Query: Defensive Distillation is Not Robust to Adversarial Examples

Relevant passages:
Distillation as a Defense to Adversarial Perturbations Against Deep Neural Networks <=SEP=> Deep learning algorithms have been shown to perform extremely well on many classical machine learning problems. However, recent studies have shown that deep learning, like other machine learning techniques, is vulnerable to adversarial samples: inputs crafted to force a deep neural network (DNN) to provide adversary-selected outputs. Such attacks can seriously undermine the security of the system supported by the DNN, sometimes with devastating consequences. For example, autonomous vehicles can be crashed, illicit or illegal content can bypass content filters, or biometric authentication systems can be manipulated to allow improper access. In this work, we introduce a defensive mechanism called defensive distillation to reduce the effectiveness of adversarial samples on DNNs. We analytically investigate the generalizability and robustness properties granted by the use of defensive distillation when training DNNs. We also empirically study the effectiveness of our defense mechanisms on two DNNs placed in adversarial settings. The study shows that defensive distillation can reduce effectiveness of sample creation from 95% to less than 0.5% on a studied DNN. Such dramatic gains can be explained by the fact that distillation leads gradients used in adversarial sample creation to be reduced by a factor of 1030. We also find that distillation increases the average minimum number of features that need to be modified to create adversarial samples by about 800% on one of the DNNs we tested.

The Limitations of Deep Learning in Adversarial Settings <=SEP=> Deep learning takes advantage of large datasets and computationally efficient training algorithms to outperform other approaches at various machine learning tasks. However, imperfections in the training phase of deep neural networks make them vulnerable to adversarial samples: inputs crafted by adversaries with the intent of causing deep neural networks to misclassify. In this work, we formalize the space of adversaries against deep neural networks (DNNs) and introduce a novel class of algorithms to craft adversarial samples based on a precise understanding of the mapping between inputs and outputs of DNNs. In an application to computer vision, we show that our algorithms can reliably produce samples correctly classified by human subjects but misclassified in specific targets by a DNN with a 97% adversarial success rate while only modifying on average 4.02% of the input features per sample. We then evaluate the vulnerability of different sample classes to adversarial perturbations by defining a hardness measure. Finally, we describe preliminary work outlining defenses against adversarial samples by defining a predictive measure of distance between a benign input and a target classification.

TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems <=SEP=> Martı́n Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dan Mané, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng Google Research∗ Abstract

Intriguing properties of neural networks <=SEP=> Deep neural networks are highly expressive models that have recently achieved state of the art performance on speech and visual recognition tasks. While their expressiveness is the reason they succeed, it also causes them to learn uninterpretable solutions that could have counter-intuitive properties. In this paper we report two such properties. First, we find that there is no distinction between individual high level units and random linear combinations of high level units, according to various methods of unit analysis. It suggests that it is the space, rather than the individual units, that contains the semantic information in the high layers of neural networks. Second, we find that deep neural networks learn input-output mappings that are fairly discontinuous to a significant extent. We can cause the network to misclassify an image by applying a certain hardly perceptible perturbation, which is found by maximizing the network’s prediction error. In addition, the specific nature of these perturbations is not a random artifact of learning: the same perturbation can cause a different network, that was trained on a different subset of the dataset, to misclassify the same input.

Gradient-based learning applied to document recognition <=SEP=> 

Millimeter wave cavity backed aperture coupled microstrip patch antenna <=SEP=> In this paper, a new single element broad band cavity backed multi-layer microstrip patch antenna is presented. This antenna is designed to be used in 79 GHz MIMO radar applications, and is fabricated with advanced high resolution multi-layer PCB technology. It has a wide bandwidth of 11.43 GHz, which is 14.5% of the center frequency. The beam widths in both E and H-planes are very wide, and are 144 degrees and 80 degrees respectively. Also, the antenna gain and efficiency are respectively 4.7 dB and 87%. Furthermore, measurements have been done, and show a good agreement with simulations.

ROBUST CYLINDER FITTING IN THREE-DIMENSIONAL POINT CLOUD DATA <=SEP=> This paper investigates the problems of cylinder fitting in laser scanning three-dimensional Point Cloud Data (PCD). Most existing methods require full cylinder data, do not study the presence of outliers, and are not statistically robust. But especially mobile laser scanning often has incomplete data, as street poles for example are only scanned from the road. Moreover, existence of outliers is common. Outliers may occur as random or systematic errors, and may be scattered and/or clustered. In this paper, we present a statistically robust cylinder fitting algorithm for PCD that combines Robust Principal Component Analysis (RPCA) with robust regression. Robust principal components as obtained by RPCA allow estimating cylinder directions more accurately, and an existing efficient circle fitting algorithm following robust regression principles, properly fit cylinder. We demonstrate the performance of the proposed method on artificial and real PCD. Results show that the proposed method provides more accurate and robust results: (i) in the presence of noise and high percentage of outliers, (ii) for incomplete as well as complete data, (iii) for small and large number of points, and (iv) for different sizes of radius. On 1000 simulated quarter cylinders of 1m radius with 10% outliers a PCA based method fit cylinders with a radius of on average 3.63meter (m); the proposed method on the other hand fit cylinders of on average 1.02 m radius. The algorithm has potential in applications such as fitting cylindrical (e.g., light and traffic) poles, diameter at breast height estimation for trees, and building and bridge information modelling.

Feature integration analysis of bag-of-features model for image retrieval <=SEP=> One of the biggest challenges in content based image retrieval is to solve the problem of “semantic gaps” between low-level features and high-level semantic concepts. In this paper, we aim to investigate various combinations of mid-level features to build an effective image retrieval system based on the bag-offeatures (BoF) model. Specifically, we study two ways of integrating the SIFT and LBP descriptors, HOG and LBP descriptors, respectively. Based on the qualitative and quantitative evaluations on two benchmark datasets, we show that the integrations of these features yield complementary and substantial improvement on image retrieval even with noisy background and ambiguous objects. Two integration models are proposed: the patch-based integration and image-based integration. By using a weighted K-means clustering algorithm, the image-based SIFT-LBP integration achieves the best performance on the given benchmark problems comparing to the existing algorithms. & 2013 Elsevier B.V. All rights reserved.

Emotion Regulation Ability 1 Running head : EMOTION REGULATION The Ability to Regulate Emotion is Associated with Greater Well-Being , Income , and Socioeconomic Status <=SEP=> Are people who are best able to implement strategies to regulate their emotional expressive behavior happier and more successful than their counterparts? Although past research has examined individual variation in knowledge of the most effective emotion regulation strategies, little is known about how individual differences in the ability to actually implement these strategies, as assessed objectively in the laboratory, is associated with external criteria. In two studies, we examined how individual variation in the ability to modify emotional expressive behavior in response to evocative stimuli is related to well-being and financial success. Study 1 showed that individuals who can best suppress their emotional reaction to an acoustic startle are happiest with their lives. Study 2 showed that individuals who can best amplify their emotional reaction to a disgust-eliciting movie are happiest with their lives and have the highest disposable income and socioeconomic status. Thus, being able to implement emotion regulation strategies in the laboratory is closely linked to well-being and financial success. Emotion Regulation Ability 3 The Ability to Regulate Emotion is Associated with Greater Well-Being, Income, and Socioeconomic Status Individual variation in cognitive abilities, such as language and mathematics, has been shown to relate strongly to a number of important life criteria, including performance at school and at work (Kunzel, Hezlett, & Ones, 2004; Schmidt & Hunter, 1998). Research in recent years has suggested that there is also important variation among individuals in emotional abilities (see Mayer, Roberts, & Barsade, 2008; Mayer, Salovey, & Caruso, 2008, for reviews). In particular, the ability to regulate emotions reflects variation in how well people adjust emotional responses to meet current situational demands (Gross & Thompson, 2007; Salovey & Mayer, 1990). Equipped with this ability, individuals can aptly modify which emotions they have, when they have them, and how they experience and express them (Gross, 1998). This ability is arguably one of the most critical elements of our emotion repertoire, and it is the focus of the present research. Past research has begun to examine whether individual variation in the ability to regulate emotions is associated with various criteria. This research has found that variation in knowledge of how to best regulate emotions – whether people know the rules of emotion regulation – is associated with well-being, close social relationships, high grades in school, and high job performance (e.g., Côté & Miners, 2006; Lopes, Salovey, Côté, & Beers, 2005; MacCann & Roberts, 2008). The measures used in these studies assess the degree to which people know how to best manage emotions. Specifically, they reflect how closely respondents’ judgments of how to best regulate emotion in hypothetical scenarios match the judgments of experts. For instance, the Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT; Mayer, Salovey, & Caruso, 2002) asks respondents to rate the effectiveness of a series of strategies to manage emotions in several hypothetical scenarios, and their responses are compared to those provided by expert emotion researchers. Emotion Regulation Ability 4 Notwithstanding the importance of knowing how to best manage emotions, knowledge does not fully represent the domain of emotion regulation ability. People who know the best strategies may not implement them well. The distinction between knowledge and the ability to implement is established in the larger literature on intelligence (cf. Ackerman, 1996), and it is also theoretically useful to describe emotional abilities. For example, a customer service agent who knows that cognitively reframing an interaction with a difficult customer is the best strategy may not implement that strategy well during the interaction. Thus, to understand fully how emotion regulation ability is associated with criteria such as well-being and financial success, researchers must also examine the ability to implement strategies to regulate emotions – whether people can actually operate the machinery of emotion regulation. Several of the measures used in studies of the relationship between emotion regulation and other criteria do not assess actual ability to implement emotion regulation strategies. For example, the MSCEIT (Mayer et al., 2002) does not ask respondents to implement the strategy that they believe best addresses the issues depicted in the scenarios. Recent advances in affective science, however, provide tools to objectively assess the ability to implement emotion regulation strategies (Gross & Levenson, 1993; Hagemann, Levenson, & Gross, 2006; Jackson, Malmstadt, Larson, & Davidson, 2000; Kunzmann, Kupperbusch, & Levenson, 2005). In these laboratory paradigms, individuals receive specific instructions about how to regulate their emotions (e.g., reduce the intensity of their emotional expressive behaviors) when encountering emotional stimuli, such as loud noises or emotionally evocative film clips. Success at implementing the emotion regulation strategy can be measured objectively, for example, by coding how much respondents change their emotional expressive behavior when being instructed to do so. Several studies have used this paradigm to examine how regulating emotions is associated with cognitive task performance (Baumeister, Bratslavsky, Muraven, & Tice, 1998; Emotion Regulation Ability 5 Bonanno, Papa, Lalande, Westphal, & Coifman, 2004; Schmeichel, Demaree, Robinson, & Pu, 2006), the activation of neural systems (Beauregard, Levesque, & Bourgouin, 2001; Oschner, Bunge, Gross, & Gabrieli, 2002), and emotion experience, emotional expressive behavior, and autonomic physiology (Demaree, Schmeichel, Robinson, Pu, Everhart, & Berntson, 2006; Giuliani, McCrae, & Gross, 2008; Gross & Levenson, 1993, 1997; Hagemann et al., 2006). This paradigm has also been used as an individual difference measure to test how the ability to implement emotion regulation strategies is associated with age (Kunzmann et al., 2005; Scheibe & Blanchard-Fields, 2009), working memory (Schmeichel, Volokhov, & Demaree, 2008), and executive function (Gyurak, Goodkind, Madan, Kramer, Miller, & Levenson, 2008). In addition, one study employed this paradigm to assess people’s flexibility in using different emotion regulation strategies depending on the situation, showing that flexibility is associated with lower distress after a traumatic event (Bonanno, Papa, Lalande, Westphal, & Coifman, 2004). Thus, this body of research supports the utility of these laboratory paradigms for assessing individual variation in the ability to implement emotion regulation strategies and the correlates of this ability. In this report, we present the results of two studies that examine whether individual variation in the ability to implement strategies to regulate emotions is associated with well-being and financial success and, if so, in what direction. Most people regulate their emotions daily, and more than half the time, they do so by modifying the expression of emotions in their face, voice, and posture (Gross, Richards, & John, 2006). Given the frequency with which we regulate our emotional expressive behavior, it is reasonable to expect that the individual’s ability in this realm would exhibit important associations with other constructs. The regulation of visible expressive behavior encompasses both up-regulation (amplifying emotional expressive behavior) and downEmotion Regulation Ability 6 regulation (reducing emotional expressive behavior). We considered the association of both with our criteria. We now turn to our theoretical development. A review of the existing literature suggests the possibility of both a positive and a negative association between the ability to implement emotion regulation strategies assessed in the laboratory and well-being and financial success. Furthermore, because we do not test the direction of causality in our studies, we consider theoretical arguments for both causal directions of associations, reviewing literatures that suggest that emotion regulation ability has consequences for well-being and financial success (both positive and negative), and also that well-being and financial success have consequences for emotion regulation ability (both positive and negative). The Ability to Regulate Emotional Behavior and Well-Being and Financial Success: Positive Associations In this section, we present theoretical arguments suggesting that the ability to regulate emotion and well-being and financial success are positively associated. We first describe why high emotion regulation ability may help people become happier and garner more financial resources, and then we examine whether happiness and financial resources may help people develop better abilities to regulate their emotions. Why Would Emotion Regulation Ability Increase Well-Being and Financial Success? Philosophers have argued that rational thought and a happy life requires the ability to rein in on emotional impulses (Aristotle, 1884; Solomon, 1993). The ability to modify emotional expressive behavior effectively may help people adapt flexibly to situational demands. Equipped with this ability, individuals might be more successful in communicating attitudes, goals, and intentions that are appropriate in various situations (Keltner & Haidt, 1999) and that might be Emotion Regulation Ability 7 rewarded and fulfilled. The ability to adapt successfully to situational demands then could be associated with various indicators of well-being and success. At a more micro-level, modifying emotional expressive behavior effectively may help people conform to display rules about who can show which emotions to whom and when they can do so (Friesen, 1972). People often attain rewards for conforming to displays rules in various settings. For instance, employees who conform to display rules at wo

Optimizing Space Amplification in RocksDB <=SEP=> RocksDB is an embedded, high-performance, persistent keyvalue storage engine developed at Facebook. Much of our current focus in developing and configuring RocksDB is to give priority to resource efficiency instead of giving priority to the more standard performance metrics, such as response time latency and throughput, as long as the latter remain acceptable. In particular, we optimize space efficiency while ensuring read and write latencies meet service-level requirements for the intended workloads. This choice is motivated by the fact that storage space is most often the primary bottleneck when using Flash SSDs under typical production workloads at Facebook. RocksDB uses log-structured merge trees to obtain significant space efficiency and better write throughput while achieving acceptable read performance. This paper describes methods we used to reduce storage usage in RocksDB. We discuss how we are able to trade off storage efficiency and CPU overhead, as well as read and write amplification. Based on experimental evaluations of MySQL with RocksDB as the embedded storage engine (using TPC-C and LinkBench benchmarks) and based on measurements taken from production databases, we show that RocksDB uses less than half the storage that InnoDB uses, yet performs well and in many cases even better than the B-tree-based InnoDB storage engine. To the best of our knowledge, this is the first time a Log-structured merge treebased storage engine has shown competitive performance when running OLTP workloads at large scale.

Ultra-Reliable and Low-Latency 5 G Communication <=SEP=> Machine-to-machine communication, M2M, will make up a large portion of the new types of services and use cases that the fifth generation (5G) systems will address. On the one hand, 5G will connect a large number of low-cost and low-energy devices in the context of the Internet of things; on the other hand it will enable critical machine type communication use cases, such as smart factory, automotive, energy, and e-health – which require communication with very high reliability and availability, as well as very low end-to-end latency. In this paper, we will discuss the requirements, enablers and challenges to support these emerging mission-critical 5G use cases. Keywords— 5G, NR, M2M, MTC, URLLC, Tactile Internet, Reliability, Latency.

A GLOBAL MEASURE OF PERCEIVED STRESS <=SEP=> Aim: To evaluate validity of the Greek version of a global measure of perceived stress PSS−14 (Perceived Stress Scale – 14 item). Materials and Methods: The original PSS−14 (theoretical range 0−56) was translated into Greek and then back-translated. One hundred men and women (39±10 years old, 40 men) participated in the validation process. Firstly, participants completed the Greek PSS−14 and, then they were interviewed by a psychologist specializing in stress management. Cronbach’s alpha (α) evaluated internal consistency of the measurement, whereas Kendall’s tau-b and Bland & Altman methods assessed consistency with the clinical evaluation. Exploratory and Confirmatory Factor analyses were conducted to reveal hidden factors within the data and to confirm the two-dimensional character of the scale. Results: Mean (SD) PSS−14 score was 25(7.9). Strong internal consistency (Cronbach’s α = 0.847) as well as moderate-to-good concordance between clinical assessment and PSS−14 (Kendall’s tau-b = 0.43, p<0.01) were observed. Two factors were extracted. Factor one explained 34.7% of variability and was heavily laden by positive items, and factor two that explained 10.6% of the variability by negative items. Confirmatory factor analysis revealed that the model with 2 factors had chi-square equal to 241.23 (p<0.001), absolute fix indexes were good (i.e. GFI=0.733, AGFI=0.529), and incremental fix indexes were also adequate (i.e. NFI=0.89 and CFI=0.92). Conclusion: The developed Greek version of PSS−14 seems to be a valid instrument for the assessment of perceived stress in the Greek adult population living in urban areas; a finding that supports its local use in research settings as an evaluation tool measuring perceived stress, mainly as a risk factor but without diagnostic properties.

Polysemy: Theoretical and Computational Approaches Yael Ravin and Claudia Leacock (editors) (IBM T. J. Watson Research Center and Educational Testing Services) New York: Oxford University Press, 2000, xi+227 pp; hardbound, ISBN 0-19-823842-8, $74.00, £45.00; paperbound, ISBN 0-19-925086-3, $21.95, £ <=SEP=> As the editors of this volume remind us, polysemy has been a vexing issue for the understanding of language since antiquity. For half a century, it has been a major bottleneck for natural language processing. It contributed to the failure of early machine translation research (remember Bar-Hillel’s famous pen and box example) and is still plaguing most natural language processing and information retrieval applications. A recent issue of this journal described the state of the art in automatic sense disambiguation (Ide and Véronis 1998), and Senseval system competitions have revealed the immense difficulty of the task (http://www.sle.sharp.co.uk/senseval2). However, no significant progress can be made on the computational aspects of polysemy without serious advances in theoretical issues. At the same time, theoretical work can be fostered by computational results and problems, and language-processing applications can provide a unique test bed for theories. It was therefore an excellent idea to gather both theoretical and applied contributions in the same book. Yael Ravin and Claudia Leacock are well-known names to those who work on the theoretical and computational aspects of word meaning. In this volume, they bring together a collection of essays from leading researchers in the field. As far as I can tell, these essays are not reprints or expanded versions of conference papers, as is often the case for edited works; instead, they seem to have been specially commissioned for the purposes of this book, which makes it even more exciting to examine. The book is composed of 11 chapters. It is not formally divided into parts, but chapters dealing more specifically with the computational aspects of polysemy are grouped together at the end (and constitute about one-third of the volume). Chapter 1 is an overview written by the volume editors. Yael Ravin and Claudia Leacock survey the main theories of meaning and their treatment of polysemy. These include the classical Aristotelian approach revived by Katz and Fodor (1963); Rosch’s (1977) prototypical approach, which has its roots in Wittgenstein’s Philosophical Investigations (1953); and the relational approach recently exemplified by WordNet (Fellbaum 1998), which (although the authors do not mention it) can be traced back to Peirce’s (1931–1958) and Selz’s (1913, 1922) graphs and which gained popularity with Quillian’s (1968) semantic networks. In the course of this overview, Ravin and Leacock put the individual chapters into perspective by relating them to the various theories.

Effects of a Dynamic Warm-Up, Static Stretching or Static Stretching with Tendon Vibration on Vertical Jump Performance and EMG Responses <=SEP=> The purpose of this study was to investigate the short-term effects of static stretching, with vibration given directly over Achilles tendon, on electro-myographic (EMG) responses and vertical jump (VJ) performances. Fifteen male, college athletes voluntarily participated in this study (n=15; age: 22±4 years old; body height: 181±10 cm; body mass: 74±11 kg). All stages were completed within 90 minutes for each participant. Tendon vibration bouts lasted 30 seconds at 50 Hz for each volunteer. EMG analysis for peripheral silent period, H-reflex, H-reflex threshold, T-reflex and H/M ratio were completed for each experimental phases. EMG data were obtained from the soleus muscle in response to electro stimulation on the popliteal post tibial nerve. As expected, the dynamic warm-up (DW) increased VJ performances (p=0.004). Increased VJ performances after the DW were not statistically substantiated by the EMG findings. In addition, EMG results did not indicate that either static stretching (SS) or tendon vibration combined with static stretching (TVSS) had any detrimental or facilitation effect on vertical jump performances. In conclusion, using TVSS does not seem to facilitate warm-up effects before explosive performance.

Relation between language experiences in preschool classrooms and children's kindergarten and fourth-grade language and reading abilities. <=SEP=> Indirect effects of preschool classroom indexes of teacher talk were tested on fourth-grade outcomes for 57 students from low-income families in a longitudinal study of classroom and home influences on reading. Detailed observations and audiotaped teacher and child language data were coded to measure content and quantity of verbal interactions in preschool classrooms. Preschool teachers' use of sophisticated vocabulary during free play predicted fourth-grade reading comprehension and word recognition (mean age=9; 7), with effects mediated by kindergarten child language measures (mean age=5; 6). In large group preschool settings, teachers' attention-getting utterances were directly related to later comprehension. Preschool teachers' correcting utterances and analytic talk about books, and early support in the home for literacy predicted fourth-grade vocabulary, as mediated by kindergarten receptive vocabulary.

Static secure page allocation for light-weight dynamic information flow tracking <=SEP=> Dynamic information flow tracking (DIFT) is an effective security countermeasure for both low-level memory corruptions and high-level semantic attacks. However, many software approaches suffer large performance degradation, and hardware approaches have high logic and storage overhead. We propose a flexible and light-weight hardware/software co-design approach to perform DIFT based on secure page allocation. Instead of associating every data with a taint tag, we aggregate data according to their taints, i.e., putting data with different attributes in separate memory pages. Our approach is a compiler-aided process with architecture support. The implementation and analysis show that the memory overhead is little, and our approach can protect critical information, including return address, indirect jump address, and system call IDs, from being overwritten by malicious users.

A concept for automated construction progress monitoring using BIM-based geometric constraints and photogrammetric point clouds <=SEP=> On-site progress monitoring is essential for keeping track of the ongoing work on construction sites. Currently, this task is a manual, time-consuming activity. The research presented here, describes a concept for an automated comparison of the actual state of construction with the planned state for the early detection of deviations in the construction process. The actual state of the construction site is detected by photogrammetric surveys. From these recordings, dense point clouds are generated by the fusion of disparity maps created with semi-global-matching (SGM). These are matched against the target state provided by a 4D Building Information Model (BIM). For matching the point cloud and the BIM, the distances between individual points of the cloud and a component’s surface are aggregated using a regular cell grid. For each cell, the degree of coverage is determined. Based on this, a confidence value is computed which serves as basis for the existence decision concerning the respective component. Additionally, processand dependency-relations are included to further enhance the detection process. Experimental results from a real-world case study are presented and discussed.

Evolutionary computation and evolutionary deep learning for image analysis, signal processing and pattern recognition <=SEP=> Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s). GECCO ’18 Companion, July 15--19, 2018, Kyoto, Japan © 2018 Copyright held by the owner/author(s). 978-1-4503-57647/18/07...$15.00 DOI https://doi.org/10.1145/3205651.3207859

Serotonin alterations in anorexia and bulimia nervosa: New insights from imaging studies <=SEP=> Anorexia nervosa (AN) and bulimia nervosa (BN) are related disorders with relatively homogenous presentations such as age of onset and gender distribution. In addition, they share symptoms, such as extremes of food consumption, body image distortion, anxiety and obsessions, and ego-syntonic neglect, raises the possibility that these symptoms reflect disturbed brain function that contributes to the pathophysiology of this illness. Recent brain imaging studies have identified altered activity in frontal, cingulate, temporal, and parietal cortical regions in AN and BN. Importantly, such disturbances are present when subjects are ill and persist after recovery, suggesting that these may be traits that are independent of the state of the illness. Emerging data point to a dysregulation of serotonin pathways in cortical and limbic structures that may be related to anxiety, behavioral inhibition, and body image distortions. In specific, recent studies using PET with serotonin specific radioligands implicate alterations of 5-HT1A and 5-HT2A receptors and the 5-HT transporter. Alterations of these circuits may affect mood and impulse control as well as the motivating and hedonic aspects of feeding behavior. Such imaging studies may offer insights into new pharmacology and psychotherapy approaches.

Thunderstrike: EFI firmware bootkits for Apple MacBooks <=SEP=> There are several flaws in Apple's MacBook firmware security that allows untrusted modifications to be written to the SPI Flash boot ROM of these laptops. This capability represents a new class of persistent firmware rootkits, or 'bootkits', for the popular Apple MacBook product line. Stealthy bootkits can conceal themselves from detection and prevent software attempts to remove them. Malicious modifications to the boot ROM are able to survive re-installation of the operating system and even hard-drive replacement. Additionally, the malware can install a copy of itself onto other Thunderbolt devices' Option ROMs as a means to spread virally across air-gap security perimeters. Apple has fixed some of these flaws as part of CVE 2014-4498, but there is no easy solution to this class of vulnerability, since the MacBook lacks trusted hardware to perform cryptographic validation of the firmware at boot time.

High-Q MEMS Resonators for Laser Beam Scanning Displays <=SEP=> This paper reports on design, fabrication and characterization of high-Q MEMS resonators to be used in optical applications like laser displays and LIDAR range sensors. Stacked vertical comb drives for electrostatic actuation of single-axis scanners and biaxial MEMS mirrors were realized in a dual layer polysilicon SOI process. High Q-factors up to 145,000 have been achieved applying wafer level vacuum packaging technology including deposition of titanium thin film getters. The effective reduction of gas damping allows the MEMS actuator to achieve large amplitudes at high oscillation frequencies while driving voltage and power consumption can be minimized. Exemplarily shown is a micro scanner that achieves a total optical scan angle of 86 degrees at a resonant frequency of 30.8 kHz, which fulfills the requirements for HD720 resolution. Furthermore, results of a new wafer based glass-forming technology for fabrication of three dimensionally shaped glass lids with tilted optical windows are presented.

Supplementary material for the paper : Discriminative Correlation Filter with Channel and Spatial Reliability DCF-CSR <=SEP=> This is the supplementary material for the paper ”Discriminative Correlation Filter with Channel and Spatial Reliability” submitted to the CVPR 2017. Due to spatial constraints, parts not crucial for understanding the DCF-CSR tracker formulation, but helpful for gaining insights, were moved here. 1. Derivation of the augmented Lagrangian minimizer This section provides the complete derivation of the closed-form solutions for the relations (9,10) in the submitted paper [3]. The augmented Lagrangian from Equation (5) in [3] is L(ĥc,h, l̂) = ‖ĥc diag(f̂)− ĝ‖ + λ 2 ‖hm‖ + (1) [̂l(ĥc − ĥm) + l̂(ĥc − ĥm)] + μ‖ĥc − ĥm‖, with hm = (m h). For the purposes of derivation we will rewrite (1) into a fully vectorized form L(ĥc,h, l̂) = ‖ĥc diag(f̂)− ĝ‖ + λ 2 ‖hm‖+ (2) [̂ l(ĥc − √ DFMh) + l̂(ĥc − √ DFMh) ] + μ‖ĥc − √ DFMh‖, where F denotes D × D orthonormal matrix of Fourier coefficients, such that the Fourier transform is defined as x̂ = F(x) = √ DFx and M = diag(m). For clearer representation we denote the four terms in the summation (2) as L(ĥc,h, l̂) = L1 + L2 + L3 + L4, (3) where L1 = ( ĥc diag(f̂)− ĝ )( ĥc diag(f̂)− ĝ )T , (4)

Efficient Energy-Optimal Routing for Electric Vehicles <=SEP=> Traditionally routing has focused on finding shortest paths in networks with positive, static edge costs representing the distance between two nodes. Energy-optimal routing for electric vehicles creates novel algorithmic challenges, as simply understanding edge costs as energy values and applying standard algorithms does not work. First, edge costs can be negative due to recuperation, excluding Dijkstra-like algorithms. Second, edge costs may depend on parameters such as vehicle weight only known at query time, ruling out existing preprocessing techniques. Third, considering battery capacity limitations implies that the cost of a path is no longer just the sum of its edge costs. This paper shows how these challenges can be met within the framework of A* search. We show how the specific domain gives rise to a consistent heuristic function yielding an O(n) routing algorithm. Moreover, we show how battery constraints can be treated by dynamically adapting edge costs and hence can be handled in the same way as parameters given at query time, without increasing run-time complexity. Experimental results with real road networks and vehicle data demonstrate the advantages of our solution.

Matching RGB Images to CAD Models for Object Pose Estimation <=SEP=> We propose a novel method for 3D object pose estimation in RGB images, which does not require pose annotations of objects in images in the training stage. We tackle the pose estimation problem by learning how to establish correspondences between RGB images and rendered depth images of CAD models. During training, our approach only requires textureless CAD models and aligned RGB-D frames of a subset of object instances, without explicitly requiring pose annotations for the RGB images. We employ a deep quadruplet convolutional neural network for joint learning of suitable keypoints and their associated descriptors in pairs of rendered depth images which can be matched across modalities with aligned RGB-D views. During testing, keypoints are extracted from a query RGB image and matched to keypoints extracted from rendered depth images, followed by establishing 2D-3D correspondences. The object’s pose is then estimated using the RANSAC and PnP algorithms. We conduct experiments on the recently introduced Pix3D [33] dataset and demonstrate the efficacy of our proposed approach in object pose estimation as well as generalization to object instances not seen during training.

Field-weakening Control Algorithm for Interior Permanent Magnet Synchronous Motor Based on Space-Vector Modulation Technique <=SEP=> We investigate the implementation of field-weakening control for interior permanent magnet synchronous motor (IPMSM), and analyze the algorithm of field-weakening control in d-q axes. To deal with the problem that the dc-link voltage is not fully utilized when the voltage regulation is employed, we propose a new field-weakening scheme based on the space-vector modulation technique. The duty-time of zero-voltage vector is used as the feedback signal to decide the switching of fieldweakening. To avoid the regulation lag in the q-axis component, we apply the lead-angle control during the field-weakening progress. The proposed scheme is validated with Matlab/Simulink tool. Simulation results show that the scheme is feasible. It not only improves the utilization ratio of the space-vector-pulse-width-modulation (SVPWM) inverter, but also achieves a smooth transition between the constant-torque mode below the base-speed and the constant-power mode above the basespeed.

An Image Matching Algorithm Based on SIFT and Improved LTP <=SEP=> SIFT is one of the most robust and widely used image matching algorithms based on local features. But the key-points descriptor of SIFT algorithm have 128 dimensions. Aiming to the problem of its high dimension and complexity, a novel image matching algorithm is proposed. The descriptors of SIFT key-points are constructed by the rotation invariant LTP, city-block distance is also employed to reduce calculation of key-points matching. The experiment is achieved through different lighting, blur changes and rotation of images, the results show that this method can reduce the processing time and raise image matching efficiency.

An Over-110-GHz-Bandwidth 2:1 Analog Multiplexer in 0.25-μm InP DHBT Technology <=SEP=> This paper presents an over-110-GHz-bandwidth 2:1 analog multiplexer (AMUX) for ultra-broadband digital-to-analog (D/A) conversion subsystems. The AMUX was designed and fabricated by using newly developed $\pmb{0.25-\mu \mathrm{m}}$ -emitter-width InP double heterojunction bipolar transistors (DHBTs), which have a peak $\pmb{f_{\mathrm{T}}}$ and $\pmb{ f\displaystyle \max}$ of 460 and 480 GHz, respectively. The AMUX IC consists of lumped building blocks, including data-input linear buffers, a clock-input limiting buffer, an AMUX core, and an output linear buffer. The measured 3-dB bandwidth for data and clock paths are both over 110 GHz. In addition, it measures and obtains time-domain large-signal sampling operations of up to 180 GS/s. A 224-Gb/s (112-GBaud) four-level pulse-amplitude modulation (PAM4) signal was successfully generated by using this AMUX. To the best of our knowledge, this AMUX IC has the broadest bandwidth and the fastest sampling rate compared with any other previously reported AMUXes.

Field-stop layer optimization for 1200V FS IGBT operating at 200˚C <=SEP=> This paper is concerned with design considerations for enabling the operation of Field-Stop Insulated Gate Bipolar Transistors (FS IGBTs) at 200 C. It is found that through a careful optimization of the Field-Stop layer doping profile the device has a low leakage current and delivers a favorable tradeoff between the on-state voltage (Von) and turn-off loss (Eoff). An investigation of the adverse effects of increasing the junction temperature on the temperature-dependent properties of the FS IGBTs is also discussed herein.

Automatic indexing: an approach using an index term corpus and combining linguistic and statistical methods <=SEP=> This thesis discusses the problems and the methods of finding relevant information in large collections of documents. The contribution of this thesis to this problem is to develop better content analysis methods which can be used to describe document content with

Theory of Outlier Ensembles <=SEP=> Outlier detection is an unsupervised problem, in which labels are not available with data records [2]. As a result, it is generally more challenging to design ensemble analysis algorithms for outlier detection. In particular, methods that require the use of labels in intermediate steps of the algorithm cannot be generalized to outlier detection. For example, in the case of boosting, the classifier algorithm needs to be evaluated in the intermediate steps of the algorithm with the use of training-data labels. Such methods are generally not possible in the case of outlier analysis. As discussed in [1], there are unique reasons why ensemble analysis is generally more difficult in the case of outlier analysis as compared to classification. In spite of the unsupervised nature of outlier ensemble analysis, we show that the theoretical foundations of outlier analysis and classification are surprisingly similar. A number of useful discussions on the theory of classification ensembles may be found in [27, 29, 33]. Further explanations on the use of the bias-variance decomposition in different types of classifiers such as neural networks, support vector machines, and rule-based classifiers are discussed in [17, 30, 31]. It is noteworthy that the biasvariance decomposition is often used in customized ways for different types of base classifiers and combination methods; this general principle is also true in outlier detection. Several arguments have recently been proposed on the theory explaining the accuracy improvements of outlier ensembles. In some cases, incorrect new arguments (such as those in [32]) are proposed to justify experimental results that can be explained by well-known ideas, and an artificial distinction is made between the theory of classification ensembles and outlier ensembles. A recent paper [4]