Assessment of Electrical Shorting and Metal Vapor Arcing Potential of Tin Whiskers

Tin whiskers are conductive crystal growths that form unpredictively from tin and tin alloy surfaces. The growth of tin whiskers presents a reliability concern in electronic equipment due to their potential to create electrical shorts and metal vapor arcs. Concern with tin whiskers is increasing due to the ever tightening conductor spacing in smaller electronic products and the increased use of pure tin and lead-free tin alloys. While tin whiskers present a failure risk for electronics, a tin whisker mechanical bridging between two differently electrically biased conductors doesn't guaranteed electrical shorts due to surface films on tin whisker and conductors. The voltage must exceed a threshold level in order to produce the current flow through the tin whisker. However, the influence of contact force and presence of surface contaminations on breakdown voltage of tin whiskers has not been adequately investigated. Furthermore, whisker-induced electrical shorts can initiate destructive metal vapor arcs. The potential for metal vapor arc formation is affected by several factors, including whisker geometry, bias voltage and pressure. Previous studies demonstrated metal vapor arc formation using gold- and tin-wires; however, material and geometry differences between these test articles and actual tin whiskers have not been examined. Further, a practical guide for assessing the potential for tin whisker-induced metal vapor arc formation has not been provided. This dissertation provides characteristics and assessment of tin whisker-induced electrical shorts and metal vapor arcs. The breakdown voltage of tin whisker was measured using gold- and tin-coated probes to characterize the influence of two different contact materials on breakdown voltage. As a part of this effort, the effect of contact force on breakdown voltage and its current-voltage characteristics related with the failure mode and the possibility of electrical shorting by tin whiskers were also investigated. With regards to tin whisker-induced metal vapor arc formation, the effect of whisker geometry, bias voltage and pressure was investigated. Based on the experimental evidence, a metric defined as a function of bias voltage and resistance was proposed and the logistic regression model that can assess the likelihood of tin whisker-induced metal vapor arc formation was developed

Lightweight and Robust Representation of Economic Scales from Satellite Imagery

Satellite imagery has long been an attractive data source that provides a wealth of information on human-inhabited areas. While super resolution satellite images are rapidly becoming available, little study has focused on how to extract meaningful information about human habitation patterns and economic scales from such data. We present READ, a new approach for obtaining essential spatial representation for any given district from high-resolution satellite imagery based on deep neural networks. Our method combines transfer learning and embedded statistics to efficiently learn critical spatial characteristics of arbitrary size areas and represent them into a fixed-length vector with minimal information loss. Even with a small set of labels, READ can distinguish subtle differences between rural and urban areas and infer the degree of urbanization. An extensive evaluation demonstrates the model outperforms the state-of-the-art in predicting economic scales, such as population density for South Korea (R^2=0.9617), and shows a high potential use for developing countries where district-level economic scales are not known.Comment: Accepted for oral presentation at AAAI 202

Towards Attack-tolerant Federated Learning via Critical Parameter Analysis

Federated learning is used to train a shared model in a decentralized way without clients sharing private data with each other. Federated learning systems are susceptible to poisoning attacks when malicious clients send false updates to the central server. Existing defense strategies are ineffective under non-IID data settings. This paper proposes a new defense strategy, FedCPA (Federated learning with Critical Parameter Analysis). Our attack-tolerant aggregation method is based on the observation that benign local models have similar sets of top-k and bottom-k critical parameters, whereas poisoned local models do not. Experiments with different attack scenarios on multiple datasets demonstrate that our model outperforms existing defense strategies in defending against poisoning attacks.Comment: ICCV'23 Accepte

FedDefender: Client-Side Attack-Tolerant Federated Learning

Federated learning enables learning from decentralized data sources without compromising privacy, which makes it a crucial technique. However, it is vulnerable to model poisoning attacks, where malicious clients interfere with the training process. Previous defense mechanisms have focused on the server-side by using careful model aggregation, but this may not be effective when the data is not identically distributed or when attackers can access the information of benign clients. In this paper, we propose a new defense mechanism that focuses on the client-side, called FedDefender, to help benign clients train robust local models and avoid the adverse impact of malicious model updates from attackers, even when a server-side defense cannot identify or remove adversaries. Our method consists of two main components: (1) attack-tolerant local meta update and (2) attack-tolerant global knowledge distillation. These components are used to find noise-resilient model parameters while accurately extracting knowledge from a potentially corrupted global model. Our client-side defense strategy has a flexible structure and can work in conjunction with any existing server-side strategies. Evaluations of real-world scenarios across multiple datasets show that the proposed method enhances the robustness of federated learning against model poisoning attacks.Comment: KDD'23 research track accepte

FedX: Unsupervised Federated Learning with Cross Knowledge Distillation

This paper presents FedX, an unsupervised federated learning framework. Our model learns unbiased representation from decentralized and heterogeneous local data. It employs a two-sided knowledge distillation with contrastive learning as a core component, allowing the federated system to function without requiring clients to share any data features. Furthermore, its adaptable architecture can be used as an add-on module for existing unsupervised algorithms in federated settings. Experiments show that our model improves performance significantly (1.58--5.52pp) on five unsupervised algorithms.Comment: Accepted and will be published at ECCV202

Optimal Pricing Effect on Equilibrium Behaviors of Delay-Sensitive Users in Cognitive Radio Networks

This paper studies price-based spectrum access control in cognitive radio networks, which characterizes network operators' service provisions to delay-sensitive secondary users (SUs) via pricing strategies. Based on the two paradigms of shared-use and exclusive-use dynamic spectrum access (DSA), we examine three network scenarios corresponding to three types of secondary markets. In the first monopoly market with one operator using opportunistic shared-use DSA, we study the operator's pricing effect on the equilibrium behaviors of self-optimizing SUs in a queueing system. %This queue represents the congestion of the multiple SUs sharing the operator's single \ON-\OFF channel that models the primary users (PUs) traffic. We provide a queueing delay analysis with the general distributions of the SU service time and PU traffic using the renewal theory. In terms of SUs, we show that there exists a unique Nash equilibrium in a non-cooperative game where SUs are players employing individual optimal strategies. We also provide a sufficient condition and iterative algorithms for equilibrium convergence. In terms of operators, two pricing mechanisms are proposed with different goals: revenue maximization and social welfare maximization. In the second monopoly market, an operator exploiting exclusive-use DSA has many channels that will be allocated separately to each entering SU. We also analyze the pricing effect on the equilibrium behaviors of the SUs and the revenue-optimal and socially-optimal pricing strategies of the operator in this market. In the third duopoly market, we study a price competition between two operators employing shared-use and exclusive-use DSA, respectively, as a two-stage Stackelberg game. Using a backward induction method, we show that there exists a unique equilibrium for this game and investigate the equilibrium convergence.Comment: 30 pages, one column, double spac

Depression is associated with reduced outcome sensitivity in a dual valence, magnitude learning task

BACKGROUND: Learning from rewarded and punished choices is perturbed in depressed patients, suggesting that abnormal reinforcement learning may be a cognitive mechanism of the illness. However, previous studies have disagreed about whether this behavior is produced by alterations in the rate of learning or sensitivity to experienced outcomes. This previous work has generally assessed learning in response to binary outcomes of one valence, rather than to both rewarding and punishing continuous outcomes. METHODS: A novel drifting reward and punishment magnitude reinforcement-learning task was administered to patients with current (n = 40) and remitted depression (n = 39), and healthy volunteers (n = 40) to capture potential differences in learning behavior. Standard questionnaires were administered to measure self-reported depressive symptom severity, trait and state anxiety and level of anhedonic symptoms. RESULTS: Our findings demonstrate that patients with current depression adjust their learning behaviors to a lesser degree in response to trial-by-trial variations in reward and loss magnitudes than the other groups. Computational modeling revealed that this behavioral signature of current depressive state is better accounted for by reduced reward and punishment sensitivity (all p < 0.031), rather than a change in learning rate (p = 0.708). However, between-group differences were not related to self-reported symptom severity or comorbid anxiety disorders in the current depression group. CONCLUSION: These findings suggest that current depression is associated with reduced outcome sensitivity rather than altered learning rate. Previous findings reported in this domain mainly from binary learning tasks seem to generalize to learning from continuous outcomes