March 2024 arXiv papers — page 24

Amin Hosseini, Antonio M. Peralta, Shanshan Su

Let $\mathcal{M}$ be a Banach bimodule over an associative Banach algebra $\mathcal{A}$, and let $F: \mathcal{A}\to \mathcal{M}$ be a linear mapping. Three main uses of the term \emph{generalized derivation} are identified in the available literature, namely, ($\checkmark$) $F$ is a generalized derivation of the first type if there exists a derivation $ d :

J. J. Relancio, L. Santamaría-Sanz

Doubly special relativity (DSR) is usually regarded as a low-energy limit of a quantum gravity theory with testable predictions. On the other hand, non-local quantum field theories have been presented as a solution to the inconsistencies arising when quantizing gravity. Here, we present a new formulation of quantum field theories in DSR with non-local behavi

Yukyung Lee, Joonghoon Kim, Jaehee Kim, Hyowon Cho

Existing LLM-as-a-Judge approaches for evaluating text generation suffer from rating inconsistencies, with low agreement and high rating variance across different evaluator models. We attribute this to subjective evaluation criteria combined with Likert scale scoring in existing protocols. To address this issue, we introduce CheckEval, a checklist-based eval

Bo Yang, Jianke Yang

We report new rogue wave patterns whose wave crests form closed or open curves in the spatial plane, which we call rogue curves, in the Davey-Stewartson I equation. These rogue curves come in various striking shapes, such as rings, double rings, and many others. They emerge from a uniform background (possibly with a few lumps on it), reach high amplitude in

Liang Lu, Jingzhi Wang, David R. Mortensen

Protolanguage reconstruction is central to historical linguistics. The comparative method, one of the most influential theoretical and methodological frameworks in the history of the language sciences, allows linguists to infer protoforms (reconstructed ancestral words) from their reflexes (related modern words) based on the assumption of regular sound chang

Akel Hashim, Ming Yuan, Pranav Gokhale, Larry Chen

Quantum entanglement is one of the primary features which distinguishes quantum computers from classical computers. In gate-based quantum computing, the creation of entangled states or the distribution of entanglement across a quantum processor often requires circuit depths which grow with the number of entangled qubits. However, in teleportation-based quant

Daniel Reem, Yair Censor

In the classical best approximation pair (BAP) problem, one is given two nonempty, closed, convex and disjoint subsets in a finite- or an infinite-dimensional Hilbert space, and the goal is to find a pair of points, each from each subset, which realizes the distance between the subsets. We discuss the problem in more general normed spaces and with possibly n

Rustam Mussabayev, Ravil Mussabayev

This paper introduces a novel K-means clustering algorithm, an advancement on the conventional Big-means methodology. The proposed method efficiently integrates parallel processing, stochastic sampling, and competitive optimization to create a scalable variant designed for big data applications. It addresses scalability and computation time challenges typica

Elliot Chane-Sane, Pierre-Alexandre Leziart, Thomas Flayols, Olivier Stasse

Deep Reinforcement Learning (RL) has demonstrated impressive results in solving complex robotic tasks such as quadruped locomotion. Yet, current solvers fail to produce efficient policies respecting hard constraints. In this work, we advocate for integrating constraints into robot learning and present Constraints as Terminations (CaT), a novel constrained RL

Jesse Reimann, Nico Mansion, James Haydon, Benjamin Bray

As the development of autonomous vehicles progresses, efficient safety assurance methods become increasingly necessary. Safety assurance methods such as monitoring and scenario-based testing call for formalisation of driving scenarios. In this paper, we develop a temporal-logic formalisation of an important class of critical scenarios in the ISO standard 345

Fei Ren, Kay Rülling

Given an effective Cartier divisor D with simple normal crossing support on a smooth and proper scheme X over a perfect field of positive characteristic p, there is a natural notion of de Rham-Witt sheaves on X with zeros along D. We show that these sheaves correspond under Grothendieck duality for coherent sheaves to de Rham-Witt sheaves on X with modulus (

Weidong Xie, Lun Luo, Nanfei Ye, Yi Ren

Place recognition is an important task for robots and autonomous cars to localize themselves and close loops in pre-built maps. While single-modal sensor-based methods have shown satisfactory performance, cross-modal place recognition that retrieving images from a point-cloud database remains a challenging problem. Current cross-modal methods transform image

Ningna Wang, Hui Huang, Shibo Song, Bin Wang

We present a novel topology-preserving 3D medial axis computation framework based on volumetric restricted power diagram (RPD), while preserving the medial features and geometric convergence simultaneously, for both 3D CAD and organic shapes. The volumetric RPD discretizes the input 3D volume into sub-regions given a set of medial spheres. With this intermed

Yike Wu, Jiatao Zhang, Nan Hu, LanLing Tang

In the realm of data-driven AI technology, the application of open-source large language models (LLMs) in robotic task planning represents a significant milestone. Recent robotic task planning methods based on open-source LLMs typically leverage vast task planning datasets to enhance models' planning abilities. While these methods show promise, they struggle

Giovanni Montani, Nakia Carlevaro

We present a new perspective on the symmetries that govern the formation of large-scale structures across the Universe, particularly focusing on the transition from the seeds of galaxy clusters to the seeds of galaxies themselves. We address two main features of cosmological fluid dynamics pertaining to both the linear and non-linear regimes. The linear dyna

Sam P. Fisher

We prove that if $G$ is a finitely generated RFRS group of cohomological dimension $2$, then $G$ is virtually free-by-cyclic if and only if $b_2^{(2)}(G) = 0$. This answers a question of Wise and generalises and gives a new proof of a recent theorem of Kielak and Linton, where the same result is obtained under the additional hypotheses that $G$ is virtually

Stuart James Hall

Following the ideas of Gasqui and Goldschmidt, we give an explicit description of the infinitesimal Einstein deformations admitted by the Fubini--Study metric on complex Grassmannians $G_{m}(\mathbb{C}^{n+m})$ with $m,n\geq 2$. The deformations were first shown to exist by Koiso in the 1980s but it has remained an open question as to whether they can be inte

Guglielmo Gallone, Francesco Iodice, Alberto Presta, Davide Tore

Aims. To develop a deep-learning based system for recognition of subclinical atherosclerosis on a plain frontal chest x-ray. Methods and Results. A deep-learning algorithm to predict coronary artery calcium (CAC) score (the AI-CAC model) was developed on 460 chest x-ray (80% training cohort, 20% internal validation cohort) of primary prevention patients (58.

Elia Cunegatti, Leonardo Lucio Custode, Giovanni Iacca

The Influence Maximization (IM) problem seeks to discover the set of nodes in a graph that can spread the information propagation at most. This problem is known to be NP-hard, and it is usually studied by maximizing the influence (spread) and, optionally, optimizing a second objective, such as minimizing the seed set size or maximizing the influence fairness

Lorenzo Bazzanini, Lisa Ferro, Cristiano Guidorzi, Giuseppe Angora

Context. The complexity and variety exhibited by the light curves of long gamma-ray bursts (GRBs) enclose a wealth of information that still awaits being fully deciphered. Despite the tremendous advance in the knowledge of the energetics, structure, and composition of the relativistic jet that results from the core collapse of the progenitor star, the nature

Chung-Yun Hsieh, Manuel Gessner

In developing quantum science and technologies, it is essential to demonstrate so-called quantum advantages, which are performances that can be achieved only with the assistance of quantum resources. Most of the time, different quantum features lead to different advantages. Interestingly, there are certain classes of tasks where quantum advantages are achiev

A. Campitelli, L. Mastrototaro

In this paper, we explore the effects of General Relativity modification on the Mass-Radius relations of Neutron Stars induced by the presence of the Quintessence field. We consider, in particular, the Kiselev model, according to which the Quintessence field, being present in the entire Universe, might also be present around massive objects. Considering the

Chengcheng Xin, Zoltan Haiman

The Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) will catalogue the light-curves of up to 100 million quasars. Among these there can be up to approximately 100 ultra-compact massive black hole (MBH) binaries, which 5-15 years later can be detected in gravitational waves (GWs) by the Laser Interferometer Space Antenna (LISA). Here we ass

Jitendra Kethepalli, Manas Kulkarni, Anupam Kundu, Satya N. Majumdar

We investigate the full counting statistics (FCS) of a harmonically confined 1d short-range Riesz gas consisting of $N$ particles in equilibrium at finite temperature. The particles interact with each other through a repulsive power-law interaction with an exponent $k>1$ which includes the Calogero-Moser model for $k=2$. We examine the probability distributi

Emma R. Cobian, Jonathan D. Hauenstein, Charles W. Wampler

The field of numerical algebraic geometry consists of algorithms for numerically solving systems of polynomial equations. When the system is exact, such as having rational coefficients, the solution set is well-defined. However, for a member of a parameterized family of polynomial systems where the parameter values may be measured with imprecision or arise f

Li Jiale

A complex scalar k is said to be an extended eigenvalue of a bounded linear operator A on a complex Hilbert space if there is a nonzero operator X such that AX=kXA. There are some solutions to the problem of computing the extended eigenvalues for composition operators induced on the Fock space and Bergman space by linear fractional transformationsof the comp

S. D. Seddon, C. R. S. Haines, T. P. A. Hase, M. R. Lees

Pyrrhotite, Fe$_7$S$_8$, provides an example of exceptionally strong magnetoelastic coupling through pinning of ferromagnetic domains by ferroelastic twins. Using direct imaging of both magnetic and ferroelastic domains by magnetic force microscopy (MFM), the mechanism by which this coupling controls local magnetic switching behaviour of regions on the pyrrh

Yangruibo Ding, Marcus J. Min, Gail Kaiser, Baishakhi Ray

Pre-trained code language models have achieved promising performance in code generation and improved the programming efficiency of human developers. However, their self-refinement capability is typically overlooked by the existing evaluations of code LMs, which focus only on the accuracy of the one-time prediction. For the cases when code LMs fail to impleme

Pablo Fondo-Ferreiro, Saber Mhiri, Cristina López-Bravo, Francisco Javier González-Castaño

Global optimization of access point (AP) assignment to user terminals requires efficient monitoring of user behavior, fast decision algorithms, efficient control signaling, and fast AP reassignment mechanisms. In this scenario, software defined networking (SDN) technology may be suitable for network monitoring, signaling, and control. We recently proposed em

Guilherme Mazanti, Thibault Moquet, Laurent Pfeiffer

An extension of the Frank-Wolfe Algorithm (FWA), also known as Conditional Gradient algorithm, is proposed. In its standard form, the FWA allows to solve constrained optimization problems involving $\beta$-smooth cost functions, calling at each iteration a Linear Minimization Oracle. More specifically, the oracle solves a problem obtained by linearization of

Lin Nan, Andrea Mancini, Thomas Weber, Geok Leng Seah

Squeezing light into subwavelength dimensions is vital for on-chip integration of photonic technologies. One approach to overcome the diffraction limit is coupling light to material excitations, leading to polariton states. Here, we showcase how low-loss mid-infrared surface phonon polaritons enable metasurfaces supporting quasi-bound states in the continuum

Shawn Im, Yixuan Li

Aligning large language models (LLMs) with human intentions has become a critical task for safely deploying models in real-world systems. While existing alignment approaches have seen empirical success, theoretically understanding how these methods affect model behavior remains an open question. Our work provides an initial attempt to theoretically analyze t

Valery E. Lyubovitskij, Werner Vogelsang, Fabian Wunder, Alexey S. Zhevlakov

We calculate the perturbative $T$-odd contributions to the lepton angular distribution in the Drell-Yan process. Using collinear factorization, we work at the first order in QCD perturbation theory where these contributions appear, ${\cal O}(\alpha_s^2)$, and address both $W^\pm$ and $\gamma/Z^0$ boson exchange. A major focus of our calculation is on the reg

Swarnim Shirke, Bikram Keshari Pradhan, Debarati Chatterjee, Laura Sagunski

The effect of dark matter (DM) on $f$-mode oscillations in DM admixed neutron stars (NSs) is investigated in a comprehensive analysis with particular attention to the role of the nuclear equation of state. Hadronic matter is modeled by the relativistic mean field model and the DM model is based on the neutron decay anomaly. The non-radial $f$-mode oscillatio

Olov Holmer, Mattias Krysander, Erik Frisk

Accurate predictions of when a component will fail are crucial when planning maintenance, and by modeling the distribution of these failure times, survival models have shown to be particularly useful in this context. The presented methodology is based on conventional neural network-based survival models that are trained using data that is continuously gather

Jean Van Schaftingen

The compact Riemannian manifolds $\mathcal{M}$ and $\mathcal{N}$ for which the trace operator from the first-order Sobolev space of mappings $\smash{\dot{W}}^{1, p} (\mathcal{M}, \mathcal{N})$ to the fractional Sobolev-Slobodecki\u{\i} space $\smash{\smash{\dot{W}}^{1 - 1/p, p}} (\partial \mathcal{M}, \mathcal{N})$ is surjective when $1 < p < \dim \mathcal{M

Matthew Willetts, Christian Harrington

Dynamic AMM pools, as found in Temporal Function Market Making, rebalance their holdings to a new desired ratio (e.g. moving from being 50-50 between two assets to being 90-10 in favour of one of them) by introducing an arbitrage opportunity that disappears when their holdings are in line with their target. Structuring this arbitrage opportunity reduces to t

Indranil Biswas, Manish Kumar, A. J. Parameswaran

We give a characterization of genuinely ramified maps of formal orbifolds in the Tannakian framework. In particular we show that a morphism is genuinely ramified if and only if the pullback of every stable bundle remains stable in the orbifold category. We also give some other characterizations of genuine ramification. This generalizes the results of [BKP1]

Hamidreza Eivazi, Stefan Wittek, Andreas Rausch

Operator learning provides methods to approximate mappings between infinite-dimensional function spaces. Deep operator networks (DeepONets) are a notable architecture in this field. Recently, an extension of DeepONet based on model reduction and neural networks, proper orthogonal decomposition (POD)-DeepONet, has been able to outperform other architectures i

Rafic Nader, Florent Autrusseau, Vincent L'Allinec, Romain Bourcier

We hereby present a full synthetic model, able to mimic the various constituents of the cerebral vascular tree: the cerebral arteries, the bifurcations and the intracranial aneurysms. By building this model, our goal was to provide a substantial dataset of brain arteries which could be used by a 3D Convolutional Neural Network (CNN) to either segment or dete

Yudai Suzuki, Rei Sakuma, Hideaki Kawaguchi

Feature selection plays an essential role in improving the predictive performance and interpretability of trained models in classical machine learning. On the other hand, the usability of conventional feature selection could be limited for quantum machine learning tasks; the technique might not provide a clear interpretation on embedding quantum circuits for

Neda Darvishi, Yining Wang, Jiang-Hao Yu

Building on our automated framework that uses ring diagrams for classifying CP basis invariants [Phys. Rev. D 108, 115030 (2023)], this paper broadens the application of the methodology with more extensive examples and a wider scope of theoretical frameworks. Here, we showcase its versatility through detailed analyses of specific operators in the Standard Mo

Dennis Gross, Helge Spieker, Arnaud Gotlieb, Ricardo Knoblauch

This research presents a method that utilizes explainability techniques to amplify the performance of machine learning (ML) models in forecasting the quality of milling processes, as demonstrated in this paper through a manufacturing use case. The methodology entails the initial training of ML models, followed by a fine-tuning phase where irrelevant features

Florin-Alexandru Vasluianu, Tim Seizinger, Zongwei Wu, Rakesh Ranjan

Lighting normalization is a crucial but underexplored restoration task with broad applications. However, existing works often simplify this task within the context of shadow removal, limiting the light sources to one and oversimplifying the scene, thus excluding complex self-shadows and restricting surface classes to smooth ones. Although promising, such sim

Avaljot Singh, Yasmin Sarita, Charith Mendis, Gagandeep Singh

We develop a declarative DSL - \cf - that can be used to specify Abstract Interpretation-based DNN certifiers. In \cf, programmers can easily define various existing and new abstract domains and transformers, all within just a few 10s of Lines of Code as opposed to 1000s of LOCs of existing libraries. We provide lightweight automatic verification, which can

W. Erick Rogers, Laurie T. Fialkowski, Daniel J. Brooker, Gleb Panteleev

This study is concerned with prediction of the "wind noise" component of ambient noise (AN) in the ocean. It builds on the seminal paper by Felizardo and Melville (1995), in which the authors quantified the correlation between AN and individual wind/wave parameters. Acoustic data are obtained from hydrophones at six diverse locations, and wind/wave parameter

Hao Chang, Jinxin Hu, Lewis Topley

Let $Y_2$ be the Yangian associated to the general linear Lie algebra $\mathfrak{gl}_2$, defined over an algebraically closed field $\mathbbm{k}$ of characteristic $p > 0$. In this paper, we study the representation theory of the restricted Yangian $Y^{[p]}_2$. This leads to a description of the representations of $\mathfrak{gl}_{2n}$, whose $p$-character is

Allana G. Iwanicki, Brandon Wilfong, Eli Zoghlin, Wyatt Bunstine

Innovative synthetic approaches can yield new phases containing novel structural and magnetic motifs. In this work, we show the synthesis and magnetic characterization of three new and one previously reported layered phase in the K-Cu-Te-O(H) phase space using a tunable hydroflux technique. The hydroflux, with a roughly equal molar ratio of water and alkali

Dennis Gross, Helge Spieker

We introduce a method to verify stochastic reinforcement learning (RL) policies. This approach is compatible with any RL algorithm as long as the algorithm and its corresponding environment collectively adhere to the Markov property. In this setting, the future state of the environment should depend solely on its current state and the action executed, indepe

Laura Río-Martín, Firas Dhaouadi, Michael Dumbser

We present a new second order accurate structure-preserving finite volume scheme for the solution of the compressible barotropic two-phase model of Romenski et. al in multiple space dimensions. The governing equations fall into the wider class of symmetric hyperbolic and thermodynamically compatible (SHTC) systems and consist of a set of first-order hyperbol

Hubert Garavel, Bas Luttik

We revisit the IEEE 1394 high-performance serial bus ("FireWire"), which became a success story in formal methods after three PhD students, by using process algebra and model checking, detected a deadlock error in this IEEE standard. We present four formal models for the asynchronous mode of the Link Layer of IEEE 1394: the original model in muCRL, a simplif

Kevin H. J. Jilissen, Peter Dieleman, Jan Friso Groote

Rijkswaterstaat, the National Dutch body responsible for infrastructure, recognised the importance of formal modelling and set up a program to model the control of road tunnels. This is done to improve the standardisation of tunnel control and make communication with suppliers smoother. A subset of SysML is used to formulate the models, which are substantial

Ehsan Latif, Ramviyas Parasuraman, Xiaoming Zhai

Robot systems in education can leverage Large language models' (LLMs) natural language understanding capabilities to provide assistance and facilitate learning. This paper proposes a multimodal interactive robot (PhysicsAssistant) built on YOLOv8 object detection, cameras, speech recognition, and chatbot using LLM to provide assistance to students' physics l

Philippe Ledent, Radu Mateescu, Wendelin Serwe

Ensuring resource isolation at the hardware level is a crucial step towards more security inside the Internet of Things. Even though there is still no generally accepted technique to generate appropriate tests, it became clear that tests should be generated at the system level. In this paper, we illustrate the modeling aspects in test generation for resource

Sami Wirtensohn, Peng Qi, Christian David, Julia Herzen

The dark-field signal uncovers details beyond conventional X-ray attenuation contrast, which is especially valuable for material sciences. In particular, dark-field techniques are able to reveal structures beyond the spatial resolution of a setup. However, its implementation is yet limited to the micrometer regime. Therefore, we propose a technique to extend

Guillaume Chapuy

We consider a Tamari interval of size $n$ (i.e., a pair of Dyck paths which are comparable for the Tamari relation) chosen uniformly at random. We show that the height of a uniformly chosen vertex on the upper or lower path scales as $n^{3/4}$, and has an explicit limit law. By the Bernardi-Bonichon bijection, this result also describes the height of points

Matthieu Cadiot

In this manuscript, we present a method to prove constructively the existence and spectral stability of solitary waves in both the Whitham and the capillary-gravity Whitham equations. By employing Fourier series analysis and computer-aided techniques, we successfully approximate the Fourier multiplier operator in this equation, allowing the construction of a

Yasin Ibrahim, Hermione Warr, Konstantinos Kamnitsas

Developing models that are capable of answering questions of the form "How would x change if y had been z?'" is fundamental to advancing medical image analysis. Training causal generative models that address such counterfactual questions, though, currently requires that all relevant variables have been observed and that the corresponding labels are available

Cameron Foreman, Richie Yeung, Florian J. Curchod

Random number generators (RNGs) are notoriously challenging to build and test, especially for cryptographic applications. While statistical tests cannot definitively guarantee an RNG's output quality, they are a powerful verification tool and the only universally applicable testing method. In this work, we design, implement, and present various post-processi

Xintong Wang, Jingheng Pan, Liang Ding, Chris Biemann

Large Vision-Language Models (LVLMs) are increasingly adept at generating contextually detailed and coherent responses from visual inputs. However, their application in multimodal decision-making and open-ended generation is hindered by a notable rate of hallucinations, where generated text inaccurately represents the visual contents. To address this issue,

Bowen Qu, Haohui Li, Wei Gao

AI-Generated Images (AGIs) have inherent multimodal nature. Unlike traditional image quality assessment (IQA) on natural scenarios, AGIs quality assessment (AGIQA) takes the correspondence of image and its textual prompt into consideration. This is coupled in the ground truth score, which confuses the unimodal IQA methods. To solve this problem, we introduce

Sylvain E. Cappell, Laurenţiu Maxim, Jörg Schürmann, Julius L. Shaneson

We survey recent developments in the study of torus equivariant motivic Chern and Hirzebruch characteristic classes of projective toric varieties, with applications to calculating equivariant Hirzebruch genera of torus-invariant Cartier divisors in terms of torus characters, as well as to general Euler-Maclaurin type formulae for full-dimensional simple latt

Chathuri Weragama, Joonas Kokkoniemi, Mar Francis De Guzman, Katsuyuki Haneda

Millimeter-wave (mmWave) and D Band (110--170~GHz) frequencies are poised to play a pivotal role in the advancement of sixth-generation (6G) systems and beyond, owing to their ability to enhance performance metrics such as capacity, ultra-low latency, and spectral efficiency. This paper concentrates on deriving statistical insights into power, delay, and the

Maxime Dumont, Carlos M. Correia, Jean-François Sauvage, Noah Schwartz

Capturing high resolution imagery of the Earth's surface often calls for a telescope of considerable size, even from Low Earth Orbits (LEO). A large aperture often requires large and expensive platforms. For instance, achieving a resolution of 1m at visible wavelengths from LEO typically requires an aperture diameter of at least 30cm. Additionally, ensuring

Camille Billouard, Dawa Derksen, Emmanuelle Sarrazin, Bruno Vallet

Current stereo-vision pipelines produce high accuracy 3D reconstruction when using multiple pairs or triplets of satellite images. However, these pipelines are sensitive to the changes between images that can occur as a result of multi-date acquisitions. Such variations are mainly due to variable shadows, reflexions and transient objects (cars, vegetation).

Zhaohui Yang, Kshitij Jerath

Recent endeavors aimed at forecasting future traffic flow states through deep learning encounter various challenges and yield diverse outcomes. A notable obstacle arises from the substantial data requirements of deep learning models, a resource often scarce in traffic flow systems. Despite the abundance of domain knowledge concerning traffic flow dynamics, p

Zheng-Cheng Liang, Zhi-Yuan Li, En-Kun Li, Jian-dong Zhang

Weak-signal limit is often used in estimating stochastic gravitational-wave background (SGWB) intensities. This approximation fails and the signal-to-noise ratio (SNR) can be much weaker when background signals are loud compared to the detector noise. In this work, we highlight this limitation for the SGWB detection using space-borne detector networks. For t

Hanxiao Zhang, Yifan Zhou, Guo-Hua Wang, Jianxin Wu

Few-shot model compression aims to compress a large model into a more compact one with only a tiny training set (even without labels). Block-level pruning has recently emerged as a leading technique in achieving high accuracy and low latency in few-shot CNN compression. But, few-shot compression for Vision Transformers (ViT) remains largely unexplored, which

Juho Bae, Ji Hoon Bai, Byung-Yoon Lee, Jun-Yong Lee

This paper presents the reachability analysis of curves in $\mathbb{R}^3$ with a prescribed curvature bound. Based on Pontryagin Maximum Principle, we leverage the existing knowledge on the structure of solutions to minimum-time problems, or Markov-Dubins problem, to reachability considerations. Based on this development, two types of reachability are discus

J. Haidenbauer, U. -G. Meißner

Near-threshold $p\bar\Lambda$ mass spectra for the reactions $e^+e^- \to K^- p\bar\Lambda$ and $J/\psi \to K^- p\bar\Lambda$ are investigated with an emphasis on the role played by the interaction in the $p\bar\Lambda$ system. As guideline for the $p\bar\Lambda$ interaction a variety of $\Lambda\bar\Lambda$ potential models is considered that have been estab

Jannis Chemseddine, Paul Hagemann, Gabriele Steidl, Christian Wald

In inverse problems, many conditional generative models approximate the posterior measure by minimizing a distance between the joint measure and its learned approximation. While this approach also controls the distance between the posterior measures in the case of the Kullback--Leibler divergence, this is in general not hold true for the Wasserstein distance

Barbara Kaltenbacher

This paper is devoted to proving convergence rates of variational and iterative regularization methods under variational source conditions VSCs for inverse problems whose linearization satisfies a range invariance condition. In order to achieve this, often an appropriate relaxation of the problem needs to be found that is usually based on an augmentation of

Sharif Azem, David Scheunert, Mengguang Li, Jonas Gehrunger

The advent of unmanned aerial vehicles (UAVs) has improved a variety of fields by providing a versatile, cost-effective and accessible platform for implementing state-of-the-art algorithms. To accomplish a broader range of tasks, there is a growing need for enhanced on-board computing to cope with increasing complexity and dynamic environmental conditions. R

Zhe Zhou, Yiqi Chen, Tao Zhang, Yang Wang

The Compute Express Link (CXL) interconnect makes it feasible to integrate diverse types of memory into servers via its byte-addressable SerDes links. Considering the various access latency, harnessing the full potential of CXL-based heterogeneous memory systems requires efficient memory tiering. However, prior work can hardly make a fundamental progress owi

Joshua Althüser, Götz S. Uhrig

We investigate the superconducting (SC), charge-density wave (CDW), and antiferromagnetic (AFM) phases in the extended Hubbard model at zero temperature and half-filling. We employ the iterated equations of motion approach to compute the two-particle Green's functions and their spectral densities. This renders a comprehensive analysis of the behavior of coll

Nicholas Miesch, Edward Shuryak, Ismail Zahed

We discuss the central and, mostly, spin-dependent potentials in heavy quarkonia $\bar b b, \bar c c$, with two goals in mind. The first is phenomenological: using the splitting between the 1S and 2S pairs, as well as the 1P and 2P quartet masses, we obtain very accurate values of all matrix elements of the spin-depepdent potentials. The second is theoretica

Huanran Li, Manh Nguyen, Daniel Pimentel-Alarcón

Contrastive learning has emerged as a powerful method in deep learning, excelling at learning effective representations through contrasting samples from different distributions. However, neural collapse, where embeddings converge into a lower-dimensional space, poses a significant challenge, especially in semi-supervised and self-supervised setups. In this p

Ursula Hamenstädt, Sebastian Hensel

The free splitting graph of a free group $F_n$ with $n\geq 2$ generators is a hyperbolic ${\rm Out}(F_n)$-graph which has a geometric realization as a sphere graph in the connected sum of $n$ copies of $S^1\times S^2$. We use this realization to construct submanifold projections of the free splitting graph into the free splitting graphs of proper free factor

Giovanni Puccetti, Maria Cassese, Andrea Esuli

While Italian is a high-resource language, there are few Italian-native benchmarks to evaluate generative Large Language Models (LLMs) in this language. This work presents three new benchmarks: Invalsi MATE to evaluate models performance on mathematical understanding in Italian, Invalsi ITA to evaluate language understanding in Italian and Olimpiadi MATE for

Ding-Kun Lian, Qi-Nan Wang, Xu-Liang Chen, Peng-Fei Yang

We revisit the masses of heavy quarkonium double-gluon hybrid mesons with exotic quantum numbers $J^{PC}=1^{-+}$ and $2^{+-}$ in the framework of the QCD sum rules. Considering the double-gluon hybrid meson operators in the octet-octet color structure, we have constructed two independent interpolating currents with $J^{PC}=1^{-+}$ and five independent curren

Luyao Zhang, George Pantazis, Shaohang Han, Sergio Grammatico

One of the critical challenges in automated driving is ensuring safety of automated vehicles despite the unknown behavior of the other vehicles. Although motion prediction modules are able to generate a probability distribution associated with various behavior modes, their probabilistic estimates are often inaccurate, thus leading to a possibly unsafe trajec

Shengwu Li

Which mechanisms are simple to play? When is it easy for participants to see that a mechanism is incentive-compatible? I will start by explaining how and why economists came to ask these questions. Then I will discuss three recent answers, that capture different aspects of what makes a mechanism simple.

Mathieu Lizée, Baptiste Coquinot, Guilhem Mariette, Alessandro Siria

The fundamental understanding of friction of liquids on solid surfaces remains one of the key knowledge gaps in the transport of fluids. While the standard perspective emphasizes the role of wettability and commensurability, recent works have unveiled the crucial role of the solid's internal excitations, whether electronic or phononic, on liquid-solid dissip

Sarah Sebo

In 2020, I designed the course CMSC 20630/30630 Human-Robot Interaction: Research and Practice as a hands-on introduction to human-robot interaction (HRI) research for both undergraduate and graduate students at the University of Chicago. Since 2020, I have taught and refined this course each academic year. Human-Robot Interaction: Research and Practice focu

Yang Ge, Shao-Kai Jian

The coupling between defects and extended critical degrees of freedom gives rise to the intriguing theory known as defect conformal field theory (CFT). In this work, we introduce a novel family of boundary and interface CFTs by coupling $N$ Majorana chains with SYK$_q$ interactions at the defect. Our analysis reveals that the interaction with $q=2$ constitut

Chen Yang, Thomas A. Cleland

Annolid is a deep learning-based software package designed for the segmentation, labeling, and tracking of research targets within video files, focusing primarily on animal behavior analysis. Based on state-of-the-art instance segmentation methods, Annolid now harnesses the Cutie video object segmentation model to achieve resilient, markerless tracking of mu

Noa Keshet, Ehud Behar, Timothy R. Kallman

Supernovae are responsible for the elemental enrichment of the galaxy and some are postulated to leave behind a black hole. In a stellar binary system the supernova pollutes its companion, and the black hole can accrete back its own debris and emit X-rays. In this sequence of events, which is only poorly understood, winds are ejected, and observed through X-

Lea Beneish, Henri Darmon, Lennart Gehrmann, Martí Roset

This article gives a new proof of the Gross--Kohnen--Zagier theorem for Shimura curves which exploits the $p$-adic uniformization of Cerednik--Drinfeld. The explicit description of CM points via this uniformization leads to an expression relating the Gross--Kohnen--Zagier generating series to the ordinary projection of the first derivative, with respect to a

Daniel Klenkert, Daniel Schaeffer, Julian Stauch

Neural networks were used to classify infrasound data. Two different approaches were compared. One based on the direct classification of time series data, using a custom implementation of the InceptionTime network. For the other approach, we generated 2D images of the wavelet transformation of the signals, which were subsequently classified using a ResNet im

Nahuel Soprano-Loto, Urtzi Ayesta, Matthieu Jonckheere, Ina Maria Verloop

A classical queuing theory result states that in a parallel-queue single-server model, the maximum stability region does not depend on the scheduling decision epochs, and in particular is the same for preemptive and non-preemptive systems. We consider here the case in which each of the queues may be connected to the server or not, depending on an exogenous p

Gustavo Sosa-Cabrera

In this work, we analyze the behavior of the multivariate symmetric uncertainty (MSU) measure through the use of statistical simulation techniques under various mixes of informative and non-informative randomly generated features. Experiments show how the number of attributes, their cardinalities, and the sample size affect the MSU. In this thesis, through o

Yan Fang, Jingtao Zhan, Qingyao Ai, Jiaxin Mao

Scaling up neural models has yielded significant advancements in a wide array of tasks, particularly in language generation. Previous studies have found that the performance of neural models frequently adheres to predictable scaling laws, correlated with factors such as training set size and model size. This insight is invaluable, especially as large-scale e

Yifei He, Ziting Chen, Haoting Zhen, Mingchen Huang

Long-range and anisotropic dipolar interactions induce complex order in quantum systems. It becomes particularly interesting in two-dimension (2D), where the superfluidity with quasi-long-range order emerges via Berezinskii-Kosterlitz-Thouless (BKT) mechanism, which still remains elusive with dipolar interactions. Here, we observe the BKT transition from a n

Otmar Ertl

Introduction. Distributed data processing and storage systems require efficient methods to distribute keys across buckets. While simple and fast, the traditional modulo-based mapping is unstable when the number of buckets changes, leading to spikes in system resource utilization, such as network or database requests. Consistent hash algorithms minimize remap

Huanran Li, Daniel Pimentel-Alarcón

Contrastive Learning (CL) has emerged as a powerful method for training feature extraction models using unlabeled data. Recent studies suggest that incorporating a linear projection head post-backbone significantly enhances model performance. In this work, we investigate the use of a transformer model as a projection head within the CL framework, aiming to e

Jakub Hoscilowicz, Adam Wiacek, Jan Chojnacki, Adam Cieslak

In this work, we explore LLM's internal representation space to identify attention heads that contain the most truthful and accurate information. We further developed the Inference Time Intervention (ITI) framework, which lets bias LLM without the need for fine-tuning. The improvement manifests in introducing a non-linear multi-token probing and multi-token

Ben Arie Tanay, Lexy Arinze, Siddhant S. Joshi, Kirsten A. Davis

Background: Large Language Models (LLMs) such as ChatGPT and CoPilot are influencing software engineering practice. Software engineering educators must teach future software engineers how to use such tools well. As of yet, there have been few studies that report on the use of LLMs in the classroom. It is, therefore, important to evaluate students' perception

Thiago R. Alves, Gustavo C. Souza

In this article, we address a problem posed by F. Bayart regarding the existence of an infinite-dimensional closed vector subspace (excluding the null operator) within the set of supercyclic operators on Banach spaces. We resolve this problem by establishing the existence of the closed subspace. Furthermore, we prove that the set of supercyclic operators on

Niccolò Preti, Nicolò Antolini, Giulio Biagioni, Andrea Fioretti

Short-wavelength repulsive potentials for quantum gases allow to realize new systems and to study new phenomena. Here we report the realization of repulsive optical potentials for dysprosium atoms in the blue region of the spectrum, at wavelengths close to 400 nm. We employ a spectrallyfiltered diode laser system to measure both scalar and tensorial componen

Axel Stenquist, Felipe Zapata, Edvin Olofsson, Yijie Liao

We show that resonant absorption of smooth laser fields can yield Mollow-like triplet patterns. General conditions for such triplets are derived and illustrated with a super-Gaussian pulse sequence. Gaussian pulses can not exhibit triplets, super-Gaussian pulses can form triplets depending on the pulse area and flat-top pulses can produce absorption triplets