December 2023 arXiv papers — page 31

Oscar Sapena, Eva Onaindia

The way of understanding online higher education has greatly changed due to the worldwide pandemic situation. Teaching is undertaken remotely, and the faculty incorporate lecture audio recordings as part of the teaching material. This new online teaching-learning setting has largely impacted university classes. While online teaching technology that enriches

Supriya Pan, Osamu Seto, Tomo Takahashi, Yo Toda

We investigate cosmological bounds on sterile neutrino masses in the light of the Hubble and $S_8$ tensions. We argue that non-zero masses for sterile neutrinos are inferred at 2$\sigma$ level in some extended models such as varying dark energy equation of state, when a direct measurement of the Hubble constant $H_0$ and weak lensing measurement of dark ener

Rong An, Shuai Sun, Li-Gang Cao, Feng-Shou Zhang

Precise knowledge of the nuclear symmetry energy can be tentatively calibrated through multimessenger constraints. The neutron skin thickness of a heavy nucleus is one of the most sensitive indicators for probing the isovector components of effective interactions in asymmetric nuclear matter. Recent studies have suggested that the experimental data from the

Yuko Kuroki, Alberto Rumi, Taira Tsuchiya, Fabio Vitale

We study best-of-both-worlds algorithms for $K$-armed linear contextual bandits. Our algorithms deliver near-optimal regret bounds in both the adversarial and stochastic regimes, without prior knowledge about the environment. In the stochastic regime, we achieve the polylogarithmic rate $\frac{(dK)^2\mathrm{poly}\log(dKT)}{\Delta_{\min}}$, where $\Delta_{\mi

Zexiang Yi, Jing Lian, Yunliang Qi, Zhaofei Yu

Spiking Neural Networks (SNNs) capture the information processing mechanism of the brain by taking advantage of spiking neurons, such as the Leaky Integrate-and-Fire (LIF) model neuron, which incorporates temporal dynamics and transmits information via discrete and asynchronous spikes. However, the simplified biological properties of LIF ignore the neuronal

Trung Thanh Nguyen, Khaled Elbassioni, Areg Karapetyan, Majid Khonji

We study a class of combinatorial scheduling problems characterized by a particular type of constraint often associated with electrical power or gas energy. This constraint appears in several practical applications and is expressed as a sum of squares of linear functions. Its nonlinear nature adds complexity to the scheduling problem, rendering it notably ch

Xu Shang, Yang Zheng

The Willems' fundamental lemma, which characterizes linear time-invariant (LTI) systems using input and output trajectories, has found many successful applications. Combining this with receding horizon control leads to a popular Data-EnablEd Predictive Control (DeePC) scheme. DeePC is first established for LTI systems and has been extended and applied for pr

Jianqiang Ren, Chao He, Lin Liu, Jiahao Chen

There is a growing demand for customized and expressive 3D characters with the emergence of AI agents and Metaverse, but creating 3D characters using traditional computer graphics tools is a complex and time-consuming task. To address these challenges, we propose a user-friendly framework named Make-A-Character (Mach) to create lifelike 3D avatars from text

Srinivas Sagar, Sheetal Kalyani

This paper investigates the performance of multiple reconfigurable intelligent surfaces (multi-RIS) communication systems where the RIS link with the highest signal-to-noise-ratio (SNR) is selected at the destination. In practice, all the RISs will not have the same number of reflecting elements. Hence, selecting the RIS link with the highest SNR will involv

Filippo Fecit

In this work, we investigate the BRST quantization of the massive $\mathcal{N}=4$ supersymmetric spinning particle, with a twofold purpose: exploring different approaches to give mass to spinning particle models and formulating a first-quantized theory for linearized massive gravity on both flat and curved spacetime. Our results suggest that achieving the ni

Parinya Chalermsook, Manoj Gupta, Wanchote Jiamjitrak, Akash Pareek

The access lemma (Sleator and Tarjan, JACM 1985) is a property of binary search trees that implies interesting consequences such as static optimality, static finger, and working set property. However, there are known corollaries of the dynamic optimality that cannot be derived via the access lemma, such as the dynamic finger, and any $o(\log n)$-competitive

Shankhanil Mitra, Rajiv Soundararajan

Perceptual quality assessment of user generated content (UGC) videos is challenging due to the requirement of large scale human annotated videos for training. In this work, we address this challenge by first designing a self-supervised Spatio-Temporal Visual Quality Representation Learning (ST-VQRL) framework to generate robust quality aware features for vid

Wenli Wu, Ye Guo, Jiantao Shi

In pursuit of carbon neutrality, many countries have adopted renewable portfolio standards to facilitate the integration of renewable energy. However, increasing penetration of renewable energy resources will also pose higher requirements on system flexibility. Allowing renewable themselves to participate in the reserve market could be a viable solution. To

Kazuyuki Sekitani, Yu Zhao

This study modifies the Russell graph measure of efficiency to find the closest target from the assessed decision-making unit to the efficient frontier of an empirical production possibility set using an extended facet approach. The closest target requires a single improvement in either an output term or an input term, and needs some positive levels of all i

Shuang Song

Projection operators are important in Analysis, Optimization and Algorithm. It is well known that these operators are firmly nonexpansive. In this paper, we provide an exact result that sharpens this well-known result. We develop the theory of averaged operators and provide a lower bound. We give a result on the avergedness of operator compositions. We also

Ming Cheung

User attribute prediction is a crucial task in various industries. However, sharing user data across different organizations faces challenges due to privacy concerns and legal requirements regarding personally identifiable information. Regulations such as the General Data Protection Regulation (GDPR) in the European Union and the Personal Information Protect

Yi Li, Qianwei Zhang

This paper investigates gradient estimates on graphs satisfying the $CD\psi(n,-K)$ condition with positive constants $n,K$, and concave $C^{1}$ functions $\psi:(0,+\infty)\rightarrow\mathbb{R}$. Our study focuses on gradient estimates for positive solutions of the heat equation $\partial_{t}u=\Delta u$. Additionally, the estimate is extended to a heat-type e

Xiaoqi Li, Mingxu Zhang, Yiran Geng, Haoran Geng

Robot manipulation relies on accurately predicting contact points and end-effector directions to ensure successful operation. However, learning-based robot manipulation, trained on a limited category within a simulator, often struggles to achieve generalizability, especially when confronted with extensive categories. Therefore, we introduce an innovative app

P. Cardaliaguet, P. E. Souganidis

We investigate how to control optimally a traffic flow through a junction on the line by acting only on speed reduction or traffic light at the junction. We show the existence of an optimal control and, under structure assumptions, provide optimality conditions. We use this analysis to investigate thoroughly the maximization of the flux on a space-time subse

Sudipta Ghosh, Zhenkun Li

We construct cobordism maps for the \textit{minus} version of instanton knot homology associated to a \textit{specially decorated} knot cobordisms of arbitrary genus between two null-homologous knots in closed oriented $3$-manifolds. As an application of our construction, we recover an inequality between the torsion order of knots in instanton theory, which

Hao Wu, Shenghua Feng, Ting Gan, Jie Wang

Barrier certificates, serving as differential invariants that witness system safety, play a crucial role in the verification of cyber-physical systems (CPS). Prevailing computational methods for synthesizing barrier certificates are based on semidefinite programming (SDP) by exploiting Putinar Positivstellensatz. Consequently, these approaches are limited by

Mageshwaran Tamilan, Kimitake Hayasaki, Takeru K. Suzuki

We present a time-dependent, one-dimensional, magnetically-driven disk wind model based on magnetohydrodynamic (MHD) equations, in the context of tidal disruption events (TDEs). We assume that the disk is geometrically thin and gas-pressure dominated, and explicitly accounts for magnetic braking and turbulent viscosity through an extended alpha-viscosity pre

Yixuan Dang, Can Cui, Marcelo Barraza-Alfaro

The vertical shear instability (VSI) is a promising mechanism to drive turbulence in protoplanetary disks. Numerical simulations in the literature demonstrate that the VSI non-linear saturation is predominated by the linear corrugation modes. These modes possess vertical wavelengths crucially longer than radial wavelengths. This paper aims to investigate the

Shunsuke Ota, Yuma Okazaki, Shuji Nakamura, Takehiko Oe

Surface acoustic waves (SAWs) hold a vast potential in various fields such as spintronics, quantum acoustics, and electron-quantum optics, but an electromagnetic wave emanating from SAW generation circuits has often been a major hurdle. Here, we investigate a differential excitation method of interdigital transducers (IDTs) to generate SAWs while reducing th

Yuchen Shi, Congying Han, Tiande Guo

This paper introduces CARSS (Cooperative Attention-guided Reinforcement Subpath Synthesis), a novel approach to address the Traveling Salesman Problem (TSP) by leveraging cooperative Multi-Agent Reinforcement Learning (MARL). CARSS decomposes the TSP solving process into two distinct yet synergistic steps: "subpath generation" and "subpath merging." In the f

Divya Jaganathan, Rahil N. Valani

Differential equations containing memory terms that depend nonlinearly on past states model a variety of non-Markovian processes. In this study, we present a Markovian embedding procedure for such equations with distributed delay by utilising an exact spectral representation of the nonlinear memory function. This allows us to transform the nonlocal system to

Sayantan Dutta, Adrian Basarab, Denis Kouamé, Bertrand Georgeot

This letter presents a novel \textit{quantum algorithm} for signal denoising, which performs a thresholding in the frequency domain through amplitude amplification and using an adaptive threshold determined by local mean values. The proposed algorithm is able to process \textit{both classical and quantum} signals. It is parametrically faster than previous cl

C. M. Varma

Transport experiments in twisted bilayer graphene (TBG) show a fan-like region near integer fillings with a resistivity linear in temperature down to the lowest temperature measured. This suggests quantum-critical points at the boundaries to long-range ordered phases. The particular order proposed by Blutinck et al. for twisted bi-layer graphene (TBG) is a l

Eric Marberg, Kam Hung Tong

Our previous work introduced a category of extended queer crystals, whose connected normal objects have unique highest weight elements and characters that are Schur $Q$-polynomials. The initial models for such crystals were based on semistandard shifted tableaux. Here, we introduce a simpler construction using certain "primed" decomposition tableaux, which s

Lingchen Sun, Jie Liang, Shuaizheng Liu, Hongwei Yong

High perceptual quality and low distortion degree are two important goals in image restoration tasks such as super-resolution (SR). Most of the existing SR methods aim to achieve these goals by minimizing the corresponding yet conflicting losses, such as the $\ell_1$ loss and the adversarial loss. Unfortunately, the commonly used gradient-based optimizers, s

Chen Zhang, Luis Fernando D'Haro, Yiming Chen, Malu Zhang

Automatic evaluation is an integral aspect of dialogue system research. The traditional reference-based NLG metrics are generally found to be unsuitable for dialogue assessment. Consequently, recent studies have suggested various unique, reference-free neural metrics that better align with human evaluations. Notably among them, large language models (LLMs),

Bailey Miller, Hanyu Chen, Alice Lai, Ioannis Gkioulekas

We develop a theory for the representation of opaque solids as volumes. Starting from a stochastic representation of opaque solids as random indicator functions, we prove the conditions under which such solids can be modeled using exponential volumetric transport. We also derive expressions for the volumetric attenuation coefficient as a functional of the pr

Yifei Yang, Xiangyao Yu, Marco Serafini, Ashraf Aboulnaga

Network is a major bottleneck in modern cloud databases that adopt a storage-disaggregation architecture. Computation pushdown is a promising solution to tackle this issue, which offloads some computation tasks to the storage layer to reduce network traffic. Existing cloud OLAP systems statically decide whether to push down computation during the query optim

Lorenzo Bartolini, Stefano Bolognesi, Sven Bjarke Gudnason, Tommaso Rainaldi

We discuss how the quark masses and their mass splitting affect the baryons in the Skyrme model as well as the Witten-Sakai-Sugimoto (WSS) model. In both cases baryons are described by solitonic objects, i.e. Skyrmions and instantons, respectively. After the quantization of their zeromodes the nucleons become quantum states of a rotor. We show how the quark

Konstantinos Prasopoulos, Ryan Kosta, Edouard Bugnion, Marios Kogias

Datacenter congestion control protocols are challenged to navigate the throughput-buffering trade-off while relative packet buffer capacity is trending lower year-over-year. In this context, receiver-driven protocols -- which schedule packet transmissions instead of reacting to congestion -- excel when the bottleneck lies at the ToR-to-receiver link. However

Juan Han, Haijun Wu, Yuanming Xiao

We propose an unfitted interface penalty Discontinuous Galerkin-Finite Element Method (UIPDG-FEM) for elliptic interface problems. This hybrid method combines the interior penalty discontinuous Galerkin (IPDG) terms near the interface-enforcing jump conditions via Nitsche method-with standard finite elements away from the interface. The UIPDG-FEM retains the

Alexandros J. Tsamopoulos, Zhen-Gang Wang

We construct a coarse-grained molecular dynamics model based on poly(ethylene oxide) and lithium bis-(trifluoromethane)sulfonimide salt to examine the combined effects of temperature and salt concentration on the transport properties. Salt doping notably slows down the dynamics of polymer chains and reduces ion diffusivity, resulting in a glass transition te

Seonghyeon Go, Kyogu Lee

In this work, we propose a symbolic music generation model with the song structure graph analysis network. We construct a graph that uses information such as note sequence and instrument as node features, while the correlation between note sequences acts as the edge feature. We trained a Graph Neural Network to obtain node representation in the graph, then w

Zijian Li, Bingbing Zheng, Chengxian Zhang, Zhenrong Zhang

In a quantum Trojan-horse attack (THA), eavesdroppers learn encoded information by injecting bright light into encoded or decoded devices of quantum key distribution (QKD) systems. These attacks severely compromise the security of non-isolated systems. Thus, analytical security bound was derived in previous studies. However, these studies achieved poor perfo

David R Johnson, Mohammad Ahmadi Gharehtoragh

Planners who wish to manage coastal flood risk with long-lived infrastructure (e.g., levees, floodwalls) under a constrained computational budget face a tradeoff. Simulating a large number of future time periods or scenarios with different assumptions about land subsidence, sea level rise, land accretion, imposes a limit on how many storm simulations can be

Abdelrahman Zayed, Goncalo Mordido, Samira Shabanian, Ioana Baldini

The increasing size of large language models (LLMs) has introduced challenges in their training and inference. Removing model components is perceived as a solution to tackle the large model sizes, however, existing pruning methods solely focus on performance, without considering an essential aspect for the responsible use of LLMs: model fairness. It is cruci

Jingjun Han, Jihao Liu, Qingyuan Xue

Prokhorov and Shokurov introduced the famous effective adjunction conjecture, also known as the effective base-point-freeness conjecture. This conjecture asserts that the moduli component of an lc-trivial fibration is effectively base-point-free. Li proposed a variation of this conjecture, which is known as the $\Gamma$-effective adjunction conjecture, and p

Hao Sun, Jieqi Tu

Immunotherapies and targeted therapies have gained popularity due to their promising therapeutic effects across multiple treatment areas. The focus of early phase dose-finding clinical trials has shifted from finding the maximum tolerated dose (MTD) to identifying the optimal biological dose (OBD), which aims to balance the toxicity and efficacy outcomes, th

Hanxi Liu, Xiaokai Mao, Haocheng Xia, Jian Lou

Large language models (LLMs) excel on new tasks without additional training, simply by providing natural language prompts that demonstrate how the task should be performed. Prompt ensemble methods comprehensively harness the knowledge of LLMs while mitigating individual biases and errors and further enhancing performance. However, more prompts do not necessa

Luyining Gan, Huajun Huang

On the space of positive definite matrices, several operator means are popular and have been studied extensively. In this paper, we investigate the near order and the L\"owner order relations on the curves defined by the Wasserstein mean and the spectral geometric mean. We show that the near order $\preceq $ is stronger than the eigenvalue entrywise order, a

Lezhi Li, Ting-Yu Chang, Hai Wang

This report outlines a transformative initiative in the financial investment industry, where the conventional decision-making process, laden with labor-intensive tasks such as sifting through voluminous documents, is being reimagined. Leveraging language models, our experiments aim to automate information summarization and investment idea generation. We seek

Chun-Hsiao Yeh, Xudong Wang, Stella X. Yu, Charles Hill

Deep learning has had remarkable success at analyzing handheld imagery such as consumer photos due to the availability of large-scale human annotations (e.g., ImageNet). However, remote sensing data lacks such extensive annotation and thus potential for supervised learning. To address this, we propose a highly effective semi-supervised approach tailored spec

Chang Chu

The security of smart contracts, which are an important part of blockchain technology, has attracted much attention. In particular, reentrancy vulnerability, which is hidden and complex, poses a great threat to smart contracts. In order to improve the existing detection methods, which exhibit low efficiency and accuracy, in this paper, we propose a smart con

Carlos Alvarado, Janelly Bautista, Alexander J. Stuart

In this paper, we study the connection between the tribimaximal and bitrimaximal mixing patterns. In doing so, we are forced to work in a non-diagonal charged lepton basis. This leads to several relations that must hold between the lepton mixing angles. After a short discussion, we analyze the underlying flavor symmetry responsible for this prediction. Final

Kun Cheng, Tianyi Tang, Wenkang Zhan, Zhenyu Sun

The direct growth of III-V semiconductors on silicon holds tremendous potential for photonics applications. However, the inherent differences in their properties lead to defects in the epitaxial layer, including threading dislocations (TDs), antiphase boundaries (APBs), and thermal cracks, significantly impacting device performance. Current processes struggl

Bo Zhang, YueLin Gao, Xia Liu, XiaoLi Huang

Quadratic constrained quadratic programming problems often occur in various fields such as engineering practice, management science, and network communication. This article mainly studies a non convex quadratic programming problem with convex quadratic constraints. Firstly, based on our existing results, the problem is reconstructed as an equivalent problem

Nikhil Behari, Akshat Dave, Kushagra Tiwary, William Yang

3D modeling from satellite imagery is essential in areas of environmental science, urban planning, agriculture, and disaster response. However, traditional 3D modeling techniques face unique challenges in the remote sensing context, including limited multi-view baselines over extensive regions, varying direct, ambient, and complex illumination conditions, an

Jingxin Mao, Xiaoyu Ma, Yanlong Bi, Rongqing Zhang

Diabetic retinopathy (DR), as a debilitating ocular complication, necessitates prompt intervention and treatment. Despite the effectiveness of artificial intelligence in aiding DR grading, the progression of research toward enhancing the interpretability of DR grading through precise lesion segmentation faces a severe hindrance due to the scarcity of pixel-l

Ekansh Agrawal, Xiangyu Sam Xu

The boom in Large Language Models (LLMs) like GPT-4 and ChatGPT has marked a significant advancement in artificial intelligence. These models are becoming increasingly complex and powerful to train and serve. This growth in capabilities comes with a substantial increase in computational requirements, both in terms of hardware resources and energy consumption

Zhaoning Yu, Hongyang Gao

Motif extraction is an important task in motif based molecular representation learning. Previously, machine learning approaches employing either rule-based or string-based techniques to extract motifs. Rule-based approaches may extract motifs that aren't frequent or prevalent within the molecular data, which can lead to an incomplete understanding of essenti

Core Francisco Park, Maya Nasr, Manuel Pérez-Carrasco, Eleanor Walker

Shadow detection and removal is a challenging problem in the analysis of hyperspectral images. Yet, this step is crucial for analyzing data for remote sensing applications like methane detection. In this work, we develop a shadow detection and removal method only based on the spectrum of each pixel and the overall distribution of spectral values. We first in

Xiangyu Cui, Xun Li, Yun Shi, Si Zhao

This paper studies a discrete-time mean-variance model based on reinforcement learning. Compared with its continuous-time counterpart in \cite{zhou2020mv}, the discrete-time model makes more general assumptions about the asset's return distribution. Using entropy to measure the cost of exploration, we derive the optimal investment strategy, whose density fun

Bo Zhang

This paper introduces a new global optimization algorithm for solving the generalized linear multiplicative problem (GLMP). The algorithm starts by introducing $\bar{p}$ new variables and applying a logarithmic transformation to convert the problem into an equivalent problem (EP). By using the strong duality of linear program, a new convex relaxation subprob

Eleanor Birrell, Jay Rodolitz, Angel Ding, Jenna Lee

Growing recognition of the potential for exploitation of personal data and of the shortcomings of prior privacy regimes has led to the passage of a multitude of new online privacy regulations. Some of these laws -- notably the European Union's General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) -- have been the focus of l

Qiansheng Han, Antti Rasila, Tommi Sottinen

In this paper, we present a stochastic method for the simulation of Laplace's equation with a mixed boundary condition in planar domains that are polygonal or bounded by circular arcs. We call this method the Reflected Walk-on-Spheres algorithm. The method combines a traditional Walk-on-Spheres algorithm with use of reflections at the Neumann boundaries. We

Marcin Łyczak

\textit{Mereological fusion}, also known as \textit{composition} and \textit{sum}, was originally used by me as a primitive notion to axiomatize \textit{Extensional Mereology} wih \textit{atoms} in \cite{Ly22}. Here, I extend this idea to axiomatize \textit{General Extensional Mereology}, also called \textit{Classical Mereology}, which is neutral regarding t

Yiwei Tang, Hualong Huang, Wenhan Zhan, Geyong Min

Being an up-and-coming application scenario of mobile edge computing (MEC), the post-disaster rescue suffers multitudinous computing-intensive tasks but unstably guaranteed network connectivity. In rescue environments, quality of service (QoS), such as task execution delay, energy consumption and battery state of health (SoH), is of significant meaning. This

Gael Kermarrec, Anna Klos, Artur Lenczuk, Janusz Bogusz

Hydrogeodesy can benefit greatly from the use of Global Positioning System (GPS) displacements to analyse local changes in the hydrosphere, which the commonly used Gravity Recovery and Climate Experiment (GRACE) mission is unable to provide due to coarse spatial resolution. Hydrosphere changes recorded by GPS are unfortunately hidden among the other signals

Justus Fasse, Bart Jacobs

A well-established approach to proving progress properties such as deadlock-freedom and termination is to associate obligations with threads. For example, in most existing work the proof rule for lock acquisition prescribes a standard usage protocol by burdening the acquiring thread with an obligation to release the lock. The fact that the obligation creatio

Dmitry Dolgopyat, Sixu Liu

We establish functional limit theorems for ergodic sums of observables with power singularities for expanding circle maps. In the regime where the observables have infinite variance, we show that when rescaled by $N^{1/s}(\ln N)^\alpha$, the partial sum process has limit points consisting precisely of increasing piecewise constant functions with finitely man

Gaurav Raut, Advait Patole

There has been significant progress made in the field of autonomous vehicles. Object detection and tracking are the primary tasks for any autonomous vehicle. The task of object detection in autonomous vehicles relies on a variety of sensors like cameras, and LiDAR. Although image features are typically preferred, numerous approaches take spatial data as inpu

Changbo Zhu, Hans-Georg Müller

Classical regression models do not cover non-Euclidean data that reside in a general metric space, while the current literature on non-Euclidean regression by and large has focused on scenarios where either predictors or responses are random objects, i.e., non-Euclidean, but not both. In this paper we propose geodesic optimal transport regression models for

Sofia Zahri, Hajar Bennouri, Ahmed M. Abdelmoniem

In today's world, the rapid expansion of IoT networks and the proliferation of smart devices in our daily lives, have resulted in the generation of substantial amounts of heterogeneous data. These data forms a stream which requires special handling. To handle this data effectively, advanced data processing technologies are necessary to guarantee the preserva

Jonathan Trejos

We give a combinatorial description of the embedded contact complex (ECC) of a certain family of contact toric lens spaces that we call concave lens spaces. We also define a notion of a concave toric domain that generalizes the usual concave toric domain in a way that possesses a singularity point and has a boundary a lens space. After desingularization thes

Kexin Chen, Jinping Guan, Ravi Seshadri, Varun Pattabhiraman

This paper proposes a multi-day needs-based model for activity and travel demand analysis. The model captures the multi-day dynamics in activity generation, which enables the modeling of activities with increased flexibility in time and space (e.g., e-commerce and remote working). As an enhancement to activity-based models, the proposed model captures the un

Viktor Martvall, Henrik Klein Moberg, Athanasios Theodoridis, David Tomeček

The ability to rapidly detect hydrogen gas upon occurrence of a leak is critical for the safe large-scale implementation of hydrogen (energy) technologies. However, to date, no technically viable sensor solution exists that meets the corresponding response time targets set by stakeholders at technically relevant conditions. Here, we demonstrate how a tailore

Ivan Pelayo, Derek Bergner, Archibald J. Williams, Jiayuwen Qi

PtTe$_2$ and PdTe$_2$ are among the first transition metal dichalcogenides that were predicted to host type-II Dirac fermions, exotic particles prohibited in free space. These materials are layered and air-stable, which makes them top candidates for technological applications that take advantage of their anisotropic magnetotransport properties. Here, we prov

Mikhail Isaev, Maksim Zhukovskii

We derive the distribution of the maximum number of common neighbours of a pair of vertices in a dense random regular graph.The proof involves two important steps. One step is to establish the extremal independence property: the asymptotic equivalence with the maximum component of a vector with independent marginal distributions. The other step is to prove t

Sebastian Casalaina-Martin, Samuel Grushevsky, Klaus Hulek

We determine the cones of effective and nef divisors on the toroidal compactification of the ball quotient model of the moduli space of complex cubic surfaces with a chosen line. From this we also compute the corresponding cones for the moduli space of unmarked cubics surfaces.

Mateusz Troka, Katarzyna Szepietowska, Izabela Lubowiecka

The study refers to the application of a type of artificial neural network called the Self-Organising Map (SOM) for the identification of areas of the human abdominal wall that perform in a similar mechanical way. The research was based on data acquired during in vivo tests using the digital image correlation technique (DIC). The mechanical behaviour of the

Stefano Biagi, Marco Bramanti

We consider a class of nonvariational degenerate elliptic operators of the kind \[ Lu=\sum_{i,j=1}^{m}a_{ij}\left( x\right) X_{i}X_{j}u \] where $\left\{ a_{ij}\left( x\right) \right\} _{i,j=1}^{m}$ is a symmetric uniformly positive matrix of bounded measurable functions defined in the whole $\mathbb{R}^{n}$ ($n>m$), possibly discontinuos but satisfying a $V

Krzysztof Bartoszek

We present here a large collection of harmonic and quadratic harmonic sums, that can be useful in applied questions, e.g., probabilistic ones. We find closed-form formulae, that we were not able to locate in the literature.

David Staines

In this paper, a mathematically rigorous solution overturns existing wisdom regarding New Keynesian Dynamic Stochastic General Equilibrium. I develop a formal concept of stochastic equilibrium. I prove uniqueness and necessity, when agents are patient, with general application. Existence depends on appropriately specified eigenvalue conditions. Otherwise, no

Chenjiao Tan, Qian Cao, Yiwei Li, Jielu Zhang

The advent of large language models (LLMs) has heightened interest in their potential for multimodal applications that integrate language and vision. This paper explores the capabilities of GPT-4V in the realms of geography, environmental science, agriculture, and urban planning by evaluating its performance across a variety of tasks. Data sources comprise s

Davide Manzini, Fouad Sahraoui, Francesco Califano

The differential heating of electrons and ions by turbulence in weakly collisional magnetized plasmas and the scales at which such energy dissipation is most effective are still debated. Using a large data sample measured in the Earth's magnetosheath by the Magnetospheric Multiscale mission and the coarse-grained energy equations derived from the Vlasov-Maxw

Kavisha Vidanapathirana, Joshua Knights, Stephen Hausler, Mark Cox

Recent progress in semantic scene understanding has primarily been enabled by the availability of semantically annotated bi-modal (camera and LiDAR) datasets in urban environments. However, such annotated datasets are also needed for natural, unstructured environments to enable semantic perception for applications, including conservation, search and rescue,

Tavis Shore, Simon Hadfield, Oscar Mendez

Cross-view image matching for geo-localisation is a challenging problem due to the significant visual difference between aerial and ground-level viewpoints. The method provides localisation capabilities from geo-referenced images, eliminating the need for external devices or costly equipment. This enhances the capacity of agents to autonomously determine the

Thomas M. Bombarde, Andrew L. Krause

Industries learn productivity improvements from their suppliers. The observed empirical importance of these interactions, often omitted by input-output models, mandates larger attention. This article embeds interdependent total factor productivity (TFP) growth into a general non-parametric input-output model. TFP growth is assumed to be Cobb-Douglas in TFP-s

Dong-Jun Han, Seyyedali Hosseinalipour, David J. Love, Mung Chiang

While network coverage maps continue to expand, many devices located in remote areas remain unconnected to terrestrial communication infrastructures, preventing them from getting access to the associated data-driven services. In this paper, we propose a ground-to-satellite cooperative federated learning (FL) methodology to facilitate machine learning service

Joohyung Park, Ayan N. Batyrkhanov, Jonas Brandhoff, Marco Gruenewald

In van der Waals materials, coupling between adjacent layers is weak, and consequently interlayer interactions are weakly screened. This opens the possibility to profoundly modify the electronic structure, e.g., by applying electric fields or with adsorbates. Here, we show for the case of the topologically trivial semimetal 1Tprime-MoTe2 that potassium dosin

Guanchu Wang, Yu-Neng Chuang, Fan Yang, Mengnan Du

Explainable machine learning significantly improves the transparency of deep neural networks. However, existing work is constrained to explaining the behavior of individual model predictions, and lacks the ability to transfer the explanation across various models and tasks. This limitation results in explaining various tasks being time- and resource-consumin

Su Jia, Fatemeh Navidi, Viswanath Nagarajan, R. Ravi

In pool-based active learning, the learner is given an unlabeled data set and aims to efficiently learn the unknown hypothesis by querying the labels of the data points. This can be formulated as the classical Optimal Decision Tree (ODT) problem: Given a set of tests, a set of hypotheses, and an outcome for each pair of test and hypothesis, our objective is

Su Jia, Andrew Li, R. Ravi, Nishant Oli

Modern platforms leverage randomized experiments to make informed decisions from a given set of items (``treatments''). As a particularly challenging scenario, these items may (i) arrive in high volume, with thousands of new items being released per hour, and (ii) have short lifetime, say, due to the item's transient nature or underlying non-stationarity tha

Claudio Meringolo, Francesco Pucci, Giuseppe Nisticò, Oreste Pezzi

We investigate the interplay of phase mixing and the nonlinear turbulent cascade in the evolution and dissipation of Alfv\'en waves using compressible magnetohydrodynamics numerical simulations. We consider perturbations in the form of torsional waves, both propagating and standing, or turbulent fluctuations, or a combination of the two. The main purpose is

Abdullah-Al-Zubaer Imran, Sen Wang, Debashish Pal, Sandeep Dutta

Purpose: Estimation of patient-specific organ doses is required for more comprehensive dose metrics, such as effective dose. Currently, available methods are performed retrospectively using the CT images themselves, which can only be done after the scan. To optimize CT acquisitions before scanning, rapid prediction of patient-specific organ dose is needed pr

Steven J. Krieg, Nitesh V. Chawla, Keith Feldman

The widespread application of machine learning techniques to biomedical data has produced many new insights into disease progression and improving clinical care. Inspired by the flexibility and interpretability of graphs (networks), as well as the potency of sequence models like transformers and higher-order networks (HONs), we propose a method that identifi

Haiming Zhou, Rex Shen, Sutan Wu, Philip He

In recent years, basket trials, which allow the evaluation of an experimental therapy across multiple tumor types within a single protocol, have gained prominence in early-phase oncology development. Unlike traditional trials, which evaluate each tumor type separately and often face challenges with limited sample sizes, basket trials offer the advantage of b

Mohamed Rossafi, Abdelilah Karara, Roumaissae El jazzar

In this paper, we will introduce the concept of biframes for Hilbert $ C^{\ast}- $modules produced by a pair of sequences, and we present various examples of biframes. Then, we examine the characteristics of biframes from the viewpoint of operator theory by establishing some properties of biframes in Hilbert $ C^{\ast}- $modules.

Oksana Firman, Philipp Kindermann, Boris Klemz, Alexander Ravsky

We study the following combinatorial problem. Given a set of $n$ y-monotone \emph{wires}, a \emph{tangle} determines the order of the wires on a number of horizontal \emph{layers} such that the orders of the wires on any two consecutive layers differ only in swaps of neighboring wires. Given a multiset~$L$ of \emph{swaps} (that is, unordered pairs of wires)

Yihao Chen, Jiahuei Lin, Bram Adams, Ahmed E. Hassan

[Context]: Containerization ensures the resilience of distributed applications by Kubernetes. Helm is a package manager for Kubernetes applications. A Helm package, namely "Chart'', is a set of pre-configured resources that one could quickly deploy a complex application. However, Helm broadens the attack surface of the distributed applications. [Objective]:

Ningkun Zheng, Bolun Xu

This paper presents a new open-source model for simulating two-stage market clearing based on the Western Electricity Coordinating Council Anchor Data Set. We model accurate two-stage market clearing with day-ahead unit commitment at hourly resolution and real-time economic dispatch with five-minute resolution. Both day-ahead unit commitment and real-time ec

Riccardo Falcone, Claudio Conti

We review the issue of localization in quantum field theory and detail the nonrelativistic limit. Three distinct localization schemes are examined: the Newton-Wigner, the algebraic quantum field theory, and the modal scheme. Among these, the algebraic quantum field theory provides a fundamental concept of localization, rooted in its axiomatic formulation. In

Javier Borquez, Kaustav Chakraborty, Hao Wang, Somil Bansal

Hamilton-Jacobi (HJ) reachability-based filtering provides a powerful framework to co-optimize performance and safety (or liveness) for autonomous systems. Under this filtering scheme, a nominal controller is minimally modified to ensure system safety or liveness. However, the resulting controllers can exhibit abrupt switching and bang-bang behavior, which i

Ramya Bhaskar, Alessandro Roggero, Martin J. Savage

Time scales associated with many-body fast neutrino flavor conversions in core-collapse supernova are explored in the context of an effective two-flavor model with axial symmetry. We present a preliminary study of time scales obtained from a linear stability analysis and from the distributions of Loschmidt echo crossing times (intimately connected to dynamic

Dingkun Guo

Learning from human demonstrations has exhibited remarkable achievements in robot manipulation. However, the challenge remains to develop a robot system that matches human capabilities and data efficiency in learning and generalizability, particularly in complex, unstructured real-world scenarios. We propose a system that processes RGBD videos to translate h