February 2024 arXiv papers — page 4

Ian M. Hayes, Tristin E. Metz, Corey E. Frank, Shanta R. Saha

The superconducting state of the heavy-fermion metal UTe$_2$ has attracted considerable interest because of evidence for spin-triplet Cooper pairing and non-trivial topology. Progress on these questions requires identifying the presence or absence of nodes in the superconducting gap function and their dimension. In this article we report a comprehensive stud

M. E. Young, E. F. Spring, J. L. Birkby

Ultra-hot Jupiters (UHJs) undergo intense irradiation by their host stars and are expected to experience non-local thermodynamic equilibrium (NLTE) effects in their atmospheres. Such effects are computationally intensive to model but, at the low pressures probed by high-resolution cross-correlation spectroscopy (HRCCS), can significantly impact the formation

Stéphane Bessy, Daniel Gonçalves, Amadeus Reinald

In 1980, Burr conjectured that every directed graph with chromatic number $2k-2$ contains any oriented tree of order $k$ as a subdigraph. Burr showed that chromatic number $(k-1)^2$ suffices, which was improved in 2013 to $\frac{k^2}{2} - \frac{k}{2} + 1$ by Addario-Berry et al. We give the first subquadratic bound for Burr's conjecture, by showing that ever

Guangming Huang, Yunfei Long, Cunjin Luo, Jiaxing Shen

Pre-trained language models (PLMs) leverage chains-of-thought (CoT) to simulate human reasoning and inference processes, achieving proficient performance in multi-hop QA. However, a gap persists between PLMs' reasoning abilities and those of humans when tackling complex problems. Psychological studies suggest a vital connection between explicit information i

Daniel McNulty, Susane Calegari, Michał Oszmaniec

An important class of fermionic observables, relevant in tasks such as fermionic partial tomography and estimating energy levels of chemical Hamiltonians, are the binary measurements obtained from the product of anti-commuting Majorana operators. In this work, we investigate efficient estimation strategies of these observables based on a joint measurement wh

Xingchen Zou, Yibo Yan, Xixuan Hao, Yuehong Hu

As cities continue to burgeon, Urban Computing emerges as a pivotal discipline for sustainable development by harnessing the power of cross-domain data fusion from diverse sources (e.g., geographical, traffic, social media, and environmental data) and modalities (e.g., spatio-temporal, visual, and textual modalities). Recently, we are witnessing a rising tre

Ankolika De, Zhicong Lu

Commencing as a photo-sharing platform, Instagram has since become multifaceted, accommodating diverse art forms, with poetry emerging as a prominent one. However, the academic understanding of Instagram's poetry community is limited, yet its significance emerges from its distinctive utilization of a primarily visual social media platform guided by recommend

Ian Shrier

There have been numerous publications on the advantages and disadvantages of estimating natural (pure) effects compared to controlled effects. One of the main criticisms of natural effects is that it requires an additional assumption for identifiability, namely that the exposure does not cause a confounder of the mediator-outcome relationship. However, every

Isabel Haasler, Filip Elvander

In this work, we introduce an optimal transport framework for inferring power distributions over both spatial location and temporal frequency. Recently, it has been shown that optimal transport is a powerful tool for estimating spatial spectra that change smoothly over time. In this work, we consider the tracking of the spatio-temporal spectrum corresponding

Dimitrios Kollias, Panagiotis Tzirakis, Alan Cowen, Stefanos Zafeiriou

This paper describes the 6th Affective Behavior Analysis in-the-wild (ABAW) Competition, which is part of the respective Workshop held in conjunction with IEEE CVPR 2024. The 6th ABAW Competition addresses contemporary challenges in understanding human emotions and behaviors, crucial for the development of human-centered technologies. In more detail, the Com

Lorenzo Gavassino

We construct a kinetic model for matter-radiation interactions whose hydrodynamic gradient expansion can be computed analytically up to infinite order in derivatives, in the fully nonlinear regime, and for arbitrary flows. The frequency dependence of the opacity of matter is chosen to mimic the relaxation time of a self-interacting scalar field. In this way,

Xiao-Xue Wei

We prove that the Drinfeld center centralized by a symmetric fusion category is a symmetric monoidal functor if we choose proper domain and codomain categories. We also compute the factorization homology of stratified surfaces with coefficients satisfying certain anomaly-free conditions.

Jonas Frey, Manthan Patel, Deegan Atha, Julian Nubert

Autonomous navigation at high speeds in off-road environments necessitates robots to comprehensively understand their surroundings using onboard sensing only. The extreme conditions posed by the off-road setting can cause degraded camera image quality due to poor lighting and motion blur, as well as limited sparse geometric information available from LiDAR s

Alexander C. Jenke, Sebastian Bodenstedt, Fiona R. Kolbinger, Marius Distler

Understanding a surgical scene is crucial for computer-assisted surgery systems to provide any intelligent assistance functionality. One way of achieving this scene understanding is via scene segmentation, where every pixel of a frame is classified and therefore identifies the visible structures and tissues. Progress on fully segmenting surgical scenes has b

Delfina Sol Martinez Pandiani, Nicolas Lazzari, Valentina Presutti

The increasing demand for automatic high-level image understanding, particularly in detecting abstract concepts (AC) within images, underscores the necessity for innovative and more interpretable approaches. These approaches need to harmonize traditional deep vision methods with the nuanced, context-dependent knowledge humans employ to interpret images at in

Pawel Szczypkowski, Monika Pawlowska, Radek Lapkiewicz

3D super-resolution fluorescence microscopy typically requires sophisticated setups, sample preparation, or long measurements. A notable exception, SOFI, only requires recording a sequence of frames and no hardware modifications whatsoever but being a wide-field method, it faces problems in thick, dense samples. We combine SOFI with temporal focusing two-pho

Bas Slotema, Matthias Hoelzl, Verena Mitterauer

Edge localized modes (ELMs) are instabilities at the tokamak edge that can have short outbursts of highly energetic particles and heat, which can severely damage the walls of a plasma reactor. Resonant magnetic perturbations (RMPs) are used to mitigate or eliminate ELMs from the plasma. One effect that can reduce the intensity of the RMP is screening, which

Begoña García-Lorenzo, Donaji Esparza-Arredondo, Jose A. Acosta-Pulido, Julio A. Castro-Almazán

The spatial coherence wavefront outer scale (L_0) characterizes the size of the largest turbulence eddies in Earth's atmosphere, determining low spatial frequency perturbations in the wavefront of the light captured by ground-based telescopes. The motivation of this work is to introduce a novel technique for estimating L_0 from seeing-limited integral field

Ramakrishna Surya, Gordon L. Koerner, Taher Hajilounezhad, Kaveh Safavigerdin

Understanding the dynamic self-assembly mechanisms of carbon nanotube (CNT) forests is necessary to advance their technological promise. Here, in-situ environmental scanning electron microscope (ESEM) chemical vapor deposition (CVD) synthesis observes the real-time nucleation, assembly, delamination, and self-termination of dense ($>$ 10$^9$ CNT/cm$^2$), tal

Silvio Dolfi, Roghayeh Hafezieh, Pablo Spiga

The structure of the character degree graphs $\Delta(G)$, i.e. the prime graphs on the set $\mathrm{cd}(G)$ of the irreducible character degrees of a finite group $G$, such that $G$ is solvable and $\Delta(G)$ has diameter three, remains an intriguing area of study. However, a comprehensive understanding of these structures remains elusive. In this paper, we

Ansh Arora, Xuanli He, Maximilian Mozes, Srinibas Swain

The democratization of pre-trained language models through open-source initiatives has rapidly advanced innovation and expanded access to cutting-edge technologies. However, this openness also brings significant security risks, including backdoor attacks, where hidden malicious behaviors are triggered by specific inputs, compromising natural language process

Tsz Kin Lam, Alexandra Birch, Barry Haddow

We propose a pretraining method to use Self-Supervised Speech (SSS) model to creating more compact Speech-to-text Translation. In contrast to using the SSS model for initialization, our method is more suitable to memory constrained scenario such as on-device deployment. Our method is based on Discrete Speech Units (DSU) extracted from the SSS model. In the f

Yunan Wang

In 2016, Fei \cite{fei2016application} established a bound on the Siegel zeros for real primitive Dirichlet characters modulo $q$, assuming the weak Hardy-Littlewood conjecture. Building on Fei's work, Jia \cite{jia2022conditional} demonstrated the same bound using a stronger version of the Hardy-Littlewood conjecture. In this paper, we present a slightly si

X. Griffin Wang

Let $k$ be a finite field and let $G$ be a reductive group over $k[[\pi]]$. Suppose $\mathrm{char}(k)$ is larger than twice the Coxeter number of $G$, we prove the standard endoscopic fundamental lemma for the spherical Hecke algebra of $G$ using multiplicative Hitchin fibrations.

Hanxi Li, Zhengxun Zhang, Hao Chen, Lin Wu

Effectively addressing the challenge of industrial Anomaly Detection (AD) necessitates an ample supply of defective samples, a constraint often hindered by their scarcity in industrial contexts. This paper introduces a novel algorithm designed to augment defective samples, thereby enhancing AD performance. The proposed method tailors the blended latent diffu

Seoyoung Park, Sanghyeok Park, Taekho You, Jinhyuk Yun

The development of the internet has allowed for the global distribution of content, redefining media communication and property structures through various streaming platforms. Previous studies successfully clarified the factors contributing to trends in each streaming service, yet the similarities and differences between platforms are commonly unexplored; mo

Chenxinran Shen, Yan Xu, Ray LC, Zhicong Lu

Online dating has become a popular way for individuals to connect with potential romantic partners. Many dating apps use personal profiles that include a headshot and self-description, allowing users to present themselves and search for compatible matches. However, this traditional model often has limitations. In this study, we explore a non-traditional voic

Fankai Xie, Tenglong Lu, Sheng Meng, Miao Liu

This study introduces a novel AI force field, namely graph-based pre-trained transformer force field (GPTFF), which can simulate arbitrary inorganic systems with good precision and generalizability. Harnessing a large trove of the data and the attention mechanism of transformer algorithms, the model can accurately predict energy, atomic forces, and stress wi

Hao Li, Ying Chen, Yifei Chen, Wenxian Yang

Whole Slide Image (WSI) classification is often formulated as a Multiple Instance Learning (MIL) problem. Recently, Vision-Language Models (VLMs) have demonstrated remarkable performance in WSI classification. However, existing methods leverage coarse-grained pathogenetic descriptions for visual representation supervision, which are insufficient to capture t

Lin Zhang, Themos Stafylakis, Federico Landini, Mireia Diez

In this paper, we apply the variational information bottleneck approach to end-to-end neural diarization with encoder-decoder attractors (EEND-EDA). This allows us to investigate what information is essential for the model. EEND-EDA utilizes attractors, vector representations of speakers in a conversation. Our analysis shows that, attractors do not necessari

Jung-Chao Ban, Wen-Guei Hu, Guan-Yu Lai, Lingmin Liao

We obtain the entropy and the surface entropy of the axial products on $\mathbb{N}^d$ and the $d$-tree $T^d$ of two types of systems: the subshift and the multiplicative subshift.

Yves van Montfort, Sébastian de Bone, David Elkouss

In this work, we introduce a method to construct fault-tolerant measurement-based quantum computation (MBQC) architectures and numerically estimate their performance over various types of networks. A possible application of such a paradigm is distributed quantum computation, where separate computing nodes work together on a fault-tolerant computation through

Anan Kabaha, Dana Drachsler-Cohen

Neural networks are successful in various applications but are also susceptible to adversarial attacks. To show the safety of network classifiers, many verifiers have been introduced to reason about the local robustness of a given input to a given perturbation. While successful, local robustness cannot generalize to unseen inputs. Several works analyze globa

Radhakrishnan Balu

The super version of imprimitivity theorem is available now to describe global supersymmetry of systems using the representations of super Lie groups (SLG). This result uses the equivalence between super Harish- Chandra pairs and super Lie groups at the categorigal level and is applicable to super Poincare group and generalizes a smooth SI to super context.

Sol Yun, Young Sun Lee, Young Kwang Kim, Timothy C. Beers

Chemical anomalies in planet-hosting stars (PHSs) are studied in order to assess how the planetary nature and multiplicity affect the atmospheric chemical abundances of their host stars. We employ APOGEE DR17 to select thin-disk stars of the Milky Way, and cross-match them with the Kepler Input Catalog to identify confirmed PHSs, which results in 227 PHSs wi

Samayita Das, Alok Shukla

We have studied the electronic and optical properties of three low-symmetry graphene quantum dots (GQDs), with the point-group symmetries $C_{2v}$, and $C_{2h}$. For the calculations of linear optical absorption spectra, we employed both the first-principles time-dependent density-functional theory (TDDFT), and the electron-correlated Pariser-Parr-Pople (PPP

Syafiq Al Atiiq, Yachao Yuan, Christian Gehrmann, Jakob Sternby

The $5^{th}$ generation of mobile networks introduces a new Network Function (NF) that was not present in previous generations, namely the Network Data Analytics Function (NWDAF). Its primary objective is to provide advanced analytics services to various entities within the network and also towards external application services in the 5G ecosystem. One of th

Pranav Khadpe, Lindy Le, Kate Nowak, Shamsi T. Iqbal

Line managers form the first level of management in organizations, and must make complex decisions, while maintaining relationships with those impacted by their decisions. Amidst growing interest in technology-supported decision-making at work, their needs remain understudied. Further, most existing design knowledge for supporting social decision-making come

John M. Wandeto, Birgitta Dresp-Langley

Novel methods of analysis are needed to help advance our understanding of the intricate interplay between landscape changes, population dynamics, and sustainable development. Self organized machine learning has been highly successful in the analysis of visual data the human expert eye may not be able to see. Thus, subtle but significant changes in fine visua

Seonghun Kim, Youngbin Kim, Young-Do Yoon, Seongjin Jeon

Nonlinear quantum photonics serves as a cornerstone in photonic quantum technologies, such as universal quantum computing and quantum communications. The emergence of integrated photonics platform not only offers the advantage of large-scale manufacturing but also provides a variety of engineering methods. Given the complexity of integrated photonics enginee

Jesse M. Hanlan, Douglas J. Durian

The time-dependent forcing and work per cycle required to drive sinusoidal spinning of a taut-line buzzer is analytically derived, both on and off resonance, from the nonlinear equation of motion. To test predictions, a model experimental system is constructed and characterized in terms of contraction versus twist angle and damped oscillations. The predicted

Chiara Gabellieri, Antonio Franchi

This work answers positively the question whether non-stop flights are possible for maintaining constant the pose of cable-suspended objects. Such a counterintuitive answer paves the way for a paradigm shift where energetically efficient fixed-wing flying carriers can replace the inefficient multirotor carriers that have been used so far in precise cooperati

Walter Winter

We discuss recent results in neutrino astronomy and their implications for the cosmic-ray acceleration in relativistic outflows, such as in Active Galactic Nuclei (AGN) jets, Gamma-Ray Bursts (GRBs), and Tidal Disruption Events (TDEs). We especially focus on challenges at the interface to particle acceleration which can be inferred from the multi-messenger c

Gregory Ashton, Nicolo Colombo, Ian Harry, Surabhi Sachdev

In astronomy, we frequently face the decision problem: does this data contain a signal? Typically, a statistical approach is used, which requires a threshold. The choice of threshold presents a common challenge in settings where signals and noise must be delineated, but their distributions overlap. Gravitational-wave astronomy, which has gone from the first

Shuntaro Sumita, Akihiro Tanaka, Yusuke Kato

Spin parity effects refer to those special situations where a dichotomy in the physical behavior of a system arises, solely depending on whether the relevant spin quantum number is integral or half-odd integral. As is the case with the Haldane conjecture in antiferromagnetic spin chains, their pursuit often derives deep insights and invokes new developments

Bowen Zhao, Lars Andersson, Shing-Tung Yau

We review Wang-Yau quasi-local definitions along the line of gravitational Hamiltonian. This makes clear the connection and difference between Wang-Yau definition and Brown-York or even global ADM definition. We make a brief comment on admissibility condition in Wang-Yau quasi-lcoal mass. We extend the positivity proof for Wang-Yau quasi-local energy to allo

E. M. Turan, J. Jäschke

The online implementation of model predictive control for constrained multivariate systems has two main disadvantages: it requires an estimate of the entire model state and an optimisation problem must be solved online. These issues have typically been treated separately. This work proposes an integrated approach for the offline training of an output feedbac

Jack Foster, Stefan Schoepf, Alexandra Brintrup

We present a machine unlearning approach that is both retraining- and label-free. Most existing machine unlearning approaches require a model to be fine-tuned to remove information while preserving performance. This is computationally expensive and necessitates the storage of the whole dataset for the lifetime of the model. Retraining-free approaches often u

Eduardo Franco Sotelo Bazan

A simplified model of an initially excited oscillator as a quantum system interacting with a large number of oscillators acting as a reservoir has been developed in this work. All these oscillators are in their ground state uncoupled each other and at the limit of the weak coupling between the system and the reservoir. This system could be an oscillator exci

Jeffrey Lazar, Santiago Giner Olavarrieta, Giancarlo Gatti, Carlos A. Argüelles

Ever-increasing amount of data is produced by particle detectors in their quest to unveil the laws of Nature. The large data rate requires the use of specialized triggers that promptly reduce the data rate to a manageable level; however, in doing so, unexpected new phenomena may escape detection. Additionally, the large data rate is increasingly difficult to

Julian Spravil, Sebastian Houben, Sven Behnke

In computer vision, a larger effective receptive field (ERF) is associated with better performance. While attention natively supports global context, its quadratic complexity limits its applicability to tasks that benefit from high-resolution input. In this work, we extend Hyena, a convolution-based attention replacement, from causal sequences to bidirection

A. A. Herasymchuk, E. V. Gorbar, P. O. Sukhachov

We study viscoelastic response in Weyl semimetals with broken time-reversal symmetry. The principal finding is that topology and anisotropy of the Fermi surface are manifested in the viscoelasticity tensor of the electron fluid. In the dynamic (interband) part of this tensor, the anisotropy leads to a qualitatively different, compared with isotropic models,

Marta Andronic, George A. Constantinides

Field-Programmable Gate Array (FPGA) accelerators have proven successful in handling latency- and resource-critical deep neural network (DNN) inference tasks. Among the most computationally intensive operations in a neural network (NN) is the dot product between the feature and weight vectors. Thus, some previous FPGA acceleration works have proposed mapping

Lee Cohen, Yishay Mansour, Shay Moran, Han Shao

In contrast with standard classification tasks, strategic classification involves agents strategically modifying their features in an effort to receive favorable predictions. For instance, given a classifier determining loan approval based on credit scores, applicants may open or close their credit cards to fool the classifier. The learning goal is to find a

Gianluca Scarpellini, Stefano Fiorini, Francesco Giuliari, Pietro Morerio

Reassembly tasks play a fundamental role in many fields and multiple approaches exist to solve specific reassembly problems. In this context, we posit that a general unified model can effectively address them all, irrespective of the input data type (images, 3D, etc.). We introduce DiffAssemble, a Graph Neural Network (GNN)-based architecture that learns to

Tim Browning, Matteo Verzobio

We investigate strong divisibility sequences and produce lower and upper bounds for the density of integers in the sequence which only have (somewhat) large prime factors. We focus on the special cases of Fibonacci numbers and elliptic divisibility sequences, discussing the limitations of our methods. At the end of the paper there is an appendix by Sandro Be

Xue Jiang, Yihong Dong, Zhiyuan Fan, Zhi Jin

Although Large Language Models (LLMs) have made significant progress in code generation, they still struggle with code generation tasks in specific scenarios. These scenarios usually necessitate the adaptation of LLMs to fulfill specific needs, but the limited training data available in practice leads to poor code generation performance. Therefore, how to ef

Zachary Greenberg, Dani Kaufman, Anna Wienhard

We study $2\times 2$ matrices over noncommutative rings with anti-involution, with a special focus on the symplectic group $\mathrm{Sp}_2(\mathcal{A},\sigma)$. We define traces and determinants of such matrices and use them to prove a Cayley Hamilton identity and trace relations which generalize well known relations for elements of $\mathrm{SL}_2(R)$ over a

Shaoteng Liu, Haoqi Yuan, Minda Hu, Yanwei Li

Large Language Models (LLMs) have demonstrated proficiency in utilizing various tools by coding, yet they face limitations in handling intricate logic and precise control. In embodied tasks, high-level planning is amenable to direct coding, while low-level actions often necessitate task-specific refinement, such as Reinforcement Learning (RL). To seamlessly

Xun Lin, Shuai Wang, Rizhao Cai, Yizhong Liu

Face Anti-Spoofing (FAS) is crucial for securing face recognition systems against presentation attacks. With advancements in sensor manufacture and multi-modal learning techniques, many multi-modal FAS approaches have emerged. However, they face challenges in generalizing to unseen attacks and deployment conditions. These challenges arise from (1) modality u

Anirudh Venkatesh, Aman Bhargava, Vivek Narsimhan

Vesicles are important surrogate structures made up of multiple phospholipids and cholesterol distributed in the form of a lipid bilayer. Tubular vesicles can undergo pearling i.e., formation of beads on the liquid thread akin to the Rayleigh-Plateau instability. Previous studies have inspected the effects of surface tension on the pearling instabilities of

Quoc Dang Vu, Caroline Fong, Anderley Gordon, Tom Lund

Gastric and oesophageal (OG) cancers are the leading causes of cancer mortality worldwide. In OG cancers, recent studies have showed that PDL1 immune checkpoint inhibitors (ICI) in combination with chemotherapy improves patient survival. However, our understanding of the tumour immune microenvironment in OG cancers remains limited. In this study, we interrog

S. M. Smith, A. J. Hughes, T. A. Dardeno, L. A. Bull

Population-based structural health monitoring (PBSHM), aims to share information between members of a population. An offshore wind (OW) farm could be considered as a population of nominally-identical wind-turbine structures. However, benign variations exist among members, such as geometry, sea-bed conditions and temperature differences. These factors could i

Ying Fu, Ye Kwon Huh, Kaibo Liu

Operating units often experience various failure modes in complex systems, leading to distinct degradation paths. Relying on a prognostic model trained on a single failure mode may lead to poor generalization performance across multiple failure modes. Therefore, accurately identifying the failure mode is of critical importance. Current prognostic approaches

Nobumasa Ishida, Yoshihiko Hasegawa

Quantum thermodynamic uncertainty relations establish fundamental trade-offs between the precision achievable in quantum systems and associated thermodynamic quantities such as entropy production or dynamical activity. While foundational, empirical demonstrations have thus far been confined to specific cases, either assuming time-reversal symmetry or involvi

Aadityan Ganesh, Jason Hartline, Atanu R Sinha, Matthew vonAllmen

This paper studies the fundamental limits of availability and throughput for independent and heterogeneous demands of a limited resource. Availability is the probability that the demands are below the capacity of the resource. Throughput is the expected fraction of the resource that is utilized by the demands. We offer a concentration inequality generator th

Haydar Can Kaya, Atabey Kaygun

We construct a model categorical equivalence between the category of simplicial vector spaces and the category of representations of a crossed simplicial group $\Delta G$ when each $G_n$ is finite and the characteristic of the ground field is 0.

Igor Makienko, Michael Grebshtein, Eli Gildish

This method solves the dual problem of blind deconvolution and estimation of the time waveform of noisy second-order cyclo-stationary (CS2) signals that traverse a Transfer Function (TF) en route to a sensor. We have proven that the deconvolution filter exists and eliminates the TF effect from signals whose statistics vary over time. This method is blind, me

Yan Wang, Yi Liu, Shijie Zhao, Junlin Li

To satisfy the rapidly increasing demands on the large image (2K-8K) super-resolution (SR), prevailing methods follow two independent tracks: 1) accelerate existing networks by content-aware routing, and 2) design better super-resolution networks via token mixer refining. Despite directness, they encounter unavoidable defects (e.g., inflexible route or non-d

Federico Fedele, Federico Cerisola, Lea Bresque, Florian Vigneau

Coupling a single spin to high-frequency mechanical motion is a fundamental bottleneck of applications such as quantum sensing, intermediate and long-distance spin-spin coupling, and classical and quantum information processing. Previous experiments have only shown single spin coupling to low-frequency mechanical resonators, such as diamond cantilevers. High

Prasad Cheema, Mahito Sugiyama

Data augmentation is an area of research which has seen active development in many machine learning fields, such as in image-based learning models, reinforcement learning for self driving vehicles, and general noise injection for point cloud data. However, convincing methods for general time series data augmentation still leaves much to be desired, especiall

Ruining Deng, Quan Liu, Can Cui, Tianyuan Yao

Understanding the anatomy of renal pathology is crucial for advancing disease diagnostics, treatment evaluation, and clinical research. The complex kidney system comprises various components across multiple levels, including regions (cortex, medulla), functional units (glomeruli, tubules), and cells (podocytes, mesangial cells in glomerulus). Prior studies h

Alexander J. Gerra, Courtney C. Jones, Sam Dillavou, Jesse M. Hanlan

Equations of motion are developed for the oscillatory rotation of a disk suspended between twisted strings kept under tension by a hanging mass, to which additional forces may be applied. In the absence of forcing, damped harmonic oscillations are observed to decay with an exponential time envelope for two different string types. This is consistent with damp

Alberto Bordin, Florian J. Bennebroek Evertsz', Gorm O. Steffensen, Tom Dvir

Detection and control of Andreev Bound States (ABSs) localized at semiconductor-superconductor interfaces are essential for their use in quantum applications. Here we investigate the impact of ABSs on the supercurrent through a Josephson junction containing a quantum dot (QD). Additional normal-metal tunneling probes on both sides of the junction unveil the

Moulay Tahar Benameur, James L. Heitsch

We give applications of the higher Lefschetz theorems for foliations of [BH10], primarily involving Haefliger cohomology. These results show that the transverse structures of foliations carry important topological and geometric information. This is in the spirit of the passage from the Atiyah-Singer index theorem for a single compact manifold to their famili

Michael Kissner, Leonardo Del Bino, Felix Päsler, Peter Caruana

Energy efficiency of electronic digital processors is primarily limited by the energy consumption of electronic communication and interconnects. The industry is almost unanimously pushing towards replacing both long-haul, as well as local chip interconnects, using optics to drastically increase efficiency. In this paper, we explore what comes after the succe

Jiantao Qiu, Haijun Lv, Zhenjiang Jin, Rui Wang

This paper presents WanJuan-CC, a safe and high-quality open-sourced English webtext dataset derived from Common Crawl data. The study addresses the challenges of constructing large-scale pre-training datasets for language models, which require vast amounts of high-quality data. A comprehensive process was designed to handle Common Crawl data, including extr

MicroBooNE collaboration, P. Abratenko, O. Alterkait, D. Andrade Aldana

We report the first double-differential neutrino-argon cross section measurement made simultaneously for final states with and without protons for the inclusive muon neutrino charged-current interaction channel. The proton kinematics of this channel are further explored with a differential cross section measurement as a function of the leading proton's kinet

Wen-Chen Hu, Sanjaikanth E Vadakkethil Somanathan Pillai, Abdelrahman Ahmed ElSaid

More than six million people died of the COVID-19 by April 2022. The heavy casualties have put people on great and urgent alert and people try to find all kinds of information to keep them from being inflected by the coronavirus. This research tries to find out whether the mobile health text information sent to peoples devices is correct as smartphones becom

Zehui Zhu, Imad L. Al-Qadi

Two-dimensional digital image correlation (2D-DIC) is a widely used optical technique to measure displacement and strain during asphalt concrete (AC) testing. An accurate 2-D DIC measurement can only be achieved when the camera's principal axis is perpendicular to the planar specimen surface. However, this requirement may not be met during testing due to dev

Deepankur Thureja, F. Emre Yazici, Tomasz Smolenski, Martin Kroner

Quantum dots are semiconductor nano-structures where particle motion is confined in all three spatial dimensions. Since their first experimental realization, nanocrystals confining the quanta of polarization waves, termed excitons, have found numerous applications in fields ranging from single photon sources for quantum information processing to commercial d

Denis E. Sergeev, Ian A. Boutle, F. Hugo Lambert, Nathan J. Mayne

Convective processes are crucial in shaping exoplanetary atmospheres but are computationally expensive to simulate directly. A novel technique of simulating moist convection on tidally locked exoplanets is to use a global 3D model with a stretched mesh. This allows us to locally refine the model resolution to 4.7 km and resolve fine-scale convective processe

Wen Wen, Mu Li, Yabin Zhang, Yiting Liao

Blind video quality assessment (BVQA) plays a pivotal role in evaluating and improving the viewing experience of end-users across a wide range of video-based platforms and services. Contemporary deep learning-based models primarily analyze video content in its aggressively subsampled format, while being blind to the impact of the actual spatial resolution an

Jingxuan Yang, Ruoxuan Bai, Haoyuan Ji, Yi Zhang

The assessment of safety performance plays a pivotal role in the development and deployment of connected and automated vehicles (CAVs). A common approach involves designing testing scenarios based on prior knowledge of CAVs (e.g., surrogate models), conducting tests in these scenarios, and subsequently evaluating CAVs' safety performances. However, substanti

Fang Pan, Junni Zhai, Jinyu Chen, Lin Yang

Stability is one of the key issues in mixed-halide perovskite alloys which are promising in emergent optoelectronics. Previous density-functional-theory (DFT) and machine learning studies indicate that the formation-energy convex hulls of these materials are very shallow, and stable alloy compositions are rare. In this work, we revisit this problem using DFT

He Zhu, Wenjia Zhang, Nuoxian Huang, Boyang Li

In the field of urban planning, general-purpose large language models often struggle to meet the specific needs of planners. Tasks like generating urban planning texts, retrieving related information, and evaluating planning documents pose unique challenges. To enhance the efficiency of urban professionals and overcome these obstacles, we introduce PlanGPT,

Thang X. Duong, Mikhail Itskov, Roger A. Sauer

We respond to David Steigmann's discussion of our paper "A general theory for anisotropic Kirchhoff-Love shells with in-plane bending of embedded fibers, Math. Mech. Solids, 28(5):1274-1317" (arXiv:2101.03122). His discussion allows us to clarify two misleading statements in our original paper, and confirm that its formulation is fully consistent with the fo

James Anderson, Abhishek Dhawan, Aiya Kuchukova

A graph $G$ is $k$-locally sparse if for each vertex $v \in V(G)$, the subgraph induced by its neighborhood contains at most $k$ edges. Alon, Krivelevich, and Sudakov showed that for $f > 0$ if a graph $G$ of maximum degree $\Delta$ is $\Delta^2/f$-locally-sparse, then $\chi(G) = O\left(\Delta/\log f\right)$. We introduce a more general notion of local spars

Rui Gong, Weide Liu, Zaiwang Gu, Xulei Yang

Geometric knowledge has been shown to be beneficial for the stereo matching task. However, prior attempts to integrate geometric insights into stereo matching algorithms have largely focused on geometric knowledge from single images while crucial cross-view factors such as occlusion and matching uniqueness have been overlooked. To address this gap, we propos

Javier Castillo-Seoane, Lidia Contreras-Bernal, T. Cristina Rojas, Juan P. Espinos

Hybrid organometal halide perovskites (HP) present exceptional optoelectronic properties, but their poor long-term stability is a major bottleneck for their commercialization. Herein, we present a solvent-free approach to growing single-crystal organic nanowires (ONW), nanoporous metal oxide scaffolds, and HP to form a core@multishell architecture. The synth

Harold D. Chiang, Ryutah Kato, Yuya Sasaki

This paper studies extremal quantiles under two-way clustered dependence. We show that the limiting distribution of unconditional intermediate-order tail quantiles is Gaussian. This result is notable because two-way clustering typically leads to non-Gaussian limiting behavior. Remarkably, extremal quantiles remain asymptotically Gaussian even in degenerate c

Seunghyun Ji, Hagai Raja Sinulingga, Darongsae Kwon

Low-resourced data presents a significant challenge for neural machine translation. In most cases, the low-resourced environment is caused by high costs due to the need for domain experts or the lack of language experts. Therefore, identifying the most training-efficient data within an unsupervised setting emerges as a practical strategy. Recent research sug

Francesco Dagnino, Fabio Pasquali

Lawvere's generalised the notion of complete metric space to the field of enriched categories: an enriched category is said to be Cauchy-complete if every left adjoint bimodule into it is represented by an enriched functor. Looking at this definition from a logical standpoint, regarding bimodules as an abstraction of relations and functors as an abstraction

Daniele Meli, Alberto Castellini, Alessandro Farinelli

Partially Observable Markov Decision Processes (POMDPs) are a powerful framework for planning under uncertainty. They allow to model state uncertainty as a belief probability distribution. Approximate solvers based on Monte Carlo sampling show great success to relax the computational demand and perform online planning. However, scaling to complex realistic d

Zhiyuan Yang, Yunjiao Zhou, Lihua Xie, Jianfei Yang

3D point cloud has been widely used in many mobile application scenarios, including autonomous driving and 3D sensing on mobile devices. However, existing 3D point cloud models tend to be large and cumbersome, making them hard to deploy on edged devices due to their high memory requirements and non-real-time latency. There has been a lack of research on how

Soumya Snigdha Kundu, Yuanhan Mo, Nicharee Srikijkasemwat, Bartłomiej W. Papiez

The development and progression of arthritis is strongly associated with osteophytes, which are small and elusive bone growths. This paper presents one of the first efforts towards automated spinal osteophyte detection in spinal X-rays. A novel automated patch extraction process, called SegPatch, has been proposed based on deep learning-driven vertebrae segm

Advait Gadhikar, Rebekka Burkholz

Learning Rate Rewinding (LRR) has been established as a strong variant of Iterative Magnitude Pruning (IMP) to find lottery tickets in deep overparameterized neural networks. While both iterative pruning schemes couple structure and parameter learning, understanding how LRR excels in both aspects can bring us closer to the design of more flexible deep learni

Gorm Ole Steffensen, Alfredo Levy Yeyati

Connecting two half-filled quantum dots to two superconducting leads induces a competition of bonds, with the dots forming either an interdot exchange bond or two individual Yu-Shiba-Rusinov (YSR) screening bonds with the leads. Defining a qubit using these singlet parity bonding states provides dot charge noise protection, attributed to the chargeless natur

Kai-An You, Ke-Jung Chen, Yen-Chen Pan, Sung-Han Tsai

Type IIP supernovae (SNe IIP) are thought to originate from the explosion of massive stars > 10 Msun. Their luminosity is primarily powered by the explosion energy and the radioactive decay energy of 56Co, with the photosphere location regulated by hydrogen recombination. However, the physical connections between SNe IIP and their progenitor stars remain unc

Thomas Bosman, Martijn van Ee, Ekin Ergen, Csanad Imreh

Scheduling jobs with given processing times on identical parallel machines so as to minimize their total completion time is one of the most basic scheduling problems. We study interesting generalizations of this classical problem involving scenarios. In our model, a scenario is defined as a subset of a predefined and fully specified set of jobs. The aim is t