April 2024 arXiv papers — page 109

Hojun Lee, Suyoung Kim, Junhoo Lee, Jaeyoung Yoo

Coreset selection is a method for selecting a small, representative subset of an entire dataset. It has been primarily researched in image classification, assuming there is only one object per image. However, coreset selection for object detection is more challenging as an image can contain multiple objects. As a result, much research has yet to be done on t

Xiao-Lei Tang, Xue-Qian Zhang, Tian-Xue Ma, Miso Kim

Topological phononic crystals (PCs) offer an innovative method for manipulating acoustic or elastic waves. In this study, we introduce the gradient PC structures with coupled interfaces, specifically designed to achieve topological rainbow trapping and broadband acoustic energy harvesting. By leveraging the geometric symmetry of PC unit cells, we merge two P

Stathes Paganis

A number of gravitation-motivated theories, as well as theories with new coloured fermions predict heavy particle towers with spectral densities $\rho(m^2)$ growing faster than $e^{m}$, a characteristic of nonlocalizable theories. It is shown that if a light scalar, like the Higgs boson, interacts strongly with a heavy scalar particle tower with exponentiall

Xuelong Li, Hongjun An, Haofei Zhao, Guangying Li

In this paper, we introduce StreakNet-Arch, a real-time, end-to-end binary-classification framework based on our self-developed Underwater Carrier LiDAR-Radar (UCLR) that embeds Self-Attention and our novel Double Branch Cross Attention (DBC-Attention) to enhance scatter suppression. Under controlled water tank validation conditions, StreakNet-Arch with Self

Paolo V. Redondo, Matheus B. Guerrero, Raphaël Huser, Hernando Ombao

This chapter illustrates how tools from univariate and multivariate statistics of extremes can complement classical methods used to study brain signals and enhance the understanding of brain activity and connectivity during specific cognitive tasks or abnormal episodes, such as an epileptic seizure.

Rishikesh Yadav, Luigi Lombardo, Raphaël Huser

In this chapter, we illustrate the use of split bulk-tail models and subasymptotic models motivated by extreme-value theory in the context of hazard assessment for earthquake-induced landslides. A spatial joint areal model is presented for modeling both landslides counts and landslide sizes, paying particular attention to extreme landslides, which are the mo

Jiang Li, Xiangdong Su, Guanglai Gao

Recent studies have highlighted the effectiveness of tensor decomposition methods in the Temporal Knowledge Graphs Embedding (TKGE) task. However, we found that inherent heterogeneity among factor tensors in tensor decomposition significantly hinders the tensor fusion process and further limits the performance of link prediction. To overcome this limitation,

Jordan Richards, Raphaël Huser

Estimation of extreme conditional quantiles is often required for risk assessment of natural hazards in climate and geo-environmental sciences and for quantitative risk management in statistical finance, econometrics, and actuarial sciences. Interest often lies in extrapolating to quantile levels that exceed any past observations. Therefore, it is crucial to

Junichiro Sugihara, Moju Zhao, Takuzumi Nishio, Kei Okada

Modular self-reconfigurable robots (MSRRs) offer enhanced task flexibility by constructing various structures suitable for each task. However, conventional terrestrial MSRRs equipped with wheels face critical challenges, including limitations in the size of constructible structures and system robustness due to elevated wrench loads applied to each module. In

Yu-de Liu, Qiang Sun, Ge Xiong

New sharp affine isoperimetric inequalities for volume decomposition functionals $X_{2}$ and $X_{3}$ in $\mathbb{R}^n$ are established. To fulfil this task, we prove the recursion formulas for volume decomposition functionals and find out the connection between the domains of these functionals and matroid polytopes. Applications of matroid theory to convex g

Siyuan Feng, Jiawei Liu, Ruihang Lai, Charlie F. Ruan

While existing machine learning (ML) frameworks focus on established platforms, like running CUDA on server-grade GPUs, there have been growing demands to enable emerging AI applications in a broader set of scenarios, such as running Large Language Models (LLMs) within browsers and mobile phones. However, deploying emerging models on new platforms (such as M

Qijin She, Shishun Zhang, Yunfan Ye, Ruizhen Hu

Reaching-and-grasping is a fundamental skill for robotic manipulation, but existing methods usually train models on a specific gripper and cannot be reused on another gripper. In this paper, we propose a novel method that can learn a unified policy model that can be easily transferred to different dexterous grippers. Our method consists of two stages: a grip

Bai Yan, Qi Zhao, Jin Zhang, J. Andrew Zhang

This paper tackles the deployment challenges of Simultaneous Transmitting and Reflecting Reconfigurable Intelligent Surface (STAR-RIS) in communication systems. Unlike existing works that use fixed deployment setups or solely optimize the location, this paper emphasizes the joint optimization of the location and orientation of STAR-RIS. This enables searchin

Lina Yang, Yixin Xu, Xianheng Wang, Yanguang Zhou

Tuning thermal transport in nanostructures is essential for many applications, such as thermal management and thermoelectrics. Nanophononic metamaterials (NPM) have shown great potential for reducing thermal conductivity by introducing local resonant hybridization. In this work, the thermal conductivity of NPM with crystalline Si (c-Si) pillar, crystalline G

Akash K Rao, Arnav Bhavsar, Shubhajit Roy Chowdhury, Sushil Chandra

Virtual Reality (VR) has made significant strides, offering users a multitude of ways to interact with virtual environments. Each sensory modality in VR provides distinct inputs and interactions, enhancing the user's immersion and presence. However, the potential of additional sensory modalities, such as haptic feedback and 360{\deg} locomotion, to improve d

Wenhao Dong, Haodong Zhu, Shaohui Lin, Xiaoyan Luo

Cross-modality fusing complementary information from different modalities effectively improves object detection performance, making it more useful and robust for a wider range of applications. Existing fusion strategies combine different types of images or merge different backbone features through elaborated neural network modules. However, these methods neg

Guochao Jiang, Ziqin Luo, Yuchen Shi, Dixuan Wang

In recent years, the fine-tuned generative models have been proven more powerful than the previous tagging-based or span-based models on named entity recognition (NER) task. It has also been found that the information related to entities, such as entity types, can prompt a model to achieve NER better. However, it is not easy to determine the entity types ind

Richard T. Pomeroy, Mark A. Norris

Intermediate-mass black holes (IMBH) are expected to exist in globular clusters (GCs) and compact stellar systems (CSS) in general, but none have been conclusively detected. Tidal disruption events (TDEs), where a star is tidally disrupted by the gravitational field of a black hole, have been observed to occur around the supermassive black holes (SMBH) found

Mahima Kaushik, Aditee Mattoo, Ritesh Rastogi

The exoplanet detection is the most exciting and challenging field of astronomy. The discovery of many exoplanets has revolutionized our understanding of the formation and evolution of planetary systems and has showed new ways to search for extra terrestrial life. In recent years, some primary methods of exoplanet detection like transit, radial velocity, gra

Mateo Díaz, Venkat Chandrasekaran

Controlling the false discovery rate (FDR) is a popular approach to multiple testing, variable selection, and related problems of simultaneous inference. In many contemporary applications, models are not specified by discrete variables, which necessitates a broadening of the scope of the FDR control paradigm. Motivated by the ubiquity of low-rank models for

Yuxiang Lu, Syed A. Jafar

In order to explore how blind interference alignment (BIA) schemes may take advantage of side-information in computation tasks, we study the degrees of freedom (DoF) of a $K$ user wireless network setting that arises in full-duplex wireless MapReduce applications. In this setting the receivers are assumed to have reconfigurable antennas and channel knowledge

Jifeng Guo, Zhulin Liu, Tong Zhang, C. L. Philip Chen

Semi-supervised learning provides a solution to reduce the dependency of machine learning on labeled data. As one of the efficient semi-supervised techniques, self-training (ST) has received increasing attention. Several advancements have emerged to address challenges associated with noisy pseudo-labels. Previous works on self-training acknowledge the import

Guoxuan Chi, Zheng Yang, Chenshu Wu, Jingao Xu

Along with AIGC shines in CV and NLP, its potential in the wireless domain has also emerged in recent years. Yet, existing RF-oriented generative solutions are ill-suited for generating high-quality, time-series RF data due to limited representation capabilities. In this work, inspired by the stellar achievements of the diffusion model in CV and NLP, we adap

Artur Kiulian, Anton Polishko, Mykola Khandoga, Oryna Chubych

In the rapidly advancing field of AI and NLP, generative large language models (LLMs) stand at the forefront of innovation, showcasing unparalleled abilities in text understanding and generation. However, the limited representation of low-resource languages like Ukrainian poses a notable challenge, restricting the reach and relevance of this technology. Our

Aminabi Thekkoth, Baheeja C, S Sahayanathan, C D Ravikumar

The blazar 4C 31.03 recently underwent a major gamma-ray outburst at the beginning of 2023 after a prolonged quiescent phase. Fermi-LAT reported a daily average flux of 5x10^-6 phs cm^-2 s^-1, which is about 60 times its average value. We investigated this extraordinary outbreak through temporal and multi-wavelength analysis. From the statistical analysis of

Spandan Das, Vinay Samuel, Shahriar Noroozizadeh

This paper introduces novel methodologies for the Natural Language Inference for Clinical Trials (NLI4CT) task. We present TLDR (T5-generated clinical-Language summaries for DeBERTa Report Analysis) which incorporates T5-model generated premise summaries for improved entailment and contradiction analysis in clinical NLI tasks. This approach overcomes the cha

Taojun Hu, Xiao-Hua Zhou

Natural Language Processing (NLP) is witnessing a remarkable breakthrough driven by the success of Large Language Models (LLMs). LLMs have gained significant attention across academia and industry for their versatile applications in text generation, question answering, and text summarization. As the landscape of NLP evolves with an increasing number of domai

Ruichen Zhang, Hongyang Du, Yinqiu Liu, Dusit Niyato

In response to the needs of 6G global communications, satellite communication networks have emerged as a key solution. However, the large-scale development of satellite communication networks is constrained by the complex system models, whose modeling is challenging for massive users. Moreover, transmission interference between satellites and users seriously

Victor H. T. Brauer, Andrea Valdés-Hernández

Quantum teleportation in the presence of noisy channels acting on a bipartite resource state is considered. We consider a family of generalized noisy channels that continuously connect the amplitude damping and the dephasing channels, encompassing a wide family of in-between scenarios, to delve into the relation between the teleportation success and the amou

João C. Getelina, Prachi Sharma, Thomas Iadecola, Peter P. Orth

Finding ground state energies on current quantum processing units (QPUs) using algorithms like the variational quantum eigensolver (VQE) continues to pose challenges. Hardware noise severely affects both the expressivity and trainability of parametrized quantum circuits, limiting them to shallow depths in practice. Here, we demonstrate that both issues can b

Xuyang Zhao. Wei Guo, Yongchao Wang

In this paper, we consider a scenario of covert communication aided by multiple friendly interference nodes. The objective is to conceal the legitimate communication link under the surveillance of a warden. The main content is as follows: first, we propose a novel strategy for generating artificial noise signals in the considered covert scenario. Then, we le

Domenico Lippolis

The established thermodynamic formalism of chaotic dynamics, valid at statistical equilibrium, is here generalized to systems out of equilibrium, that have yet to relax to a steady state. A relation between information, escape rate, and the phase-space average of an integrated observable (e.g. Lyapunov exponent, diffusion coefficient) is obtained for finite

Yanhong Li, Chenghao Yang, Allyson Ettinger

Recent studies suggest that self-reflective prompting can significantly enhance the reasoning capabilities of Large Language Models (LLMs). However, the use of external feedback as a stop criterion raises doubts about the true extent of LLMs' ability to emulate human-like self-reflection. In this paper, we set out to clarify these capabilities under a more s

Junfei Wang, Pirathayini Srikantha

The modern power grid is witnessing a shift in operations from traditional control methods to more advanced operational mechanisms. Due to the nonconvex nature of the Alternating Current Optimal Power Flow (ACOPF) problem and the need for operations with better granularity in the modern smart grid, system operators require a more efficient and reliable ACOPF

Ruixin Yang, Dheeraj Rajagopal, Shirley Anugrah Hayati, Bin Hu

Uncertainty estimation is a significant issue for current large language models (LLMs) that are generally poorly calibrated and over-confident, especially with reinforcement learning from human feedback (RLHF). Unlike humans, whose decisions and confidences not only stem from intrinsic beliefs but can also be adjusted through daily observations, existing cal

Heejun Shin, Antonio Linero, Michelle Audirac, Kezia Irene

Estimating the joint effect of a multivariate, continuous exposure is crucial, particularly in environmental health where interest lies in simultaneously evaluating the impact of multiple environmental pollutants on health. We develop novel methodology that addresses two key issues for estimation of treatment effects of multivariate, continuous exposures. We

Wenlong Yang, Zhenghui Ji, Yang Gao, Kaiyuan Zhou

The large-scale fabrication of three-terminal magnetic tunnel junctions (MTJs) with high yield is becoming increasingly crucial, especially with the growing interest in spin-orbit torque (SOT) magnetic random access memory (MRAM) as the next generation of MRAM technology. To achieve high yield and consistent device performance in MTJs with perpendicular magn

Yongkang Sun, Xin Li, Qige Ao, Wenyuan Cui

IW And-type dwarf novae are anomalous Z Cam stars featured with outbursts happening during standstill states, which are not expected in the standard disk instability model. The physical mechanisms for these variations remain unclear. In this study, we report the discovery of a new candidate IW And-type dwarf nova J0652+2436, identified with its frequent outb

Dipendra Misra, Aldo Pacchiano, Robert E. Schapire

We study interactive learning in a setting where the agent has to generate a response (e.g., an action or trajectory) given a context and an instruction. In contrast, to typical approaches that train the system using reward or expert supervision on response, we study learning with hindsight instruction where a teacher provides an instruction that is most sui

Ki-Seok Kim, Arpita Mitra, Debangshu Mukherjee, Shinsei Ryu

Based on the renormalization group (RG) flow of worldsheet bosonic string theory, we construct an effective holographic dual description of the target space theory identifying the RG scale with the emergent extra dimension. This results in an effective dilaton-gravity-gauge theory, analogous to the low-energy description of bosonic M-theory. We argue that th

Alexander Perry, Saket Shah

We construct stability conditions on crepant resolutions of certain quotients of product varieties, giving as a special case the first examples of stability conditions on strict Calabi-Yau varieties of arbitrary dimension. Along the way, we prove the crepant resolutions are derived equivalent to the corresponding quotient stacks, verifying an instance of a c

Zhe Cheng, Xiaoyang Ji, Zifeng Huang, Yutaka Ohno

Overheating has emerged as a primary challenge constraining the reliability and performance of next-generation high-performance electronics, such as chiplets and (ultra)wide bandgap electronics. Advanced heterogeneous integration not only constitutes a pivotal technique for fabricating these electronics but also offers potential solutions for thermal managem

Jin-Hong Du, Zhenghao Zeng, Edward H. Kennedy, Larry Wasserman

With the evolution of single-cell RNA sequencing techniques into a standard approach in genomics, it has become possible to conduct cohort-level causal inferences based on single-cell-level measurements. However, the individual gene expression levels of interest are not directly observable; instead, only repeated proxy measurements from each individual's cel

Frédéric Ouimet

In this short note, explicit formulas are developed for the central and noncentral moments of the multivariate hypergeometric distribution. A numerical implementation is provided in Mathematica for fast evaluations. This work complements the paper by Ouimet (2021), where analogous formulas were derived and implemented in Mathematica for the multinomial distr

Dong Bai, Zhongzhou Ren

Multiprotons and multineutrons are among the most exotic and mysterious things ever produced on earth. They provide an exceptional opportunity to understand nuclear forces and nuclear dynamics at extreme conditions, as well as neutron stars in the heaven. Quantum entanglement, referred to as ``spooky action at a distance'' by Einstein, is a ubiquitous yet de

Haifeng Xia, Hai Huang, Zhengming Ding

Deep clustering as an important branch of unsupervised representation learning focuses on embedding semantically similar samples into the identical feature space. This core demand inspires the exploration of contrastive learning and subspace clustering. However, these solutions always rely on the basic assumption that there are sufficient and category-balanc

Wenchao Wu, Hao Xu, Dongxiao Zhang, Fanyang Mo

We present an innovative of artificial intelligence with column chromatography, aiming to resolve inefficiencies and standardize data collection in chemical separation and purification domain. By developing an automated platform for precise data acquisition and employing advanced machine learning algorithms, we constructed predictive models to forecast key s

Bohan Wu, David Blei

Variational inference (VI) has emerged as a popular method for approximate inference for high-dimensional Bayesian models. In this paper, we propose a novel VI method that extends the naive mean field via entropic regularization, referred to as $\Xi$-variational inference ($\Xi$-VI). $\Xi$-VI has a close connection to the entropic optimal transport problem a

Timothy McQuaid, Daniela Calzetti, Sean T. Linden, Matteo Messa

We survey the young star cluster population in the dwarf galaxy NGC4449 with the goal of investigating how stellar feedback may depend on the clusters' properties. Using Ultraviolet(UV)-optical-NearIR(NIR) photometry obtained from the Hubble Space Telescope, we have recovered 99 compact sources exhibiting emission in the Pa$\beta$ hydrogen recombination line

Haonan Zhao, Yiting Wang, Thomas Bashford-Rogers, Valentina Donzella

Datasets are essential for training and testing vehicle perception algorithms. However, the collection and annotation of real-world images is time-consuming and expensive. Driving simulators offer a solution by automatically generating various driving scenarios with corresponding annotations, but the simulation-to-reality (Sim2Real) domain gap remains a chal

Debesh Jha, Gorkem Durak, Abhijit Das, Jasmer Sanjotra

The emergence of foundational models represents a paradigm shift in medical imaging, offering extraordinary capabilities in disease detection, diagnosis, and treatment planning. These large-scale artificial intelligence systems, trained on extensive multimodal and multi-center datasets, demonstrate remarkable versatility across diverse medical applications.

Sneihil Gopal, David Griffith, Richard A. Rouil, Chunmei Liu

The Open Radio Access Network (O-RAN), an industry-driven initiative, utilizes intelligent Radio Access Network (RAN) controllers and open interfaces to facilitate efficient spectrum sharing between LTE and NR RANs. In this paper, we introduce the Proactive Spectrum Adaptation Scheme (ProSAS), a data-driven, O-RAN-compatible spectrum sharing solution. ProSAS

Ryuji Chiba

We investigate the quark contribution to the equation of state (EOS) of the isospin QCD matter using the two-flavor quark meson model at finite isospin density. This model includes the quark degrees of freedom through the lowest order of the loop correction. This model describes the crossover of the pion condensate from the Bose-Einstein condensation (BEC) p

Albert Kim, Samuel Madden

Despite decades of research into query optimization, optimizing queries with disjunctive predicate expressions remains a challenge. Solutions employed by existing systems (if any) are often simplistic and lead to much redundant work being performed by the execution engine. To address these problems, we propose a novel form of query execution called tagged ex

Sergei M. Kuzenko, Emmanouil S. N. Raptakis

We present higher-derivative deformations of the ModMax theory which preserve both $\mathsf{U}(1)$ duality symmetry and Weyl invariance. In particular, we single out a class of deformations expected to describe a low-energy effective action for the ModMax theory. We also elaborate on (higher-derivative) deformations of the $\mathcal{N}=1$ super ModMax theory

José Edgar Madriz Aguilar, Diego Allan Reyna, Mariana Montes

In this paper we investigate a power law coupling Higgs inflationary model in which the background geometry is determined by the Palatini's variational principle. The geometrical symmetries of the background geometry determine the invariant form of the action of the model and the background geometry resulted is of the Weyl-integrable type. The invariant acti

Alison A. Silva, D. Bazeia, Fabiano M. Andrade

This work deals with quantum transport in open quantum graphs. We consider the case of complete graphs on $n$ vertices with an edge removed and attached to two leads, to represent the entrance and exit channels, from where we calculate the transmission coefficient. We include the possibility of several vertices being connected or not and associate it with a

Yuanhao Gong

The Gaussian splatting methods are getting popular. However, their loss function only contains the $\ell_1$ norm and the structural similarity between the rendered and input images, without considering the edges in these images. It is well-known that the edges in an image provide important information. Therefore, in this paper, we propose an Edge Guided Gaus

Wouter W. L. Nuijten, Vlado Menkovski

We propose a method for the classification of objects that are structured as random trees. Our aim is to model a distribution over the node label assignments in settings where the tree data structure is associated with node attributes (typically high dimensional embeddings). The tree topology is not predetermined and none of the label assignments are present

Adnan Malik

In this research manuscript, we explore cylindrically symmetric solutions within the framework of modified $f(R)$ theories of gravity, where $R$ representing the Ricci scalar. The study focuses on analyzing the cylindrical solutions within realistic space-time regions and investigates three distinct cases of exact solutions derived from the field equations o

Kuo Hai, Xuefang Deng, Qiong Chen, Wenhua Hai

We consider an electron confined in a gated nanowire quantum dot (NQD) with arbitrarily strong spin-orbit coupling (SOC) and weak static magnetic field, and treat the latter as a perturbation to seek the maximal spin-motion entangled states with the exact general solutions of the perturbed equations. From the boundedness and self-consistent conditions of the

Chao Duan, Nikhil R. Agrawal, Rui Wang

Modeling ion correlations in inhomogeneous polymers and soft matters with spatially varying ionic strength or dielectric permittivity remains a great challenge. Here, we develop a new theory which systematically incorporates electrostatic fluctuations into the self-consistent field theory for polymers. Applied to polyelectrolyte brushes, the theory predicts

A. Nobile

The Nash blowing-up (or modification) of an algebraic variety $X$ is a canonical process that produces a proper, birational morphism $\pi : X' \to X$ of varieties. It is expected that the singularities of $X'$ will be better than those of $X$. In the mid-1970's, it was proved that in characteristic zero, ${\pi}$ is an isomorphism if and only if $X$ is nonsin

Jorge Faya, Pablo Figueroa, Claudio Muñoz, Felipe Poblete

Consider the generalized Korteweg-de Vries (gKdV) equations with power nonlinearities $q=2,3,4\ldots$ in dimension $N=1$, and the Zakharov-Kuznetsov (ZK) model with integer power nonlinearities $q$ in higher dimensions $N\geq 2$. Among these power-type models, the only conjectured equation with space localized time periodic breathers is the modified KdV (mKd

Kazuo Aoki, Vincent Giovangigli, François Golse, Shingo Kosuge

A half-space problem of a linear kinetic equation for gas molecules physisorbed close to a solid surface, relevant to a kinetic model of gas-surface interactions and derived by Aoki et al. [K.~Aoki et al., in: Phys. Rev. E 106:035306, 2022], is considered. The equation contains a confinement potential in the vicinity of the solid surface and an interaction t

Marcus E. Lower, Shi Dai, Simon Johnston, Ewan D. Barr

The Galactic Center is host to a population of extraordinary radio filaments, thin linear structures that trace out magnetic field lines running perpendicular to the Galactic plane. Using Murriyang, the 64 m Parkes radio telescope, we conducted a search for pulsars centered on the position of a compact source in the filament G359.0$-$0.2. We discovered a mil

Naifeng Zhang, Stephen McAleer, Tuomas Sandholm

Counterfactual regret minimization (CFR) algorithms are a foundational class of methods for solving imperfect-information games, with the time average of their iterates converging to a Nash equilibrium in two-player zero-sum games. Prior state-of-the-art variants, Discounted CFR (DCFR) and Predictive CFR$^+$ (PCFR$^+$), achieved the fastest known practical p

Mavis Seidu, J. O. Chibueze, Gary A. Fuller, A. Avison

This study presents a detailed analysis of the GAL045.804-0.356 massive star-forming clump. A high-angular resolution and sensitivity observations were conducted using MeerKAT at 1.28 GHz and ALMA interferometer at 1.3 mm. Two distinct centimetre radio continuum emissions (source A and source B) were identified within the clump. A comprehensive investigation

Christoph Coijanovic, Akim Stark, Daniel Schadt, Thorsten Strufe

Anonymous metadata-private voice call protocols suffer from high delays and so far cannot provide group call functionality. Anonymization inherently yields delay penalties, and scaling signalling and communication to groups of users exacerbates this situation. Our protocol Pirates employs PIR, improves parallelization and signalling, and is the first group v

Anja Sheppard, Jason Brown, Nilton Renno, Katherine A. Skinner

Terrain classification is an important problem for mobile robots operating in extreme environments as it can aid downstream tasks such as autonomous navigation and planning. While RGB cameras are widely used for terrain identification, vision-based methods can suffer due to poor lighting conditions and occlusions. In this paper, we propose the novel use of G

Felix Hoffmann, Udo Kebschull

In this publication, a novel architecture for Proof-of-Useful-Work blockchain consensus which aims to replace hash-based block problems with Monte Carlo simulation-based block problems to donate computational power to real-world HEP experiments is described. Design decisions are detailed and challenges are addressed. The architecture is being implemented usi

Kai Lawonn, Monique Meuschke, Tobias Günther

Exploratory analysis of scalar fields on surface meshes presents significant challenges in identifying and visualizing important regions, particularly on the surface's backside. Previous visualization methods achieved only a limited visibility of significant features, i.e., regions with high or low scalar values, during interactive exploration. In response t

Sanat Sharma, Jayant Kumar, Twisha Naik, Zhaoyu Lu

Accurate explicit and implicit product identification in search queries is critical for enhancing user experiences, especially at a company like Adobe which has over 50 products and covers queries across hundreds of tools. In this work, we present a novel approach to training a product classifier from user behavioral data. Our semantic model led to >25% rela

Erhan Bayraktar, Asaf Cohen, April Nellis

We investigate the behavior of liquidity providers (LPs) by modeling a decentralized cryptocurrency exchange (DEX) based on Uniswap v3. LPs with heterogeneous characteristics choose optimal liquidity positions subject to uncertainty regarding the size of exogenous incoming transactions and the prices of assets in the wider market. They engage in a game among

Steven A. Frank

Organisms gain by anticipating future changes in the environment. Those environmental changes often follow stochastic trends. The greater the slope of the trend, the more likely the trend's momentum carries the future trend in the same direction. This article presents a simple biological circuit that measures the momentum, providing a prediction about future

Hsuan-Po Liu, Hessam Mahdavifar

In this paper, we study the problem of constructing projective systematic authentication schemes based on binary linear codes. In systematic authentication, a tag for authentication is generated and then appended to the information, also referred to as the source, to be sent from the sender. Existing approaches to leverage projective constructions focus prim

Jiaming Qiu, Jiayi Son, Roch Guerin, Henry Sariowan

This paper considers networks where user traffic is regulated through deterministic traffic profiles, e.g., token buckets, and requires hard delay bounds. The network's goal is to minimize the resources it needs to meet those bounds. The paper explores how reprofiling, i.e., proactively modifying how user traffic enters the network, can be of benefit. Reprof

Juan F. Pérez-Pérez, Pablo Isaza Gómez, Isis Bonet, María Solange Sánchez-Pinzón

Climate risk assessment is becoming increasingly important. For organisations, identifying and assessing climate-related risks is challenging, as they can come from multiple sources. This study identifies and assesses the main climate transition risks in the colombian processed food sector. As transition risks are vague, our approach uses Fuzzy Logic and com

Lauren I. Massaro, Connor Meese, Nancy P. Sandler, Mahmoud M. Asmar

Subjecting a massive two-dimensional Dirac material to a vortex light beam provides a mechanism for the photo-induction of multiply quantized vortices. Using Floquet theory, we show that electronic vortices, characterized by their total angular momentum, are exclusive to circularly polarized vortex beams. The equations for the driven system at the one photon

Zhi Qi

In this paper, we introduce concise expressions for the complex Bessel integral that enables us to improve the spectral large sieve inequality of Watt for $\mathrm{PGL}_2 (\mathbb{Z}[i]) \backslash \mathrm{PGL}_2 (\mathbb{C})$. Our result is optimistic in the case when the average of spectral parameters is on a square centered at $(0, 0)$.

Gelu Popescu

The goal of this paper is to study the structure of noncommutative weighted shifts, their properties, and to understand their role as models (up to similarity) for $n$-tuples of operators on Hilbert spaces as well as their implications to function theory on noncommutative (resp.commutative) Reinhardt domains. We obtain a Rota type similarity result concernin

Yunhe Bai, Yuanzhao Li, Jianli Luan, Yang Chen

Anderson localization is anticipated to play a pivotal role in the manifestation of the quantum anomalous Hall effect, akin to its role in conventional quantum Hall effects. The significance of Anderson localization is particularly pronounced in elucidating the reasons behind the fragility of the observed quantum anomalous Hall state in the intrinsic magneti

Yisheng Lei

Quantum repeaters are proposed to overcome exponential photon loss over distance in fibers. One-way quantum repeaters eliminate the need for two-way classical communications, which can potentially outperform quantum memory based quantum repeaters. I propose that rare-earth-ions doped in solids and coupled with nano-cavity can be used to generate photonic clu

Tristan Aumentado-Armstrong, Stavros Tsogkas, Sven Dickinson, Allan Jepson

In modern computer vision, the optimal representation of 3D shape continues to be task-dependent. One fundamental operation applied to such representations is differentiable rendering, as it enables inverse graphics approaches in learning frameworks. Standard explicit shape representations (voxels, point clouds, or meshes) are often easily rendered, but can

Puze Liu, Haitham Bou-Ammar, Jan Peters, Davide Tateo

Integrating learning-based techniques, especially reinforcement learning, into robotics is promising for solving complex problems in unstructured environments. However, most existing approaches are trained in well-tuned simulators and subsequently deployed on real robots without online fine-tuning. In this setting, extensive engineering is required to mitiga

Zhaolong Han, Tadele Mengesha, Xiaochuan Tian

We prove two compactness results for function spaces with finite Dirichlet energy of half-space nonlocal gradients. In each of these results, we provide sufficient conditions on a sequence of kernel functions that guarantee the asymptotic compact embedding of the associated nonlocal function spaces into the class of square-integrable functions. Moreover, we

Avijit Shee, Fabian M. Faulstich, Birgitta Whaley, Lin Lin

We develop a static quantum embedding scheme that utilizes different levels of approximations to coupled cluster (CC) theory for an active fragment region and its environment. To reduce the computational cost, we solve the local fragment problem using a high-level CC method and address the environment problem with a lower-level M{\o}ller-Plesset (MP) perturb

Zukang Yang, Zixuan Zhu, Xuan Zhu

Large Language Models (LLMs) have achieved significant success in open-domain question answering. However, they continue to face challenges such as hallucinations and knowledge cutoffs. These issues can be mitigated through in-context learning by providing LLMs with relevant context before generating answers. Recent literature proposes Knowledge Graph Prompt

Yaofeng Su

In this short note, we propose a new and short approach to polynomial escape rates, which can be applied to various open systems with intermittency. The tool of our approach is the maximal large deviations developed in \cite{mldp}.

Mingjie Li, S. A. R. Horsley

We find a new set of exact solutions to Maxwell's equations in space--time varying materials, where the refractive index is constant, while the impedance exhibits effective motion, i.e. it is a function of $x-vt$. We find that waves co--propagating with the modulation are not reflected within the material, while counter--propagating waves are continually ref

Paola Cinnella

This chapter provides an introduction to data-driven techniques for the development and calibration of closure models for the Reynolds-Averaged Navier--Stokes (RANS) equations. RANS models are the workhorse for engineering applications of computational fluid dynamics (CFD) and are expected to play an important role for decades to come. However, RANS model in

Gelu Popescu

In a recent paper, we introduced and studied the class of admissible noncommutative domains $D_{g^{-1}}(H)$ in $B(H)^n$ associated with admissible free holomorphic functions $g$ in noncommutative indeterminates $Z_1,\ldots, Z_n$. Each such a domain admits a universal model ${\bf W}:=(W_1,\ldots, W_n)$ of weighted left creation operators acting on the full Fo

Gelu Popescu

Let B(H) be the algebra of all bounded linear operators on a Hilbert space H. The main goal of the paper is to find large classes of noncommutative domains in B(H) with prescribed universal operator models, acting on the full Fock space with n generators, and to study these domains and their universal models in connection with the Hardy algebras and the C^*-

Bhavith Chandra Challagundla

Cardiovascular diseases are a pervasive global health concern, contributing significantly to morbidity and mortality rates worldwide. Among these conditions, arrhythmia, characterized by irregular heart rhythms, presents formidable diagnostic challenges. This study introduces an innovative approach utilizing deep learning techniques, specifically Convolution

Rodrigo A. González, Koen Classens, Cristian R. Rojas, James S. Welsh

Block coordinate descent is an optimization technique that is used for estimating multi-input single-output (MISO) continuous-time models, as well as single-input single output (SISO) models in additive form. Despite its widespread use in various optimization contexts, the statistical properties of block coordinate descent in continuous-time system identific

Yuriy Drozd, Andriana Plakosh

We describe integral representations of the alternating group $A_4$, in particular, the Auslander-Reiten quiver of its 2-adic representations. Using these results we calculate Tate cohomologies of all $A_4$-lattices.

Longfei Fang, Michael Tait, Mingqing Zhai

Given a graph family $\mathcal{H}$ with $\min_{H\in \mathcal{H}}\chi(H)=r+1\geq 3$. Let ${\rm ex}(n,\mathcal{H})$ and ${\rm spex}(n,\mathcal{H})$ be the maximum number of edges and the maximum spectral radius of the adjacency matrix over all $\mathcal{H}$-free graphs of order $n$, respectively. Denote by ${\rm EX}(n,\mathcal{H})$ (resp. ${\rm SPEX}(n,\mathca

Jean-Olivier Simoneau, Sébastien Jezouin, Christian Lupien, Bertrand Reulet

The statistics of quantum transport in nanostructures can be tailored by a time-dependent bias voltage $V(t)$. We demonstrate experimentally how correlations of current fluctuations at two different times $t$ and $t+\tau$ depend on the shape of $V(t)$ via the phase accumulated by the electronic wavefunctions between $t$ and $t+\tau$. For this we measure the

Avinab Saha, Shashank Gupta, Sravan Kumar Ankireddy, Karl Chahine

Image Classification and Video Action Recognition are perhaps the two most foundational tasks in computer vision. Consequently, explaining the inner workings of trained deep neural networks is of prime importance. While numerous efforts focus on explaining the decisions of trained deep neural networks in image classification, exploration in the domain of its

Amit Finkman Noah, Avishag Shapira, Eden Bar Kochva, Inbar Maimon

LLM-based code assistants are becoming increasingly popular among developers. These tools help developers improve their coding efficiency and reduce errors by providing real-time suggestions based on the developer's codebase. While beneficial, the use of these tools can inadvertently expose the developer's proprietary code to the code assistant service provi