Query: Predicting Facial Attributes in Video Using Temporal Coherence and Motion-Attention

Relevant passages:
Slow and Steady Feature Analysis: Higher Order Temporal Coherence in Video <=SEP=> How can unlabeled video augment visual learning? Existing methods perform "slow" feature analysis, encouraging the representations of temporally close frames to exhibit only small differences. While this standard approach captures the fact that high-level visual signals change slowly over time, it fails to capture how the visual content changes. We propose to generalize slow feature analysis to "steady" feature analysis. The key idea is to impose a prior that higher order derivatives in the learned feature space must be small. To this end, we train a convolutional neural network with a regularizer on tuples of sequential frames from unlabeled video. It encourages feature changes over time to be smooth, i.e., similar to the most recent changes. Using five diverse datasets, including unlabeled YouTube and KITTI videos, we demonstrate our method's impact on object, scene, and action recognition tasks. We further show that our features learned from unlabeled video can even surpass a standard heavily supervised pretraining approach.

Face recognition: A literature survey <=SEP=> As one of the most successful applications of image analysis and understanding, face recognition has recently received significant attention, especially during the past several years. At least two reasons account for this trend: the first is the wide range of commercial and law enforcement applications, and the second is the availability of feasible technologies after 30 years of research. Even though current machine recognition systems have reached a certain level of maturity, their success is limited by the conditions imposed by many real applications. For example, recognition of face images acquired in an outdoor environment with changes in illumination and/or pose remains a largely unsolved problem. In other words, current systems are still far away from the capability of the human perception system.This paper provides an up-to-date critical survey of still- and video-based face recognition research. There are two underlying motivations for us to write this survey paper: the first is to provide an up-to-date review of the existing literature, and the second is to offer some insights into the studies of machine recognition of faces. To provide a comprehensive survey, we not only categorize existing recognition techniques but also present detailed descriptions of representative methods within each category. In addition, relevant topics such as psychophysical studies, system evaluation, and issues of illumination and pose variation are covered.

Face recognition in unconstrained videos with matched background similarity <=SEP=> Recognizing faces in unconstrained videos is a task of mounting importance. While obviously related to face recognition in still images, it has its own unique characteristics and algorithmic requirements. Over the years several methods have been suggested for this problem, and a few benchmark data sets have been assembled to facilitate its study. However, there is a sizable gap between the actual application needs and the current state of the art. In this paper we make the following contributions. (a) We present a comprehensive database of labeled videos of faces in challenging, uncontrolled conditions (i.e., ‘in the wild’), the ‘YouTube Faces’ database, along with benchmark, pair-matching tests1. (b) We employ our benchmark to survey and compare the performance of a large variety of existing video face recognition techniques. Finally, (c) we describe a novel set-to-set similarity measure, the Matched Background Similarity (MBGS). This similarity is shown to considerably improve performance on the benchmark tests.

Caffe: Convolutional Architecture for Fast Feature Embedding <=SEP=> Caffe provides multimedia scientists and practitioners with a clean and modifiable framework for state-of-the-art deep learning algorithms and a collection of reference models. The framework is a BSD-licensed C++ library with Python and MATLAB bindings for training and deploying general-purpose convolutional neural networks and other deep models efficiently on commodity architectures. Caffe fits industry and internet-scale media needs by CUDA GPU computation, processing over 40 million images a day on a single K40 or Titan GPU (approx 2 ms per image). By separating model representation from actual implementation, Caffe allows experimentation and seamless switching among platforms for ease of development and deployment from prototyping machines to cloud environments.
 Caffe is maintained and developed by the Berkeley Vision and Learning Center (BVLC) with the help of an active community of contributors on GitHub. It powers ongoing research projects, large-scale industrial applications, and startup prototypes in vision, speech, and multimedia.

Learning realistic human actions from movies <=SEP=> The aim of this paper is to address recognition of natural human actions in diverse and realistic video settings. This challenging but important subject has mostly been ignored in the past due to several problems one of which is the lack of realistic and annotated video datasets. Our first contribution is to address this limitation and to investigate the use of movie scripts for automatic annotation of human actions in videos. We evaluate alternative methods for action retrieval from scripts and show benefits of a text-based classifier. Using the retrieved action samples for visual learning, we next turn to the problem of action classification in video. We present a new method for video classification that builds upon and extends several recent ideas including local space-time features, space-time pyramids and multi-channel non-linear SVMs. The method is shown to improve state-of-the-art results on the standard KTH action dataset by achieving 91.8% accuracy. Given the inherent problem of noisy labels in automatic annotation, we particularly investigate and show high tolerance of our method to annotation errors in the training set. We finally apply the method to learning and classifying challenging action classes in movies and show promising results.

Fast Computation of the Difference of Low-Pass Transform <=SEP=> This paper defines the difference of low-pass (DOLP) transform and describes a fast algorithm for its computation. The DOLP is a reversible transform which converts an image into a set of bandpass images. A DOLP transform is shown to require O(N2) multiplies and produce O(N log(N)) samples from an N sample image. When Gaussian low-pass filters are used, the result is a set of images which have been convolved with difference of Gaussian (DOG) filters from an exponential set of sizes. A fast computation technique based on ``resampling'' is described and shown to reduce the DOLP transform complexity to O(N log(N)) multiplies and O(N) storage locations. A second technique, ``cascaded convolution with expansion,'' is then defined and also shown to reduce the computational cost to O(N log(N)) multiplies. Combining these two techniques yields an algorithm for a DOLP transform that requires O(N) storage cells and requires O(N) multiplies.

Three-way decisions with probabilistic rough sets <=SEP=> The rough set theory approximates a concept by three regions, namely, the positive, boundary and negative regions. Rules constructed from the three regions are associated with different actions and decisions, which immediately leads to the notion of three-way decision rules. A positive rule makes a decision of acceptance, a negative rule makes a decision of rejection, and a boundary rule makes a decision of abstaining. This paper provides an analysis of three-way decision rules in the classical rough set model and the decision-theoretic rough set model. The results enrich the rough set theory by ideas from Bayesian decision theory and hypothesis testing in statistics. The connections established between the levels of tolerance for errors and costs of incorrect decisions make the rough set theory practical in applications.

A method for normalizing histology slides for quantitative analysis <=SEP=> Inconsistencies in the preparation of histology slides make it difficult to perform quantitative analysis on their results. In this paper we provide two mechanisms for overcoming many of the known inconsistencies in the staining process, thereby bringing slides that were processed or stored under very different conditions into a common, normalized space to enable improved quantitative analysis.

Pattern-Reconfigurable Planar Circular Ultra-Wideband Monopole Antenna <=SEP=> A novel pattern-reconfigurable compact planar ultra-wideband monopole antenna is presented. By the incorporation of four p-i-n diode switches and two parasitic elements, the antenna's radiation patterns can be shaped to concentrate energy in specific directions while minimising the gain in other unwanted directions without significantly affecting the impedance bandwidth of the antenna. A fully functional prototype has been developed and tested. The measured results of the return loss, radiation patterns, and realised gain verify the effectiveness of the proposed antenna configuration. The antenna switches its radiation patterns between an omni-directional mode and two directional modes with opposite directions in the operating range from 3 to 6 GHz. The proposed antenna could be a suitable candidate for advanced and smart radio applications such as cognitive radio (CR) as it can enhance the radio front-end flexibility and performance by adding the benefits of pattern diversity, specifically in multipath environments.

Genome plasticity a key factor in the success of polyploid wheat under domestication. <=SEP=> Wheat was domesticated about 10,000 years ago and has since spread worldwide to become one of the major crops. Its adaptability to diverse environments and end uses is surprising given the diversity bottlenecks expected from recent domestication and polyploid speciation events. Wheat compensates for these bottlenecks by capturing part of the genetic diversity of its progenitors and by generating new diversity at a relatively fast pace. Frequent gene deletions and disruptions generated by a fast replacement rate of repetitive sequences are buffered by the polyploid nature of wheat, resulting in subtle dosage effects on which selection can operate.

Empowerment - an Introduction <=SEP=> Is it better for you to own a corkscrew or not? If asked, you as a human being would likely say " yes " , but more importantly, you are somehow able to make this decision. You are able to decide this, even if your current acute problems or task do not include opening a wine bottle. Similarly, it is also unlikely that you evaluated several possible trajectories your life could take and looked at them with and without a corkscrew, and then measured your survival or reproductive fitness in each. When you, as a human cognitive agent, made this decision, you were likely relying on a behavioural " proxy " , an internal motivation that abstracts the problem of evaluating a decision impact on your overall life, but evaluating it in regard to some simple fitness function. One example would be the idea of curiosity, urging you to act so that your experience new sensations and learn about the environment. On average, this should lead to better and richer models of the world, which give you a better chance of reaching your ultimate goals of survival and reproduction. But how about questions such as, would you rather be rich than poor, sick or healthy, imprisoned or free? While each options offers some interesting new experience , there seems to be a consensus that rich, healthy and free is a preferable choice. We think that all these examples, in addition to the question of tool ownership above, share a common element of preparedness. Everything else being equal it is preferable to be prepared, to keep ones options open or to be in a state where ones actions have the greatest influence on ones direct environment. The concept of Empowerment, in a nutshell, is an attempt at formalizing and quantifying these degrees of freedom (or options) that an organism or agent has as a proxy for " preparedness " ; preparedness, in turn, is considered a proxy for prospective fitness via the hypothesis that preparedness would be a good indicator to distinguish promising from less promising regions in the prospective fitness landscape, without actually having to evaluate the full fitness landscape.

DroidMat: Android Malware Detection through Manifest and API Calls Tracing <=SEP=> Recently, the threat of Android malware is spreading rapidly, especially those repackaged Android malware. Although understanding Android malware using dynamic analysis can provide a comprehensive view, it is still subjected to high cost in environment deployment and manual efforts in investigation. In this study, we propose a static feature-based mechanism to provide a static analyst paradigm for detecting the Android malware. The mechanism considers the static information including permissions, deployment of components, Intent messages passing and API calls for characterizing the Android applications behavior. In order to recognize different intentions of Android malware, different kinds of clustering algorithms can be applied to enhance the malware modeling capability. Besides, we leverage the proposed mechanism and develop a system, called Droid Mat. First, the Droid Mat extracts the information (e.g., requested permissions, Intent messages passing, etc) from each application's manifest file, and regards components (Activity, Service, Receiver) as entry points drilling down for tracing API Calls related to permissions. Next, it applies K-means algorithm that enhances the malware modeling capability. The number of clusters are decided by Singular Value Decomposition (SVD) method on the low rank approximation. Finally, it uses kNN algorithm to classify the application as benign or malicious. The experiment result shows that the recall rate of our approach is better than one of well-known tool, Androguard, published in Black hat 2011, which focuses on Android malware analysis. In addition, Droid Mat is efficient since it takes only half of time than Androguard to predict 1738 apps as benign apps or Android malware.

B-ROC Curves for the Assessment of Classifiers over Imbalanced Data Sets <=SEP=> The class imbalance problem appears to be ubiquitous to a large portion of the machine learning and data mining communities. One of the key questions in this setting is how to evaluate the learning algorithms in the case of class imbalances. In this paper we introduce the Bayesian Receiver Operating Characteristic (B-ROC) curves, as a set of tradeoff curves that combine in an intuitive way, the variables that are more relevant to the evaluation of classifiers over imbalanced data sets. This presentation is based on section 4 of (Cárdenas, Baras, & Seamon 2006). Introduction The term class imbalance refers to the case when in a classification task, there are many more instances of some classes than others. The problem is that under this setting, classifiers in general perform poorly because they tend to concentrate on the large classes and disregard the ones with few examples. Given that this problem is prevalent in a wide range of practical classification problems, there has been recent interest in trying to design and evaluate classifiers faced with imbalanced data sets (Japkowicz 2000; Chawla, Japkowicz, & Kołcz 2003; Chawla, Japkowicz, & Kołz 2004). A number of approaches on how to address these issues have been proposed in the literature. Ideas such as data sampling methods, one-class learning (i.e. recognitionbased learning), and feature selection algorithms, appear to be the most active research directions for learning classifiers. On the other hand the issue of how to evaluate binary classifiers in the case of class imbalances appears to be dominated by the use of ROC curves (Ferri et al. 2004; 2005) (and to a lesser extent, by error curves (Drummond & Holte 2001)). The class imbalance problem is of particular importance in intrusion detection systems (IDSs). In this paper we present and expand some of the ideas introduced in our research for the evaluation of IDSs (Cárdenas, Baras, & Seamon 2006). In particular we claim that for heavily imbalanced data sets, ROC curves cannot provide the necessary Copyright c © 2006, American Association for Artificial Intelligence (www.aaai.org). All rights reserved. intuition for the choice of the operational point of the classifier and therefore we introduce the Bayesian-ROCs (BROCs). Furthermore we demonstrate how B-ROCs can deal with the uncertainty of class distributions by displaying the performance of the classifier under different conditions. Finally, we also show how B-ROCs can be used for comparing classifiers without any assumptions of misclassification costs. Performance Tradeoffs Before we present our formulation we need to introduce some notation and definitions. Assume that the input to the classifier is a feature-vector x. Let C be an indicator random variable denoting whether x belongs to class zero: C = 0 (the majority class) or class one: C = 1 (the minority class). The output of the classifier is denoted by A = 1 if the classifier assigns x to class one, and A = 0 if the classifier assigns x to class zero. Finally, the class imbalance problem is quantified by the probability of a positive example p = Pr[C = 1]. Most classifiers subject to the class imbalance problem are evaluated with the help of ROC curves. ROC curves are a tool to visualize the tradeoff between the probability of false alarm PFA ≡ Pr[A = 1|C = 0] and the probability of detection PD ≡ Pr[A = 1|C = 1]. Of interest to us in the intrusion detection community, is that classifiers with ROC curves achieving traditionally “good” operating points such as (PFA = 0.01,PD = 1) would still generate a huge amount of false alarms in realistic scenarios. This effect is due in part to the class imbalance problem, since one of the causes for the large amount of false alarms that IDSs generate, is the enormous difference between the large amount of normal activity compared to the small amount of intrusion events. The reasoning is that because the likelihood of an attack is very small, even if an IDS fires an alarm, the likelihood of having an intrusion remains relatively small. That is, when we compute the posterior probability of intrusion given that the IDS fired an alarm, (a quantity known as the Bayesian detection rate, or the positive predictive value (PPV)), we obtain: PPV ≡ Pr[C = 1|A = 1] = pPD pPD +(1− p)PFA (1) Therefore, if the rate of incidence of an attack is very small, for example on average only 1 out of 105 events is an attack

Anti-counterfeiting with hardware intrinsic security <=SEP=> Counterfeiting of goods and electronic devices is a growing problem that has a huge economic impact on the electronics industry. Sometimes the consequences are even more dramatic, when critical systems start failing due to the use of counterfeit lower quality components. Hardware Intrinsic security (i.e. security systems built on the unique electronic fingerprint of a device) offers the potential to reduce the counterfeiting problem drastically. In this paper we will show how Hardware Intrinsic Security (HIS) can be used to prevent various forms of counterfeiting and over-production. HIS technology can also be used to bind software or user data to specific hardware devices, which provides additional security to both soft- and hardware vendors as well as consumers using HIS-enabled products. Besides showing the benefits of HIS, we will also provide an extensive overview of the results (both scientific and industrial) that Intrinsic-ID has achieved studying and implementing HIS.

HDRF: Stream-Based Partitioning for Power-Law Graphs <=SEP=> Balanced graph partitioning is a fundamental problem that is receiving growing attention with the emergence of distributed graph-computing (DGC) frameworks. In these frameworks, the partitioning strategy plays an important role since it drives the communication cost and the workload balance among computing nodes, thereby affecting system performance. However, existing solutions only partially exploit a key characteristic of natural graphs commonly found in the real-world: their highly skewed power-law degree distributions. In this paper, we propose High-Degree (are) Replicated First (HDRF), a novel streaming vertex-cut graph partitioning algorithm that effectively exploits skewed degree distributions by explicitly taking into account vertex degree in the placement decision. We analytically and experimentally evaluate HDRF on both synthetic and real-world graphs and show that it outperforms all existing algorithms in partitioning quality.

Mining and ranking users’ intents behind queries <=SEP=> How to understand intents behind user queries is crucial towards improving the performance of Web search systems. NTCIR-11 IMine task focuses on this problem. In this paper, we address the NTCIR-11 IMine task with two phases referred to as Query Intent Mining (QIM) and Query Intent Ranking (QIR). (I) QIM is intended to mine users’ potential intents by clustering short text fragments related to the given query. (II) QIR focuses on ranking those mined intents in a proper way. Two challenges exist in handling these tasks. (II) How to precisely estimate the intent similarity between user queries which only consist of a few words. (2) How to properly rank intents in terms of multiple factors, e.g. relevance, diversity, intent drift and so on. For the first challenge, we first investigate two interesting phenomena by analyzing query logs and document datasets, namely “Same-Intent-Co-Click” (SICC) and “Same-Intent-Similar-Rank” (SISR). SICC means that when users issue different queries, these queries represent the same intent if they click on the same URL. SISR means that if two queries denote the same intent, we should get similar search results when issuing them to a search engine. Then, we propose similarity functions for QIM based on the two phenomena. For the second challenge, we propose a novel intent ranking model which considers multiple factors as a whole. We perform extensive experiments and an interesting case study on the Chinese dataset of NTCIR-11 IMine task. Experimental results demonstrate the effectiveness of our proposed approaches in terms of both QIM and QIR.

IR sensitivity enhancement of CMOS Image Sensor with diffractive light trapping pixels <=SEP=> We report on the IR sensitivity enhancement of back-illuminated CMOS Image Sensor (BI-CIS) with 2-dimensional diffractive inverted pyramid array structure (IPA) on crystalline silicon (c-Si) and deep trench isolation (DTI). FDTD simulations of semi-infinite thick c-Si having 2D IPAs on its surface whose pitches over 400 nm shows more than 30% improvement of light absorption at λ = 850 nm and the maximum enhancement of 43% with the 540 nm pitch at the wavelength is confirmed. A prototype BI-CIS sample with pixel size of 1.2 μm square containing 400 nm pitch IPAs shows 80% sensitivity enhancement at λ = 850 nm compared to the reference sample with flat surface. This is due to diffraction with the IPA and total reflection at the pixel boundary. The NIR images taken by the demo camera equip with a C-mount lens show 75% sensitivity enhancement in the λ = 700–1200 nm wavelength range with negligible spatial resolution degradation. Light trapping CIS pixel technology promises to improve NIR sensitivity and appears to be applicable to many different image sensor applications including security camera, personal authentication, and range finding Time-of-Flight camera with IR illuminations.

Content-adaptive low rank regularization for image denoising <=SEP=> Prior knowledge plays an important role in image denoising tasks. This paper utilizes the data of the input image to adaptively model the prior distribution. The proposed scheme is based on the observation that, for a natural image, a matrix consisted of its vectorized non-local similar patches is of low rank. We use a non-convex smooth surrogate for the low-rank regularization, and view the optimization problem from the empirical Bayesian perspective. In such framework, a parameter-free distribution prior is derived from the grouped non-local similar image contents. Experimental results show that the proposed approach is highly competitive with several state-of-art denoising methods in PSNR and visual quality.

Geometry Guided Adversarial Facial Expression Synthesis <=SEP=> Facial expression synthesis has drawn much attention in the field of computer graphics and pattern recognition. It has been widely used in face animation and recognition. However, it is still challenging due to the high-level semantic presence of large and non-linear face geometry variations. This paper proposes a Geometry-Guided Generative Adversarial Network (G2-GAN) for continuously-adjusting and identity-preserving facial expression synthesis. We employ facial geometry (fiducial points) as a controllable condition to guide facial texture synthesis with specific expression. A pair of generative adversarial subnetworks is jointly trained towards opposite tasks: expression removal and expression synthesis. The paired networks form a mapping cycle between neutral expression and arbitrary expressions, with which the proposed approach can be conducted among unpaired data. The proposed paired networks also facilitate other applications such as face transfer, expression interpolation and expression-invariant face recognition. Experimental results on several facial expression databases show that our method can generate compelling perceptual results on different expression editing tasks.

Elliptic curve cryptography <=SEP=> This paper describes the Elliptic Curve Cryptography algorithm and its suitability for smart cards.

Fault Tolerance in Cloud Using Reactive and Proactive Techniques <=SEP=> Fault tolerance plays a vital role in ensuring high serviceability and reliability in cloud. A lot of research is currently under way to analyze how cloud can provide fault tolerance for an application. The work proposes a reactive fault tolerant technique that uses check pointing to tolerate the fault. The work proposes a VMμ Checkpoint framework to protect both VMs and applications in the VMs against transient errors. The VM-μCheckpoint mechanism is implemented using CoW-PC (Copy on Write – Presave in cache) algorithm. The CoW-PC algorithm presaves all the tasks running on the VM’s in a cache memory. When there is any transient failure happening in VMs, it is noted and it is recovered using last presaved checkpoint from the cache memory. Once the tasks are executed successfully, the presaved checkpoints are deleted automatically from the cache memory. Thus the algorithm uses in place and in memory recovery of checkpoints that reduces the checkpoint overhead and improves the performance.

A High Efficiency two-stage ZVS AC/DC converter with all SiC MOSFET <=SEP=> A High Efficiency two-stage AC/DC converter is presented and discussed in this paper, which can be applied in on-board charger for electric vehicles. The front-end converter is a high efficiency ZVS half bridge power factor correction (PFC) topology with only one device in the current flowing path. Owing to the high voltage stress of the half bridge rectifier, the SiC MOSFET is applied to reduce the conduction and switching losses and achieve high frequency. The downstream stage is a LLC resonant DC/DC converter. The peak efficiency reaches 97.5%.

CPR: Composable performance regression for scalable multiprocessor models <=SEP=> Uniprocessor simulators track resource utilization cycle by cycle to estimate performance. Multiprocessor simulators, however, must account for synchronization events that increase the cost of every cycle simulated and shared resource contention that increases the total number of cycles simulated. These effects cause multiprocessor simulation times to scale superlinearly with the number of cores. Composable performance regression (CPR) fundamentally addresses these intractable multiprocessor simulation times, estimating multiprocessor performance with a combination of uniprocessor, contention, and penalty models. The uniprocessor model predicts baseline performance of each core while the contention models predict interfering accesses from other cores. Uniprocessor and contention model outputs are composed by a penalty model to produce the final multiprocessor performance estimate. Trained with a production quality simulator, CPR is accurate with median errors of 6.63, 4.83 percent for dual-, quad-core multiprocessors. Furthermore, composable regression is scalable, requiring 0.33× the simulations required by prior regression strategies.

The Internet of Things - promise for the future? An introduction <=SEP=> The Internet is a living entity, always changing and evolving. New applications and businesses are created continuously. In addition to an evolving Internet, technology is also changing the landscape. Broadband connectivity is becoming cheap and ubiquitous; devices are becoming more powerful and smaller with a variety of on-board sensors. The proliferation of more devices becoming connected is leading to a new paradigm: the Internet of Things. The Internet of Things is driven by an expansion of the Internet through the inclusion of physical objects combined with an ability to provide smarter services to the environment as more data becomes available. Various application domains ranging from Green-IT and energy efficiency to logistics are already starting to benefit from Internet of Things concepts. There are challenges associated with the Internet of Things, most explicitly in areas of trust and security, standardisation and governance required to ensure a fair and trustworthy open Internet of Things which provides value to all of society. Internet of Things is high on the research agenda of several multinationals as well as the European Commission and countries such as China. The research conducted is driving the creation of a useful and powerful Internet of Things. The benefits of Internet of Things to the developing and emerging economies are significant, and strategies to realise these need to be found.

Reinforcement Learning through Asynchronous Advantage Actor-Critic on a GPU <=SEP=> We introduce a hybrid CPU/GPU version of the Asynchronous Advantage ActorCritic (A3C) algorithm, currently the state-of-the-art method in reinforcement learning for various gaming tasks. We analyze its computational traits and concentrate on aspects critical to leveraging the GPU’s computational power. We introduce a system of queues and a dynamic scheduling strategy, potentially helpful for other asynchronous algorithms as well. Our hybrid CPU/GPU version of A3C, based on TensorFlow, achieves a significant speed up compared to a CPU implementation; we make it publicly available to other researchers at https://github.com/NVlabs/GA3C.

An Empirical Evaluation of Thompson Sampling <=SEP=> Thompson sampling is one of oldest heuristic to address the e xploration / exploitation trade-off, but it is surprisingly unpopular in t he literature. We present here some empirical results using Thompson sampling on simu lated and real data, and show that it is highly competitive. And since this heuris tic is very easy to implement, we argue that it should be part of the standard bas eline to compare against.

Forwarding metamorphosis: fast programmable match-action processing in hardware for SDN <=SEP=> In Software Defined Networking (SDN) the control plane is physically separate from the forwarding plane. Control software programs the forwarding plane (e.g., switches and routers) using an open interface, such as OpenFlow. This paper aims to overcomes two limitations in current switching chips and the OpenFlow protocol: i) current hardware switches are quite rigid, allowing ``Match-Action'' processing on only a fixed set of fields, and ii) the OpenFlow specification only defines a limited repertoire of packet processing actions. We propose the RMT (reconfigurable match tables) model, a new RISC-inspired pipelined architecture for switching chips, and we identify the essential minimal set of action primitives to specify how headers are processed in hardware. RMT allows the forwarding plane to be changed in the field without modifying hardware. As in OpenFlow, the programmer can specify multiple match tables of arbitrary width and depth, subject only to an overall resource limit, with each table configurable for matching on arbitrary fields. However, RMT allows the programmer to modify all header fields much more comprehensively than in OpenFlow. Our paper describes the design of a 64 port by 10 Gb/s switch chip implementing the RMT model. Our concrete design demonstrates, contrary to concerns within the community, that flexible OpenFlow hardware switch implementations are feasible at almost no additional cost or power.

3-D TCAD simulation to optimize the trench termination design for higher and robust BVdss <=SEP=> Optimizing the edge termination design around the periphery of active area is critically important for achieving the highest and stable breakdown voltage (BVDSS) for any power devices. Active cell structures can be assumed as two dimensional (2-D) in the central part of the die, however as the active cells terminate to the termination regions at the periphery of the die, 2-D and 3-D transition regions are formed at different locations of the die layout with respect to the last edge termination trench. Optimization of the 3-D termination region is imperative to ascertain equal or higher BVDSS of the termination region than the active cell region. Synopsys advanced multi-dimensional TCAD device simulation tool - “Sentaurus Device Editor (SDE) [1]” is adopted for designing and optimizing the 3-D termination transition region for a higher and robust BVDSS, which is validated by the experimental data.

Dual-Band Filter Design With Flexible Passband Frequency and Bandwidth Selections <=SEP=> In this paper, improved dual-band filter design is studied. The dual-band resonators are composed of shunt open- and short-circuited stubs. In order to fulfil the requirements of dual-band inverters, a structure of stepped-impedance asymmetric coupled lines is proposed and its equivalent circuit is also derived. The dual-band filter is then designed based on this equivalent circuit. This type of filter can achieve relatively large practical passband center frequency ratios (in theory infinite), and it has more freedom of bandwidth ratio. The circuit size is also reduced. Detailed design procedure is presented and, finally, a filter example is given to validate the theoretical study

A Comparison of Tactile, Visual, and Auditory Warnings for Rear-End Collision Prevention in Simulated Driving <=SEP=> OBJECTIVE
This study examined the effectiveness of rear-end collision warnings presented in different sensory modalities as a function of warning timing in a driving simulator.


BACKGROUND
The proliferation of in-vehicle information and entertainment systems threatens driver attention and may increase the risk of rear-end collisions. Collision warning systems have been shown to improve inattentive and/or distracted driver response time (RT) in rear-end collision situations. However, most previous rear-end collision warning research has not directly compared auditory, visual, and tactile warnings.


METHOD
Sixteen participants in a fixed-base driving simulator experienced four warning conditions: no warning, visual, auditory, and tactile. The warnings activated when the time-to-collision (TTC) reached a critical threshold of 3.0 or 5.0 s. Driver RT was captured from a warning below critical threshold to brake initiation.


RESULTS
Drivers with a tactile warning had the shortest mean RT. Drivers with a tactile warning had significantly shorter RT than drivers without a warning and had a significant advantage over drivers with visual warnings.


CONCLUSION
Tactile warnings show promise as effective rear-end collision warnings.


APPLICATION
The results of this study can be applied to the future design and evaluation of automotive warnings designed to reduce rear-end collisions.