March 2024 arXiv papers — page 37

Rinaldo M. Colombo, Vincent Perrollaz

Consider the inverse design problem for a scalar conservation law, i.e., the problem of finding initial data evolving into a given profile at a given time. The solution we present below takes into account localizations both in the final interval where the profile is assigned and in the initial interval where the datum is sought, as well as additional a prior

Jordan McGinn, Arunava Mukherjee, Jessica Irwin, Christopher Messenger

The first direct detection of gravitational waves from binary neutron stars on the 17th of August, 2017, (GW170817) heralded the arrival of a new messenger for probing neutron star astrophysics and provided the first constraints on neutron star equation of state from gravitational wave observations. Significant computational effort was expended to obtain the

Seun Osonuga, Vincent Imard, Benoit Delinchant, Frederic Wurtz

The proliferation of sensors in buildings has given us access to more data than before. To shepherd this rise in data, many open data lifecycles have been proposed over the past decade. However, many of the proposed lifecycles do not reflect the necessary complexity in the built environment. In this paper, we present a new open data lifecycle model: Open Ene

Jin Hong, Yuchen Yang, Yong Wang

In this paper, we define the spectral Einstein functional associated with the sub-Dirac operator for manifolds with boundary. A proof of the Dabrowski-Sitarz-Zalecki type theorem for spectral Einstein functions associated with the sub-Dirac operator on four-dimensional manifolds with boundary is also given.

Aman Chawla

In this brief paper, the authors study the tuning curves of starburst amacrine cells (SACs) and introduce a quantity called the irresolution or ambiguity of a SAC. They show that the rate of data generated by a starburst amacrine cell is inversely proportional to its irresolution. This is done by providing bounds on the rate required to encode the generated

Runmin Dong, Shuai Yuan, Bin Luo, Mengxuan Chen

Reference-based super-resolution (RefSR) has the potential to build bridges across spatial and temporal resolutions of remote sensing images. However, existing RefSR methods are limited by the faithfulness of content reconstruction and the effectiveness of texture transfer in large scaling factors. Conditional diffusion models have opened up new opportunitie

Shogo Makino, Kento Kawaharazuka, Masaya Kawamura, Yuki Asano

Human can not only support their body during standing or walking, but also support them by hand, so that they can dangle a bar and others. But most humanoid robots support their body only in the foot and they use their hand just to manipulate objects because their hands are too weak to support their body. Strong hands are supposed to enable humanoid robots t

Jake Hesford, Daniel Cheng, Alan Wan, Larry Huynh

Our paper provides empirical comparisons between recent IDSs to provide an objective comparison between them to help users choose the most appropriate solution based on their requirements. Our results show that no one solution is the best, but is dependent on external variables such as the types of attacks, complexity, and network environment in the dataset.

Akio Hosoya, Shunsuke Fujii

We revisit the old problem of the energy-momentum tensor in general relativistic field theories. On the basis of the general covariance we derive a simple equation for the Hilbert and Noether energy-momentum tensors for the scalar and electromagnetic field theories. We see that the two definitions of energy-momentum tensors coincide and identify the Noether

Qian Shao, Pradeep Varakantham, Shih-Fen Cheng

Complex planning and scheduling problems have long been solved using various optimization or heuristic approaches. In recent years, imitation learning that aims to learn from expert demonstrations has been proposed as a viable alternative to solving these problems. Generally speaking, imitation learning is designed to learn either the reward (or preference)

Upalaparna Banerjee, Christoph Englert, Wrishik Naskar

The juxtaposition of the precision of lepton flavour measurements and the limited energy range of the Large Hadron Collider (LHC) to discover dynamical degrees of freedom linked to the generation of the observed lepton mass patterns naively suggests only a limited relevance of the LHC's high luminosity phase. This, potentially, extends to future colliders. U

Shining Yang, Jianbo Lu, Xinping Yu, Jingyang Xu

This study investigates the dynamical effects of particles orbiting a celestial body in rotating Simpson-Visser (RSV) spacetime. The results show that, compared to Kerr and rotating regular black holes, the innermost stable circular orbit (ISCO) of an RSV wormhole is closer to the central object. Using high-frequency quasi-periodic oscillation (HFQPO) data f

Jungyun Lee, Daniel K. Park

Classification is at the core of data-driven prediction and decision-making, representing a fundamental task in supervised machine learning. Recently, several quantum machine learning algorithms that use quantum kernels as a measure of similarities between data have emerged to perform binary classification on datasets encoded as quantum states. The potential

Shogo Makino, Kento Kawaharazuka, Ayaka Fujii, Masaya Kawamura

Human hands can not only grasp objects of various shape and size and manipulate them in hands but also exert such a large gripping force that they can support the body in the situations such as dangling a bar and climbing a ladder. On the other hand, it is difficult for most robot hands to manage both. Therefore in this paper we developed the hand which can

Dorothee Knees, Sebastian Owczarek, Patrizio Neff

In this paper, we investigate the global higher regularity properties of weak solutions for a linear elliptic system coupled with a nonlinear Maxwell-type system defined on Lipschitz domains. The regularity result is established using a modified finite difference approach. These adjusted finite differences involve inner variations in conjunction with a Piola

Bujin Li, Shaohua Pan, Tieyong Zeng

This paper concerns a class of composite image reconstruction models for impluse noise removal, which is rather general and covers existing convex and nonconvex models proposed for reconstructing images with impluse noise. For this nonconvex and nonsmooth optimization problem, we propose a proximal majorization-minimization (MM) algorithm with an implementab

V. Guzey

In this contribution, we summarize NLO pQCD predictions for inclusive and diffractive dijet photoproduction in Pb-Pb UPCs at the LHC. We demonstrate that the theory describes well the preliminary ATLAS data on the inclusive cross section, which probes nuclear parton distributions (PDFs) down to $x_A \approx 0.005$ and which can reduce current uncertainties o

Jie Qi, Ronghua Wanga, Nailong Wu

The focus of this paper is to develop a methodology that enables an unmanned surface vehicle (USV) to efficiently track a planned path. The introduction of a vector field-based adaptive line of-sight guidance law (VFALOS) for accurate trajectory tracking and minimizing the overshoot response time during USV tracking of curved paths improves the overall line-

Yingtao Shen, Minqing Sun, Jianzhe Lin, Jie Zhao

Model compression has gained significant popularity as a means to alleviate the computational and memory demands of machine learning models. Each compression technique leverages unique features to reduce the size of neural networks. Although intuitively combining different techniques may enhance compression effectiveness, we find that the order in which they

Matthew H. Holden, Eva E. Plagányi, Elizabeth A. Fulton, Alexander B. Campbell

Mathematical and statistical models underlie many of the world's most important fisheries management decisions. Since the 19th century, difficulty calibrating and fitting such models has been used to justify the selection of simple, stationary, single-species models to aid tactical fisheries management decisions. Whereas these justifications are reasonable,

Jiqun Chu, Zuoquan Lin

Modeling long-range dependencies in sequential data is a crucial step in sequence learning. A recently developed model, the Structured State Space (S4), demonstrated significant effectiveness in modeling long-range sequences. However, It is unclear whether the success of S4 can be attributed to its intricate parameterization and HiPPO initialization or simpl

Hai-Ling Liu, Ya-Qian Zhao, Ren-Gang Li, Xin Zhang

Multi-view Feature Extraction (MvFE) has wide applications in machine learning, image processing and other fields. When dealing with massive high-dimensional data, the performance of classical computer faces severe challenges due to MvFE involves expensive matrix calculation. To address this challenge, a quantum-accelerated cross-regression algorithm for MvF

Yannick Neyt, James Parkinson, Hendrik Van Maldeghem

An automorphism of a building is called uniclass if the Weyl distance between any chamber and its image lies in a single (twisted) conjugacy class of the Coxeter group. In this paper we characterise uniclass automorphisms of spherical buildings in terms of their fixed structure. For this purpose we introduce the notion of a Weyl substructure in a spherical b

Xing Tang, Yang Qiao, Fuyuan Lyu, Dugang Liu

As user behaviors become complicated on business platforms, online recommendations focus more on how to touch the core conversions, which are highly related to the interests of platforms. These core conversions are usually continuous targets, such as \textit{watch time}, \textit{revenue}, and so on, whose predictions can be enhanced by previous discrete conv

Chiyun Noh, Ayoung Kim

Robust odometry estimation in perceptually degraded environments represents a key challenge in the field of robotics. In this paper, we propose a LiDAR-radar fusion method for robust odometry for adverse environment with LiDAR degeneracy. By comparing the LiDAR point cloud with the radar static point cloud obtained through preprocessing module, it is possibl

Sofoclis Zambirinis, Fragkiskos Papadopoulos

We extend a recent model of temporal random hyperbolic graphs by allowing connections and disconnections to persist across network snapshots with different probabilities, $\omega_1$ and $\omega_2$. This extension, while conceptually simple, poses analytical challenges involving the Appell $F_1$ series. Despite these challenges, we are able to analyze key pro

V. Medvedev, E. Zhuzhoma

Let $M^n$, $n\geq 3$, be a closed orientable $n$-manifold and $\mathbb{D}_k(M^n;a,b,c)$ the set of axiom A diffeomorp\-hisms $f: M^n\to M^n$ satisfying the following conditions: (1) $f$ has $k\geq 1$ nontrivial basic sets each is either an orientable codimension one expanding attractor or an orientable codimension one contracting repeller, and other trivial

Thomas Selig, Haoyue Zhu

We present a bijection between two well-known objects in the ubiquitous Catalan family: non-decreasing parking functions and {\L}ukasiewicz paths. This bijection maps the maximal displacement of a parking function to the height of the corresponding {\L}ukasiewicz path, and the total displacement to the area of the path. We also study this bijection restricte

Zheng Lin, Neng Zhang, Chao Liu, Zibin Zheng

Since the launch of ChatGPT in 2022, an increasing number of ChatGPT-related projects are being published on GitHub, sparking widespread discussions. However, GitHub does not provide a detailed classification of these projects to help users effectively explore interested projects. Additionally, the issues raised by users for these projects cover various aspe

Maja Karwowska, Łukasz Graczykowski, Kamil Deja, Miłosz Kasak

The ALICE experiment at the LHC measures properties of the strongly interacting matter formed in ultrarelativistic heavy-ion collisions. Such studies require accurate particle identification (PID). ALICE provides PID information via several detectors for particles with momentum from about 100 MeV/c up to 20 GeV/c. Traditionally, particles are selected with r

Manimugdha Saikia

We associate quantum states with subsets of a product of two compact connected K\"ahler manifolds $M_1$ and $M_2$. To associate the quantum state with the subset, we use the map that restricts holomorphic sections of the quantum line bundle over the product of the two K\"ahler manifolds to the subset. We present a description of the kernel of this restrictio

Xingguang Jin, Kei Fong Lam, Changqing Ye

In this work we propose a phase field model based on a Caginalp system with mechanical effects to study the underlying physical and chemical processes behind stereolithography, which is an additive manufacturing (3D printing) technique that builds objects in a layer-by-layer fashion by using an ultraviolet laser to solidify liquid polymer resins. Existence o

Yaping Yang, Paul Zinn-Justin

We study higher spin (pure and mixed spin) representations of the Yangian of $\mathfrak{sl}_2$. We provide a geometric realization in terms of the critical cohomology of representations of the quiver with potential of Bykov and Zinn-Justin [BZJ20]. When the framing dimension is 1, it recovers the evaluation pullback of the $\ell+1$-dimensional irreducible re

Kazi Shahriar Sanjid, Md. Tanzim Hossain, Md. Shakib Shahariar Junayed, Mohammad Monir Uddin

Integrating components from convolutional neural networks and state space models in medical image segmentation presents a compelling approach to enhance accuracy and efficiency. We introduce Mamba HUNet, a novel architecture tailored for robust and efficient segmentation tasks. Leveraging strengths from Mamba UNet and the lighter version of Hierarchical Upsa

Yingfa Chen, Zhengyan Zhang, Xu Han, Chaojun Xiao

Large language models (LLMs) can make predictions using parametric knowledge--knowledge encoded in the model weights--or contextual knowledge--knowledge presented in the context. In many scenarios, a desirable behavior is that LLMs give precedence to contextual knowledge when it conflicts with the parametric knowledge, and fall back to using their parametric

Anna Sukhova, Alexey Akhundov, Efim Verzakov, Yegor Bugayenko

In object-oriented programming languages, a belief exists that classes with -Er/-Or and -Utils suffixes are "code smells" because they take over a lot of functional responsibility, turning out to be bulky and complicated, and therefore making it more difficult to maintain the code. In order to validate this intuition, we analyzed complexity and cohesion of 1

Mi-Ra Hwang, Eylee Jung, DaeKil Park

The generalized version of the Arthurs-Kelly inequality is derived when the initial state is a tripartite separable state. When each initial substate obeys the minimal uncertainty, the generalized version reduces to the well-known inequality, i.e. twice of the Heisenberg uncertainty. If the initial probe state is entangled, it is shown that the generalized v

Jae-hee So, Joonhwan Chang, Eunji Kim, Junho Na

Background: Advancements in large language models (LLMs) have opened new possibilities in psychiatric interviews, an underexplored area where LLMs could be valuable. This study focuses on enhancing psychiatric interviews by analyzing counseling data from North Korean defectors who have experienced trauma and mental health issues. Objective: The study investi

Runnan Zhang, Yuuki Yasui, Masahiro Fukuda, Taisuke Ozaki

To achieve atomic-level characterization of the diamond (001) surface, persistent efforts have been made over the past few decades. The motivation behind the pursuit extends beyond investigating surface defects and adsorbates; it also involves unraveling the mystery of the smooth growth of diamond. However, the inherently low conductivity and the short C-C b

Yi C. Huang, Sijie Luo

Sukochev and Zanin resolved an open problem due to B. Simon concerning optimal constants in H\"older inequality for the weak Schatten classes of compact operators. In this note we observe that these constants, by introducing the modified weak Schatten quasi-norms, can be renormalised so that the original Simon's conjecture (with optimal constant 1) does hold

Saurav Joshi, Filip Ilievski, Jay Pujara

According to WWF, 1.1 billion people lack access to water, and 2.7 billion experience water scarcity at least one month a year. By 2025, two-thirds of the world's population may be facing water shortages. This highlights the urgency of managing water usage efficiently, especially in water-intensive sectors like food. This paper proposes a recommendation engi

Jianwei Wang, Kai Wang, Xuemin Lin, Wenjie Zhang

Community search has aroused widespread interest in the past decades. Among existing solutions, the learning-based models exhibit outstanding performance in terms of accuracy by leveraging labels to 1) train the model for community score learning, and 2) select the optimal threshold for community identification. However, labeled data are not always available

Wentao Ouyang, Xiuwu Zhang, Chaofeng Guo, Shukui Ren

In real-world advertising systems, conversions have different types in nature and ads can be shown in different display scenarios, both of which highly impact the actual conversion rate (CVR). This results in the multi-type and multi-scenario CVR prediction problem. A desired model for this problem should satisfy the following requirements: 1) Accuracy: the

Lin Hou, Tom J. P. Irons, Yanyong Wang, James W. Furness

Accurate approximation of the exchange-correlation (XC) energy in density functional theory (DFT) calculations is essential for reliably modelling electronic systems. Many such approximations are developed from models of the XC hole; accurate reference XC holes for real electronic systems are crucial for evaluating the accuracy of these models however the av

Shohei Enomoto, Naoya Hasegawa, Kazuki Adachi, Taku Sasaki

Deep neural networks have achieved remarkable success in a variety of computer vision applications. However, there is a problem of degrading accuracy when the data distribution shifts between training and testing. As a solution of this problem, Test-time Adaptation~(TTA) has been well studied because of its practicality. Although TTA methods increase accurac

Jihyun Lee, Shunsuke Saito, Giljoo Nam, Minhyuk Sung

We present InterHandGen, a novel framework that learns the generative prior of two-hand interaction. Sampling from our model yields plausible and diverse two-hand shapes in close interaction with or without an object. Our prior can be incorporated into any optimization or learning methods to reduce ambiguity in an ill-posed setup. Our key observation is that

Yiqun Chen, Jiaxin Mao, Yi Zhang, Dehong Ma

Search result diversification (SRD), which aims to ensure that documents in a ranking list cover a broad range of subtopics, is a significant and widely studied problem in Information Retrieval and Web Search. Existing methods primarily utilize a paradigm of "greedy selection", i.e., selecting one document with the highest diversity score at a time or optimi

Dongjin Kim, Sung Jin Um, Sangmin Lee, Jung Uk Kim

The goal of the multi-sound source localization task is to localize sound sources from the mixture individually. While recent multi-sound source localization methods have shown improved performance, they face challenges due to their reliance on prior information about the number of objects to be separated. In this paper, to overcome this limitation, we prese

Deepak P

This article critically examines the recent hype around AI safety. We first start with noting the nature of the AI safety hype as being dominated by governments and corporations, and contrast it with other avenues within AI research on advancing social good. We consider what 'AI safety' actually means, and outline the dominant concepts that the digital footp

Sumanth A. Rotti, Berkay Aydin, Petrus C. Martens

Solar energetic particle (SEP) events are one of the most crucial aspects of space weather that require continuous monitoring and forecasting. Their prediction depends on various factors including source eruptions. In the present work, we use the Geostationary Solar Energetic Particle (GSEP) data set covering solar cycles 22, 23, and 24. We develop a framewo

Anna Fujioka, Masaki Ogura, Naoki Wakamiya

A multi-agent system comprises numerous agents that autonomously make decisions to collectively accomplish tasks, drawing significant attention for their wide-ranging applications. Within this context, formation control emerges as a prominent task, wherein agents collaboratively shape and maneuver while preserving formation integrity. Our focus centers on cy

Wei Wu, Chao Wang, Dazhong Shen, Chuan Qin

Collaborative filtering methods based on graph neural networks (GNNs) have witnessed significant success in recommender systems (RS), capitalizing on their ability to capture collaborative signals within intricate user-item relationships via message-passing mechanisms. However, these GNN-based RS inadvertently introduce excess linear correlation between user

Gaoming Wang

Given a bounded $C^2$ domain in $\mathbb{R}^{n+1}$ and an integral $n$-rectifiable varifold $V$ with bounded first variation and bounded generalized mean curvature. Given a $C^1$ function $\theta$ defined on the boundary of the domain with range $(0,\pi)$, we assume $V$ has prescribed contact angle $\theta$ with $\partial \Omega$ and the tangent cone of $V$

Maryam Majedi, Ken Barker

Organizations use privacy policies to communicate their data collection practices to their clients. A privacy policy is a set of statements that specifies how an organization gathers, uses, discloses, and maintains a client's data. However, most privacy policies lack a clear, complete explanation of how data providers' information is used. We propose a model

Yixuan Wang, Baoxin Wang, Yijun Liu, Dayong Wu

Over-correction is a critical problem in Chinese grammatical error correction (CGEC) task. Recent work using model ensemble methods based on voting can effectively mitigate over-correction and improve the precision of the GEC system. However, these methods still require the output of several GEC systems and inevitably lead to reduced error recall. In this li

Nobuya Maeshima, Ken-ichi Hino

We study low-lying spin-singlet excitations of two-leg Hubbard ladders with site-dependent potentials. Using general formulas of the charge disproportionation induced by the site-dependent potentials, we derive the contributions of spin degrees of freedom to the spectral functions such as the dynamical charge structure factor $N(\boldsymbol{k},\omega)$ and t

Jinyi Li, Yihuai Lan, Lei Wang, Hao Wang

Prompt compression is an innovative method for efficiently condensing input prompts while preserving essential information. To facilitate quick-start services, user-friendly interfaces, and compatibility with common datasets and metrics, we present the Prompt Compression Toolkit (PCToolkit). This toolkit is a unified plug-and-play solution for compressing pr

Masanari Kimura, Ryotaro Shimizu, Yuki Hirakawa, Ryosuke Goto

Conventional machine learning algorithms have traditionally been designed under the assumption that input data follows a vector-based format, with an emphasis on vector-centric paradigms. However, as the demand for tasks involving set-based inputs has grown, there has been a paradigm shift in the research community towards addressing these challenges. In rec

Guikun Chen, Xia Li, Yi Yang, Wenguan Wang

We investigate a fundamental aspect of machine vision: the measurement of features, by revisiting clustering, one of the most classic approaches in machine learning and data analysis. Existing visual feature extractors, including ConvNets, ViTs, and MLPs, represent an image as rectangular regions. Though prevalent, such a grid-style paradigm is built upon en

Yusuke Tajima, Junichi Shiogai, Kohei Ueda, Hirotake Suzaki

An antiferromagnetic metal with two-dimensional triangular network offers a unique playground of intriguing magneto-transport properties and functionalities stemming from interplay between conducting electrons and intricate magnetic phases. A NiAs-type CrSe is one of the candidates owing to alternate stackings of Cr and Se triangular atomic networks in its c

Qi-Ning Hsu, L. L. Cowie, Chian-Chou Chen, A. J. Barger

The extragalactic background light (EBL) is the cumulative radiation outside the Milky Way. The determination of its corresponding primary emitting sources as well as its total energy level across the entire electromagnetic spectrum has profound implications for both cosmology and galaxy formation. However, the detailed origin of the EBL at far-infrared wave

Jo-An Occhipinti, Ante Prodan, William Hynes, Roy Green

Generative Artificial Intelligence (AI) stands as a transformative force that presents a paradox; it offers unprecedented opportunities for productivity growth while potentially posing significant threats to economic stability and societal wellbeing. Many consider generative AI as akin to previous technological advancements, using historical precedent to arg

Jinze Zhao, Peihao Wang, Zhangyang Wang

Mixture-of-Experts (MoE) represents an ensemble methodology that amalgamates predictions from several specialized sub-models (referred to as experts). This fusion is accomplished through a router mechanism, dynamically assigning weights to each expert's contribution based on the input data. Conventional MoE mechanisms select all available experts, incurring

L. -R. Liu, S. -C. Wu, T. -W. Liu, H. -Y. Hsu

Gauge fields are ubiquitous in modern quantum physics. In superfluids, quantized vortices can be induced by gauge fields. Here we demonstrate the first experimental observation of vortex nucleations in light-dressed spinor Bose-Einstein condensates under radially-localized synthetic magnetic fields. The light-induced spin-orbital-angular-momentum coupling cr

Satoki Ogiso, Yoshiaki Bando, Takeshi Kurata, Takashi Okuma

Human and/or asset tracking using an attached sensor units helps understand their activities. Most common indoor localization methods for human tracking technologies require expensive infrastructures, deployment and maintenance. To overcome this problem, environmental sounds have been used for infrastructure-free localization. While they achieve room-level c

Pramath Anamby, Soumya Das

In the first half of the paper, we lay down a classical approach to the study of Saito-Kurokawa (SK) lifts of (Hecke congruence) square-free level, including the allied new-oldform theory. Our treatment of this relies on a novel idea of computing ranks of certain matrices whose entries are $L^2$-norms of eigenforms. For computing the $L^2$ norms we work with

Qian Chen, Zheng Guo, Weixiao Meng, Shuai Han

The advent of the 6G era aims for ubiquitous connectivity, with the integration of non-terrestrial networks (NTN) offering extensive coverage and enhanced capacity. As manufacturing advances and user demands evolve, space-air-ground integrated networks (SAGIN) with computational capabilities emerge as a viable solution for services requiring low latency and

Baptiste Chevalier, Wojciech Roga, Masahiro Takeoka

We present a framework to deal with a range of large scale compressive sensing problems using a quantum subroutine. We apply a quantum approximate optimization algorithm (QAOA) to support detection in a sparse signal reconstruction algorithm: matching pursuit. The constrained optimization required in this algorithm is difficult to handle when the size of the

Silas L. Carvalho, Alexander Condori

In this work, we are interested in characterizing typical (generic) dimensional properties of invariant measures associated with the full-shift system, $T$, in a product space whose alphabet is a countable set. More specifically, we show that the set of invariant measures with infinite packing dimension equal to infinity is a dense $G_\delta$ subset of $\mat

Parnashree Ghosh, Neena Gupta, Ananya Pal

In recent decades, linear affine threefolds have enabled researchers to solve some of the challenging problems on affine spaces. Koras-Russell threefolds, especially the Russell Cubic over $\mathbb{C}$ and Asanuma threefolds over a field of positive characteristic, are striking examples of such linear threefolds.In this paper, we apply tools from $K$-theory

S. Ghazaleh Dashti, Katherine J. Lee, Julie A. Simpson, John B. Carlin

The interventional effects approach to causal mediation analysis is increasingly common in epidemiologic research, given its potential to address policy-relevant questions about hypothetical mediator interventions. Multiple imputation (MI) is widely used for handling missing data in epidemiologic studies. However, guidance is lacking on best practices for us

Zhen Li, Kaixiang Zhu, Xuegong Zhou, Lingli Wang

We propose an open-source end-to-end logic optimization framework for large-scale boolean network with reinforcement learning.

Aitang Xing, Gary Goozee, Alison Gray, Vaughan Moutrie

The integral quality monitor (IQM) system compares the signal measured with a large volume chamber mounted to the linear accelerator's head to the signal calculated using the patient DICOM RT plan for patient-specific quality assurance (PSQA). A method was developed to reconstruct the dose in patients using the signal measured by IQM chamber and patient plan

Piljin Yi, Yi Zhang

We revisit anomalies of $(4,0)$ and $(3,1)$ maximally supersymmetric tensor theories in $d=6$. A $(4,0)$ on-shell tensor multiplet descends to that of the $d=5$ maximal supergravity upon a dimensional reduction, hypothesized to offer a strong-coupled UV completion of the latter in the same sense of $(2,0)$ theories as the UV completion of $d=5$ $\mathcal{N}=

Yang Bai, Phuoc Thanh Tran Ngoc, Huu Duoc Nguyen, Duc Long Le

Cyborg insects refer to hybrid robots that integrate living insects with miniature electronic controllers to enable robotic-like programmable control. These creatures exhibit advantages over conventional robots in adaption to complex terrain and sustained energy efficiency. Nevertheless, there is a lack of literature on the control of multi-cyborg systems. T

Steven H. Low

We consider the problem of identifying the admittance matrix of a three-phase radial network from voltage and current measurements at a subset of nodes. These measurements are used to estimate a virtual network represented by the Kron reduction (Schur complement) of the full admittance matrix. We focus on recovering exactly the full admittance matrix from it

Songbur Wong

SSF3D modified the semi-supervised 3D object detection (SS3DOD) framework, which designed specifically for point cloud data. Leveraging the characteristics of non-coincidence and weak correlation of target objects in point cloud, we adopt a strategy of retaining only the truth-determining pseudo labels and trimming the other fuzzy labels with points, instead

Monit Sharma, Hoong Chuin Lau, Rudy Raymond

Simulation-based optimization is a widely used method to solve stochastic optimization problems. This method aims to identify an optimal solution by maximizing the expected value of the objective function. However, due to its computational complexity, the function cannot be accurately evaluated directly, hence it is estimated through simulation. Exploiting t

Vadim Petruhanov, Alexander Pechen

In this work, we review several results on development and application of incoherent version of GRAPE (Gradient Ascent Pulse Engineering) approach, inGRAPE, to optimization for open quantum systems driven by both coherent and incoherent controls. In the incoherent control approach, the environment serves as a control together with coherent field, and decoher

Jiacheng Zhang, Jiaming Li, Xiangru Lin, Wei Zhang

We delve into pseudo-labeling for semi-supervised monocular 3D object detection (SSM3OD) and discover two primary issues: a misalignment between the prediction quality of 3D and 2D attributes and the tendency of depth supervision derived from pseudo-labels to be noisy, leading to significant optimization conflicts with other reliable forms of supervision. We

Deepak Kumar, Subramaniam Pushpavanam

In this work, we focus on an autocatalytic reaction-diffusion model and carry out multiple scale weakly nonlinear analysis. A cubic and a quadratic autocatalytic reaction system is analysed. We develop a framework to identify the critical surfaces in parameter space across which the nature of the Turing bifurcation changes from supercritical to subcritical.

Haris Riaz, Razvan-Gabriel Dumitru, Mihai Surdeanu

In this work, we revisit the problem of semi-supervised named entity recognition (NER) focusing on extremely light supervision, consisting of a lexicon containing only 10 examples per class. We introduce ELLEN, a simple, fully modular, neuro-symbolic method that blends fine-tuned language models with linguistic rules. These rules include insights such as ''O

Hyeon-Ju Jeon, Jeon-Ho Kang, In-Hyuk Kwon, O-Joun Lee

This paper investigates the impact of observations on atmospheric state estimation in weather forecasting systems using graph neural networks (GNNs) and explainability methods. We integrate observation and Numerical Weather Prediction (NWP) points into a meteorological graph, extracting $k$-hop subgraphs centered on NWP points. Self-supervised GNNs are emplo

Komal Negi, Madeti Prabhakar

The concepts of twisted knot theory and singular knot theory inspire the introduction of singular twisted knot theory. This study showcases similar findings for singular twisted links, including the Alexander theorem and the Markov theorem derived from knot theory. Moreover, in this paper we define singular twisted virtual braids and their monoid structure.

David Rolnick, Alan Aspuru-Guzik, Sara Beery, Bistra Dilkina

In this position paper, we argue that application-driven research has been systemically under-valued in the machine learning community. As applications of machine learning proliferate, innovative algorithms inspired by specific real-world challenges have become increasingly important. Such work offers the potential for significant impact not merely in domain

Nobutatsu Kobayashi

We study the Cauchy problem for the Zakharov system in one space dimension with the Diriclet boundary conditions. We establish the global well-posedness and the growth of higher-order Sobolev norms of solutions to the Zakharov system by using the modified energy method.

Sundaraparipurnan Narayanan

The research investigates the crucial role of clear and intelligible terms of service in cultivating user trust and facilitating informed decision-making in the context of AI, in specific GenAI. It highlights the obstacles presented by complex legal terminology and detailed fine print, which impede genuine user consent and recourse, particularly during insta

Yang Ai, Zhen-Hua Ling

This paper presents a novel neural speech phase prediction model which predicts wrapped phase spectra directly from amplitude spectra. The proposed model is a cascade of a residual convolutional network and a parallel estimation architecture. The parallel estimation architecture is a core module for direct wrapped phase prediction. This architecture consists

Donghoon Ahn, Hyoungwon Cho, Jaewon Min, Wooseok Jang

Recent studies have demonstrated that diffusion models are capable of generating high-quality samples, but their quality heavily depends on sampling guidance techniques, such as classifier guidance (CG) and classifier-free guidance (CFG). These techniques are often not applicable in unconditional generation or in various downstream tasks such as image restor

Shengzi Zhao, Le Shen, Yuxing Xing

Photon counting detectors (PCDs) bring valuable advantages to diagnostic computed tomography (CT), including lower noise and higher resolution than energy integrating detectors. However, there are still several nonideal factors preventing PCDs from meeting people's expectations, for example, charge sharing and pile up. In this paper, we did some preliminary

Hyunjun Ju, SeongKu Kang, Dongha Lee, Junyoung Hwang

Recently, web platforms have been operating various service domains simultaneously. Targeting a platform that operates multiple service domains, we introduce a new task, Multi-Domain Recommendation to Attract Users (MDRAU), which recommends items from multiple ``unseen'' domains with which each user has not interacted yet, by using knowledge from the user's

Mingfu Liang, Jong-Chyi Su, Samuel Schulter, Sparsh Garg

Autonomous vehicle (AV) systems rely on robust perception models as a cornerstone of safety assurance. However, objects encountered on the road exhibit a long-tailed distribution, with rare or unseen categories posing challenges to a deployed perception model. This necessitates an expensive process of continuously curating and annotating data with significan

Youhua Li, Hanwen Du, Yongxin Ni, Yuanqi He

Sequential Recommendation (SR) aims to predict future user-item interactions based on historical interactions. While many SR approaches concentrate on user IDs and item IDs, the human perception of the world through multi-modal signals, like text and images, has inspired researchers to delve into constructing SR from multi-modal information without using IDs

Jacob Spainhour, David Gunderman, Kenneth Weiss

Point containment queries for regions bound by watertight geometric surfaces, i.e., closed and without self-intersections, can be evaluated straightforwardly with a number of well-studied algorithms. When this assumption on domain geometry is not met, such methods are either unusable, or prone to misclassifications that can lead to cascading errors in downst

Bernardo Subercaseaux, Wojciech Nawrocki, James Gallicchio, Cayden Codel

A recent breakthrough in computer-assisted mathematics showed that every set of $30$ points in the plane in general position (i.e., without three on a common line) contains an empty convex hexagon, thus closing a line of research dating back to the 1930s. Through a combination of geometric insights and automated reasoning techniques, Heule and Scheucher cons

Ziyang Gong, Fuhao Li, Yupeng Deng, Deblina Bhattacharjee

Unsupervised Domain Adaptation (UDA) aims to adapt models from labeled source domains to unlabeled target domains. When adapting to adverse scenes, existing UDA methods fail to perform well due to the lack of instructions, leading their models to overlook discrepancies within all adverse scenes. To tackle this, we propose CoDA which instructs models to disti

Fan Huang, Haewoon Kwak, Kunwoo Park, Jisun An

As AI becomes more integral in our lives, the need for transparency and responsibility grows. While natural language explanations (NLEs) are vital for clarifying the reasoning behind AI decisions, evaluating them through human judgments is complex and resource-intensive due to subjectivity and the need for fine-grained ratings. This study explores the alignm

Guoping Pan, Qingwei Ben, Zhecheng Yuan, Guangqi Jiang

Fully leveraging the loco-manipulation capabilities of a quadruped robot equipped with a robotic arm is non-trivial, as it requires controlling all degrees of freedom (DoFs) of the quadruped robot to achieve effective whole-body coordination. In this letter, we propose a novel framework RoboDuet, which employs two collaborative policies to realize locomotion

Samuel J van Bohemen, Joe O Nardo, Jeffrey M Rogers, Eleanor Stephens

The Electrocardiography Brain Perfusion index (EBPi) is a novel electrocardiography (ECG)-based metric that may function as a proxy for cerebral blood flow (CBF). We investigated the spatio-temporal correlation between EBPi and epileptic seizure events. EBPi was computed retrospectively from clinical EEG and ECG data captured previously from 30 epilepsy pati

Aitang Xing, Gary Goozee, Gary Liney, Sankar Arumugam

A software system named AutoMRISimQA was developed to monitor the daily performance of a wide-bore 3T scanner(MRI) which was designed and dedicated to radiotherapy simulation. The system can monitor the performance of the MRI simulator not only by using image quality indices such as signal-to-noise ratio (SNR), uniformity, ghosting and contrast but also perf