October 2023 arXiv papers — page 110

Peizheng Li, Adnan Aijaz

Improving sustainability, enhancing spectral and energy efficiency, and bringing in-network intelligence and reasoning are the driving forces for 6G. In this context, semantic communications (SemCom) and open radio access networks (Open RAN) are emerging as focal points of research. SemCom is widely viewed as a disruptive paradigm that creates the possibilit

Thomas A. Waigh, Nickolay Korabel

It is well established that a wide variety of phenomena in cellular and molecular biology involve anomalous transport e.g. the statistics for the motility of cells and molecules are fractional and do not conform to the archetypes of simple diffusion or ballistic transport. Recent research demonstrates that anomalous transport is in many cases heterogeneous i

Wenqi Jiang, Marco Zeller, Roger Waleffe, Torsten Hoefler

A Retrieval-Augmented Language Model (RALM) combines a large language model (LLM) with a vector database to retrieve context-specific knowledge during text generation. This strategy facilitates impressive generation quality even with smaller models, thus reducing computational demands by orders of magnitude. To serve RALMs efficiently and flexibly, we propos

Nikolaos Karastathis, Remy Prechelt, Juan Ammerman-Yebra, Maximilian Reininghaus

CORSIKA 8 is a new framework for air shower simulations implemented in modern C++17, based on past experience with existing codes like CORSIKA 7. The flexible and modular structure of the project allows the development of independent modules that can produce a fully customizable air shower simulation. The radio module in particular is designed to treat the s

Michael Ruzhansky, Alibek Yeskermessuly

This article considers the initial boundary value problem for the heat equation with the time-dependent Sturm-Liouville operator with singular potentials. To obtain a solution by the method of separation of variables, the problem is reduced to the problem of eigenvalues of the Sturm-Liouville operator. Further on, the solution to the initial boundary value p

Di Wu, Shaomu Tan, David Stap, Ali Araabi

This paper describes the UvA-MT's submission to the WMT 2023 shared task on general machine translation. We participate in the constrained track in two directions: English <-> Hebrew. In this competition, we show that by using one model to handle bidirectional tasks, as a minimal setting of Multilingual Machine Translation (MMT), it is possible to achieve co

Marko Mićić, Brenna N. Wells, Olivia J. Holmes, Jimmy A. Irwin

In this paper, we present the discovery of a dual AGN in a $\sim$11:1 minor merger between the galaxy SDSS J125417.98+274004.6 and its unnamed dwarf satellite. We calculated stellar masses of the primary and secondary galaxy to be 2.5$\times$10$^{10}$M$_{\odot}$ and 2.2$\times$10$^{9}$M$_{\odot}$, respectively. We used archival Chandra X-ray observations to

Polina Perstneva

We construct elliptic operators with scalar coefficients on the complements $(\mathbb{R}^2 \setminus S)^+$ of some Koch-type snowflakes $S$, whose Hausdorff dimensions cover the full range $(1, \ln{(4)}/\ln{(3)})$, such that the operator's elliptic measures are equal to the Hausdorff measure on the boundary. This provides another example of the phenomenon th

Chahyon Ku, Carl Winge, Ryan Diaz, Wentao Yuan

This paper primarily focuses on evaluating and benchmarking the robustness of visual representations in the context of object assembly tasks. Specifically, it investigates the alignment and insertion of objects with geometrical extrusions and intrusions, commonly referred to as a peg-in-hole task. The accuracy required to detect and orient the peg and the ho

Yael Cohen, Amit Schiller, Dong Wang, Joshua Dijksman

Disordered solids, straddling the solid-fluid boundary, lack a comprehensive continuum mechanical description. They exhibit a complex microstructure wherein multiple meta-stable states exist. Deforming disordered solids induces particles rearrangements enabling the system to transition between meta-stable states. A dramatic consequence of these transitions i

D. J. W. Simpson

For piecewise-linear maps the stable and unstable manifolds of hyperbolic periodic solutions are themselves piecewise-linear. Hence compact subsets of these manifolds can be represented using polytopes (i.e. polygons, in the case of two-dimensional manifolds). Such representations are efficient and exact so for computational purposes are superior to represen

José Miguel Mateos-Ramos, Baptiste Chatelier, Christian Häger, Musa Furkan Keskin

Integrated sensing and communications (ISAC) is envisioned as one of the key enablers of next-generation wireless systems, offering improved hardware, spectral, and energy efficiencies. In this paper, we consider an ISAC transceiver with an impaired uniform linear array that performs single-target detection and position estimation, and multiple-input single-

Malcolm Druett, Wenzhi Ruan, Rony Keppens

Context. Multi-dimensional solar flare simulations have not yet included detailed analysis of the lower atmospheric responses such as down-flowing chromospheric compressions and chromospheric evaporation processes. Aims. We present an analysis of multi-dimensional flare simulations, including analysis of chromospheric up-flows and down-flows that provide imp

Suguru Otani, Takuma Matsuda

We construct a novel unified merger list in the global container shipping industry between 1966 (the beginning of the industry) and 2022. Combining the list with proprietary data, we construct a structural matching model to describe the historical transition of the importance of a firm's age, size, and geographical proximity on merger decisions. We find that

Long Bai, Shilong Yao, Kun Gao, Yanjun Huang

Considering that Coupled Dictionary Learning (CDL) method can obtain a reasonable linear mathematical relationship between resource images, we propose a novel CDL-based Synthetic Aperture Radar (SAR) and multispectral pseudo-color fusion method. Firstly, the traditional Brovey transform is employed as a pre-processing method on the paired SAR and multispectr

Álvaro Castañeda, Fernanda Torres

We study the smoothness of the topological equivalence between a linear equation and its nonlinear perturbation, which is regarded as unbounded. To the best of our knowledge, it has not previously been considered such study in the literature. Therefore, the main result of this work copes with this lack, that is, it is shown, on the positive half line, that s

Xiuqiang He, Florian Dörfler

We prove that the popular grid-forming control, i.e., dispatchable virtual oscillator control (dVOC), also termed complex droop control, exhibits output-feedback passivity in its large-signal model, featuring an explicit and physically meaningful passivity index. Using this passivity property, we derive decentralized stability conditions for the transient st

David Radice, Sebastiano Bernuzzi

We study mass ejection from a binary neutron star merger producing a long-lived massive neutron star remnant with general-relativistic neutrino-radiation hydrodynamics simulations. In addition to outflows generated by shocks and tidal torques during and shortly after the merger, we observe the appearance of a wind driven by spiral density waves in the disk.

Xiuqiang He, Linbin Huang, Irina Subotić, Verena Häberle

In this paper, we analytically study the transient stability of grid-connected converters with grid-forming complex droop control, also known as dispatchable virtual oscillator control. We prove theoretically that complex droop control, as a state-of-the-art grid-forming control, always possesses steady-state equilibria whereas classical droop control does n

Yi Xiao, Harshit Sharma, Zhongyang Zhang, Dessa Bergen-Cico

Stress impacts our physical and mental health as well as our social life. A passive and contactless indoor stress monitoring system can unlock numerous important applications such as workplace productivity assessment, smart homes, and personalized mental health monitoring. While the thermal signatures from a user's body captured by a thermal camera can provi

Bobby Wilson, Xueying Yu

In this paper, we prove that the initial value problem for the mass-critical defocusing nonlinear Schr\"odinger equation on the three-dimensional hyperbolic space $\mathbb{H}^3$ is globally well-posed and scatters for data with radial symmetry in the critical space $L^2 (\mathbb{H}^3)$.

Jiaze Qiu

The Na\"ive Mean Field (NMF) approximation is widely employed in modern Machine Learning due to the huge computational gains it bestows on the statistician. Despite its popularity in practice, theoretical guarantees for high-dimensional problems are only available under strong structural assumptions (e.g., sparsity). Moreover, existing theory often does not

Tianxiao Shen, Hao Peng, Ruoqi Shen, Yao Fu

Language models have become the backbone of today's AI systems. However, their predominant left-to-right generation limits the use of bidirectional context, which is essential for tasks that involve filling text in the middle. We propose the Fill-in Language Model (FiLM), a new language modeling approach that allows for flexible generation at any position wi

Shubham Parashar, Zhiqiu Lin, Yanan Li, Shu Kong

Trained on web-scale image-text pairs, Vision-Language Models (VLMs) such as CLIP can recognize images of common objects in a zero-shot fashion. However, it is underexplored how to use CLIP for zero-shot recognition of highly specialized concepts, e.g., species of birds, plants, and animals, for which their scientific names are written in Latin or Greek. Ind

Valerio Proietti, Makoto Yamashita

We consider the homology theory of \'etale groupoids introduced by Crainic and Moerdijk, with particular interest to groupoids arising from topological dynamical systems. We prove a K\"unneth formula for products of groupoids and a Poincar\'e-duality type result for groupoids which are principal with orbits homeomorphic to a Euclidean space. We conclude with

Xun Lin, Shizhuo Zhang

Let $X$ be a smooth Fano variety. We attach a bi-graded associative algebra $\mathrm{HS}(\mathcal{K}u(X))=\bigoplus_{i,j\in \mathbb{Z}} \mathrm{Hom}(\mathrm{Id},S_{\mathcal{K}u(X)}^{i}[j])$ to the Kuznetsov component $\mathcal{K}u(X)$ whenever it is defined. Then we construct a natural sub-algebra of $\mathrm{HS}(\mathcal{K}u(X))$ when $X$ is a Fano hypersur

Shiladitya Dutta, Hongbo Wei, Lars van der Laan, Ahmed M. Alaa

Foundation models are trained on vast amounts of data at scale using self-supervised learning, enabling adaptation to a wide range of downstream tasks. At test time, these models exhibit zero-shot capabilities through which they can classify previously unseen (user-specified) categories. In this paper, we address the problem of quantifying uncertainty in the

Hosein Mohebbi, Grzegorz Chrupała, Willem Zuidema, Afra Alishahi

Transformers have become a key architecture in speech processing, but our understanding of how they build up representations of acoustic and linguistic structure is limited. In this study, we address this gap by investigating how measures of 'context-mixing' developed for text models can be adapted and applied to models of spoken language. We identify a ling

Francisco Munguia-Galeano, Ah-Hwee Tan, Ze Ji

Reinforcement learning (RL) has shown an outstanding capability for solving complex computational problems. However, most RL algorithms lack an explicit method that would allow learning from contextual information. Humans use context to identify patterns and relations among elements in the environment, along with how to avoid making wrong actions. On the oth

Zhihao Tao, Zhaoyi Xu, Athina Petropulu

In this paper, if the time-modulated array (TMA)-enabled directional modulation (DM) communication system can be cracked is investigated and the answer is YES! We first demonstrate that the scrambling data received at the eavesdropper can be defied by using grid search to successfully find the only and actual mixing matrix generated by TMA. Then, we propose

Anton Freund

This introduction begins with a section on fundamental notions of mathematical logic, including propositional logic, predicate or first-order logic, completeness, compactness, the L\"owenheim-Skolem theorem, Craig interpolation, Beth's definability theorem and Herbrand's theorem. It continues with a section on G\"odel's incompleteness theorems, which include

Lingdong Wang, Simran Singh, Jacob Chakareski, Mohammad Hajiesmaili

Accessing high-quality video content can be challenging due to insufficient and unstable network bandwidth. Recent advances in neural enhancement have shown promising results in improving the quality of degraded videos through deep learning. Neural-Enhanced Streaming (NES) incorporates this new approach into video streaming, allowing users to download low-qu

Dylan Possamaï, Ludovic Tangpi

Commuters looking for the shortest path to their destinations, the security of networked computers, hedge funds trading on the same stocks, governments and populations acting to mitigate an epidemic, or employers and employees agreeing on a contact, are all examples of (dynamic) stochastic differential games. In essence, game theory deals with the analysis o

Jiahao Xia, Gavin Gong, Jiawei Liu, Zhigang Zhu

In this paper, a Segment Anything Model (SAM)-based pedestrian infrastructure segmentation workflow is designed and optimized, which is capable of efficiently processing multi-sourced geospatial data including LiDAR data and satellite imagery data. We used an expanded definition of pedestrian infrastructure inventory which goes beyond the traditional transpo

Nadezhda Chirkova, Sheng Liang, Vassilina Nikoulina

Zero-shot cross-lingual knowledge transfer enables the multilingual pretrained language model (mPLM), finetuned on a task in one language, make predictions for this task in other languages. While being broadly studied for natural language understanding tasks, the described setting is understudied for generation. Previous works notice a frequent problem of ge

Ricarte Ribeiro, Plinio Moreno

The objective of this work is to expand upon previous works, considering socially acceptable behaviours within robot navigation and interaction, and allow a robot to closely approach static and dynamic individuals or groups. The space models developed in this dissertation are adaptive, that is, capable of changing over time to accommodate the changing circum

Nikola Opačak, Barbara Schneider, Jérôme Faist, Benedikt Schwarz

Frequency-modulated (FM) combs form spontaneously in free-running semiconductor lasers and possess a vast potential for spectroscopic applications. Despite recent progress in obtaining a conclusive theoretical description, experimental FM combs often exhibit non-ideal traits, which prevents their widespread use. Here we explain this by providing a clear theo

A. Patrón, B. Sánchez-Rey, A. Prados

In this paper we investigate, both analytically and numerically, the emergence of a kinetic glass transition in two different model systems: a uniformly heated granular gas and a molecular fluid with nonlinear drag. Despite the profound differences between these two physical systems, their behavior in thermal cycles share strong similarities, which stem from

Ziyang Zhou, Suyi Li

This study presents a modular, electronics-free, and fully onboard control and actuation approach for SMA-based soft robots to achieve locomotion tasks. This approach exploits the nonlinear mechanics of compliant curved beams and carefully designed mechanical control circuits to create and synchronize rhythmic deformation cycles, mimicking the central patter

Zijian Zhang, Luping Liu, Zhijie Lin, Yichen Zhu

We propose the first unsupervised and learning-based method to identify interpretable directions in h-space of pre-trained diffusion models. Our method is derived from an existing technique that operates on the GAN latent space. Specifically, we employ a shift control module that works on h-space of pre-trained diffusion models to manipulate a sample into a

Sergio F. Chevtchenko, Monalisa C. M. dos Santos, Diego M. Vieira, Ricardo L. Mota

With the support of Internet of Things (IoT) devices, it is possible to acquire data from degradation phenomena and design data-driven models to perform anomaly detection in industrial equipment. This approach not only identifies potential anomalies but can also serve as a first step toward building predictive maintenance policies. In this work, we demonstra

Christian Venturella, Christopher Hillenbrand, Jiachen Li, Tianyu Zhu

We present a machine learning (ML) framework for predicting Green's functions of molecular systems, from which photoemission spectra and quasiparticle energies at quantum many-body level can be obtained. Kernel ridge regression is adopted to predict self-energy matrix elements on compact imaginary frequency grids from static and dynamical mean-field electron

Zhichao Carton Zeng, Annika H. G. Peter, Xiaolong Du, Shengqi Yang

One of the hottest questions in the cosmology of self-interacting dark matter (SIDM) is whether scatterings can induce detectable core-collapse in halos by the present day. Because gravitational tides can accelerate core-collapse, the most promising targets to observe core-collapse are satellite galaxies and subhalo systems. However, simulating small subhalo

Chaoyi Wu, Jiayu Lei, Qiaoyu Zheng, Weike Zhao

Driven by the large foundation models, the development of artificial intelligence has witnessed tremendous progress lately, leading to a surge of general interest from the public. In this study, we aim to assess the performance of OpenAI's newest model, GPT-4V(ision), specifically in the realm of multimodal medical diagnosis. Our evaluation encompasses 17 hu

Fernando Febres Cordero

We present a calculation of the conservative two-body Hamiltonian of a compact binary system including a spinning black hole. We include up-to third order corrections in Newton's constant $G$, all orders in velocity, and linear and quadratic terms in spin. The results are obtained from the classical limit of two-loop scattering amplitudes involving two massi

Aqsa Mehmood, M. Umair Shahzad

In this work, we investigate the thermodynamic properties of black holes (BHs) that have non-trivial topological features in their phase diagrams. We consider three different models of BHs: (1) a class of BHs in dRGT massive gravity, which adds a mass term to general relativity; (2) a class of BHs in 5D Yang-Mills massive gravity, which combines dRGT massive

Mawo Ito

Lattice paths called $\ell$-Schr\"oder paths are introduced. They are paths on the upper half-plane consisting of $\ell+2$ types of steps: $(i,\ell-i)$ for $i=0,\ldots,\ell$, and $(1,-1)$. Those paths generalize Schr\"oder paths and some variants, such as $m$-Schr\"oder paths by Yang and Jiang and Motzkin-Schr\"oder paths by Kim and Stanton. We show that $\e

Harkirat Singh Sahota

Sharply peaked quantum states are conjectured to be conducive to the notion of a quantum-corrected spacetime. We investigate this conjecture for a flat-FLRW model with perfect fluid, where a generalized ordering scheme is considered for the gravitational Hamiltonian. We study the implications of different ordering choices on the dynamics of the quantum unive

Yu Liu, M. O. Ajeesh, A. O. Scheie, C. R. dela Cruz

We report a study of the structural, magnetic, transport, and thermodynamic properties of polycrystalline samples of CeRhC$_2$. CeRhC$_2$ crystallizes in a tetragonal structure with space group $P4_1$ and it orders antiferromagnetically below $T_\textrm{N1} \approx$ 1.8 K. Powder neutron diffraction measurements reveal a chiral magnetic structure with a sing

Fatemeh Moodi, Amir Jahangard-Rafsanjani

Stock price prediction is influenced by a variety of factors, including technical indicators, which makes Feature selection crucial for identifying the most relevant predictors. This study examines the impact of feature selection on stock price prediction accuracy using technical indicators. A total of 123 technical indicators and 10 regression models were e

Jean-Baptiste Ruffio, Marshall D. Perrin, Kielan K. W. Hoch, Jens Kammerer

The JWST NIRSpec integral field unit (IFU) presents a unique opportunity to observe directly imaged exoplanets from 3-5 um at moderate spectral resolution (R~2,700) and thereby better constrain the composition, disequilibrium chemistry, and cloud properties of their atmospheres. In this work, we present the first NIRSpec IFU high-contrast observations of a s

Mahir Bilen Can, Pinakinath Saha

In this article, we provide characterizations of toric Richardson varieties across all types through three distinct approaches: 1) poset theory, 2) root theory, and 3) geometry.

Alvaro Cornejo, Mariel Supina

The Brion morphism maps a generalized permutahedron to a collection of posets associated to its vertices. We compute this map explicitly for the Hopf monoids of permutahedra, associahedra, and orbit polytopes, and we explore the dual Brion map of the primitive Lie monoids associated to these three Hopf monoids. We describe the Lie monoid structure of the pri

Mingrui Yu, Kangchen Lv, Changhao Wang, Yongpeng Jiang

Constrained environments are common in practical applications of manipulating deformable linear objects (DLOs), where movements of both DLOs and robots should be constrained. This task is high-dimensional and highly constrained owing to the highly deformable DLOs, dual-arm robots with high degrees of freedom, and 3-D complex environments, which render global

Matthew Kleban, Cameron E. Norton

During slow-roll inflation, non-perturbative transitions can produce bubbles of metastable vacuum. These bubbles expand exponentially during inflation to super-horizon size, and later collapse into black holes when the expansion of the universe is decelerating. Estimating the rate for these transitions during a time-dependent slow-roll phase requires the dev

Dimitris Gkoumas, Matthew Purver, Maria Liakata

Dementia is associated with language disorders which impede communication. Here, we automatically learn linguistic disorder patterns by making use of a moderately-sized pre-trained language model and forcing it to focus on reformulated natural language processing (NLP) tasks and associated linguistic patterns. Our experiments show that NLP tasks that encapsu

Pavel Gotovski, Sergej Orlov

Weber-type parabolic beams have a transverse intensity profile, which is parabolically-shaped and can be flexibly controlled. On the other hand, this type of beams belongs to the family of the so-called nondiffracting beams and have properties, promising for applications where the shape of the beam is of an importance. Vector electromagnetic theory has to be

Sciacovelli L., Cannici A., Passiatore D., Cinnella P

A priori tests of turbulence models for the compressible Reynolds-Averaged Navier--Stokes (RANS) are performed by using Direct Numerical Simulations (DNS) data of zero-pressure-gradient flat-plate turbulent boundary layers. The DNS database covers a wide range of operating conditions, ranging from supersonic ($M_\infty = 2.25$) up to the high-enthalpy hypers

Vitalis Vosylius, Sergej Orlov

Nondiffracting pulsed beams are well studied nowadays and can be as short as a few femtoseconds. The nondiffracting pulsed beams not only resist diffraction but also propagate without changes due to the dispersion of a linear dispersive medium. A promising member of non-diffracting beam family is the so-called Mathieu beam which is a solution of Helmholtz wa

Wei-Cheng Huang, Alp Aydinoglu, Wanxin Jin, Michael Posa

The hybrid nature of multi-contact robotic systems, due to making and breaking contact with the environment, creates significant challenges for high-quality control. Existing model-based methods typically rely on either good prior knowledge of the multi-contact model or require significant offline model tuning effort, thus resulting in low adaptability and r

Siming He, Christopher D. Hsu, Dexter Ong, Yifei Simon Shao

We study active perception from first principles to argue that an autonomous agent performing active perception should maximize the mutual information that past observations posses about future ones. Doing so requires (a) a representation of the scene that summarizes past observations and the ability to update this representation to incorporate new observati

Yulong Yang, Chenhao Lin, Xiang Ji, Qiwei Tian

Transfer-based adversarial attacks raise a severe threat to real-world deep learning systems since they do not require access to target models. Adversarial training (AT), which is recognized as the strongest defense against white-box attacks, has also guaranteed high robustness to (black-box) transfer-based attacks. However, AT suffers from heavy computation

Eric Lei, Arman Adibi, Hamed Hassani

Discrete optimization problems often arise in deep learning tasks, despite the fact that neural networks typically operate on continuous data. One class of these problems involve objective functions which depend on neural networks, but optimization variables which are discrete. Although the discrete optimization literature provides efficient algorithms, they

Kai Wan, Hua Sun, Mingyue Ji, Tiebin Mi

This paper considers the secure aggregation problem for federated learning under an information theoretic cryptographic formulation, where distributed training nodes (referred to as users) train models based on their own local data and a curious-but-honest server aggregates the trained models without retrieving other information about users' local data. Secu

Yuki Koto

We construct an I-function for toric bundles obtained as a fiberwise GIT quotient of a (not necessarily split) vector bundle. This is a generalization of Brown's I-function for split toric bundles and the I-function for non-split projective bundles. In order to prove the mirror theorem, we establish a characterization of points on the Givental Lagrangian con

S. Biswas, S. Mahata, B. P. Nayak, S. Sahoo

Inspired by various updated tantalizing results of LHCb on baryonic sector we study the {\Lambda}_b decays into {\Lambda}^* (1520)l^+ l^- decay in non-universal Z^' model. We present the four-fold angular distributions of the decay in terms of transversity amplitudes. We structure the observables: differential branching ratio, lepton side forward backward as

Chengwei Qin, Chen Chen, Shafiq Joty

Lifelong sequence generation (LSG), a problem in continual learning, aims to continually train a model on a sequence of generation tasks to learn constantly emerging new generation patterns while avoiding the forgetting of previous knowledge. Existing LSG methods mainly focus on maintaining old knowledge while paying little attention to knowledge transfer ac

Filippo Fabiani, Simone Sagratella

We characterize the convergence properties of traditional best-response (BR) algorithms in computing solutions to mixed-integer Nash equilibrium problems (MI-NEPs) that turn into a class of monotone Nash equilibrium problems (NEPs) once relaxed the integer restrictions. We show that the sequence produced by a Jacobi/Gauss-Seidel BR method always approaches a

Luca Luceri, Valeria Pantè, Keith Burghardt, Emilio Ferrara

Social media platforms, particularly Twitter, have become pivotal arenas for influence campaigns, often orchestrated by state-sponsored information operations (IOs). This paper delves into the detection of key players driving IOs by employing similarity graphs constructed from behavioral pattern data. We unveil that well-known, yet underutilized network prop

Xinting Li, Shiguang Zhang, Yue LU, Kerry Dang

This paper investigates the zero-shot object goal visual navigation problem. In the object goal visual navigation task, the agent needs to locate navigation targets from its egocentric visual input. "Zero-shot" means that the target the agent needs to find is not trained during the training phase. To address the issue of coupling navigation ability with targ

Alperen Govdeli, John N. Straguzzi, Zheng Yong, Yiding Lin

Single photon detection is important for a wide range of low-light applications, including quantum information processing, spectroscopy, and light detection and ranging (LiDAR). A key challenge in these applications has been to integrate single-photon detection capability into photonic circuits for the realization of complex photonic microsystems. Short-wave

Chengwei Qin, Aston Zhang, Chen Chen, Anirudh Dagar

Spurred by advancements in scale, large language models (LLMs) have demonstrated strong few-shot learning ability via in-context learning (ICL). However, the performance of ICL has been shown to be highly sensitive to the selection of few-shot demonstrations. Selecting the most suitable examples as context remains an ongoing challenge and an open problem. Ex

Frederik Ravn Klausen, Simone Warzel

We study the time evolution of single-particle quantum states described by a Lindblad master equation with local terms. By means of a geometric resolvent equation derived for Lindblad generators, we establish a finite-volume-type criterion for the decay of the off-diagonal matrix elements in the position basis of the time-evolved or steady states. This crite

Christopher P. Chambers, Yusufcan Masatlioglu, Collin Raymond

Many models of economics assume that individuals distort objective probabilities. We propose a simple consistency condition on distortion functions, which we term distortion coherence, that ensures that the function commutes with conditioning on an event. We show that distortion coherence restricts belief distortions to have a particular function form: power

Daniel López-Bruna, Mauro Recchia, Palak Jain, Italo Predebon

This work documents the initial 3D calculations to simulate the coupling between radio-frequency (RF) waves and plasma in discharges of the SPIDER device. Axisymmetric 3D calculations in the plasma domain alone compare well against equivalent 2D cases. A model of SPIDER driver, the cylindrical chamber where the plasma is heated by the RF drive, is then defin

Chitu Okoli

Accumulated Local Effects (ALE) is a model-agnostic approach for global explanations of the results of black-box machine learning (ML) algorithms. There are at least three challenges with conducting statistical inference based on ALE: ensuring the reliability of ALE analyses, especially in the context of small datasets; intuitively characterizing a variable'

Zheng Ma, Changxin Wang, Bo Huang, Zixuan Zhu

Most image captioning models following an autoregressive manner suffer from significant inference latency. Several models adopted a non-autoregressive manner to speed up the process. However, the vanilla non-autoregressive manner results in subpar performance, since it generates all words simultaneously, which fails to capture the relationships between words

J. P. Rodríguez-Gómez, R. Tapia, M. M. Guzmán, J. R. Martínez-de Dios

Autonomous flight of flapping-wing robots is a major challenge for robot perception. Most of the previous sense-and-avoid works have studied the problem of obstacle avoidance for flapping-wing robots considering only static obstacles. This paper presents a fully onboard dynamic sense-and-avoid scheme for large-scale ornithopters using event cameras. These se

Jiahao Wu, Qijiong Liu, Hengchang Hu, Wenqi Fan

Modern techniques in Content-based Recommendation (CBR) leverage item content information to provide personalized services to users, but suffer from resource-intensive training on large datasets. To address this issue, we explore the dataset condensation for textual CBR in this paper. The goal of dataset condensation is to synthesize a small yet informative

Yu-Ming Chen, Hien Bui, Michael Posa

Model-based approaches for planning and control for bipedal locomotion have a long history of success. It can provide stability and safety guarantees while being effective in accomplishing many locomotion tasks. Model-free reinforcement learning, on the other hand, has gained much popularity in recent years due to computational advancements. It can achieve h

Jianxiang Yu, Yuxiang Ren, Chenghua Gong, Jiaqi Tan

Text-attributed graphs have recently garnered significant attention due to their wide range of applications in web domains. Existing methodologies employ word embedding models for acquiring text representations as node features, which are subsequently fed into Graph Neural Networks (GNNs) for training. Recently, the advent of Large Language Models (LLMs) has

Dimitrios Loukrezis, Eric Diehl, Herbert De Gersem

This work develops a novel basis-adaptive method for constructing anisotropic polynomial chaos expansions of multidimensional (vector-valued, multi-output) model responses. The adaptive basis selection is based on multivariate sensitivity analysis metrics that can be estimated by post-processing the polynomial chaos expansion and results in a common anisotro

Matteo Becchetti

We report on recent progress for the QCD corrections to top quark pair plus jet production. In particular, we discuss a recent computation for the two-loop master integrals associated to a two-loop five-point pentagon-box integral configuration with one internal massive propagator, that contributes to top quark pair production in association with a jet in th

Debjyoti Majumdar, Ido Regev

The discovery that memory of particle configurations and plastic events can be stored in amorphous solids subject to oscillatory shear has spurred research into methods for storing and retrieving information from these materials. However, it is unclear to what extent the ability to store memory is affected by thermal fluctuations and other environmental nois

Setsuo Misawa

The quasiparticles in the normal state of cuprate superconductors have been shown to behave universally as a 3-dimensional Fermi liquid. Because of interactions and the presence of the Fermi surfaces (or Fermi energies), the quasiparticle energy contains, as a function of the momentum $\boldmath{p}$, a term of the form $(p-p_0)^3 \ln ( | p-p_0 | / p_0 )$, wh

F. Gran, G. Kordopatis, M. Zoccali, V. Hill

After the second data release of Gaia, the number of new globular cluster candidates has increased importantly. However, most of them need to be properly characterised, both spectroscopically and photometrically, by means of radial velocities, metallicities, and deeper photometric observations. Our goal is to provide an independent confirmation of the cluste

Andoni Zozaya

Ado's Theorem had been extended to principal ideal domains independently by Churkin and Weigel. They demonstrated that if $R$ is a principal ideal domain of characteristic zero and $\mathfrak{L}$ is a Lie algebra over $R$ which is also a free $R$-module of finite rank, then $\mathfrak{L}$ admits a finite faithful Lie algebra representation over $R$. We prese

Haibo Yang, Zhuqing Liu, Jia Liu, Chaosheng Dong

In recent years, multi-objective optimization (MOO) emerges as a foundational problem underpinning many multi-agent multi-task learning applications. However, existing algorithms in MOO literature remain limited to centralized learning settings, which do not satisfy the distributed nature and data privacy needs of such multi-agent multi-task learning applica

Jiaxin Jin, Chanwoo Kim

Motivated by the stellar wind ejected from the upper atmosphere (Corona) of a star, we explore a boundary problem of the two-species nonlinear relativistic Vlasov-Poisson systems in the 3D half space in the presence of a constant vertical magnetic field and strong background gravity. We allow species to have different mass and charge (as proton and electron,

A. D. Chaikovskaia, D. V. Karlovets, V. G. Serbo

We present a theoretical investigation of the Vavilov-Cherenkov (VC) radiation by a plane-wave or twisted electron. Special emphasis is put on the question whether and at what conditions the emitted VC photons can be twisted. For this aim we obtain a general expression in the coordinate and momentum representations for the quantum state of the final electron

Greger Torgrimsson

In previous works we derived equations for the average momentum of high-energy electrons experiencing quantum radiation reaction (RR) in strong electromagnetic plane-wave background fields. In this paper we derive similar equations for the momentum spectrum. We formulate the equations in terms of the cumulative function and study the relation between the equ

D. Bazeia, M. A. Liao, M. A. Marques

We investigate the presence of localized structures for relativistic scalar fields coupled to impurities in arbitrary spatial dimensions. Such systems present spatial inhomogeneity, realized through the inclusion of explicit coordinate dependence in the Lagrangian. It is shown that, in stark contrast to the impurity-free scenario, Derrick's argument does not

Jiancheng An, Chau Yuen, Marco Di Renzo, Merouane Debbah

Staked intelligent metasurface (SIM) based techniques are developed to perform two-dimensional (2D) direction-of-arrival (DOA) estimation. In contrast to the conventional designs, an advanced SIM in front of the receiving array automatically performs the 2D discrete Fourier transform (DFT) as the incident waves propagate through it. To arrange for the SIM to

Miloš S. Kurilić, Stevo Todorčević

The poset of copies of a relational structure ${\mathbb X}$ is the partial order ${\mathbb P} ({\mathbb X} ) := \langle \{ Y \subset X: {\mathbb Y} \cong {\mathbb X}\}, \subset \rangle$ and each similarity of such posets (e.g. isomorphism, forcing equivalence) determines a classification of structures. We consider the countable ultrahomogeneous tournaments:

L. Guzman-Vargas, A. Zabaleta-Ortega, A. Guzman-Saenz

The complex behavior of many systems in nature requires the application of robust methodologies capable of identifying changes in their dynamics. In the case of time series (which are sensed values of a system during a time interval), several methods have been proposed to evaluate their irregularity. However, for some types of dynamics such as stochastic and

Kaidi Xu, Shenglong Zhou, Geoffrey Ye Li

Resource allocation significantly impacts the performance of vehicle-to-everything (V2X) networks. Most existing algorithms for resource allocation are based on optimization or machine learning (e.g., reinforcement learning). In this paper, we explore resource allocation in a V2X network under the framework of federated reinforcement learning (FRL). On one h

Florian Gut, Thilo Krill, Florian Reich

A digraph $H$ is called ubiquitous if every digraph that contains arbitrarily many vertex-disjoint copies of $H$ also contains infinitely many vertex-disjoint copies of $H$. We study oriented double rays, that is, digraphs $H$ whose underlying undirected graphs are double rays. Calling a vertex of an oriented double ray a turn if it has in-degree or out-degr

Ke Chen, Jasen Lai, Chunmei Wang

Partial differential equations (PDEs) play a foundational role in modeling physical phenomena. This study addresses the challenging task of determining variable coefficients within PDEs from measurement data. We introduce a novel neural network, "pseudo-differential IAEnet" (pd-IAEnet), which draws inspiration from pseudo-differential operators. pd-IAEnet ac

Son Cao

The well-established neutrino oscillation phenomenon, which confirms neutrinos have mass and the charged and neutral leptons are mixed, has been widely exploited to investigate the physics of this elusive particle. The complete description of neutrino oscillations, which are dictated by two mass-squared splittings, three mixing angles, and one Dirac CP-viola

Ke Chen, Chunmei Wang, Haizhao Yang

Regularization plays a pivotal role in integrating prior information into inverse problems. While many deep learning methods have been proposed to solve inverse problems, determining where to apply regularization remains a crucial consideration. Typical methods regularize neural networks via architecture, wherein neural network functions parametrize the para