March 2024 arXiv papers — page 115

Pavel Exner, Jan Pekař

We analyze band spectrum of the periodic quantum graph in the form of a chain of rings connected by line segments with the vertex coupling which violates the time reversal invariance, interpolating between the $\delta$ coupling and the one determined by a simple circulant matrix. We find that flat bands are generically absent and that the negative spectrum i

Long Nguyen-Phuoc, Renald Gaboriau, Dimitri Delacroix, Laurent Navarro

In order to provide a more objective and quicker way to diagnose post-traumatic stress disorder (PTSD), we present PTSD-MDNN which merges two unimodal convolutional neural networks and which gives low detection error rate. By taking only videos and audios as inputs, the model could be used in the configuration of teleconsultation sessions, in the optimizatio

Jagdeep Singh, Vaidy Sivaraman

A class $\mathcal{G}$ of graphs is called hereditary if it is closed under taking induced subgraphs. We denote by $G^{epex}$ the class of graphs that are at most one edge away from being in $\mathcal{G}$. We note that $G^{epex}$ is hereditary and prove that if a hereditary class $\mathcal{G}$ has finitely many forbidden induced subgraphs, then so does $G^{ep

Eric Grinstein, Toon van Waterschoot, Mike Brookes, Patrick A. Naylor

Steered Response Power (SRP) is a widely used method for the task of sound source localization using microphone arrays, showing satisfactory localization performance on many practical scenarios. However, its performance is diminished under highly reverberant environments. Although Deep Neural Networks (DNNs) have been previously proposed to overcome this lim

Adrien Gallet, Andrew Liew, Iman Hajirasouliha, Danny Smyl

This work develops a machine learned structural design model for continuous beam systems from the inverse problem perspective. After demarcating between forward, optimisation and inverse machine learned operators, the investigation proposes a novel methodology based on the recently developed influence zone concept which represents a fundamental shift in appr

Fatemeh Mohammadi, Francesca Zaffalon

Positroids are a family of matroids introduced by Postnikov in the study of non-negative Grassmannians. Postnikov identified several combinatorial objects in bijections with positroids, among which are bounded affine permutations. On the other hand, the notion of essential sets, introduced for permutations by Fulton, was used by Knutson in the study of the s

ATLAS Collaboration

Inclusive and differential cross-sections are measured at particle level for the associated production of a top quark pair and a photon ($t\bar{t}\gamma$). The analysis is performed using an integrated luminosity of 140 fb$^{-1}$ of proton-proton collisions at a centre-of-mass energy of 13 TeV collected by the ATLAS detector. The measurements are performed i

Long Nguyen-Phuoc, Renald Gaboriau, Dimitri Delacroix, Laurent Navarro

This paper introduces the M&M model, a novel multimodal-multitask learning framework, applied to the AVCAffe dataset for cognitive load assessment (CLA). M&M uniquely integrates audiovisual cues through a dual-pathway architecture, featuring specialized streams for audio and video inputs. A key innovation lies in its cross-modality multihead attention mechan

Zhangheng Li, Junyuan Hong, Bo Li, Zhangyang Wang

While diffusion models have recently demonstrated remarkable progress in generating realistic images, privacy risks also arise: published models or APIs could generate training images and thus leak privacy-sensitive training information. In this paper, we reveal a new risk, Shake-to-Leak (S2L), that fine-tuning the pre-trained models with manipulated data ca

Mickaël Montessinos

We represent vector bundles over a regular algebraic curve as pairs of lattices over the maximal orders of its function field and we give polynomial time algorithms for several tasks: computing determinants of vector bundles, kernels and images of global homomorphisms, isomorphisms between vector bundles, cohomology groups, extensions, and splitting into a d

Samuel I Watson

The R package rts2 provides data manipulation and model fitting tools for Log Gaussian Cox Process (LGCP) models. LGCP models are a key method for disease and other types of surveillance, and provide a means of predicting risk across an area of interest based on spatially-referenced and time-stamped case data. However, these models can be difficult to specif

Marina Ivanović, François Wirz, Jordi Subirà Nieto, Adrian Perrig

Internet censorship and global Internet reachability are prevalent topics of today's Internet. Nonetheless, the impact of network topology and Internet architecture to these aspects of the Internet is under-explored. With the goal of informing policy discussions with an objective basis, we present an approach for evaluating both censorship resilience and glo

Jacques Martinet

We construct Euclidean lattices whose sets of minimal vectors support some large equiangular families of lines, using notably reduction modulo~$2$ of lattices. %as considered in \cite{Ma1} and \cite{Ma2}. We also consider some related problems, and answer a question raised by Greaves ([G], Subsection~1.3.2).

Bekir Turkkan, Elvis Rodrigues, Tevfik Kosar, Aleksey Charapko

Coordination services and protocols are critical components of distributed systems and are essential for providing consistency, fault tolerance, and scalability. However, due to the lack of standard benchmarking and evaluation tools for distributed coordination services, coordination service developers/researchers either use a NoSQL standard benchmark and om

Guoying Zhao, Rong-Feng Shen

Expanding X-ray halo or rings appear when short pulses of X-ray radiation from a background source are scattered by clouds of dust in the Milky Way. We study the X-ray rings of the brightest gamma-ray burst (GRB) 221009A, detected by the {\it Swift} X-Ray Telescope. The rings center on the GRB position and their angular radii increase with time. We identify

L. Chakhchi, H. El Moumni, K. Masmar

Recently, the Event Horizon Telescope (EHT) achieved the realization of an image of the supermassive black hole $\textrm{Sgr~A}^\star$ showing an angular shadow diameter $\mathcal{D}= 48.7 \pm 7\mu as$ and the fractional deviation $\mathbf{\delta} = -0.08^{+0.09}_{-0.09}~\text{(VLTI)},-0.04^{+0.09}_{-0.10}~\text{(Keck)}$, alongside the earlier image of $\tex

Martin Bubel, Jochen Schmid, Volodymyr Kozachynskyi, Erik Esche

We propose a general methodology of sequential locally optimal design of experiments for explicit or implicit nonlinear models, as they abound in chemical engineering and, in particular, in vapor-liquid equilibrium modeling. As a sequential design method, our method iteratively alternates between performing experiments, updating parameter estimates, and comp

Zheying Zhang, Maruf Rayhan, Tomas Herda, Manuel Goisauf

In agile software development, maintaining high-quality user stories is crucial, but also challenging. This study explores the use of large language models to automatically improve the user story quality in Austrian Post Group IT agile teams. We developed a reference model for an Autonomous LLM-based Agent System and implemented it at the company. The qualit

James Rawson

Bjorn Poonen asked whether there exists a polynomial giving a surjection $\mathbb{Z} \times \mathbb{Z} \to \mathbb{N}$. We answer this question in the negative, conditional on a conjecture of Vojta. More precisely, we show that if such a function exists, there is a family of open surfaces with dense integral points despite the surfaces being of log general t

Frank Zhang, Yibo Zhang, Quan Zheng, Rui Ma

Text-driven 3D scene generation techniques have made rapid progress in recent years. Their success is mainly attributed to using existing generative models to iteratively perform image warping and inpainting to generate 3D scenes. However, these methods heavily rely on the outputs of existing models, leading to error accumulation in geometry and appearance t

Natalya Pya Arnqvist

Regression models that incorporate smooth functions of predictor variables to explain the relationships with a response variable have gained widespread usage and proved successful in various applications. By incorporating smooth functions of predictor variables, these models can capture complex relationships between the response and predictors while still al

Pawel Knap, Peter Hardy, Alberto Tamajo, Hwasup Lim

Current human pose estimation systems focus on retrieving an accurate 3D global estimate of a single person. Therefore, this paper presents one of the first 3D multi-person human pose estimation systems that is able to work in real-time and is also able to handle basic forms of occlusion. First, we adjust an off-the-shelf 2D detector and an unsupervised 2D-3

Leonardo Di Bari, Matteo Bisardi, Sabrina Cotogno, Martin Weigt

We introduce a data-driven epistatic model of protein evolution, capable of generating evolutionary trajectories spanning very different time scales reaching from individual mutations to diverged homologs. Our in silico evolution encompasses random nucleotide mutations, insertions and deletions, and models selection using a fitness landscape, which is inferr

Martin Berggren, Anders Bernland, André Massing, Daniel Noreland

The compression driver, the standard sound source for midrange acoustic horns, contains a cylindrical compression chamber connected to the horn throat through a system of channels known as a phase plug. The main challenge in the design of the phase plug is to avoid resonance and interference phenomena. The complexity of these phenomena makes it difficult to

Antonin Reitz, Aymeric Fromherz, Jonathan Protzenko

In this work, we present StarMalloc, a verified, security-oriented, concurrent memory allocator that can be used as a drop-in replacement in real-world projects. Using the Steel separation logic framework, we show how to specify and verify StarMalloc, relying on dependent types and modular abstractions to enable efficient verification. As part of StarMalloc,

Licheng Zhong, Hong-Xing Yu, Jiajun Wu, Yunzhu Li

Reconstructing and simulating elastic objects from visual observations is crucial for applications in computer vision and robotics. Existing methods, such as 3D Gaussians, model 3D appearance and geometry, but lack the ability to estimate physical properties for objects and simulate them. The core challenge lies in integrating an expressive yet efficient phy

Zhao Wang, Aoxue Li, Fengwei Zhou, Zhenguo Li

Classical object detectors are incapable of detecting novel class objects that are not encountered before. Regarding this issue, Open-Vocabulary Object Detection (OVOD) is proposed, which aims to detect the objects in the candidate class list. However, current OVOD models are suffering from overfitting on the base classes, heavily relying on the large-scale

Zhao Wang, Aoxue Li, Zhenguo Li, Qi Dou

Large-scale pre-training followed by downstream fine-tuning is an effective solution for transferring deep-learning-based models. Since finetuning all possible pre-trained models is computational costly, we aim to predict the transferability performance of these pre-trained models in a computational efficient manner. Different from previous work that seek ou

B. M. Huddart, A. Hernández-Melián, G. D. A. Wood, D. A. Mayoh

Centrosymmetric GdRu$_2$Si$_2$ exhibits a variety of multi-Q magnetic states as a function of temperature and applied magnetic field, including a square skyrmion-lattice phase. The material's behavior is strongly dependent on the direction of the applied field, with different phase diagrams resulting for fields applied parallel or perpendicular to the crysta

CMS Collaboration

A search for Higgs boson pair (HH) production with one Higgs boson decaying to two bottom quarks and the other to two W bosons are presented. The search is done using proton-proton collisions data at a centre-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 138 fb$^{-1}$ recorded by the CMS detector at the LHC from 2016 to 2018. The fin

Heiko Zimmermann, Christian A. Naesseth, Jan-Willem van de Meent

We present variational inference with sequential sample-average approximation (VISA), a method for approximate inference in computationally intensive models, such as those based on numerical simulations. VISA extends importance-weighted forward-KL variational inference by employing a sequence of sample-average approximations, which are considered valid insid

Zhuo Zhi, Ziquan Liu, Moe Elbadawi, Adam Daneshmend

Multimodal machine learning with missing modalities is an increasingly relevant challenge arising in various applications such as healthcare. This paper extends the current research into missing modalities to the low-data regime, i.e., a downstream task has both missing modalities and limited sample size issues. This problem setting is particularly challengi

Alexander Pukhov, Lars Reichwein

Wakefield excitation by structured electron bunches in hollow gaps between plasma wedges is studied using three-dimensional particle-in-cell simulations. The main part of the electron bunch has a triangular current distribution in the longitudinal direction with a smooth head and short tail. These bunches propagate stably in the hollow gap while being attach

Jiyuan Yang, Jing Wu, Jingxuan Li, Chao Zhou

The high coercive field ($E_c$) of hafnia-based ferroelectrics presents a major obstacle to their applications. The ferroelectric switching mechanisms in hafnia that dictate $E_c$, especially those related to domain nucleation in the Nucleation-Limited-Switching (NLS) model and domain wall motion in the Kolmogorov-Avrami-Ishibas (KAI) model, have remained el

Francesca Lisi, Luca Sabatini

Consider a finite primitive solvable group. We observe that a result of Y. Yang implies that there exist two points whose pointwise stabilizer has derived length at most $9$. We show that, if the group has odd cardinality, then there exist two points whose pointwise stabilizer is abelian.

X. Y. Zheng, D. T. Adroja, B. Chevalier, Z. Y. Shan

CeNiSn is a Kondo semimetal where a gap opens at low temperatures due to hybridization between 4$f$ and conduction electrons, but a full insulating state fails to develop. Upon the insertion of hydrogen, long range magnetic order is induced. Here we report zero-field muon-spin relaxation and inelastic neutron scattering measurements of polycrystalline sample

P. J. Cameron, F. E. Jannat, R. K. Nath, R. Sharafdini

There are several graphs defined on groups. Among them we consider graphs whose vertex set consists conjugacy classes of a group $G$ and adjacency is defined by properties of the elements of conjugacy classes. In particular, we consider commuting/nilpotent/solvable conjugacy class graph of $G$ where two distinct conjugacy classes $a^G$ and $b^G$ are adjacent

Tian Xia, Mélanie Roschewitz, Fabio De Sousa Ribeiro, Charles Jones

Causal generative modelling is gaining interest in medical imaging due to its ability to answer interventional and counterfactual queries. Most work focuses on generating counterfactual images that look plausible, using auxiliary classifiers to enforce effectiveness of simulated interventions. We investigate pitfalls in this approach, discovering the issue o

Javier Jiménez

High-dimensional dynamical systems projected onto a reduced-order model cease to be deterministic and are best described by probability distributions in state space. Their equations of motion map onto an evolution operator with a deterministic component describing the projected dynamics, and a stochastic one from the neglected dimensions. It is shown that, f

Ruizhe Liu, Qian Luo, Yanchao Yang

We focus on the self-supervised discovery of manipulation concepts that can be adapted and reassembled to address various robotic tasks. We propose that the decision to conceptualize a physical procedure should not depend on how we name it (semantics) but rather on the significance of the informativeness in its representation regarding the low-level physical

Asuka Kumon, Donghyeok Lim

We prove that the Krull-Schmidt decomposition of the Galois module of the $p$-adic completion of algebraic units is controlled by the primes that are ramified in the Galois extension and the $S$-ideal class group. We also compute explicit upper bounds for the number of possible Galois module structures of algebraic units when the Galois group is cyclic of or

Martin Plávala, Otfried Gühne, Marco Túlio Quintino

Bell nonlocality is a fundamental phenomenon of quantum physics as well as an essential resource for various tasks in quantum information processing. It is known that for the observation of nonlocality the measurements on a quantum system have to be incompatible, but the question which incompatible measurements are useful, remained open. Here we prove that a

Thang-Anh-Quan Nguyen, Luis Roldão, Nathan Piasco, Moussab Bennehar

The task of separating dynamic objects from static environments using NeRFs has been widely studied in recent years. However, capturing large-scale scenes still poses a challenge due to their complex geometric structures and unconstrained dynamics. Without the help of 3D motion cues, previous methods often require simplified setups with slow camera motion an

Yidong Liao, Chris Ferrie

Large Language Models (LLMs) such as ChatGPT have transformed how we interact with and understand the capabilities of Artificial Intelligence (AI). However, the intersection of LLMs with the burgeoning field of Quantum Machine Learning (QML) is only in its nascent stages. This paper presents an exploration of this niche by detailing a comprehensive framework

Hela Mhiri, Leo Monbroussou, Mario Herrero-Gonzalez, Slimane Thabet

In this work, we highlight an unforeseen behavior of the expressivity of Parameterized Quantum Circuits (PQCs) for machine learning. A large class of these models, seen as Fourier Series which frequencies are derived from the encoding gates, were thought to have their Fourier coefficients mostly determined by the trainable gates. Here, we demonstrate a new c

Filippo Ascolani, Gareth O. Roberts, Giacomo Zanella

We study general coordinate-wise MCMC schemes (such as Metropolis-within-Gibbs samplers), which are commonly used to fit Bayesian non-conjugate hierarchical models. We relate their convergence properties to the ones of the corresponding (potentially not implementable) Gibbs sampler through the notion of conditional conductance. This allows us to study the pe

Rishabh Vallabh Varsha Haria, Amin El Abed, Sebastian Maneth

We present a new dataset of "free roaming" (FR) and "targeted roaming" (TR): a pool of 41 participants is asked to walk around a university campus (FR) or is asked to find a particular room within a library (TR). Eye movements are recorded using a commodity wearable eye tracker (Pupil Labs Neon at 200Hz). On this dataset we investigate the accuracy of user i

Mengyu Li, Magnus Magnusson, Thilo van Eimeren, Lotta M. Ellingsen

Segmentation of brain structures on MRI is the primary step for further quantitative analysis of brain diseases. Manual segmentation is still considered the gold standard in terms of accuracy; however, such data is extremely time-consuming to generate. This paper presents a deep learning-based segmentation approach for 12 deep-brain structures, utilizing mul

Jaewoo Jung, Jisang Han, Honggyu An, Jiwon Kang

3D Gaussian splatting (3DGS) has recently demonstrated impressive capabilities in real-time novel view synthesis and 3D reconstruction. However, 3DGS heavily depends on the accurate initialization derived from Structure-from-Motion (SfM) methods. When the quality of the initial point cloud deteriorates, such as in the presence of noise or when using randomly

Yinan Deng, Jiahui Wang, Jingyu Zhao, Xinyu Tian

Environment representations endowed with sophisticated semantics are pivotal for facilitating seamless interaction between robots and humans, enabling them to effectively carry out various tasks. Open-vocabulary maps, powered by Visual-Language models (VLMs), possess inherent advantages, including zero-shot learning and support for open-set classes. However,

Tierui Gong, Li Wei, Chongwen Huang, George C. Alexandropoulos

Empowered by the latest progress on innovative metamaterials/metasurfaces and advanced antenna technologies, holographic multiple-input multiple-output (H-MIMO) emerges as a promising technology to fulfill the extreme goals of the sixth-generation (6G) wireless networks. The antenna arrays utilized in H-MIMO comprise massive (possibly to extreme extent) numb

Yequan Bie, Luyang Luo, Zhixuan Chen, Hao Chen

Utilizing potent representations of the large vision-language models (VLMs) to accomplish various downstream tasks has attracted increasing attention. Within this research field, soft prompt learning has become a representative approach for efficiently adapting VLMs such as CLIP, to tasks like image classification. However, most existing prompt learning meth

Simon Briend, Christophe Giraud, Gábor Lugosi, Déborah Sulem

This paper studies the problem of estimating the order of arrival of the vertices in a random recursive tree. Specifically, we study two fundamental models: the uniform attachment model and the linear preferential attachment model. We propose an order estimator based on the Jordan centrality measure and define a family of risk measures to quantify the qualit

Seth Bernstein, Paul Denny, Juho Leinonen, Lauren Kan

Grasping complex computing concepts often poses a challenge for students who struggle to anchor these new ideas to familiar experiences and understandings. To help with this, a good analogy can bridge the gap between unfamiliar concepts and familiar ones, providing an engaging way to aid understanding. However, creating effective educational analogies is dif

Wenjie Yin, Xuejiao Zhao, Yi Yu, Hang Yin

Dance typically involves professional choreography with complex movements that follow a musical rhythm and can also be influenced by lyrical content. The integration of lyrics in addition to the auditory dimension, enriches the foundational tone and makes motion generation more amenable to its semantic meanings. However, existing dance synthesis methods tend

Takanori Ayano

Let $V$ be a hyperelliptic curve of genus 2 defined by $Y^2=f(X)$, where $f(X)$ is a polynomial of degree 5. The sigma function associated with $V$ is a holomorphic function on $\mathbb{C}^2$. For a point $P$ on $V$, we consider the problem to express the $X$-coordinate of $P$ in terms of the image of $P$ under the Abel-Jacobi map. Two meromorphic functions

Ali Ladak, Jamie Harris, Jacy Reese Anthis

Many studies have identified particular features of artificial intelligences (AI), such as their autonomy and emotion expression, that affect the extent to which they are treated as subjects of moral consideration. However, there has not yet been a comparison of the relative importance of features as is necessary to design and understand increasingly capable

Anirban Mukherjee, Hannah Hanwen Chang

Deviating from conventional perspectives that frame artificial intelligence (AI) systems solely as logic emulators, we propose a novel program of heuristic reasoning. We distinguish between the 'instrumental' use of heuristics to match resources with objectives, and 'mimetic absorption,' whereby heuristics manifest randomly and universally. Through a series

Philipp Strasberg, Joseph Schindler

We study the time evolution of eleven microscopic entropy definitions (of Boltzmann-surface, Gibbs-volume, canonical, coarse-grained-observational, entanglement and diagonal type) and three microscopic temperature definitions (based on Boltzmann, Gibbs or canonical entropy). This is done for the archetypal nonequilibrium setup of two systems exchanging energ

Nicolas Boltz, Sebastian Hahner, Christopher Gerking, Robert Heinrich

The growing interconnection between software systems increases the need for security already at design time. Security-related properties like confidentiality are often analyzed based on data flow diagrams (DFDs). However, manually analyzing DFDs of large software systems is bothersome and error-prone, and adjusting an already deployed software is costly. Add

Tingtian Li, Zixun Sun, Xinyu Xiao

Identifying highlight moments of raw video materials is crucial for improving the efficiency of editing videos that are pervasive on internet platforms. However, the extensive work of manually labeling footage has created obstacles to applying supervised methods to videos of unseen categories. The absence of an audio modality that contains valuable cues for

Aleksandr Matsun, Numan Saeed, Fadillah Adamsyah Maani, Mohammad Yaqub

Medical data often exhibits distribution shifts, which cause test-time performance degradation for deep learning models trained using standard supervised learning pipelines. This challenge is addressed in the field of Domain Generalization (DG) with the sub-field of Single Domain Generalization (SDG) being specifically interesting due to the privacy- or logi

Yunze Gao, Astrid Weston, Vladimir Enaldiev, Eli Castanon

Van der Waals (vdW) heterostructures have opened new opportunities to develop atomically thin (opto)electronic devices with a wide range of functionalities. The recent focus on manipulating the interlayer twist angle has led to the observation of out-of-plane room temperature ferroelectricity in twisted rhombohedral (R) bilayers of transition metal dichalcog

Valéria Carvalho, Márcio Ferreira, Constança Providência, Michał Bejger

We explore the anomaly detection framework based on Normalizing Flows (NF) models introduced in \cite{PhysRevC.106.065802} to detect the presence of a large (destabilising) dense matter phase transition in neutron star (NS) observations of masses and radii, and relate the feasibility of detection with parameters of the underlying mass-radius sequence, which

Hao Jia

We reformulate results from the paper ``Linear vortex symmetrization: The spectral density function" by Ionescu and the author in simplified forms and derive rigorously the bounds given in Bassom and Gilbert (J. Fluid Mech., 1998), which provided interesting insights on the vortex symmetrization phenomenon.

Ambroise Mathey, Mickaël Le Fur, Patrick Chasle, Axelle Amon

In this manuscript, we describe a scientific device specifically designed for the study of the plasticity fluctuations preceding the fracture of granular media. Biaxial tests on model granular media are performed using a commercial uniaxial loading system. Strain field fluctuations are measured using a method based on the interference of coherent light scatt

Bin Hu, Hui Chen, Yuhan Ye, Zihao Huang

The collective modes of the superconducting order parameter fluctuation can provide key insights into the nature of the superconductor. Recently, a family of superconductors has emerged in non-magnetic kagome material AV3Sb5 (A=K, Rb, Cs), exhibiting fertile emergent phenomenology. However, the collective behaviors of Cooper pairs have not been studied. Here

Haiyang Wang, Hao Tang, Li Jiang, Shaoshuai Shi

This paper proposes a simple, yet effective framework, called GiT, simultaneously applicable for various vision tasks only with a vanilla ViT. Motivated by the universality of the Multi-layer Transformer architecture (e.g, GPT) widely used in large language models (LLMs), we seek to broaden its scope to serve as a powerful vision foundation model (VFM). Howe

Dhiren Panda, Manas Kumar Mohapatra, Rukmani Mohanta

We perform an analysis of the consequences of various new physics operators on the lepton flavor violating (LFV) decay modes mediated through $b \to s \ell _1 \ell _2$ transitions. We scrutinize the imprints of the (pseudo)scalar and axial(vector) operators on the exclusive LFV decay channels $ B_{(s)} \rightarrow (\phi, K^{*}, K_{2}^{*})\ell_{1}\ell_{2}$ an

Yuliang Wu, Ganchao Tan, Jinze Chen, Wei Zhai

Dynamic Range (DR) is a pivotal characteristic of imaging systems. Current frame-based cameras struggle to achieve high dynamic range imaging due to the conflict between globally uniform exposure and spatially variant scene illumination. In this paper, we propose AsynHDR, a Pixel-Asynchronous HDR imaging system, based on key insights into the challenges in H

D. Septier, G. Brévalle-Wasilewski, E. Lefebvre, N. Gajendra Kumar

Multimodal nonlinear endoscopes have been a topic of intense research over the past two decades, enabling sub-cellular and label-free imaging in areas not reachable with table-top microscopes. They are sophisticated systems that can be implemented on an optical table in a lab environment, but they cannot be easily moved within or out of the lab. We present h

H. -T. Ding, O. Kaczmarek, F. Karsch, P. Petreczky

We analyze the dependence of the chiral phase transition temperature on baryon number and strangeness chemical potentials by calculating the leading order curvature coefficients in the light and strange quark flavor basis as well as in the conserved charge ($B, S$) basis. Making use of scaling properties of the magnetic equation of state (MEoS) and including

Andrea Martin, Luca Furieri

The increasing reliance on numerical methods for controlling dynamical systems and training machine learning models underscores the need to devise algorithms that dependably and efficiently navigate complex optimization landscapes. Classical gradient descent methods offer strong theoretical guarantees for convex problems; however, they demand meticulous hype

Che-Yu Chen, Yuki Yokokura

The recent advancements in black hole imaging have opened a new era of probing horizon-scale physics with electromagnetic radiation. However, a feature of the observed images, a bright ring encircling a relatively dark region, has not sufficiently proved the existence of event horizons. It thus requires extreme care when studying the possibility of using suc

Zhi-Peng Ma, Kai Wang

Merging of stellar-mass binary black holes (BBH) could take place within the accretion disk of active galactic nuclei (AGN). The resulting BH remnant is likely to accrete the disk gas at a super-Eddington rate, launching a fast, quasi-spherical outflow (wind). Particles will be accelerated by shocks driven by the wind, subsequently interacting with the shock

Athokpam Langlen Chanu, R. K. Brojen Singh, Jae-Hyung Jeon

The concentration of intracellular calcium ion (Ca$^{2+}$) exhibits complex oscillations, including bursting and chaos, as observed experimentally. These dynamics are influenced by inherent fluctuations within cells, which serve as crucial determinants in cellular decision-making processes and fate determination. In this study, we systematically explore the

C. Pallis

We consider F-term hybrid inflation (FHI) and SUSY breaking in the context of a B-L extension of MSSM which largely respects a global U(1) R symmetry. The hidden sector Kaehler manifold enjoys an enhanced SU(1,1)/U(1) symmetry with the scalar curvature determined by the achievement of a SUSY-breaking de Sitter vacuum without ugly tuning. FHI turns out to be

Xin Jin, Qiling Ou, Haoran Wei, Xianyong Ding

Thermoelectric (TE) conversion technology, capable of transforming heat into electricity, is critical for sustainable energy solutions. Many promising TE materials contain rare or toxic elements, so the development of cost-effective and eco-friendly high-performance TE materials is highly urgent. Herein, we explore the thermal transport and TE properties of

Rune Kjærsgaard, Ahcène Boubekki, Line Clemmensen

Prototypical self-explainable classifiers have emerged to meet the growing demand for interpretable AI systems. These classifiers are designed to incorporate high transparency in their decisions by basing inference on similarity with learned prototypical objects. While these models are designed with diversity in mind, the learned prototypes often do not suff

Nikolay Kuznetsov

A new characterization of convexity of a planar domain is obtained. Its derivation involves two classical facts: the Varadhan's formula, expressing the distance function with respect to the domain's boundary via real-valued solutions of the modified Helmholtz equation, and the convexity of a planar domain in which the distance function is superharmonic

Dongni Chen, Jiahui Li, Stefano Chesi, Ying-Dan Wang

We consider generating maximally entangled states (Bell states) between two qubits coupled to a common bosonic mode, based on f-STIRAP. Utilizing the systematic approach developed in New J. Phys. 19 093016 (2017), we quantify the effects of non-adiabatic leakage and system dissipation on the entanglement generation, and optimize the entanglement by balancing

Juan Tapia, Christoph Busch

This paper evaluated the impact of synthetic images on Morphing Attack Detection (MAD) using a Siamese network with a semi-hard-loss function. Intra and cross-dataset evaluations were performed to measure synthetic image generalisation capabilities using a cross-dataset for evaluation. Three different pre-trained networks were used as feature extractors from

Elena Caviglia

We generalize principal bundles and quotient stacks to the two-categorical context of bisites. We introduce a notion of principal 2-bundle that makes sense for a 2-category with finite flexible limits, endowed with a bitopology. We then use principal 2-bundles to explicitly construct quotient-pre-2-stacks, which are the analogues of quotient stacks one dimen

Ludovico Bruni Bruno, Wolfgang Erb

In this article, we study the Fekete problem in segmental and combined nodal-segmental univariate polynomial interpolation by investigating sets of segments, or segments combined with nodes, such that the Vandermonde determinant for the respective polynomial interpolation problem is maximized. For particular families of segments, we will be able to find expl

Tingyu Qu, Tinne Tuytelaars, Marie-Francine Moens

Mainstream parameter-efficient fine-tuning (PEFT) methods, such as LoRA or Adapter, project a model's hidden states to a lower dimension, allowing pre-trained models to adapt to new data through this low-rank bottleneck. However, PEFT tasks involving multiple modalities, like vision-language (VL) tasks, require not only adaptation to new data but also learni

Tianshuo Xu, Peng Mi, Ruilin Wang, Yingcong Chen

Diffusion models (DMs) are a powerful generative framework that have attracted significant attention in recent years. However, the high computational cost of training DMs limits their practical applications. In this paper, we start with a consistency phenomenon of DMs: we observe that DMs with different initializations or even different architectures can pro

Xiaoqi Liu, Haiying Shan

This paper investigates the influence of two graft transformations on the distance spectral radius of connected uniform hypergraphs. Specifically, we study $k$-uniform hypertrees with given size, maximum degree and number of vertices of maximum degree, and give the structure of such hypergraph with maximum distance spectral radius.

Qirui Mi, Zhiyu Zhao, Chengdong Ma, Siyu Xia

Macroeconomic outcomes emerge from individuals' decisions, making it essential to model how agents interact with macro policy via consumption, investment, and labor choices. We formulate this as a dynamic Stackelberg game: the government (leader) sets policies, and agents (followers) respond by optimizing their behavior over time. Unlike static models, this

Afreen Islam, Guido Herrmann, Joaquin Carrasco

In this paper, the solvability of the Inverse Optimal Control (IOC) problem based on two existing minimum principal methods, is analysed. The aim of this work is to answer the question regarding what kinds of trajectories, that is depending on the initial conditions of the closed-loop system and system dynamics, of the original optimal control problem, will

Christian Sivertsen, Guido Salimbeni, Anders Sundnes Løvlie, Steve Benford

Ongoing efforts to turn Machine Learning (ML) into a design material have encountered limited success. This paper examines the burgeoning area of AI art to understand how artists incorporate ML in their creative work. Drawing upon related HCI theories, we investigate how artists create ambiguity by analyzing nine AI artworks that use computer vision and imag

Mark D. Groves, Dan J. Hill

This paper is concerned with complex Banach-space valued functions of the form $$ \hat{f}_k(r\cos\theta,r\sin\theta,z)=\mathrm{e}^{\mathrm{i} k \theta}f_k(r,z), \qquad r \in [0,\infty), \theta \in \mathbb{T}^1, z \in \mathbb{R}, $$ for some $k \in \mathbb{Z}$. It is demonstrated how classical and Sobolev spaces for the radial function $f_k$ can be constructe

Steven Hurder

We construct families of non-trivial universal rigid secondary classes for foliations, and then discuss their application to prove that foliations are not homotopic. An observation of Lawson about the non-triviality of the normal Pontrjagin classes of foliations is extended, and then used to construct new families of examples of foliations with non-trivial r

Pei-Fang Wu, Wei-Chen Guo, Liang He

In the presence of system-environment coupling, classical complex systems undergo stochastic dynamics, where rich phenomena can emerge at large spatio-temporal scales. To investigate these phenomena, numerical approaches for simulating stochastic dynamics are indispensable and can be computationally expensive. In light of the recent fast development in machi

Fuliang Li, Haozhi Lang, Jiajie Zhang, Jiaxing Shen

Manual network configuration automation (NCA) tools face significant challenges in versatility and flexibility due to their reliance on extensive domain expertise and manual design, limiting their adaptability to diverse scenarios and complex application needs. This paper introduces PreConfig, an innovative NCA tool that leverages a pretrained language model

Shuang-Kai Yang, Wei-Min Zhang

Utilizing the general theory of open quantum systems to investigate the exact dynamical evolution of simple bilinear systems, we discover a mechanism of the dynamical genesis of quantum entanglement. We focus in detail on the exact quantum evolution dynamics of two photonic modes (or any two bosonic modes) coupled to each other through a linear interaction,

Qianqian Wu, Xianping Ma, Jialu Sui, Man-On Pun

Recent advancements in remote sensing (RS) technologies have shown their potential in accurately classifying local climate zones (LCZs). However, traditional scene-level methods using convolutional neural networks (CNNs) often struggle to integrate prior knowledge of ground objects effectively. Moreover, commonly utilized data sources like Sentinel-2 encount

Liejun Shen, Marco Squassina

We investigate the existence and concentration of normalized solutions for a $p$-Laplacian problem with logarithmic nonlinearity of type \[ \left\{ \begin{array}{ll} \displaystyle -\varepsilon^p\Delta_p u+V(x)|u|^{p-2}u=\lambda |u|^{p-2}u+|u|^{p-2}u\log|u|^p ~\text{in}~\mathbb R^N,\newline \displaystyle \int_{\mathbb R^N}|u|^pdx=a^p\varepsilon^N, \end{array}

Hedieh Ajam, Vahid Jamali, Bernhard Schmauss, Robert Schober

The line-of-sight (LOS) requirement of free-space optical (FSO) systems can be relaxed by employing optical intelligent reflecting surfaces (IRSs). In this paper, we model the impact of the IRS-induced delay dispersion and derive the channel impulse response (CIR) of IRS-assisted FSO links. The proposed model takes into account the characteristics of the inc

Etera R. Livine

In the quest of a physical theory of quantum gravity, spin foam models, or in short spinfoams, propose a well-defined path integral summing over quantized discrete space-time geometries. At the crossroad of topological quantum field theory, dynamical triangulations, Regge calculus, and loop quantum gravity, this framework provides a non-perturbative and back

Fan Wan, Xingyu Miao, Haoran Duan, Jingjing Deng

With increasing concerns over data privacy and model copyrights, especially in the context of collaborations between AI service providers and data owners, an innovative SG-ZSL paradigm is proposed in this work. SG-ZSL is designed to foster efficient collaboration without the need to exchange models or sensitive data. It consists of a teacher model, a student