March 2023 arXiv papers — page 27

Raphael Keusch, Hans-Andrea Loeliger

Normals with unknown variance (NUV) and, more generally, normals with unknown parameters (NUP) can represent many useful priors including L_p norms and other sparsifying priors, and they blend well with linear-Gaussian models and Gaussian message passing algorithms. In this paper, we elaborate on recently proposed NUP representations of half-space constraint

Rogier H. Wuijts, Laurens P. Stoop, Jing Hu, Arno Haverkamp

The integration of renewable energy sources into power systems is expected to increase significantly in the coming decades. This can result in critical situations related to the strong variability in space and time of weather patterns. During these critical situations the power system experiences a structural shortage of energy across multiple time steps and

Yunfan Zhang, Hao Wang, Guosheng Lin, Vun Chan Hua Nicholas

This paper investigates an open research task of reconstructing and generating 3D point clouds. Most existing works of 3D generative models directly take the Gaussian prior as input for the decoder to generate 3D point clouds, which fail to learn disentangled latent codes, leading noisy interpolated results. Most of the GAN-based models fail to discriminate

Johannes Heiny, Carolin Kleemann

The convergence of a sequence of point processes with dependent points, defined by a symmetric function of iid high-dimensional random vectors, to a Poisson random measure is proved. This also implies the convergence of the joint distribution of a fixed number of upper order statistics. As applications of the result a generalization of maximum convergence to

Jayakant Kumar, Himanshu, Harikumar Kandath, Pooja Agrawal

This research paper presents a novel approach for navigating a micro UAV (Unmanned Aerial Vehicle) through narrow passages using only its onboard camera feed and a PID control system. The proposed method uses edge detection and homography techniques to extract the key features of the passage from the camera feed and then employs a tuned PID controller to gui

Yongle Luo, Jiaqun Wei

Let $A$ be a basic finite-dimensional algebra and denote by $\operatorname{tors} A$ the collection of all all torsion classes of $A$. It has been proved in \cite{Demonet} that $\operatorname{tors} A$ is always a completely semidistributive lattice. In the present paper, we investigate the distributivity of this lattice and proved that the lattice is distribu

Sukhminder Singh, Lukas Pflug, Julia Mergheim, Michael Stingl

We propose a novel approach to optimize the design of heterogeneous materials, with the goal of enhancing their effective fracture toughness under mode-I loading. The method employs a Gaussian processes-based Bayesian optimization framework to determine the optimal shapes and locations of stiff elliptical inclusions within a periodic microstructure in two di

Florian Müller, Arantxa Ye, Dominik Schön, Julian Rasch

When we get lost in Virtual Reality (VR) or want to return to a previous location, we use the same methods of locomotion for the way back as for the way forward. This is time-consuming and requires additional physical orientation changes, increasing the risk of getting tangled in the headsets' cables. In this paper, we propose the use of undo actions to reve

Mohammad Rasras, Iuliana Marin, Serban Radu

A smart home can be considered a place of residence that enables the management of appliances and systems to help with day-to-day life by automated technology. In the current paper is described a prototype that simulates a context-aware environment, developed in a designed smart home. The smart home environment has been simulated using three agents and five

Wenhao Yuan, Xuehe Wang

Federated Learning (FL) has emerged as a crucial distributed training paradigm, enabling discrete devices to collaboratively train a shared model under the coordination of a central server, while leveraging their locally stored private data. Nonetheless, the non-independent-and-identically-distributed (Non-IID) data generated on heterogeneous clients and the

Jiaxin Liu, Shijie Yuan, B. Nebiyou Bekele, Yuan Ji

We consider a formal statistical design that allows simultaneous enrollment of a main cohort and a backfill cohort of patients in a dose-finding trial. The goal is to accumulate more information at various doses to facilitate dose optimization. The proposed design, called Bi3+3, combines the simple dose-escalation algorithm in the i3+3 design and a model-bas

Yongle Luo, Zhengpan Wang, Jiaqun Wei

Let {\Gamma} be a directed graph and Inv({\Gamma}) be the graph inverse semigroup of {\Gamma}. Luo and Wang [7] showed that the congruence lattice C(Inv({\Gamma})) of any graph inverse semigroup Inv({\Gamma}) is upper semimodular, but not lower semimodular in general. Anagnostopoulou-Merkouri, Mesyan and Mitchell characterized the directed graph {\Gamma} for

Xiangyang Li, Zihan Wang, Jiahao Yang, Yaowei Wang

Vision-and-language navigation (VLN) is the task to enable an embodied agent to navigate to a remote location following the natural language instruction in real scenes. Most of the previous approaches utilize the entire features or object-centric features to represent navigable candidates. However, these representations are not efficient enough for an agent

Yang Liu, Wei-Jun Zhang, Cong Jiang, Jiu-Peng Chen

Quantum key distribution (QKD) aims to generate secure private keys shared by two remote parties. With its security being protected by principles of quantum mechanics, some technology challenges remain towards practical application of QKD. The major one is the distance limit, which is caused by the fact that a quantum signal cannot be amplified while the cha

Kana Moriwaki, Takahiro Nishimichi, Naoki Yoshida

An array of large observational programs using ground-based and space-borne telescopes is planned in the next decade. The forthcoming wide-field sky surveys are expected to deliver a sheer volume of data exceeding an exabyte. Processing the large amount of multiplex astronomical data is technically challenging, and fully automated technologies based on machi

Pravin K. Dahal, Fil Simovic, Ioannis Soranidis, Daniel R. Terno

Working in a semi-classical setting, we consider solutions of the Einstein equations that exhibit light trapping in finite time according to distant observers. In spherical symmetry, we construct near-horizon quantities from the assumption of regularity of the renormalized expectation value of the energy-momentum tensor, and derive explicit coordinate transf

Yuval Becker, Raz Z. Nossek, Tomer Peleg

Image demosaicing is an important step in the image processing pipeline for digital cameras. In data centric approaches, such as deep learning, the distribution of the dataset used for training can impose a bias on the networks' outcome. For example, in natural images most patches are smooth, and high-content patches are much rarer. This can lead to a bias i

Akara Supratak, Peter Haddawy

Deep learning models for scoring sleep stages based on single-channel EEG have been proposed as a promising method for remote sleep monitoring. However, applying these models to new datasets, particularly from wearable devices, raises two questions. First, when annotations on a target dataset are unavailable, which different data characteristics affect the s

Morten Nielsen

We study a class of almost diagonal matrices compatible with the mixed-norm $\alpha$-modulation spaces $M_{\vec{p},q}^{s,\alpha}(\mathbb{R}^n)$, $\alpha\in [0,1]$, introduced recently by Cleanthous and Georgiadis [Trans.\ Amer.\ Math.\ Soc.\ 373 (2020), no. 5, 3323-3356]. The class of almost diagonal matrices is shown to be closed under matrix multiplication

M. Achasov, X. C. Ai, R. Aliberti, L. P. An

The Super $\tau$-Charm facility (STCF) is an electron-positron collider proposed by the Chinese particle physics community. It is designed to operate in a center-of-mass energy range from 2 to 7 GeV with a peak luminosity of $0.5\times 10^{35}{\rm cm}^{-2}{\rm s}^{-1}$ or higher. The STCF will produce a data sample about a factor of 100 larger than that by t

Tao Yang, Yuan Ma, Peng Zhang

Small-scale flickering buoyant diffusion flames in externally swirling flows were computationally investigated with a particular interest in identifying and characterizing various distinct dynamical behaviors of the flame oscillators under different swirling flow conditions. By varying the external swirl, six distinct flame dynamical modes, such as the flick

Tikhon Pshenitsyn

We study how to relate well-known hypergraph grammars based on the double pushout (DPO) approach and grammars over the hypergraph Lambek calculus HL (called HL-grammars). It turns out that DPO rules can be naturally encoded by types of HL using methods similar to those used by Kanazawa for multiplicative-exponential linear logic. In order to generalize his r

Nathan Couchet, Robert Yuncken

Originally, the noncommutative residue was studied in the 80's by Wodzicki in his thesis and Guillemin. In this article we give a definition of the Wodzicki residue, using the langage of r-fibered distributions in the context of filtered manifolds. We show that this groupoidal residue behaves like a trace on the algebra of pseudodifferential operators on fil

Shan Ning, Longtian Qiu, Yongfei Liu, Xuming He

Human-Object Interaction (HOI) detection aims to localize human-object pairs and recognize their interactions. Recently, Contrastive Language-Image Pre-training (CLIP) has shown great potential in providing interaction prior for HOI detectors via knowledge distillation. However, such approaches often rely on large-scale training data and suffer from inferior

A. V. Ivanov

The paper is devoted to a local heat kernel, which is a special part of the standard heat kernel. Locality means that all considerations are produced in an open convex set of a smooth Riemannian manifold. We study such properties and concepts as uniqueness, a symmetry of the Seeley-DeWitt coefficients, extension on the whole manifold, a family of special fun

Stephen Mell, Osbert Bastani, Steve Zdancewic

We introduce Ideograph, a language for expressing and manipulating structured data. Its types describe kinds of structures, such as natural numbers, lists, multisets, binary trees, syntax trees with variable binding, directed multigraphs, and relational databases. Fully normalized terms of a type correspond exactly to members of the structure, analogous to a

Vincent van Oostrom

We introduce proof terms for string rewrite systems and, using these, show that various notions of equivalence on reductions known from the literature can be viewed as different perspectives on the notion of causal equivalence. In particular, we show that permutation equivalence classes (as known from the lambda-calculus and term rewriting) are uniquely repr

Nick Heppert, Muhammad Zubair Irshad, Sergey Zakharov, Katherine Liu

We present CARTO, a novel approach for reconstructing multiple articulated objects from a single stereo RGB observation. We use implicit object-centric representations and learn a single geometry and articulation decoder for multiple object categories. Despite training on multiple categories, our decoder achieves a comparable reconstruction accuracy to metho

Li-Na Hu, Orkash Amat, Li Wang, Adiljan Sawut

Spirals in multiphoton pair production are revisited by two counter-rotating fields with time delay for different cycles in pulse. Novel findings include that for subcycle fields, the remarkable spiral structure in the momentum spectrum can be still caused by a large time delay compared to the previous study for supercycle case where it is easier to be gener

Hiromichi Kamata, Yuiko Sakuma, Akio Hayakawa, Masato Ishii

We propose a high-quality 3D-to-3D conversion method, Instruct 3D-to-3D. Our method is designed for a novel task, which is to convert a given 3D scene to another scene according to text instructions. Instruct 3D-to-3D applies pretrained Image-to-Image diffusion models for 3D-to-3D conversion. This enables the likelihood maximization of each viewpoint image a

Karen Sargsyan

The paper discusses the limitations of deep learning models in identifying and utilizing features that remain invariant under a bijective transformation on the data entries, which we refer to as combinatorial patterns. We argue that the identification of such patterns may be important for certain applications and suggest providing neural networks with inform

Juan-Pablo Ortega, Daiying Yin

A complete structure-preserving learning scheme for single-input/single-output (SISO) linear port-Hamiltonian systems is proposed. The construction is based on the solution, when possible, of the unique identification problem for these systems, in ways that reveal fundamental relationships between classical notions in control theory and crucial properties in

Jeppe R. Andersen, Bertrand Ducloué, Conor Elrick, Hitham Hassan

We present version 2.2 of the High Energy Jets (HEJ) Monte Carlo event generator for hadronic scattering processes at high energies. The new version adds support for two further processes of central phenomenological interest, namely the production of a W boson pair with equal charge together with two or more jets and the production of a Higgs boson with at l

Yameng Wang, Yi Wan, Yongjun Zhang, Bin Zhang

Despite the good results that have been achieved in unimodal segmentation, the inherent limitations of individual data increase the difficulty of achieving breakthroughs in performance. For that reason, multi-modal learning is increasingly being explored within the field of remote sensing. The present multi-modal methods usually map high-dimensional features

Luis Crespo Ruiz

Let $Ass_k(n)$ denote the simplicial complex of $(k+1)$-crossing-free subsets of edges in $\binom{n}{2}$. Here $k,n\in \mathbb{N}$ and $n\ge 2k+1$. It is conjectured that this simplicial complex is polytopal (Jonsson 2005). However, despite several recent advances, this is still an open problem. In this paper we attack this problem using as a vector configur

Jan-Henrik Metsch

We establish the existence and smoothness of minimizers of the Willmore energy among axially symmetric surfaces of spherical type with prescribed isoperimetric ratio. Afterwards, we study the behavior of these minimizers as the isoperimetric ratio tends to zero.

Gongcheng Yue, Yang Li

Optical phased arrays (OPAs) with high speed, low power consumption, and low insertion loss are appealing for many applications, such as light detection and ranging, free-space communication, image projection, and imaging. These OPAs can be achieved by fully leveraging the high electro-optic modulation speed, low driving voltage, and low optical loss of inte

Luca Razzoli, Fabio Cavaliere, Matteo Carrega, Maura Sassetti

In the quest for high-performance quantum thermal machines, looking for an optimal thermodynamic efficiency is only part of the issue. Indeed, at the level of quantum devices, fluctuations become extremely relevant and need to be taken into account. In this paper we study the thermodynamic uncertainty relations for a quantum thermal machine with a quantum ha

Nafisa Shamim Rafa, Basma Binte Azmal, Abdur Rab Dhruba, Mohammad Monirujjaman Khan

COVID19 and asthma are respiratory diseases that can be life threatening in uncontrolled circumstances and require continuous monitoring. A poverty stricken South Asian country like Bangladesh has been bearing the brunt of the COVID19 pandemic since its beginning. The majority of the country's population resides in rural areas, where proper healthcare is dif

Mina Karimi, Kaushik Dayal, Matteo Pozzi

We investigate the effect of using local and non-local second derivative information on the performance of Hamiltonian Monte Carlo (HMC) sampling methods, for high-dimension non-Gaussian distributions, with application to Bayesian inference and nonlinear inverse problems. The Riemannian Manifold Hamiltonian Monte Carlo (RMHMC) method uses second and third de

Rishi Bommasani, Dilara Soylu, Thomas I. Liao, Kathleen A. Creel

Foundation models (e.g. ChatGPT, StableDiffusion) pervasively influence society, warranting immediate social attention. While the models themselves garner much attention, to accurately characterize their impact, we must consider the broader sociotechnical ecosystem. We propose Ecosystem Graphs as a documentation framework to transparently centralize knowledg

Xiangyun Meng, Nathan Hatch, Alexander Lambert, Anqi Li

Effective use of camera-based vision systems is essential for robust performance in autonomous off-road driving, particularly in the high-speed regime. Despite success in structured, on-road settings, current end-to-end approaches for scene prediction have yet to be successfully adapted for complex outdoor terrain. To this end, we present TerrainNet, a visio

Peng Geng, Huizhen Hao, Annan Yang, Yan Liu

In the understanding of important edges in complex networks, the edges with larger degree are naturally considered more important, and they will cause greater destructiveness when attacked. However, through simulation analysis, we conclude that this understanding needs to be based on certain preconditions. In this article, the robustness of BA scale-free net

Xiaoyue Li, Kai Shang, Gaoang Wang, Mark D. Butala

Reducing the radiation dose in computed tomography (CT) is important to mitigate radiation-induced risks. One option is to employ a well-trained model to compensate for incomplete information and map sparse-view measurements to the CT reconstruction. However, reconstruction from sparsely sampled measurements is insufficient to uniquely characterize an object

Kotomi Taniguchi, Prasanta Gorai, Jonathan C. Tan

The presence of carbon-chain molecules in the interstellar medium (ISM) has been known since the early 1970s and $>130$ such species have been identified to date, making up $\sim 43$% of the total of detected ISM molecules. They are prevalent not only in star-forming regions in our Galaxy but also in other galaxies. These molecules provide important informat

Jaeseong Lee, Taewoo Kim, Sunghyun Park, Younggun Lee

Face swapping aims at injecting a source image's identity (i.e., facial features) into a target image, while strictly preserving the target's attributes, which are irrelevant to identity. However, we observed that previous approaches still suffer from source attribute leakage, where the source image's attributes interfere with the target image's. In this pap

Francesco Benetti, Andrea Lapi, Giovanni Gandolfi, Paolo Salucci

[abridged] We explore the possibility that the dark matter (DM) component in galaxies may originate fractional gravity. In such a framework, the standard law of inertia continues to hold, but the gravitational potential associated to a given DM density distribution is determined by a modified Poisson equation including fractional derivatives (i.e., derivativ

Jiaxuan Wang

We introduce the the fractional Laplacian on a subgraph of a graph with Dirichlet boundary condition. For a lattice graph, we prove the upper and lower estimates for the sum of the first $k$ Dirichlet eigenvalues of the fractional Laplacian, extending the classical results by Li-Yau and Kr\"{o}ger.

James F. Davis, Wolfgang Lueck

We consider the problem of whether, for a given virtually torsionfree discrete group $\Gamma$, there exists a cocompact proper topological $\Gamma$-manifold, which is equivariantly homotopy equivalent to the classifying space for proper actions. This problem is related to Nielsen Realization. We will make the assumption that the expected manifold model has a

Yiwei Ma, Xiaioqing Zhang, Xiaoshuai Sun, Jiayi Ji

Text-driven 3D stylization is a complex and crucial task in the fields of computer vision (CV) and computer graphics (CG), aimed at transforming a bare mesh to fit a target text. Prior methods adopt text-independent multilayer perceptrons (MLPs) to predict the attributes of the target mesh with the supervision of CLIP loss. However, such text-independent arc

Hao Xu, Shuang Song, Ze Mao

Throughput-oriented computing via co-running multiple applications in the same machine has been widely adopted to achieve high hardware utilization and energy saving on modern supercomputers and data centers. However, efficiently co-running applications raises new design challenges, mainly because applications with diverse requirements can stress out shared

Shlomi Steinberg, Ravi Ramamoorthi, Benedikt Bitterli, Eugene d'Eon

Under ray-optical light transport, the classical ray serves as a linear and local "point query" of light's behaviour. Linearity and locality are crucial to the formulation of sophisticated path tracing and sampling techniques, that enable efficient solutions to light transport problems in complex, real-world settings and environments. However, such formulati

Xinyi Ye, Weiyue Zhao, Hao Lu, Zhiguo Cao

Correspondence pruning aims to search consistent correspondences (inliers) from a set of putative correspondences. It is challenging because of the disorganized spatial distribution of numerous outliers, especially when putative correspondences are largely dominated by outliers. It's more challenging to ensure effectiveness while maintaining efficiency. In t

Jingbo Wang

Topological order (long-range entanglement) is a new type of order that beyond Landau's symmetry breaking theory. This concept plays important roles in modern condensed matter physics. The topological entanglement entropy provides a universal quantum number to characterize the topological order in a system. The topological entanglement entropy of the BTZ bla

Haoxiang Luo, Xiangyue Yang, Hongfang Yu, Gang Sun

Due to advantages in security and privacy, blockchain is considered a key enabling technology to support 6G communications. Practical Byzantine Fault Tolerance (PBFT) is seen as the most applicable consensus mechanism in blockchain-enabled wireless networks. However, previous studies on PBFT do not consider the channel performance of the physical layer, such

Leping Li, Hui Tian, Huadong Chen, Hongqiang Song

How structures, e.g., magnetic loops, in the upper atmosphere, i.e., the transition region and corona, are heated and sustained is one of the major unresolved issues in solar and stellar physics. Various theoretical and observational studies on the heating of coronal loops have been undertaken. The heating of quiescent loops caused by eruptions is, however,

Peter Kling, Enno Giese

Despite exhibiting novel radiation features, the operation of the proposed quantum free-electron laser would have the drawback that the number of emitted photons is limited by one per electron, significantly reducing the output power of such a device. We show that relying on different resonances of the initial momentum of the electrons increases the number o

Thomas Selig, Haoyue Zhu

We study a link between complete non-ambiguous trees (CNATs) and permutations exhibited by Daniel Chen and Sebastian Ohlig in recent work. In this, they associate a certain permutation to the leaves of a CNAT, and show that the number of $n$-permutations that are associated with exactly one CNAT is $2^{n-2}$. We connect this to work by the first author and c

Nathan Keller, Noam Lifshitz, Dor Minzer, Ohad Sheinfeld

We prove that there exists a constant $c_0$ such that for any $t \in \mathbb{N}$ and any $n\geq c_0 t$, if $A \subset S_n$ is a $t$-intersecting family of permutations then$|A|\leq (n-t)!$. Furthermore, if $|A|\ge 0.75(n-t)!$ then there exist $i_1,\ldots,i_t$ and $j_1,\ldots,j_t$ such that $\sigma(i_1)=j_1,\ldots,\sigma(i_t)=j_t$ holds for any $\sigma \in A$

Jianping Zhang, Yizhan Huang, Weibin Wu, Michael R. Lyu

Vision transformers (ViTs) have been successfully deployed in a variety of computer vision tasks, but they are still vulnerable to adversarial samples. Transfer-based attacks use a local model to generate adversarial samples and directly transfer them to attack a target black-box model. The high efficiency of transfer-based attacks makes it a severe security

Oto Brzobohaty, Martin Duchan, Petr Jakl, Jan Jezek

We explore, experimentally and theoretically, the emergence of coherent coupled oscillations and synchronization between a pair of non-Hermitian, stochastic, opto-mechanical oscillators, levitated in vacuum. Each oscillator consists of a polystyrene microsphere trapped in a circularly polarized, counter-propagating Gaussian laser beam. Non-conservative, azim

Tianze Hao, Yuhao Hu, Peng Liu, Yuguang Shi

We show that the hyperbolic manifold $\mathbb{H}^n/\mathbb{Z}^{n-2}$ is not rigid under all compactly supported deformations that preserve the scalar curvature lower bound $-n(n-1)$, and that it is rigid under deformations that are further constrained by certain topological conditions. In addition, we prove two related splitting results.

Funa Hanada, Nobuyuki Sawado

We consider a baby--Skyrme model with Dzyaloshinskii--Moriya interaction (DMI) and two types of potential terms. The model has a close connection with the vacuum functional of fermions coupled with $O(3)$ nonlinear $\bm{n}$-fields and with a constant $SU(2)$ gauge background. The energy functional is derived from the heat-kernel expansion for the fermion det

Ozelot Vanilla, Jingxiang Yu, Hemn Barzan Abdalla

Programming languages are incredibly versatile, enabling developers to create applications and programs that suit their individual requirements. This article introduces a new language called Cesno, designed from the ground up to offer an advanced, user-friendly, and easy-to-use programming environment. Cesno's syntax is similar to other popular languages, ma

Hongyi Wang, Luyang Luo, Fang Wang, Ruofeng Tong

Whole Slide Image (WSI) classification remains a challenge due to their extremely high resolution and the absence of fine-grained labels. Presently, WSI classification is usually regarded as a Multiple Instance Learning (MIL) problem when only slide-level labels are available. MIL methods involve a patch embedding module and a bag-level classification module

Marco Nittscher, Michael Lameter, Riccardo Barbano, Johannes Leuschner

The deep image prior (DIP) is a well-established unsupervised deep learning method for image reconstruction; yet it is far from being flawless. The DIP overfits to noise if not early stopped, or optimized via a regularized objective. We build on the regularized fine-tuning of a pretrained DIP, by adopting a novel strategy that restricts the learning to the a

Eric Chu, Jacob Andreas, Stephen Ansolabehere, Deb Roy

Public opinion reflects and shapes societal behavior, but the traditional survey-based tools to measure it are limited. We introduce a novel approach to probe media diet models -- language models adapted to online news, TV broadcast, or radio show content -- that can emulate the opinions of subpopulations that have consumed a set of media. To validate this m

Soma Onishi, Kenta Oono, Kohei Hayashi

We present \emph{TabRet}, a pre-trainable Transformer-based model for tabular data. TabRet is designed to work on a downstream task that contains columns not seen in pre-training. Unlike other methods, TabRet has an extra learning step before fine-tuning called \emph{retokenizing}, which calibrates feature embeddings based on the masked autoencoding loss. In

Raul Astudillo, Zhiyuan Jerry Lin, Eytan Bakshy, Peter I. Frazier

Preferential Bayesian optimization (PBO) is a framework for optimizing a decision maker's latent utility function using preference feedback. This work introduces the expected utility of the best option (qEUBO) as a novel acquisition function for PBO. When the decision maker's responses are noise-free, we show that qEUBO is one-step Bayes optimal and thus equ

Arunav Gupta, Rohit Mishra, William Luu, Mehdi Bouassami

In this technical report, we explore the behavior of Recursive Feature Machines (RFMs), a type of novel kernel machine that recursively learns features via the average gradient outer product, through a series of experiments on regression datasets. When successively adding random noise features to a dataset, we observe intriguing patterns in the Mean Squared

Jia Zhao, Yu Qiao, Senrong Xu

In this paper, we introduce the notion of crossed homomorphisms between Lie-Yamaguti algebras and establish the cohomology theory of crossed homomorphisms via the Yamaguti cohomology. Consequently, we use this cohomology to characterize linear deformations of crossed homomorphisms between Lie-Yamaguti algebras. We show that if two linear or formal deformatio

Linfang Zheng, Chen Wang, Yinghan Sun, Esha Dasgupta

In this paper, we focus on the problem of category-level object pose estimation, which is challenging due to the large intra-category shape variation. 3D graph convolution (3D-GC) based methods have been widely used to extract local geometric features, but they have limitations for complex shaped objects and are sensitive to noise. Moreover, the scale and tr

Lin Geng Foo, Jia Gong, Zhipeng Fan, Jun Liu

Recent years have witnessed great progress in deep neural networks for real-time applications. However, most existing works do not explicitly consider the general case where the device's state and the available resources fluctuate over time, and none of them investigate or address the impact of varying computational resources for online video understanding t

W. Bednarek, J. Sitarek

Novae were discovered to emit transient gamma rays during the period of several days to a few weeks after initial explosion, indicating presence of acceleration processes of particles in their expanding shells. In the case of recurrent novae, electrons can be in principle accelerated in the nova shells for the whole recurrence period of nova producing delaye

Zaiwei Chen, Siva Theja Maguluri, Martin Zubeldia

In this paper, we establish maximal concentration bounds for the iterates generated by a stochastic approximation (SA) algorithm under a contractive operator with respect to some arbitrary norm (for example, the $\ell_\infty$-norm). We consider two settings where the iterates are potentially unbounded: SA with bounded multiplicative noise and SA with sub-Gau

Shuya Nagayasu, Sumio Watanabe

In many research fields in artificial intelligence, it has been shown that deep neural networks are useful to estimate unknown functions on high dimensional input spaces. However, their generalization performance is not yet completely clarified from the theoretical point of view because they are nonidentifiable and singular learning machines. Moreover, a ReL

Tetsuya Ito, Kimihiko Motegi, Masakazu Teragaito

Each $r$-Dehn filling of the exterior $E(K)$ of a knot $K$ in $S^3$ produces a $3$-manifold $K(r)$, and induces an epimorphism from the knot group $G(K) = \pi_1(E(K))$ to $\pi_1(K(r))$, which trivializes elements in its kernel. To each element $g \in G(K)$, consider all the non-trivial Dehn fillings and assign $\mathcal{S}_K(g) = \{ r \in \mathbb{Q} \mid \te

Tao He, Sheng Huang, Wenhao Tang, Bo Liu

Scene text detection is a challenging computer vision task due to the high variation in text shapes and ratios. In this work, we propose a scene text detector named Deformable Kernel Expansion (DKE), which incorporates the merits of both segmentation and contour-based detectors. DKE employs a segmentation module to segment the shrunken text region as the tex

Amr S. Mohamed, Deepa Kundur

The electric grid is an attractive target for cyberattackers given its critical nature in society. With the increasing sophistication of cyberattacks, effective grid defense will benefit from proactively identifying vulnerabilities and attack strategies. We develop a deep reinforcement learning-based method that recognizes vulnerabilities in load frequency c

Shallu Sharma, Iqbal Kour, Pooja Saproo

The concept of cone metric spaces with $w-$distance was introduced by H. Lakzian and F. Arabyani [16] in $2009.$ In $2020,$ Branga and Olaru [4] put forth the idea of cone metric spaces over topological module. In this paper, we compose these concepts together and introduce cone metric spaces with $w-$distance over topological module and then prove some fixe

Jianping Zhang, Jen-tse Huang, Wenxuan Wang, Yichen Li

Deep neural networks have achieved unprecedented success on diverse vision tasks. However, they are vulnerable to adversarial noise that is imperceptible to humans. This phenomenon negatively affects their deployment in real-world scenarios, especially security-related ones. To evaluate the robustness of a target model in practice, transfer-based attacks cra

Sunwoong Yang, Kwanjung Yee

This study aims to comprehensively investigate the deep ensemble approach, an approximate Bayesian inference, in the multi-output regression task for predicting the aerodynamic performance of a missile configuration. To this end, the effect of the number of neural networks used in the ensemble, which has been blindly adopted in previous studies, is scrutiniz

Ibrahim Khan, Thai Van Nguyen, Chollakorn Nimpattanavong, Ruck Thawonmas

This paper presents our work to enhance the background music (BGM) in DareFightingICE by adding an adaptive BGM. The adaptive BGM consists of five different instruments playing a classical music piece called "Air on G-String." The BGM adapts by changing the volume of the instruments. Each instrument is connected to a different element of the game. We then ru

Xin Tong, Shing Shin Cheng

This paper is about the construction of a family of centrally synergistic potential functions from a single modified trace function on SO(3). First, we demonstrate that it is possible to complete the construction through angular warping with multiple directions, particularly effective in the unresolved cases in the literature. Second, it can be shown that fo

Farhan Rozaidi, Emma Waters, Olivia Dawes, Jennifer Yang

Snake robots are characterized by their ability to navigate through small spaces and loose terrain by utilizing efficient cyclic forms of locomotion. Soft snake robots are a subset of these robots which utilize soft, compliant actuators to produce movement. Prior work on soft snake robots has primarily focused on planar gaits, such as undulation. More effici

Tiago Koketsu Rodrigues, Shikhar Verma, Yuichi Kawamoto, Nei Kato

WiGig networks and 60 GHz frequency communications have a lot of potential for commercial and personal use. They can offer extremely high transmission rates but at the cost of low range and penetration. Due to these issues, WiGig systems are unstable and need to rely on frequent handovers to maintain high-quality connections. However, this solution is proble

Siyu Lv, Xiao Yang

This paper studies a class of zero-sum stopping game in a regime switching model. A verification theorem as a sufficient criterion for Nash equilibriums is established based on a set of variational inequalities (VIs). Under an appropriate regularity condition for solutions to the VIs, a suitable system of algebraic equations is derived via the so-called smoo

Hoa T. Nguyen, Muhammad Usman, Rajkumar Buyya

Today's quantum computers are primarily accessible through the cloud and potentially shifting to the edge network in the future. With the rapid advancement and proliferation of quantum computing research worldwide, there has been a considerable increase in demand for using cloud-based quantum computation resources. This demand has highlighted the need for de

Peiang Zhao, Han Li, Ruiyang Jin, S. Kevin Zhou

Universal Lesion Detection (ULD) in computed tomography (CT) plays an essential role in computer-aided diagnosis. Promising ULD results have been reported by anchor-based detection designs, but they have inherent drawbacks due to the use of anchors: i) Insufficient training targets and ii) Difficulties in anchor design. Diffusion probability models (DPM) hav

Bishal Lamichhane

Large language models (LLM) have been successful in several natural language understanding tasks and could be relevant for natural language processing (NLP)-based mental health application research. In this work, we report the performance of LLM-based ChatGPT (with gpt-3.5-turbo backend) in three text-based mental health classification tasks: stress detectio

Mingsong Zhang, Bin Peng, Wanli Zhang, Wenxu Zhang

In order to understand the anti-site disorder effect on the anisotropic magnetoresistance (AMR) effect in alloys, $\rm{Fe}_{50}Co_{50}$ alloys were studied in this work using the fully relativistic spin-polarized screened (KKR) method. The anti-site effect was modeled by interchanging Fe and Co atoms and treated by the coherent potential approximation (CPA).

Zhihang Li, Zhao Song, Tianyi Zhou

In modern machine learning, attention computation is a fundamental task for training large language models such as Transformer, GPT-4 and ChatGPT. In this work, we study exponential regression problem which is inspired by the softmax/exp unit in the attention mechanism in large language models. The standard exponential regression is non-convex. We study the

Satoshi Ikehata

In this paper, we introduce SDM-UniPS, a groundbreaking Scalable, Detailed, Mask-free, and Universal Photometric Stereo network. Our approach can recover astonishingly intricate surface normal maps, rivaling the quality of 3D scanners, even when images are captured under unknown, spatially-varying lighting conditions in uncontrolled environments. We have ext

Mark Whitmeyer, Kun Zhang

We revisit Popper's falsifiability criterion. A tester hires a potential expert to produce a theory, offering payments contingent on the observed performance of the theory. In our model, instead of knowing the true data-generating process, the expert knows the state-of-the-art belief over data-generating processes. A non-expert does not. We argue that if the

Weiwei He, Yan Yin, Qihua Gong, Richard F. L. Evans

Two-dimensional magnets could potentially revolutionize information technology, but their potential application to cooling technology and magnetocaloric effect (MCE) in a material down to the monolayer limit remain unexplored. Herein, we reveal through multiscale calculations the existence of giant MCE and its strain tunability in monolayer magnets such as C

Amin Shafiee, Benoit Charbonnier, Sudeep Pasricha, Mahdi Nikdast

The integration of silicon photonics (SiPh) and phase change materials (PCMs) has created a unique opportunity to realize adaptable and reconfigurable photonic systems. In particular, the nonvolatile programmability in PCMs has made them a promising candidate for implementing optical memory systems. In this paper, we describe the design of an optical memory

Zhiyong Cheng, Sai Han, Fan Liu, Lei Zhu

Multi-behavior recommendation, which exploits auxiliary behaviors (e.g., click and cart) to help predict users' potential interactions on the target behavior (e.g., buy), is regarded as an effective way to alleviate the data sparsity or cold-start issues in recommendation. Multi-behaviors are often taken in certain orders in real-world applications (e.g., cl

Borui Miao, Yi Zhu

Honeycomb structures lead to conically degenerate points on the dispersion surfaces. These spectral points, termed as Dirac points, are responsible for various topological phenomena. In this paper, we investigate the generalized honeycomb-structured materials, which have six inclusions in a hexagonal cell. We obtain the asymptotic band structures and corresp

Congyi Wang, Feida Zhu, Shilei Wen

Existing methods proposed for hand reconstruction tasks usually parameterize a generic 3D hand model or predict hand mesh positions directly. The parametric representations consisting of hand shapes and rotational poses are more stable, while the non-parametric methods can predict more accurate mesh positions. In this paper, we propose to reconstruct meshes

Haibo Gou, Huanyin Chen

We investigate the Hirano invertibility of block-operator matrices in Banach algebras, and obtain the Hirano inverse of matrix $\begin{bmatrix} A&B\\ C&D \end{bmatrix}$ under two types of new perturbation conditions. Furthermore, we provide a new operator matrix decomposed into the sum of tripotent and nilpotent elements on Banach spaces.