March 2024 arXiv papers — page 171

Dominik Dürrschnabel, Uta Priss

Euler diagrams are a tool for the graphical representation of set relations. Due to their simple way of visualizing elements in the sets by geometric containment, they are easily readable by an inexperienced reader. Euler diagrams where the sets are visualized as aligned rectangles are of special interest. In this work, we link the existence of such rectangu

M. Hinderling, S. C. ten Kate, M. Coraiola, D. Z. Haxell

We demonstrate microwave measurements of the Andreev bound state (ABS) spectrum in planar Josephson junctions (JJs) defined in Ge high mobility two-dimensional hole gases contacted by superconducting PtSiGe. The JJs and readout circuitry are located on separate chips and inductively coupled via flip-chip bonding. For a device with $350~\mathrm{nm}$ junction

Jordan Krywonos, Simone Paradiso, Alex Krolewski, Shahab Joudaki

When fitting cosmological models to data, a Bayesian framework is commonly used, requiring assumptions on the form of the likelihood and model prior. In light of current tensions between different data, it is interesting to investigate the robustness of cosmological measurements to statistical assumptions about the likelihood distribution from which the data

Duncan Neill, David Tsang, William G. Newton

With current and planned gravitational-wave (GW) observing runs, coincident multimessenger timing of Resonant Shattering Flares (RSFs) and GWs may soon allow for neutron star (NS) asteroseismology to be used to constrain the nuclear symmetry energy, an important property of fundamental nuclear physics that influences the composition and equation of state of

Tillmann Carl, Clarissa Schoenecker

In this study, we investigate the thermocapillary rotation of microgears at fluid interfaces and extend the concept of geometric asymmetry to the translational propulsion of micron-sized particles. We introduce a transient numerical model that couples the Navier-Stokes equations with heat transfer, displaying particle motion through a moving mesh interface.

Daniel Stremmer, Malgorzata Worek

We compute for the first time the so-called complete NLO corrections to top-quark pair production with one and two isolated photons in the di-lepton top-quark decay channel. The Narrow Width Approximation is used for the modeling of unstable top quarks and $W$ bosons. Higher-order QCD and EW effects as well as photon bremsstrahlung are consistently included

Mary Jane Brundage, David E. Meltzer, Chandralekha Singh

We use the Survey of Thermodynamic Processes and First and Second Laws-Long (STPFaSL-Long), a research-based survey instrument with 78 items at the level of introductory physics, to investigate introductory and advanced students' difficulties with internal energy, work, and heat transfer. We present analysis of data from 12 different introductory and advance

Billel Guelmame, Haroune Houamed

This work revisits a recent finding by the first author concerning the local convergence of a regularized scalar conservation law. We significantly improve the original statement by establishing a global convergence result within the Lebesgue spaces $L^\infty_{\mathrm{loc}}(\mathbb{R}^+;L^p(\mathbb{R}))$, for any $p \in [1,\infty)$, as the regularization par

Shuai Yuan, Jiaojie Yan, Ke Huang, Zhimou Chen

A two dimensional system with extra degrees of freedom, such as spin and valley, is of great interest in the study of quantum phase transitions. The critical condition when a transition between different multicomponent fractional quantum Hall states appears is one of the very few junctions for many body problems between theoretical calculations and experimen

Ben Van Dusen, Heidi Cian, Jayson Nissen, Lucy Arellano

This investigation examines the efficacy of multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) over fixed effect models when performing intersectional studies. The research questions are: 1) What are typical strata representation rates and outcomes on physics research-based assessments? 2) To what extent do MAIHDA models cre

Rajdeep Haldar, Yue Xing, Qifan Song

The existence of adversarial attacks on machine learning models imperceptible to a human is still quite a mystery from a theoretical perspective. In this work, we introduce two notions of adversarial attacks: natural or on-manifold attacks, which are perceptible by a human/oracle, and unnatural or off-manifold attacks, which are not. We argue that the existe

Dario Pasquini, Martin Strohmeier, Carmela Troncoso

We introduce a new family of prompt injection attacks, termed Neural Exec. Unlike known attacks that rely on handcrafted strings (e.g., "Ignore previous instructions and..."), we show that it is possible to conceptualize the creation of execution triggers as a differentiable search problem and use learning-based methods to autonomously generate them. Our res

Ruoqi Liu, Lingfei Wu, Ping Zhang

Treatment effect estimation (TEE) is the task of determining the impact of various treatments on patient outcomes. Current TEE methods fall short due to reliance on limited labeled data and challenges posed by sparse and high-dimensional observational patient data. To address the challenges, we introduce a novel pre-training and fine-tuning framework, KG-TRE

Wei Zhang, Miaoxin Cai, Tong Zhang, Guoqiang Lei

Ship detection needs to identify ship locations from remote sensing (RS) scenes. Due to different imaging payloads, various appearances of ships, and complicated background interference from the bird's eye view, it is difficult to set up a unified paradigm for achieving multi-source ship detection. To address this challenge, in this article, leveraging the l

Vadim Tynchenko, Aleksei Kudryavtsev, Vladimir Nelyub, Aleksei Borodulin

This report presents the test results Python library BaumEvA, which implements evolutionary algorithms for optimizing various types of problems, including computer vision tasks accompanied by the search for optimal model architectures. Testing was carried out to evaluate the effectiveness and reliability of the pro-posed methods, as well as to determine thei

Vikash Kumar, Francisco Marcellán, A. Swaminathan

In this contribution, quasi-orthogonality of polynomials generated by Geronimus and Uvarov transformations is analyzed. An attempt is made to discuss the recovery of the source orthogonal polynomial from the quasi-Geronimus and quasi-Uvarov polynomials of order one. Moreover, the discussion on the difference equation satisfied by quasi-Geronimus and quasi-Uv

Zekai Zhang, Yiduo Guo, Yaobo Liang, Dongyan Zhao

The growing dependence on Large Language Models (LLMs) for finishing user instructions necessitates a comprehensive understanding of their robustness to complex task completion in real-world situations. To address this critical need, we propose the PowerPoint Task Completion Robustness benchmark (PPTC-R) to measure LLMs' robustness to the user PPT task instr

V. L. Gorshenin

We show that injecting a light pulse prepared in the Shroedinger cat quantum state into the dark port of a two-arm interferometer and the strong classical light into the bright one, it is possible, in principle, to detect a given phase shift unambiguously. The value of this phase shift is inversely proportional to the amplitudes of both the classical carrier

Herman Sletmoen, Hans A. Winther

Brans-Dicke (BD), one of the first proposed scalar-tensor theories of gravity, effectively makes the gravitational constant of general relativity (GR) time-dependent. Constraints on the BD parameter $\omega$ serve as a benchmark for testing GR, which is recovered in the limit $\omega \rightarrow \infty$. Current small-scale astrophysical constraints $\omega

Karina Silina, Francesco Ciompi

Quantification of lymphoid aggregates including tertiary lymphoid structures with germinal centers in histology images of cancer is a promising approach for developing prognostic and predictive tissue biomarkers. In this article, we provide recommendations for identifying lymphoid aggregates in tissue sections from routine pathology workflows such as hematox

Rambod Rahmani, Marco Parola, Mario G. C. A. Cimino

Due to the recent increase in interest in Financial Technology (FinTech), applications like credit default prediction (CDP) are gaining significant industrial and academic attention. In this regard, CDP plays a crucial role in assessing the creditworthiness of individuals and businesses, enabling lenders to make informed decisions regarding loan approvals an

Yuqing Wang, Yuan Zhou

Let $\Omega$ be a domain of $\mathbb R^n$ with $n\ge 2$ and $p(\cdot)$ be a local Lipschitz funcion in $\Omega$ with $1<p(x)<\infty$ in $\Omega$. We build up an interior quantitative second order Sobolev regularity for the normalized $p(\cdot)$-Laplace equation $-\Delta^N_{p(\cdot)}u=0$ in $\Omega$ as well as the corresponding inhomogeneous equation $-\Delta

Paolo Dai Pra, Elisa Marini

We study a dissipative version of the contact process, with mean-field interaction, which admits a simple epidemiological interpretation. The propagation of chaos and the corresponding normal fluctuations reveal that the noise present in the finite-size system induces oscillations with a nearly deterministic period and a randomly varying amplitude. This is r

Jakob Stoye, Ralf Zimmermann

Arguably, geodesics are the most important geometric objects on a differentiable manifold. They describe candidates for shortest paths and are guaranteed to be unique shortest paths when the starting velocity stays within the so-called injectivity radius of the manifold. In this work, we investigate the injectivity radius of the Stiefel manifold under the ca

Séamus Lankford, Diarmuid Grimes

Neural network models have a number of hyperparameters that must be chosen along with their architecture. This can be a heavy burden on a novice user, choosing which architecture and what values to assign to parameters. In most cases, default hyperparameters and architectures are used. Significant improvements to model accuracy can be achieved through the ev

F. De Zela

For almost three decades in the twentieth century, the physics community believed that John von Neumann had proved the impossibility of completing quantum mechanics by a local realist, hidden-variables theory. Although Grete Hermann had raised strong objections to von Neumann's proof, she was largely ignored. This situation lasted, until John Bell rediscover

S. Andersson, H. Havir, A. Ranni, S. Haldar

In this article, we present an experimental study of a Josephson junction -based high-impedance resonator. By taking the resonator to the limit of consisting effectively only of one junction, results in strong non-linear effects already for the second photon while maintaining a high impedance of the resonance mode. Our experiment yields thus resonators with

Christoph Schultheiss, Markus Ulmer, Peter Bühlmann

We present a new method for causal discovery in linear structural vector autoregressive models. We adapt an idea designed for independent observations to the case of time series while retaining its favorable properties, i.e., explicit error control for false causal discovery, at least asymptotically. We apply our method to several real-world bivariate time s

Nazar Buzun, Maksim Bobrin, Dmitry V. Dylov

We present a new approach for Neural Optimal Transport (NOT) training procedure, capable of accurately and efficiently estimating optimal transportation plan via specific regularization on dual Kantorovich potentials. The main bottleneck of existing NOT solvers is associated with the procedure of finding a near-exact approximation of the conjugate operator (

Bengt E. W. Nilsson, Björn Jonson

We give some personal reflections on the person and scientist Lars Brink and on some of his scientific achievements. Our relations to Lars are briefly described in [1] and [2], while the sources relevant for this text are summarised in [3].

Mahmoud Shaqfa, Gary P. T. Choi, Guillaume Anciaux, Katrin Beyer

When two bodies get into contact, only a small portion of the apparent area is actually involved in producing contact and friction forces, because of the surface roughnesses. It is therefore crucial to accurately describe the morphology of rough surfaces for instance by extracting the fractal dimension and the so-called Hurst exponent which is a typical sign

Weikang Zhang, Matěj Hejda, Qusay Raghib Ali Al-Taai, Dafydd Owen-Newns

We report a multi-modal spiking neuron that allows optical and electronic input and control, and wavelength-multiplexing operation, for use in novel high-speed neuromorphic sensing and computing functionalities. The photonic-electronic neuron is built with a micro-scale, nanostructure resonant tunnelling diode (RTD) with photodetection (PD) capability. Lever

Liina Chung-Jukko, Eugene A. Lim, David J. E. Marsh

Axion dark matter can form stable, self-gravitating, and coherent configurations known as axion stars, which are rendered unstable above a critical mass by the Chern-Simons coupling to electromagnetism. We study, using numerical relativity, the merger and subsequent decay of compact axion stars. We show that two sub-critical stars can merge, and form a more

Anna P. Meyer, Yuhao Zhang, Aws Albarghouthi, Loris D'Antoni

Counterfactual explanations (CEs) enhance the interpretability of machine learning models by describing what changes to an input are necessary to change its prediction to a desired class. These explanations are commonly used to guide users' actions, e.g., by describing how a user whose loan application was denied can be approved for a loan in the future. Exi

Victor Akinwande, J. Zico Kolter

Existing causal discovery methods based on combinatorial optimization or search are slow, prohibiting their application on large-scale datasets. In response, more recent methods attempt to address this limitation by formulating causal discovery as structure learning with continuous optimization but such approaches thus far provide no statistical guarantees.

Kuo Meng, Shaoshi Yang, Xiao-Yang Wang, Yan Bu

We propose a channel estimation scheme based on joint sparsity pattern learning (JSPL) for massive multi-input multi-output (MIMO) orthogonal time-frequency-space (OTFS) modulation aided systems. By exploiting the potential joint sparsity of the delay-Doppler-angle (DDA) domain channel, the channel estimation problem is transformed into a sparse recovery pro

P. G. Ortega, D. R. Entem, F. Fernández, J. Segovia

The $\Upsilon(10753)$ state has been recently observed by the Belle and Belle~II collaborations with enough global significance to motivate an assessment of the high-energy spectrum usually predicted by any reasonable \emph{na\"ive} quark model. In the framework of a constituent quark model which satisfactorily describes a wide range of properties of convent

Marek Lewicki, Marco Merchand, Laura Sagunski, Philipp Schicho

Different computational techniques for cosmological phase transition parameters can impact the Gravitational Wave (GW) spectra predicted in a given particle physics model. To scrutinize the importance of this effect, we perform large-scale parameter scans of the dynamical real-singlet extended Standard Model using three perturbative approximations for the ef

Yushuai Wu, Ting Zhang, Hao Zhou, Hainan Wu

The fields of therapeutic application and drug research and development (R&D) both face substantial challenges, i.e., the therapeutic domain calls for more treatment alternatives, while numerous promising pre-clinical drugs have failed in clinical trials. One of the reasons is the inadequacy of Cross-drug Response Evaluation (CRE) during the late stages of d

Christoforos Brozos, Jan G. Rittig, Sandip Bhattacharya, Elie Akanny

The critical micelle concentration (CMC) of surfactant molecules is an essential property for surfactant applications in industry. Recently, classical QSPR and Graph Neural Networks (GNNs), a deep learning technique, have been successfully applied to predict the CMC of surfactants at room temperature. However, these models have not yet considered the tempera

Lukas M. Rachbauer, Dorian Bouchet, Ulf Leonhardt, Stefan Rotter

The interaction of quantum light with matter is of great importance to a wide range of scientific disciplines, ranging from optomechanics to high precision measurements. A central issue we discuss here, is how to make optimal use of both the spatial and the quantum degrees of freedom of light for characterizing and manipulating arbitrary observable parameter

Yasuko Kawahata

This note considers an innovative interdisciplinary methodology that bridges the gap between the fundamental principles of quantum mechanics applied to the study of materials such as tellurium nanoparticles (TeNPs) and graphene and the complex dynamics of social systems. The basis for this approach lies in the metaphorical parallels drawn between the structu

Sunit Das, Suvankar Chakraverty, Amit Agarwal

Quantum oscillations offer a powerful probe for the geometry and topology of the Fermi surface in metals. Onsager's semiclassical quantization relation governs these periodic oscillations in 1/B, leading to a linear Landau fan diagram. However, higher-order magnetic susceptibility-induced corrections give rise to a generalized Onsager's relation, manifesting

Xuanyi Zhang, Athby Al-Tawhid, Padraic Schafer, Zhan Zhang

The interplay of lattice, electronic, and spin degrees of freedom at epitaxial complex oxide interfaces provides a route to tune their magnetic ground states. Unraveling the competing contributions is critical for tuning their functional properties. We investigate the relationship between magnetic ordering and magnetic anisotropy and the lattice symmetry, ox

Masood Aryapoor, Per Bäck

We introduce and study flipped non-associative polynomial rings. In particular, we show that all Cayley-Dickson algebras naturally appear as quotients of a certain type of such rings; this extends the classical construction of the complex numbers (and quaternions) as a quotient of a (skew) polynomial ring to the octonions, and beyond. We also extend some cla

David Sundström, Anton Björkman, Andreas Jakobsson, Filip Elvander

The ability to accurately estimate room impulse responses (RIRs) is integral to many applications of spatial audio processing. Regrettably, estimating the RIR using ambient signals, such as speech or music, remains a challenging problem due to, e.g., low signal-to-noise ratios, finite sample lengths, and poor spectral excitation. Commonly, in order to improv

Yin Mo, Lei Zhang, Yu-Ao Chen, Yingjian Liu

Quantum combs play a vital role in characterizing and transforming quantum processes, with wide-ranging applications in quantum information processing. However, obtaining the explicit quantum circuit for the desired quantum comb remains a challenging problem. We propose PQComb, a novel framework that employs parameterized quantum circuits (PQCs) or quantum n

Francesco Alessio, Michele Arzano

Infrared effects in the scattering of particles in gravity and electrodynamics entail an exchange of relativistic angular momentum between pairs of particles and the gauge field. Due to this exchange particles can carry an asymptotically non-vanishing "pairwise" boost-like angular momentum proportional to the product of their couplings to the field. At the q

Yuri Lima, Mauricio Poletti

Given a $C^{1+\beta}$ flow $\varphi$ with positive speed on a closed smooth Riemannian manifold, we code two homoclinically related $\varphi$-invariant probabilities by an irreducible countable topological Markov flow. As an application, we give proofs using symbolic dynamics of the theorem of Knieper on the uniqueness of the measure of maximal entropy and t

Niels Böttner, Joe Bentley, Roman Schnabel, Mikhail Korobko

The observation of gravitational waves from binary neutron star mergers offers insights into properties of extreme nuclear matter. However, their high-frequency signals in the kHz range are often masked by quantum noise of the laser light used. Here, we propose the "quantum expander with coherent feedback", a new detector design that features an additional o

Andrea Nava, Reinhold Egger, Fabian Hassler, Domenico Giuliano

Demonstrating the non-Abelian Ising anyon statistics of Majorana zero modes in a physical platform still represents a major open challenge in physics. We here show that the linear low-frequency charge conductance of a Majorana interferometer containing a floating superconducting island can reveal the topological spin of quantum edge vortices. The latter are

Xiaoyan Hu, Pengle Wen, Han Xiao, Wenjie Wang

A Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) scheme with simultaneous wireless information and power transfer (SWIPT) is proposed in this paper. Unlike existing MEC-WPT schemes that disregard the downlink period for returning computing results to the ground equipment (GEs), our proposed scheme actively considers and capitalizes on thi

Jan Głowacki

In this note, we provide some categorical perspectives on the relativization construction arising from quantum measurement theory in the presence of symmetries and occupying a central place in the operational approach to quantum reference frames. This construction provides, for any quantum system, a quantum channel from the system's algebra to the invariant

Joe Boninger

The perturbed Alexander invariant $\rho_1$, defined by Bar-Natan and van der Veen, is a powerful, easily computable polynomial knot invariant with deep connections to the Alexander and colored Jones polynomials. We study the behavior of $\rho_1$ for families of knots $\{K_t\}$ given by performing $t$ full twists on a set of coherently oriented strands in a k

Boujemaa Agrebaoui, Walid Mhiri

Let $A_n=\mathbb{C}[t_i^{\pm1},~1\leq i\leq n]$ be the algebra of Laurent polynomials in $n$-variables. Let $\mu=(\mu_1,\ldots,\mu_n)$ be a generic vector in $\mathbb{C}^n$ and $\Gamma_{\mu}=\{\mu\cdot\alpha,\alpha\in \mathbb{Z}^n\}$ where $\mu\cdot\alpha=\displaystyle\sum_{i=1}^n\mu_i\alpha_i$ for $\alpha=(\alpha_1,\ldots,\alpha_n)\in \mathbb{Z}^n$. Denote

Giacomo Sommani, Anna Franckowiak, Massimiliano Lincetto, Ralf-Jürgen Dettmar

In 2013, the IceCube collaboration announced the detection of a diffuse high-energy astrophysical neutrino flux. The origin of this flux is still largely unknown. The most significant individual source is the close-by Seyfert galaxy NGC 1068 at 4.2-sigma level with a soft spectral index. To identify sources based on their counterpart, IceCube releases realti

Zakhar Iakovlev, Alexey Chulkov, Nikita Golikov, Vyacheslav Lukianov

One common way to speed up the find operation within a set of text files involves a trigram index. This structure is merely a map from a trigram (sequence consisting of three characters) to a set of files which contain it. When searching for a pattern, potential file locations are identified by intersecting the sets related to the trigrams in the pattern. Th

Laura Mascarell, Ribin Chalumattu, Annette Rios

The advent of Large Language Models (LLMs) has led to remarkable progress on a wide range of natural language processing tasks. Despite the advances, these large-sized models still suffer from hallucinating information in their output, which poses a major issue in automatic text summarization, as we must guarantee that the generated summary is consistent wit

Pavel Šťovíček

A series of the form $\sum_{\ell=0}^{\infty}c(\kappa,\ell)\,M_{\kappa,\ell+1/2}(r_{0})W_{\kappa,\ell+1/2}(r)P_{\ell}(\cos(\gamma))$ is evaluated explicitly where $c(\kappa,\ell)$ are suitable complex coefficients, $M_{\kappa,\mu}$ and $W_{\kappa,\mu}$ are the Whittaker functions, $P_{\ell}$ are the Legendre polynomials, $r_{0}<r$ are radial variables, $\gamm

Eduard Looijenga

A topological theorem that appears in a paper by Deligne-Goncharov (and which they attribute to Beilinson) states the following. Let $(X,*)$ be a path connected pointed space with a reasonable topology and denote by $I$ the augmentation ideal of its fundamental group ring. Then for every field F and positive integer n, the space of F-valued linear forms on $

H. Jóźwiak, N. Stolarczyk, K. Stankiewicz, M. Zaborowski

The hydrogen deuteride (HD) molecule is an important deuterium tracer in astrophysical studies. The atmospheres of gas giants are dominated by molecular hydrogen, and simultaneous observation of H$_2$ and HD lines provides reliable information on the D/H ratios on these planets. The reference spectroscopic parameters play a crucial role in such studies. Unde

Julian Kaduk, Friederike Weilbeer, Heiko Hamann

In human-robot interaction (HRI), we study how humans interact with robots, but also the effects of robot behavior on human perception and well-being. Especially, the influence on humans by tandem robots with one human controlled and one autonomous robot or even semi-autonomous multi-robot systems is not yet fully understood. Here, we focus on a leader-follo

S. F. Poole, O. J. Amin, A. Solomon, L. X. Barton

The design, manufacture and characterisation of an inexpensive, temperature controlled vacuum chamber with millikelvin stability for electrical transport measurements at and near room temperature is reported. A commercially available Peltier device and high-precision temperature controller are used to actively heat and cool the sample space. The system was d

Tessa Han, Aounon Kumar, Chirag Agarwal, Himabindu Lakkaraju

As large language models (LLMs) develop increasingly sophisticated capabilities and find applications in medical settings, it becomes important to assess their medical safety due to their far-reaching implications for personal and public health, patient safety, and human rights. However, there is little to no understanding of the notion of medical safety in

Domingo García-Senz, Rubén M. Cabezón, Moritz Reichert, Axel S. Lechuga

We present here an extended nuclear network, with 90 species, designed for being coupled with hydrodynamic simulations, which includes neutrons, protons, electrons, positrons, and the corresponding neutrino and anti-neutrino emission. This network is also coupled with temperature, making it extremely robust and, together with its size, unique of its kind. Th

Sanjeet S. Kaintura, Swati Thakur, Sarabjot Kaur, Soni Devi

In this work, radioactivity investigations of soil samples from neutral and agricultural sites in Punjab/India have been carried out to study the impact of land use patterns. The analysis of radiological, mineralogical, physicochemical, and morphological attributes of soil samples has been performed employing state-of-the-art techniques. The mean activity co

Ruotong Zou, Shuyu Yin, Tianqi Song, Peinuan Qin

As virtual reality (VR) becomes more popular for intergenerational collaboration, there is still a significant gap in research regarding understanding the potential for reducing ageism. Our study aims to address this gap by analyzing ageism levels before and after VR escape room collaborative experiences. We recruited 28 participants to collaborate with an o

Yuta Ono, Till Aczel, Benjamin Estermann, Roger Wattenhofer

Active learning is a machine learning paradigm designed to optimize model performance in a setting where labeled data is expensive to acquire. In this work, we propose a novel active learning method called SUPClust that seeks to identify points at the decision boundary between classes. By targeting these points, SUPClust aims to gather information that is mo

Zhaoran Zhao, Peng Lu, Xujun Peng, Wenhao Guo

In the domain of image layout representation learning, the critical process of translating image layouts into succinct vector forms is increasingly significant across diverse applications, such as image retrieval, manipulation, and generation. Most approaches in this area heavily rely on costly labeled datasets and notably lack in adapting their modeling and

Igor V. Kolokolov, Vladimir V. Lebedev

We examine statistics of fluctuations of the laser beam intensity at its propagating in turbulent atmosphere. We are interested in relatively large propagating distances and the remote tail of the probability density function. The tail is determined by the stretched exponent, we find its index.

Arik Reuter, Anton Thielmann, Christoph Weisser, Benjamin Säfken

Topic modelling was mostly dominated by Bayesian graphical models during the last decade. With the rise of transformers in Natural Language Processing, however, several successful models that rely on straightforward clustering approaches in transformer-based embedding spaces have emerged and consolidated the notion of topics as clusters of embedding vectors.

Naifu Xue, Qi Mao, Zijian Wang, Yuan Zhang

Recent progress in generative compression technology has significantly improved the perceptual quality of compressed data. However, these advancements primarily focus on producing high-frequency details, often overlooking the ability of generative models to capture the prior distribution of image content, thus impeding further bitrate reduction in extreme co

Dmitry V. Averyanov, Ivan S. Sokolov, Alexander N. Taldenkov, Oleg E. Parfenov

Layered magnets are stand-out materials because of their range of functional properties that can be controlled by external stimuli. Regretfully, the class of such compounds is rather narrow, prompting the search for new members. Graphitization - stabilization of layered graphitic structures in the 2D limit - is being discussed for cubic materials. We suggest

Simon Riche

These notes present an application of the geometric Satake equivalence to the description of characters of indecomposable tilting modules for reductive algebraic groups over fields of positive characteristic, obtained in joint work with G. Williamson.

Khalid Hassouna, Vincent Boudry

In high-granularity calorimetry, as proposed for detectors at future Higgs factories, the requirements on electronics can have a strong impact on the design of the detector, especially via the cooling and acquisition systems. This project aims to establish the typical fluxes in the calorimeters: deposited energy, number of cells above the electronics thresho

Nuno Arala, Sam Chow

We establish expansion properties for suitably generic polynomials of degree $d$ in $d+1$ variables over finite fields. In particular, we show that if $P\in\mathbb{F}_q[x_1,\ldots,x_{d+1}]$ is a polynomial of degree $d$ coming from an explicit, Zariski dense set, and $X_1,\ldots,X_{d+1}\subseteq\mathbb{F}_q$ are suitably large, then $|P(X_1,\ldots,X_{d+1})|=

Silvia Celli, Giada Peron

Context. The recent discovery of several ultra high-energy gamma-ray emitters in our Galaxy represents a significant advancement towards the characterisation of its most powerful accelerators. Nonetheless, in order to unambiguously locate the regions where the highest energy particles are produced and understand the responsible physical mechanisms, detailed

John Day, Tushar Arora, Jirui Liu, Li Erran Li

As part of human core knowledge, the representation of objects is the building block of mental representation that supports high-level concepts and symbolic reasoning. While humans develop the ability of perceiving objects situated in 3D environments without supervision, models that learn the same set of abilities with similar constraints faced by human infa

Cun Xue, Qing-Yu Wang, Han-Xi Ren, An He

The macroscopic electromagnetic properties of type II superconductors are primarily influenced by the behavior of microscopic superconducting flux quantum units. Time-dependent Ginzburg-Landau (TDGL) equations provide an elegant and powerful tool for describing and examining both the statics and dynamics of these superconducting entities. They have been inst

Paul Doucet, Benjamin Estermann, Till Aczel, Roger Wattenhofer

This study addresses the integration of diversity-based and uncertainty-based sampling strategies in active learning, particularly within the context of self-supervised pre-trained models. We introduce a straightforward heuristic called TCM that mitigates the cold start problem while maintaining strong performance across various data levels. By initially app

Alexis Linard, Anna Gautier, Daniel Duberg, Jana Tumova

In environments like offices, the duration of a robot's navigation between two locations may vary over time. For instance, reaching a kitchen may take more time during lunchtime since the corridors are crowded with people heading the same way. In this work, we address the problem of routing in such environments with tasks expressed in Metric Interval Tempora

Viacheslav Meshchaninov, Pavel Strashnov, Andrey Shevtsov, Fedor Nikolaev

Protein sequence design has seen significant advances through discrete diffusion and autoregressive approaches, yet the potential of continuous diffusion remains underexplored. Here, we present DiMA, a latent diffusion framework that operates on protein language model representations. Through systematic exploration of architectural choices and diffusion comp

Riccardo Colini-Baldeschi, Sophie Klumper, Guido Schäfer, Artem Tsikiridis

The realm of algorithms with predictions has led to the development of several new algorithms that leverage (potentially erroneous) predictions to enhance their performance guarantees. The challenge is to devise algorithms that achieve optimal approximation guarantees as the prediction quality varies from perfect (consistency) to imperfect (robustness). This

Maxim Buzdalov, Pavel Martynov, Sergey Pankratov, Vitaly Aksenov

Demand-aware communication networks are networks whose topology is optimized toward the traffic they need to serve. These networks have recently been enabled by novel optical communication technologies and are investigated intensively in the context of datacenters. In this work, we consider networks with one of the most common topologies~ -- a binary tree. W

Chiara Caprini, Ryusuke Jinno, Marek Lewicki, Eric Madge

We develop a tool for the analysis of stochastic gravitational wave backgrounds from cosmological first-order phase transitions with LISA: we initiate a template databank for these signals, prototype their searches, and forecast their reconstruction. The templates encompass the gravitational wave signals sourced by bubble collisions, sound waves and turbulen

Sarah Leyder, Jakob Raymaekers, Peter J. Rousseeuw

Distance covariance is a popular measure of dependence between random variables. It has some robustness properties, but not all. We prove that the influence function of the usual distance covariance is bounded, but that its breakdown value is zero. Moreover, it has an unbounded sensitivity function, converging to the bounded influence function for increasing

Gyusam Chang, Wonseok Roh, Sujin Jang, Dongwook Lee

Recent LiDAR-based 3D Object Detection (3DOD) methods show promising results, but they often do not generalize well to target domains outside the source (or training) data distribution. To reduce such domain gaps and thus to make 3DOD models more generalizable, we introduce a novel unsupervised domain adaptation (UDA) method, called CMDA, which (i) leverages

Casper van Elteren, Vítor V. Vasconcelos, Mike Lees

The interplay between criminal organizations and law enforcement disruption strategies is crucial in criminology. Criminal enterprises, like legitimate businesses, balance visibility and security to thrive. This study uses evolutionary game theory to analyze criminal networks' dynamics, resilience to interventions, and responses to external conditions. We fi

Emanuele Vivoli, Joan Lafuente Baeza, Ernest Valveny Llobet, Dimosthenis Karatzas

This work explores a closure task in comics, a medium where visual and textual elements are intricately intertwined. Specifically, Text-cloze refers to the task of selecting the correct text to use in a comic panel, given its neighboring panels. Traditional methods based on recurrent neural networks have struggled with this task due to limited OCR accuracy a

Andreas Chatziafratis, Telemachos Hatziafratis

In this note, we prove that the Fourier-Laplace transform of the typical function (i.e., generic in the sense of Baire category theorem) in the Schwartz class of the half-line, being analytic in the lower half of the complex plane, has natural boundary the axis of the real numbers. We also provide variations and generalizations.

Peng Zhao

In state-of-the-art superconducting quantum processors, each qubit is controlled by at least one control line that delivers control pulses generated at room temperature to qubits operating at millikelvin temperatures. While this strategy has been successfully applied to control hundreds of qubits, it is unlikely to be scalable to control thousands of qubits,

Xue-Jia Yu, Wei-Lin Li

By constructing an exactly solvable spin model, we investigate the critical behaviors of transverse field Ising chains interpolated with cluster interactions, which exhibit various types of topologically distinct Ising critical points. Using fidelity susceptibility as an indicator, we establish the global phase diagram, including ferromagnetic, trivial param

Zequn Zeng, Yan Xie, Hao Zhang, Chiyu Chen

Zero-shot image captioning (IC) without well-paired image-text data can be divided into two categories, training-free and text-only-training. Generally, these two types of methods realize zero-shot IC by integrating pretrained vision-language models like CLIP for image-text similarity evaluation and a pre-trained language model (LM) for caption generation. T

Yuling Wang, Xiao Wang, Xiangzhou Huang, Yanhua Yu

Graph neural network (GNN) based recommender systems have become one of the mainstream trends due to the powerful learning ability from user behavior data. Understanding the user intents from behavior data is the key to recommender systems, which poses two basic requirements for GNN-based recommender systems. One is how to learn complex and diverse intents e

Cong Pan, Kaiyuan Zhang, Shuangquan Zhang

Based on the point-coupling density functional, the time-odd deformed relativistic Hartree-Bogoliubov theory in continuum (TODRHBc) is developed. Then the effects of nuclear magnetism on halo phenomenon are explored by taking the experimentally suggested deformed halo nucleus $^{31}$Ne as an example. For $^{31}$Ne, nuclear magnetism contributes 0.09 MeV to t

Pamina Georgiou, Márton Hajdu, Laura Kovács

We present a first-order theorem proving framework for establishing the correctness of functional programs implementing sorting algorithms with recursive data structures. We formalize the semantics of recursive programs in many-sorted first-order logic and integrate sortedness/permutation properties within our first-order formalization. Rather than focusing

Helena M. Richie, Evan E. Schneider, Matthew W. Abruzzo, Paul Torrey

We present a suite of high-resolution numerical simulations to study the evolution and survival of dust in hot galactic winds. We implement a novel dust framework in the Cholla hydrodynamics code and use wind tunnel simulations of cool, dusty clouds to understand how thermal sputtering affects the dust content of galactic winds. Our simulations illustrate ho

LHCb collaboration, R. Aaij, A. S. W. Abdelmotteleb, C. Abellan Beteta

The resonant structure of the radiative decay $\Lambda_b^0\to pK^-\gamma$ in the region of proton-kaon invariant-mass up to 2.5 GeV$/c^2$ is studied using proton-proton collision data recorded at centre-of-mass energies of 7, 8, and 13 TeV collected with the LHCb detector, corresponding to a total integrated luminosity of 9 fb$^{-1}$. Results are given in te

Stephen Hudson, Jeffrey Larson, John-Luke Navarro, Stefan M. Wild

libEnsemble is a Python-based toolkit for running dynamic ensembles, developed as part of the DOE Exascale Computing Project. The toolkit utilizes a unique generator--simulator--allocator paradigm, where generators produce input for simulators, simulators evaluate those inputs, and allocators decide whether and when a simulator or generator should be called.

Chryssis Georgiou, Nicolas Nicolaou, Andria Trigeorgi

Ares is a modular framework, designed to implement dynamic, reconfigurable, fault-tolerant, read/write and strongly consistent distributed shared memory objects. Recent enhancements of the framework have realized the efficient implementation of large objects, by introducing versioning and data striping techniques. In this work, we identify performance bottle