March 2024 arXiv papers — page 170

Asmita, Yaroslav Oliinyk, Michael Scott, Ryan Tsang

BusyBox, an open-source software bundling over 300 essential Linux commands into a single executable, is ubiquitous in Linux-based embedded devices. Vulnerabilities in BusyBox can have far-reaching consequences, affecting a wide array of devices. This research, driven by the extensive use of BusyBox, delved into its analysis. The study revealed the prevalenc

Tianshu Huang, John Miller, Akarsh Prabhakara, Tao Jin

Simulation is an invaluable tool for radio-frequency system designers that enables rapid prototyping of various algorithms for imaging, target detection, classification, and tracking. However, simulating realistic radar scans is a challenging task that requires an accurate model of the scene, radio frequency material properties, and a corresponding radar syn

Evert Provoost, Wim Michiels

We reformulate the Lanczos tau method for the discretization of time-delay systems in terms of a pencil of operators, allowing for new insights into this approach. As a first main result, we show that, for the choice of a shifted Legendre basis, this method is equivalent to Pad\'e approximation in the frequency domain. We illustrate that Lanczos tau methods

Indraneil Paul, Goran Glavaš, Iryna Gurevych

Code understanding and generation have fast become some of the most popular applications of language models (LMs). Nonetheless, research on multilingual aspects of Code-LMs (i.e., LMs for code generation) such as cross-lingual transfer between different programming languages, language-specific data augmentation, and post-hoc LM adaptation, alongside exploita

Luiza Pozzobon, Patrick Lewis, Sara Hooker, Beyza Ermis

To date, toxicity mitigation in language models has almost entirely been focused on single-language settings. As language models embrace multilingual capabilities, it's crucial our safety measures keep pace. Recognizing this research gap, our approach expands the scope of conventional toxicity mitigation to address the complexities presented by multiple lang

Sujan Pal, Anik Pramanick

Furstenberg introduced the notion of Central sets in 1981. Later in 1990 V. Bergelson and N. Hindman proved a different but an equivalent version of the central set theorem. In 2008 D. De, N. Hindman and D. Strauss proved a stronger version of central sets theorem. Hindman and Leader first introduced the concept of near zero. Recently S. Goswami, L. Baglini

Omar S. M. El Nahhas, Georg Wölflein, Marta Ligero, Tim Lenz

Deep Learning (DL) can predict biomarkers directly from digitized cancer histology in a weakly-supervised setting. Recently, the prediction of continuous biomarkers through regression-based DL has seen an increasing interest. Nonetheless, clinical decision making often requires a categorical outcome. Consequently, we developed a weakly-supervised joint multi

Xiao Ma, Sumit Patidar, Iain Haughton, Stephen James

This paper introduces Hierarchical Diffusion Policy (HDP), a hierarchical agent for multi-task robotic manipulation. HDP factorises a manipulation policy into a hierarchical structure: a high-level task-planning agent which predicts a distant next-best end-effector pose (NBP), and a low-level goal-conditioned diffusion policy which generates optimal motion t

Amirhossein Omidi Soroor, Pablo A. Tarazaga

In the present study, the authors introduce a geometrically improved model inspired by the mammalian basilar membrane's properties and special vibratory behavior while conducting a parametric investigation. The goal of this model is to mimic the broadband non-reflective traveling wave response to excitation frequencies, as observed in the basilar membrane. S

Vasileios Katranidis, Gabor Barany

The demand for accurate and efficient verification of information in texts generated by large language models (LMs) is at an all-time high, but remains unresolved. Recent efforts have focused on extracting and verifying atomic facts from these texts via prompting LM evaluators. However, we demonstrate that this method of prompting is unreliable when faced wi

Nicholas G. Vlamis

Building on the work of Mann and Rafi, we introduce an expanded definition of a telescoping 2-manifold and proceed to study the homeomorphism group of a telescoping 2-manifold. Our main result shows that it is strongly distorted. We then give a simple description of its commutator subgroup, which is index one, two, or four depending on the topology of the ma

P. F. Antonietti, L. Beirao da Veiga, M. Botti, G. Vacca

In this paper, we design and analyze a Virtual Element discretization for the steady motion of non-Newtonian, incompressible fluids. A specific stabilization, tailored to mimic the monotonicity and boundedness properties of the continuous operator, is introduced and theoretically investigated. The proposed method has several appealing features, including the

Idael Martinez-Perez

We consider cycle decompositions of even, $2an$-dimensional hypercubes $Q_{2an},$ where $a \geq 3$ is odd and $n \geq 1.$ Prior work done by Axenovich, Offner, and Tompkins focused on obtaining the existence of cycle decompositions for even-dimensional hypercubes using long cycles of a given form, leaving out cycles of shorter lengths and, in fact, cycles of

Espen R. Jakobsen, Artur Rutkowski

We survey some results on Lipschitz and Schauder regularity estimates for viscous Hamilton--Jacobi equations with subcritical L\'evy diffusions. The Schauder estimates, along with existence of smooth solutions, are obtained with the help of a Duhamel formula and $L^1$ bounds on the spatial derivatives of the heat kernel. Our results cover very general nonloc

Pierre Colombo, Telmo Pessoa Pires, Malik Boudiaf, Dominic Culver

In this paper, we introduce SaulLM-7B, a large language model (LLM) tailored for the legal domain. With 7 billion parameters, SaulLM-7B is the first LLM designed explicitly for legal text comprehension and generation. Leveraging the Mistral 7B architecture as its foundation, SaulLM-7B is trained on an English legal corpus of over 30 billion tokens. SaulLM-7B

Jianfei Liu, Christopher Parnell, Ronald M. Summers

Accurate training labels are a key component for multi-class medical image segmentation. Their annotation is costly and time-consuming because it requires domain expertise. This work aims to develop a dual-branch network and automatically improve training labels for multi-class image segmentation. Transfer learning is used to train the network and improve in

Brian B. Moser, Federico Raue, Sebastian Palacio, Stanislav Frolov

Dataset distillation seeks to condense datasets into smaller but highly representative synthetic samples. While diffusion models now lead all generative benchmarks, current distillation methods avoid them and rely instead on GANs or autoencoders, or, at best, sampling from a fixed diffusion prior. This trend arises because naive backpropagation through the l

Sam Adam-Day, Michael Benedikt, İsmail İlkan Ceylan, Ben Finkelshtein

We present a new angle on the expressive power of graph neural networks (GNNs) by studying how the predictions of real-valued GNN classifiers, such as those classifying graphs probabilistically, evolve as we apply them on larger graphs drawn from some random graph model. We show that the output converges to a constant function, which upper-bounds what these

Meryem Amaouche, Ouassim Karrakchou, Mounir Ghogho, Anouar El Ghazzaly

Bladder cancer ranks within the top 10 most diagnosed cancers worldwide and is among the most expensive cancers to treat due to the high recurrence rates which require lifetime follow-ups. The primary tool for diagnosis is cystoscopy, which heavily relies on doctors' expertise and interpretation. Therefore, annually, numerous cases are either undiagnosed or

Barbara Fantechi, Andrea T. Ricolfi

Let $X$ be a quasiprojective scheme. In this expository note we collect a series of useful structural results on the stack $\mathscr{C}oh^n(X)$ parametrising $0$-dimensional coherent sheaves of length $n$ over $X$. For instance, we discuss its functoriality (in particular its behaviour along \'etale maps), the support morphism to $\mathrm{Sym}^n(X)$, and its

Chungang Shi

In the paper, the Kolmogorov distance is used to study the Smoluchowski-Kramers approximation for diffusions with jumps. The convergence rate is derived by Malliavin calculus.

Ioana Banicescu, Trisha Chakraborty, Seth Gilbert, Maxwell Young

Contention resolution addresses the challenge of coordinating access by multiple processes to a shared resource such as memory, disk storage, or a communication channel. Originally spurred by challenges in database systems and bus networks, contention resolution has endured as an important abstraction for resource sharing, despite decades of technological ch

JangHyeon Lee, Lawrence H. Kim

Smartphones are integral to modern life, yet research highlights the cognitive drawbacks associated with their mere presence. While physically removing them can mitigate these effects, it is often inconvenient and may heighten anxiety due to prolonged separation. To address this, we use holographic augmented reality (AR) displays to visually diminish distrac

Amanda Cercas Curry, Zeerak Talat, Dirk Hovy

Since Labov's (1964) foundational work on the social stratification of language, linguistics has dedicated concerted efforts towards understanding the relationships between socio-demographic factors and language production and perception. Despite the large body of evidence identifying significant relationships between socio-demographic factors and language p

Marcantonio Gagliardi, Marina Bonadeni, Sara Billai, Gian Luca Marcialis

Background. Personality is a primary object of interest in clinical psychology and psychiatry. It is most often measured using questionnaires, which rely on Factor Analysis (FA) to identify essential domains corresponding to highly correlated questions/items that define a (sub)scale. This procedure implies the rigid assignment of each question to one scale -

Ignacio Bono Parisi, Inés Pacharoni

The Matrix Bochner Problem aims to classify weight matrices $W$ such that the algebra $\mathcal D(W)$, of all differential operators that have a sequence of matrix-valued orthogonal polynomials for $W$ as eigenfunctions, contains a second-order differential operator. In \cite{CY18} it is proven that, under certain assumptions, the solutions to the Matrix Boc

Lucas Kreiss, Melissa Wu, Michael Wayne, Shiqi Xu

Diffuse Correlation Spectroscopy (DCS) allows the label-free investigation of microvascular dynamics deep within living tissue. However, common implementations of DCS are currently limited to measurement depths of $\sim 1-1.5cm$, which can limit the accuracy of cerebral hemodynamics measurement. Here we present massively parallelized DCS (pDCS) using novel s

Ignas Juodžbalis, Roberto Maiolino, William M. Baker, Sandro Tacchella

Recent observations have found a large number of supermassive black holes already in place in the first few hundred million years after Big Bang. The channels of formation and growth of these early, massive black holes are not clear, with scenarios ranging from heavy seeds to light seeds experiencing bursts of high accretion rate. Here we present the detecti

Avi Amalanshu, Yash Sirvi, David I. Inouye

Vertical Federated Learning (VFL) is an emergent distributed machine learning paradigm for collaborative learning between clients who have disjoint features of common entities. However, standard VFL lacks fault tolerance, with each participant and connection being a single point of failure. Prior attempts to induce fault tolerance in VFL focus on the scenari

Shannon Zejiang Shen, Hunter Lang, Bailin Wang, Yoon Kim

We propose a method to teach multiple large language models (LLM) to collaborate by interleaving their generations at the token level. We model the decision of which LLM generates the next token as a latent variable. By optimizing the marginal likelihood of a training set under our latent variable model, the base LLM automatically learns when to generate its

Salvatore Spina

The digital age introduced the Digital Ecological Niche (DEN), revolutionizing human interactions. The advent of Digital History (DHy) has marked a methodological shift in historical studies, tracing its roots to Babbage and Lovelace's 19th-century work on "coding" as a foundational communication process, fostering a new interaction paradigm between humans a

Ying Jin, Zhimei Ren

Conformal prediction builds marginally valid prediction intervals that cover the unknown outcome of a randomly drawn test point with a prescribed probability. However, in practice, data-driven methods are often used to identify specific test unit(s) of interest, requiring uncertainty quantification tailored to these focal units. In such cases, marginally val

Yibo Jiang, Goutham Rajendran, Pradeep Ravikumar, Bryon Aragam

Recent works have argued that high-level semantic concepts are encoded "linearly" in the representation space of large language models. In this work, we study the origins of such linear representations. To that end, we introduce a simple latent variable model to abstract and formalize the concept dynamics of the next token prediction. We use this formalism t

Fangyuan Xu, Kyle Lo, Luca Soldaini, Bailey Kuehl

Large language models (LLMs) adapted to follow user instructions are now widely deployed as conversational agents. In this work, we examine one increasingly common instruction-following task: providing writing assistance to compose a long-form answer. To evaluate the capabilities of current LLMs on this task, we construct KIWI, a dataset of knowledge-intensi

Pedro Baiz

The increasing significance of sustainability considerations within both public spheres (such as policies and regulations) and private sectors (including voluntary commitments by major multinational corporations) underscores the imperative to harness cutting-edge technological advancements. This is essential to ensure that the momentum of this trend translat

Deepanway Ghosal, Vernon Toh Yan Han, Chia Yew Ken, Soujanya Poria

This paper introduces the novel task of multimodal puzzle solving, framed within the context of visual question-answering. We present a new dataset, AlgoPuzzleVQA designed to challenge and evaluate the capabilities of multimodal language models in solving algorithmic puzzles that necessitate both visual understanding, language understanding, and complex algo

Hanzi Xu, Muhao Chen, Lifu Huang, Slobodan Vucetic

In recent years, few-shot and zero-shot learning, which learn to predict labels with limited annotated instances, have garnered significant attention. Traditional approaches often treat frequent-shot (freq-shot; labels with abundant instances), few-shot, and zero-shot learning as distinct challenges, optimizing systems for just one of these scenarios. Yet, i

Benjamin Lucas

In December 2023 the Florida State Seminoles became the first Power 5 school to have an undefeated season and miss selection for the College Football Playoff. In order to assess this decision, we employed an Elo ratings model to rank the teams and found that the selection committee's decision was justified and that Florida State were not one of the four best

Rishabh Adiga, Lakshminarayanan Subramanian, Varun Chandrasekaran

Pretrained language models (PLMs) have shown remarkable few-shot learning capabilities when provided with properly formatted examples. However, selecting the "best" examples remains an open challenge. We propose a complexity-based prompt selection approach for sequence tagging tasks. This approach avoids the training of a dedicated model for selection of exa

Luke Lozenski, Refik Mert Cam, Mark D. Pagel, Mark A. Anastasio

Accurate spatiotemporal image reconstruction methods are needed for a wide range of biomedical research areas but face challenges due to data incompleteness and computational burden. Data incompleteness arises from the undersampling often required to increase frame rates, while computational burden emerges due to the memory footprint of high-resolution image

Matteo Beltrame, Marco Bonici, Carmelita Carbone

In this work we have investigated the synergy between Stage-IV galaxy surveys and future GW observatories for constraining the underlying cosmological model of the Universe, focussing on photometric galaxy clustering, cosmic shear and GW magnification as cosmological probes. We have implemented a Fisher matrix approach for the evaluation of the full $6\times

Yassine Mekdad, Abbas Acar, Ahmet Aris, Abdeslam El Fergougui

Recent advancements in drone technology have shown that commercial off-the-shelf Micro Aerial Drones are more effective than large-sized drones for performing flight missions in narrow environments, such as swarming, indoor navigation, and inspection of hazardous locations. Due to their deployments in many civilian and military applications, safe and reliabl

Lars Henning Klein, Roland Aydin, Robert West

Emoji have become ubiquitous in written communication, on the Web and beyond. They can emphasize or clarify emotions, add details to conversations, or simply serve decorative purposes. This casual use, however, barely scratches the surface of the expressive power of emoji. To further unleash this power, we present Emojinize, a method for translating arbitrar

Enayat Ullah, Michael Menart, Raef Bassily, Cristóbal Guzmán

We study the limits and capability of public-data assisted differentially private (PA-DP) algorithms. Specifically, we focus on the problem of stochastic convex optimization (SCO) with either labeled or unlabeled public data. For complete/labeled public data, we show that any $(\epsilon,\delta)$-PA-DP has excess risk $\tilde{\Omega}\big(\min\big\{\frac{1}{\s

Rebeca Fernández-Fernández, Laura Bonavera, David Crespo, Joaquín González-Nuevo

Magnification bias, an observational effect of gravitational lensing in the weak regime, allows testing the cosmological model through angular correlations of sources at different redshifts. This effect has been observed in various contexts, particularly with sub-millimeter galaxies (SMGs), offering astrophysical and cosmological insights. The study aims to

Erik Brorsson, Knut Åkesson, Lennart Svensson, Kristofer Bengtsson

We consider unsupervised domain adaptation (UDA) for semantic segmentation in which the model is trained on a labeled source dataset and adapted to an unlabeled target dataset. Unfortunately, current self-training methods are susceptible to misclassified pseudo-labels resulting from erroneous predictions. Since certain classes are typically associated with l

Xin Men, Mingyu Xu, Qingyu Zhang, Bingning Wang

As Large Language Models (LLMs) continue to advance in performance, their size has escalated significantly, with current LLMs containing billions or even trillions of parameters. However, in this study, we discovered that many layers of LLMs exhibit high similarity, and some layers play a negligible role in network functionality. Based on this observation, w

Gen Li, Yu Huang, Timofey Efimov, Yuting Wei

Score-based diffusion models, while achieving remarkable empirical performance, often suffer from low sampling speed, due to extensive function evaluations needed during the sampling phase. Despite a flurry of recent activities towards speeding up diffusion generative modeling in practice, theoretical underpinnings for acceleration techniques remain severely

Alan Riquier, Emmanuel Dormy

We introduce a numerical strategy to study the evolution of 2D water waves in the presence of a plunging jet. The free-surface Navier-Stokes solution is obtained with a finite but small viscosity. We observe the formation of a surface boundary layer where the vorticity is localised. We highlight convergence to the inviscid solution. The effects of dissipatio

Chen Xu, Hanyang Jiang, Yao Xie

Conformal prediction (CP) has been a popular method for uncertainty quantification because it is distribution-free, model-agnostic, and theoretically sound. For forecasting problems in supervised learning, most CP methods focus on building prediction intervals for univariate responses. In this work, we develop a sequential CP method called $\texttt{MultiDimS

Yubiao Yue, Zhenzhang Li

Since the era of deep learning, convolutional neural networks (CNNs) and vision transformers (ViTs) have been extensively studied and widely used in medical image classification tasks. Unfortunately, CNN's limitations in modeling long-range dependencies result in poor classification performances. In contrast, ViTs are hampered by the quadratic computational

Zifan Xu, Amir Hossain Raj, Xuesu Xiao, Peter Stone

Recent advances of locomotion controllers utilizing deep reinforcement learning (RL) have yielded impressive results in terms of achieving rapid and robust locomotion across challenging terrain, such as rugged rocks, non-rigid ground, and slippery surfaces. However, while these controllers primarily address challenges underneath the robot, relatively little

Andrea Simonetto

Can we allow humans to pick among different, yet reasonably similar, decisions? Are we able to construct optimization problems whose outcome are sets of feasible, close-to-optimal decisions for human users to pick from, instead of a single, hardly explainable, do-as-I-say ``optimal'' directive? In this paper, we explore two complementary ways to render optim

Arik Reuter, Anton Thielmann, Benjamin Saefken

Understanding how images influence the world, interpreting which effects their semantics have on various quantities and exploring the reasons behind changes in image-based predictions are highly difficult yet extremely interesting problems. By adopting a holistic modeling approach utilizing Neural Additive Models in combination with Diffusion Autoencoders, w

TIngxu Han, Wei Song, Weisong Sun, Ziqi Ding

Self-Supervised Learning (SSL) has become a prominent paradigm for pre-training encoders to learning general-purpose representations from unlabeled data and releasing them on third-party platforms for broad downstream deep learning tasks. However, SSL is vulnerable to backdoor attacks, where an adversary may train and distribute poisoned pre-training encoder

Xu Zhang, Keping Qiu, Qizhou Zhang, Yue Cao

The physical properties, evolution, and fragmentation of massive dense cores (MDCs, $\sim$ 0.1 pc) are fundamental pieces in our understanding of high-mass star formation. We aim to characterize the temperature, velocity dispersion, and fragmentation of the MDCs in the Cygnus X giant molecular cloud and to investigate the stability and dynamics of these core

Liliana Borcea, Yiyang Liu, Jörn Zimmerling

The inverse wave scattering problem seeks to estimate a heterogeneous, inaccessible medium, modeled by unknown variable coefficients in wave equations, from transient recordings of waves generated by probing signals. It is a widely studied inverse problem with important applications, that is typically formulated as a nonlinear least squares data fit optimiza

Adam Shostack

Recent work arXiv.2310.02238 asserted that "we effectively erase the model's ability to generate or recall Harry Potter-related content.'' This claim is shown to be overbroad. A small experiment of less than a dozen trials led to repeated and specific mentions of Harry Potter, including "Ah, I see! A "muggle" is a term used in the Harry Potter book series by

Sally D. Jiang, Lynne A. Hillenbrand

Monoceros R2 (Mon R2) is one of the closest large active star-forming regions. This extremely young and partially embedded region provides an excellent laboratory for studying star formation and the early evolution of young stellar objects (YSOs). In this paper, we conduct an optical study of the greater Mon R2 region. Beginning with 1690 previously identifi

Antti Gronow, Arttu Malkamäki

The study analyses polarisation on Finnish social media with data from the platform X, which was known as Twitter during the time of data collection (during the Sipil\"a and Marin governments, 2015-2023). The users were clustered into three different ideological groups - the Conservative Right, the Moderate Right, and the Liberal Left - based on their retwee

Hassan Firouzjahi

We calculate the one-loop corrections in bispectrum of CMB scale perturbations induced from the small scale modes undergoing an intermediate phase of USR inflation in scenarios employed for PBHs formation. Using the formalism of effective field theory of inflation we calculate the cubic and quartic Hamiltonians and perform the in-in analysis for a subset of

Gavin Armstrong, Krzysztof Bogdan, Artur Rutkowski

We give Martin representation of nonnegative functions caloric with respect to the fractional Laplacian in Lipschitz open sets. The caloric functions are defined in terms of the mean value property for the space-time isotropic $\alpha$-stable L\'evy process. To derive the representation, we first establish the existence of the parabolic Martin kernel. This i

Matthew Gorton, Anne M. Green

Primordial black holes (PBHs) can make up all of the dark matter (DM) if their mass, $m$, is in the so-called 'asteroid-mass window', $10^{17} \, {\rm g} \lesssim m \lesssim 10^{22} \, {\rm g}$. Observational constraints on the abundance of PBHs are usually calculated assuming they all have the same mass, however this is unlikely to be a good approximation.

Wangyang Ying, Dongjie Wang, Haifeng Chen, Yanjie Fu

Feature selection aims to identify the most pattern-discriminative feature subset. In prior literature, filter (e.g., backward elimination) and embedded (e.g., Lasso) methods have hyperparameters (e.g., top-K, score thresholding) and tie to specific models, thus, hard to generalize; wrapper methods search a feature subset in a huge discrete space and is comp

Christian Capezza, Giovanna Capizzi, Fabio Centofanti, Antonio Lepore

In many modern industrial scenarios, the measurements of the quality characteristics of interest are often required to be represented as functional data or profiles. This motivates the growing interest in extending traditional univariate statistical process monitoring (SPM) schemes to the functional data setting. This article proposes a new SPM scheme, which

S. Sannyamath, R. Vetter, H. Bonart, M. Hartmann

A liquid drop containing a long fiber is a complex system whose configuration is determined by an interplay of elastic stresses in the fiber and capillary forces due to the liquid. We study the morphological evolution of fibers that are much longer than the drop diameter in evaporating sessile drops. After insertion, the fibers are either found in an ordered

Xin Lian, Sashank Varma, Christopher J. MacLellan

Cobweb, a human-like category learning system, differs from most cognitive science models in incrementally constructing hierarchically organized tree-like structures guided by the category utility measure. Prior studies have shown that Cobweb can capture psychological effects such as basic-level, typicality, and fan effects. However, a broader evaluation of

Yuqing Wang, Yizhe Zhu

Denote by $\Delta$ the Laplacian and by $\Delta_\infty$ the $\infty$-Laplacian. A fundamental inequality is proved for the algebraic structure of $\Delta v\Delta_\infty v$: for every $v\in C^{\infty}$, $$\bigg| |D^2vDv|^2-\Delta v\Delta_\infty v-\frac{1}{2}[|D^2v|^2-(\Delta v)^2]|Dv|^2\bigg| \le\frac{n-2}{2}[|D^2v|^2|Dv|^2-|D^2vDv|^2]$$ Based on this, we pro

Lorenzo Cipriani, Massimo Mannarelli, Fabrizio Nesti, Silvia Trabucco

We present a superfluid dark star model consisting of relativistic dark bosons with two-body self-interaction. The obtained masses, radii, and tidal deformability depend in a simple way on the boson mass and interaction strength. We report first results on binary mergers: the distinctive amplitude and frequency of the emitted gravitational waves are well wit

Pedro Gomes do Nascimento, Pidge Witiak, Tucker MacCallum, Zachary Winterfeldt

This research aims to further understanding in the field of continuous authentication using behavioral biometrics. We are contributing a novel dataset that encompasses the gesture data of 15 users playing Minecraft with a Samsung Tablet, each for a duration of 15 minutes. Utilizing this dataset, we employed machine learning (ML) binary classifiers, being Ran

Jingyao Dou, Philip J. Carter, Zoë M. Leinhardt

During the final stage of planetary formation, different formation pathways of planetary embryos could significantly influence the observed variations in planetary densities. Of the approximately 5,000 exoplanets identified to date, a notable subset exhibit core fractions reminiscent of Mercury, potentially a consequence of high-velocity giant impacts. In or

Jayakrishnan Madathil, Kitty Meeks

We study {\sc Cluster Edge Modification} problems with constraints on the size of the clusters. A graph $G$ is a cluster graph if every connected component of $G$ is a clique. In a typical {\sc Cluster Edge Modification} problem such as the widely studied {\sc Cluster Editing}, we are given a graph $G$ and a non-negative integer $k$ as input, and we have to

Soufyan Lakbir, Caterina Buranelli, Gerrit A. Meijer, Jaap Heringa

Background: Genomic instability is a hallmark of cancer, leading to many somatic alterations. Identifying which alterations have a system-wide impact is a challenging task. Nevertheless, this is an essential first step for prioritizing potential biomarkers. We developed CIBRA (Computational Identification of Biologically Relevant Alterations), a method that

Rushit Dave, Marcho Handoko, Ali Rashid, Cole Schoenbauer

In the realm of computer security, the importance of efficient and reliable user authentication methods has become increasingly critical. This paper examines the potential of mouse movement dynamics as a consistent metric for continuous authentication. By analyzing user mouse movement patterns in two contrasting gaming scenarios, "Team Fortress" and Poly Bri

Alberto Bemporad

In this paper, we propose a very efficient numerical method based on the L-BFGS-B algorithm for identifying linear and nonlinear discrete-time state-space models, possibly under $\ell_1$ and group-Lasso regularization for reducing model complexity. For the identification of linear models, we show that, compared to classical linear subspace methods, the appro

Lars Simon, Holger Eble, Hagen-Henrik Kowalski, Manuel Radons

In this article we introduce an algorithm for mitigating the adverse effects of noise on gradient descent in variational quantum algorithms. This is accomplished by computing a {\emph{regularized}} local classical approximation to the objective function at every gradient descent step. The computational overhead of our algorithm is entirely classical, i.e., t

Jeremias Gerner, Klaus Bogenberger, Stefanie Schmidtner

Floating Car Observers (FCOs) are an innovative method to collect traffic data by deploying sensor-equipped vehicles to detect and locate other vehicles. We demonstrate that even a small penetration rate of FCOs can identify a significant amount of vehicles at a given intersection. This is achieved through the emulation of detection within a microscopic traf

CRESST Collaboration, G. Angloher, S. Banik, G. Benato

Cryogenic scintillating calorimeters are ultrasensitive particle detectors for rare event searches, particularly for the search for dark matter and the measurement of neutrino properties. These detectors are made from scintillating target crystals generating two signals for each particle interaction. The phonon (heat) signal precisely measures the deposited

Louis Mahon, Mirella Lapata

In this paper we address the task of summarizing television shows, which touches key areas in AI research: complex reasoning, multiple modalities, and long narratives. We present a modular approach where separate components perform specialized sub-tasks which we argue affords greater flexibility compared to end-to-end methods. Our modules involve detecting s

Zezheng Feng, Fang Zhu, Hongjun Wang, Jianing Hao

Higher-order patterns reveal sequential multistep state transitions, which are usually superior to origin-destination analysis, which depicts only first-order geospatial movement patterns. Conventional methods for higher-order movement modeling first construct a directed acyclic graph (DAG) of movements, then extract higher-order patterns from the DAG. Howev

Yeqi Fang, Wei Hong, Jun Tao

The EHT has captured a series of images of black holes. These images could provide valuable information about the gravitational environment near the event horizon. However, accurate detection and parameter estimation for candidate black holes are necessary. This paper explores the potential for identifying black holes in the ultraviolet band using space tele

Zu-En Su, Boaz Taitler, Ido Schwartz, Dan Cogan

Cluster states are key resources for measurement-based quantum information processing. Photonic cluster and graph states, in particular, play indispensable roles in quantum network and quantum metrology. We demonstrate a semiconductor quantum dot based device in which the confined hole spin acts as a needle in a quantum knitting machine producing continuousl

Aaron Imani, Shiva Radmanesh, Iftekhar Ahmed, Mohammad Moshirpour

In recent years, open-source software (OSS) has become increasingly prevalent in developing software products. While OSS documentation is the primary source of information provided by the developers' community about a product, its role in the industry's adoption process has yet to be examined. We conducted semi-structured interviews and an online survey to p

Hyeonguk Bahk, Ho Seong Hwang

We present the updated galaxy cluster catalog of the second Planck catalog of Sunyaev-Zeldovich sources (PSZ2) through the compilation of the data for clusters and galaxies with spectroscopically measured redshifts in the literature. The original version of PSZ2 comprises 1653 SZ sources, of which 1203 have been validated as genuine galaxy clusters, while th

J. S. Dowker

An oldish question is resurrected concerning the significance of the ambiguous `b-type' terms encountered in calculations of the vacuum, Casimir energy on the Einstein Universe for conformally coupled scalar fields. Some remarks in the literature are hopefully clarified and the relevance of much earlier evaluations is pointed out. A consistency principle is

John Joshua Miller, Simon Mak

The optimization of a black-box simulator over control parameters $\mathbf{x}$ arises in a myriad of scientific applications. In such applications, the simulator often takes the form $f(\mathbf{x},\boldsymbol{\theta})$, where $\boldsymbol{\theta}$ are parameters that are uncertain in practice. Robust optimization aims to optimize the objective $\mathbb{E}[f(

Shouzhi Wang, Zhongzu Wu, Bo Zhang, Yu. Sotnikova

We present the results of our observations using the Giant Meterwave Radio Telescope (GMRT) to investigate the radio continuum and OH line emission of 10 OHM candidates from the Arecibo Legacy Fast ALFA (ALFALFA) survey. Among these candidates, we have identified two sources, AGC115713 and AGC249507, which display compact OH line emission that are spatially

Carolin Holtermann, Paul Röttger, Timm Dill, Anne Lauscher

Large language models (LLMs) need to serve everyone, including a global majority of non-English speakers. However, most LLMs today, and open LLMs in particular, are often intended for use in just English (e.g. Llama2, Mistral) or a small handful of high-resource languages (e.g. Mixtral, Qwen). Recent research shows that, despite limits in their intended use,

Waqas Iqbal, Najeeb-ur-Rehman, Niaz Wali

This study reports the successful synthesis of multilayered graphene sheets via microwave atmospheric pressure plasma. This innovative approach streamlines and expedites graphene production and other carbon nanostructures, eliminating the need for catalysts, solvents, or complex processing conditions. Ethanol is directly injected into a microwave-generated a

Kiran Madhusudhanan, Gunnar Behrens, Maximilian Stubbemann, Lars Schmidt-Thieme

Used car pricing is a critical aspect of the automotive industry, influenced by many economic factors and market dynamics. With the recent surge in online marketplaces and increased demand for used cars, accurate pricing would benefit both buyers and sellers by ensuring fair transactions. However, the transition towards automated pricing algorithms using mac

Antoine Scheid, Daniil Tiapkin, Etienne Boursier, Aymeric Capitaine

This work considers a repeated principal-agent bandit game, where the principal can only interact with her environment through the agent. The principal and the agent have misaligned objectives and the choice of action is only left to the agent. However, the principal can influence the agent's decisions by offering incentives which add up to his rewards. The

Martin Ehler, Karlheinz Gröchenig, Andreas Klotz

In order to compute the Fourier transform of a function $f$ on the real line numerically, one samples $f$ on a grid and then takes the discrete Fourier transform. We derive exact error estimates for this procedure in terms of the decay and smoothness of $f$. The analysis provides a new recipe of how to relate the number of samples, the sampling interval, and

Yunfei Li, Yiting Luo, Weiqiang Tan, Chunguo Li

This paper focuses on the challenge of jointly optimizing location and path loss exponent (PLE) in distance-dependent noise. Departing from the conventional independent noise model used in localization and path loss exponent estimation problems, we consider a more realistic model incorporating distance-dependent noise variance, as revealed in recent theoreti

Ajith Anil Meera, Pablo Lanillos

Self-reflecting about our performance (e.g., how confident we are) before doing a task is essential for decision making, such as selecting the most suitable tool or choosing the best route to drive. While this form of awareness -- thinking about our performance or metacognitive performance -- is well-known in humans, robots still lack this cognitive ability.

Daniela Grandón, Gabriela A. Marques, Leander Thiele, Sihao Cheng

Baryonic feedback is a major systematic in weak lensing cosmology. Its most studied effect is the suppression of the lensing power spectrum, a second-order statistic, on small scales. Motivated by the growing interest in statistics beyond the second order, we investigate the effect of baryons on lensing non-Gaussian statistics and the resulting biases in the

Joshua Springer, Gylfi Þór Guðmundsson, Marcel Kyas

We propose a method for autonomous precision drone landing with fiducial markers and a gimbal-mounted, multi-payload camera with wide-angle, zoom, and IR sensors. The method has minimal data requirements; it depends primarily on the direction from the drone to the landing pad, enabling it to switch dynamically between the camera's different sensors and zoom

Wibke Bronsch, Manuel Tuniz, Denny Puntel, Alessandro Giammarino

Complex materials encompassing different phases of matter can display new photoinduced metastable states differing from those attainable under equilibrium conditions. These states can be realized when energy is injected in the material following a non-equilibrium pathway, unbalancing the unperturbed energy landscape of the material. Guided by the fact that p

Hugues Chaté, Alexandre Solon

We propose a hydrodynamic description of the homogeneous ordered phase of polar flocks. Starting from symmetry principles, we construct the appropriate equation for the dynamics of the Goldstone mode associated with the broken rotational symmetry. We then focus on the two-dimensional case considering both "Malthusian flocks" for which the density field is a

Vladimir M. Krasnov

Current-biased Josephson junctions can act as detectors of electromagnetic radiation. At optimal conditions, their sensitivity is limited by fluctuations causing stochastic switching from the superconducting to the resistive state. This work provides a quantitative description of a stochastic switching current detector, based on an underdamped Josephson junc

Tommaso Lando Idir Arab, Paulo Eduardo Oliveira

We introduce a comprehensive method for establishing stochastic orders among order statistics in the i.i.d. case. This approach relies on the assumption that the underlying distribution is linked to a reference distribution through a transform order. Notably, this method exhibits broad applicability, particularly since several well-known nonparametric distri