December 2024 arXiv papers — page 90

Moritz Egert, Benjamin W. Kosmala

Kalton and Mitrea characterized complex interpolation spaces of quasi-Banach function spaces as Calder\'on products if both interpolants are separable. We show that one separability assumption may be omitted and establish a Wolff-reiteration result with one non-separable endpoint space.

Liangru Xie, Hui Liu, Jingying Zeng, Xianfeng Tang

Large Language Models (LLMs) demonstrate remarkable performance in semantic understanding and generation, yet accurately assessing their output reliability remains a significant challenge. While numerous studies have explored calibration techniques, they primarily focus on White-Box LLMs with accessible parameters. Black-Box LLMs, despite their superior perf

Vivek Madhavaram, Shivangana Rawat, Chaitanya Devaguptapu, Charu Sharma

Text driven diffusion models have shown remarkable capabilities in editing images. However, when editing 3D scenes, existing works mostly rely on training a NeRF for 3D editing. Recent NeRF editing methods leverages edit operations by deploying 2D diffusion models and project these edits into 3D space. They require strong positional priors alongside text pro

Yingyan Xu, Kate Gadola, Prashanth Chandran, Sebastian Weiss

We present a new method for reconstructing the appearance properties of human faces from a lightweight capture procedure in an unconstrained environment. Our method recovers the surface geometry, diffuse albedo, specular intensity and specular roughness from a monocular video containing a simple head rotation in-the-wild. Notably, we make no simplifying assu

J. A. Rueda, L. Becerra, C. L. Bianco, M. Della Valle

Long and short gamma-ray bursts (GRBs) are thought to arise from different and unrelated astrophysical progenitors. The association of long GRBs with supernovae (SNe) and the difference in the distributions of galactocentric offsets of long and short GRBs within their host galaxies have often been considered strong evidence of their unrelated origins. Long G

Francesco Coti Zelati, Alice Borghese

At the Seventeenth Marcel Grossman meeting, researchers gathered to discuss significant advances in the study of ultra-long period sources. Presentations covered key aspects, including emission properties, evolutionary scenarios, and models for their emission. In this proceeding, we summarize key observational breakthroughs and touch upon the proposed evolut

Ammar Daskin

In this paper, we consider matrices given as a linear combination of permutations and analyze the impact of bit and phase-flips on the perturbation of the eigenvalues. When the coefficients in the linear combination are positive, we observe that the eigenvalues of the resulting matrices exhibit resilience to quantum bit-flip errors. In addition, we analyze t

Debajyoti Mazumder, Aakash Kumar, Jasabanta Patro

In this paper, we reported our experiments with various strategies to improve code-mixed humour and sarcasm detection. Particularly, we tried three approaches: (i) native sample mixing, (ii) multi-task learning (MTL), and (iii) prompting and instruction finetuning very large multilingual language models (VMLMs). In native sample mixing, we added monolingual

He Wang, Xucheng Wan, Naijun Zheng, Kai Liu

Code-switching automatic speech recognition (ASR) aims to transcribe speech that contains two or more languages accurately. To better capture language-specific speech representations and address language confusion in code-switching ASR, the mixture-of-experts (MoE) architecture and an additional language diarization (LD) decoder are commonly employed. Howeve

Zehua Yu, Weihan Zhang, Sihan Pan, Jun Tao

Ordering has been extensively studied in many visualization applications, such as axis and matrix reordering, for the simple reason that the order will greatly impact the perceived pattern of data. Many quality metrics concerning data pattern, perception, and aesthetics are proposed, and respective optimization algorithms are developed. However, the optimiza

G. Charbel N. Kindji, Lina Maria Rojas-Barahona, Elisa Fromont, Tanguy Urvoy

Detecting synthetic tabular data is essential to prevent the distribution of false or manipulated datasets that could compromise data-driven decision-making. This study explores whether synthetic tabular data can be reliably identified ''in the wild''-meaning across different generators, domains, and table formats. This challenge is unique to tabular data, w

Christophe Creusot, Antonio Morandi, Francesco Mimmi, Emiliano Guerra

The European project SCARLET aims to study and realize a demonstrator of a MVDC (Medium Voltage Direct Current) high-power superconducting cable. This device might be employed to connect offshore wind farms with land, expecting to significantly simplify the offshore platform by eliminating the need for its conversion function. For this purpose, windmill conv

Jiamin Jiang, Jingrun Chen, Zhouwang Yang

Numerical simulation of multi-phase fluid dynamics in porous media is critical to a variety of geoscience applications. Data-driven surrogate models using Convolutional Neural Networks (CNNs) have shown promise but are constrained to regular Cartesian grids and struggle with unstructured meshes necessary for accurately modeling complex geological features in

Heinz-Jürgen Schmidt

In this study, we present a modified quantum theory, denoted as $QT^\ast$, which introduces mass-dependent decoherence effects. These effects are derived by averaging the influence of a proposed global quantum fluctuation in position and velocity. While $QT^\ast$ is initially conceived as a conceptual framework - a ``toy theory" - to demonstrate the internal

Vidya Prasad, Anna Vilanova, Nicola Pezzotti

While deep generative models (DGMs) have gained popularity, their susceptibility to biases and other inefficiencies that lead to undesirable outcomes remains an issue. With their growing complexity, there is a critical need for early detection of issues to achieve desired results and optimize resources. Hence, we introduce a progressive analysis framework to

Gregor Donabauer, Udo Kruschwitz

The classification of short texts is a common subtask in Information Retrieval (IR). Recent advances in graph machine learning have led to interest in graph-based approaches for low resource scenarios, showing promise in such settings. However, existing methods face limitations such as not accounting for different meanings of the same words or constraints fr

M. A. Rego-Monteiro, E. M. F. Curado

We construct a nonlinear multiparametric Klein-Gordon for complex and real fields with mass dimension depending on a real parameter $\alpha$ as $\delta = 2/(1+\alpha)$ where $\delta$ is the mass dimension of the fields. We show that there are three classes of generalized models, one class for complex fields and two different classes for real fields. All mode

Kamil Iwanowski, Gábor Csányi, Michele Simoncelli

Understanding how the vibrational and thermal properties of solids are influenced by atomistic structural disorder is of fundamental scientific interest, and paramount to designing materials for next-generation energy technologies. While several studies indicate that structural disorder strongly influences the thermal conductivity, the fundamental physics go

Simon Alexander Wiese, Johannes Lehmann, Michael Beckmann

Using novel establishment-level observational data from Switzerland, we empirically examine whether the usage of key technologies of Industry 4.0 distinguishes across firms with different types of organizational culture. Based on the Technology-Organization-Environment and the Competing Values framework, we hypothesize that the developmental culture has the

Andreas Casparsen, Van-Phuc Bui, Shashi Raj Pandey, Jimmy Jessen Nielsen

Current Adaptive Bit Rate (ABR) methods react to network congestion after it occurs, causing application layer buffering and latency spikes in live video streaming. We introduce a proactive semantic control channel that enables coordination between Open Radio Access Network (ORAN) xApp, Mobile Edge computing (MEC), and User Equipment (UE) components for seam

S. Van Thurenhout, V. Del Duca, C. Duhr, L. Fekésházy

In order to numerically compute scattering cross sections in QCD, one needs to deal with various kinematic divergences that appear at intermediate stages of the calculation. One way of doing this is by setting up an IR subtraction scheme. In this talk we give an update on the status of extending the CoLoRFul subtraction scheme, which has been successfully us

Florian Klein-Helmkamp, Tina Möllemann, Irina Zettl, Steffen Kortmann

Flexibility provision from active distribution grids requires efficient and robust methods of optimization and control suitable to online operation. In this paper we introduce conditions for the safe operation of feedback optimization based controllers. We use the feasible operating region of a controlled system as bounds for safe system states and evaluate

Antonio Maria Tagliente, Carlos Mejuto-Zaera, Michele Fabrizio

The ghost-Gutzwiller variational wavefunction within the Gutzwiller approximation is shown to stabilize a genuine paramagnetic Mott insulator in the half-filled single-band Hubbard model. This phase hosts quasiparticles that are crucial to the paramagnetic response without showing up in the single-particle spectrum, and, as such, they can be legitimately reg

Xinlin Cao, Ahcene Ghandriche, Mourad Sini

We know that the electric field generated by a plasmonic nano-particle (with negative permittivity) is given as a polarization of the incident electric field. Similarly, the electric field produced by a dielectric nano-particle (with positive but high permittivity) is given as a polarization of the incident magnetic field. In this work, we demonstrate that a

Florian Hofhammer, Qinying Wang, Atri Bhattacharyya, Majid Salehi

Dynamic analysis and especially fuzzing are challenging tasks for embedded firmware running on modern low-end Microcontroller Units (MCUs) due to performance overheads from instruction emulation, the difficulty of emulating the vast space of available peripherals, and low availability of open-source embedded firmware. Consequently, efficient security testing

Charilaos Skandylas, Mikael Asplund

Recent changes in standards and regulations, driven by the increasing importance of software systems in meeting societal needs, mandate increased security testing of software systems. Penetration testing has been shown to be a reliable method to asses software system security. However, manual penetration testing is labor-intensive and requires highly skilled

Nan Li, Bo Kang, Tijl De Bie

Text classification with hierarchical labels is a prevalent and challenging task in natural language processing. Examples include assigning ICD codes to patient records, tagging patents into IPC classes, assigning EUROVOC descriptors to European legal texts, and more. Despite its widespread applications, a comprehensive understanding of state-of-the-art meth

Khen Cohen, Liav Hen, Ariel Lellouch

Distributed Acoustic Sensing (DAS) has emerged as a promising tool for real-time traffic monitoring in densely populated areas. In this paper, we present a novel concept that integrates DAS data with co-located visual information. We use YOLO-derived vehicle location and classification from camera inputs as labeled data to train a detection and classificatio

Wenqi Huang, Veronika Spieker, Siying Xu, Gastao Cruz

Conventional cardiac cine MRI methods rely on retrospective gating, which limits temporal resolution and the ability to capture continuous cardiac dynamics, particularly in patients with arrhythmias and beat-to-beat variations. To address these challenges, we propose a reconstruction framework based on subspace implicit neural representations for real-time c

Charles Meynard, Charles Bertucci

This paper is concerned with the study of mean field games master equations involving an additional variable modelling common noise. We address cases in which the dynamics of this variable can depend on the state of the game, which requires in general additional monotonicity assumptions on the coefficients. We explore the link between such a common noise and

Matteo Sodano, Federico Magistri, Jens Behley, Cyrill Stachniss

Perception is a key building block of autonomously acting vision systems such as autonomous vehicles. It is crucial that these systems are able to understand their surroundings in order to operate safely and robustly. Additionally, autonomous systems deployed in unconstrained real-world scenarios must be able of dealing with novel situations and object that

Kassem Kallas

This paper introduces a deep learning-based framework for resilient decision fusion in adversarial multi-sensor networks, providing a unified mathematical setup that encompasses diverse scenarios, including varying Byzantine node proportions, synchronized and unsynchronized attacks, unbalanced priors, adaptive strategies, and Markovian states. Unlike traditi

Takahiro Orito, Yoshihito Kuno, Ikuo Ichinose

Discovering and categorizing quantum orders in mixed many-body systems are currently one of the most important problems. Specific types of decoherence applied to typical quantum many-body states can induce a novel kind of mixed state accompanying characteristic symmetry orders, which has no counterparts in pure many-body states. We study phenomena generated

Yuqing Wang, Zhongling Huang, Shuxin Yang, Hao Tang

PolSAR data presents unique challenges due to its rich and complex characteristics. Existing data representations, such as complex-valued data, polarimetric features, and amplitude images, are widely used. However, these formats often face issues related to usability, interpretability, and data integrity. Most feature extraction networks for PolSAR are small

Ju-Young Kim, Minhaeng Cho

Optical beams carrying orbital angular momentum (OAM) have gained significant interest due to their unique properties, enhancing various communication systems and enabling applications such as the characterization of material or molecular chirality. Generating and detecting the OAM of light is thus crucial for numerous applications but poses challenges. This

Mukai Li, Lei Li, Shansan Gong, Qi Liu

Visual Language Models (VLMs) demonstrate impressive capabilities in processing multimodal inputs, yet applications such as visual agents, which require handling multiple images and high-resolution videos, demand enhanced long-range modeling. Moreover, existing open-source VLMs lack systematic exploration into extending their context length, and commercial m

Sebastian Weiss, Derek Bradley

Gaussian Splatting has recently emerged as the go-to representation for reconstructing and rendering 3D scenes. The transition from 3D to 2D Gaussian primitives has further improved multi-view consistency and surface reconstruction accuracy. In this work we highlight the similarity between 2D Gaussian Splatting (2DGS) and billboards from traditional computer

Alon Eirew, Eviatar Nachshoni, Aviv Slobodkin, Ido Dagan

Event relation detection is a fundamental NLP task, leveraged in many downstream applications, whose modeling requires datasets annotated with event relations of various types. However, systematic and complete annotation of these relations is costly and challenging, due to the quadratic number of event pairs that need to be considered. Consequently, many cur

Tita Alissa Bach, Aleksandar Babic, Narae Park, Tor Sporsem

The maritime industry requires effective communication among diverse stakeholders to address complex, safety-critical challenges. Industrial AI, including Large Language Models (LLMs), has the potential to augment human experts' workflows in this specialized domain. Our case study investigated the utility of LLMs in drafting replies to stakeholder inquiries

Kshitij Dave, Nouhaila Innan, Bikash K. Behera, Zahid Mumtaz

Sentiment analysis is an essential component of natural language processing, used to analyze sentiments, attitudes, and emotional tones in various contexts. It provides valuable insights into public opinion, customer feedback, and user experiences. Researchers have developed various classical machine learning and neuro-fuzzy approaches to address the exponen

Jason Stafford, Nwachukwu Uzo, Enrico Piccoli, Camille Petit

The dynamics of thin liquid films flowing over a spinning disc is studied through a combination of experiments and direct numerical simulations. We consider a comprehensive range of interfacial flow regimes from waveless through to three-dimensional (3D) waves, and for previously unexplored inertia-dominated conditions that have practical relevance. The tran

I. Vidana, V. Mantovani Sarti, J. Haidenbauer, D. L. Mihaylov

We construct the equation of state of hypernuclear matter and study the structure of neutron stars employing a chiral hyperon-nucleon interaction of the J\"{u}lich--Bonn group tuned to femtoscopic $\Lambda p$ data of the ALICE Collaboration, and $\Lambda\Lambda$ and $\Xi$N interactions determined from lattice QCD calculations by the HAL QCD Collaboration tha

Blake Pehrson

We introduce stochastically resetting deterministic processes -- the simplest subclass of general resetting stochastic processes -- finding them to be repackaged renewal processes. In particular, we consider the stochastically resetting deterministic process undergoing linear growth of rate 1 subject to Poissonian resetting and deduce its marginal, expectati

Zhiyu Liao, Boxuan Li, Shaohui Yi, Lincong Zheng

We report an infrared spectroscopy study of the antiferromagnetic (AFM) insulator EuZn$_2$As$_2$ over a broad frequency range, spanning temperatures both above and below the AFM transition $T_{\rm N} \simeq$ 20 K. The optical response reveals an insulating behavior, featuring two prominent infrared-active phonon modes at around 95 and 190 cm$^{-1}$, and two

Florian Peißker, Michal Zajacek, Lucas Labadie, Emma Bordier

High-velocity stars and peculiar G objects orbit the central supermassive black hole (SMBH) Sagittarius A* (Sgr A*). Together, the G objects and high-velocity stars constitute the S cluster. In contrast with theoretical predictions, no binary system near Sgr A* has been identified. Here, we report the detection of a spectroscopic binary system in the S clust

Ágnes Kis-Tóth, Zoltán Haiman, Zsolt Frei

The stochastic gravitational wave background (GWB) recently discovered by several pulsar timing array (PTA) experiments is consistent with arising from a population of coalescing super-massive black hole binaries (SMBHBs). The amplitude of the background is somewhat higher than expected in most previous population models or from the local mass density of SMB

Xiaomeng Chu, Jiajun Deng, Guoliang You, Yifan Duan

We propose Radar-Camera fusion transformer (RaCFormer) to boost the accuracy of 3D object detection by the following insight. The Radar-Camera fusion in outdoor 3D scene perception is capped by the image-to-BEV transformation--if the depth of pixels is not accurately estimated, the naive combination of BEV features actually integrates unaligned visual conten

Shaik Basheeruddin Shah, Satish Mulleti, Yonina C. Eldar

Analog-to-Digital Converters (ADCs) are essential components in modern data acquisition systems. A key design challenge is accommodating high dynamic range (DR) input signals without clipping. Existing solutions, such as oversampling, automatic gain control (AGC), and compander-based methods, have limitations in handling high-DR signals. Recently, the Unlimi

Lingxiao Yang, Xuewen Dong, Zhiguo Wan, Sheng Gao

Sidechain techniques improve blockchain scalability and interoperability, providing decentralized exchange and cross-chain collaboration solutions for Internet of Things (IoT) data across various domains. However, current state-of-the-art (SOTA) schemes for IoT multi-domain data exchange are constrained by the need for synchronous networks, hindering efficie

Qi Zhou, Tianlin Li, Qing Guo, Dongxia Wang

Recent studies have raised significant concerns regarding the vulnerability of Large Vision Language Models (LVLMs) to maliciously injected or perturbed input images, which can mislead their responses. Existing defense methods show that such vision attacks are sensitive to image modifications especially cropping, using majority voting across responses of mod

Ke Fei, Xinyue Zhang, Jingjing Li

In recommender systems, post-click conversion rate (CVR) estimation is an essential task to model user preferences for items and estimate the value of recommendations. Sample selection bias (SSB) and data sparsity (DS) are two persistent challenges for post-click conversion rate (CVR) estimation. Currently, entire-space approaches that exploit unclicked samp

Dan Mikulincer, Arianna Piana

We present generalizations and modifications of Eldan's Stochastic Localization process, extending it to incorporate non-Gaussian tilts, making it useful for a broader class of measures. As an application, we introduce new processes that enable the decomposition and analysis of non-quadratic potentials on the Boolean hypercube, with a specific focus on quart

Parviz Asefi Nazarlou, Ali Bajravani, George H. Hitching

Generalizing the Martens theorem for line bundles over a curve $C$, we obtain upper bounds on the dimension of the Brill--Noether locus $B^k_{n, d}$ parametrizing stable bundles of rank $n \ge 2$ and degree $d$ over $C$ with at least $k$ independent sections. This proves a conjecture of the second author and generalizes bounds obtained by him in the rank two

Zhenxing Zhang, Yaxiong Wang, Lechao Cheng, Zhun Zhong

We present ASAP, a new framework for detecting and grounding multi-modal media manipulation (DGM4).Upon thorough examination, we observe that accurate fine-grained cross-modal semantic alignment between the image and text is vital for accurately manipulation detection and grounding. While existing DGM4 methods pay rare attention to the cross-modal alignment,

Shi-Chen Bai, Ruilong Guo, Yuchen Xiao, Quanqi Shi

Magnetic reconnection is the key to explosive phenomena in the universe. The flux rope is crucial in three-dimensional magnetic reconnection theory and are commonly considered to be generated by secondary tearing mode instability. Here we show that the parallel electron flow moving toward the reconnection diffusion region can spontaneously form flux ropes. T

Hanfang Liang, Yizhuo Yang, Jinming Hu, Jianfei Yang

Compact UAV systems, while advancing delivery and surveillance, pose significant security challenges due to their small size, which hinders detection by traditional methods. This paper presents a cost-effective, unsupervised UAV detection method using spatial-temporal sequence processing to fuse multiple LiDAR scans for accurate UAV tracking in real-world sc

Marissa Vlasblom, Milou Temmink, Sierra L. Grant, Nicolas Kurtovic

Radial drift of icy pebbles can have a large impact on the chemistry of the inner regions of protoplanetary disks. Compact dust disks ($\lesssim$50 au) are suggested to have a higher (cold) H$_2$O flux than more extended disks, likely due to efficient radial drift bringing H$_2$O-rich material to the inner disk, where it can be observed with JWST. We present

Nguyen Viet Dang, András Vasy, Michał Wrochna

For small perturbations of Minkowski space, we show that the square of the Lorentzian Dirac operator $P= -D^2$ has real spectrum apart from possible poles in a horizontal strip. Furthermore, for $\varepsilon>0$ we relate the poles of the spectral zeta function density of $P-i\varepsilon$ to local invariants, in particular to the Lorentzian scalar curvature.

Jean Van Schaftingen

We prove that a mapping $u \colon \mathcal{M}'\to \mathcal{N}$, where $\mathcal{M}'$ and $ \mathcal{N}$ are compact Riemannian manifolds, is the trace of a Sobolev mapping $U \colon \mathcal{M}' \times [0, 1) \to \mathcal{N}$ if and only if it is on some open covering of $\mathcal{M}'$. In the global case where $\mathcal{M}$ is a compact Riemannian manifold

Mauro Garavello, Elena Rossi, Abraham Sylla

Motivated by a vaccination coverage problem, we consider here a zero-sum differential game governed by a differential system consisting of a hyperbolic partial differential equation (PDE) and an ordinary differential equation (ODE). Two players act through their respective controls to influence the evolution of the system with the aim of minimizing their obj

Matthias J. Ehrhardt, Marco Mauritz

Reconstructing high-quality images from undersampled dynamic MRI data is a challenging task and important for the success of this imaging modality. To remedy the naturally occurring artifacts due to measurement undersampling, one can incorporate a motion model into the reconstruction so that information can propagate across time frames. Current models for MR

Syed Zohaib Hassan, Pierre Lison, Pål Halvorsen

Disfluencies are a natural feature of spontaneous human speech but are typically absent from the outputs of Large Language Models (LLMs). This absence can diminish the perceived naturalness of synthesized speech, which is an important criteria when building conversational agents that aim to mimick human behaviours. We show how the insertion of disfluencies c

Irham T. Andika, Stefan Schuldt, Sherry H. Suyu, Satadru Bag

Strongly lensed quasars provide valuable insights into the rate of cosmic expansion, the distribution of dark matter in foreground deflectors, and the characteristics of quasar hosts. However, detecting them in astronomical images is difficult due to the prevalence of non-lensing objects. To address this challenge, we developed a generative deep learning mod

J. Okamoto, G. Shibata, Yu. S. Posonov, H. Hayashi

Transition-metal ions with $5d^2$ electronic configuration in a cubic crystal field are prone to have a vanishing dipolar magnetic moment but finite higher-order multipolar moments, and they are expected to exhibit exotic physical properties. Through an investigation using resonant inelastic X-ray spectroscopy (RIXS), Raman spectroscopy, and theoretical liga

Gioacchino Antonelli, Marco Pozzetta, Kai Xu

We prove a sharp spectral generalization of the Cheeger--Gromoll splitting theorem. We show that if a complete non-compact Riemannian manifold $M$ of dimension $n\geq 2$ has at least two ends and \[ \lambda_1(-\gamma\Delta+\mathrm{Ric})\geq 0, \] for some $\gamma<\frac{4}{n-1}$, then $M$ splits isometrically as $\mathbb R\times N$ for some compact manifold $

Jiebin Zhang, Dawei Zhu, Yifan Song, Wenhao Wu

As large language models (LLMs) process increasing context windows, the memory usage of KV cache has become a critical bottleneck during inference. The mainstream KV compression methods, including KV pruning and KV quantization, primarily focus on either token or precision dimension separately. However, these works leaving the trade-off between these two ort

Nouhaila Innan, Bikash K. Behera, Saif Al-Kuwari, Ahmed Farouk

The escalating complexity of urban transportation systems, exacerbated by factors such as traffic congestion, diverse transportation modalities, and shifting commuter preferences, necessitates the development of more sophisticated analytical frameworks. Traditional computational approaches often struggle with the voluminous datasets generated by real-time se

Dapeng Zhang, Dayu Chen, Peng Zhi, Yinda Chen

Constructing online High-Definition (HD) maps is crucial for the static environment perception of autonomous driving systems (ADS). Existing solutions typically attempt to detect vectorized HD map elements with unified models; however, these methods often overlook the distinct characteristics of different non-cubic map elements, making accurate distinction c

Sébastien Roux, Jules Fraunié, Kenji Watanabe, Takashi Taniguchi

When two BN layers are stacked in parallel in an AB or BA arrangement, a spontaneous out-of-plane electric polarization arises due to charge transfer in the out-of-plane B-N bonds. The ferroelectric switching from AB to BA (or BA to AB) can be achieved with a relatively small out-of-plane electric field through the in-plane sliding of one atomic layer over t

Zhengwei Liu, Shuang Ming, Yilong Wang, Jinsong Wu

We propose a topological paradigm in alterfold topological quantum field theory to explore various concepts, including modular invariants, $\alpha$-induction and connections in Morita contexts within a modular fusion category of non-zero global dimension over an arbitrary field. Using our topological perspective, we provide streamlined quick proofs and broad

Kepu Zhang, Zhongxiang Sun, Xiao Zhang, Xiaoxue Zang

In search scenarios, user experience can be hindered by erroneous queries due to typos, voice errors, or knowledge gaps. Therefore, query correction is crucial for search engines. Current correction models, usually small models trained on specific data, often struggle with queries beyond their training scope or those requiring contextual understanding. While

Ruixuan Miao, Xu Lu, Cong Tian, Bin Yu

The commonly used Reinforcement Learning (RL) model, MDPs (Markov Decision Processes), has a basic premise that rewards depend on the current state and action only. However, many real-world tasks are non-Markovian, which has long-term memory and dependency. The reward sparseness problem is further amplified in non-Markovian scenarios. Hence learning a non-Ma

Yijie Liu, Yuyang Chen, Zhaoyang Guo, Zhi-Kang Lin

We investigate a novel higher-order topological behavior in elastic lattices characterized by nonsymmorphic symmetries. In the theoretical spring-mass lattice, altering the vertex mass allows for fine-tuning of the topological features within the bandgap. We analyze the quadrupole topological behavior in square lattices with nonsymmorphic symmetries using ne

Allen Lei, Tianchen Deng, Han Wang, Jianfei Yang

As small unmanned aerial vehicles (UAVs) become increasingly prevalent, there is growing concern regarding their impact on public safety and privacy, highlighting the need for advanced tracking and trajectory estimation solutions. In response, this paper introduces a novel framework that utilizes audio array for 3D UAV trajectory estimation. Our approach inc

Baekjune Kang, Junhee Shin, Myeongjun Kang, Uksam Choi

Colossal optical anisotropy in the entire visible spectrum is crucial for advanced photonic applications, enabling precise light manipulation without optical loss across a broad spectral range. Here, we demonstrate that CuAlO2 exhibits colossal optical anisotropy and transparency across the visible spectrum, enabled by its unique three-dimensional O-Cu-O dum

Wenyao Ni, Jiangrong Shen, Qi Xu, Huajin Tang

Inspired by the human brain's ability to adapt to new tasks without erasing prior knowledge, we develop spiking neural networks (SNNs) with dynamic structures for Class Incremental Learning (CIL). Our comparative experiments reveal that limited datasets introduce biases in logits distributions among tasks. Fixed features from frozen past-task extractors can

Antoni Szczurek, Pawel Jucha

Light-by-light scattering is a relatively new area of experimental physics. Our recent, theoretical research shows that studying two photon measurements in regions with lower transverse momentum ($p_{t,\gamma}$) and invariant mass ($M_{\gamma\gamma}$) allows us to observe not only the main contribution of photon scattering, known as fermionic loops but also

Zhenxing Zhang, Tianxian Zhang, Xiangliang Xu

A series of modified cognitive-only particle swarm optimization (PSO) algorithms effectively mitigate premature convergence by constructing distinct vectors for different particles. However, the underutilization of these constructed vectors hampers convergence accuracy. In this paper, an adaptive balance search based complementary heterogeneous PSO architect

Wenyu Zhang, Wei En Ng, Lixin Ma, Yuwen Wang

Current vision-language models may grasp basic spatial cues and simple directions (e.g. left, right, front, back), but struggle with the multi-dimensional spatial reasoning necessary for human-like understanding and real-world applications. To address this gap, we develop SPHERE (Spatial Perception and Hierarchical Evaluation of REasoning), a hierarchical ev

Jan Moser

In this paper we show that there is an infinite set of points of contact between the set of all Dirichlet's series and Fermat-Wiles theorem. The proof is independent on the Jacob's ladders.

Philippe Humbert

Neutron time correlation measurements are used to characterize fissile systems. For this purpose, detailed information are obtained using list mode measurements where the neutron detection times are recorded in a file called the time list file. In this article, we present the simulation and analysis of list mode measurements performed on SILENE reactor. Anal

Renlong Wang

Ordinal Priority Approach (OPA) has recently been proposed to determine the weights of experts, attributes, and alternatives using ordinal preference without precise information for multi-attribute ranking and selection (MARS). This study extends OPA with preference elicitation under incomplete information to counter the parametric and preference uncertainty

Yun Shi, Wei Wang

Holomorphic functions in several complex variables are generalized to regular functions in several quaternionic variables, and further to monogenic functions of several vector variables, which are annihilated by several Dirac operators on $k$ copies of the Euclidean space $\mathbb R^n$. As the Dolbeault complex in complex analysis, the Dirac complex resolvin

Yuwei Miao, Yuzhi Guo, Hehuan Ma, Jingquan Yan

Gene studies are crucial for fields such as protein structure prediction, drug discovery, and cancer genomics, yet they face challenges in fully utilizing the vast and diverse information available. Gene studies require clean, factual datasets to ensure reliable results. Ontology graphs, neatly organized domain terminology graphs, provide ideal sources for d

Seungeun Oh, Jinhyuk Kim, Jihong Park, Seung-Woo Ko

This paper studies a hybrid language model (HLM) architecture that integrates a small language model (SLM) operating on a mobile device with a large language model (LLM) hosted at the base station (BS) of a wireless network. The HLM token generation process follows the speculative inference principle: the SLM's vocabulary distribution is uploaded to the LLM,

Haoyi Zhang, Shizhao Sun, Yibo Lin, Runsheng Wang

Analog circuits are crucial in modern electronic systems, and automating their design has attracted significant research interest. One of major challenges is topology synthesis, which determines circuit components and their connections. Recent studies explore large language models (LLM) for topology synthesis. However, the scenarios addressed by these studie

Yangfan Ye, Xiaocheng Feng, Xiachong Feng, Libo Qin

Current large language models (LLMs) often exhibit imbalances in multilingual capabilities and cultural adaptability, largely attributed to their English-centric pre-training data. In this paper, we introduce and investigate cross-lingual latent transplantation (XTransplant), a probing framework which aims to further exploit the model's internalized multilin

Chen Chen, Liangjin Zhao, Yuanchun He, Yingxuan Long

Semantic segmentation and 3D reconstruction are two fundamental tasks in remote sensing, typically treated as separate or loosely coupled tasks. Despite attempts to integrate them into a unified network, the constraints between the two heterogeneous tasks are not explicitly modeled, since the pioneering studies either utilize a loosely coupled parallel struc

D. Kong, A. Kovács, M. Charilaou, M. Altthaler

The ability to control magnetism with strain offers innovative pathways for the modulation of magnetic domain configurations and for the manipulation of magnetic states in materials on the nanoscale. Although the effect of strain on magnetic domains has been recognized since the early work of C. Kittel, detailed local observations have been elusive. Here, we

Rixin Zhou, Honglin Pang, Qian Zhang, Ruihua Qi

In real-world applications across specialized domains, addressing complex out-of-distribution (OOD) challenges is a common and significant concern. In this study, we concentrate on the task of fine-grained bronze ware dating, a critical aspect in the study of ancient Chinese history, and developed a benchmark dataset named ShiftedBronzes. By extensively expa

Lijun Bo, Dongfang Yang, Shihua Wang

We study the mean field game problem for a nervous system consisting of a large number of neurons with mean-field interaction. In this system, each neuron can modulate its spiking activity by controlling its membrane potential to synchronize with others, thereby giving rise to a finite-player game problem. To address this, we first examine the corresponding

Leonid Schwenke, Martin Atzmueller

With their increase in performance, neural network architectures also become more complex, necessitating explainability. Therefore, many new and improved methods are currently emerging, which often generate so-called saliency maps in order to improve interpretability. Those methods are often evaluated by visual expectations, yet this typically leads towards

Ryo Suzuki, Mar Gonzalez-Franco, Misha Sra, David Lindlbauer

This workshop brings together experts and practitioners from augmented reality (AR) and artificial intelligence (AI) to shape the future of AI-in-the-loop everyday AR experiences. With recent advancements in both AR hardware and AI capabilities, we envision that everyday AR -- always-available and seamlessly integrated into users' daily environments -- is be

Paul Renault, Patrick Yard, Raphael C. Pooser, Miller Eaton

Photonics represents one of the most promising approaches to large-scale quantum computation with millions of qubits and billions of gates, owing to the potential for room-temperature operation, high clock speeds, miniaturization of photonic circuits, and repeatable fabrication processes in commercial photonic foundries. We present an end-to-end architecture

Yupei Li, Manuel Milling, Lucia Specia, Björn W. Schuller

The availability of high-quality APIs for Large Language Models (LLMs) has facilitated the widespread creation of Machine-Generated Content (MGC), posing challenges such as academic plagiarism and the spread of misinformation. Existing MGC detectors often focus solely on surface-level information, overlooking implicit and structural features. This makes them

Hongwei Xu, Zai Yang

This paper addresses the challenge of Toeplitz covariance matrix estimation from partial entries of random quantized samples. To balance trade-offs among the number of samples, the number of entries observed per sample, and the data resolution, we propose a ruler-based quantized Toeplitz covariance estimator. We derive non-asymptotic error bounds and analyze

Yili Deng, Jie Fan, Jiguang He, Baojia Luo

Real-time, high-precision localization in large-scale wireless networks faces two primary challenges: clock offsets caused by network asynchrony and non-line-of-sight (NLoS) conditions. To tackle these challenges, we propose a low-complexity real-time algorithm for joint synchronization and NLoS identification-based localization. For precise synchronization,

Ying Xue Li, Burkhard C. Schipper

When bidders bid on complex objects, they might be unaware of characteristics effecting their valuations. We assume that each buyer's valuation is a sum of independent random variables, one for each characteristic. When a bidder is unaware of a characteristic, he omits the random variable from the sum. We study the seller's decision to raise bidders' awarene

Wangyu Xue, Chen Qian, Jiayi Wu, Yang Zhou

Existing works on human-centric video understanding typically focus on analyzing specific moment or entire videos. However, many applications require higher precision at the frame level. In this work, we propose a novel task, BestShot, which aims to locate highlight frames within human-centric videos via language queries. This task demands not only a deep se

Jenny Kunz

Smaller LLMs still face significant challenges even in medium-resourced languages, particularly when it comes to language-specific knowledge -- a problem not easily resolved with machine-translated data. In this case study on Icelandic, we aim to enhance the generation performance of an LLM by specialising it using unstructured text corpora. A key focus is o