May 2024 arXiv papers — page 112

Yifan Xu, Xiaoshan Yang, Yaguang Song, Changsheng Xu

In this work, we introduce Libra, a prototype model with a decoupled vision system on a large language model (LLM). The decoupled vision system decouples inner-modal modeling and cross-modal interaction, yielding unique visual information modeling and effective cross-modal comprehension. Libra is trained through discrete auto-regressive modeling on both visi

Maria Teresa Ortoleva, Rita Borgo, Alfie Abdul-Rahman

Data physicalization is a research area in quick expansion whose necessity and popularity are motivated by the pervasiveness of data in our everyday. While the reflective ability of personal data physicalization has been vastly documented, their mental health and emotional well-being benefits remain largely unexplored. We present a qualitative study where we

Rafał Poświata, Sławomir Dadas, Michał Perełkiewicz

In this paper, we introduce the Polish Massive Text Embedding Benchmark (PL-MTEB), a comprehensive benchmark for text embeddings in the Polish language. PL-MTEB comprises 30 diverse NLP tasks across five categories: classification, clustering, pair classification, information retrieval, and semantic text similarity. Within the scope of this work, we added 12

Thomas Hartman, Grégoire Mathys

In a four-dimensional quantum field theory that flows between two fixed points under the renormalization group, the change in the conformal anomaly $\Delta a$ has been related to the average null energy. We extend this result to derive a sum rule for the other anomaly coefficient, $\Delta c$, in terms of the stress tensor three-point function. While the sum

Fernando Szechtman

We compute the automorphism tower of the centerless Mennicke group $M(-1,-1,-1)$

Patxi Fernandez-Zelaia, Jason Mayeur, Jiahao Cheng, Yousub Lee

Machine learning surrogate emulators are needed in engineering design and optimization tasks to rapidly emulate computationally expensive physics-based models. In micromechanics problems the local full-field response variables are desired at microstructural length scales. While there has been a great deal of work on establishing architectures for these tasks

Tobias Demmler, Andreas Tamke, Thao Dang, Karsten Haug

In autonomous driving, accurately interpreting the movements of other road users and leveraging this knowledge to forecast future trajectories is crucial. This is typically achieved through the integration of map data and tracked trajectories of various agents. Numerous methodologies combine this information into a singular embedding for each agent, which is

Togay Yazar, Mucahid Kutlu, İsa Kerem Bayırlı

Over the past century, the Turkish language has undergone substantial changes, primarily driven by governmental interventions. In this work, our goal is to investigate the evolution of the Turkish language since the establishment of T\"urkiye in 1923. Thus, we first introduce Turkronicles which is a diachronic corpus for Turkish derived from the Official Gaz

Xinyu Zhang, Yijin Xiong, Qianxin Qu, Renjie Wang

In the typical urban intersection scenario, both vehicles and infrastructures are equipped with visual and LiDAR sensors. By successfully integrating the data from vehicle-side and road monitoring devices, a more comprehensive and accurate environmental perception and information acquisition can be achieved. The Calibration of sensors, as an essential compon

Jordi Thijsman, Merlijn Sebrechts, Filip De Turck, Bruno Volckaert

A Kubernetes cluster typically consists of trusted nodes, running within the confines of a physically secure datacenter. With recent advances in edge orchestration, this is no longer the case. This poses a new challenge: how can we trust a device that an attacker has physical access to? This paper presents an architecture and open-source implementation that

Yue Yang, Chenhui Lin, Luo Xu, Wenchuan Wu

This paper introduces the design and implementation of PyOptInterface, a modeling language for mathematical optimization embedded in Python programming language. PyOptInterface uses lightweight and compact data structure to bridge high-level entities in optimization models like variables and constraints to internal indices of optimizers efficiently. It suppo

Chidimma Opara

The emergence of large language models (LLMs) capable of generating realistic texts and images has sparked ethical concerns across various sectors. In response, researchers in academia and industry are actively exploring methods to distinguish AI-generated content from human-authored material. However, a crucial question remains: What are the unique characte

Xiaofei Wen, Bangzheng Li, Tenghao Huang, Muhao Chen

Most language models currently available are prone to self-contradiction during dialogues. To mitigate this issue, this study explores a novel contradictory dialogue processing task that aims to detect and modify contradictory statements in a conversation. This task is inspired by research on context faithfulness and dialogue comprehension, which have demons

Kabir Chakravarti, Amit Reza, Leonardo G. Trombetta

Hairy black-holes are a unique prediction of certain theories that extend General Relativity (GR) with a scalar field. The presence of scalar hair is reflected non-trivially in the entropy of the black hole along with any topological coupling that may be present in the action. Demanding that a system of two merging black holes obeys the global second law of

Eunji Lim

We consider the problem of estimating an unknown function f* and its partial derivatives from a noisy data set of n observations, where we make no assumptions about f* except that it is smooth in the sense that it has square integrable partial derivatives of order m. A natural candidate for the estimator of f* in such a case is the best fit to the data set t

Raimel A. Medina, Maksym Serbyn

The Quantum Approximate Optimization Algorithm (QAOA) uses a quantum computer to implement a variational method with $2p$ layers of alternating unitary operators, optimized by a classical computer to minimize a cost function. While rigorous performance guarantees exist for the QAOA at small depths $p$, the behavior at large depths remains less clear, though

Meenatchi Sundaram Muthu Selva Annamalai, Georgi Ganev, Emiliano De Cristofaro

Differentially private synthetic data generation (DP-SDG) algorithms are used to release datasets that are structurally and statistically similar to sensitive data while providing formal bounds on the information they leak. However, bugs in algorithms and implementations may cause the actual information leakage to be higher. This prompts the need to verify w

Hao Yan, Cong Ling

The concept of the smoothing parameter plays a crucial role in both lattice-based and code-based cryptography, primarily due to its effectiveness in achieving nearly uniform distributions through the addition of noise. Recent research by Pathegama and Barg has determined the optimal smoothing bound for random codes under R\'enyi Divergence for any order $\al

Charikleia Iakovidou, Kibaek Kim

Federated learning (FL) was recently proposed to securely train models with data held over multiple locations (``clients'') under the coordination of a central server. Prolonged training times caused by slow clients may hinder the performance of FL; while asynchronous communication is a promising solution, highly heterogeneous client response times under non

João Bordalo, Vasco Ramos, Rodrigo Valério, Diogo Glória-Silva

Multistep instructions, such as recipes and how-to guides, greatly benefit from visual aids, such as a series of images that accompany the instruction steps. While Large Language Models (LLMs) have become adept at generating coherent textual steps, Large Vision/Language Models (LVLMs) are less capable of generating accompanying image sequences. The most chal

Bo Zhang, Hui Ma, Jian Ding, Jian Wang

Integrating multimodal knowledge into large language models (LLMs) represents a significant advancement in dialogue generation capabilities. However, the effective incorporation of such knowledge in zero-resource scenarios remains a substantial challenge due to the scarcity of diverse, high-quality dialogue datasets. To address this, we propose the Visual Im

Jiahuan Pei, Irene Viola, Haochen Huang, Junxiao Wang

Autonomous artificial intelligence (AI) agents have emerged as promising protocols for automatically understanding the language-based environment, particularly with the exponential development of large language models (LLMs). However, a fine-grained, comprehensive understanding of multimodal environments remains under-explored. This work designs an autonomou

Nejc Blaznik, Jasper Smits, Marc Duran Gutierrez, Peter van der Straten

We introduce a novel, non-invasive imaging technique based on spin-dependent off-axis holography (SOAH) for spin-1 Bose-Einstein condensates (BECs). Utilizing a dual reference beam strategy, this method records two orthogonal circular polarization components of a single probe beam. The circular birefringence of spin-polarized atoms induces differing complex

Thomas Z. Li, Kaiwen Xu, Aravind Krishnan, Riqiang Gao

Statistical models for predicting lung cancer have the potential to facilitate earlier diagnosis of malignancy and avoid invasive workup of benign disease. Many models have been published, but comparative studies of their utility in different clinical settings in which patients would arguably most benefit are scarce. This study retrospectively evaluated prom

Piotr Mironowicz, Akshata Shenoy H., Antonio Mandarino, A. Ege Yilmaz

Machine learning and quantum machine learning (QML) have gained significant importance, as they offer powerful tools for tackling complex computational problems across various domains. This work gives an extensive overview of QML uses in quantitative finance, an important discipline in the financial industry. We examine the connection between quantum computi

Elad Sunray, Gil Weinberg, Moriya Rosenfeld, Ori Katz

Imaging inside scattering media at optical resolution is a longstanding challenge affecting multiple fields, from bio-medicine to astronomy. In recent years, several groundbreaking techniques for imaging inside scattering media, in particular scattering-matrix based approaches, have shown great promise, but usually suffer from a restricted field of view (FOV

Sitai Li, Gino Biondini, Gregor Kovacic

We study an initial-boundary-value problem (IBVP) for a system of coupled Maxwell-Bloch equations (CMBE) that model two colors or polarizations of light resonantly interacting with a degenerate, two-level, active optical medium with an excited state and a pair of degenerate ground states. We assume that the electromagnetic field approaches non-vanishing plan

Xuanyu Liang, Ahmed Al-Tahmeesschi, Qiao Wang, Swarna Chetty

The proliferation of 5G technology presents an unprecedented challenge in managing the energy consumption of densely deployed network infrastructures, particularly Base Stations (BSs), which account for the majority of power usage in mobile networks. The O-RAN architecture, with its emphasis on open and intelligent design, offers a promising framework to add

Eleni Antonopoulou, Antonios Nathanail

Intense flaring events in the near-infrared and X-ray wavebands of our Galactic Center have been the subject of research for decades. In recent years, the GRAVITY instrument of the Very Large Telescope captured the motion and polarimetric signature of such a flare in close proximity to the supermassive black hole. This study aims to investigate a broad param

Yuki Amari, Sergei Antsipovich, Muneto Nitta, Yakov Shnir

We study stationary rotating topological solitons in (2+1)-dimensional ${\mathbb C}P^2$ non-linear sigma model with a stabilizing potential term. We find families of $U(1)\times U(1)$ symmetric solutions with topological degrees larger than 2, which have two angular frequencies and are labelled by two (one topological and the other non-topological) winding n

Jacopo Angeletti

Quantum cryptography has undergone substantial growth and development within the multi-disciplinary field of quantum information in recent years. The field is constantly advancing with new protocols being developed, security measures being improved, and the first practical applications of these technologies being deployed in optical fibers and free space opt

Robert Worden

Animals build Bayesian 3D models of their surroundings, to control their movements. There is strong selection pressure to make these models as precise as possible, given their sense data. A previous paper has described how a precise 3D model of space can be built by object tracking. This only works if 3D locations are stored with high spatial precision. Neur

A. Le Yaouanc, J. -P. Leroy, P. Roudeau

Using recent measurements of exclusive B-meson decays we extract the $a_1$ factorization parameter in ${\overline{B}^{0}_{d}} \to D^{(*)^+} K^-/\pi^-$ decay channels. { The values obtained in the four channels are very similar, in agreement with the theoretical expectations obtained in the $m_Q \to \infty$ limit, but the measured values differ definitely fro

Jan Friedrich, Sarah Schraven, Fabian Kiessling, Michael Herty

A consensus-based optimization (CBO) algorithm, which enables derivative and mesh-free optimization, is presented to localize a bioluminescent source. The light propagation is modeled by the radiative transfer equation approximated by spherical harmonics. The approach is investigated for a hierarchy of simplified diffusion models in simulated environments an

Hanke Feng, Tong Ge, Yaowen Hu, Zhenzheng Wang

Optical vector analysis (OVA) is an enabling technology for comprehensively characterizing both amplitude and phase responses of optical devices or systems. Conventional OVA technologies are mostly based on discrete optoelectronic components, leading to unsatisfactory system sizes, complexity, and stability. They also encounter challenges in revealing the on

Yu-Cun Xie

In this thesis, we investigate the dynamical Casimir effect, the creation of particles from vacuum by dynamical boundary conditions or dynamical background, and its backreaction to the motion of the boundary. The backreaction of particle creation to the boundary motion is studied using quantum field theory in curved spacetime technique, in 1+1 dimension and

Luis H. Gallardo

Assume that $H$ is a circulant Hadamard matrix of order $n\geq 4$. We consider an appropriate stochastic matrix $S$ of order $n$ depending on $H$. This allows us to prove that $n = 4$. Thus, there are only $10$ circulant Hadamard matrices.

Alec Yen, Yufeng Ye, Kaidong Peng, Jennifer Wang

In superconducting qubits, suppression of spontaneous emission is essential to achieve fast dispersive measurement and reset without sacrificing qubit lifetime. We show that resonator-mediated decay of the qubit mode to the feedline can be suppressed using destructive interference, where the readout resonator is coupled to the feedline at two points. This "i

Dmitrii Kobylianskii, Nathalie Soybelman, Nilotpal Kakati, Etienne Dreyer

The computational intensity of detector simulation and event reconstruction poses a significant difficulty for data analysis in collider experiments. This challenge inspires the continued development of machine learning techniques to serve as efficient surrogate models. We propose a fast emulation approach that combines simulation and reconstruction. In othe

Do Kien Hoang

Consider a simple algebraic group $G$ of classical type and its Lie algebra $\mathfrak{g}$. Let $(e,h,f) \subset \mathfrak{g}$ be an $\mathfrak{sl}_2$-triple and $Q_e= C_G(e,h,f)$. The torus $T_e$ that comes from the $\mathfrak{sl}_2$-triple acts on the Springer fiber $\mathcal{B}_e$. Let $\mathcal{B}_e^{gr}$ denote the fixed point loci of $\mathcal{B}_e$ un

Zhan-Ying Ma, Xian-Jin Liu, Yu-Qi Peng, Da-Sen Zhang

On-chip integrated metasurface driven by in-plane guided waves is of great interests in various light field manipulation applications such as colorful augmented reality and holographic display. However, it remains a challenge to design colorful multichannel holography by a single on-chip metasurface. Here we present metasurfaces integrated on top of guided-w

Subrata Ghosh, Giacomo Pagani, Massimilano Righi, Chengxi Hou

Advanced material composite of nanocarbons and metal-based materials provides a synergistic effect to obtain excellent electrochemical charge-storage performance and other properties. Herein, 3D porous carbon-metal oxynitride nanocomposites with tunable carbon/metal and oxygen/nitrogen ratio are synthesized uniquely by simultaneous ablation from two differen

Zhongju Yuan, Geraint Wiggins, Dick Botteldooren

This paper introduces a novel approach to predicting periodic time series using reservoir computing. The model is tailored to deliver precise forecasts of rhythms, a crucial aspect for tasks such as generating musical rhythm. Leveraging reservoir computing, our proposed method is ultimately oriented towards predicting human perception of rhythm. Our network

Riccardo Bartocci

The $U(3)^5$ symmetry within the SMEFT framework restricts the inclusion of only fully flavor-conserving operators at dimension six. This proceeding presents a global analysis of the SMEFT under this assumption. We provide global constraints on all 41 Wilson coefficients, utilizing leading-order and next-to-leading-order SMEFT predictions for various experim

Joan Quirant

We explore the classical Regge growth conjecture in the 4d effective field theory that results from compactifying $D$-dimensional General Relativity on a compact, Ricci-flat manifold. While the higher dimensional description is given in terms of pure Einstein gravity and the conjecture is automatically satisfied, it imposes several non-trivial constraints in

Kazuki Watanabe, Marck van der Vegt, Sebastian Junges, Ichiro Hasuo

The de-facto standard approach in MDP verification is based on value iteration (VI). We propose compositional VI, a framework for model checking compositional MDPs, that addresses efficiency while maintaining soundness. Concretely, compositional MDPs naturally arise from the combination of individual components, and their structure can be expressed using, e.

Sen Huang, Kaixiang Yang, Sheng Qi, Rui Wang

Optimization algorithms and large language models (LLMs) enhance decision-making in dynamic environments by integrating artificial intelligence with traditional techniques. LLMs, with extensive domain knowledge, facilitate intelligent modeling and strategic decision-making in optimization, while optimization algorithms refine LLM architectures and output qua

Peter Gladbach, Heiner Olbermann

We consider a thin elastic sheet with a finite number of disclinations in a variational framework in the F\"oppl-von K\'arm\'an approximation. Under the non-physical assumption that the out-of-plane displacement is a convex function, we prove that minimizers display ridges between the disclinations. We prove the associated energy scaling law with upper and l

Tianqu Kang, Lumin Liu, Hengtao He, Jun Zhang

Federated Learning (FL) is an emerging paradigm that holds great promise for privacy-preserving machine learning using distributed data. To enhance privacy, FL can be combined with Differential Privacy (DP), which involves adding Gaussian noise to the model weights. However, FL faces a significant challenge in terms of large communication overhead when trans

Sidiney G. Alves, Silvio C. Ferreira, Marcelo M. de Oliveira

The weighted planar stochastic (WPS) lattice introduces a topological disorder that emerges from a multifractal structure. Its dual network has a power-law degree distribution and is embedded in a two-dimensional space, forming a planar network. We modify the original recipe to construct WPS networks with degree distributions interpolating smoothly between t

Piotr Ostropolski-Nalewaja, Tim S. Lyon

The decidability of axiomatic extensions of the modal logic K with modal reduction principles, i.e. axioms of the form $\Diamond^{k} p \rightarrow \Diamond^{n} p$, has remained a long-standing open problem. In this paper, we make significant progress toward solving this problem and show that decidability holds for a large subclass of these logics, namely, fo

Stijn Verdenius, Andrea Zerio, Roy L. M. Wang

We introduce LatentTimePFN (LaT-PFN), a foundational Time Series model with a strong embedding space that enables zero-shot forecasting. To achieve this, we perform in-context learning in latent space utilizing a novel integration of the Prior-data Fitted Networks (PFN) and Joint Embedding Predictive Architecture (JEPA) frameworks. We leverage the JEPA frame

Pei-Jie Chang, Jinghui Pi, Muxi Zheng, Yu-Ting Lei

Despite extensive studies on the one-dimensional Su-Schrieffer-Heeger-Hubbard (SSHH) model, the variant incorporating next-nearest neighbour hopping remains largely unexplored. Here, we investigate the ground-state properties of this extended SSHH model using the constrained-path auxiliary-field quantum Monte Carlo (CP-AFQMC) method. We show that this model

Chenjing Bu

We prove a motivic integral identity relating the motivic Behrend function of a $(-1)$-shifted symplectic stack to that of its stack of graded points. This generalizes analogous identities for moduli stacks of objects in $3$-Calabi$\unicode{x2013}$Yau abelian categories obtained by Kontsevich$\unicode{x2013}$Soibelman and Joyce$\unicode{x2013}$Song, which ar

Benoit F. Sehba

We characterize the multipliers from the little BMO of Cotlar-Sadosky to the product BMO of Chang-Fefferman on the polydisk.

V. A. Kuz'min, T. V. Tetereva

The appearance of like nucleon pair correlations in the ground state of spherical even-even nuclei is considered within a special Bogoliubov transformation. It is confirmed that in closed subshell nuclei superconducting pair correlations start to form if the coupling constant $G$ exceeds a certain threshold value. Rough upper and lower estimates are obtained

Xaver Fabian, Marco Patrignani, Marco Guarnieri, Michael Backes

Mainstream compilers implement different countermeasures to prevent specific classes of speculative execution attacks. Unfortunately, these countermeasures either lack formal guarantees or come with proofs restricted to speculative semantics capturing only a subset of the speculation mechanisms supported by modern CPUs, thereby limiting their practical appli

Antonio Montero, Primož Potočnik

The motion of a graph is the minimum number of vertices that are moved by a non-trivial automorphism. Equivalently, it can be defined as the minimal degree of its automorphism group (as a permutation group on the vertices). In this paper we develop some results on permutation groups (primitive and imprimitive) with small minimal degree. As a consequence of s

Chenrui Sun, Gianluca Fontanesi, Swarna Bindu Chetty, Xuanyu Liang

Deep Reinforcement Learning (DRL) emerges as a prime solution for Unmanned Aerial Vehicle (UAV) trajectory planning, offering proficiency in navigating high-dimensional spaces, adaptability to dynamic environments, and making sequential decisions based on real-time feedback. Despite these advantages, the use of DRL for UAV trajectory planning requires signif

Johannes Schleischitz

We refine upper bounds for the classical exponents of uniform approximation for a linear form on the Veronese curve in dimension from $3$ to $9$. For dimension three, this in particular shows that a bound previously obtained by two different methods is not sharp. Our proof involves parametric geometry of numbers and investigation of geometric properties of b

Pashupati Dhakal, Bashu Dev Khanal, Eric Lechner, Gianluigi Ciovati

The effect of mid-T heat treatment on flux trapping sensitivity was measured on several 1.3 GHz single cell cavities subjected to vacuum annealing at temperature of 150 - 400 $^\circ$C for a duration of 3 hours. The cavity was cooldown with residual magnetic field $\sim$0 and $\sim$20 mG in the Dewar with cooldown condition of full flux trapping. The quality

Manh Luong, Khai Nguyen, Nhat Ho, Reza Haf

The Learning-to-match (LTM) framework proves to be an effective inverse optimal transport approach for learning the underlying ground metric between two sources of data, facilitating subsequent matching. However, the conventional LTM framework faces scalability challenges, necessitating the use of the entire dataset each time the parameters of the ground met

Fan Wu, Xun Li, Xin Zhang

This paper investigates an inhomogeneous non-zero-sum linear-quadratic (LQ, for short) differential game problem whose state process and cost functional are regulated by a Markov chain. Under the $L^2$ stabilizability framework, we first provide a sufficient condition to ensure the $L^2$-integrability of the state process and study a class of linear backward

Konstantinos Tsigos, Evlampios Apostolidis, Vasileios Mezaris

In this paper, we propose an integrated framework for multi-granular explanation of video summarization. This framework integrates methods for producing explanations both at the fragment level (indicating which video fragments influenced the most the decisions of the summarizer) and the more fine-grained visual object level (highlighting which visual objects

Kenta Kiuchi

Binary neutron star mergers used to be the most promising candidate for gravitational waves for ground-based gravitational wave detectors, such as advanced LIGO and advanced VIRGO. This was proved by the detection of gravitational waves from a binary neutron star merger in 2017. Numerical modeling is pivotal in predicting and interpreting binary neutron star

Suman Chatterjee, Khandakar Md Asif Elahi, Somnath Bharadwaj, Shouvik Sarkar

Drift scan observations provide the broad sky coverage and instrumental stability needed to measure the Epoch of Reionization (EoR) 21-cm signal. In such observations, the telescope's pointing center (PC) moves continuously on the sky. The Tracking Tapered Gridded Estimator (TTGE) combines observations from different PC to estimate $P(k_{\perp}, k_{\parallel

Tianyu Zhang, Chengxin Xiao, Hongxia Xue, Wang Yao

Moir\'e superlattices formed in stacks of two or more 2D crystals with similar lattice structures have recently become excellent platforms to reveal new physics in low-dimensional systems. They are, however, highly sensitive to the angle and lattice constant differences between the associated crystals, limiting the range of the material choice and the possib

Ken Shirakawa, Yoshihiro Nagano, Misato Tanaka, Shuntaro C. Aoki

Advances in brain decoding, particularly visual image reconstruction, have sparked discussions about the societal implications and ethical considerations of neurotechnology. As these methods aim to recover visual experiences from brain activity and achieve prediction beyond training samples (zero-shot prediction), it is crucial to assess their capabilities a

Muqsit Azeem, Marta Grobelna, Sudeep Kanav, Jan Kretinsky

The behavior of neural networks (NNs) on previously unseen types of data (out-of-distribution or OOD) is typically unpredictable. This can be dangerous if the network's output is used for decision-making in a safety-critical system. Hence, detecting that an input is OOD is crucial for the safe application of the NN. Verification approaches do not scale to pr

Shinnosuke Tanaka, James Barry, Vishnudev Kuruvanthodi, Movina Moses

This paper describes the KnowledgeHub tool, a scientific literature Information Extraction (IE) and Question Answering (QA) pipeline. This is achieved by supporting the ingestion of PDF documents that are converted to text and structured representations. An ontology can then be constructed where a user defines the types of entities and relationships they wan

Jacopo Bonari, Lisa Kühn, Max von Danwitz, Alexander Popp

Urban populations continue to grow, highlighting the critical need to safeguard civilians against potential disruptions, such as dangerous gas contaminant dispersion. The digital twin (DT) framework offers promise in analyzing and predicting such events. This study presents a computational framework for modelling airborne contaminant dispersion in built envi

Samuel Jelbart, Kristian Uldall Kristiansen, Peter Szmolyan

We prove the existence of a family of travelling wave solutions in a variant of the $\textit{Zeldovich-Frank-Kamenetskii (ZFK) equation}$, a reaction-diffusion equation which models the propagation of planar laminar premixed flames in combustion theory. Our results are valid in an asymptotic regime which corresponds to a reaction with high activation energy,

Kun Yuan, Vinkle Srivastav, Nassir Navab, Nicolas Padoy

Natural language could play an important role in developing generalist surgical models by providing a broad source of supervision from raw texts. This flexible form of supervision can enable the model's transferability across datasets and tasks as natural language can be used to reference learned visual concepts or describe new ones. In this work, we present

Marie Schmidt, Anita Schöbel

For all line-based transit systems like bus, metro and tram, the routes of the lines and the frequencies at which they are operated are determining for the operational performance of the system. However, as transit line planning happens early in the planning process, it is not straightforward to predict the effects of line planning decisions on relevant perf

Mana Ito, Kazuyuki Omukai

First stars play crucial roles in development of the universe, influencing events like cosmic reionization and the chemical enrichment. While first stars are conventionally thought to form at around $z \sim 20-30$ in the standard $\Lambda$ Cold Dark Matter ($\Lambda$CDM) cosmology, observational constraints on small-scale density fluctuations remain limited,

Redi Haderi, Özgün Ünlü

We define a category $\mathsf{List}$ whose objects are sets and morphisms are mappings which assign to an element in the domain an ordered sequence (list) of elements in the codomain. We introduce and study a category of simplicial objects $\mathsf{slist}$ whose objects are functors $\Delta^{op} \to \mathsf{List}$, which we call simplicial lists, and morphis

Andrei Tokovinin, Brian D. Mason, Rene A. Mendez, Edgardo Costa

Results of the speckle-interferometry observations at the 4.1 m Southern Astrophysical Research Telescope (SOAR) obtained during 2023 are presented: 1913 measurements of 1533 resolved pairs or subsystems (median separation 0.16") and non-resolutions of 552 targets; 42 pairs are resolved here for the first time. This work continues our long-term effort to mon

Johannes G. Borgqvist, Christoffer Gretarsson Alexandersen

Prion-like proteins play crucial parts in biological processes in organisms ranging from yeast to humans. For instance, many neurodegenerative diseases are believed to be caused by the production of prion-like proteins in neural tissue. As such, understanding the dynamics of prion-like protein production is a vital step toward treating neurodegenerative dise

Ayhan Günaydın, Ebru Nayir

We show that the VC-density of any partitioned formula in a pair of ordered vector spaces is bounded above by twice the number of parameter variables. We also show that this bound is optimal and, as a by-product, we prove that no dense pair of o-minimal structures is dp-minimal.

Ruizhe Li, Grazziela Figueredo, Dorothee Auer, Christian Wagner

Deformable image registration (alignment) is highly sought after in numerous clinical applications, such as computer aided diagnosis and disease progression analysis. Deep Convolutional Neural Network (DCNN)-based image registration methods have demonstrated advantages in terms of registration accuracy and computational speed. However, while most methods exc

Felix Held, Jacob Lindbäck, Rebecka Jörnsten

Interest in unsupervised methods for joint analysis of heterogeneous data sources has risen in recent years. Low-rank latent factor models have proven to be an effective tool for data integration and have been extended to a large number of data source layouts. Of particular interest is the separation of variation present in data sources into shared and indiv

Marta Magnani

We generalize arc coordinates for maximal representations from a hyperbolic surface with boundary into $\text{PSp}(4,\mathbb{R})$, focusing on the case where the surface is a pair of pants. We introduce geometric parameters within the space of right-angled hexagons in the Siegel space $\mathcal{X}$. These parameters enable the visualization of a right-angled

Nima Monshizadeh, Claudio De Persis, Pietro Tesi

This note aims to provide a systematic investigation of direct data-driven control, enriching the existing literature not by adding another isolated result, but rather by offering a unifying, versatile, and broad framework that enables the generation of novel results in this domain. We formulate the nonlinear design problem from a high-level perspective as a

Xinlei Liu, Andrey Belogaev, Jonathan Oostvogels, Bingwu Fang

Wireless sensor networks (WSNs) have received great interest due to their scalability, energy efficiency, and low-cost deployment. By utilizing multi-hop communication, WSNs can cover a wide area using low transmission power without the need for any communication infrastructure. Traditionally, WSNs rely on store-and-forward routing protocols and Time Divisio

Adith Sai Aramthottil, Piotr Sierant, Maciej Lewenstein, Jakub Zakrzewski

Many-body localization (MBL) hinders the thermalization of quantum many-body systems in the presence of strong disorder. In this work, we study the MBL regime in bond-disordered spin-1/2 XXZ spin chain, finding the multimodal distribution of entanglement entropy in eigenstates, sub-Poissonian level statistics, and revealing a relation between operators and i

Xuhao Jiang, Jianfei Xu

In this paper, we analyze the symmetry properties of the complete family of type D spacetimes generalized form the Pleba\'nski-Demia\'nski solution in four dimensions holographically in terms of a warped CFT. The generalized Pleba\'nski-Demia\'nski solutions are black hole-like spacetimes characterized by seven physical parameters. Most of the black holes in

Qiqi Gu, Wei Ke

Model-Driven Engineering (MDE) is a technique that aims to boost productivity in software development and ensure the safety of critical systems. Central to MDE is the refinement of high-level requirement models into executable code. Given that requirement models form the foundation of the entire development process, ensuring their correctness is crucial. RM2

Jana Franceska Funke, Enrico Rukzio

In a world where in-person context transitions more into remote and hybrid concepts, we should consider new concepts of interaction in health and rehabilitation and what advantages and disadvantages they bring. One of the rising topics is mixed reality, where we can use the advantages of immersive 3D, 360-degree environments. Meanwhile, physical activity is

Fabien Dufoulon, Pierre Fraigniaud, Mikaël Rabie, Hening Zheng

In distributed network computing, a variant of the LOCAL model has been recently introduced, referred to as the SLEEPING model. In this model, nodes have the ability to decide on which round they are awake, and on which round they are sleeping. Two (adjacent) nodes can exchange messages in a round only if both of them are awake in that round. The SLEEPING mo

Timothé Albouy, Antonio Fernández Anta, Chryssis Georgiou, Mathieu Gestin

In this work, we introduce a modular framework for specifying distributed systems that we call AMECOS. Specifically, our framework departs from the traditional use of sequential specification, which presents limitations both on the specification expressiveness and implementation efficiency of inherently concurrent objects, as documented by Casta\~neda, Rajsb

An-Ping Chen, Xiaohui Liu, Yan-Qing Ma

We propose to measure the energy correlator in quarkonium production, which tracks the energy deposited in the calorimeter $\chi$-angular distance away from the identified quarkonium. The observable eliminates the need for jets while sustaining the perturbative predictive power. Analyzing the power correction to the energy correlator, we demonstrate the nove

Yuri Gurevich, Andreas Blass

According to Dirac's bra-ket notation, in an inner-product space, the inner product $\langle x\,|\,y\rangle$ of vectors $x,y$ can be viewed as an application of the bra $\langle x|$ to the ket $|y\rangle$. Here $\langle x|$ is the linear functional $|y\rangle \mapsto \langle x\,|\,y\rangle$ and $|y\rangle$ is the vector $y$. But often -- though not always --

Daniel Racz, Martin Gonzalez, Mihaly Petreczky, Andras Benczur

One of the main theoretical challenges in learning dynamical systems from data is providing upper bounds on the generalization error, that is, the difference between the expected prediction error and the empirical prediction error measured on some finite sample. In machine learning, a popular class of such bounds are the so-called Probably Approximately Corr

Mingxuan Liu, Tyler L. Hayes, Elisa Ricci, Gabriela Csurka

Open-vocabulary object detection (OvOD) has transformed detection into a language-guided task, empowering users to freely define their class vocabularies of interest during inference. However, our initial investigation indicates that existing OvOD detectors exhibit significant variability when dealing with vocabularies across various semantic granularities,

Leyi Pan, Aiwei Liu, Zhiwei He, Zitian Gao

LLM watermarking, which embeds imperceptible yet algorithmically detectable signals in model outputs to identify LLM-generated text, has become crucial in mitigating the potential misuse of large language models. However, the abundance of LLM watermarking algorithms, their intricate mechanisms, and the complex evaluation procedures and perspectives pose chal

Alexander Sikorski, Martin Heida

The computation of Voronoi Diagrams, or their dual Delauney triangulations is difficult in high dimensions. In a recent publication Polianskii and Pokorny propose an iterative randomized algorithm facilitating the approximation of Voronoi tesselations in high dimensions. In this paper, we provide an improved vertex search method that is not only exact but ev

Dawson Beatty

Methods for global navigation satellite system fault detection using Euclidean Distance Matrices have been presented recently in the literature. Published methods define a test statistic in terms of eigenvalues of a certain matrix, but the distribution of the test statistic was not known, which presented a barrier to practical implementation. This document w

Arnaud Vanhaecke

Colmez, Dospinescu and Niziol have shown that the only $p$-adic representations of $\rm{Gal}(\bar{\mathbb{Q}}_p/\mathbb{Q}_p)$ appearing in the $p$-adic \'etale cohomology of the coverings of Drinfeld's half-plane are the $2$-dimensional cuspidal representations (i.e. potentially semi-stable, whose associated Weil-Deligne representation is irreducible) with

Weitao Zhang, Jun Zhang, Han He, Ali Luo

The dependence of stellar magnetic activity on stellar parameters would be inspired by the chromospheric activity studies based on the large-scale spectroscopic surveys. The Ca II H and K lines are employed to construct indicators for assessing and studying the chromospheric activity of solar-like stars. We investigate the widely used bolometric and photosph

Andrea Matteazzi, Pascal Colling, Michael Arnold, Dietmar Tutsch

In recent years considerable research in LiDAR semantic segmentation was conducted, introducing several new state of the art models. However, most research focuses on single-scan point clouds, limiting performance especially in long distance outdoor scenarios, by omitting time-sequential information. Moreover, varying-density and occlusions constitute signif