280 research outputs found
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
Beyond Traditional Approaches: Multi-Task Network for Breast Ultrasound Diagnosis
Breast Ultrasound plays a vital role in cancer diagnosis as a non-invasive
approach with cost-effective. In recent years, with the development of deep
learning, many CNN-based approaches have been widely researched in both tumor
localization and cancer classification tasks. Even though previous single
models achieved great performance in both tasks, these methods have some
limitations in inference time, GPU requirement, and separate fine-tuning for
each model. In this study, we aim to redesign and build end-to-end multi-task
architecture to conduct both segmentation and classification. With our proposed
approach, we achieved outstanding performance and time efficiency, with 79.8%
and 86.4% in DeepLabV3+ architecture in the segmentation task.Comment: 7 pages, 3 figure
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
Adjustment of Vietnamese labour market in time of economic fluctuations and structural changes
Dans cet article, nous examinons les ajustements du marché du travail aux fluctuations économiques, compte tenu des transformations structurelles en cours ainsi que des changements à court terme. Nous utilisons pour cela des données des recensements de la population ou publiées dans les annuaires statistiques de l’Office Général de la Statistique pour les séries à long terme, et les enquêtes emploi conduites entre 2007 à 2012 pour les données à court terme. Cet article souligne la profonde transformation du marché du travail au cours des dernières décennies. La population active a doublé en 25 ans et la part de l'agriculture est passée en dessous du seuil de 50 %. L’absorption de l'offre de travail a donc été l'un des principaux défis pour l'économie vietnamienne sur cette période. Le secteur des entreprises familiales agricoles et non-agricoles a été le principal pourvoyeur d'emplois au cours de ces années. Le marché du travail s'est adapté au récent ralentissement économique à travers différents canaux. Le chômage est resté stable mais le nombre de personnes inactives a augmenté. La quantité de travail a également été affectée par une réduction significative du nombre d'heures travaillées. Alors que le secteur non agricole a généré plus d'emplois pour les travailleurs qualifiés, un flux de travailleurs non-qualifiés vers l’agriculture a été observé. En raison de facteurs démographiques, l'absorption de l'offre de travail et la création de nouveaux emplois ne sont plus le principal problème. En revanche, l’évolution récente du marché du travail appelle à la mise en oeuvre de politiques structurelles en vue d’améliorer les conditions de travail, la période étant particulièrement favorable pour mener ces politiques puisque le Vietnam profite actuellement du dividende démographique
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
TCT-14 ABSORB FIRST: 1-year clinical outcomes from a prospective, multi-center, global, real-world registry
D2D Communication Network with the Assistance of Power Beacon under the Impact of Co-channel Interferences and Eavesdropper: Performance Analysis
In this paper, we study and demonstrate the
performance analysis of a device-to-device (D2D) com-
munication network. Specifically, a source node trans-
mits data to the destination node using the power bea-
con’s harvested energy in order to overcome the limited
energy budget. Besides, an eavesdropper located in the
proximal region of a source is trying to overhear secure
information. Notably, both eavesdropper and destina-
tion are affected by co-channel interferences from other
sources when they utilize the same frequency. By con-
sidering the above discussions, we derived the closed-
form expressions for outage probability (OP), intercept
probability (IP), and secrecy outage probability (SOP)
in connection with using the system model. The derived
analytical expressions are then verified by utilizing both
simulation and numerical results. Finally, the inten-
sive parameters’ influences on the OP, IP, and SOP
are also investigated
- …