under sparsity. The number of predictors can be nearly exponentially large relative to the sample size. We put a conjugate normal prior initially disregarding sparsity, but for making an inference, instead of

Avner Peleg, Debananda Chakraborty

We study transmission stabilization of optical solitons against emission of radiation in nonlinear optical waveguides in the presence of weak linear gain-loss, cubic loss, and the collisional Raman frequency shift. We first show how the collisional Raman frequency shift perturbation arises in three different physical setups. We then show by numerical simulat

Jieyu Lu, Zhangde Song, Qiyuan Zhao, Yuanqi Du

Designing functional transition metal complexes (TMCs) faces challenges due to the vast search space of metals and ligands, requiring efficient optimization strategies. Traditional genetic algorithms (GAs) are commonly used, employing random mutations and crossovers driven by explicit mathematical objectives to explore this space. Transferring knowledge betw

Kirill Batrakov, Sergei N. Yurchenko, Alec Owens, Jonathan Tennyson

New line lists for four isotopologues of nickel monohydride, $^{58}$NiH, $^{60}$NiH, $^{62}$NiH, and $^{58}$NiD are presented covering the wavenumber range $<10000$ cm$^{-1}$ ($\lambda > 1$ $\mu$m), $J$ up to 37.5 for transitions within and between the three lowest-lying electronic states, ${X}\,^{2}\Delta$, ${W}\,^{2}\Pi$, and ${V}\,^{2}\Sigma^{+}$. The lin

Kenza Benkirane, Jackie Kay, Maria Perez-Ortiz

Recent advancements in Large Language Models (LLMs) have positioned them as powerful tools for clinical decision-making, with rapidly expanding applications in healthcare. However, concerns about bias remain a significant challenge in the clinical implementation of LLMs, particularly regarding gender and ethnicity. This research investigates the evaluation a

Shirmohammad Tavangari, Zahra Shakarami, Aref Yelghi, Asef Yelghi

This paper explores the challenges of PAC learning in semi-enclosed environments that face persistent disruptive noise and demonstrates the weaknesses of traditional learning models based on noise-free data. We present a novel algorithm that enhances noise robustness in semiconservative learning by using robust optimization techniques and advanced error corr

Ruth Medeiros, Valentin de la Rubia

Time domain simulations of electromagnetic problems are highly valuable in engineering applications, as they allow for the analysis of transient behavior and broadband responses. These simulations utilize time stepping schemes, where each solution is derived from the solutions of previous time steps. Although each time step involves relatively straightforwar