1,720,977 research outputs found
WhiskEras 2.0: Fast and Accurate Whisker Tracking in Rodents
No full textMice and rats can rapidly move their whiskers when exploring the environment. Accurate description of these movements is important for behavioral studies in neuroscience. Whisker tracking is, however, a notoriously difficult task due to the fast movements and frequent crossings and juxtapositionings among whiskers. We have recently developed WhiskEras, a computer-vision-based algorithm for whisker tracking in untrimmed, head-restrained mice. Although WhiskEras excels in tracking the movements of individual unmarked whiskers over time based on high-speed videos, the initial version of WhiskEras still had two issues preventing its widespread use: it involved tuning a great number of parameters manually to adjust for different experimental setups, and it was slow, processing less than 1 frame per second. To overcome these problems, we present here WhiskEras 2.0, in which the unwieldy stages of the initial algorithm were improved. The enhanced algorithm is more robust, not requiring intense parameter tuning. Furthermore, it was accelerated by first porting the code from MATLAB to C++ and then using advanced parallelization techniques with CUDA and OpenMP to achieve a speedup of at least 75x when processing a challenging whisker video. The improved WhiskEras 2.0 is made publicly available and is ready for processing high-speed videos, thus propelling behavioral research in neuroscience, in particular on sensorimotor integration.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Quantum & Computer EngineeringComputer Engineerin
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Algorithm-Centric Design of Reliable and Efficient Deep Learning Processing Systems
Get PDFArtificial intelligence techniques driven by deep learning have experienced significant advancements in the past decade. The usage of deep learning methods has increased dramatically in practical application domains such as autonomous driving, healthcare, and robotics, where the utmost hardware resource efficiency, as well as strict hardware safety and reliability requirements, are often imposed. The increasing computational cost of deep learning models has been traditionally tackled through model compression and domain-specific accelerator design. As the cost of conventional fault tolerance methods is often prohibitive in consumer electronics, the question of functional safety and reliability for deep learning hardware is still in its infancy. This dissertation outlines a novel approach to deliver dramatic boosts in hardware safety, reliability, and resource efficiency through a synergistic co-design paradigm. We first observe and make use of the unique algorithmic characteristics of deep neural networks, including plasticity in the design process, resiliency to small numerical perturbations, and their inherent redundancy, as well as the unique micro-architectural properties of deep learning accelerators such as regularity. The advocated approach is accomplished by reshaping deep neural networks, enhancing deep neural network accelerators strategically, prioritizing the overall functional correctness, and minimizing the associated costs through the statistical nature of deep neural networks. To illustrate, our analysis demonstrates that deep neural networks equipped with the proposed techniques can maintain accuracy gracefully, even at extreme rates of hardware errors. As a result, the described methodology can embed strong safety and reliability characteristics in mission-critical deep learning applications at a negligible cost. The proposed approach further offers a promising avenue for handling the micro-architectural challenges of deep neural network accelerators and boosting resource efficiency through the synergistic co-design of deep neural networks and hardware micro-architectures
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Attacking and Improving Received Signal Strength Ratio for Secure Localization and Proximity-based Authentication
Get PDFThe cheap versatility of modern embedded systems is being harnessed to attack sprawling tasks with teams of cooperating machines - infrastructure monitoring, automated patient health monitoring, and the management of cyber-physical systems are some prime examples. Parallel advances in networking technology, particularly MIMO signal processing capabilities,facilitate wireless tracking and localization for coordination of physical activities among independent nodes. As these technologies enter wider markets, they inevitably become targets of malicious adversaries, and the ability to detect and mitigate attacks becomes necessary. In many applications, the ability to securely determine the location or proximity of one entity relative to another is important for maintaining system security. This work concentrates on a specific and relatively new RF-based localization scheme called Received Signal Strength Ratio (RSSR). Derived from a more primitive signal-strength-based localization technique, RSSR takes the ratio of signal strength measurements at 2 (or more) receivers to determine the distance of a transmitter from the device. This construct has attracted interest for its potential utility in securing ad-hoc networks and body-area networks. However, researchers proposing RSSR as the backbone of proximity-based authentication systems have not thoroughly considered certain realistic attacker capabilities. In this work, we present a threat model that characterizes the security of an RSSR-based proximity authentication system in more detail than previous research; describe a generic attack on the security of such systems; and discuss a set of mitigation strategies that ultimately restore the effectiveness of RSSR as a secure distance and proximity verification scheme
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A Holistic Sybil Detection Framework for Participatory Cyber-Physical Systems
Get PDFThe increasing ubiquity of communication-capable devices such as smartphones and smart vehicles has paved the way for a plethora of useful Participatory Cyber-Physical Systems (CPSs) in the defense, medical, and commercial sectors. Such systems promise to collect and analyze vast amounts of timestamped, location-contextualized user data, allowing various agencies to act on the results. However, given that the utility of these systems depends absolutely on the number of willing participants and the quality of their data, CPSs must be accessible to attract as large a user base as possible, while also possessing countermeasures against adversarial data intended to mislead the decision-making of the systems.We begin by defining the structure of these CPS systems and the capabilities of the adversary, showing the particular danger of fake, software-simulated Sybil nodes that provide the adversary immense deceptive power at no meaningful cost or risk. We claim that no currently existing Sybil countermeasure successfully addresses both the requirements posed by CPS systems and the deceptive techniques available to the adversary---namely the introduction of a small number of physical malicious nodes who would help disguise the Sybils---and argue that a new approach is needed.To address the Sybil threat, we develop a holistic 3-stage Sybil detection framework that may be employed on-demand to detect Sybil nodes around a target location. The first stage comprises of an inter-node communication scheme that uses local radio broadcasts as proof-of-physical-presence, and is designed specifically to address the CPS requirements and the adversarial deception techniques. The second stage consists of a detection layer that examines each node's observed pairwise RSSI readings, searching for those inconsistent with their expected distributions given their claimed locations. Due to the time-constrained nature of the communication scheme however, many pairwise connections randomly fail and therefore do not have any RSSI readings attached to them. This binary connection success/failure information is utilized by the third stage, wherein we develop a probabilistic framework to assess the likelihood of a node's observed combination of connection outcomes. To evaluate our approach, we have built an extensive simulation environment that allows for complex adversarial obfuscation strategies. Using this environment, we demonstrate both the efficacy and robustness of our detection methodology
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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