July 2022 arXiv papers — page 74

Yitao Peng, Longzhen Yang, Yihang Liu, Lianghua He

The Class Activation Map (CAM) lookup of a neural network tells us to which regions the neural network focuses when it makes a decision. In the past, the CAM search method was dependent upon a specific internal module of the network. It has specific constraints on the structure of the neural network. To make the search of CAM have generality and high perform

Xing-Guo Ye, Peng-Fei Zhu, Wen-Zheng Xu, Zhihao Zang

The anomalous Hall effect (AHE) has been extended into the nonlinear regime, where the Hall voltage shows higher-order response to the applied current. Nevertheless, the microscopic mechanism of the nonlinear AHE remains unclear. Here we report the orbital polarization and its induced third-order AHE in few-layer WTe2 flakes. Through angle-dependent electric

Xiangyu Yin, Wenjie Ruan, Jonathan Fieldsend

The adversarial attack can force a CNN-based model to produce an incorrect output by craftily manipulating human-imperceptible input. Exploring such perturbations can help us gain a deeper understanding of the vulnerability of neural networks, and provide robustness to deep learning against miscellaneous adversaries. Despite extensive studies focusing on the

Stefan Steinerberger

We discuss a phenomenon where Optimal Transport leads to a remarkable amount of combinatorial regularity. Consider infinite sequences $(x_k)_{k=1}^{\infty}$ in $[0,1]$ constructed in a greedy manner: given $x_1, \dots, x_n$, the new point $x_{n+1}$ is chosen so as to minimize the Wasserstein distance $W_2$ between the empirical measure of the $n+1$ points an

Theodosis Alexandrou

Let $X$ be a Godeaux surface over $\mathbb{C}$ and $q_{X}\colon Y\to X$ be its universal cover. We show that the pullback map $q^{*}_{X}\colon Br(X)\to Br(Y)$ is injective if $\rho(Y)=9$. Our arguments rely on a degeneration technique that also applies to other examples.

Naichen Shi, Raed Al Kontar

In this paper, we tackle a significant challenge in PCA: heterogeneity. When data are collected from different sources with heterogeneous trends while still sharing some congruency, it is critical to extract shared knowledge while retaining the unique features of each source. To this end, we propose personalized PCA (PerPCA), which uses mutually orthogonal g

Thesath Nanayakkara

There has been considerable interest in leveraging RL and stochastic control methods to learn optimal treatment strategies for critically ill patients, directly from observational data. However, there is significant ambiguity on the control objective and on the best reward choice for the standard RL objective. In this work, we propose a clinically motivated

Ivan Motorin

We construct a Springer-type resolution of singularities of the odd nilpotent cone of the orthosymplectic Lie superalgebras osp(m|2n).

Shusen Ding, Dylan Helliwell, Gavin Pandya, Arya Yae

We establish constructive geometric tools for determining when a domain is $L^s$-averaging and obtain upper and lower bounds for the $L^s$-integrals of the quasihyperbolic distance. We also construct examples which are helpful to understand our geometric tools and the relationship between $p$-Poincar\'{e} domains and $L^s$-averaging domains. Finally, finite

Siavash Ameli, Shawn C. Shadden

We study a matrix that arises from a singular form of the Woodbury matrix identity. We present generalized inverse and pseudo-determinant identities for this matrix, which have direct applications for Gaussian process regression, specifically its likelihood representation and precision matrix. We extend the definition of the precision matrix to the Bott-Duff

Rie Kamikubo, Lining Wang, Crystal Marte, Amnah Mahmood

As data-driven systems are increasingly deployed at scale, ethical concerns have arisen around unfair and discriminatory outcomes for historically marginalized groups that are underrepresented in training data. In response, work around AI fairness and inclusion has called for datasets that are representative of various demographic groups. In this paper, we c

Shawkh Ibne Rashid, Elham Shakibapour, Mehran Ebrahimi

Spatial resolution of medical images can be improved using super-resolution methods. Real Enhanced Super Resolution Generative Adversarial Network (Real-ESRGAN) is one of the recent effective approaches utilized to produce higher resolution images, given input images of lower resolution. In this paper, we apply this method to enhance the spatial resolution o

Fraser Binns, Subhankar Dey

We apply sutured Floer homology techniques to study the knot and link Floer homologies of various links with annuli embedded in their exteriors. Our main results include, for large $m$, characterizations of links with the same link Floer homology as $(n,nm)$-cables of $L$-space knots or the same knot Floer homology as $(2,2m)$-cables of $L$-space knots. Note

Amro Abbas, Stéphane Deny

Deep networks should be robust to rare events if they are to be successfully deployed in high-stakes real-world applications (e.g., self-driving cars). Here we study the capability of deep networks to recognize objects in unusual poses. We create a synthetic dataset of images of objects in unusual orientations, and evaluate the robustness of a collection of

Konstantin Zimenko, Denis Efimov, Andrey Polyakov

Hyperexponential stability is investigated for dynamical systems with the use of both, explicit and implicit, Lyapunov function methods. A nonlinear hyperexponential control is designed for stabilizing linear systems. The tuning procedure is formalized in LMI form. Through numeric experiments, it is observed that the proposed hyperexponential control is less

Mahmoudreza Moghimhanjani, Ali Taghavirashidizadeh

Medical imaging is the most important tool for detecting complications in the inner body of medicine. Nowadays, with the development of image processing technology as well as changing the size of photos to higher resolution images in the field of digital medical imaging, there is an efficient and accurate system for segmenting this. Real-world images that fo

Tim Alderson, Benjamin Morine

A combinatorial problem concerning the maximum size of the (hamming) weight set of an $[n,k]_q$ linear code was recently introduced. Codes attaining the established upper bound are the Maximum Weight Spectrum (MWS) codes. Those $[n,k]_q $ codes with the same weight set as $ \mathbb{F}_q^n $ are called Full Weight Spectrum (FWS) codes. FWS codes are necessari

Thomas Karam

Let $d \ge 2$ be a positive integer. We show that for a class of notions $R$ of rank for order-$d$ tensors, which includes in particular the tensor rank, the slice rank and the partition rank, there exist functions $F_{d,R}$ and $G_{d,R}$ such that if an order-$d$ tensor has $R$-rank at least $G_{d,R}(l)$ then we can restrict its entries to a product of sets

Joseanne Viana, Hamed Farkhari, Luis Miguel Campos, Pedro Sebastiao

When users exchange data with Unmanned Aerial vehicles - (UAVs) over air-to-ground (A2G) wireless communication networks, they expose the link to attacks that could increase packet loss and might disrupt connectivity. For example, in emergency deliveries, losing control information (i.e data related to the UAV control communication) might result in accidents

Chang Liu, Keith Julien, Edgar Knobloch

Bifurcation analysis of confined salt-finger convection using single-mode equations obtained from a severely truncated Fourier expansion in the horizontal is performed. Strongly nonlinear staircase-like solutions having, respectively, one (S1), two (S2) and three (S3) regions of mixed salinity in the vertical direction are computed using numerical continuati

Kyle Bushick, Emmanouil Kioupakis

We present a consistent first-principles methodology to study both direct and phonon-assisted Auger-Meitner recombination (AMR) in indirect-gap semiconductors that we apply to investigate the microscopic origin of AMR processes in silicon. Our results are in excellent agreement with experimental measurements and show that phonon-assisted contributions domina

Jeffrey Uhlmann

Extends previous work on block-partitioned mixed generalized inverses from two subsets of system variables with distinct consistency requirements to three subsets. Does not include any notable theoretical contributions.

Jakub Bober, Anthea Monod, Emil Saucan, Kevin N. Webster

Information over-squashing is a phenomenon of inefficient information propagation between distant nodes on networks. It is an important problem that is known to significantly impact the training of graph neural networks (GNNs), as the receptive field of a node grows exponentially. To mitigate this problem, a preprocessing procedure known as rewiring is often

Mujahid I. Ashqer, Huthaifa I. Ashqar, Mohammed Elhenawy, Mohammed Almannaa

This paper presents a novel method to compute various measures of effectiveness (MOEs) at a signalized intersection using vehicle trajectory data collected by flying drones. MOEs are key parameters in determining the quality of service at signalized intersections. Specifically, this study investigates the use of drone raw data at a busy three-way signalized

Sumanth Gurram, Andy Fang, David Chan, John Canny

Generating representations of video data is of key importance in advancing the field of machine perception. Most current techniques rely on hand-annotated data, which can be difficult to work with, expensive to generate, and hard to scale. In this work, we propose a novel learning approach based on contrastive learning, LAVA, which is capable of learning joi

Daniel T. Chang

Deep learning for molecular science has so far mainly focused on 2D molecular graphs. Recently, however, there has been work to extend it to 3D molecular geometry, due to its scientific significance and critical importance in real-world applications. The 3D distance-geometric graph representation (DG-GR) adopts a unified scheme (distance) for representing th

Joshua Fan, Di Chen, Jiaming Wen, Ying Sun

Monitoring vegetation productivity at extremely fine resolutions is valuable for real-world agricultural applications, such as detecting crop stress and providing early warning of food insecurity. Solar-Induced Chlorophyll Fluorescence (SIF) provides a promising way to directly measure plant productivity from space. However, satellite SIF observations are on

Srirangan Madhavan, Anwesan Pal, Henrik I. Christensen

Deep reinforcement learning approaches have been a popular method for visual navigation tasks in the computer vision and robotics community of late. In most cases, the reward function has a binary structure, i.e., a large positive reward is provided when the agent reaches goal state, and a negative step penalty is assigned for every other state in the enviro

Haoyue Tang, Yin Sun, Leandros Tassiulas

In this paper, we study an online sampling problem of the Wiener process. The goal is to minimize the mean squared error (MSE) of the remote estimator under a sampling frequency constraint when the transmission delay distribution is unknown. The sampling problem is reformulated into an optional stopping problem, and we propose an online sampling algorithm th

Adrian Ramsingh, Jeremy Singer, Phil Trinder

Web applications are structured as multi-tier stacks of components. Each component may be written in a different language and interoperate using a variety of protocols. Such interoperation increases developer effort, can introduce security vulnerabilities, may reduce performance and require additional resources. A range of approaches have been explored to mi

Nahid Ebrahimi, Ali Taghavirashidizadeh, Seyyed Saeed Hosseini

Wireless sensor networks have significant potential to increase our ability to view and control the physical environment, but the issue of power consumption in these networks has become an important parameter in their reliability and since in many applications of networks. Wireless sensor needs to guarantee end-to-end quality parameters, support for quality

Nelson Fernandez, Ricardo Bernal

Understanding soccer as a complex system we base on nature and the collective behavior of many organisms that "do calculations," seeking to generate solutions in a bioinspired way. When soccer mysteries appear, complex systems science emerges as a means to provide explanations. However, given the variety of interpretations that complexity and its associated

Ruirui Liu, Serdar Charyyev, Niklas Wahl, Wei Liu

Purpose: Patient-specific ridge filters can modulate proton energy to obtain a conformal dose. We describe a new framework for optimization of filter design and spot maps to meet the unique demands of FLASH radiotherapy. We demonstrate an Integrated Biological Optimization IMPT (IBO-IMPT) approach for optimization of dose, dose-averaged dose rate (DADR), and

Nikolai Karpov, Qin Zhang

In this paper, we study the tradeoffs between the time and the number of communication rounds of the best arm identification problem in the heterogeneous collaborative learning model, where multiple agents interact with possibly different environments and they want to learn in parallel an objective function in the aggregated environment. By proving almost ti

Ali Taghavirashidizadeh, Arash Bahram Zarei, Arman Farsi

Several years ago, wireless sensor networks were used only by the military. These networks, which have many uses and are subject to limitations, the most important of which is the energy constraint, this energy constraint creates the requirement that the number And the length of the messages exchanged between the sensors is low. Sensor networks do not have a

R. B. Menezes, J. E. Steiner, T. V. Ricci, Patrícia da Silva

This work presents the first results of the Deep IFS View of Nuclei of Galaxies (DIVING$^\mathrm{3D}$) survey. We analysed the nuclear emission-line spectra of a sub-sample we call mini-DIVING$^\mathrm{3D}$, which includes all Southern galaxies with B < 11.2 and |b| > 15 degrees. We verified that $23\% \pm 4\%$ of the galaxies show nuclear emission-line prop

Kevin Denamganaï, Sondess Missaoui, James Alfred Walker

Human beings use compositionality to generalise from past experiences to novel experiences. We assume a separation of our experiences into fundamental atomic components that can be recombined in novel ways to support our ability to engage with novel experiences. We frame this as the ability to learn to generalise compositionally, and we will refer to behavio

Osman Darici, Arthur D. Kuo

Humans experience small fluctuations in their gait when walking on uneven terrain. The fluctuations deviate from the steady, energy-minimizing pattern for level walking, and have no obvious organization. But humans often look ahead when they walk, and could potentially plan anticipatory fluctuations for the terrain. Such planning is only sensible if it serve

Max Kölbl

We study a class of polynomials that has all of its roots on the critical line and shares many properties with Ehrhart polynomials. Braun showed that the roots of Ehrhart polynomials are bounded quadratically and Higashitani provided examples for polytopes whose Ehrhart polynomial roots come close to this bound. In the case of polytopes which have their root

Rami Atar, Eyal Castiel, Marty Reiman

This paper studies a 2-class, 2-server parallel server system under the recently introduced extended heavy traffic condition, which states that the underlying 'static allocation' linear program (LP) is critical, but does not require that it has a unique solution. The main result is the construction of policies that asymptotically achieve a lower bound, prove

Thomas Perrau, Maksim Stojkovic, Gregor Verbic

This paper demonstrates the value of power hardware-in-the-loop (PHIL) testing for the study of peer-to-peer (P2P) energy trading. P2P has emerged as a promising candidate for coordinating large numbers of distributed energy resources (DER) that pose a risk to network operations if left unmanaged. The existing literature has so far relied on pure software si

P. -O. Downey, O. Gingras, J. Fournier, C. -D. Hébert

The Mott transition is observed experimentally in materials that are magnetically frustrated so that long-range order does not hide the Mott transition at finite temperature. The Hubbard model on the triangular lattice at half-filling is a paradigmatic model to study the interplay of interactions and frustration on the normal-state phase diagram. We use the

Caroline Aparecida de Paula Silva, Cândida Nunes da Silva, Orlando Lee

Let $D$ be a digraph. A stable set $S$ of $D$ and a path partition $\mathcal{P}$ of $D$ are orthogonal if every path $P \in \mathcal{P}$ contains exactly one vertex of $S$. In 1982, Berge defined the class of $\alpha$-diperfect digraphs. A digraph $D$ is $\alpha$-diperfect if for every maximum stable set $S$ of $D$ there is a path partition $\mathcal{P}$ of

Ian En-Hsu Yen, Zhibin Xiao, Dongkuan Xu

Exploiting sparsity underlying neural networks has become one of the most potential methodologies to reduce the memory footprint, I/O cost, and computation workloads during inference. And the degree of sparsity one can exploit has become higher as larger model sizes have been considered along with the trend of pre-training giant models. On the other hand, co

Ipsita Koley, Sunandan Adhikary, Soumyajit Dey

Modern Cyber-Physical Systems (CPSs) are often designed as networked, software-based controller implementations which have been found to be vulnerable to network-level and physical level attacks. A number of research works have proposed CPS-specific attack detection schemes as well as techniques for attack resilient controller design. However, such schemes a

Hootan Amani, Paul Halpern

We consider a modified version of Brans-Dicke theory (MBDT) in four dimensions obtained by applying the induced matter method of Wesson to a 5D generalized Brans-Dicke theory. In 5D the model consists of pure vacuum, with no self-interacting potential, except for a scalar field. Constraining the 5D geometry to be a generalization of the anisotropic Bianchi t

Antonio Barbalau, Radu Tudor Ionescu, Mariana-Iuliana Georgescu, Jacob Dueholm

A self-supervised multi-task learning (SSMTL) framework for video anomaly detection was recently introduced in literature. Due to its highly accurate results, the method attracted the attention of many researchers. In this work, we revisit the self-supervised multi-task learning framework, proposing several updates to the original method. First, we study var

David Bethge, Philipp Hallgarten, Tobias Grosse-Puppendahl, Mohamed Kari

There is a growing need for sparse representational formats of human affective states that can be utilized in scenarios with limited computational memory resources. We explore whether representing neural data, in response to emotional stimuli, in a latent vector space can serve to both predict emotional states as well as generate synthetic EEG data that are

Madeline C. Schiappa, Yogesh S. Rawat

In this work, we focus on generating graphical representations of noisy, instructional videos for video understanding. We propose a self-supervised, interpretable approach that does not require any annotations for graphical representations, which would be expensive and time consuming to collect. We attempt to overcome "black box" learning limitations by pres

Ruizhi Shao, Zerong Zheng, Hongwen Zhang, Jingxiang Sun

We propose DiffuStereo, a novel system using only sparse cameras (8 in this work) for high-quality 3D human reconstruction. At its core is a novel diffusion-based stereo module, which introduces diffusion models, a type of powerful generative models, into the iterative stereo matching network. To this end, we design a new diffusion kernel and additional ster

Mobasshir Mahbub, Raed M. Shubair

With the advancement of sensing technologies over the years, it has become critical to ensure the seamless connectivity of the Internet of Things (IoT) gadgets. With the advancement of communication technology, cellular networks are increasingly being utilized to link IoT systems. An IRS is a rectangular metasurface made up of a vast number of reflecting com

Andrew McNutt

There has been substantial growth in the use of JSON-based grammars, as well as other standard data serialization languages, to create visualizations. Each of these grammars serves a purpose: some focus on particular computational tasks (such as animation), some are concerned with certain chart types (such as maps), and some target specific data domains (suc

Ji Qiang

The nonlinear space-charge effects in a high intensity or high brightness accelerator can have a significant impact on the beam properties through the accelerator. These effects are included in the accelerator design via self-consistent multi-particle tracking simulations. In order to study the sensitivity of the final beam's properties with respect to the a

Matheus V. X. Ferreira, Ye Lin Sally Hahn, S. Matthew Weinberg, Catherine Yu

Cryptographic Self-Selection is a subroutine used to select a leader for modern proof-of-stake consensus protocols, such as Algorand. In cryptographic self-selection, each round $r$ has a seed $Q_r$. In round $r$, each account owner is asked to digitally sign $Q_r$, hash their digital signature to produce a credential, and then broadcast this credential to t

Saeed Rasouli, Amin Dehghani

This paper studies a fascinating type of filter in residuated lattices, the so-called pure filters. A combination of algebraic and topological methods on the pure filters of a residuated lattice is applied to obtain some new structural results. The notion of purely-prime filters of a residuated lattice has been investigated, and a Cohen-type theorem has been

Per Bäck, Johan Richter

We introduce non-associative skew Laurent polynomial rings and characterize when they are simple. Thereby, we generalize results by Jordan, Voskoglou, and Nystedt and \"Oinert.

Mohammed Shalaby, Charles Champagne Cossette, James Richard Forbes, Jerome Le Ny

The ability to accurately estimate the position of robotic agents relative to one another, in possibly GPS-denied environments, is crucial to execute collaborative tasks. Inter-agent range measurements are available at a low cost, due to technologies such as ultra-wideband radio. However, the task of three-dimensional relative position estimation using range

Conner Bender

Drones are widely used in the energy, construction, agriculture, transportation, warehousing, real estate and movie industries. Key applications include surveys, inspections, deliveries and cinematography. With approximately 70-80% of the global market share of commercial off-the-shelf drones, Da-Jiang Innovations (DJI), headquartered in Shenzhen, China, ess

Yi Song, Xiaoyuan Xie, Quanming Liu, Xihao Zhang

The clustering technique has attracted a lot of attention as a promising strategy for parallel debugging in multi-fault scenarios, this heuristic approach (i.e., failure indexing or fault isolation) enables developers to perform multiple debugging tasks simultaneously through dividing failed test cases into several disjoint groups. When using statement ranki

Jens Harlander, Stephan Rosebrock

Labeled oriented trees, LOT's, encode spines of ribbon discs in the 4-ball and ribbon 2-knots in the 4-sphere. The unresolved asphericity question for these spines is a major test case for Whitehead's asphericity conjecture. In this paper we give a complete description of the link of a reduced injective LOT complex. An important case is the following: If $\G

Xize Wang, Tao Liu

As a vital aspect of individual's quality of life, mental health has been included as an important component of the U.N. Sustainable Development Goals. This study focuses on a specific aspect of mental health: depression, and examines its relationship with commute patterns. Using survey data from 1,528 residents in Beijing, China, we find that every 10 addit

Sumati Thareja, Gustavo Rohde, Rocio Diaz Martin, Ivan Medri

We describe a method for signal parameter estimation using the signed cumulative distribution transform (SCDT), a recently introduced signal representation tool based on optimal transport theory. The method builds upon signal estimation using the cumulative distribution transform (CDT) originally introduced for positive distributions. Specifically, we show t

Yongcheng Qi, Mengzi Xie, Jingping Yang

In this paper we consider the estimation problem for high quantiles of a heavy-tailed distribution from block data when only a few largest values are observed within blocks. We propose estimators for high quantiles and prove that these estimators are asymptotically normal. Furthermore, we employ empirical likelihood method and adjusted empirical likelihood m

Jinqi Huang, Spyros Stathopoulos, Alex Serb, Themis Prodromakis

Memristors have shown promising features for enhancing neuromorphic computing concepts and AI hardware accelerators. In this paper, we present a user-friendly software infrastructure that allows emulating a wide range of neuromorphic architectures with memristor models. This tool empowers studies that exploit memristors for online learning and online classif

Shreya Bhatt, Aayush Jain, Parv Maheshwari, Animesh Jha

The following paper is a reproducibility report for "Path Planning using Neural A* Search" published in ICML2 2021 as part of the ML Reproducibility Challenge 2021. The original paper proposes the Neural A* planner, and claims it achieves an optimal balance between the reduction of node expansions and path accuracy. We verify this claim by reimplementing the

Elena Shablovinskaya, Mikhail Piotrovich, Eugene Malygin, Stanislava Buliga

Here we present the detailed investigation of AGNs in two Seyfert 1 galaxies, LEDA 3095839 and VII Zw 244. Both of them were observed within the photometric reverberation mapping project in Special Astrophysical Observatory of the Russian Academy of Sciences (SAO RAS), during which we earlier obtained the SMBHs masses. After that, both galaxies were observed

Xize Wang, Tao Liu

Although residential crowding has many well-being implications, its connection to mental health is yet to be widely examined. Using survey data from 1613 residents in Beijing, China, we find that living in a crowded place - measured by both square metres per person and persons per bedroom - is significantly associated with a higher risk of depression. We tes

Gogulapati Sreedurga, Soumyarup Sadhukhan, Souvik Roy, Yadati Narahari

We study fairness in social choice settings under single-peaked preferences. Construction and characterization of social choice rules in the single-peaked domain has been extensively studied in prior works. In fact, in the single-peaked domain, it is known that unanimous and strategy-proof deterministic rules have to be min-max rules and those that also sati

R. Ravi, Weizhong Zhang, Michael Zlatin

In the Steiner Tree Augmentation Problem (STAP), we are given a graph $G = (V,E)$, a set of terminals $R \subseteq V$, and a Steiner tree $T$ spanning $R$. The edges $L := E \setminus E(T)$ are called links and have non-negative costs. The goal is to augment $T$ by adding a minimum cost set of links, so that there are 2 edge-disjoint paths between each pair

Maria Papathanasaki, Panagiotis Fountas, Kostas Kolomvatsos

The ever-increasing demands of end-users on the Internet of Things (IoT), often cause great congestion in the nodes that serve their requests. Therefore, the problem of node overloading arises. In this article we attempt to solve the problem of heavy traffic in a node, by proposing a mechanism that keeps the node from overloading, regardless of the load ente

Payal Kaushik, Sayantan Pramanik, M Girish Chandra, C V Sridhar

Time series forecasting has always been a thought-provoking topic in the field of machine learning. Machine learning scientists define a time series as a set of observations recorded over consistent time steps. And, time series forecasting is a way of analyzing the data and finding how variables change over time and hence, predicting the future value. Time i

Gogulapati Sreedurga, Yadati Narahari

Participatory budgeting (PB) has attracted much attention in recent times due to its wide applicability in social choice settings. In this paper, we consider indivisible PB which involves allocating an available, limited budget to a set of indivisible projects, each having a certain cost, based on the preferences of agents over projects. The specific, import

T. Mitchell Roddenberry, Santiago Segarra

We develop a theory of limits for sequences of dense abstract simplicial complexes, where a sequence is considered convergent if its homomorphism densities converge. The limiting objects are represented by stacks of measurable [0,1]-valued functions on unit cubes of increasing dimension, each corresponding to a dimension of the abstract simplicial complex. W

Jingjia Huang, Baixiang Yang

Human Object Interaction (HOI) detection is a challenging task that requires to distinguish the interaction between a human-object pair. Attention based relation parsing is a popular and effective strategy utilized in HOI. However, current methods execute relation parsing in a "bottom-up" manner. We argue that the independent use of the bottom-up parsing str

Christian Capezza, Fabio Centofanti, Antonio Lepore, Biagio Palumbo

In modern Industry 4.0 applications, a huge amount of data is acquired during manufacturing processes that are often contaminated with anomalous observations in the form of both casewise and cellwise outliers. These can seriously reduce the performance of control charting procedures, especially in complex and high-dimensional settings. To mitigate this issue

Hans Dermot Doran

In this relatively informal discussion-paper we summarise issues in the domains of safety and security in machine learning that will affect industry sectors in the next five to ten years. Various products using neural network classification, most often in vision related applications but also in predictive maintenance, have been researched and applied in real

Juan J. Abellan, Nicolas Bonnet, Alex Carlton, Paul Frewer

Quantitative decision-making (QDM) principles address the issues related to the mapping of results to decisions, the synthesis of information and the quantification of uncertainty. Since the clinical drug development involves a succession of decisions to be made, QDM methods can be applied at various levels. At the study level, it can be used to properly des

Filip Matzner

Echo State Networks represent a type of recurrent neural network with a large randomly generated reservoir and a small number of readout connections trained via linear regression. The most common topology of the reservoir is a fully connected network of up to thousands of neurons. Over the years, researchers have introduced a variety of alternative reservoir

Erdem Cil, Icaro V. Soares, David Renaudeau, Ronan Lucas

During their travel through the gastrointestinal tract, ingestible antennas encounter detuning in their impedance response due to varying electromagnetic properties of the surrounding tissues. This paper investigates the possibility of using this impedance detuning to detect in which segment of the gastrointestinal tract - stomach, small intestine, or large

Binghui Peng, Fred Zhang

We provide the first sub-linear space and sub-linear regret algorithm for online learning with expert advice (against an oblivious adversary), addressing an open question raised recently by Srinivas, Woodruff, Xu and Zhou (STOC 2022). We also demonstrate a separation between oblivious and (strong) adaptive adversaries by proving a linear memory lower bound o

Xinyi Zou, Yan Yan, Jing-Hao Xue, Si Chen

Most existing compound facial expression recognition (FER) methods rely on large-scale labeled compound expression data for training. However, collecting such data is labor-intensive and time-consuming. In this paper, we address the compound FER task in the cross-domain few-shot learning (FSL) setting, which requires only a few samples of compound expression

Arpit Bansal, Ping-yeh Chiang, Michael Curry, Rajiv Jain

Watermarking is a commonly used strategy to protect creators' rights to digital images, videos and audio. Recently, watermarking methods have been extended to deep learning models -- in principle, the watermark should be preserved when an adversary tries to copy the model. However, in practice, watermarks can often be removed by an intelligent adversary. Sev

Xiuxian Li, Min Meng, Yiguang Hong, Jie Chen

Game theory has by now found numerous applications in various fields, including economics, industry, jurisprudence, and artificial intelligence, where each player only cares about its own interest in a noncooperative or cooperative manner, but without obvious malice to other players. However, in many practical applications, such as poker, chess, evader pursu

Matheus Cavalcante, Domenic Wüthrich, Matteo Perotti, Samuel Riedel

While parallel architectures based on clusters of Processing Elements (PEs) sharing L1 memory are widespread, there is no consensus on how lean their PE should be. Architecting PEs as vector processors holds the promise to greatly reduce their instruction fetch bandwidth, mitigating the Von Neumann Bottleneck (VNB). However, due to their historical associati

Shintaro Negishi

There is a strong need to reduce greenhouse gas emissions to deal with climate change. In the power sector, changing the power generation method in the medium and long term is needed to reduce greenhouse gas emissions. This paper proposes a long-term energy-mix optimization mod-el to obtain the process of carbon neutrality in the power system. The proposed m

Philipp Linnartz, Alexander Winkens, Andreas Ulbig

Successful cyber attacks on power systems cause severe disruptions. One possible manipulation strategy is the utilization of distributed energy resources (DERs) to disturb power system operation. In addition to the impact on bulk power system frequency, local cascading effects caused by DER control and protection can increase the severity of this strategy. T

L. -M. Seillé, V. Buat, W. Haddad, A. Boselli

The detection with of the Atacama Large Millimeter Array (ALMA) of dust-rich high redshift galaxies whose cold dust emission is spatially disconnected from the ultraviolet emission bears a challenge for modelling their spectral energy distributions (SED) with codes based on an energy budget between the stellar and dust components. We test the validity of ene

João Azevedo, Pavel Shumyatsky

If $H$ is a subgroup of a compact group $G$, the probability that a random element of $H$ commutes with a random element of $G$ is denoted by $Pr(H,G)$. Let $\langle g\rangle$ stand for the monothetic subgroup generated by an element $g\in G$ and let $K$ be a subgroup of $G$. We prove that $Pr(\langle x\rangle,G)>0$ for any $x\in K$ if and only if $G$ has an

Qihang Yao, Manoj Chandrasekaran, Constantine Dovrolis

Diffusion MRI imaging and tractography algorithms have enabled the mapping of the macro-scale connectome of the entire brain. At the functional level, probably the simplest way to study the dynamics of macro-scale brain activity is to compute the "activation cascade" that follows the artificial stimulation of a source region. Such cascades can be computed us

Mohammad Anagreh, Peeter Laud

In this paper, we present a secure multiparty computation (SMC) protocol for single-source shortest distances (SSSD) in undirected graphs, where the location of edges is public, but their length is private. The protocol works in the Arithmetic Black Box (ABB) model on top of the separator tree of the graph, achieving good time complexity if the subgraphs of

Hongtao Yan, Qiang Gao, Chunyao Song, Chaohui Yin

The pseudogap state is one of the most enigmatic characteristics in the anomalous normal state properties of the high temperature cuprate superconductors. A central issue is to reveal whether there is a symmetry breaking and which symmetries are broken across the pseudogap transition. By performing high resolution laser-based angle-resolved photoemission mea

Sangyun Lee, Hyungjin Chung, Jaehyeon Kim, Jong Chul Ye

Recently, diffusion models have shown remarkable results in image synthesis by gradually removing noise and amplifying signals. Although the simple generative process surprisingly works well, is this the best way to generate image data? For instance, despite the fact that human perception is more sensitive to the low frequencies of an image, diffusion models

Jonathan A. Sobel, Ronit Almog, Leo Anthony Celi, Michal Gaziel-Yablowitz

A datathon is a time-constrained competition involving data science applied to a specific problem. In the past decade, datathons have been shown to be a valuable bridge between fields and expertise . Biomedical data analysis represents a challenging area requiring collaboration between engineers, biologists and physicians to gain a better understanding of pa

Peize Liu

This dissertation is an exposition of Kontsevich's proof of the formality theorem and the classification of deformation quantisation on a Poisson manifold. We begin with an account of the physical background and introduce the Weyl-Moyal product as the first example. Then we develop the deformation theory via differential graded Lie algebras and $\mathrm{L}_\

Hongping Deng, Gordon I. Ogilvie

Astrophysical disks that are sufficiently cold and dense are linearly unstable to the formation of axisymmetric rings as a result of the disk's gravity. In practice, spiral structures are formed, which may in turn produce bound fragments. We study a nonlinear dynamical path that can explain the development of spirals in a local model of a gaseous disk on the

Alessandro Camasta, Genni Fragnelli

In this paper we consider the fourth order operators A1u := (au")" and A2u := au"" in divergence form and non divergence form, respectively, where a, defined in [0, 1] with values in R+, degenerates in an interior point of the interval. Using the semigroup technique, under suitable assumptions on a, we study the generation property of these operators associa

Javier Duarte, Nhan Tran, Ben Hawks, Christian Herwig

Applications of machine learning (ML) are growing by the day for many unique and challenging scientific applications. However, a crucial challenge facing these applications is their need for ultra low-latency and on-detector ML capabilities. Given the slowdown in Moore's law and Dennard scaling, coupled with the rapid advances in scientific instrumentation t

Abdelmalek Bedhouche, Bakir Farhi

This paper is devoted to study some expressions of the type $\prod_{p} p^{\lfloor\frac{x}{f(p)}\rfloor}$, where $x$ is a nonnegative real number, $f$ is an arithmetic function satisfying some conditions, and the product is over the primes $p$. We begin by proving that such expressions can be expressed by using the $\mathrm{lcm}$ function, without any referen

Hridesh Kedia, Shunhao Oh, Dana Randall

We present local distributed, stochastic algorithms for \emph{alignment} in self-organizing particle systems (SOPS) on two-dimensional lattices, where particles occupy unique sites on the lattice, and particles can make spatial moves to neighboring sites if they are unoccupied. Such models are abstractions of programmable matter, composed of individual compu

Sakshi Chhabra, Ashutosh Kumar Singh

Cloud computing has become the backbone of the computing industry and offers subscription-based on-demand services. Through virtualization, which produces a virtual instance of a computer system running in an abstracted hardware layer, it has made it possible for us to share resources among many users. Contrary to early distributed computing models, it guara