March 2024 arXiv papers — page 22

Pavlin G. Poličar, Blaž Zupan

With the increasing availability of high-dimensional data, analysts often rely on exploratory data analysis to understand complex data sets. A key approach to exploring such data is dimensionality reduction, which embeds high-dimensional data in two dimensions to enable visual exploration. However, popular embedding techniques, such as t-SNE and UMAP, typica

Arthur Y. Lin, Kevin K. Huguenin-Dumittan, Yong-Cheol Cho, Jigyasa Nigam

Physics-based, atom-centered machine learning (ML) representations have been instrumental to the effective integration of ML within the atomistic simulation community. Many of these representations build off the idea of atoms as having spherical, or isotropic, interactions. In many communities, there is often a need to represent groups of atoms, either to in

J. Richard Bond, George M. Fuller, Evan Grohs, Joel Meyers

Very different processes characterize the decoupling of neutrinos to form the cosmic neutrino background (C$\nu$B) and the much later decoupling of photons from thermal equilibrium to form the cosmic microwave background (CMB). The C$\nu$B emerges from the fuzzy, energy-dependent neutrinosphere and encodes the physics operating in the early universe in the t

Florence E. Enock, Francesca Stevens, Tvesha Sippy, Jonathan Bright

Online harms, such as hate speech, trolling and self-harm promotion, continue to be widespread. There are growing concerns that these harms may disproportionately affect women, reflecting and reproducing existing structural inequalities within digital spaces. Using a nationally representative survey of UK adults (N=1992), we examine how gender shapes exposur

Philip Claude Caplan

This paper addresses two problems needed to support four-dimensional ($3d + t$) spacetime numerical simulations. The first contribution is a general algorithm for producing conforming spacetime meshes of moving geometries. Here, the surface points of the geometry are embedded in a four-dimensional space as the geometry moves in time. The geometry is first te

Andrej Antalík, Andrea Levy, Sonata Kvedaravičiūtė, Sophia K. Johnson

MiMiC is a framework for performing multiscale simulations in which loosely coupled external programs describe individual subsystems at different resolutions and levels of theory. To make it highly efficient and flexible, we adopt an interoperable approach based on a multiple-program multiple-data (MPMD) paradigm, serving as an intermediary responsible for f

Uwe Hernandez Acosta, Burkhard Kämpfer

Suitably normalized differential probabilities of one-photon emission in external electromagnetic fields are compared to quantify the transit of nonlinear Compton scattering to linear Compton scattering, described by the Klein-Nishina formula, and to constant crossed field treatment. The known Klein-Nishina suppression at large energies is further enforced b

Kazunori Ando, Hyeonbae Kang, Yoshihisa Miyanishi, Mihai Putinar

One of the unexplored benefits of studying layer potentials on smooth, closed hypersurfaces of Euclidean space is the factorization of the Neumann-Poincar\'e operator into a product of two self-adjoint transforms. Resurrecting some pertinent indications of Carleman and M. G. Krein, we exploit this grossly overlooked structure by confining the spectral analys

Kyri Baker, Harsha Gangammanavar

Electricity markets often utilize the DC approximation of the AC power flow equations to facilitate solving an otherwise complex nonconvex optimization problem. These DC power flow equations have analogies to DC circuit laws such as Kirchhoff's Laws, resulting in an intuitive understanding of power flows under this model. Variables derived from the Lagrangia

Yuting Guo, Anthony Ovadje, Mohammed Ali Al-Garadi, Abeed Sarker

Large language models (LLMs) have demonstrated remarkable success in NLP tasks. However, there is a paucity of studies that attempt to evaluate their performances on social media-based health-related natural language processing tasks, which have traditionally been difficult to achieve high scores in. We benchmarked one supervised classic machine learning mod

Kyle McKee, John H. Lienhard

Lienhard (2019) reported that the shape factor of the interior of a simply-connected region ($\Omega$) is equal to that of its exterior ($\mathbb{R}^2\backslash\Omega$) under the same boundary conditions. In that study, numerical examples supported the claim in particular cases; for example, it was shown that for certain boundary conditions on circles and sq

Wenlei Chen, Patrick L. Kelly, Brenda L. Frye, Justin Pierel

SN H0pe is a triply imaged supernova (SN) at redshift $z=1.78$ discovered using the James Webb Space Telescope (JWST). In order to classify the SN spectroscopically and measure the relative time delays of its three images (designated A, B, and C), we acquired NIRSpec follow-up spectroscopy spanning 0.6 to 5 microns. From the high signal-to-noise spectra of t

Daniel Menges, Trym Tengesdal, Adil Rasheed

This article proposes an approach for collision avoidance, path following, and anti-grounding of autonomous surface vessels under consideration of environmental forces based on Nonlinear Model Predictive Control (NMPC). Artificial Potential Fields (APFs) set the foundation for the cost function of the optimal control problem in terms of collision avoidance a

Vivienne Bihe Chi, Shashank Mehrotra, Teruhisa Misu, Kumar Akash

We propose leveraging prosocial observations to cultivate new social norms to encourage prosocial behaviors toward delivery robots. With an online experiment, we quantitatively assess updates in norm beliefs regarding human-robot prosocial behaviors through observational learning. Results demonstrate the initially perceived normativity of helping robots is i

Kevin S. Becker, Kristina D. Launey, Andreas Ekström, Tomáš Dytrych

We perform an uncertainty estimate of quadrupole moments and B(E2) transition rates that inform nuclear collectivity. In particular, we study the low-lying states of 6Li and 12C using the ab initio symmetry-adapted no-core shell model. For a narrow standard deviation of approximately 1% on the low-energy constants which parametrize high-precision chiral pote

Weizhuo Wang, C. Karen Liu, Monroe Kennedy

Wearable collaborative robots stand to assist human wearers who need fall prevention assistance or wear exoskeletons. Such a robot needs to be able to constantly adapt to the surrounding scene based on egocentric vision, and predict the ego motion of the wearer. In this work, we leveraged body-mounted cameras and sensors to anticipate the trajectory of human

Roy Aleksan, Luis Oliver

An important experimental effort has been accomplished in recent years in the measurement of rates, polarization and CP observables in $B$ decays into two light vector mesons. On the theoretical side, after a very consistent effort done within the framework of QCD Factorization, the comparison of the theory with the present experimental data has not been upd

Yasin Sonmez, Neelay Junnarkar, Murat Arcak

Recent work in reinforcement learning has leveraged symmetries in the model to improve sample efficiency in training a policy. A commonly used simplifying assumption is that the dynamics and reward both exhibit the same symmetry; however, in many real-world environments, the dynamical model exhibits symmetry independent of the reward model. In this paper, we

Sven Bachmann, Richard Froese, Severin Schraven

We prove upper and lower bounds for the number of eigenvalues of semi-bounded Schr\"odinger operators in all spatial dimensions. As a corollary, we obtain two-sided estimates for the sum of the negative eigenvalues of atomic Hamiltonians with Kato potentials. Instead of being in terms of the potential itself, as in the usual Lieb-Thirring result, the bounds

Khiem Vuong, N. Dinesh Reddy, Robert Tamburo, Srinivasa G. Narasimhan

Current methods for 2D and 3D object understanding struggle with severe occlusions in busy urban environments, partly due to the lack of large-scale labeled ground-truth annotations for learning occlusion. In this work, we introduce a novel framework for automatically generating a large, realistic dataset of dynamic objects under occlusions using freely avai

Juntao Tan, Shuyuan Xu, Wenyue Hua, Yingqiang Ge

Generative recommendation based on Large Language Models (LLMs) have transformed the traditional ranking-based recommendation style into a text-to-text generation paradigm. However, in contrast to standard NLP tasks that inherently operate on human vocabulary, current research in generative recommendations struggles to effectively encode recommendation items

Sondre Tesdal Galtung

We show how the sticky dynamics for the one-dimensional pressureless Euler-alignment system can be obtained as an $L^2$-gradient flow of a convex functional. This is analogous to the Lagrangian evolution introduced by Natile and Savar\'{e} for the pressureless Euler system, and by Brenier et al. for the corresponding system with a self-interacting force fiel

Mohammadamin Sanaei, Stephen B. Gilbert, Nikoo Javadpour, Hila Sabouni

This investigation examined the relationships among scene complexity, workload, presence, and cybersickness in virtual reality (VR) environments. Numerous factors can influence the overall VR experience, and existing research on this matter is not yet conclusive, warranting further investigation. In this between-subjects experimental setup, 44 participants e

Armin Khayyer, Alexander Vinel, Joseph J. Kennedy

We consider the problem of evaluating risk for a system that is modeled by a complex stochastic simulation with many possible input parameter values. Two sources of computational burden can be identified: the effort associated with extensive simulation runs required to accurately represent the tail of the loss distribution for each set of parameter values, a

Xudong Chen

We consider discrete ensembles of linear, scalar control systems with single-inputs. Assuming that all the individual systems are unstable, we investigate whether there exist linear feedback control laws that can asymptotically stabilize the ensemble system. We provide necessary/sufficient conditions for feasibility of pole placement in the left half plane a

Chang Liu, Jun Zhao

In the upcoming 6G era, vehicular networks are shifting from simple Vehicle-to-Vehicle (V2V) communication to the more complex Vehicle-to-Everything (V2X) connectivity. At the forefront of this shift is the incorporation of Large Language Models (LLMs) into vehicles. Known for their sophisticated natural language processing abilities, LLMs change how users i

Yanhao Jin, Krishnakumar Balasubramanian, Debashis Paul

Meta-learning involves training models on a variety of training tasks in a way that enables them to generalize well on new, unseen test tasks. In this work, we consider meta-learning within the framework of high-dimensional multivariate random-effects linear models and study generalized ridge-regression based predictions. The statistical intuition of using g

Julia Monika Koulen, Stefano Profumo, Nolan Smyth

The evolution of old, compact stellar structures provides strong constraints on macroscopic dark matter candidates such as primordial black holes. In view of recent observational data for the Eridanus II dwarf galaxy, we perform the first $N$-body simulations of its central stellar cluster to model dynamical heating by PBHs. We find evidence that such candid

Darlene Barker, Haim Levkowitz

In this study, we present a method for emotion recognition in Virtual Reality (VR) using pupillometry. We analyze pupil diameter responses to both visual and auditory stimuli via a VR headset and focus on extracting key features in the time-domain, frequency-domain, and time-frequency domain from VR generated data. Our approach utilizes feature selection to

Santanu Pakhira, D. C. Johnston

EuSn$_2$As$_2$ is a trigonal A-type antiferromagnetic topological insulator with the moments aligned in the $ab$ plane and with a N\'eel temperature $T_{\rm N} = 23.5$ K. Here we report that an EuSn$_2$As$_2$ crystal exhibits a broad peak at $H_{\rm c1} = 1100$ Oe in the field derivative $dM_{ab}/dH$ of the $ab$-plane magnetization $M_{ab}(H)$ at temperature

Yang Zhong, Mohamed Elaraby, Diane Litman, Ahmed Ashraf Butt

This paper introduces ReflectSumm, a novel summarization dataset specifically designed for summarizing students' reflective writing. The goal of ReflectSumm is to facilitate developing and evaluating novel summarization techniques tailored to real-world scenarios with little training data, %practical tasks with potential implications in the opinion summariza

Alan D. Kaplan, Priyadip Ray, John D. Greene, Vincent X. Liu

In the dynamic hospital setting, decision support can be a valuable tool for improving patient outcomes. Data-driven inference of future outcomes is challenging in this dynamic setting, where long sequences such as laboratory tests and medications are updated frequently. This is due in part to heterogeneity of data types and mixed-sequence types contained in

Abe Leininger, Mahmoud Ali, Hassan Jardali, Lantao Liu

Efficient navigation through uneven terrain remains a challenging endeavor for autonomous robots. We propose a new geometric-based uneven terrain mapless navigation framework combining a Sparse Gaussian Process (SGP) local map with a Rapidly-Exploring Random Tree* (RRT*) planner. Our approach begins with the generation of a high-resolution SGP local map, pro

Syed Mhamudul Hasan, Abdur R. Shahid, Ahmed Imteaj

The widespread adoption of machine learning (ML) across various industries has raised sustainability concerns due to its substantial energy usage and carbon emissions. This issue becomes more pressing in adversarial ML, which focuses on enhancing model security against different network-based attacks. Implementing defenses in ML systems often necessitates ad

Eoin Mackall

We show that algebraizability of the functors $R^1\pi_*\mathcal{K}^M_{2,X}$ and $R^2\pi_*\mathcal{K}^M_{2,X}$ is a stable birational invariant for smooth and proper varieties $\pi:X\rightarrow k$ defined over an algebraic extension $k$ of $\mathbb{Q}$. The same is true for the \'etale sheafifications of these functors as well. To get these results we introdu

Jonathan de Brusse, Mathieu Granzotto, Romain Postoyan, Dragan Nešić

Given a discounted cost, we study deterministic discrete-time systems whose inputs are generated by policy iteration (PI). We provide novel near-optimality and stability properties, while allowing for non stabilizing initial policies. That is, we first give novel bounds on the mismatch between the value function generated by PI and the optimal value function

Patrick Wolf

Autonomous driving vehicles provide a vast potential for realizing use cases in the on-road and off-road domains. Consequently, remarkable solutions exist to autonomous systems' environmental perception and control. Nevertheless, proof of safety remains an open challenge preventing such machinery from being introduced to markets and deployed in real world. T

D. A. Ivanov, A. Kudlis, I. S. Burmistrov

We investigate the elastic behavior of two-dimensional crystalline membrane embedded into real space taking into account the presence an arbitrary number of flexural phonon modes $d_c$ (the number of out-of-plane deformation field components). The bending rigidity exponent $\eta$ is extracted by numerical simulation via Fourier Monte Carlo technique of the s

Yukun Yue

In this paper, we derive discrete Poincar\'e and trace inequalities for the hybridizable discontinuous Galerkin (HDG) method. We employ the Crouzeix-Raviart space as a bridge, connecting classical discrete functional tools from Brenner's foundational work \cite{brenner2003poincare} with hybridizable finite element spaces comprised of piecewise polynomial fun

Maximilian Ruth, David Bindel

In many applications, one is interested in classifying trajectories of Hamiltonian systems as invariant tori, islands, or chaos. The convergence rate of ergodic Birkhoff averages can be used to categorize these regions, but many iterations of the return map are needed to implement this directly. Recently, it has been shown that a weighted Birkhoff average ca

Siva Sai Nagender Vasireddy, Chenxu Zhang, Xiaohu Guo, Yapeng Tian

This paper addresses the issue of active speaker detection (ASD) in noisy environments and formulates a robust active speaker detection (rASD) problem. Existing ASD approaches leverage both audio and visual modalities, but non-speech sounds in the surrounding environment can negatively impact performance. To overcome this, we propose a novel framework that u

Yui Lo, Yuqian Chen, Dongnan Liu, Wan Liu

Shape plays an important role in computer graphics, offering informative features to convey an object's morphology and functionality. Shape analysis in brain imaging can help interpret structural and functionality correlations of the human brain. In this work, we investigate the shape of the brain's 3D white matter connections and its potential predictive re

Domenico Ribezzo, Roberto Salazar, Jakub Czartowski, Flora Segur

In a world where Quantum Networks are rapidly becoming a reality, the development of the Quantum Internet is gaining increasing interest. Nevertheless, modern quantum networks are still in the early stages of development and have limited capacity to distribute resources among different users -- a constraint that needs to be taken into account. In this work w

Tomáš Dacík, Adam Rogalewicz, Tomáš Vojnar, Florian Zuleger

We present a novel decision procedure for a fragment of separation logic (SL) with arbitrary nesting of separating conjunctions with boolean conjunctions, disjunctions, and guarded negations together with a support for the most common variants of linked lists. Our method is based on a model-based translation to SMT for which we introduce several optimisation

Yuqing Wang, Mika V. Mäntylä, Serge Demeyer, Mutlu Beyazit

Microservice-based systems (MSS) may fail with various fault types. While existing AIOps methods excel at detecting abnormal traces and locating the responsible service(s), human efforts are still required for diagnosing specific fault types and failure causes.This paper presents TraFaultDia, a novel AIOps framework to automatically classify abnormal traces

Anthony M. Smaldone, Victor S. Batista

Toxicity is a roadblock that prevents an inordinate number of drugs from being used in potentially life-saving applications. Deep learning provides a promising solution to finding ideal drug candidates; however, the vastness of chemical space coupled with the underlying $\mathcal{O}(n^3)$ matrix multiplication means these efforts quickly become computational

Anees Ur Rehman Hashmi, Dwarikanath Mahapatra, Mohammad Yaqub

Explaining Deep Learning models is becoming increasingly important in the face of daily emerging multimodal models, particularly in safety-critical domains like medical imaging. However, the lack of detailed investigations into the performance of explainability methods on these models is widening the gap between their development and safe deployment. In this

Peter Habermehl, Vojtěch Havlena, Michal Hečko, Lukáš Holík

We present a new angle on solving quantified linear integer arithmetic based on combining the automata-based approach, where numbers are understood as bitvectors, with ideas from (nowadays prevalent) algebraic approaches, which work directly with numbers. This combination is enabled by a fine-grained version of the duality between automata and arithmetic for

Yaxin Fang, Faming Liang

With the advancement of data science, the collection of increasingly complex datasets has become commonplace. In such datasets, the data dimension can be extremely high, and the underlying data generation process can be unknown and highly nonlinear. As a result, the task of making causal inference with high-dimensional complex data has become a fundamental p

Xinya Bian, G Andrew D Briggs, Jan A Mol

To build a large scale quantum circuit comprising millions of cryogenic qubits will require an efficient way to supply large numbers of classic control signals. Given the limited number of direct connections allowed from room temperature, multiple level of signal multiplexing becomes essential. The stacking of hardware to accomplish this task is highly depen

Tian Yu, Yunhe Li, Michael E. Kim, Chenyu Gao

Diffusion MRI (dMRI) streamline tractography, the gold standard for in vivo estimation of brain white matter (WM) pathways, has long been considered indicative of macroscopic relationships with WM microstructure. However, recent advances in tractography demonstrated that convolutional recurrent neural networks (CoRNN) trained with a teacher-student framework

Howard Baer, Vernon Barger, Kairui Zhang

Natural supersymmetry (SUSY) with light higgsinos is perhaps the most plausible of all weak scale SUSY models while a variety of motivations point to (right) tau sleptons as the lightest of all the sleptons. We examine a SUSY model line with rather light right-staus embedded within natural SUSY. For light stau_1 of a few hundred GeV, then the decays stau_1 -

Harsha Gurram, Jason R. Shuster, Li-Jen Chen, Matthew R. Argall

We present a comprehensive study of Magnetospheric Multiscale (MMS) spacecraft encounter with KHI during a geomagnetic storm, focusing on elucidating key turbulence properties and reconnection signatures observed at the edges of KH vortices. The spectral slope for electric field stays approximately constant for frequencies below the ion cyclotron frequency a

Jesse Atuhurra, Takanori Hara, Yuanyu Zhang, Masahiro Sasabe

With the rapidly spreading usage of Internet of Things (IoT) devices, a network intrusion detection system (NIDS) plays an important role in detecting and protecting various types of attacks in the IoT network. To evaluate the robustness of the NIDS in the IoT network, the existing work proposed a realistic botnet dataset in the IoT network (Bot-IoT dataset)

Varun Chaturmutha, Bernhard Fleck, Stuart Jefferies

We present evidence supporting wave reflection in the lower solar chromosphere based on helioseismic analysis of multi-height Doppler data. This evidence is derived through a wave propagation model that incorporates both upward- and downward-traveling (reflected) waves. Moreover, we find that the height of the reflecting region varies with magnetic field str

Xinya Bian, Hannah J Joyce, Charles G Smith, Michael J Kelly

In this paper, we present a reconfigurable multiplex (MUX) setup that increases the throughput of electrical characterisation at cryogenic temperature. The setup separates the MUX circuitry from quantum device under test (qDUT), allowing qDUT chips to be exchanged easily and MUX chips to be reused. To interface with different types of qDUTs, board-level desi

Yuto Bekki, Robert H. Cameron, Laurent Gizon

Rapidly rotating fluids have a rotation profile which depends only on the distance from the rotation axis, in accordance with the Taylor-Proudman theorem. Although the Sun was expected to be such a body, helioseismology showed that the rotation rate in the convection zone is closer to constant on radii. It has been postulated that this deviation is due to th

Soumyendu Sarkar, Ashwin Ramesh Babu, Sajad Mousavi, Vineet Gundecha

We present a generic Reinforcement Learning (RL) framework optimized for crafting adversarial attacks on different model types spanning from ECG signal analysis (1D), image classification (2D), and video classification (3D). The framework focuses on identifying sensitive regions and inducing misclassifications with minimal distortions and various distortion

Fabrice Baudoin, Quanjun Lang, Yannick Sire

We investigate regularity properties of some non-local equations defined on Dirichlet spaces equipped with sub-gaussian estimates for the heat kernel associated to the generator. We prove that weak solutions for homogeneous equations involving pure powers of the generator are actually H\"older continuous and satisfy an Harnack inequality. Our methods are bas

Thomas Werkmeister, James R. Ehrets, Marie E. Wesson, Danial H. Najafabadi

The search for anyons, quasiparticles with fractional charge and exotic exchange statistics, has inspired decades of condensed matter research. Quantum Hall interferometers enable direct observation of the anyon braiding phase via discrete interference phase jumps when the quasiparticle number changes. Here, we observe the universal anyonic braiding phase in

Zachary E. Ross

Accurate models of fault zone geometry are important for scientific and hazard applications. While seismicity can provide high-resolution point measurements of fault geometry, extrapolating these measurements to volumes may involve making strong assumptions. This is particularly problematic in distributed fault zones, which are commonly observed in immature

Trey V. Wenger, Daniel R. Rybarczyk, Snežana Stanimirović

The vertical distribution of cold neutral hydrogen (HI) clouds is a constraint on models of the structure, dynamics, and hydrostatic balance of the interstellar medium. In 1978, Crovisier pioneered a method to infer the vertical distribution of HI absorbing clouds in the solar neighborhood. Using data from the Nan\c{c}ay 21-cm absorption survey, they determi

Olivier Bernardi, Éric Fusy, Shizhe Liang

We present two graph drawing algorithms based on the recently defined "grand-Schnyder woods", which are a far-reaching generalization of the classical Schnyder woods. The first is a straight-line drawing algorithm for plane graphs with faces of degree 3 and 4 with no separating 3-cycle, while the second is a rectangular drawing algorithm for the dual of such

Hanlin Yang, Chunlan Jin, Zifan Wang, Jingxiu Wang

As a relatively active region, ephemeral region (ER) exhibits highly complex pattern of magnetic flux emergence. We aim to study detailed secondary flux emergences (SFEs) which we define as bipoles that they appear close to ERs and finally coalesce with ERs after a period. We study the SFEs during the whole process from emergence to decay of 5 ERs observed b

Yanyu Li, Xian Liu, Anil Kag, Ju Hu

Diffusion-based text-to-image generative models, e.g., Stable Diffusion, have revolutionized the field of content generation, enabling significant advancements in areas like image editing and video synthesis. Despite their formidable capabilities, these models are not without their limitations. It is still challenging to synthesize an image that aligns well

Xiaoan Shen, Christof Sparber

We consider semiclassically scaled, weakly nonlinear Schr\"odinger equations with external confining potentials and additional angular-momentum rotation term. This type of model arises in the Gross-Pitaevskii theory of trapped, rotating quantum gases. We construct asymptotic solutions in the form of semiclassical wave-packets, which are concentrated in both

Vipula Rawte, S. M Towhidul Islam Tonmoy, S M Mehedi Zaman, Prachi Priya

Hallucination has emerged as the most vulnerable aspect of contemporary Large Language Models (LLMs). In this paper, we introduce the Sorry, Come Again (SCA) prompting, aimed to avoid LLM hallucinations by enhancing comprehension through: (i) optimal paraphrasing and (ii) injecting [PAUSE] tokens to delay LLM generation. First, we provide an in-depth analysi

Namu Park, Kevin Lybarger, Giridhar Kaushik Ramachandran, Spencer Lewis

Medical imaging is critical to the diagnosis, surveillance, and treatment of many health conditions, including oncological, neurological, cardiovascular, and musculoskeletal disorders, among others. Radiologists interpret these complex, unstructured images and articulate their assessments through narrative reports that remain largely unstructured. This unstr

Anna S. Bodrova, Aleksei V. Chechkin, Awadhesh Kumar Dubey

We investigate the granular temperatures in force-free granular gases under exponential resetting. When a resetting event occurs, the granular temperature attains its initial value, whereas it decreases because of the inelastic collisions between the resetting events. We develop a theory and perform computer simulations for granular gas cooling in the presen

Floris den Hengst, Ralf Wolter, Patrick Altmeyer, Arda Kaygan

We present Conformal Intent Classification and Clarification (CICC), a framework for fast and accurate intent classification for task-oriented dialogue systems. The framework turns heuristic uncertainty scores of any intent classifier into a clarification question that is guaranteed to contain the true intent at a pre-defined confidence level. By disambiguat

Prithvi Akella, Anushri Dixit, Mohamadreza Ahmadi, Lars Lindemann

The need for a systematic approach to risk assessment has increased in recent years due to the ubiquity of autonomous systems that alter our day-to-day experiences and their need for safety, e.g., for self-driving vehicles, mobile service robots, and bipedal robots. These systems are expected to function safely in unpredictable environments and interact seam

M. Mokhtarzadeh, F Lopez Jimenez, K. Maute

By adjusting both the structural shape and fiber orientation, this research aims to optimize the design of Fiber Reinforced Composite structures. The structural geometry is represented by a level set function, which is approximated by quadratic B-spline functions. The fiber orientation field is parameterized with quadratic/cubic B-splines on hierarchically r

Jongho Park

We propose a novel universal construction of two-level overlapping Schwarz preconditioners for $2m$th-order elliptic boundary value problems, where $m$ is a positive integer. The word "universal" here signifies that the coarse space construction can be applied to any finite element discretization for any $m$ that satisfies some common assumptions. We present

Chen Wang, Jin Zhao, Jiaqi Gong

Recent advancements in Large Language Models (LLMs), particularly those built on Transformer architectures, have significantly broadened the scope of natural language processing (NLP) applications, transcending their initial use in chatbot technology. This paper investigates the multifaceted applications of these models, with an emphasis on the GPT series. T

Mehdi Rezaei

In this paper we have performed a comparative study of different types of oscillating dark energy models using the Metropolis algorithm of MCMC. Eight different oscillating parameterization being examined herein which have demonstrated considerable ability to fit the overall cosmological observations including Pantheon sample of SnIa, Baryon Acoustic Oscilla

Delin Chu, Volker Mehrmann

The structure preserving stabilization of (possibly non-regular) linear port-Hamiltonian descriptor (pHDAE) systems by output feedback is discussed. For general descriptor systems the characterization when there exist output feedbacks that lead to an asymptotically stable closed loop system is a very hard and partially an open problem. In contrast to this it

Ilya A. Krishtal, Götz E. Pfander

We show that the classical Prony's method for recovery of a sparse signal from its consecutive Fourier coefficients can be viewed as a spectral identification problem for an unknown restriction of a known linear operator. This presents a unified point of view on various existing and novel generalizations and applications of the method, some of which are disc

Xin Ye, Feng Tao, Abhirup Mallik, Burhaneddin Yaman

Reinforcement learning (RL) based autonomous driving has emerged as a promising alternative to data-driven imitation learning approaches. However, crafting effective reward functions for RL poses challenges due to the complexity of defining and quantifying good driving behaviors across diverse scenarios. Recently, large pretrained models have gained signific

Tianhan Liu, Yuwaraj Adhikari, Hailong Wang, Yiyang Jiang

Electrical generation and transduction of polarized electron spins in semiconductors are of central interest in spintronics and quantum information science. While spin generation in semiconductors has been frequently realized via electrical injection from a ferromagnet, there are significant advantages in nonmagnetic pathways of creating spin polarization. O

Sergey G. Bobkov, Devraj Duggal

We start with a brief survey on H\"offding's kernels, its properties, related spectral decompositions, and discuss marginal distributions of H\"offding measures. In the second part of this note, one-dimensional covariance representations are considered over compactly supported probability distributions in the class of periodic smooth functions. H\"offding's

Gabriel A. Silva

The exploration of new problem classes for quantum computation is an active area of research. In this paper, we introduce and solve a novel problem class related to dynamics on large-scale networks relevant to neurobiology and machine learning. Specifically, we ask if a network can sustain inherent dynamic activity beyond some arbitrary observation time or i

Gineke Wiggers, Suzan Verberne, Arjen de Vries, Roel van der Burg

This paper illustrates some challenges of common ranking evaluation methods for legal information retrieval (IR). We show these challenges with log data from a live legal search system and two user studies. We provide an overview of aspects of legal IR, and the implications of these aspects for the expected challenges of common evaluation methods: test colle

Fan Zhou

This study explores the distinctions between neural machine translation (NMT) and human translation (HT) through the lens of translation relations. It benchmarks HT to assess the translation techniques produced by an NMT system and aims to address three key research questions: the differences in overall translation relations between NMT and HT, how each util

David Bolin, Jonas Wallin

The estimation of regression parameters in spatially referenced data plays a crucial role across various scientific domains. A common approach involves employing an additive regression model to capture the relationship between observations and covariates, accounting for spatial variability not explained by the covariates through a Gaussian random field. Whil

Boyuan Liang, Kei Ota, Masayoshi Tomizuka, Devesh Jha

We present in-hand manipulation tasks where a robot moves an object in grasp, maintains its external contact mode with the environment, and adjusts its in-hand pose simultaneously. The proposed manipulation task leads to complex contact interactions which can be very susceptible to uncertainties in kinematic and physical parameters. Therefore, we propose a r

Tamanna Chatterjee, Laura Rider

In the context of the Springer correspondence, the Weyl group action on the Springer sheaf can be defined in two ways: via restriction or the Fourier transform. It is well-known that these two actions differ by the sign character. This was proven by Hotta for sheaves with characteristic 0 coefficients in 1981, and more recently extended to arbitrary coeffici

Harsh Patel, Dominique Boucher, Emad Fallahzadeh, Ahmed E. Hassan

This paper investigates the complexities of integrating Large Language Models (LLMs) into software products, with a focus on the challenges encountered for determining their readiness for release. Our systematic review of grey literature identifies common challenges in deploying LLMs, ranging from pre-training and fine-tuning to user experience consideration

Michał Szafarczyk, Piotr Ludynia, Przemysław Kukla

Machine learning solutions are very popular in the field of chemoinformatics, where they have numerous applications, such as novel drug discovery or molecular property prediction. Molecular fingerprints are algorithms commonly used for vectorizing chemical molecules as a part of preprocessing in this kind of solution. However, despite their popularity, there

Lauren Conger, Yiheng Lin, Adam Wierman, Eric Mazumdar

This paper presents a closed-form notion of controllability and observability for systems with communication delays, actuation delays, and locality constraints. The formulation reduces to classical notions of controllability and observability in the unconstrained setting. As a consequence of our formulation, we show that the addition of locality and communic

J. D. R. Pierel, B. L. Frye, M. Pascale, G. B. Caminha

Supernova (SN) H0pe is a gravitationally lensed, triply-imaged, Type Ia SN (SN Ia) discovered in James Webb Space Telescope imaging of the PLCK G165.7+67.0 cluster of galaxies. Well-observed multiply-imaged SNe provide a rare opportunity to constrain the Hubble constant ($H_0$), by measuring the relative time delay between the images and modeling the foregro

Bernhard Müller

Magnetohydrodynamic simulations of core-collapse supernovae have become increasingly mature and important in recent years. Magnetic fields take center stage in scenarios for explaining hypernova explosions, but are now also considered in supernova theory more broadly as an important factor even in neutrino-driven explosions, especially in the context of neut

Ufuk Kilic, Matthew Hilfiker, Shawn Wimer, Alexander Ruder

The inherently weak chiroptical responses of natural materials limit their usage for controlling and enhancing chiral light-matter interactions. Recently, several nanostructures with subwavelength scale dimensions were demonstrated, mainly due to the advent of nanofabrication technologies, as a potential alternative to efficiently enhance chirality. However,

Mingxuan Ju, William Shiao, Zhichun Guo, Yanfang Ye

Collaborative filtering (CF) has exhibited prominent results for recommender systems and been broadly utilized for real-world applications. A branch of research enhances CF methods by message passing used in graph neural networks, due to its strong capabilities of extracting knowledge from graph-structured data, like user-item bipartite graphs that naturally

G. Gonzalez, O. Cornejo-Perez, J. de la Cruz, H. C. Rosu

Isochronous waveform solutions of homogeneous Li\'enard equations are obtained by a modification of the nonlinear factorization method of Rosu and Cornejo-P\'erez. The scheme is based on the assumption that the intermediate function $\Phi$ that can be introduced in this factorization method depends on both the dependent and independent variables of the nonli

Hermann Schulz-Baldes

This contribution describes the mathematical theory of topological indices in solid state systems composed of non-interacting Fermions. In particular, this covers the spectral localizer and the bulk-boundary correspondence.

Lorenzo Roberti, Marco Limongi, Alessandro Chieffi

We present an extension of the set of models published in Limongi & Chieffi, 2018, ApJS, 237, 13, at metallicity two times solar, i.e. [Fe/H]=0.3. The key physical properties of these models at the onset of the core collapse are mainly due to the higher mass loss triggered by the higher metallicity: the super solar metallicity (SSM) models reach the core col

Hermann Schulz-Baldes, Tom Stoiber

This note presents an elementary iterative construction of the generators for the complex $K$-groups $K_i(C(\SM^d))$ of the $d$-dimensional spheres. These generators are explicitly given as the restrictions of Dirac or Weyl Hamiltonians to the unit sphere. Connections to solid state physics are briefly elaborated on.

C. Dalfó, G. Erskine, G. Exoo, M. A. Fiol

Mixed graphs can be seen as digraphs with arcs and edges (or digons, that is, two opposite arcs). In this paper, we consider the case where such graphs are bipartite and in which the undirected and directed degrees are one. The best graphs, in terms of the number of vertices, are presented for small diameters. Moreover, two infinite families of such graphs w

Lars Koekenbier, Hermann Schulz-Baldes

Transfer matrix techniques are used to provide a new proof of Widom's results on the asymptotic spectral theory of finite block Toeplitz matrices. Furthermore, a rigorous treatment of the skin effect, spectral outliers, the generalized Brillouin zone and the bulk-boundary correspondence in such systems is given. This covers chiral Hamiltonians with topologic

Gabriella Contardo, David W. Hogg

Stellar infrared excesses can indicate various phenomena of interest, from protoplanetary disks to debris disks, or (more speculatively) techno-signatures along the lines of Dyson spheres. In this paper, we conduct a large search for such excesses, designed as a data-driven contextual anomaly detection pipeline. We focus our search on FGK stars close to the