March 2023 arXiv papers — page 20

Yeqi Gao, Sridhar Mahadevan, Zhao Song

Over the past few years, there has been a significant amount of research focused on studying the ReLU activation function, with the aim of achieving neural network convergence through over-parametrization. However, recent developments in the field of Large Language Models (LLMs) have sparked interest in the use of exponential activation functions, specifical

Jing Wang, Bing Ji, Yang Lei, Tian Liu

Background: Magnetic resonance spectroscopy (MRS) enables non-invasive detection and measurement of biochemicals and metabolites. However, MRS has low signal-to-noise ratio (SNR) when concentrations of metabolites are in the range of the million molars. Standard approach of using a high number of signal averaging (NSA) to achieve sufficient NSR comes at the

Eduard Gorbunov

This note focuses on a simple approach to the unified analysis of SGD-type methods from (Gorbunov et al., 2020) for strongly convex smooth optimization problems. The similarities in the analyses of different stochastic first-order methods are discussed along with the existing extensions of the framework. The limitations of the analysis and several alternativ

Paul Hongsuck Seo, Arsha Nagrani, Cordelia Schmid

Audiovisual automatic speech recognition (AV-ASR) aims to improve the robustness of a speech recognition system by incorporating visual information. Training fully supervised multimodal models for this task from scratch, however is limited by the need for large labelled audiovisual datasets (in each downstream domain of interest). We present AVFormer, a simp

Xiao Lin, Chen Wang

Line detection is widely used in many robotic tasks such as scene recognition, 3D reconstruction, and simultaneous localization and mapping (SLAM). Compared to points, lines can provide both low-level and high-level geometrical information for downstream tasks. In this paper, we propose a novel learnable edge-based line detection algorithm, AirLine, which ca

Cansu Dogan, Selim Yilmaz, Sevil Sen

The Internet of Things (IoT) is becoming ubiquitous in our daily life. IoT networks that are made up of devices low power, low memory, and low computing capability appears in many applications such as healthcare, home, agriculture. IPv6 Routing Protocol for Low Power and Lossy Network (RPL) has become a standardized routing protocol for such low-power and lo

Tomohisa Kojima, Ryohei Momokawa, Takuma Matsuo, Mitsuo Notomi

Shatterproof polymer films are widely for windows used because they can be easily installed on existing glass windows to improve safety. Applying them to glass plates has been reported to not only prevent fragments from scattering but also increase load-bearing capacity and penetration resistance. However, the clarification of their mechanism and quantitativ

Bon-Chul Koo, Dongkok Kim, Sung-Chul Yoon, John C. Raymond

We report on the result of He abundance analysis of dense circumstellar clumps in the young supernova remnant Cassiopeia A. These clumps, which are called quasi-stationary flocculi (QSFs), are known from previous optical studies to be enriched in He along with N, but the degree of He overabundance relative to H has remained uncertain. For several QSFs with n

Gabriel E. Monsalve, Mihai Tibăr

We develop two methods for expressing the global index of the gradient of a 2 variable polynomial function $f$: in terms of the atypical fibres of $f$, and in terms of the clusters of Milnor arcs at infinity. These allow us to derive upper bounds for the global index, in particular refining the one found by Durfee in terms of the degree of $f$.

Nicolas Scharowski, Sebastian A. C. Perrig

Trust is a key motivation in developing explainable artificial intelligence (XAI). However, researchers attempting to measure trust in AI face numerous challenges, such as different trust conceptualizations, simplified experimental tasks that may not induce uncertainty as a prerequisite for trust, and the lack of validated trust questionnaires in the context

Brian Irwin, Sebastian Reich

In this paper, we introduce the Ensemble Kalman-Stein Gradient Descent (EnKSGD) class of algorithms. The EnKSGD class of algorithms builds on the ensemble Kalman filter (EnKF) line of work, applying techniques from sequential data assimilation to unconstrained optimization and parameter estimation problems. The essential idea is to exploit the EnKF as a blac

Hyunyoung Jung, Zhuo Hui, Lei Luo, Haitao Yang

To apply optical flow in practice, it is often necessary to resize the input to smaller dimensions in order to reduce computational costs. However, downsizing inputs makes the estimation more challenging because objects and motion ranges become smaller. Even though recent approaches have demonstrated high-quality flow estimation, they tend to fail to accurat

Yajie Yu, Hanyu Li, Jingchun Zhou

Tensor ring (TR) decomposition is a simple but effective tensor network for analyzing and interpreting latent patterns of tensors. In this work, we propose a doubly randomized optimization framework for computing TR decomposition. It can be regarded as a sensible mix of randomized block coordinate descent and stochastic gradient descent, and hence functions

Xinxin Hu, Haotian Chen, Junjie Zhang, Hongchang Chen

Along with the rapid evolution of mobile communication technologies, such as 5G, there has been a drastically increase in telecom fraud, which significantly dissipates individual fortune and social wealth. In recent years, graph mining techniques are gradually becoming a mainstream solution for detecting telecom fraud. However, the graph imbalance problem, c

Sicheng Gao, Xuhui Liu, Bohan Zeng, Sheng Xu

Image super-resolution (SR) has attracted increasing attention due to its wide applications. However, current SR methods generally suffer from over-smoothing and artifacts, and most work only with fixed magnifications. This paper introduces an Implicit Diffusion Model (IDM) for high-fidelity continuous image super-resolution. IDM integrates an implicit neura

Soichiro Yamazaki, Fumio Uchida, Kotaro Fujisawa, Koichi Miyamoto

The collisionless Boltzmann equation (CBE) is a fundamental equation that governs the dynamics of a broad range of astrophysical systems from space plasma to star clusters and galaxies. It is computationally expensive to integrate the CBE directly in a multi-dimensional phase space, and thus the applications to realistic astrophysical problems have been limi

Ikkei Hotta, Sebastian Schleißinger, Toshiyuki Sugawa

In this article we prove that nonlinear resolvents of infinitesimal generators on bounded and convex subdomains of $\C^n$ are decreasing Loewner chains. Furthermore, we consider the problem of the existence of nonlinear resolvents on unbounded convex domains in $\C$. In the case of the upper half-plane, we obtain a complete solution by using that nonlinear r

K. Uzawa, K. Hagino

We model fission at barrier-top energies in a simplified model space that permits comparison of different components of the residual nucleon-nucleon interaction. The model space is built on particle-hole excitations of reference configurations. These are Slater determinants of uniformly spaced orbitals characterized only by their quantum numbers and orbital

Yang Shen, Guang-Ming Zhang, Mingpu Qin

We revisit the ground state of the Hubbard model on 2-legged ladders in this work. We perform DMRG calculation on large system sizes with large kept states and perform extrapolation of DMRG results with truncation errors in the converged region. We find the superconducting correlation exponent $K_{sc}$ extracted from the pair-pair correlation is very sensiti

Shao-Bo Tang, Hao Qin, D. -Y. Wang, Kaifeng Cui

The precision of quantum sensing could be improved by exploiting quantum phase transitions, where the physical quantity tends to diverge when the system approaches the quantum critical point. This critical enhancement phenomenon has been applied to the quantum Rabi model in a dynamic framework, showing a promising sensing enhancement without the need for com

Tao Hu, Xiaogang Xu, Ruihang Chu, Jiaya Jia

Existing learning-based methods for point cloud rendering adopt various 3D representations and feature querying mechanisms to alleviate the sparsity problem of point clouds. However, artifacts still appear in rendered images, due to the challenges in extracting continuous and discriminative 3D features from point clouds. In this paper, we present a dense whi

Dimpi, Hemant Kumar Singh

Let $X$ be a finite CW-complex having mod $p$ cohomology isomorphic to a wedge of three spheres $\mathbb{S}^n\vee \mathbb{S}^m \vee \mathbb{S}^l,~ 1\leq n \leq m \leq l$. The aim of this paper is to determine the fixed point sets of actions of the cyclic group of prime order on $X.$ We also classify the orbit spaces of free actions of $G=\mathbb{Z}_p, p$ a p

Y. Omura, H. Nakada, K. Abe, M. Takahashi

Quadrupole collectivity of the lowest-lying states, focusing on $E_x(2^+_1)$ and $B(E2;0^+_1\to 2^+_1)$, have been investigated for the $N=50-82$ Sn nuclei by applying the self-consistent approaches with the semi-realistic interaction M3Y-P6. Both $E_x(2^+_1)$ and $B(E2;0^+_1\to 2^+_1)$ are well reproduced by the spherical Hartree-Fock-Bogolyubov (HFB) plus

Tao Hu, Xiaogang Xu, Shu Liu, Jiaya Jia

Synthesizing photo-realistic images from a point cloud is challenging because of the sparsity of point cloud representation. Recent Neural Radiance Fields and extensions are proposed to synthesize realistic images from 2D input. In this paper, we present Point2Pix as a novel point renderer to link the 3D sparse point clouds with 2D dense image pixels. Taking

Lavinia Corina Ciungu

Starting from involutive BE algebras, we redefine the quantum-MV algebras, by introducing and studying the notion of quantum-Wajsberg algebras. We define the $\vee$-commutative quantum-Wajsberg algebras and we investigate their properties. We also prove that any Wajsberg algebra is a quantum-Wajsberg algebra, and give conditions for quantum-Wajsberg algebras

Xiaojun Zhang, Chengge Liu, Zhirui Gong, Zhihai Wang

We study the Markovian and Non-Markovian dynamics in a giant atom system which couples to a coupled resonator waveguide (CRW) via two distant sites. Under certain conditions, we find that the giant atom population can exhibit an oscillating behavior and the photon can be trapped in the giant atom regime. These phenomena are induced by the interference effect

Xianghui Xie, Bharat Lal Bhatnagar, Gerard Pons-Moll

Capturing the interactions between humans and their environment in 3D is important for many applications in robotics, graphics, and vision. Recent works to reconstruct the 3D human and object from a single RGB image do not have consistent relative translation across frames because they assume a fixed depth. Moreover, their performance drops significantly whe

Marc P. Hauer, Tobias D Krafft, Andreas Sesing-Wagenpfeil, Katharina Zweig

With the Proposal for a Regulation laying down harmonised rules on Artificial Intelligence (AI Act) the European Union provides the first regulatory document that applies to the entire complex of AI systems. While some fear that the regulation leaves too much room for interpretation and thus bring little benefit to society, others expect that the regulation

Ahmed Boughammoura

This paper presents a concise mathematical framework for investigating both feed-forward and backward process, during the training to learn model weights, of an artificial neural network (ANN). Inspired from the idea of the two-step rule for backpropagation, we define a notion of F-adjoint which is aimed at a better description of the backpropagation algorit

Dimpi, Hemant Kumar Singh

Let $G=\mathbb{Z}_2$ act on a finite CW-complex $X$ having mod 2 cohomology isomorphic to the product of projective space and sphere $\mathbb{F}P^n\times \mathbb{S}^m,$ where $\mathbb{F}=\mathbb{R}$ or $\mathbb{C}.$ In this paper, we have determined the connected fixed point sets and the orbit spaces of free involutions on $X.$ As an application, we derive t

Krishna Hanumanthu, Jagadish Pine

Let $X$ be a smooth complex projective curve and let $E$ be a vector bundle on $X$ which is not semistable. We consider a flag bundle $\pi: \text{Fl}(E) \to X$ parametrizing certain flags of fibers of $E$. The dimensions of the successive quotients of the flags are determined by the ranks of vector bundles appearing in the Harder-Narasimhan filtration of $E$

Stanley Yao Xiao

In this paper we refine recent work due to A. Shankar, A. N. Shankar, and X. Wang on counting elliptic curves by conductor to the case of elliptic curves with a rational 2-torsion point. This family is a small family, as opposed to the large families considered by the aforementioned authors. We prove the analogous counting theorem for elliptic curves with so

Dmitriy Kunisky

We study subgraphs of Paley graphs of prime order $p$ induced on the sets of vertices extending a given independent set of size $a$ to a larger independent set. Using a sufficient condition proved in the author's recent companion work, we show that a family of character sum estimates would imply that, as $p \to \infty$, the empirical spectral distributions o

Dimpi, Hemant Kumar Singh

In this paper, we have determined the orbit spaces of free involutions on a finitistic space having mod 2 cohomology of the product of three spheres $\mathbb{S}^n\times \mathbb{S}^m \times \mathbb{S}^l, 1 \leq n \leq m \leq l$. This paper generalizes the results proved by Dotzel et al. [6] for free involutions on the product of two sphere $\mathbb{S}^n \time

S. Chatterjee, A. Sen, R. Paul, S. Sahai

Gas Electron Multiplier (GEM) detector, one of the advanced members of the Micro Pattern Gas Detector (MPGD) group, is widely used in High Energy Physics (HEP) experiments. The high rate handling capability and spatial resolution make it a desired tracking detector for high rate HEP experiments. Investigation of the long-term stability is an essential criter

Thomas Brian Winkler, Jan Rothörl, Maarten A. Brems, Hans Fangohr

Magnetic skyrmions are magnetic quasi-particles with enhanced stability and different manipulation mechanisms using external fields and currents making them promising candidates for future applications for instance in neuromorphic computing. Recently, several measurements and simulations have shown that thermally activated skyrmions in confined geometries, a

Krish Desai

Parkinsons Disease (PD) is a neurodegenerative disorder resulting in motor deficits due to advancing degeneration of dopaminergic neurons. PD patients report experiencing tremor, rigidity, visual impairment, bradykinesia, and several cognitive deficits. Although Electroencephalography (EEG) indicates abnormalities in PD patients, one major challenge is the l

B. V. Simonov, A. A. Jumabayeva

In this paper we prove Ulyanov-type inequalities between mixed moduli of smoothness of positive orders in different metrics.

Jacek Gulgowski, Anna Kamont, Markus Passenbrunner

Assume that we are given a filtration $(\mathscr F_n)$ on a probability space $(\Omega,\mathscr F,\mathbb P)$ of the form that each $\mathscr F_n$ is generated by the partition of one atom of $\mathscr F_{n-1}$ into two atoms of $\mathscr F_n$ having positive measure. Additionally, assume that we are given a finite-dimensional linear space $S$ of $\mathscr F

Liu Haofeng, Chen Yiwen, Tan Jiayi, Marcelo H Ang

Combined with demonstrations, deep reinforcement learning can efficiently develop policies for manipulators. However, it takes time to collect sufficient high-quality demonstrations in practice. And human demonstrations may be unsuitable for robots. The non-Markovian process and over-reliance on demonstrations are further challenges. For example, we found th

Sahan Wijethunga, Shehan Kaushalya Senavirathna, Kavishka Dissanayake, Janith Bandara Senanayake

Human motion analysis is used in many different fields and applications. Currently, existing systems either focus on one single limb or one single class of movements. Many proposed systems are designed to be used in an indoor controlled environment and must possess good technical know-how to operate. To improve mobility, a less restrictive, modularized, and

Daniel McGinnis

A \textit{$k$-transversal} to family of sets in $\mathbb{R}^d$ is a $k$-dimensional affine subspace that intersects each set of the family. In 1957 Hadwiger provided a necessary and sufficient condition for a family of pairwise disjoint, planar convex sets to have a $1$-transversal. After a series of three papers among the authors Goodman, Pollack, and Wenge

Kaishu Kawaguchi, Kenta Kuroda, Z. Zhao, S. Tani

We describe a setup of time-, spin-, and angle-resolved photoemission spectroscopy (tr-SARPES) employing a 10.7-eV ($\lambda$=115.6 nm) pulse laser at 1-MHz repetition rate as a probe photon source. This equipment effectively combines technologies of a high-power Yb:fiber laser, ultraviolet-driven harmonic generation in Xe gas, and a SARPES apparatus equippe

Xiangyu Zhu, Dong Du, Weikai Chen, Zhiyou Zhao

Extracting parametric edge curves from point clouds is a fundamental problem in 3D vision and geometry processing. Existing approaches mainly rely on keypoint detection, a challenging procedure that tends to generate noisy output, making the subsequent edge extraction error-prone. To address this issue, we propose to directly detect structured edges to circu

Mohammad Lashkari, Amin Gheibi

The generalization performance of deep neural networks with regard to the optimization algorithm is one of the major concerns in machine learning. This performance can be affected by various factors. In this paper, we theoretically prove that the Lipschitz constant of a loss function is an important factor to diminish the generalization error of the output m

Vikram Narayanan, Claudio Carvalho, Angelo Ruocco, Gheorghe Almási

Trying to address the security challenges of a cloud-centric software deployment paradigm, silicon and cloud vendors are introducing confidential computing - an umbrella term aimed at providing hardware and software mechanisms for protecting cloud workloads from the cloud provider and its software stack. Today, Intel SGX, AMD SEV, Intel TDX, etc., provide a

Cho Win Aung, Ashutosh Dwibedi, Jayanta Dey, Sabyasachi Ghosh

Shear viscosity becomes anisotropic in a rotating medium. It is discovered here that for rotating thermalized quantum systems such as those created in relativistic heavy-ion collisions, the coeffficient of shear viscosity breaks up into five independent components. Similar phenomena were also discovered for quark-gluon plasma in the presence of the magnetic

Chris Fields, James F. Glazebrook, Antonino Marciano

Topological quantum field theories (TQFTs) provide a general, minimal-assumption language for describing quantum-state preparation and measurement. They therefore provide a general language in which to express multi-agent communication protocols, e.g. local operations, classical communication (LOCC) protocols. Here we construct LOCC protocols using TQFT, and

Lukáš Kachtík, Daniel Citterberg, Kristýna Bukvišová, Lukáš Kejík

Fabrication of chiral assemblies of plasmonic nanoparticles is a highly attractive and challenging task with promising applications in light emission, detection, and sensing. So far, primarily organic chiral templates have been used for chirality inscription. However, this significantly limits the variety of nanoparticle preparation techniques to an in-pot a

Stefan Abi-Karam, Cong Hao

There are plenty of graph neural network (GNN) accelerators being proposed. However, they highly rely on users' hardware expertise and are usually optimized for one specific GNN model, making them challenging for practical use. Therefore, in this work, we propose GNNBuilder, the first automated, generic, end-to-end GNN accelerator generation framework. It fe

Yuxuan Cao, Jiarong Xu, Carl Yang, Jiaan Wang

In recent years, graph pre-training has gained significant attention, focusing on acquiring transferable knowledge from unlabeled graph data to improve downstream performance. Despite these recent endeavors, the problem of negative transfer remains a major concern when utilizing graph pre-trained models to downstream tasks. Previous studies made great effort

Sebin Gracy, Mengbin Ye, Brian D. O. Anderson, Cesar A. Uribe

This paper studies the endemic behavior of a multi-competitive networked susceptible-infected-susceptible (SIS) model. Specifically, the paper deals with three competing virus systems (i.e., tri-virus systems). First, we show that a tri-virus system, unlike a bi-virus system, is not a monotone dynamical system. Using the Parametric Transversality Theorem, we

Wenhao Chai, Zhongyu Jiang, Jenq-Neng Hwang, Gaoang Wang

When applying a pre-trained 2D-to-3D human pose lifting model to a target unseen dataset, large performance degradation is commonly encountered due to domain shift issues. We observe that the degradation is caused by two factors: 1) the large distribution gap over global positions of poses between the source and target datasets due to variant camera paramete

Yu-Hang Xiao, Lei Huang, David Ramírez, Cheng Qian

Covariance matrix reconstruction is a topic of great significance in the field of one-bit signal processing and has numerous practical applications. Despite its importance, the conventional arcsine law with zero threshold is incapable of recovering the diagonal elements of the covariance matrix. To address this limitation, recent studies have proposed the us

Bangti Jin, Xiyao Li, Qimeng Quan, Zhi Zhou

In this work we develop a novel approach using deep neural networks to reconstruct the conductivity distribution in elliptic problems from one measurement of the solution over the whole domain. The approach is based on a mixed reformulation of the governing equation and utilizes the standard least-squares objective, with deep neural networks as ansatz functi

Chinami Kato, Hiroki Nagakura, Masamichi Zaizen

Fast neutrino flavor conversions (FFCs) and collisional flavor instabilities (CFIs) potentially affect the dynamics of core-collapse supernovae (CCSNe) and binary neutron star mergers (BNSMs). Under the assumption of homogeneous neutrinos, we investigate effects of neutrino emission and absorption (EA) by matters through both single and multi-energy numerica

Ishaan R Kale, Mandar S Sapre, Ayush Khedkar, Kaustubh Dhamankar

A range of complicated real-world problems have inspired the development of several optimization methods. Here, a novel hybrid version of the Ant colony optimization (ACO) method is developed using the sample space reduction technique of the Cohort Intelligence (CI) Algorithm. The algorithm is developed, and accuracy is tested by solving 35 standard benchmar

Youhan Lee, Hasun Yu

Protein language models (pLMs), pre-trained via causal language modeling on protein sequences, have been a promising tool for protein sequence design. In real-world protein engineering, there are many cases where the amino acids in the middle of a protein sequence are optimized while maintaining other residues. Unfortunately, because of the left-to-right nat

J. A. Taboada, H. H. Segnorile, C. E. González, R. C. Zamar

Quasi-equilibrium states that can be prepared in solids through Nuclear Magnetic Resonance (NMR) techniques are out-of-equilibrium states that slowly relax towards thermodynamic equilibrium with the lattice. In this work, we use the quantum discord dynamics as a witness of the quantum correlation in this kind of state. The studied system is a dipole interact

Jinyoung Park, Sanghyeok Lee, Sihyeon Kim, Yunyang Xiong

Transformers have shown superior performance on various computer vision tasks with their capabilities to capture long-range dependencies. Despite the success, it is challenging to directly apply Transformers on point clouds due to their quadratic cost in the number of points. In this paper, we present a Self-Positioning point-based Transformer (SPoTr), which

Francesco Campaioli, Jared H. Cole, Harini Hapuarachchi

Quantum master equations are an invaluable tool to model the dynamics of a plethora of microscopic systems, ranging from quantum optics and quantum information processing, to energy and charge transport, electronic and nuclear spin resonance, photochemistry, and more. This tutorial offers a concise and pedagogical introduction to quantum master equations, ac

Declan Jagt, Peter Seiler, Matthew Peet

It has recently been shown that the evolution of a linear Partial Differential Equation (PDE) can be more conveniently represented in terms of the evolution of a higher spatial derivative of the state. This higher spatial derivative (termed the `fundamental state') lies in $L_2$ - requiring no auxiliary boundary conditions or continuity constraints. Such a r

Xu Cao, Hiroaki Santo, Fumio Okura, Yasuyuki Matsushita

We present a method for 3D reconstruction only using calibrated multi-view surface azimuth maps. Our method, multi-view azimuth stereo, is effective for textureless or specular surfaces, which are difficult for conventional multi-view stereo methods. We introduce the concept of tangent space consistency: Multi-view azimuth observations of a surface point sho

Tian-wen Cao, Hong Wu, Gaspar Galaz, Venu M. Kalari

We present a sample of 330 blue edge-on low surface brightness galaxies (ELSBGs). To understand the chemical evolution of LSBGs, we derived the gas-phase abundance and the [$\alpha$/Fe] ratio. Compared with star-forming galaxies, ELSBGs show a flatter trend in the mass-metallicity ($M_*-Z$) relation, suggesting that the oxygen abundance enhancement is ineffi

Namrata Shivagunde, Vladislav Lialin, Anna Rumshisky

Language model probing is often used to test specific capabilities of models. However, conclusions from such studies may be limited when the probing benchmarks are small and lack statistical power. In this work, we introduce new, larger datasets for negation (NEG-1500-SIMP) and role reversal (ROLE-1500) inspired by psycholinguistic studies. We dramatically e

Jaehwan Jeong, Katherine Tian, Andrew Li, Sina Hartung

Automated generation of clinically accurate radiology reports can improve patient care. Previous report generation methods that rely on image captioning models often generate incoherent and incorrect text due to their lack of relevant domain knowledge, while retrieval-based attempts frequently retrieve reports that are irrelevant to the input image. In this

Jianfeng Wang

In this paper we will discuss the Dirichlet problem of nonlinear second order partial differential equations resolved with any derivatives. First, we transform it into generalized integral equations. Next, we discuss the existence of the classical solution by Leray-Schauder degree and Sobolev space\ $H^{-m_{1}}(\Omega_{1})$.

Pratim Guha Niyogi, Martin A. Lindquist, Tapabrata Maiti

All neuroimaging modalities have their own strengths and limitations. A current trend is toward interdisciplinary approaches that use multiple imaging methods to overcome limitations of each method in isolation. At the same time neuroimaging data is increasingly being combined with other non-imaging modalities, such as behavioral and genetic data. The data s

Jieying Zhang, Simin Liu, Erpeng Dai, Xin Shao

Purpose: This study aims to propose a model-based reconstruction algorithm for simultaneous multi-slab diffusion MRI acquired with blipped-CAIPI gradients (blipped-SMSlab), which can also incorporate distortion correction. Methods: We formulate blipped-SMSlab in a 4D k-space with kz gradients for the intra-slab slice encoding and km (blipped-CAIPI) gradients

Tyler Foster, Sam Payne

We introduce adic tropicalizations for subschemes of toric varieties as limits of Gubler models associated to polyhedral covers of the ordinary tropicalization. Our main result shows that Huber's adic analytification of a subscheme of a toric variety is naturally isomorphic to the inverse limit of its adic tropicalizations, in the category of locally topolog

Jan Grebík

We prove a full measurable version of Vizing's theorem for bounded degree Borel graphs, that is, we show that every Borel graph $\mathcal{G}$ of degree uniformly bounded by $\Delta\in \mathbb{N}$ defined on a standard probability space $(X,\mu)$ admits a $\mu$-measurable proper edge coloring with $(\Delta+1)$-many colors. This answers a question of Marks [Qu

Xue Zaifa, Lu Huibin, Zhang Tao, Li Tao

As an emerging concept cognitive learning model, partial order formal structure analysis (POFSA) has been widely used in the field of knowledge processing. In this paper, we propose the method named three-way causal attribute partial order structure (3WCAPOS) to evolve the POFSA from set coverage to causal coverage in order to increase the interpretability a

Bruce Balick, Lars Borchert, Joel H. Kastner, Adam Frank

NGC 6302 (The ''Butterfly Nebula'') is an extremely energetic bipolar nebula whose central star is among the most massive, hottest, and presumably rapidly evolving of all central stars of planetary nebulae. Our proper-motion study of NGC 6302, based on excellent HST WFC3 images spanning 11 yr, has uncovered at least four different pairs of expanding internal

Man Zhou, Naishan Zheng, Jie Huang, Xiangyu Rui

In this paper, orthogonal to the existing data and model studies, we instead resort our efforts to investigate the potential of loss function in a new perspective and present our belief ``Random Weights Networks can Be Acted as Loss Prior Constraint for Image Restoration''. Inspired by Functional theory, we provide several alternative solutions to implement

Daisuke Nakai, Masaki Muramatsu, Susumu Nishimura

The logical method proposed by Goubault, Ledent, and Rajsbaum provides a novel way to show the unsolvability of distributed tasks by means of a logical obstruction, which is an epistemic logic formula describing the reason of unsolvability. In this paper, we introduce the notion of partial product update, which refines that of product update in the original

Hongyi Yan, Haiwen Liu

In this study, we investigate the weak localization (WL) and weak antilocalization (WAL) effects in twisted bilayer graphene positioned on a hexagonal boron nitride substrate. The bottom graphene layer aligns with the hexagonal boron nitride. The top layer of the system features a Dirac cone with a negligible gap, while the bottom layer possesses a relativel

Yaqian Guo, Xin Wang, Ce Li, Shihui Ying

Scene segmentation is widely used in the field of autonomous driving for environment perception, and semantic scene segmentation (3S) has received a great deal of attention due to the richness of the semantic information it contains. It aims to assign labels to pixels in an image, thus enabling automatic image labeling. Current approaches are mainly based on

Yaobo Liang, Chenfei Wu, Ting Song, Wenshan Wu

Artificial Intelligence (AI) has made incredible progress recently. On the one hand, advanced foundation models like ChatGPT can offer powerful conversation, in-context learning and code generation abilities on a broad range of open-domain tasks. They can also generate high-level solution outlines for domain-specific tasks based on the common sense knowledge

Yuanhanqing Huang, Jianghai Hu

Learning in multi-player games can model a large variety of practical scenarios, where each player seeks to optimize its own local objective function, which at the same time relies on the actions taken by others. Motivated by the frequent absence of first-order information such as partial gradients in solving local optimization problems and the prevalence of

Ahmed M. Farouk, I. I. Beterov, Peng Xu, I. I. Ryabtsev

Based on our recent paper [arXiv:2206.12176 (2022)], we propose a scalable heteronuclear architecture of parallel implementation of CNOT gates in arrays of alkali-metal neutral atoms for quantum information processing. We considered a scheme where we perform CNOT gates in a parallel manner within the array, while they are performed sequentially between the p

Tadashi Wadayama, Ayano Nakai-Kasai

This study investigates the use of continuous-time dynamical systems for sparse signal recovery. The proposed dynamical system is in the form of a nonlinear ordinary differential equation (ODE) derived from the gradient flow of the Lasso objective function. The sparse signal recovery process of this ODE-based approach is demonstrated by numerical simulations

Yuanhanqing Huang, Jianghai Hu

Non-cooperative games serve as a powerful framework for capturing the interactions among self-interested players and have broad applicability in modeling a wide range of practical scenarios, ranging from power management to drug delivery. Although most existing solution algorithms assume the availability of first-order information or full knowledge of the ob

Tiago Lubiana, Rafael Lopes, Pedro Medeiros, Juan Carlo Silva

The rise of advanced chatbots, such as ChatGPT, has sparked curiosity in the scientific community. ChatGPT is a general-purpose chatbot powered by large language models (LLMs) GPT-3.5 and GPT-4, with the potential to impact numerous fields, including computational biology. In this article, we offer ten tips based on our experience with ChatGPT to assist comp

Luis E. Padilla, Juan Carlos Hidalgo, Gabriel German

If, after primordial inflation, the universe undergoes a relatively long reheating period, it could present a phase of matter domination supported by the oscillating inflaton field. During this epoch, small perturbations from the inflaton that reenter the cosmological horizon could virialize to form \textit{inflaton} structures. If the primordial overdensiti

Shiyu Jin, Zhixian Ye, Liangjun Zhang

Autonomous excavation is a challenging task. The unknown contact dynamics between the excavator bucket and the terrain could easily result in large contact forces and jamming problems during excavation. Traditional model-based methods struggle to handle such problems due to complex dynamic modeling. In this paper, we formulate the excavation skills with thre

Prasenjit Ghosh, T. K. Samanta

The idea of best approximation in linear n-normed space is presented and some examples showing various possibilities of best approximations in linear n-normed space is given. Also, we study strictly convex n-norm and enquire about the uniqueness of best approximations in n-normed linear space. Furthermore, best approximations in n-Hilbert space is discussed.

Zhaoyang Zhang, Yitong Jiang, Wenqi Shao, Xiaogang Wang

Controllable image denoising aims to generate clean samples with human perceptual priors and balance sharpness and smoothness. In traditional filter-based denoising methods, this can be easily achieved by adjusting the filtering strength. However, for NN (Neural Network)-based models, adjusting the final denoising strength requires performing network inferen

Mohammad Vahid Jamali, Hamid Saber, Homayoon Hatami, Jung Hyun Bae

While decades of theoretical research have led to the invention of several classes of error-correction codes, the design of such codes is an extremely challenging task, mostly driven by human ingenuity. Recent studies demonstrate that such designs can be effectively automated and accelerated via tools from machine learning (ML), thus enabling ML-driven class

Alexander E. Patkowski

We establish new Fourier integral evaluations involving the Riemann xi function related to a series involving Bessel function of the first kind. We show this infinite series involving the Bessel function of the first kind solves a boundary value problem for the cylindrical heat equation.

Brian Jabarian, Elia Sartori

In a stylized voting model, we establish that increasing the share of critical thinkers -- individuals who are aware of the ambivalent nature of a certain issue -- in the population increases the efficiency of surveys (elections) but might increase surveys' bias. In an incentivized online social media experiment on a representative US population (N = 706), w

Ning Bian, Xianpei Han, Le Sun, Hongyu Lin

Large language models (LLMs) have made significant progress in NLP. However, their ability to memorize, represent, and leverage commonsense knowledge has been a well-known pain point. In this paper, we specifically focus on ChatGPT, a widely used and easily accessible LLM, and ask the following questions: (1) Can ChatGPT effectively answer commonsense questi

Qiong Wu, Sainan Zhang, Wei Wang, Huifu Xu

In this paper, we consider a multi-attribute decision making problem where the decision maker's (DM's) objective is to maximize the expected utility of outcomes but the true utility function which captures the DM's risk preference is ambiguous. We propose a maximin multi-attribute utility preference robust optimization (UPRO) model where the optimal decision

Paratat Bejrakarbum, Paolo Bertozzini

We investigate the notion of involutive weak globular $\omega$-categories via T.Leinster's approach: as algebras for the initial contracted globular operad in the bicategory of globular collections induced by the Cartesian monad of the free involutive strict $\omega$-category functor on globular $\omega$-sets. An apparently more restrictive notion of involut

Robert M. Parrish

The MultiExp Gaussian quadrature rule proposed by Gill and Chien has many compelling properties for the integration of radial integrands encountered in electronic structure, but thus far has only been computed up to N = 50, with some nontrivial holes in the existing tables for 27 <= N < 50. In this work, a simple recipe is developed to compute the MultiExp G

Yutaka Hosotani, Shuichiro Funatsu, Hisaki Hatanaka, Yuta Orikasa

In GUT inspired $SO(5) \times U(1)_X \times SU(3)_C$ gauge-Higgs unification (GHU) in the Randall-Sundrum warped spacetime, the $W$ and $Z$ couplings of all 4D fermion modes become nontrivial. The $W$ and $Z$ couplings of zero-mode quarks and leptons slightly deviate from those in the SM, and the couplings take the matrix form in the space of Kaluza-Klein (K

Trevor Tsue, Brent Mombourquette, Ahmed Taha, Thomas Paul Matthews

This work reveals undiscovered challenges in the performance and generalizability of deep learning models. We (1) identify spurious shortcuts and evaluation issues that can inflate performance and (2) propose training and analysis methods to address them. We trained an AI model to classify cancer on a retrospective dataset of 120,112 US exams (3,467 cancers)

Yan Hu, Qingyu Chen, Jingcheng Du, Xueqing Peng

Objective: This study quantifies the capabilities of GPT-3.5 and GPT-4 for clinical named entity recognition (NER) tasks and proposes task-specific prompts to improve their performance. Materials and Methods: We evaluated these models on two clinical NER tasks: (1) to extract medical problems, treatments, and tests from clinical notes in the MTSamples corpus

Pratik Aghor, Mohammad Atif

A Taylor-Couette setup with radial heating is considered where a Boussinesq fluid is sheared in the annular region between two concentric, independently rotating cylinders maintained at different temperatures. Linear stability analysis is performed to determine the Taylor number for the onset of instability. Two radius ratios corresponding to wide and thin g

Tadashi Wadayama, Kensho Nakajima, Ayano Nakai-Kasai

The power consumption of the integrated circuit is becoming a significant burden, particularly for large-scale signal processing tasks requiring high throughput. The decoding process of LDPC codes is such a heavy signal processing task that demands power efficiency and higher decoding throughput. A promising approach to reducing both power and latency of a d

Edward Pyne, Ran Raz, Wei Zhan

Let $\mathcal{L}$ be a language that can be decided in linear space and let $\epsilon >0$ be any constant. Let $\mathcal{A}$ be the exponential hardness assumption that for every $n$, membership in $\mathcal{L}$ for inputs of length~$n$ cannot be decided by circuits of size smaller than $2^{\epsilon n}$. We prove that for every function $f :\{0,1\}^* \righta