EMaP: Explainable AI with Manifold-based Perturbations

In the last few years, many explanation methods based on the perturbations of input data have been introduced to improve our understanding of decisions made by black-box models. The goal of this work is to introduce a novel perturbation scheme so that more faithful and robust explanations can be obtained. Our study focuses on the impact of perturbing directions on the data topology. We show that perturbing along the orthogonal directions of the input manifold better preserves the data topology, both in the worst-case analysis of the discrete Gromov-Hausdorff distance and in the average-case analysis via persistent homology. From those results, we introduce EMaP algorithm, realizing the orthogonal perturbation scheme. Our experiments show that EMaP not only improves the explainers' performance but also helps them overcome a recently-developed attack against perturbation-based methods.Comment: 29 page

NeuCEPT: Locally Discover Neural Networks' Mechanism via Critical Neurons Identification with Precision Guarantee

Despite recent studies on understanding deep neural networks (DNNs), there exists numerous questions on how DNNs generate their predictions. Especially, given similar predictions on different input samples, are the underlying mechanisms generating those predictions the same? In this work, we propose NeuCEPT, a method to locally discover critical neurons that play a major role in the model's predictions and identify model's mechanisms in generating those predictions. We first formulate a critical neurons identification problem as maximizing a sequence of mutual-information objectives and provide a theoretical framework to efficiently solve for critical neurons while keeping the precision under control. NeuCEPT next heuristically learns different model's mechanisms in an unsupervised manner. Our experimental results show that neurons identified by NeuCEPT not only have strong influence on the model's predictions but also hold meaningful information about model's mechanisms.Comment: 6 main page

UIT-ADrone: A Novel Drone Dataset for Traffic Anomaly Detection

Anomaly detection plays an increasingly important role in video surveillance and is one of the issues that have attracted various communities, such as computer vision, machine learning, and data mining in recent years. Moreover, drones equipped with cameras have quickly been deployed to a wide range of applications, starting from border security applications to street monitoring systems. However, there is a notable lack of adequate drone-based datasets available to detect unusual events in the urban traffic environment, especially in roundabouts, due to the density of interaction between road users and vehicles. To promote the development of anomalous event detection with drones in the complex traffic environment, we construct a novel large-scale drone dataset to detect anomalies involving realistic roundabouts in Vietnam, covering a large variety of anomalous events. Traffic at a total of three different roundabouts in Ho Chi Minh City was recorded with a camera-equipped drone. The resulting dataset contains 51 videos with total data traffic of nearly 6.5 h, captured across 206K frames with ten abnormal event types. Based on this dataset, we comprehensively evaluate the current state-of-the-art algorithms and what anomaly detection can do in drone-based video surveillance. This study presents a detailed description of the proposed UIT-ADrone dataset, along with information regarding data distribution, protocols for evaluation, baseline experimental results on our dataset, and other benchmark datasets, discussions, and paves the way for future work

High energy storage responses in all-oxide epitaxial relaxor ferroelectric thin films with the coexistence of relaxor and antiferroelectric-like behaviors

Relaxor ferroelectric Pb0.9La0.1(Zr0.52Ti0.48)O3 (PLZT) thin films have been epitaxially grown via pulsed laser deposition on SrRuO3/SrTiO3 single crystal with different orientations. The high recoverable energy-storage density and energy-storage efficiency in the epitaxial PLZT thin films are mainly caused by the coexistence of relaxor and antiferroelectric-like behaviors. The recoverable energy-storage density of 12.03, 12.51 and 12.74 J/cm3 and energy-storage efficiency of 86.50, 88.14 and 88.44%, respectively, for the PLZT(001), PLZT(011) and PLZT(111) thin films measured at 1000 kV/cm. The high energy density and high efficiency indicate that the relaxor epitaxial PLZT(111) thin film is a promising candidate for high pulsed power capacitors

Spectral and Energy Efficiency Maximization for Content-Centric C-RANs with Edge Caching

This paper aims to maximize the spectral and energy efficiencies of a content-centric cloud radio access network (C-RAN), where users requesting the same contents are grouped together. Data are transferred from a central baseband unit to multiple remote radio heads (RRHs) equipped with local caches. The RRHs then send the received data to each group's user. Both multicast and unicast schemes are considered for data transmission. We formulate mixed-integer nonlinear problems in which user association, RRH activation, data rate allocation, and signal precoding are jointly designed. These challenging problems are subject to minimum data rate requirements, limited fronthaul capacity, and maximum RRH transmit power. Employing successive convex quadratic programming, we propose iterative algorithms with guaranteed convergence to Fritz John solutions. Numerical results confirm that the proposed joint designs markedly improve the spectral and energy efficiencies of the considered content-centric C-RAN compared to benchmark schemes. Importantly, they show that unicasting outperforms multicasting in terms of spectral efficiency in both cache and cache-less scenarios. In terms of energy efficiency, multicasting is the best choice for the system without cache whereas unicasting is best for the system with cache. Finally, edge caching is shown to improve both spectral and energy efficiencies.This work is supported in part by an ECRHDR scholarship from The University of Newcastle, in part by the Australian Research Council Discovery Project grants DP170100939 and DP160101537

Energy-Efficient Design for Downlink Cloud Radio Access Networks

This work aims to maximize the energy efficiency of a downlink cloud radio access network (C-RAN), where data is transferred from a baseband unit in the core network to several remote radio heads via a set of edge routers over capacity-limited fronthaul links. The remote radio heads then send the received signals to their users via radio access links. We formulate a new mixed-integer nonlinear problem in which the ratio of network throughput and total power consumption is maximized. This challenging problem formulation includes practical constraints on routing, predefined minimum data rates, fronthaul capacity and maximum RRH transmit power. By employing the successive convex quadratic programming framework, an iterative algorithm is proposed with guaranteed convergence to a Fritz John solution of the formulated problem. Significantly, each iteration of the proposed algorithm solves only one simple convex program. Numerical examples with practical parameters confirm that the proposed joint optimization design markedly improves the C-RAN's energy efficiency compared to benchmark schemes.This work is supported in part by an ECR-HDR scholarship from The University of Newcastle, in part by the Australian Research Council Discovery Project grants DP170100939 and DP160101537, in part by Vietnam National Foundation for Science and Technology Development under grant number 101.02-2016.11 and in part by a startup fund from San Diego State University

Toward Design Rules for Multilayer Ferroelectric Energy Storage Capacitors – A Study Based on Lead-Free and Relaxor-Ferroelectric/Paraelectric Multilayer Devices

Future pulsed-power electronic systems based on dielectric capacitors require the use of environment-friendly materials with high energy-storage performance that can operate efficiently and reliably in harsh environments. Here, a study of multilayer structures, combining paraelectric-like Ba0.6Sr0.4TiO3 (BST) with relaxor-ferroelectric BaZr0.4Ti0.6O3 (BZT) layers on SrTiO3-buffered Si substrates, with the goal to optimize the high energy-storage performance is presented. The energy-storage properties of various stackings are investigated and an extremely large maximum recoverable energy storage density of ≈165.6 J cm−3 (energy efficiency ≈ 93%) is achieved for unipolar charging–discharging of a 25-nm-BZT/20-nm-BST/910-nm-BZT/20-nm-BST/25-nm-BZT multilayer structure, due to the extremely large breakdown field of 7.5 MV cm−1 and the lack of polarization saturation at high fields in this device. Strong indications are found that the breakdown field of the devices is determined by the outer layers of the multilayer stack and can be increased by improving the quality of these layers. Authors are also able to deduce design optimization rules for this material combination, which can be to a large extend justify by structural analysis. These rules are expected also to be useful for optimizing other multilayer systems and are therefore very relevant for further increasing the energy storage density of capacitors.</p

User Selection Approaches to Mitigate the Straggler Effect for Federated Learning on Cell-Free Massive MIMO Networks

This work proposes UE selection approaches to mitigate the straggler effect for federated learning (FL) on cell-free massive multiple-input multiple-output networks. To show how these approaches work, we consider a general FL framework with UE sampling, and aim to minimize the FL training time in this framework. Here, training updates are (S1) broadcast to all the selected UEs from a central server, (S2) computed at the UEs sampled from the selected UE set, and (S3) sent back to the central server. The first approach mitigates the straggler effect in both Steps (S1) and (S3), while the second approach only Step (S3). Two optimization problems are then formulated to jointly optimize UE selection, transmit power and data rate. These mixed-integer mixed-timescale stochastic nonconvex problems capture the complex interactions among the training time, the straggler effect, and UE selection. By employing the online successive convex approximation approach, we develop a novel algorithm to solve the formulated problems with proven convergence to the neighbourhood of their stationary points. Numerical results confirm that our UE selection designs significantly reduce the training time over baseline approaches, especially in the networks that experience serious straggler effects due to the moderately low density of access points.Comment: submitted for peer review