May 2024 arXiv papers — page 14

Jing Wen

Deep neural networks (DNNs) can easily be cheated by some imperceptible but purposeful noise added to images, and erroneously classify them. Previous defensive work mostly focused on retraining the models or detecting the noise, but has either shown limited success rates or been attacked by new adversarial examples. Instead of focusing on adversarial images

Antonio Pich

The super tau-charm facility will provide excellent conditions to perform a high-precision investigation of the tau-lepton properties: very high statistics, controllable systematics and low backgrounds. An overview of the broad physics program that could be addressed at this facility is presented.

Ehud Malul, Yair Meidan, Dudu Mimran, Yuval Elovici

A key challenge associated with Kubernetes configuration files (KCFs) is that they are often highly complex and error-prone, leading to security vulnerabilities and operational setbacks. Rule-based (RB) tools for KCF misconfiguration detection rely on static rule sets, making them inherently limited and unable to detect newly-discovered misconfigurations. RB

Eoin Ó Colgáin, M. M. Sheikh-Jabbari, Lu Yin

The James Webb Space Telescope (JWST) is reporting massive high redshift galaxies that appear challenging from the $\Lambda$CDM perspective. Interpreted as a cosmological problem, this necessitates the Planck collaboration underestimating either matter density $\Omega_m$ or physical matter density $\Omega_m h^2$ at higher redshifts. Through standard frequent

Yue Liu, Xingjie Yan, Jinbiao Wang, Jipeng Cheng

Bigraded Toda hierarchy $L_1^M(n)=L_2^N(n)$ is generalized to $L_1^M(n)=L_2^{N}(n)+\sum_{j\in \mathbb Z}\sum_{i=1}^{m}q^{(i)}_n\Lambda^jr^{(i)}_{n+1}$, which is the analogue of the famous constrained KP hierarchy $L^{k}= (L^{k})_{\geq0}+\sum_{i=1}^{m}q_{i}\partial^{-1}r_i$. It is known that different bosonizations of fermionic KP hierarchy will give rise to

Laurent Condat, Peter Richtárik

Point-SAGA is a randomized algorithm for minimizing a sum of convex functions using their proximity operators (proxs), proposed by Defazio (2016). At every iteration, the prox of only one randomly chosen function is called. We generalize the algorithm to any number of prox calls per iteration, not only one, and propose a simple proof of linear convergence wh

Konstantin Hemker, Nikola Simidjievski, Mateja Jamnik

Learning holistic computational representations in physical, chemical or biological systems requires the ability to process information from different distributions and modalities within the same model. Thus, the demand for multimodal machine learning models has sharply risen for modalities that go beyond vision and language, such as sequences, graphs, time

Jake Vasilakes, Zhixue Zhao, Ivan Vykopal, Michal Gregor

Addressing online disinformation requires analysing narratives across languages to help fact-checkers and journalists sift through large amounts of data. The ExU project focuses on developing AI-based models for multilingual disinformation analysis, addressing the tasks of rumour stance classification and claim retrieval. We describe the ExU project proposal

Jianxiong Gao, Xuelin Qian, Longfei Liang, Junwei Han

Amodal object completion is a complex task that involves predicting the invisible parts of an object based on visible segments and background information. Learning shape priors is crucial for effective amodal completion, but traditional methods often rely on two-stage processes or additional information, leading to inefficiencies and potential error accumula

Mukta Debnath, Animesh Basak Chowdhury, Debasri Saha, Susmita Sur-Kolay

High-Level Synthesis (HLS) has transformed the development of complex Hardware IPs (HWIP) by offering abstraction and configurability through languages like SystemC/C++, particularly for Field Programmable Gate Array (FPGA) accelerators in high-performance and cloud computing contexts. These IPs can be synthesized for different FPGA boards in cloud, offering

Alvaro Corral

Bifurcations are one of the most remarkable features of dynamical systems. Corral et al. [Sci. Rep. 8(11783), 2018] showed the existence of scaling laws describing the transient (finite-time) dynamics in discrete dynamical systems close to a bifurcation point, following an approach that was valid for the transcritical as well as for the saddle-node bifurcati

Xuanfa Jin, Ziyan Wang, Yali Du, Meng Fang

Communication is a fundamental aspect of human society, facilitating the exchange of information and beliefs among people. Despite the advancements in large language models (LLMs), recent agents built with these often neglect the control over discussion tactics, which are essential in communication scenarios and games. As a variant of the famous communicatio

Carlos A. Cruz, Xavier Massaneda, Joaquim Ortega-Cerdà

We study sampling and interpolation arrays with multiplicities for the spaces P_k of holomorphic polynomials of degree at most k. We find that the geometric conditions satisfied by these arrays are in accordance with the conditions satisfied by the sampling and interpolating sequences with unbounded multiplicities in the Fock space, which can be seen as a li

Mohammed Alkrunz, Yaprak Yalcin

In this paper, a discrete-time I&I-based adaptive IDA-PBC controller for uncertain nonlinearly parameterized port-controlled Hamiltonian systems (PCH), where the parameter uncertainties are assumed in the energy function, is constructed. A proper formulation for the uncertain system dynamics is established where the uncertainties appear in nonlinearly parame

Qi Zhang, Yunfei Gong, Daijie Chen, Antoni B. Chan

Recent deep learning-based multi-view people detection (MVD) methods have shown promising results on existing datasets. However, current methods are mainly trained and evaluated on small, single scenes with a limited number of multi-view frames and fixed camera views. As a result, these methods may not be practical for detecting people in larger, more comple

Yang ya, Sun ge, Li jing, Lu jing

Quantum optics with giant atoms provides a new approach for implementing optical memory devices at the atomic scale. Here, we theoretically study the relaxation dynamics of a single driven three-level atom interacting with a one-dimensional waveguide, via two coupling points. Under certain conditions, after the long-time dynamics, we found that the populatio

Simon N. Chu, Alex J. Goodell

Problem: Effective patient-centered communication is a core competency for physicians. However, both seasoned providers and medical trainees report decreased confidence in leading conversations on sensitive topics such as goals of care or end-of-life discussions. The significant administrative burden and the resources required to provide dedicated training i

Derek Holt

We prove that a quotient G/N of a subgroup G of Sym(n) by a nonabelian minimal normal subgroup N of G embeds into Sym(m) for some $m<n$. This result was proved previously by Robert Chamberlain, and we also prove that,if G is transitive, then we can take m \le 2n/5.

Michele Scalco, Luigi Bedin, Enrico Vesperini

In this paper we present the analysis of Hubble Space Telescope (HST) observations of the globular cluster Omega Centauri. Our analysis combines data obtained in this work with previously published HST data from an earlier article of this series and encompasses a broad portion of the cluster's radial extension. Our findings reveal a significant radial variat

Nicolas Lerner

We explore the new proofs and extensions of the Heisenberg Uncertainty Principle introduced by A.~Widgerson & Y.~Widgerson in [MR4229152], developed in [MR4453622] by N.C.~Dias, F.~Luef and J.N.~Prata and also in [MR4337266] by Y.~Tang. In particular we give here a proof of the Uncertainty Principle for operators in the Metaplectic group in any dimension.

A. Chakraborty, B. K. Sahoo

Relativistic coupled-cluster (RCC) theory at the singles and doubles approximation has been implemented to estimate nuclear spin dependent (NSD) parity violating (PV) electric dipole (E1) transition amplitudes ($E1_{PV}^{NSD}$) among hyperfine levels of the $6s ~^2S_{1/2} \rightarrow 7s ~^2S_{1/2}$ transition in $^{133}$Cs. To validate our calculations, we r

Srijaya Nandi, Mousumi Chakraborty, Aesha Lahiri, Hindolii Gope

Individual human recognition is important for species that live in close proximity to humans. Numerous studies on domesticated species and urban-adapted birds have highlighted this ability. One such species which is heavily reliant on humans is the free-ranging dog. Very little knowledge exists on the amount of time taken by free-ranging dogs to learn and re

Armin Ghazi, Seyed Akbar Jafari

In quantum anomalous Hall (QAH) systems, the Hall conductance is quantized and the corresponding effective topological theory of the system is the Chern-Simons theory. The conductance quantum is given by the universal constant $e^2/h$ -- the inverse von Klitzing constant -- that is independent of the bulk gap, as well as the size of the system. This picture

Elizabeth Hunter, John D. Kelleher

Stroke is one of the leading causes of death and disability worldwide but it is believed to be highly preventable. The majority of stroke prevention focuses on targeting high-risk individuals but its is important to understand how the targeting of high-risk individuals might impact the overall societal burden of stroke. We propose using an agent-based model

Alessandro Manenti, Daniele Zambon, Cesare Alippi

Graph neural networks use relational information as an inductive bias to enhance prediction performance. Not rarely, task-relevant relations are unknown and graph structure learning approaches have been proposed to learn them from data. Given their latent nature, no graph observations are available to provide a direct training signal to the learnable relatio

Per Alexandersson, Olivia Nabawanda

The $(P, w)$-partition generating function $K_{(P,w)}(x)$ is a quasisymmetric function obtained from a labeled poset. Recently, Liu and Weselcouch gave a formula for the coefficients of $K_{(P,w)}(x)$ when expanded in the quasisymmetric power sum function basis. This formula generalizes the classical Murnaghan--Nakayama rule for Schur functions. We extend th

Xiaoyu Wu, Jiaru Zhang, Yang Hua, Bohan Lyu

Few-shot fine-tuning of Diffusion Models (DMs) is a key advancement, significantly reducing training costs and enabling personalized AI applications. However, we explore the training dynamics of DMs and observe an unanticipated phenomenon: during the training process, image fidelity initially improves, then unexpectedly deteriorates with the emergence of noi

Hannah P. Menke, Katherine M. Hood, Kamaljit Singh, Gabriela M. Medero

Permeability and heat transport through building materials ultimately dictates their insulatory performance over a buildings service lifetime. Experiments combining XCT with numerical modelling are an accepted method of studying pore scale processes and have been used extensively in the oil and gas industry to study highly complex reservoir rocks. However, d

Yusuke Kato, Hiroshi Kori

The coexistence of an abnormal rhythm and a normal steady state is often observed in nature (e.g., epilepsy). Such a system is modeled as a bistable oscillator that possesses both a limit cycle and a fixed point. Although bistable oscillators under several perturbations have been addressed in the literature, the mechanism of oscillation quenching, a transiti

Xiaoyun Xu, Zhuoran Liu, Stefanos Koffas, Shujian Yu

Backdoor attacks on deep learning represent a recent threat that has gained significant attention in the research community. Backdoor defenses are mainly based on backdoor inversion, which has been shown to be generic, model-agnostic, and applicable to practical threat scenarios. State-of-the-art backdoor inversion recovers a mask in the feature space to loc

Puneet Kumar Shaw, Jehan Taraporewalla, Sohaib Raza, Akash Kumar

Applications like high density information storage, neuromorphic computing, nanophotonics, etc. require ultra-thin electronic devices which can be controlled with applied electric field. Of late, atomically thin two-dimensional (2D) materials and van der Waals (vdW) heterointerface of those have emerged as suitable candidates for such ultra-low power nanoele

Arvind Kumar Nath

In this paper, we first explore exponential stability by using Monotonicity inequality and use this information to obtain the existence of Invariant measure for linear Stochastic PDEs with potential in the space of tempered distributions. The uniqueness of Invariant Measure follows from Monotonicity inequality.

Hongliang Luo, Tengyu Zhang, Chuanbin Zhao, Yucong Wang

In this paper, we propose a novel integrated sensing and communications (ISAC) framework for the sixth generation (6G) mobile networks, in which we decompose the real physical world into static environment, dynamic targets, and various object materials. The ubiquitous static environment occupies the vast majority of the physical world, for which we design st

Jose Manuel Garcia Calcines

The concept of relative sectional category expands upon classical sectional category theory by incorporating the pullback of a fibration along a map. Our paper aims not only to explore this extension but also to thoroughly investigate its properties. We seek to uncover how the relative sectional category unifies several homotopic numerical invariants found i

Yuya Kodama

We prove that higher-dimensional Thompson's groups have linear divergence functions. By the work of Dru\c{t}u, Mozes, and Sapir, this implies none of the asymptotic cones of $nV$ has a cut-point.

S. Massalkhi, M. Agundez, J. P. Fonfria, J. R. Pardo

The spatial distribution of molecules in AGB circumstellar envelopes is regulated by different processes. In the outer layers all molecules are destroyed due to the interaction with interstellar ultraviolet photons. Here we aim to characterize in a coherent and uniform way the radial extent of three molecules (SiO, CS, and SiS) in envelopes around AGB stars

Zihao Li, Weiwei Yi, Jiahong Chen

The accuracy of Generative AI is increasingly critical as Large Language Models become more widely adopted. Due to potential flaws in training data and hallucination in outputs, inaccuracy can significantly impact individuals interests by distorting perceptions and leading to decisions based on flawed information. Therefore, ensuring these models accuracy is

Angel Villar-Corrales, Moritz Austermann, Sven Behnke

Autonomous systems, such as self-driving cars, rely on reliable semantic environment perception for decision making. Despite great advances in video semantic segmentation, existing approaches ignore important inductive biases and lack structured and interpretable internal representations. In this work, we propose MCDS-VSS, a structured filter model that lear

David Kohns, Noa Kallioinen, Yann McLatchie, Aki Vehtari

We present the ARR2 prior, a joint prior over the auto-regressive components in Bayesian time-series models and their induced $R^2$. Compared to other priors designed for times-series models, the ARR2 prior allows for flexible and intuitive shrinkage. We derive the prior for pure auto-regressive models, and extend it to auto-regressive models with exogenous

Yuhao Wu, Jiangchao Yao, Bo Han, Lina Yao

While Positive-Unlabeled (PU) learning is vital in many real-world scenarios, its application to graph data still remains under-explored. We unveil that a critical challenge for PU learning on graph lies on the edge heterophily, which directly violates the irreducibility assumption for Class-Prior Estimation (class prior is essential for building PU learning

Y. H. Chen, Thomas Y. He

Bressoud introduced the partition function $B(\alpha_1,\ldots,\alpha_\lambda;\eta,k,r;n)$, which counts the number of partitions with certain difference conditions. Bressoud posed a conjecture on the generating function for the partition function $B(\alpha_1,\ldots,\alpha_\lambda;\eta,k,r;n)$ in multi-summation form. In this article, we introduce a bijection

Masashi Hatano, Ryo Hachiuma, Ryo Fujii, Hideo Saito

We address a novel cross-domain few-shot learning task (CD-FSL) with multimodal input and unlabeled target data for egocentric action recognition. This paper simultaneously tackles two critical challenges associated with egocentric action recognition in CD-FSL settings: (1) the extreme domain gap in egocentric videos (e.g., daily life vs. industrial domain)

Anzal Memon, Albert van Rees, Jesse Mak, Youwen Fan

We demonstrate absorber-free passive and hybrid mode-locking at sub-GHz repetition rates using a hybrid integrated extended cavity diode laser around 1550 nm. The laser is based on InP as gain medium and a long Si$_3$N$_4$ feedback circuit, with three highly frequency selective microring resonators. The feedback resonators not only increases the cavity lengt

Huihong Shi, Xin Cheng, Wendong Mao, Zhongfeng Wang

Vision Transformers (ViTs) have excelled in computer vision tasks but are memory-consuming and computation-intensive, challenging their deployment on resource-constrained devices. To tackle this limitation, prior works have explored ViT-tailored quantization algorithms but retained floating-point scaling factors, which yield non-negligible re-quantization ov

Huadong Li, Shichao Dong, Jin Wang, Rong Fu

This paper focuses on the area of RGB(visible)-NIR(near-infrared) cross-modality image registration, which is crucial for many downstream vision tasks to fully leverage the complementary information present in visible and infrared images. In this field, researchers face two primary challenges - the absence of a correctly-annotated benchmark with viewpoint va

M. Karthick Selvan, S. Balakrishnan

In this brief report, we discuss the characteristics of B$^{\alpha}$ gates. We provide the conditions for the two-qubit gates generated by two applications of a B$^{\alpha}$ gate. We propose an experimental scheme to implement B$^{\alpha}$ gates in ion-trap system. In this scheme, we assume that only a single vibrational mode contributes to spin-spin couplin

Antonin Schrab, Ilmun Kim

We propose a general method for constructing robust permutation tests under data corruption. The proposed tests effectively control the non-asymptotic type I error under data corruption, and we prove their consistency in power under minimal conditions. This contributes to the practical deployment of hypothesis tests for real-world applications with potential

Chiara Castello, Olga Polverino, Ferdinando Zullo

For a linear Hamming metric code of length n over a finite field, the number of distinct weights of its codewords is at most n. The codes achieving the equality in the above bound were called full weight spectrum codes. In this paper we will focus on the analogous class of codes within the framework of cyclic subspace codes. Cyclic subspace codes have garner

M. Demiański, A. Doroshkevich, T. Larchenkova

Cosmic objects with magnetic fields (quasars, radiogalaxies) are observed at redshifts $z\geq 7$ (Wang et al., 2021, Fan et al., 2023, Yang et al., 2024) and more (for instance, for $z = 10.073\pm 0.002$, Goulding et al., 2023) indicates the early creation of magnetic fields. The observations of the cosmic telescope JWST show that the first galaxies were for

Tenglong Liu, Yang Li, Yixing Lan, Hao Gao

In offline reinforcement learning, the challenge of out-of-distribution (OOD) is pronounced. To address this, existing methods often constrain the learned policy through policy regularization. However, these methods often suffer from the issue of unnecessary conservativeness, hampering policy improvement. This occurs due to the indiscriminate use of all acti

Kunle Adegoke, Robert Frontczak

We present a range of difficult integration formulas involving Fibonacci and Lucas numbers and trigonometric functions. These formulas are often expressed in terms of special functions like the dilogarithm and Clausen's function. We also prove complements of integral identities of Dilcher (2000) and Stewart (2022). Many of our results are based on a fundamen

Surajit Chakraborty, Kabir Ramola

We explore the behavior of spatially heterogeneous elastic moduli as well as the correlations between local moduli in model solids with short-range repulsive potentials. We show through numerical simulations that local elastic moduli exhibit long-range correlations, similar to correlations in the local stresses. Specifically, the correlations in local shear

Irina Zhang, Jim Denholm, Azam Hamidinekoo, Oskar Ålund

Accurate segmentation of glomerulus instances attains high clinical significance in the automated analysis of renal biopsies to aid in diagnosing and monitoring kidney disease. Analyzing real-world histopathology images often encompasses inter-observer variability and requires a labor-intensive process of data annotation. Therefore, conventional supervised l

Kamal Kumar, Anjali Kumari, Soni Mishra, Ramesh Sharma

The structural, electronic, and dielectric (optical) properties of graphene-like 2D MgO monolayer have been explored through first-principles calculations under bi-axial tensile and compressive mechanical strain within a range of -10% to +10%. Our findings revealed that the pristine MgO monolayer is an indirect band gap semiconducting material and the semico

Raschid Abedin, Wenjun Niu

In this paper, we present a canonical quantization of Lie bialgebra structures on the formal power series $\mathfrak{d}[\![t]\!]$ with coefficients in the cotangent Lie algebra $\mathfrak{d} = T^*\mathfrak{g} = \mathfrak{g} \ltimes \mathfrak{g}^*$ to a simple complex Lie algebra $\mathfrak{g}$. We prove that these quantizations produce twists to the natural

D. M. -A. Meyer, E. Vorobyov

In recent years, it has been demonstrated that massive stars see their infant circumstellar medium shaped into a large, irradiated, gravitationally unstable accretion disc during their early formation phase. Such discs constitute the gas reservoir in which nascent high-mass stars gain substantial fraction of their mass by episodic accretion of dense gaseous

A. Ramezanpour, M. A. Rajabpour

Determinants are useful to represent the state of an interacting system of (effectively) repulsive and independent elements, like fermions in a quantum system and training samples in a learning problem. A computationally challenging problem is to compute the sum of powers of principal minors of a matrix which is relevant to the study of critical behaviors in

Suyeon Kim, Dongha Lee, SeongKu Kang, Sukang Chae

Label noise, commonly found in real-world datasets, has a detrimental impact on a model's generalization. To effectively detect incorrectly labeled instances, previous works have mostly relied on distinguishable training signals, such as training loss, as indicators to differentiate between clean and noisy labels. However, they have limitations in that the t

Junling Hu

This paper presents a novel approach for predicting molecular properties with high accuracy without the need for extensive pre-training. Employing ensemble learning and supervised fine-tuning of BERT, RoBERTa, and XLNet, our method demonstrates significant effectiveness compared to existing advanced models. Crucially, it addresses the issue of limited comput

Giacomo Blanco, Luca Barco, Lorenzo Innocenti, Claudio Rossi

Air pollution poses a significant threat to public health and well-being, particularly in urban areas. This study introduces a series of machine-learning models that integrate data from the Sentinel-5P satellite, meteorological conditions, and topological characteristics to forecast future levels of five major pollutants. The investigation delineates the pro

Alexey E. Rastegin

The current study aims to examine uncertainty relations for measurements from generalized equiangular tight frames. Informationally overcomplete measurements are a valuable tool in quantum information processing, including tomography and state estimation. The maximal sets of mutually unbiased bases are the most common case of such measurements. The existence

Seun-An Choe, Ah-Hyung Shin, Keon-Hee Park, Jinwoo Choi

Unsupervised domain adaptation (UDA) for semantic segmentation aims to transfer the pixel-wise knowledge from the labeled source domain to the unlabeled target domain. However, current UDA methods typically assume a shared label space between source and target, limiting their applicability in real-world scenarios where novel categories may emerge in the targ

Robin Chemnitz, Maximilian Engel, Guillermo Olicón-Mendez

We study the synchronization behavior of discrete-time Markov chains on countable state spaces. Representing a Markov chain in terms of a random dynamical system, which describes the collective dynamics of trajectories driven by the same noise, allows for the characterization of synchronization via random attractors. We establish the existence and uniqueness

Hanyuan Jiang

In the study of the Political Resource Curse (Brollo et al.,2013), the authors identified a new channel to investigate whether the windfalls of resources are unambiguously beneficial to society, both with theory and empirical evidence. This paper revisits the framework with a new dataset. Specifically, we implemented a regression discontinuity design and dif

Fernando Henriquez, Jan S. Hesthaven

We extend our previous work [F. Henr'iquez and J. S. Hesthaven, arXiv:2403.02847 (2024)] to the linear, second-order wave equation in bounded domains. This technique uses two widely known mathematical tools to construct a fast and efficient method for the solution of linear, time-dependent problems: the Laplace transform (LT) and the model-order reduction (M

Jaroslav Horáček, Miroslav Rada

There are many entities that disseminate in the physical space - information, gossip, mood, innovation etc. Personal spaces are also entities that disperse and interplay. In this work we study the emergence of configurations formed by participants when choosing a place to sit in a rectangular auditorium. Based on experimental questionnaire data we design sev

Volodymyr Mazorchuk, Xiaoyu Zhu

We study locally finitary realizations of simple transitive module categories of infinite rank over the monoidal category $\mathscr{C}$ of finite dimensional modules for the complex Lie algebra $\mathfrak{sl}_2$. Combinatorics of such realizations is governed by six infinite Coxeter diagrams. We show that five of these are realizable in our setup, while one

Chunjing Gan, Dan Yang, Binbin Hu, Hanxiao Zhang

In recent years, large language models (LLMs) have made remarkable achievements in various domains. However, the untimeliness and cost of knowledge updates coupled with hallucination issues of LLMs have curtailed their applications in knowledge intensive tasks, where retrieval augmented generation (RAG) can be of help. Nevertheless, existing retrieval augmen

Han Miao, Jianyu Zhang

Utilizing the large quantity of hyperons and antihyperons produced by the decay of 10 billion $J/\psi$ and 2.7 billion $\psi(3686)$ collected at BESIII, the cross sections of several specific elastic or inelastic (anti-)hyperon-nucleus/nucleon rections have been measured via the scattering between the (anti-)hyperons and the nucleus in the dense objects of B

Dohun Kim, Minyoung Kim, Sarah Meng Li, Michele Mosca

We introduce an improved CNOT synthesis algorithm that considers nearest-neighbour interactions and CNOT gate error rates in noisy intermediate-scale quantum (NISQ) hardware. Compared to IBM's Qiskit compiler, it improves the fidelity of a synthesized CNOT circuit by about 2 times on average (up to 9 times). It lowers the synthesized CNOT count by a factor o

ALICE Collaboration

This Letter presents the first measurement of event-by-event fluctuations of the net number (difference between the particle and antiparticle multiplicities) of multistrange hadrons $\Xi^-$ and $\overline{\Xi}^+$ and its correlation with the net-kaon number using the data collected by the ALICE Collaboration in pp, p$-$Pb, and Pb$-$Pb collisions at a center-

Minghui Wu, Zhen Gao, Zhaocheng Wang, Dusit Niyato

Near-space airship-borne communication network is recognized to be an indispensable component of the future integrated ground-air-space network thanks to airships' advantage of long-term residency at stratospheric altitudes, but it urgently needs reliable and efficient Airship-to-X link. To improve the transmission efficiency and capacity, this paper propose

Chaofan Lin, Zhenhua Han, Chengruidong Zhang, Yuqing Yang

The rise of large language models (LLMs) has enabled LLM-based applications (a.k.a. AI agents or co-pilots), a new software paradigm that combines the strength of LLM and conventional software. Diverse LLM applications from different tenants could design complex workflows using multiple LLM requests to accomplish one task. However, they have to use the over-

C. Gaafele, Edmond B. Madimabe, K. Ndebele, P. Otlaadisa

We study the time-fractional Ivancevic option pricing model and the coupled nonlinear volatility and option price model via both modulational instability (MI) analysis and direct simulations. For the coupled volatility and option pricing model the coupling term for both the volatility and the option price equation is the same, the MI results are dependent on

Henrik Rydén, Reza Moosavi, Erik G. Larsson

In federated learning, a server must periodically broadcast a model to the agents. We propose to use multi-resolution coding and modulation (also known as non-uniform modulation) for this purpose. In the simplest instance, broadcast transmission is used, whereby all agents are targeted with one and the same transmission (typically without any particular favo

Hengkai Tan, Songming Liu, Kai Ma, Chengyang Ying

Transformer has shown promise in reinforcement learning to model time-varying features for obtaining generalized low-level robot policies on diverse robotics datasets in embodied learning. However, it still suffers from the issues of low data efficiency and high inference latency. In this paper, we propose to investigate the task from a new perspective of th

Antonio Picano, Giulio Biroli, Marco Schirò

Isolated quantum many-body systems which thermalize under their own dynamics are expected to act as their own thermal baths, thereby bringing their local subsystems to thermal equilibrium. Here we show that the infinite-dimensional limit of a quantum lattice model, as described by Dynamical Mean-Field theory (DMFT), provides a natural framework to understand

Jianliang He, Siyu Chen, Fengzhuo Zhang, Zhuoran Yang

In this work, from a theoretical lens, we aim to understand why large language model (LLM) empowered agents are able to solve decision-making problems in the physical world. To this end, consider a hierarchical reinforcement learning (RL) model where the LLM Planner and the Actor perform high-level task planning and low-level execution, respectively. Under t

Tomáš Vojíř, Jan Šochman, Jiří Matas

We propose a dense image prediction out-of-distribution detection algorithm, called PixOOD, which does not require training on samples of anomalous data and is not designed for a specific application which avoids traditional training biases. In order to model the complex intra-class variability of the in-distribution data at the pixel level, we propose an on

Hibiki Nagata, Hiroya Sakurai, Yuta Ueki, Kazuki Yamane

The superconducting transition temperatures, $T_{\mathrm{c}}$, of La$_{4}$Ni$_{3}$O$_{10+\delta}$($\delta$ = 0.04 and -0.01) were determined under various pressures up to 124.9 GPa by electrical resistance measurements with a diamond anvil cell. $T_{\mathrm{c}}$ exhibits a strong dependence on oxygen content within the pressure range of approximately 20 GPa

H. S. Zhang, M. Beretta, S. Cialdi, C. X. Yang

In the field of rare event physics, it is common to have huge masses of organic liquid scintillator as detection medium. In particular, they are widely used to study neutrino properties or astrophysical neutrinos. Thanks to its safety properties (such as low toxicity and high flash point) and easy scalability, linear alkyl benzene is the most common solvent

Timothy Horscroft

Theoretical computer science plays an important role in the understanding of social networks and their properties. We can model information rippling throughout social networks, or the opinions of social media users for example, using graph theory and Markov chains. In this thesis, we model social networks as graphs, and consider two such processes: 1. Nodes

Zeyu Fang, Tian Lan

Generative models such as diffusion have been employed as world models in offline reinforcement learning to generate synthetic data for more effective learning. Existing work either generates diffusion models one-time prior to training or requires additional interaction data to update it. In this paper, we propose a novel approach for offline reinforcement l

Arto Bendiken

We present KNOW--the Knowledge Navigator Ontology for the World--the first ontology designed to capture everyday knowledge to augment large language models (LLMs) in real-world generative AI use cases such as personal AI assistants. Our domain is human life, both its everyday concerns and its major milestones. We have limited the initial scope of the modeled

Alessio Mazzucchelli, Adrian Garcia-Garcia, Elena Garces, Fernando Rivas-Manzaneque

Advances in NERFs have allowed for 3D scene reconstructions and novel view synthesis. Yet, efficiently editing these representations while retaining photorealism is an emerging challenge. Recent methods face three primary limitations: they're slow for interactive use, lack precision at object boundaries, and struggle to ensure multi-view consistency. We intr

Yuxia Liang, Jonathan R. Partington

Recently, it was shown that the image of a Toeplitz kernel of dimension greater than $1$ under composition by an inner function is nearly $S^*$-invariant if and only if the inner function is an automorphism. Building on this, we determine the minimal Toeplitz kernel containing the image of a Toeplitz kernel under a composition operator with a general inner s

Hao Zhao, Maksym Andriushchenko, Francesco Croce, Nicolas Flammarion

In-context learning (ICL) allows LLMs to learn from examples without changing their weights: this is a particularly promising capability for long-context LLMs that can potentially learn from many examples. Recently, Lin et al. (2024) proposed URIAL, a method using only three in-context examples to align base LLMs, achieving non-trivial instruction following

C. Pashartis, M. J. van Setten, M. Houssa, G. Pourtois

Advancements in modern semiconductor devices increasingly depend on the utilization of amorphous materials and the reduction of material thickness, pushing the boundaries of their physical capabilities. The mechanical properties of these thin layers are critical in determining both the operational efficacy and mechanical integrity of these devices. Unlike bu

Igor Podlubny

Based on the analysis of the data obtainable from the Web of Science publication and citation database, typical signs of possible papermilling behavior are described, quantified, and illustrated by examples. A MATLAB function is provided for the analysis of the outputs from the Web of Science. A new quantitative indicator -- integrity index, or I-index -- is

Yevheniya Nosyk, Maciej Korczyński, Carlos H. Gañán, Michał Król

DNS dynamic updates represent an inherently vulnerable mechanism deliberately granting the potential for any host to dynamically modify DNS zone files. Consequently, this feature exposes domains to various security risks such as domain hijacking, compromise of domain control validation, and man-in-the-middle attacks. Originally devised without the implementa

Andreas Tritsarolis, Nikos Pelekis, Konstantina Bereta, Dimitris Zissis

The wide spread of Automatic Identification System (AIS) has motivated several maritime analytics operations. Vessel Location Forecasting (VLF) is one of the most critical operations for maritime awareness. However, accurate VLF is a challenging problem due to the complexity and dynamic nature of maritime traffic conditions. Furthermore, as privacy concerns

Wouter Jansen, Jan Steckel

In challenging environments where traditional sensing modalities struggle, in-air sonar offers resilience to optical interference. Placing a priori known landmarks in these environments can eliminate accumulated errors in autonomous mobile systems such as Simultaneous Localization and Mapping (SLAM) and autonomous navigation. We present a novel approach usin

Xu-Liang Chen, Peng-Fei Yang, Wei Chen

We derive the discontinuities of banana integrals using the dispersion relation iteratively. We find a series of identities between the parameterized discontinuities of banana integrals (p-DOBIs). Similar to elliptic integrals, these identities enable the reduction of various p-DOBIs to be a linear combination of some fundamental ones. We present a practical

Chia-Ying Chung, Sean M. Andrews, Mark A. Gurwell, Melvyn Wright

We present a new SMA survey of 47 Class II sources in the Taurus-Auriga region. Our observations made 12 independent samples of flux densities over the 200-400 GHz frequency range. We tightly constrained the spectral indices of most sources to a narrow range of $2.0\pm0.2$; only a handful of spatially resolved (e.g., diameter $>$250 au) disks present larger

Shaked Bader, Robert Kropholler, Vladimir Vankov

In 1996, Gersten proved that finitely presented subgroups of a word hyperbolic group of integral cohomological dimension 2 are hyperbolic. We use isoperimetric inequalities over arbitrary rings to extend this result to any ring. In particular, we study the discrete isoperimetric function and show that its linearity is equivalent to hyperbolicity, which is al

Mark de Rooij, Lorenza Cotugno, Roberta Siciliano

In this paper, we propose the generalized mixed reduced rank regression method, GMR$^3$ for short. GMR$^3$ is a regression method for a mix of numeric, binary, and ordinal response variables. The predictor variables can be a mix of binary, nominal, ordinal, and numeric variables. For dealing with the categorical predictors we use optimal scaling. A majorizat

Taisei Tosaki, Eiichiro Uchino, Ryosuke Kojima, Yohei Mineharu

Machine learning is increasingly used to predict lifestyle-related disease onset using health and medical data. However, its predictive accuracy for use is often hindered by dataset shift, which refers to discrepancies in data distribution between the training and testing datasets. This issue leads to the misclassification of out-of-distribution (OOD) data.

Jeremy B. Hume, Michael F. Whittaker

We show that the dynamical system associated by Putnam to a pair of graph embeddings is identical to the shift map on the limit space of a self-similar groupoid action on a graph. Moreover, performing a certain out-split on said graph gives rise to a Katsura-Exel-Pardo groupoid action on the out-split graph whose associated limit space dynamical system is co

ATLAS Collaboration

A search is presented for the pair-production of heavy vector-like quarks (VLQs) that each decay into a $W$ boson and a light quark. This study focuses on events where one $W$ boson decays into leptons and the other into hadrons. The search analyzed 140 fb$^{-1}$ of $pp$ collision data with $\sqrt{s} = 13$ TeV, recorded by the ATLAS detector from 2015 to 201

Young Chol Song

Temporal grounding of activities, the identification of specific time intervals of actions within a larger event context, is a critical task in video understanding. Recent advancements in multimodal large language models (LLMs) offer new opportunities for enhancing temporal reasoning capabilities. In this paper, we evaluate the effectiveness of combining ima