December 2024 arXiv papers — page 184

Pierre Romanet, Marco Maria Scuderi, Jean-Paul Ampuero, Stephanie Chaillat

To better understand the mechanics of injection-induced seismicity, we developed a two-dimensional numerical code to simulate both seismic and aseismic slip on non-planar faults and fault networks driven by fluid diffusion along permeable faults. Our approach integrates a boundary element method to model fault slip governed by rate-and-state friction with a

Jorge Gabín, Javier Parapar, Craig Macdonald

While question-like queries are gaining popularity and search engines' users increasingly adopt them, keyphrase search has traditionally been the cornerstone of web search. This query type is also prevalent in specialised search tasks such as academic or professional search, where experts rely on keyphrases to articulate their information needs. However, cur

Luca Ciampi, Gabriele Lagani, Giuseppe Amato, Fabrizio Falchi

We propose a novel bio-inspired semi-supervised learning approach for training downsampling-upsampling semantic segmentation architectures. The first stage does not use backpropagation. Rather, it exploits the Hebbian principle ``fire together, wire together'' as a local learning rule for updating the weights of both convolutional and transpose-convolutional

Matteo Crotti, Luca Rossini, Balint K. Hodossy, Anna Pace

In recent years, artificial feet based on soft robotics and under-actuation principles emerged to improve mobility on challenging terrains. This paper presents the application of the MuJoCo physics engine to realize a digital twin of an adaptive soft foot developed for use with legged robots. We release the MuJoCo soft foot digital twin as open source to all

Uday Bhaskar, Jayadratha Gayen, Charu Sharma, Naresh Manwani

One of the key tasks in graph learning is node classification. While Graph neural networks have been used for various applications, their adaptivity to reject option setting is not previously explored. In this paper, we propose NCwR, a novel approach to node classification in Graph Neural Networks (GNNs) with an integrated reject option, which allows the mod

Jacopo Stoppa

We obtain results that relate Donaldson-Futaki type invariants (that is, the numerical invariants used to define K-stability for general polarised manifolds) for a toric polarised manifold and for a compactification of its mirror Landau-Ginzburg model, nearby the large volume limit. In general, these have the form of expansions containing terms which involve

Ivan Kralj, Lodovico Giaretta, Gordan Ježić, Ivana Podnar Žarko

In smart mobility, large networks of geographically distributed sensors produce vast amounts of high-frequency spatio-temporal data that must be processed in real time to avoid major disruptions. Traditional centralized approaches are increasingly unsuitable to this task, as they struggle to scale with expanding sensor networks, and reliability issues in cen

Ziyi Yang, Fanqi Wan, Longguang Zhong, Tianyuan Shi

While fusing heterogeneous open-source LLMs with varying architectures and sizes can potentially integrate the strengths of different models, existing fusion methods face significant challenges, such as vocabulary alignment and merging distribution matrices. These procedures are not only complex but also prone to introducing noise and errors. In this paper,

Vivienn Weru, Annette Kopp-Schneider, Manuel Wiesenfarth, Sebastian Weber

External data borrowing in clinical trial designs has increased in recent years. This is accomplished in the Bayesian framework by specifying informative prior distributions. To mitigate the impact of potential inconsistency (bias) between external and current data, robust approaches have been proposed. One such approach is the robust mixture prior arising a

Enric Florit, Ariel Pacetti

In a remarkable article Ribet showed how to attach rational $2$-dimensional representations to elliptic ${\mathbb Q}$-curves. An abelian variety $A$ is a (weak) $K$-variety if it is isogenous to all of its $\text{Gal}_K$-conjugates. In this article we study the problem of attaching an absolutely irreducible $\ell$-adic representation of $\text{Gal}_K$ to an

Shiro Ikeda, Takeshi Nakazato, Takashi Tsukagoshi, Tsutomu T. Takeuchi

We reformulate the gain correction problem of the radio interferometry as an optimization problem with regularization, which is solved efficiently with an iterative algorithm. Combining this new method with our previously proposed imaging method, PRIISM, the whole process of the self-calibration of radio interferometry is redefined as a single optimization p

Anderson Melchor Hernandez, Filippo Girardi, Davide Pastorello, Giacomo De Palma

We study quantum neural networks where the generated function is the expectation value of the sum of single-qubit observables across all qubits. In [Girardi \emph{et al.}, arXiv:2402.08726], it is proven that the probability distributions of such generated functions converge in distribution to a Gaussian process in the limit of infinite width for both untrai

Hafiz Faheem Shahid, Erkki Harjula

The rapid growth of the Internet of Things (IoT) applications inflicts high requirements for computing resources and network bandwidth. A growing number of service providers are applying edge-cloud computing to improve the quality of their services. Deploying IoT applications to optimal computing nodes to minimize energy consumption and enhance system perfor

Sudipta Goswami, Shubhankar Mishra, Kishor Kumar Sahoo, Kumar Brajesh

We investigated the magnetic transitions in BiFeO$_3$ at low temperature (5-300 K) and observed nearly 90$^o$ rotation of magnetic domains (imaged by vertical magnetic force microscopy) across 150 K in an epitaxial thin film of thickness $\sim$36 nm. It offers a clear evidence of spin reorientation transition. It also corroborates the transition observed bel

Maxime Fontana, Michael Spratling, Miaojing Shi

Multi-Task Learning (MTL) involves the concurrent training of multiple tasks, offering notable advantages for dense prediction tasks in computer vision. MTL not only reduces training and inference time as opposed to having multiple single-task models, but also enhances task accuracy through the interaction of multiple tasks. However, existing methods face li

Gianni Franchi, Dat Nguyen Trong, Nacim Belkhir, Guoxuan Xia

Uncertainty quantification in text-to-image (T2I) generative models is crucial for understanding model behavior and improving output reliability. In this paper, we are the first to quantify and evaluate the uncertainty of T2I models with respect to the prompt. Alongside adapting existing approaches designed to measure uncertainty in the image space, we also

Qihan Huang, Weilong Dai, Jinlong Liu, Wanggui He

Finetuning-free personalized image generation can synthesize customized images without test-time finetuning, attracting wide research interest owing to its high efficiency. Current finetuning-free methods simply adopt a single training stage with a simple image reconstruction task, and they typically generate low-quality images inconsistent with the referenc

Leon-Paul Schaub Torre, Pelayo Quiros, Helena Garcia Mieres

In this paper we present a hybrid method for the automatic detection of dermatological pathologies in medical reports. We use a large language model combined with medical ontologies to predict, given a first appointment or follow-up medical report, the pathology a person may suffer from. The results show that teaching the model to learn the type, severity an

Xuesong Pan, Zhong Zheng, Xueqing Huang, Zesong Fei

In this paper, we consider cell-free communication systems with several access points (APs) serving terrestrial users (UEs) simultaneously. To enhance the uplink multi-user multiple-input multiple-output communications, we adopt a hybrid-CSI-based two-layer distributed multi-user detection scheme comprising the local minimum mean-squared error (MMSE) detecti

Jianhua Yang, Mir Seyedebrahimi, Margaret Low, Holly Heshmati

Programming is a crucial skill in today's world and being taught worldwide at different levels. However, in the literature there is little research investigating a formal approach to embedding public engagement into programming module design. This paper explores the integration of public engagement into an introductory programming module, at the University o

Geesara Kulathunga, Abdurrahman Yilmaz, Zhuoling Huang, Ibrahim Hroob

In autonomous navigation, trajectory replanning, refinement, and control command generation are essential for effective motion planning. This paper presents a resilient approach to trajectory replanning addressing scenarios where the initial planner's solution becomes infeasible. The proposed method incorporates a hybrid A* algorithm to generate feasible tra

Saksham Sharma, Akshit Raizada, Suresh Sundaram

Autonomous off-road navigation is required for applications in agriculture, construction, search and rescue and defence. Traditional on-road autonomous methods struggle with dynamic terrains, leading to poor vehicle control in off-road conditions. Recent deep-learning models have used perception sensors along with kinesthetic feedback for navigation on such

Nowar E. Koning, Sergei M. Kuzenko, Emmanouil S. N. Raptakis

In a supergravity framework, the $\cal N$-extended anti-de Sitter (AdS) superspace in four spacetime dimensions, $\text{AdS}^{4|4\cal N} $, is a maximally symmetric background that is described by a curved superspace geometry with structure group $\mathsf{SL}(2, \mathbb{C}) \times \mathsf{U}({\cal N})$. On the other hand, within the group-theoretic setting,

Evgeny N. Bulgakov, Galina V. Shadrina

We investigate sorting Rayleigh optical particles up to several nanometers in size during Brownian motion in an tilted periodic potential with multiple deep wells. The wells are induced which by optical bound states in the continuum in a system of parallel photonic crystal slabs immersed in a liquid. The Brownian dynamics of the particles is significantly al

Masoud Jamshidiyan Tehrani, Jinhan Kim, Rosmael Zidane Lekeufack Foulefack, Alessandro Marchetto

The advent of deep learning and its astonishing performance has enabled its usage in complex systems, including autonomous vehicles. On the other hand, deep learning models are susceptible to mispredictions when small, adversarial changes are introduced into their input. Such mis-predictions can be triggered in the real world and can result in a failure of t

Nurlan Abiev

We proved that on every Stiefel manifold $V_2\mathbb{R}^n\cong \operatorname{SO}(n)/\operatorname{SO}(n-2)$ with $n\ge 3$ the normalized Ricci flow preserves the positivity of the Ricci curvature of invariant Riemannian metrics with positive Ricci curvature. Moreover, the normalized Ricci flow evolves all metrics with mixed Ricci curvature into metrics with

Max van Horssen, Philip Schlösser

We classify the shift operators for the symmetric Askey-Wilson polynomials and construct shift operators for the non-symmetric Askey-Wilson polynomials using two decompositions of non-symmetric Askey-Wilson polynomials in terms of symmetric ones. These shift operators are difference-reflection operators, and we discuss the conditions under which they restric

Cai Heng Li, Hanyue Yi, Yan Zhou Zhu

A transitive permutation group is said to be semiprimitive if each of its normal subgroups is either semiregular or transitive.The class of semiprimitive groups properly contains primitive groups, quasiprimitive groups and innately transitive groups.The latter three classes of groups of rank $3$ have been classified, forming significant progresses on the lon

Aniruddha Mukherjee, Rekha Singhal, Gautam Shroff

We consider the application of machine learning models for short-term intra-day trading in equities. We envisage a scenario wherein machine learning models are submitted by independent data scientists to predict discretised ten-candle returns every five minutes, in response to five-minute candlestick data provided to them in near real-time. An ensemble model

Rosa Y. G. Paccotacya-Yanque, Alceu Bissoto, Sandra Avila

Deep Learning has shown outstanding results in computer vision tasks; healthcare is no exception. However, there is no straightforward way to expose the decision-making process of DL models. Good accuracy is not enough for skin cancer predictions. Understanding the model's behavior is crucial for clinical application and reliable outcomes. In this work, we i

Przemyslaw Chelminiak

The Ornstein-Uhlenbeck process of diffusion in the harmonic potential is re-examined in the context of the first-passage time problem. We investigate this problem to the extent that it has not yet been fully resolved and demonstrate exact novel results. They mainly concern the mean first-passage time for a particle diffusing downward and upward in the harmon

Josef Dick, Friedrich Pillichshammer

In this short note we report on a coincidence of two mathematical quantities that, at first glance, have little to do with each other. On the one hand, there are the Lebesgue constants of the Walsh function system that play an important role in approximation theory, and on the other hand there is the star discrepancy of the van der Corput sequence that plays

Zhanfang Chen, Feng Huang, Taotao Fang

Low-frequency radio emission from the Large Magellanic Cloud~(LMC) is assumed to be dominated by nonthermal synchrotron radiation from energy loss of energetic $e^+/e^-$ in magnetic field. Two different kinds of sources of $e^+/e^-$, dark matter~(DM) annihilation and cosmic rays~(CR) related to massive stars, are taken into account in this paper. We fit the

Sunwoong Kim, Jongho Jeong, Jin Soo Han, Donghyuk Shin

Surveys are widely used in social sciences to understand human behavior, but their implementation often involves iterative adjustments that demand significant effort and resources. To this end, researchers have increasingly turned to large language models (LLMs) to simulate human behavior. While existing studies have focused on distributional similarities, i

Sung Woong Cho, Hwijae Son

Inverse problems involving partial differential equations (PDEs) can be seen as discovering a mapping from measurement data to unknown quantities, often framed within an operator learning approach. However, existing methods typically rely on large amounts of labeled training data, which is impractical for most real-world applications. Moreover, these supervi

Saibo Geng, Sankalp Gambhir, Chris Wendler, Robert West

Tokenization is an important preprocessing step in the training and inference of large language models (LLMs). While there has been extensive research on the expressive power of the neural achitectures used in LLMs, the impact of tokenization has not been well understood. In this work, we demonstrate that tokenization, irrespective of the algorithm used, act

Yunkai Dang, Min Zhang, Zhengyu Chen, Xinliang Zhang

Few-shot image classification(FSIC) aims to recognize novel classes given few labeled images from base classes. Recent works have achieved promising classification performance, especially for metric-learning methods, where a measure at only image feature level is usually used. In this paper, we argue that measure at such a level may not be effective enough t

Nouhaila Innan, Alberto Marchisio, Mohamed Bennai, Muhammad Shafique

Predicting loan eligibility with high accuracy remains a significant challenge in the finance sector. Accurate predictions enable financial institutions to make informed decisions, mitigate risks, and effectively adapt services to meet customer needs. However, the complexity and the high-dimensional nature of financial data have always posed significant chal

Federico Mason, Jacopo Pegoraro

In Integrated Sensing And Communication (ISAC) systems, estimating the micro-Doppler (mD) spectrogram of a target requires combining channel estimates retrieved from communication with ad-hoc sensing packets, which cope with the sparsity of the communication traffic. Hence, the mD quality depends on the transmission strategy of the sensing packets, which is

P. O. Mchedlov-Petrosyan, L. N. Davydov, O. A. Osmaev

To describe highly heterogeneous systems using the Cahn-Hilliard equation, the standard form of the thermodynamic potential with a constant coefficient in the gradient term and a polynomial of the fourth degree may not be sufficient. The modification of the form of the thermodynamic potential with a polynomial of the sixth degree and the quadratic dependence

Yiwen Pan, Wenbin Yan

This is the companion paper of the letter arXiv:2410.15695, containing all the details and series of examples on a 4d mirror symmetry for the class-$\mathcal{S}$ theories which relates the representation theory of the chiral quantization of the Higgs branch and the geometry of the Coulomb branch. We study the representation theory by using the 4d/VOA corresp

Xingjian Zhou, Keyi Shen, Andy Xu, Hongji Xu

Neural network (NN) verification aims to formally verify properties of NNs, which is crucial for ensuring the behavior of NN-based models in safety-critical applications. In recent years, the community has developed many NN verifiers and benchmarks to evaluate them. However, existing benchmarks typically lack ground-truth for hard instances where no current

Yanzun Meng, Zuoqiang Shi

Based on the development in dealing with nonlocal boundary conditions, we propose a seamless local-nonlocal coupling diffusion model in this paper. In our model, a finite constant interaction horizon is equipped in the nonlocal part and transmission conditions are imposed on a co-dimension one interface. To achieve a seamless coupling, we introduce an auxili

Pius von Däniken, Jan Deriu, Mark Cieliebak

Automated metrics for Machine Translation have made significant progress, with the goal of replacing expensive and time-consuming human evaluations. These metrics are typically assessed by their correlation with human judgments, which captures the monotonic relationship between human and metric scores. However, we argue that it is equally important to ensure

Luning Sun, Yuzhuo Yuan, Yuan Yao, Yanyan Li

Artificial intelligence has, so far, largely automated routine tasks, but what does it mean for the future of work if Large Language Models (LLMs) show creativity comparable to humans? To measure the creativity of LLMs holistically, the current study uses 13 creative tasks spanning three domains. We benchmark the LLMs against individual humans, and also take

Siyoon Jin, Jisu Nam, Jiyoung Kim, Dahyun Chung

Exemplar-based semantic image synthesis generates images aligned with semantic content while preserving the appearance of an exemplar. Conventional structure-guidance models like ControlNet, are limited as they rely solely on text prompts to control appearance and cannot utilize exemplar images as input. Recent tuning-free approaches address this by transfer

Chuandi Zhang, Junsen Xiang, Cheng Su, Denis Sheptyakov

Motivated by the recent observations of various exotic quantum states in the equilateral triangular-lattice phosphates Na$_2$BaCo(PO$_4$)$_2$ with $J\rm_{eff}$ = 1/2 and Na$_2$BaNi(PO$_4$)$_2$ with $S$ = 1, the magnetic properties of spin-5/2 antiferromagnet Na$_2$BaMn(PO$_4$)$_2$, their classical counterpart, are comprehensively investigated experimentally.

Kun Li, Chenwei Dai, Wei Zhou, Songlin Hu

Large language models (LLMs) have demonstrated impressive capabilities in role-playing tasks. However, there is limited research on whether LLMs can accurately simulate user behavior in real-world scenarios, such as social media. This requires models to effectively analyze a user's history and simulate their role. In this paper, we introduce \textbf{FineRob}

M. Parzer, F. Garmroudi, A. Riss, T. Mori

Tailoring charge transport in solids on demand is the overarching goal of condensed-matter research as it is crucial for electronic applications. Yet, often the proper tuning knob is missing and extrinsic factors such as impurities and disorder impede coherent conduction. Here we control the very buildup of an electronic band from impurity states within the

Huai Yu, Junhao Wang, Yao He, Wen Yang

Making multi-camera visual SLAM systems easier to set up and more robust to the environment is attractive for vision robots. Existing monocular and binocular vision SLAM systems have narrow sensing Field-of-View (FoV), resulting in degenerated accuracy and limited robustness in textureless environments. Thus multi-camera SLAM systems are gaining attention be

Vincent P. Grande, Josef Hoppe, Florian Frantzen, Michael T. Schaub

We consider the problem of classifying trajectories on a discrete or discretised 2-dimensional manifold modelled by a simplicial complex. Previous works have proposed to project the trajectories into the harmonic eigenspace of the Hodge Laplacian, and then cluster the resulting embeddings. However, if the considered space has vanishing homology (i.e., no "ho

Stephane Vennes, Adela Kawka

We revisit the binary and stellar properties of the double-degenerate system NLTT 16249. An analysis of new echelle spectra, supported by a joint study of a DQZ velocity template NLTT 44303, confirms the orbital period and constrains the mass ratio revealing a carbon-polluted DQ white dwarf that is up to ~6 percent more massive than its hydrogen-rich DA comp

J. A. Grundy, N. Seymour, O. I. Wong, K. Lee-Waddell

The broad-band radio spectral energy distribution (SED) of star-forming galaxies (SFGs) contains a wealth of complex physics. We aim to determine the physical emission and loss processes causing radio SED curvature and steepening to see which observed global astrophysical properties are correlated with radio SED complexity. We have acquired radio continuum d

Shijie Wu, Yihang Zhu, Yunao Huang, Kaizhen Zhu

Diffusion-based policies have shown impressive performance in robotic manipulation tasks while struggling with out-of-domain distributions. Recent efforts attempted to enhance generalization by improving the visual feature encoding for diffusion policy. However, their generalization is typically limited to the same category with similar appearances. Our key

Da Song, Hong-bo Cai, Shen Wang, Jing Wang

Impact flashes on the moon are caused by high-speed collisions of celestial bodies with the lunar surface. The study of the impacts is critical for exploring the evolutionary history and formation of the Moon, and for quantifying the risk posed by the impacts to future human activity. Although the impacts have been monitored from the Earth by a few projects

Mehmet Demirci, M. Fauzi Mustamin

In the presence of a transition magnetic moment between active and sterile neutrinos, sterile neutrinos could be produced by neutrino beams electromagnetically upscattering on nuclei. We study the active-sterile neutrino transition magnetic moment through this upscattering in the coherent elastic neutrino-nucleus scattering process induced by solar neutrinos

Tomer Eini, M. F. C. Martins Quintela, J. C. G. Henriques, R. M. Ribeiro

Collective excitations of charged particles under the influence of an electromagnetic field give rise to a rich variety of hybrid light-matter quasiparticles with unique properties. In metals, intraband collective response manifested by negative permittivity leads to plasmon-polaritons with extreme field confinement, wavelength squeezing, and potentially low

Jack Gardiner, Lachlan L. H. Andrew, Junhao Gan, Jean Honorio

This paper tightens the best known analysis of Hein's 1989 algorithm to infer the topology of a weighted tree based on the lengths of paths between its leaves. It shows that the number of length queries required for a degree-$k$ tree of $n$ leaves is $O(n k \log_k n)$, which is the lower bound. It also presents a family of trees for which the performance is

Zhe Shen, Ning Liu

Optical tweezers (OTs) with structured light expand degrees of freedom of particle manipulation. However, the studies of structured optical tweezers are usually accompanied by complex theoretical models, strict simulation conditions, and uncertain experimental factors, which may bring about high time costs and insufficiently precise results. In this work, we

Xudong Li, Zhixiang Wang, Zihao Liu, Yizhai Zhang

Recent works have combined monocular event camera and inertial measurement unit to estimate the $SE(3)$ trajectory. However, the asynchronicity of event cameras brings a great challenge to conventional fusion algorithms. In this paper, we present an asynchronous event-inertial odometry under a unified Gaussian Process (GP) regression framework to naturally f

Jaime Muñoz Masqué, Luis Miguel Pozo Coronado

Let $\mathbb{F}$ be a field of characteristic $\neq 2$ and $3$, let $V$ be a $\mathbb{F}$-vector space of dimension $6$, and let $\Omega \in \wedge ^2V^\ast $ be a non-degenerate form. A system of generators for polynomial invariant functions under the tensorial action of the group $Sp(\Omega )$ on $\wedge ^3 V^\ast $, is given explicitly. Applications of th

Takuro Kutsuna

Diffusion models have become fundamental tools for modeling data distributions in machine learning. Despite their success, these models face challenges when generating data with extreme brightness values, as evidenced by limitations observed in practical large-scale diffusion models. Offset noise has been proposed as an empirical solution to this issue, yet

J. S. Alvarado, M. Hoballah, E. Voutier

Generalized Parton Distributions (GPDs) are multidimensonal structure functions that encode the information about the internal structure of hadrons. Using privileged channels such as Deeply Virtual Compton Scattering (DVCS) or Timelike Compton Scattering (TCS), it is possible to make direct measurements at points where the momentum fraction of the parton equ

Wenhe Lyu, Hong-Xin Zhang, Sanjaya Paudel, Tie Li

Nuclear Star Clusters (NSCs) are commonly found in galaxy centers, but their dominant formation mechanisms remain elusive. We perform a consistent analysis of stellar populations of 97 nearby NSCs, based on VLT spectroscopic data. The sample covers a galaxy stellar mass range of 10$^{7}$ to 10$^{11}$ M$_{\odot}$ and is more than 3$\times$ larger than any pre

Yanqi Zhang, Yuwei Hu, Runyuan Zhao, John C. S. Lui

Large language models (LLMs) demonstrate remarkable capabilities but face substantial serving costs due to their high memory demands, with the key-value (KV) cache being a primary bottleneck. State-of-the-art KV cache compression techniques, such as quantization and pruning, apply uniform treatment to both keys and values, and discard unimportant tokens enti

Jürg Meierhofer, Nikola Pascher, Jochen Wulf

We introduce a novel framework aimed at identifying and quantifying the value of customer pains as a critical element in service innovation. The proposed approach enhances existing end-to-end frameworks by offering a structured method to elaborate on and measure the value derived from solving these customer challenges. The effectiveness of the framework is v

Elias Arnold, Eike-Manuel Edelmann, Alexander von Bank, Eric Müller

Spiking neural networks (SNNs) emulated on dedicated neuromorphic accelerators promise to offer energy-efficient signal processing. However, the neuromorphic advantage over traditional algorithms still remains to be demonstrated in real-world applications. Here, we describe an intensity-modulation, direct-detection (IM/DD) task that is relevant to high-speed

Philipp Spilger, Eric Müller, Johannes Schemmel

The study of plasticity in spiking neural networks is an active area of research. However, simulations that involve complex plasticity rules, dense connectivity/high synapse counts, complex neuron morphologies, or extended simulation times can be computationally demanding. The BrainScaleS-2 neuromorphic architecture has been designed to address this challeng

Olivier Danvy

Seventy years on, Moessner's theorem and Moessner's process -- i.e., the additive computation of integral powers -- continue to fascinate. They have given rise to a variety of elegant proofs, to an implementation in hardware, to generalizations, and now even to a popular video, "The Moessner Miracle.'' The existence of this video, and even more its title, in

Martin Pluemicke

Over the last two decades practically all object-oriented programming languages have introduced features that are well-known from functional programming languages. But many features that were introduced were fragmentary. In Java-TX we address the latter features and propose a completion. Java-TX (i.e. Type eXtended) is a language based on Java. The predomina

Jeremy G. Siek

The gradual guarantee is an important litmus test for gradually typed languages, that is, languages that enable a mixture of static and dynamic typing. The gradual guarantee states that changing the precision of a type annotation does not change the behavior of the program, except perhaps to trigger an error if the type annotation is incorrect. Siek et al. (

Philip Wadler

I present a novel formulation of substitution, where facts about substitution that previously required tens or hundreds of lines to justify in a proof assistant now follow immediately - they can be justified by writing the four letters "refl". The paper is an executable literate Agda script, and source of the paper is available as an artifact in the file Wea

Xiaojun Xu, Jinghan Jia, Yuanshun Yao, Yang Liu

We propose an imperceptible multi-bit text watermark embedded by paraphrasing with LLMs. We fine-tune a pair of LLM paraphrasers that are designed to behave differently so that their paraphrasing difference reflected in the text semantics can be identified by a trained decoder. To embed our multi-bit watermark, we use two paraphrasers alternatively to encode

Robert Glück, Louis Marott Normann

A computational limit of combining partial evaluation and program inversion is investigated. Using a reversible Turing machine interpreter, we show that the first Futamura and inversion projections can produce not only functionally but also textually equivalent programs. The construction of the interpreter in a reversible flowchart language is shown in full.

Yijia Guo, Wenkai Huang, Yang Li, Gaolei Li

3D Gaussian splatting (3DGS) has demonstrated impressive 3D reconstruction performance with explicit scene representations. Given the widespread application of 3DGS in 3D reconstruction and generation tasks, there is an urgent need to protect the copyright of 3DGS assets. However, existing copyright protection techniques for 3DGS overlook the usability of 3D

Kazuki Watanabe, Noboru Isobe

Sinkhorn algorithm is the de-facto standard approximation algorithm for optimal transport, which has been applied to a variety of applications, including image processing and natural language processing. In theory, the proof of its convergence follows from the convergence of the Sinkhorn--Knopp algorithm for the matrix scaling problem, and Altschuler et al.

Taekyun Kim, Dae san Kim

The aim of this paper is to study degenerate Eulerian polynomials and degenerate Eulerian numbers, respectively as degenerate versions of the Eulerian polynomials and the Eulerian numbers, and to derive some of their properties. Specifically, we derive an identity, recursive relations, generating function and degenerate version of Worpitzky's identity for th

Ruiping Liu, Jiaming Zhang, Angela Schön, Karin Müller

Searching for objects in unfamiliar scenarios is a challenging task for blind people. It involves specifying the target object, detecting it, and then gathering detailed information according to the user's intent. However, existing description- and detection-based assistive technologies do not sufficiently support the multifaceted nature of interactive objec

Simone Ragoni

Ultra-peripheral collisions (UPC) are events characterised by large impact parameters between the two projectiles, larger than the sum of their radii. In UPCs, the protons and ions accelerated by the LHC do not interact via the strong interaction and can be regarded as sources of quasireal photons. Using the Run 2 data, the ALICE Collaboration has carried ou

M. T. Abbas, M. Szymczak, V. Kinzhybalo, M. Drozd

Among the various techniques used in luminescence thermometry, luminescence kinetics is considered the least sensitive to perturbations related to the optical properties of the medium containing the phosphor. For this reason, temperature sensing and imaging using lifetime-based luminescence thermometers is of high interest for wide range of specific applicat

Putra Farrel Azhar

To achieve the federal goal to make half of all new vehicles sold in the U.S. in 2030 zero-emissions vehicles, the U.S. Department of Transportation's (DOT) Federal Highway Administration (FHWA) has employed the National Electric Vehicle Infrastructure (NEVI) Formula Program, which aims to promote an interconnected network of publicly accessible electric veh

Arno B. J. Kuijlaars

Matrix valued orthogonal polynomials (MVOP) appear in the study of doubly periodic tiling models. Of particular interest is their limiting behavior as the degree tends to infinity. In recent years, MVOP associated with doubly periodic domino tilings of the Aztec diamond have been successfully analyzed. The MVOP related to doubly periodic lozenge tilings of a

Ujjwal Sen

We present here a brief discussion, in Bangla (Bengali), on what is entanglement and why it is interesting.

X. Wang, X. F. Wu, B. Yang, B. Zhang

Understanding the mechanism behind the buildup of inner correlations is crucial for studying nonequilibrium dynamics in complex, strongly interacting many-body systems. Here we investigate both analytically and numerically the buildup of antiferromagnetic (AF) correlations in a dynamically tuned Ising model with various geometries, realized in a Rydberg atom

Rei Murakami

Assuming Calabi symmetry, we prove that a numerical condition ensures the solvability of the complex Hessian quotient equation, as conjectured by Sz\'ekelyhidi. We also propose a conjecture on the existence of a $k$-subharmonic representative in a given cohomology class and confirm it under the assumption of Calabi symmetry or when the class is semiample.

W. Tang, M. Hoelzl, M. Lehnen, D. Hu

Shattered pellet injection (SPI) is selected for the disruption mitigation system in ITER, due to deeper penetration, expected assimilation efficiency and prompt material delivery. This article describes non-linear magnetohydrodynamic (MHD) simulations of SPI in the ASDEX Upgrade tokamak to test the mitigation efficiency of different injection parameters for

Ruiqi He, Falk Lieder

One explanation for how people can plan efficiently despite limited cognitive resources is that we possess a set of adaptive planning strategies and know when and how to use them. But how are these strategies acquired? While previous research has studied how individuals learn to choose among existing strategies, little is known about the process of forming n

Yannick Mvondo-She

From our work on partition functions in log gravity, we show that the palindromic numerators in two variables of bigraded symmetric orbifold Hilbert series take the form of sums of products of Kostka-Foulkes polynomials associated with a pair of partition $\lambda$ and $\mu=(1^n)$. The log partition function also being a KP $\tau$-function, our work gives a

Zhong-Xia Shang

We propose a new quantum computing formalism named Pauli quantum computing. In this formalism, we use the Pauli basis $I$ and $X$ on the non-diagonal blocks of density matrices to encode information and treat them as the computational basis $|0\rangle$ and $|1\rangle$ in standard quantum computing. There are significant differences between Pauli quantum comp

Wenying Sun, Zhen Xu, Wenqing Zhang, Kunyuan Ma

This paper aims to study the prediction of the bank stability index based on the Time Series Transformer model. The bank stability index is an important indicator to measure the health status and risk resistance of financial institutions. Traditional prediction methods are difficult to adapt to complex market changes because they rely on single-dimensional m

Seongyeon Youn, Beomjoon Goh, Geng-Dong Zhou, Zhi-Da Song

We characterize gap-opening mechanisms in the topological heavy fermion (THF) model of magic-angle twisted bilayer graphene (MATBG), with and without electron-phonon coupling, using dynamical mean-field theory (DMFT) with the numerical renormalization group (NRG) impurity solver. In the presence of symmetry breaking associated with valley-orbital ordering (t

Haoyu Jiang, Xuhong Wang, Ping Yi, Shanzhe Lei

Large Language Models (LLMs) are widely used in complex natural language processing tasks but raise privacy and security concerns due to the lack of identity recognition. This paper proposes a multi-party credible watermarking framework (CredID) involving a trusted third party (TTP) and multiple LLM vendors to address these issues. In the watermark embedding

Zhuohang He, Junjie Ma, Xiaojun Yuan

Compressive Robust Principal Component Analysis (CRPCA) naturally arises in various applications as a means to recover a low-rank matrix low-rank matrix $\boldsymbol{L}$ and a sparse matrix $\boldsymbol{S}$ from compressive measurements. In this paper, we approach the problem from a Bayesian inference perspective. We establish a probabilistic model for the p

Jiacheng Hu, Zhen Qi, Jianjun Wei, Jiajing Chen

Deep learning has revolutionized various fields, yet its efficacy is hindered by overfitting and the requirement of extensive annotated data, particularly in few-shot learning scenarios where limited samples are available. This paper introduces a novel approach to few-shot learning by employing a Residual Weight Masking Conditional Generative Adversarial Net

Mohan Jiang, Yaxin Liang, Siyuan Han, Kunyuan Ma

This study explores the application of generative adversarial networks in financial market supervision, especially for solving the problem of data imbalance to improve the accuracy of risk prediction. Since financial market data are often imbalanced, especially high-risk events such as market manipulation and systemic risk occur less frequently, traditional

Zhe Xie, Zeyan Li, Xiao He, Longlong Xu

Understanding time series is crucial for its application in real-world scenarios. Recently, large language models (LLMs) have been increasingly applied to time series tasks, leveraging their strong language capabilities to enhance various applications. However, research on multimodal LLMs (MLLMs) for time series understanding and reasoning remains limited, p

Gangjian Zhang, Nanjie Yao, Shunsi Zhang, Hanfeng Zhao

This paper investigates the research task of reconstructing the 3D clothed human body from a monocular image. Due to the inherent ambiguity of single-view input, existing approaches leverage pre-trained SMPL(-X) estimation models or generative models to provide auxiliary information for human reconstruction. However, these methods capture only the general hu

Shanding Diao, Yang Zhao, Yuan Chen, Zhao Zhang

With the rapid development of stereoscopic display technologies, especially glasses-free 3D screens, and virtual reality devices, stereoscopic conversion has become an important task to address the lack of high-quality stereoscopic image and video resources. Current stereoscopic conversion algorithms typically struggle to balance reconstruction performance a

Yiming Fang, Li Chen, Yunfei Chen, Weidong Wang

Mixed-precision quantization offers superior performance to fixed-precision quantization. It has been widely used in signal processing, communication systems, and machine learning. In mixed-precision quantization, bit allocation is essential. Hence, in this paper, we propose a new bit allocation framework for mixed-precision quantization from a search perspe

Alexander V. Khoperskov, Sergey S. Khrapov, Danila S. Sirotin

The dynamics of the merger of a dwarf disc galaxy with a massive spiral galaxy of the Milky Way type have been studied in detail. The remnant of such interaction after numerous crossings of the satellite through the disc of the main galaxy is a compact stellar core, the characteristics of which are close to small compact elliptical galaxies (cEs) or large ul