May 2024 arXiv papers — page 59

Claudio Battiloro, Ege Karaismailoğlu, Mauricio Tec, George Dasoulas

Graph neural networks excel at modeling pairwise interactions, but they cannot flexibly accommodate higher-order interactions and features. Topological deep learning (TDL) has emerged recently as a promising tool for addressing this issue. TDL enables the principled modeling of arbitrary multi-way, hierarchical higher-order interactions by operating on combi

Deepak Narayan Gadde, Sebastian Simon, Djones Lettnin, Thomas Ziller

The verification throughput is becoming a major challenge bottleneck, since the complexity and size of SoC designs are still ever increasing. Simply adding more CPU cores and running more tests in parallel will not scale anymore. This paper discusses various methods of improving verification throughput: ranking and the new machine learning (ML) based technol

Ajay John Alex, Chloe M. Barnes, Pedro Machado, Isibor Ihianle

In an era of rapid climate change and its adverse effects on food production, technological intervention to monitor pollinator conservation is of paramount importance for environmental monitoring and conservation for global food security. The survival of the human species depends on the conservation of pollinators. This article explores the use of Computer V

Nandini Sahu, Kim-Vy Tran, Sherry H. Suyu, Anowar J. Shajib

Observed evolution of the total mass distribution with redshift is crucial to testing galaxy evolution theories. To measure the total mass distribution, strong gravitational lenses complement the resolved dynamical observations currently limited to $z \lesssim 0.5$. Here we present the lens models for a pilot sample of seven galaxy-scale lenses from the ASTR

Yuling Cai, Fan Xiang, Guozhu Meng, Yinzhi Cao

Model stealing, i.e., unauthorized access and exfiltration of deep learning models, has become one of the major threats. Proprietary models may be protected by access controls and encryption. However, in reality, these measures can be compromised due to system breaches, query-based model extraction or a disgruntled insider. Security hardening of neural netwo

Jorge Condor, Sebastien Speierer, Lukas Bode, Aljaz Bozic

Efficient scene representations are essential for many computer graphics applications. A general unified representation that can handle both surfaces and volumes simultaneously, remains a research challenge. Inspired by recent methods for scene reconstruction that leverage mixtures of 3D Gaussians to model radiance fields, we formalize and generalize the mod

Vinod Raman, Unique Subedi, Ambuj Tewari

We study online classification under smoothed adversaries. In this setting, at each time point, the adversary draws an example from a distribution that has a bounded density with respect to a fixed base measure, which is known apriori to the learner. For binary classification and scalar-valued regression, previous works \citep{haghtalab2020smoothed, block202

Nataša Krčo, Florent Guépin, Matthieu Meeus, Bogdan Kulynych

Synthetic data generators and machine learning models can memorize their training data, posing privacy concerns. Membership inference attacks (MIAs) are a standard method of estimating the privacy risk of these systems. The risk of individual records is typically computed by evaluating MIAs in a record-specific privacy game. We analyze the record-specific pr

Aaron Fitzpatrick, N. Walter Talarico, Roberto Di Remigio Eikås, Stefan Knecht

We present a measurement-cost efficient implementation of Strongly-Contracted $N$-Electron Valence Perturbation Theory (SC-NEVPT2) for use on near-term quantum devices. At the heart of our algorithm we exploit the properties of adaptive Informationally Complete positive operator valued measures (IC-POVMs) to recycle the measurement outcomes from a ground sta

Lea Richtmann, Viktoria-S. Schmiesing, Dennis Wilken, Jan Heine

Setting up and controlling optical systems is often a challenging and tedious task. The high number of degrees of freedom to control mirrors, lenses, or phases of light makes automatic control challenging, especially when the complexity of the system cannot be adequately modeled due to noise or non-linearities. Here, we show that reinforcement learning (RL)

Giacomo Venturi, Andrea Mancini, Nicola Melchioni, Stefano Chiodini

Controlling light at subwavelength scales is one of the main challenges of nanophotonics. Leveraging hyperbolic polaritons supporting arbitrarily large wavevectors can lead to extreme light confinement, effectively overcoming the diffraction limit. Hyperbolicity was initially realized in artificial metamaterials, but their performances are limited by high lo

Simon Hubmer, Victoria Laidlaw, Ronny Ramlau, Ekaterina Sherina

In this paper, we derive a new class of methods for the classic 2D phase unwrapping problem of recovering a phase function from its wrapped form. For this, we consider the wrapped phase as a wavefront aberration in an optical system, and use reconstruction methods for (digital) wavefront sensors for its recovery. The key idea is that mathematically, common w

Jan Kozisek, Jana Hrncirova, Miroslav Slouf, Ivana Sloufova

Plasmon-driven reactions on plasmonic nanoparticles (NPs) occur under significantly different conditions from those of classical organic synthesis and provide a promising pathway for enhancing the efficiency of various chemical processes. However, these reactions can also have undesirable effects, such as 4-mercaptophenylboronic acid (MPBA) deboronation. MPB

Valerio Belcamino, Alessandro Carfì, Fulvio Mastrogiovanni

Hands are a fundamental tool humans use to interact with the environment and objects. Through hand motions, we can obtain information about the shape and materials of the surfaces we touch, modify our surroundings by interacting with objects, manipulate objects and tools, or communicate with other people by leveraging the power of gestures. For these reasons

Aditya Y Dalwadi, Kapil R Shenvi Pause, Ajit A Diwan, Nishad Kothari

For most problems pertaining to perfect matchings, one may restrict attention to matching covered graphs - that is, connected nontrivial graphs with the property that each edge belongs to some perfect matching. There is extensive literature on these graphs that are also known as 1-extendable graphs (since each edge extends to a perfect matching) including an

Houssem Sifaou, Osvaldo Simeone

In many wireless application scenarios, acquiring labeled data can be prohibitively costly, requiring complex optimization processes or measurement campaigns. Semi-supervised learning leverages unlabeled samples to augment the available dataset by assigning synthetic labels obtained via machine learning (ML)-based predictions. However, treating the synthetic

Yuxuan Guo, Shaohui Peng, Jiaming Guo, Di Huang

Building open agents has always been the ultimate goal in AI research, and creative agents are the more enticing. Existing LLM agents excel at long-horizon tasks with well-defined goals (e.g., `mine diamonds' in Minecraft). However, they encounter difficulties on creative tasks with open goals and abstract criteria due to the inability to bridge the gap betw

Ladan Khaloopour, Yanpeng Su, Florian Raskob, Tobias Meuser

The sixth generation (6G) mobile communication networks are expected to intelligently integrate into various aspects of modern digital society, including smart cities, homes, health-care, transportation, and factories. While offering a multitude of services, it is likely that societies become increasingly reliant on 6G infrastructure. Any disruption to these

Shiyu Qin, Jinpeng Wang, Yimin Zhou, Bin Chen

Learned visual compression is an important and active task in multimedia. Existing approaches have explored various CNN- and Transformer-based designs to model content distribution and eliminate redundancy, where balancing efficacy (i.e., rate-distortion trade-off) and efficiency remains a challenge. Recently, state-space models (SSMs) have shown promise due

Zijie Guo, Pumeng Lyu, Fenghua Ling, Lei Bai

Accurate ocean dynamics modeling is crucial for enhancing understanding of ocean circulation, predicting climate variability, and tackling challenges posed by climate change. Despite improvements in traditional numerical models, predicting global ocean variability over multi-year scales remains challenging. Here, we propose ORCA-DL (Oceanic Reliable foreCAst

Ildikó Pethes, Andrea Piarristeguy, Annie Pradel, Raphael Escalier

The structure of evaporated amorphous Ge$_x$Sb$_x$Te$_{100-2x}$ ($x =$ 6, 9, 13) alloys was investigated by neutron diffraction, X-ray diffraction and extended X-ray absorption spectroscopy (EXAFS) at the Ge, Sb and Te K-edges. Large scale structural models were generated by fitting the experimental datasets (5 for each composition) simultaneously in the fra

Chaoxi Cui, Run-Wu Zhang, Yilin Han, Zhi-Ming Yu

Exploring new Hall effect is always a fascinating research topic. The ordinary Hall effect and the quantum Hall effect, initially discovered in two-dimensional (2D) non-magnetic systems, are the phenomena that a transverse current is generated when a system carrying an electron current is placed in a magnetic field perpendicular to the currents. In this work

Donglei Yang, Fan Yang

For a graph $G$ and a constant $\alpha>0$, we denote by $C_{\alpha}(G)$ the minimum order of a subgraph $H\subseteq G$ with $d(H)\ge \alpha d(G)$. Liu and Montgomery conjectured that every graph $G$ contains $K_{\Omega(t)}$ as a subdivision for $t=\min \{d(G), \sqrt{\tfrac{C_{\alpha}(G)}{\log C_{\alpha}(G)}}\}$. In the paper, we prove this conjecture.

Niren Bhoja, Kirill Krasnov

In differential geometry, geometric structures can often be encoded by differential forms satisfying algebraic and differential constraints. This is in particular the case for spinorial G-structures, where the defining tensors are differential forms arising as spinor bilinears and their exterior derivatives determine the intrinsic torsion. In this paper we s

Songze Li, Chenqing Zhu

Conventional federated learning (FL) frameworks follow a server-driven model where the server determines session initiation and client participation, which faces challenges in accommodating clients' asynchronous needs for model updates. We introduce Client-Driven Federated Learning (CDFL), a novel FL framework that puts clients at the driving role. In CDFL,

Ali Kashefi

In this technical report, we extensively investigate the accuracy of outputs from well-known generative artificial intelligence (AI) applications in response to prompts describing common fluid motion phenomena familiar to the fluid mechanics community. We examine a range of applications, including Midjourney, Dall-E, Runway ML, Microsoft Designer, Gemini, Me

Barış Büyüktaş, Kenneth Weitzel, Sebastian Völkers, Felix Zailskas

Federated learning (FL) aims to collaboratively learn deep learning model parameters from decentralized data archives (i.e., clients) without accessing training data on clients. However, the training data across clients might be not independent and identically distributed (non-IID), which may result in difficulty in achieving optimal model convergence. In th

Nagendra Singh, Akhlad Iqbal, Shahid Ali

In this paper, we discuss the concepts of bifunction and geodesic convexity for vector valued functions on Hadamard manifold. The Hadamard manifold is a particular type of Riemannian manifold with non-positive sectional curvature. Using bifunction, we introduce a definition of generalized geodesic convexity in the context of the Hadamard manifold. To support

Eloy Serrano-Seco, Edgar Ramirez-Laboreo, Eduardo Moya-Lasheras, Carlos Sagues

Electromechanical relays and contactors suffer from strong collisions at the end of the switching operations. This causes several undesirable phenomena, such as clicking, mechanical wear and contact bounce. Thus, there is great interest in mitigating these switching impacts while keeping the advantageous features of these devices. This paper proposes a compl

Mingming Ha, Xuewen Tao, Wenfang Lin, Qionxu Ma

In most practical applications such as recommendation systems, display advertising, and so forth, the collected data often contains missing values and those missing values are generally missing-not-at-random, which deteriorates the prediction performance of models. Some existing estimators and regularizers attempt to achieve unbiased estimation to improve th

Nagendra Singh, Akhlad Iqbal, Shahid Ali

An important concept of convexificators has been extended to Hadamard manifolds in this paper. The mean value theorem for convexificators on the Hadamard manifold has also been derived. Monotonicity of the bounded convexificators has been discussed and an important characterization for the bounded convexificators to be $\partial_{*}^{*}$-geodesic convexity h

Stein Meereboer

Let $\mathfrak{g}$ be a complex semisimple Lie algebra and let $\mathbf{U}_q(\mathfrak{g})$ denote the associated Drinfel'd Jimbo quantized enveloping algebra. In this paper we study spherical functions of $\mathbf{U}_q(\mathfrak{g})$ related to characters. We show invariance under the Wang-Zhang braid group operators and show relative Weyl group invariance,

Xuezhi Chen, Changxing Miao

Let $\gamma(t)=(P_1(t),\ldots,P_n(t))$ where $P_i$ is a real polynomial with zero constant term for each $1\leq i\leq n$. We will show the existence of the configuration $\{x,x+\gamma(t)\}$ in sets of positive density $\epsilon$ in $[0,1]^n$ with a gap estimate $t\geq \delta(\epsilon)$ when $P_i$'s are arbitrary, and in $[0,N]^n$ with a gap estimate $t\geq \

Emile Pierret, Bruno Galerne

Super-Resolution (SR) is the problem that consists in reconstructing images that have been degraded by a zoom-out operator. This is an ill-posed problem that does not have a unique solution, and numerical approaches rely on a prior on high-resolution images. While optimization-based methods are generally deterministic, with the rise of image generative model

Yuandou Wang, Neel Kanwal, Kjersti Engan, Chunming Rong

Running deep neural networks for large medical images is a resource-hungry and time-consuming task with centralized computing. Outsourcing such medical image processing tasks to hybrid clouds has benefits, such as a significant reduction of execution time and monetary cost. However, due to privacy concerns, it is still challenging to process sensitive medica

Ahmed Mohamed Abdelmoaty, Ibrahim Ihab Ibrahim

This research conducts a comparative analysis of four Ant Colony Optimization (ACO) variants -- Ant System (AS), Rank-Based Ant System (ASRank), Max-Min Ant System (MMAS), and Ant Colony System (ACS) -- for solving the Traveling Salesman Problem (TSP). Our findings demonstrate that algorithm performance is significantly influenced by problem scale and instan

Bojun Li, Nariya Uchida

An Ott-Antonsen reduced $M$-population of Kuramoto-Sakaguchi oscillators is investigated, focusing on the influence of the phase-lag parameter $\alpha$ on the collective dynamics. For oscillator populations coupled on a ring, we obtained a wide variety of spatiotemporal patterns, including coherent states, traveling waves, partially synchronized states, modu

Hyeonjae Gil, Myung-Hwan Jeon, Ayoung Kim

Thermal infrared (TIR) cameras are emerging as promising sensors in safety-related fields due to their robustness against external illumination. However, RAW TIR image has 14 bits of pixel depth and needs to be rescaled into 8 bits for general applications. Previous works utilize a global 1D look-up table to compute pixel-wise gain solely based on its intens

Hoàng-Ân Lê, Minh-Tan Pham

Partial multi-task learning where training examples are annotated for one of the target tasks is a promising idea in remote sensing as it allows combining datasets annotated for different tasks and predicting more tasks with fewer network parameters. The na\"ive approach to partial multi-task learning is sub-optimal due to the lack of all-task annotations fo

Thomas Nagler, Lennart Schneider, Bernd Bischl, Matthias Feurer

Hyperparameter optimization is crucial for obtaining peak performance of machine learning models. The standard protocol evaluates various hyperparameter configurations using a resampling estimate of the generalization error to guide optimization and select a final hyperparameter configuration. Without much evidence, paired resampling splits, i.e., either a f

Yuandou Wang, Sheejan Tripathi, Siamak Farshidi, Zhiming Zhao

Today, scientific research is increasingly data-centric and compute-intensive, relying on data and models across distributed sources. However, it still faces challenges in the traditional cooperation mode, due to the high storage and computing cost, geo-location barriers, and local confidentiality regulations. The Jupyter environment has recently emerged and

Fabio Scarabotti

We construct the ordinary irreducible representations of the group of automorphisms of a finite rooted tree and we get a natural parametrization of them. To achieve this goals, we introduce and study the combinatorics of tree compositions, a natural generalization of set compositions but with new features and more complexity. These combinatorial structures l

Luis Enrique Espinosa Castro, Simone Rossoni, Günter Sigl

The propagation of very-high-energy gamma-rays (VHEGRs) in the extragalactic space offers the opportunity to study astrophysical phenomena not reproducible in laboratories. In particular, the deviation from predictions of the observed photon flux from distant sources at the GeV energy scale still represents an open problem. Commonly, this deviation is interp

Peter Lippmann, Gerrit Gerhartz, Roman Remme, Fred A. Hamprecht

In numerous applications of geometric deep learning, the studied systems exhibit spatial symmetries and it is desirable to enforce these. For the symmetry of global rotations and reflections, this means that the model should be equivariant with respect to the transformations that form the group of $\mathrm O(d)$. While many approaches for equivariant message

Junkai Xia, Chenxin Xu, Qingyao Xu, Chen Xie

Realistic trajectory generation with natural language control is pivotal for advancing autonomous vehicle technology. However, previous methods focus on individual traffic participant trajectory generation, thus failing to account for the complexity of interactive traffic dynamics. In this work, we propose InteractTraj, the first language-driven traffic traj

Zhongquan Chen, Pim van der Hoorn, Björn Baumeier

We present a graph random walk (GRW) method for the study of charge transport properties of complex molecular materials in the time-of-flight regime. The molecules forming the material are represented by the vertices of a directed weighted graph, and the charge carriers are random walkers. The edge weights are rates for elementary jumping processes for a cha

Xing-Yu Han, Shu-Min Zhao, Long Ruan, Xi Wang

It is well known that the muon magnetic dipole moment (MDM) has close relation with the new physics (NP) in the development of the Standard Model (SM). Combined with the Fermilab National Accelerator Laboratory (FNAL) and the Brookhaven National Laboratory (BNL) E821 result, the departure from the SM prediction is about 5.0 $\sigma$. We study the electroweak

Stefan Diehl, Kyungseon Joo, Kirill Semenov-Tian-Shansky, Christian Weiss

QCD gives rise to a rich spectrum of excited baryon states. Understanding their internal structure is important for many areas of nuclear physics, such as nuclear forces, dense matter, and neutrino-nucleus interactions. Generalized parton distributions (GPDs) are an established tool for characterizing the QCD structure of the ground-state nucleon. They are u

Xia Li, Runzhao Yang, Xiangtai Li, Antony Lomax

Motion information from 4D medical imaging offers critical insights into dynamic changes in patient anatomy for clinical assessments and radiotherapy planning and, thereby, enhances the capabilities of 3D image analysis. However, inherent physical and technical constraints of imaging hardware often necessitate a compromise between temporal resolution and ima

Fan-Ming Luo, Zuolin Tu, Zefang Huang, Yang Yu

Real-world decision-making tasks are usually partially observable Markov decision processes (POMDPs), where the state is not fully observable. Recent progress has demonstrated that recurrent reinforcement learning (RL), which consists of a context encoder based on recurrent neural networks (RNNs) for unobservable state prediction and a multilayer perceptron

Nicola Dainese, Matteo Merler, Minttu Alakuijala, Pekka Marttinen

In this work we consider Code World Models, world models generated by a Large Language Model (LLM) in the form of Python code for model-based Reinforcement Learning (RL). Calling code instead of LLMs for planning has potential to be more precise, reliable, interpretable, and extremely efficient. However, writing appropriate Code World Models requires the abi

Viktor Rindert, Vanya Darakchieva, Tapati Sarkar, Mathias Schubert

In this letter, we derive an expression for magnetic dipole transitions that is analogous to the Lyddane-Sachs-Teller relation for dielectric polar lattice vibrations. We thereby define transverse and longitudinal optical frequencies at which paramagnetic resonance and antiresonance occurs, respectively. The relation found here thus permits non-invasive opti

Youzhi Qu, Junfeng Xia, Xinyao Jian, Wendu Li

Data reconstruction is a widely used pre-training task to learn the generalized features for many downstream tasks. Although reconstruction tasks have been applied to neural signal completion and denoising, neural signal reconstruction is less studied. Here, we employ the masked autoencoder (MAE) model to reconstruct functional magnetic resonance imaging (fM

Naïn Jonckers, Toon Vinck, Gert Dekkers, Peter Karsmakers

Deep Neural Network (DNN) accelerators are extensively used to improve the computational efficiency of DNNs, but are prone to faults through Single-Event Upsets (SEUs). In this work, we present an in-depth analysis of the impact of SEUs on a Systolic Array (SA) based DNN accelerator. A fault injection campaign is performed through a Register-Transfer Level (

Debjyoti Bhattacharjee, Anmol, Tommaso Marinelli, Karan Pathak

Architectural simulators hold a vital role in RISC-V research, providing a crucial platform for workload evaluation without the need for costly physical prototypes. They serve as a dynamic environment for exploring innovative architectural concepts, enabling swift iteration and thorough analysis of performance metrics. As deep learning algorithms become incr

Pabitra Barman, Subhojoy Gupta

Let $S$ be a punctured surface of negative Euler characteristic. We show that given a generic representation $\rho:\pi_1(S) \rightarrow \mathrm{PSL}_n(\mathbb{C})$, there exists a positive representation $\rho_0:\pi_1(S) \rightarrow \mathrm{PSL}_n(\mathbb{R})$ that dominates $\rho$ in the Hilbert length spectrum as well as in the translation length spectrum,

Zhitian Li, Yida Niu, Yao Su, Hangxin Liu

The dynamic Sequential Mobile Manipulation Planning (SMMP) framework is essential for the safe and robust operation of mobile manipulators in dynamic environments. Previous research has primarily focused on either motion-level or task-level dynamic planning, with limitations in handling state changes that have long-term effects or in generating responsive mo

Nicolas Béreux, Aurélien Decelle, Cyril Furtlehner, Lorenzo Rosset

Restricted Boltzmann Machines (RBMs) are powerful tools for modeling complex systems and extracting insights from data, but their training is hindered by the slow mixing of Markov Chain Monte Carlo (MCMC) processes, especially with highly structured datasets. In this study, we build on recent theoretical advances in RBM training and focus on the stepwise enc

Martin Böckling, Heiko Paulheim, Sarah Detzler

Geospatial data plays a central role in modeling our world, for which OpenStreetMap (OSM) provides a rich source of such data. While often spatial data is represented in a tabular format, a graph based representation provides the possibility to interconnect entities which would have been separated in a tabular representation. We propose in our paper a framew

Runsong Jia, Bowen Zhang, Sergio J. Rodríguez Méndez, Pouya G. Omran

The proposed research aims to develop an innovative semantic query processing system that enables users to obtain comprehensive information about research works produced by Computer Science (CS) researchers at the Australian National University (ANU). The system integrates Large Language Models (LLMs) with the ANU Scholarly Knowledge Graph (ASKG), a structur

Anurag Mishra, Ronen Gold, Sanjeev Vijayakumar

Industrial operations have grown exponentially over the last century, driving advancements in energy utilization through vehicles and machinery.This growth has significant environmental implications, necessitating the use of sophisticated technology to monitor and analyze climate data.The surge in industrial activities presents a complex challenge in forecas

Razvan-Andrei Lascu, Mateusz B. Majka, Łukasz Szpruch

Gradient flows play a substantial role in addressing many machine learning problems. We examine the convergence in continuous-time of a \textit{Fisher-Rao} (Mean-Field Birth-Death) gradient flow in the context of solving convex-concave min-max games with entropy regularization. We propose appropriate Lyapunov functions to demonstrate convergence with explici

Jianyuan Zhong, Zhijian Xu, Saizhuo Wang, Xiangyu Wen

In quantitative investment, constructing characteristic-sorted portfolios is a crucial strategy for asset allocation. Traditional methods transform raw stock data of varying frequencies into predictive characteristic factors for asset sorting, often requiring extensive manual design and misalignment between prediction and optimization goals. To address these

Yuchun Guo, Zhiqing Lu, Yanling Zhou, Xin Jiang

In this work, we propose a novel strategy to ensure infants, who inadvertently displace their quilts during sleep, are promptly and accurately re-covered. Our approach is formulated into two subsequent steps: interference resolution and quilt spreading. By leveraging the DWPose human skeletal detection and the Segment Anything instance segmentation models, t

Sushmita Gupta, Tanmay Inamdar, Pallavi Jain, Daniel Lokshtanov

Classical work on metric space based committee selection problem interprets distance as ``near is better''. In this work, motivated by real-life situations, we interpret distance as ``far is better''. Formally stated, we initiate the study of ``obnoxious'' committee scoring rules when the voters' preferences are expressed via a metric space. To this end, we

Matthew Akram, Nikolai Maas, Peter Sanders, Dominik Schreiber

The NP-hard scheduling problem P||C_max encompasses a set of tasks with known execution time which must be mapped to a set of identical machines such that the overall completion time is minimized. In this work, we improve existing techniques for optimal P||C_max scheduling with a combination of new theoretical insights and careful practical engineering. Most

Jun Liu, Chaoyun Zhang, Jiaxu Qian, Minghua Ma

Time series anomaly detection (TSAD) plays a crucial role in various industries by identifying atypical patterns that deviate from standard trends, thereby maintaining system integrity and enabling prompt response measures. Traditional TSAD models, which often rely on deep learning, require extensive training data and operate as black boxes, lacking interpre

Jiafei Lyu, Chenjia Bai, Jingwen Yang, Zongqing Lu

It is vital to learn effective policies that can be transferred to different domains with dynamics discrepancies in reinforcement learning (RL). In this paper, we consider dynamics adaptation settings where there exists dynamics mismatch between the source domain and the target domain, and one can get access to sufficient source domain data, while can only h

Peter Bürgisser, Mahmut Levent Doğan, Visu Makam, Michael Walter

When a group acts on a set, it naturally partitions it into orbits, giving rise to orbit problems. These are natural algorithmic problems, as symmetries are central in numerous questions and structures in physics, mathematics, computer science, optimization, and more. Accordingly, it is of high interest to understand their computational complexity. Recently,

Till Jahnke, Sebastian Mai, Surjendu Bhattacharyya, Keyu Chen

Molecular photoabsorption results in an electronic excitation/ionization which couples to the rearrangement of the nuclei. The resulting intertwined change of nuclear and electronic degrees of freedom determines the conversion of photoenergy into other molecular energy forms. Nucleobases are excellent candidates for studying such dynamics, and great effort h

T. Tano, T. Horai, Y. Ashida, Y. Hino

Deep understanding of $\gamma$-ray production from the fast neutron reaction in water is crucial for various physics studies at large-scale water Cherenkov detectors. We performed test experiments using quasi-mono energetic neutron beams ($E_n = 30$ and 250~MeV) at Osaka University's Research Center for Nuclear Physics to measure $\gamma$-rays originating fr

Bingyu Li, Da Zhang, Zhiyuan Zhao, Junyu Gao

Multimodal semantic segmentation is a pivotal component of computer vision and typically surpasses unimodal methods by utilizing rich information set from various sources.Current models frequently adopt modality-specific frameworks that inherently biases toward certain modalities. Although these biases might be advantageous in specific situations, they gener

Meng You, Zhiyu Zhu, Hui Liu, Junhui Hou

By harnessing the potent generative capabilities of pre-trained large video diffusion models, we propose NVS-Solver, a new novel view synthesis (NVS) paradigm that operates \textit{without} the need for training. NVS-Solver adaptively modulates the diffusion sampling process with the given views to enable the creation of remarkable visual experiences from si

Irina S. Proshina, Olga K. Sil'chenko, Alexei Yu. Kniazev

We have fulfilled a detailed long-slit spectroscopic analysis for two SB0 galaxies -- NGC 1533 and NGC 1543, -- belonging to the Dorado group. Our spectral data reveal asymmetric decoupled kinematics of the stars and ionised gas in these barred lenticular galaxies that give evidences for external origin of the gas in the rings. We have calculated the star fo

Penghui Qi, Xinyi Wan, Nyamdavaa Amar, Min Lin

Pipeline parallelism has been widely explored, but most existing schedules lack a systematic methodology. In this paper, we propose a framework to decompose pipeline schedules as repeating a building block, and show that the lifespan of the building block decides the peak activation memory of the pipeline schedule. Guided by the observations, we find that al

A. Miguel-Torcal, A. González-Tudela, F. J. García-Vidal, A. I. Fernández-Domínguez

Using topology optimization, we inverse-design nanophotonic cavities enabling the preparation of pure states of pairs and triples of quantum emitters. Our devices involve moderate values of the dielectric constant, operate under continuous laser driving, and yield fidelities to the target (Bell and W) states approaching unity for distant qubits (several natu

Yan Liu, Jonathan Chuah, Yishu Huang, Andre C. Stiel

Optoacoustic (OA) imaging combined with reversibly photoswitchable proteins has emerged as a promising technology for the high-sensitivity and multiplexed imaging of cells in live tissues in preclinical research. Through carefully-designed illumination schedules of ON and OFF laser pulses, the resulting OA signal is a multiplex of different reporter species

Grégoire Dutot, Margaux Zaffran, Olivier Féron, Yannig Goude

Electricity price forecasting (EPF) plays a major role for electricity companies as a fundamental entry for trading decisions or energy management operations. As electricity can not be stored, electricity prices are highly volatile which make EPF a particularly difficult task. This is all the more true when dramatic fortuitous events disrupt the markets. Tra

Stephen Smith, Qing Zhou

Learning the structure of causal directed acyclic graphs (DAGs) is useful in many areas of machine learning and artificial intelligence, with wide applications. However, in the high-dimensional setting, it is challenging to obtain good empirical and theoretical results without strong and often restrictive assumptions. Additionally, it is questionable whether

Fabio Feser, Marina Evangelou

Tuning the regularization parameter in penalized regression models is an expensive task, requiring multiple models to be fit along a path of parameters. Strong screening rules drastically reduce computational costs by lowering the dimensionality of the input prior to fitting. We develop strong screening rules for group-based Sorted L-One Penalized Estimation

Xinyu Lyu, Beitao Chen, Lianli Gao, Jingkuan Song

Although Large Visual Language Models (LVLMs) have demonstrated exceptional abilities in understanding multimodal data, they invariably suffer from hallucinations, leading to a disconnect between the generated text and the corresponding images. Almost all current visual contrastive decoding methods attempt to mitigate these hallucinations by introducing visu

Jouni J. Takalo

We analyse the monthly sunspot group (SG) data for Solar Cycles C8-C23 and calculate the average latitude of the drift path for the northern and southern hemisphere of the Sun. We find that exponential function fits slightly better than the second-order polynomial to the average drift of the SGs. The drift velocities are are 0.30 and 0.27 degrees/month (meri

Deblina Dey, A. V. Jayanthan, Kamalesh Saha

Let $G$ be a finite simple graph, and $J_G$ denote the binomial edge ideal of $G$. In this article, we first compute the $\mathrm{v}$-number of binomial edge ideals corresponding to Cohen-Macaulay closed graphs. As a consequence, we obtain the $\mathrm{v}$-number for paths. For cycle and binary tree graphs, we obtain a sharp upper bound for $\mathrm{v}(J_G)$

J. Pascal Gollin, Kevin Hendrey, Hao Huang, Tony Huynh

Motivated by the analysis of consensus formation in the Deffuant model for social interaction, we consider the following procedure on a graph $G$. Initially, there is one unit of tea at a fixed vertex $r \in V(G)$, and all other vertices have no tea. At any time in the procedure, we can choose a connected subset of vertices $T$ and equalize the amount of tea

Shu-Ping Lin

Analyses of $B$ meson decays to charmless hadronic final states are an important part of the Belle II program. They are sensitive to effects from non-standard model physics and provide experimentally precise constraints on the weak interactions of quarks. We present recent Belle II results on branching fractions and direct $CP$-violating asymmetries of the d

Bin Liu, Xiaodong Liu

A morphological and photometric analysis of the naked-eye long-period comet C/2022 E3 (ZTF) before perihelion is presented in this study. The observation images taken by the Zwicky Transient Facility survey telescope from July 2022 to October 2022 show a gradually brightening dust coma and a tail with a clear structure. The morphology of the dust coma reveal

Zhiyun Cheng, Hongzhu Gao

Let $\Sigma_g$ be a closed oriented surface of genus $g$, in this paper we discuss how to define coloring invariants and its generalizations for links in $\Sigma_g\times S^1$.

Jingcheng Deng, Zihao Wei, Liang Pang, Hanxing Ding

Recent knowledge editing methods have primarily focused on modifying structured knowledge in large language models. However, this task setting overlooks the fact that a significant portion of real-world knowledge is stored in an unstructured format, characterized by long-form content, noise, and a complex yet comprehensive nature. Techniques like "local laye

G. P. Mikitik, Yu. V. Sharlai

Using a known distribution of the Meissner currents over the surface of an infinitely long superconducting slab with a rectangular cross section, we find an applied magnetic field at which vortices begin to penetrate into the superconductor. This vortex-penetration field is determined by an interplay of the geometrical and Bean-Livingston barriers. The obtai

Juan Huang, Sheng Wang

This paper concerns the normalized ground states for the nonlinear Schr\"{o}dinger equation in the Bopp-Podolsky electrodynamics. This equation has a nonlocal nonlinearity and a mass supercritical power nonlinearity, both of which have deep impact on the geometry of the corresponding functional, and thus on the existence, limit behavior and stability of the

Minghui Zou, Ronghui Guo, Sai Zhang, Xiaowang Zhang

As the size and context length of Large Language Models (LLMs) grow, weight-activation quantization has emerged as a crucial technique for efficient deployment of LLMs. Compared to weight-only quantization, weight-activation quantization presents greater challenges due to the presence of outliers in activations. Existing methods have made significant progres

Álvaro Huertas-García, Javier Muñoz, Enrique De Miguel Ambite, Marcos Avilés Camarmas

The integration of predictive maintenance and cybersecurity represents a transformative advancement for small and medium-sized enterprises (SMEs) operating within the Industry 4.0 paradigm. Despite their economic importance, SMEs often face significant challenges in adopting advanced technologies due to resource constraints and knowledge gaps. The DETECTA 2.

Hao Lei, Jiayi Zhang, Zhe Wang, Bo Ai

Extremely large-scale multiple-input multiple-output (XL-MIMO) is crucial for satisfying the high data rate requirements of the sixth-generation (6G) wireless networks. In this context, ensuring accurate acquisition of channel state information (CSI) with low complexity becomes imperative. Moreover, deploying an extremely large antenna array at the base stat

Run Jiang, Haijun Wu, Yifeng Xu, Jun Zou

A nonlinear Helmholtz (NLH) equation with high frequencies and corner singularities is discretized by the linear finite element method (FEM). After deriving some wave-number-explicit stability estimates and the singularity decomposition for the NLH problem, a priori stability and error estimates are established for the FEM on shape regular meshes including t

Qiang Guo

Pedestrian detection has been a hot spot in computer vision over the past decades due to the wide spectrum of promising applications, the major challenge of which is False Positives (FPs) that occur during pedestrian detection. The emergence various Convolutional Neural Network-based detection strategies substantially enhance the pedestrian detection accurac

Lichuan Ji, Yingqi Lin, Zhenhua Huang, Yan Han

The development of AI-Generated Content (AIGC) has empowered the creation of remarkably realistic AI-generated videos, such as those involving Sora. However, the widespread adoption of these models raises concerns regarding potential misuse, including face video scams and copyright disputes. Addressing these concerns requires the development of robust tools

Aamir Ali, Muhammad Imran, Valentin Kuznetsov, Spyridon Trigazis

The CMSWEB cluster is pivotal to the activities of the Compact Muon Solenoid (CMS) experiment, as it hosts critical services required for the operational needs of the CMS experiment. The security of these services and the corresponding data is crucial to CMS. Any malicious attack can compromise the availability of our services. Therefore, it is important to

Abdur Rahman, Rajat Chawla, Muskaan Kumar, Arkajit Datta

In the rapidly evolving landscape of AI research and application, Multimodal Large Language Models (MLLMs) have emerged as a transformative force, adept at interpreting and integrating information from diverse modalities such as text, images, and Graphical User Interfaces (GUIs). Despite these advancements, the nuanced interaction and understanding of GUIs p

Jouni J. Takalo

We show using superposed epoch analysis (SEA) that the most energetic protons (greater than 60 MeV) in near-Earth IMF have a peak almost immediately (less than a day) after peak in solar flare index (SFI), while protons greater than 10 MeV peak one day after the SFI and protons greater than 1 MeV two days after the SFI. The geomagnetic indices AU, -AL, PC, A

Shunyu Liu, Wei Luo, Yanzhen Zhou, Kaixuan Chen

Transmission interface power flow adjustment is a critical measure to ensure the security and economy operation of power systems. However, conventional model-based adjustment schemes are limited by the increasing variations and uncertainties occur in power systems, where the adjustment problems of different transmission interfaces are often treated as severa