title: Image Segmentation Using Disjunctive Normal Bayesian Shape and Appearance Models

abstract: The use of appearance and shape priors in image segmentation is known to improve accuracy; however, existing techniques have several drawbacks. For instance, most active shape and appearance models require landmark points and assume unimodal shape and appearance distributions, and the level set representation does not support construction of local priors. In this paper, we present novel appearance and shape models for image segmentation based on a differentiable implicit parametric shape representation called a disjunctive normal shape model (DNSM). The DNSM is formed by the disjunction of polytopes, which themselves are formed by the conjunctions of half-spaces. The DNSM’s parametric nature allows the use of powerful local prior statistics, and its implicit nature removes the need to use landmarks and easily handles topological changes. In a Bayesian inference framework, we model arbitrary shape and appearance distributions using nonparametric density estimations, at any local scale. The proposed local shape prior results in accurate segmentation even when very few training shapes are available, because the method generates a rich set of shape variations by locally combining training samples. We demonstrate the performance of the framework by applying it to both 2-D and 3-D data sets with emphasis on biomedical image segmentation applications.


title: Adaptive Control of Nonlinear TRMS Model by Using Gradient Descent Optimizers

abstract: This study demonstrates an application of direct gradient descent control for adaptively control of a nonlinear stable system models. The approach is based on utilization of gradient descent optimization techniques for the synthesis of control signals to control a specific plant model. In a former work, gradient descent optimizers were designed by considering a first degree instant input-output relation model assumption of the controlled system and this can allow model-independent adaptive control of a class of plant models that can approximate to first order stable plant dynamics. The current study is an extension of this scheme for the purpose of nonlinear adaptive control. Here, we consider a higher degree polynomial assumption of instant input-output relations of the controlled system to obtain gradient descent optimizers that can be applied for adaptive control of a class of nonlinear systems. For evaluation of control performance of gradient descent optimizers, it is applied for the control of nonlinear TRMS model and the results are compared with performance of conventional PID control.


title: Reinforcement Learning: A Survey

abstract: This paper surveys the field of reinforcement learning from a computer-science perspective. It is written to be accessible to researchers familiar with machine learning. Both the historical basis of the field and a broad selection of current work are summarized. Reinforcement learning is the problem faced by an agent that learns behavior through trial-and-error interactions with a dynamic environment. The work described here has a resemblance to work in psychology, but differs considerably in the details and in the use of the word ``reinforcement.'' The paper discusses central issues of reinforcement learning, including trading off exploration and exploitation, establishing the foundations of the field via Markov decision theory, learning from delayed reinforcement, constructing empirical models to accelerate learning, making use of generalization and hierarchy, and coping with hidden state. It concludes with a survey of some implemented systems and an assessment of the practical utility of current methods for reinforcement learning.


title: Social Media Analytics: The Kosmix Story

abstract: Kosmix was a Silicon Valley startup founded in 2005 by Anand Rajaraman and Venky Harinarayan. Initially targeting Deep Web search, in early 2010 Kosmix shifted its main focus to social media, and built a large infrastructure to perform social media analytics, for a variety of real-world applications. In 2011 Kosmix was acquired by Walmart and converted into @WalmartLabs, the advanced research and development arm of Walmart. The goals of the acquisition were to provide a core of technical people in the Valley and attract more, to help improve traditional e-commerce for Walmart, and to explore the future of e-commerce. This future looks increasingly social, mobile, and local. Accordingly, @WalmartLabs continues to develop the social media analytics infrastructure pioneered by Kosmix, and uses it to explore a range of social e-commerce applications. In this paper we describe social media analytics, as carried out at Kosmix. While our framework can handle many types of social media data, for concreteness we will focus mostly on tweets. Section 2 describes the analytics architecture, the applications, and the challenges. We describe in particular the Social Genome, a large real-time social knowledge base that lied at the heart of Kosmix and powered most of its applications. Section 3 describes how the Social Genome was built, using Wikipedia, a set of other data sources, and social media data. Section 4 describes how we classify and tag tweets, and extract entities from tweets and link them to a knowledge base. Section 5 describes how we detect and monitor events in the Twittersphere. Section 6 discusses how we process the high-speed Twitter stream using Muppet, a scalable distributed stream processing engine built in house [1]. Section 7 discusses lessons learned and related work, and Section 8 concludes. Parts of the work described here have been open sourced [1] and described in detail in recent papers [18, 23, 25, 32].


title: Dual coordinate solvers for large-scale structural SVMs

abstract: This manuscript describes a method for training linear SVMs (including binary SVMs, SVM regression, and structural SVMs) from large, out-of-core training datasets. Current strategies for large-scale learning fall into one of two camps; batch algorithms which solve the learning problem given a finite datasets, and online algorithms which can process out-of-core datasets. The former typically requires datasets small enough to fit in memory. The latter is often phrased as a stochastic optimization problem; such algorithms enjoy strong theoretical properties but often require manual tuned annealing schedules, and may converge slowly for problems with large output spaces (e.g., structural SVMs). We discuss an algorithm for an "intermediate" regime in which the data is too large to fit in memory, but the active constraints (support vectors) are small enough to remain in memory. In this case, one can design rather efficient learning algorithms that are as stable as batch algorithms, but capable of processing out-of-core datasets. We have developed such a MATLAB-based solver and used it to train a collection of recognition systems for articulated pose estimation, facial analysis, 3D object recognition, and action classification, all with publicly-available code. This writeup describes the solver in detail.


title: A mobility prediction algorithm for the seamless handoff

abstract: In recent years, many applications based on movement prediction have appeared very strongly. One of those is in the smooth handover process and resource reservation to improve Quality of Service (QoS). For effective productivity of these processes, time factor is very important. It needs exact information such as when and how long the user will go to a next location. Active Lezi algorithm and order-2 Markov model algorithm were appreciated in accuracy implementation, but the time factors were not significantly considered. In this paper, we propose a novel location prediction method constructed from a list of time and location history. A tree based on Lezi family of data compression is constructed and then, combined with Hidden Markov Model (HMM) for movement prediction driven by a sequence of time series. From our experiment, we used a part of dataset come from Dartmouth including information of 281 users. We achieved an accuracy rate of 85.52% location prediction. Moreover, the length of tree and the lost information were both reduced significantly.


title: Characterizing Cloud Federation in IoT

abstract: The advent of both Cloud computing and Internet of Things (IoT) is changing the way of conceiving information and communication systems. Generally, we talk about IoT Cloud to indicate a new type of distributed system consisting of a set of smart devices interconnected with a remote Cloud infrastructure, platform, or software through the Internet and able to provide IoT as a Service (IoTaaS). In this paper, we discuss the near future evolution of IoT Clouds towards federated ecosystems, where IoT providers cooperate to offer more flexible services. Moreover, we present a general three-layer IoT Cloud Federation architecture, highlighting new business opportunities and challenges.


Solving Small TSPs with Constraints


title: Robust Detection of Region-Duplication Forgery in Digital Image

abstract: Region duplication forgery, in which a part of a digital image is copied and then pasted to another portion of the same image in order to conceal an important object in the scene, is one of the common image forgery techniques. In this paper, we describe an efficient and robust algorithm for detecting and localizing this type of malicious tampering. We present experimental results which show that our method is robust and can successfully detect this type of tampering for images that have been subjected to various forms of post region duplication image processing, including blurring, noise contamination, severe lossy compression, and a mixture of these processing operations.


title: Multiphase LLC Series Resonant Converter for Microprocessor Voltage Regulation

abstract: In order to further reduce the power loss, and increase the computation speed, future micro-processors desires lower voltage, higher current, tighter voltage regulation, and faster transient from their voltage regulators. In the last decade, interleaved buck converters have been studied and used extensively for powering microprocessors. Due to their inherent limitations in current sharing, capacitor size, step down ratio, and energy flow pattern, it becomes very difficult for interleaved buck converters to keep up with the demands of the future microprocessors. In this paper, a new candidate, a multiphase LLC resonant converter is proposed, which features zero voltage switching for both inverter and rectifier switches; wide load range; limited frequency range; fast dynamic response; high efficiency; and automatic current sharing. These features ensure the proposed multiphase LLC resonant converter a good voltage regulator candidate for the next generation microprocessors. All theoretical analyses are verified by a 1 V 50 W prototype and its experimental results.


title: A Convolutional Neural Network for Fault Classification and Diagnosis in Semiconductor Manufacturing Processes

abstract: Many studies on the prediction of manufacturing results using sensor signals have been conducted in the field of fault detection and classification (FDC) for semiconductor manufacturing processes. However, fault diagnosis used to find clues as to root causes remains a challenging area. In particular, process monitoring using neural networks has been employed to only a limited extent because it is a black box model, making the relationships between input data and output results difficult to interpret in actual manufacturing settings, despite its high classification performance. In this paper, we propose a convolutional neural network (CNN) model, named FDC-CNN, in which a receptive field tailored to multivariate sensor signals slides along the time axis, to extract fault features. This approach enables the association of the output of the first convolutional layer with the structural meaning of the raw data, making it possible to locate the variable and time information that represents process faults. In an experiment on a chemical vapor deposition process, the proposed method outperformed other deep learning models.


title: Computational thinking through programming and algorithmic art

abstract: General education students can be taught computational thinking skills through courses that marry computer programming with algorithmic art. Algorithmic art is a varied and growing field where images are generated on the computer using mathematical and computer algorithms (see http://processing.org/exhibition/index.html for examples). Visually oriented students may be motivated to learn programming when it is taught in a context that is as much focused on art, artists, and design principles as it is on mathematics and programming. This presentation will include 1) a discussion of the challenges of teaching programming, 2) the role of teaching style, motivation, and programming environment, 3) a summary of common algorithms and their relation to design principles, and 4) examples of artwork that has been created by current artists.


title: On the Shape of the Probability Weighting Function

abstract: Empirical studies have shown that decision makers do not usually treat probabilities linearly. Instead, people tend to overweight small probabilities and underweight large probabilities. One way to model such distortions in decision making under risk is through a probability weighting function. We present a nonparametric estimation procedure for assessing the probability weighting function and value function at the level of the individual subject. The evidence in the domain of gains supports a two-parameter weighting function, where each parameter is given a psychological interpretation: one parameter measures how the decision maker discriminates probabilities, and the other parameter measures how attractive the decision maker views gambling. These findings are consistent with a growing body of empirical and theoretical work attempting to establish a psychological rationale for the probability weighting function.


title: Adaptive Re-ranking of Deep Feature for Person Re-identification

abstract: Typical person re-identification (re-ID) methods train a deep CNN to extract deep features and combine them with a distance metric for the final evaluation. In this work, we focus on exploiting the full information encoded in the deep feature to boost the re-ID performance. First, we propose a Deep Feature Fusion (DFF) method to exploit the diverse information embedded in a deep feature. DFF treats each sub-feature as an information carrier and employs a diffusion process to exchange their information. Second, we propose an Adaptive Re-Ranking (ARR) method to exploit the contextual information encoded in the features of neighbors. ARR utilizes the contextual information to re-rank the retrieval results in an iterative manner. Particularly, it adds more contextual information after each iteration automatically to consider more matches. Third, we propose a strategy that combines DFF and ARR to enhance the performance. Extensive comparative evaluations demonstrate the superiority of the proposed methods on three large benchmarks.


title: Replay Attack: A Prevalent Pattern of Fraudulent Online Transactions

abstract: The rapid advancement in the electronic commerce technology makes electronic transaction an indispensable part of our daily life. While, this way of transaction has always been facing security problems. Researchers persevere in looking for fraud transaction detection methodologies. A promising paradigm is to devise dedicated detectors for the typical patterns of fraudulent transactions. Unfortunately, this paradigm is really constrained by the lack of real electronic transaction data, especially real fraudulent samples. In this paper, by analyzing real B2C electronic transaction data provided by an Asian bank, from the perspective of transaction sequence, we discover a typical pattern of fraud transactions: Most of the fraud transactions are fast and repeated transactions between the same customer and the same vendor, and all the transaction amounts are nearly the same. We name this pattern Replay Attack. We prove the prominent existence of Replay Attack by comprehensive statistics, and we propose a novel fraud transaction detector, Replay Attack Killer (RAK). By experiment, we show that RAK can catch up to 92% fraud transactions in real time but only disturb less than 0.06% normal transactions.


Modeling and Prediction of Online Product Review Helpfulness: A Survey


title: IoT Data Integrity Verification for Cyber-Physical Systems Using Blockchain

abstract: Blockchain technologies can enable decentralized and trustful features for the Internet of Things (IoT). Although, existing blockchain based solutions to provide data integrity verification for semi-trusted data storages (e.g. cloud providers) cannot respect the time determinism required by Cyber-Physical Systems (CPS). Additionally, they cannot be applied to resource-constrained IoT devices. We propose an architecture that can take advantage of blockchain features to allow further integrity verification of data produced by IoT devices even in the realm of CPS. Our architecture is divided into three levels, each of them responsible for tasks compatible with their resources capabilities. The first level, composed of sensors, actuators, and gateways, introduces the concept of Proof-of-Trust (PoT), an energy-efficient, time-deterministic and secure communication based on the Trustful Space-Time Protocol (TSTP). Upper levels are responsible for data persistence and integrity verification in the Cloud. The work also comprises a performance evaluation of the critical path of data to demonstrate that the architecture respect time-bounded operations demanded by the sense-decide-actuate cycle of CPSs. The additional delay of 5.894us added by our architecture is negligible for a typical TSTP with IEEE 802.15.4 radios which has communication latencies in the order of hundreds of milisseconds in each hop.


title: An improved symbol timing error detector for QPSK signals

abstract: In this paper, based on analyzing the characteristics of the traditional timing error detectors, a new detector is proposed for quadratic phase shift keying (QPSK) signals. It makes use of the implicit timing information where there is no transition between consecutive symbols. Compared with the conventional timing error detector, the proposed one can improve the open loop performance. With the improved slope of the proposed detector's S-curve, its residual timing jitter is investigated by simulation. Results show that the proposed estimator can reduce the self noise jitter in some degree, especially at medium-to-high SNR range.


title: Neweyes: A System for Comparing Biological Sequences Using the Running Karp-Rabin Greedy String-Tiling Algorithm

abstract: A system for aligning nucleotide or amino acid biosequences is described. The system, called Neweyes, employs a novel string matching algorithm, Running Karp-Rabin Greedy String T’ding (RKR--GST), which involves tiling one string with matching substrings of a second string. In practice, RKR-GST has a computational complexity that appears close to linear. With RKR-GST, Neweyes is able to detect transposed substrings or substrings of one biosequence that appears rearranged in a second sequence. Repeated substrings can also be detected. Neweyes also supports a form of matching-by-group that gives the effect of different amino acid mutation matrices. Neweyes can be used in a macro mode (searching database for a list of biosequences that are similar to a given biosequence) or in a micro mode, where two biosequences are compared and more detailed output formats are available.


title: Like: The discourse particle and semantics

abstract: Using data from interviews with high school students, I first adduce evidence that lends support to Schourup's (1985) claim that the United States English adolescent hedge like is a discourse particle signalling a possible slight mismatch between words and meaning. Such a particle would generally be included in a grammar in a post-compositional pragmatic component, but, surprisingly, like also affects basic semantic attributes. These include both truth-conditions and the weak/strong distinction-though only in existential there and sluicing sentences. I argue that the differential behaviour of like in various constructions selecting weak NP's stems from the restricted free variable it introduces, a variable which only there and sluicing require. This variable is available for binding, quantifier interpretation and other syntactic-semantic processes, yet is pragmatically conditioned. Indeed, I show that, due to its formal properties, like can be interpreted only during the assignment of model-theoretic denotations to expressions, along the lines of Lasersohn's (1999) pragmatic haloes. These results support the idea that weak/strong is not a unitary distinction and suggest that the various components of grammars must be organized to allow information from pragmatic/discourse elements to affect basic compositional semantics.


title: A Divide-and-Conquer Procedure for Sparse Inverse Covariance Estimation

abstract: We consider the composite log-determinant optimization problem, arising from the l1- regularized Gaussian maximum likelihood estimator of a sparse inverse covariance matrix, in a high-dimensional setting with a very large number of variables. Recent work has shown this estimator to have strong statistical guarantees in recovering the true structure of the sparse inverse covariance matrix, or alternatively the underlying graph structure of the corresponding Gaussian Markov Random Field, even in very high-dimensional regimes with a limited number of samples. In this paper, we are concerned with the computational cost in solving the above optimization problem. Our proposed algorithm partitions the problem into smaller sub-problems, and uses the solutions of the sub-problems to build a good approximation for the original problem. Our key idea for the divide step to obtain a sub-problem partition is as follows: we first derive a tractable bound on the quality of the approximate solution obtained from solving the corresponding sub-divided problems. Based on this bound, we propose a clustering algorithm that attempts to minimize this bound, in order to find effective partitions of the variables. For the conquer step, we use the approximate solution, i.e., solution resulting from solving the sub-problems, as an initial point to solve the original problem, and thereby achieve a much faster computational procedure.


title: 'Computing' as Information Compression by Multiple Alignment, Unification and Search

abstract: This paper argues that the operations of a 'Universal Turing Machine' (UTM) and equivalent mechanisms such as the 'Post Canonical System' (PCS) - which are widely accepted as definitions of the concept of `computing' - may be interpreted as *information compression by multiple alignment, unification and search* (ICMAUS). ::: The motivation for this interpretation is that it suggests ways in which the UTM/PCS model may be augmented in a proposed new computing system designed to exploit the ICMAUS principles as fully as possible. The provision of a relatively sophisticated search mechanism in the proposed 'SP' system appears to open the door to the integration and simplification of a range of functions including unsupervised inductive learning, best-match pattern recognition and information retrieval, probabilistic reasoning, planning and problem solving, and others. Detailed consideration of how the ICMAUS principles may be applied to these functions is outside the scope of this article but relevant sources are cited in this article.


title: GNSS/Cellular Hybrid Positioning System for Mobile Users in Urban Scenarios

abstract: This paper proposes a hybrid scheme for user positioning in an urban scenario using both a Global Navigation Satellite System (GNSS) and a mobile cellular network. To maintain receiver complexity (and costs) at a minimum, the location scheme combines the time-difference-of-arrival (TDOA) technique measurements obtained from the cellular network with GNNS pseudorange measurements. The extended Kalman filter (EKF) algorithm is used as a data integration system over the time axis. Simulated results, which are obtained starting from real measurements, demonstrate that the use of cellular network data may provide increased location accuracy when the number of visible satellites is not adequate. In every case, the obtained accuracy is within the limits required by emergency location services, e.g., Enhanced 911 (E911).


title: A Privacy-Preserving Remote Data Integrity Checking Protocol with Data Dynamics and Public Verifiability

abstract: Remote data integrity checking is a crucial technology in cloud computing. Recently, many works focus on providing data dynamics and/or public verifiability to this type of protocols. Existing protocols can support both features with the help of a third-party auditor. In a previous work, Sebe et al. propose a remote data integrity checking protocol that supports data dynamics. In this paper, we adapt Sebe et al.'s protocol to support public verifiability. The proposed protocol supports public verifiability without help of a third-party auditor. In addition, the proposed protocol does not leak any private information to third-party verifiers. Through a formal analysis, we show the correctness and security of the protocol. After that, through theoretical analysis and experimental results, we demonstrate that the proposed protocol has a good performance.


title: GEMS: the design and evaluation of a location-based storytelling game

abstract: It is now possible to capture geotagged photos and videos and share them with family and friends. Yet the reality is that applications for capturing and viewing this information are not particularly rich offering little more than maps and simple textual information about a location. Given this, we wanted to explore this design space to find new and exciting ways for people to document and share their experiences. We designed a location-based game called GEMS to support storytelling amongst family members and close friends. The game narrative and mechanics prompt players to reflect on meaningful places from their past and create geolocated digital memory. Other players can then visit the locations to collect and view the records. A user study revealed that location can provide a rich foundation for storytelling activities. We learned that location-based storytelling strategies often elicit a sense of discovery through exploration, sharing, and conscious reflection.


title: Double flap phalloplasty in transgender men: Surgical technique and outcome of pedicled anterolateral thigh flap phalloplasty combined with radial forearm free flap urethral reconstruction

abstract: INTRODUCTION ::: Radial forearm free flap (RFFF) tube-in-tube phalloplasty is the most performed phalloplasty technique worldwide. The conspicuous donor-site scar is a drawback for some transgender men. In search for techniques with less conspicuous donor-sites, we performed a series of one-stage pedicled anterolateral thigh flap (ALT) phalloplasties combined with RFFF urethral reconstruction. In this study, we aim to describe this technique and assess its surgical outcome in a series of transgender men. ::: ::: ::: PATIENTS AND METHODS ::: Between January 2008 and December 2015, nineteen transgender men (median age 37, range 21-57) underwent pedicled ALT phalloplasty combined with RFFF urethral reconstruction in one stage. The surgical procedure was described. Patient demographics, surgical characteristics, intra- and postoperative complications, hospitalization length, and reoperations were recorded. ::: ::: ::: RESULTS ::: The size of the ALT flaps ranged from 12 × 12 to 15 × 13 cm, the size of the RFFFs from 14 × 3 to 17 × 3 cm. Median clinical follow-up was 35 months (range 3-95). Total RFFF failure occurred in two patients, total ALT flap failure in one patient, and partial necrosis of the ALT flap in one patient. Long-term urinary complications occurred in 10 (53%) patients, of which 9 concerned urethral strictures. ::: ::: ::: CONCLUSIONS ::: In experienced hands, one-stage pedicled ALT phalloplasty combined with RFFF urethral reconstruction is a feasible alternative surgical option in eligible transgender men, who desire a less conspicuous forearm scar. Possible drawbacks comprise flap-related complications, difficult inner flap monitoring and urethral complications.


title: Reverse replication of virtual machines (rRVM) for low latency and high availability services

abstract: Virtualization supplies a straightforward approach to high availability through iterative replications of virtual machine (VM) checkpoints that encapsulate the protected services. Unfortunately, traditional VM replication solutions suffer from deficiencies in either response latency or state recovery consistency, which constrains the adoption of VM replication in production. In this paper, we extend the function of the secondary host to be the primary recipient of network requests so that the state of the primary VM (PVM) is retained by the secondary host in the form of network packets. In doing this, we redesign the typical consistency model and network architecture for virtual machine replication. Specifically, the secondary host is set for network redirection and packets recording. Should the primary host fail, the recorded packets are used to recreate the state on the secondary host. Experiments in this research demonstrate simultaneously strong recovery consistency and low response latency in our real-time fault tolerance system. We name the system reverse replication of virtual machines (rRVM).


title: A-priori map information and path planning for automated valet-parking

abstract: Automated valet parking can help manage space in parking garages more efficiently and increase the comfort for the driver by delegating the parking task to the system. To successfully navigate the vehicle in a parking garage, the valet parking system requires a-priori information about the environment. This paper shows an approach to derive a-priori information from available blueprints of a parking lot. Based on the derived map data, paths for Ackermann-steered vehicles are planned to navigate vehicles through the parking area and park them into desired parking spots.


Going to the MALL: Mobile Assisted Language Learning.


title: Meta-Path-Based Search and Mining in Heterogeneous Information Networks

abstract: Information networks that can be extracted from many domains are widely studied recently. Different functions for mining these networks are proposed and developed, such as ranking, community detection, and link prediction. Most existing network studies are on homogeneous networks, where nodes and links are assumed from one single type. In reality, however, heterogeneous information networks can better model the real-world systems, which are typically semi-structured and typed, following a network schema. In order to mine these heterogeneous information networks directly, we propose to explore the meta structure of the information network, i.e., the network schema. The concepts of meta-paths are proposed to systematically capture numerous semantic relationships across multiple types of objects, which are defined as a path over the graph of network schema. Meta-paths can provide guidance for search and mining of the network and help analyze and understand the semantic meaning of the objects and relations in the network. Under this framework, similarity search and other mining tasks such as relationship prediction and clustering can be addressed by systematic exploration of the network meta structure. Moreover, with user's guidance or feedback, we can select the best meta-path or their weighted combination for a specific mining task.


title: Logic Tensor Networks for Semantic Image Interpretation

abstract: Semantic Image Interpretation (SII) is the task of extracting structured semantic descriptions from images. It is widely agreed that the combined use of visual data and background knowledge is of great importance for SII. Recently, Statistical Relational Learning (SRL) approaches have been developed for reasoning under uncertainty and learning in the presence of data and rich knowledge. Logic Tensor Networks (LTNs) are an SRL framework which integrates neural networks with first-order fuzzy logic to allow (i) efficient learning from noisy data in the presence of logical constraints, and (ii) reasoning with logical formulas describing general properties of the data. In this paper, we develop and apply LTNs to two of the main tasks of SII, namely, the classification of an image's bounding boxes and the detection of the relevant part-of relations between objects. To the best of our knowledge, this is the first successful application of SRL to such SII tasks. The proposed approach is evaluated on a standard image processing benchmark. Experiments show that the use of background knowledge in the form of logical constraints can improve the performance of purely data-driven approaches, including the state-of-the-art Fast Region-based Convolutional Neural Networks (Fast R-CNN). Moreover, we show that the use of logical background knowledge adds robustness to the learning system when errors are present in the labels of the training data.


title: Effects of brain-derived neurotrophic factor (BDNF) on compression-induced spinal cord injury: BDNF attenuates down-regulation of superoxide dismutase expression and promotes up-regulation of myelin basic protein expression.

abstract: Neurotrophins enhance the survival of cells in the nervous system under both physiological and pathological conditions, such as those caused by disease or trauma. We recently demonstrated that expression of brain-derived neurotrophic factor (BDNF) was up-regulated in neurons and glia after compression-induced spinal cord injury (SCI). We show here the effects of BDNF on the oligodendrocyte survival and functional recovery after SCI. The effects of intrathecally administered BDNF on both Cu/Zn superoxide dismutase (CuZnSOD) and myelin basic protein (MBP) expression were examined using rats that had received compression-induced spinal cord injury. CuZnSOD expression in the spinal cord was down-regulated within 24 h of compression-induced injury and then recovered. Continuous infusion of BDNF inhibited the acute down-regulation of CuZnSOD expression. In situ hybridization showed that CuZnSOD was expressed in both neurons and glia. Although MBP expression was greatly reduced after injury, BDNF administration promoted the recovery of MBP expression nearly to a control level after 2 wk. Furthermore, BDNF administration also prompted behavioral recovery. These results suggest BDNF's usefulness in human clinical applications. The attenuation of CuZnSOD down-regulation may be related to a protective effect of BDNF and the promotion of MBP up-regulation may be related to a long-lasting restorative effect.


title: Improved decremental algorithms for maintaining transitive closure and all-pairs shortest paths

abstract: We present improved algorithms for maintaining transitive closure and all-pairs shortest paths/distances in a digraph under deletion of edges.(MATH) For the problem of transitive closure, the previous best known algorithms, for achieving O(1) query time, require O(\min(m, \frac{n^3}{m}))$ amortized update time, implying an upper bound of O(n^{\frac{3}{2}})$ on update time per edge-deletion. We present an algorithm that achieves $O(1)$ query time and O(n \log^2n + \frac{n^2}{\sqrt{m}}{\sqrt{\log n}})$ update time per edge-deletion, thus improving the upper bound to O(n^{\frac{4}{3}}\sqrt[3]{\log n})$.(MATH) For the problem of maintaining all-pairs shortest distances in unweighted digraph under deletion of edges, we present an algorithm that requires O(\frac{n^3}{m} \log^2 n)$ amortized update time and answers a distance query in O(1) time. This improves the previous best known update bound by a factor of log n. For maintaining all-pairs shortest paths, we present an algorithm that achieves O(\min(n^{\frac{3}{2}} \sqrt{\log n}, \frac{n^3}{m} \log ^2n))$ amortized update time and reports a shortest path in optimal time (proportional to the length of the path). For the latter problem we improve the worst amortized update time bound by a factor of O(\sqrt{\frac{n}{\log n}})$.(MATH) We also present the first decremental algorithm for maintaining all-pairs (1+e) approximate shortest paths/distances, for any e > 0, that achieves a sub-quadratic update time of O(n log2n + \frac{n^2}{\sqrt{\epsilon m}}\sqrt{\log n})$ and optimal query time.Our algorithms are randomized and have one-sided error for query (with probability O(1/nc) for any constant c).


title: Sentiment Classification and Polarity Shifting

abstract: Polarity shifting marked by various linguistic structures has been a challenge to automatic sentiment classification. In this paper, we propose a machine learning approach to incorporate polarity shifting information into a document-level sentiment classification system. First, a feature selection method is adopted to automatically generate the training data for a binary classifier on polarity shifting detection of sentences. Then, by using the obtained binary classifier, each document in the original polarity classification training data is split into two partitions, polarity-shifted and polarity-unshifted, which are used to train two base classifiers respectively for further classifier combination. The experimental results across four different domains demonstrate the effectiveness of our approach.


title: A Real-Time Visual Inspection System for Railway Maintenance: Automatic Hexagonal-Headed Bolts Detection

abstract: Rail inspection is a very important task in railway maintenance, and it is periodically needed for preventing dangerous situations. Inspection is operated manually by trained human operator walking along the track searching for visual anomalies. This monitoring is unacceptable for slowness and lack of objectivity, as the results are related to the ability of the observer to recognize critical situations. The correspondence presents a patent-pending real-time Visual Inspection System for Railway (VISyR) maintenance, and describes how presence/absence of the fastening bolts that fix the rails to the sleepers is automatically detected. VISyR acquires images from a digital line-scan camera. Data are simultaneously preprocessed according to two discrete wavelet transforms, and then provided to two multilayer perceptron neural classifiers (MLPNCs). The "cross validation" of these MLPNCs avoids (practically-at-all) false positives, and reveals the presence/absence of the fastening bolts with an accuracy of 99.6% in detecting visible bolts and of 95% in detecting missing bolts. A field-programmable gate array-based architecture performs these tasks in 8.09 mus, allowing an on-the-fly analysis of a video sequence acquired at 200 km/h


OpenGeneMed: a portable, flexible and customizable informatics hub for the coordination of next-generation sequencing studies in support of precision medicine trials


title: An analysis of BGP convergence properties

abstract: The Border Gateway Protocol (BGP) is the de facto inter-domain routing protocol used to exchange reachability information between Autonomous Systems in the global Internet. BGP is a path-vector protocol that allows each Autonomous System to override distance-based metrics with policy-based metrics when choosing best routes. Varadhan et al. [18] have shown that it is possible for a group of Autonomous Systems to independently define BGP policies that together lead to BGP protocol oscillations that never converge on a stable routing. One approach to addressing this problem is based on static analysis of routing policies to determine if they are safe. We explore the worst-case complexity for convergence-oriented static analysis of BGP routing policies. We present an abstract model of BGP and use it to define several global sanity conditions on routing policies that are related to BGP convergence/divergence. For each condition we show that the complexity of statically checking it is either NP-complete or NP-hard.


title: A brain-computer interface for high-level remote control of an autonomous, reinforcement-learning-based robotic system for reaching and grasping

abstract: We present an Internet-based brain-computer interface (BCI) for controlling an intelligent robotic device with autonomous reinforcement-learning. BCI control was achieved through dry-electrode electroencephalography (EEG) obtained during imaginary movements. Rather than using low-level direct motor control, we employed a high-level control scheme of the robot, acquired via reinforcement learning, to keep the users cognitive load low while allowing control a reaching-grasping task with multiple degrees of freedom. High-level commands were obtained by classification of EEG responses using an artificial neural network approach utilizing time-frequency features and conveyed through an intuitive user interface. The novel ombination of a rapidly operational dry electrode setup, autonomous control and Internet connectivity made it possible to conveniently interface subjects in an EEG laboratory with remote robotic devices in a closed-loop setup with online visual feedback of the robots actions to the subject. The same approach is also suitable to provide home-bound patients with the possibility to control state-of-the-art robotic devices currently confined to a research environment. Thereby, our BCI approach could help severely paralyzed patients by facilitating patient-centered research of new means of communication, mobility and independence.


title: Object-Based Multiple Foreground Video Co-segmentation

abstract: We present a video co-segmentation method that uses category-independent object proposals as its basic element and can extract multiple foreground objects in a video set. The use of object elements overcomes limitations of low-level feature representations in separating complex foregrounds and backgrounds. We formulate object-based co-segmentation as a co-selection graph in which regions with foreground-like characteristics are favored while also accounting for intra-video and inter-video foreground coherence. To handle multiple foreground objects, we expand the co-selection graph model into a proposed multi-state selection graph model (MSG) that optimizes the segmentations of different objects jointly. This extension into the MSG can be applied not only to our co-selection graph, but also can be used to turn any standard graph model into a multi-state selection solution that can be optimized directly by the existing energy minimization techniques. Our experiments show that our object-based multiple foreground video co-segmentation method (ObMiC) compares well to related techniques on both single and multiple foreground cases.


title: Did Relational Capital Matter During the Financial Crisis?

abstract: This paper aims to contribute to the body of empirical studies that address the importance of investments in companies’ relationships and the way in which they influence value creation in the global economic crisis. We employ linear panel analysis using the Hausman–Taylor model to analyse panel data for companies from the five largest European countries in the period 2004–2011. Different types of exogenous and endogenous links which a company could have in different stages of the crisis are investigated. The findings suggest that there is a statistically significant and positive link between relational capital and a firm’s value. Moreover, we identify several differences in the significance of the inputs during different crisis periods. The study provides both theoretical and practical insights into investments in intangibles for framing strategy decisions with a particular focus on the role of relational capital. This could provide scholars and practitioners with a working basis for understanding connections and the implications of strategizing in the context of a company’s networks.


title: Anomaly Detection During Lithium-ion Battery Qualification Testing

abstract: Qualification testing of Li-ion batteries usually involves battery capacity fade trend monitoring over a large number of repeated charge/discharge cycles. However, due to manufacturing-induced variations, capacity fade trends of batteries from the same as well different production lots can differ from each other. This paper discusses a real-world problem where the Li-ion batteries from a particular production lot exhibit a different capacity fade trend than that exhibited by healthy batteries from earlier production lots. The paper also outlines the approach to identify these anomalous batteries at the earliest stage of testing and provides a case study that applies one-class support vector machine (SVM) based methods to detect the anomalous capacity fade behavior.


title: A Critical Look at Smart Wheelchairs

abstract: Research into smart wheelchairs has been conducted for decades, but we have yet to see the widespread use of this technology among full-time wheelchair users. We argue that the main reason for this is that there is a mismatch between research and the actualities of using a powered mobility device in the real world. Based on our own research experiences, we enumerate some of these disparities, and offer some suggestions for where work in smart wheelchairs might focus in the coming years.


title: Synergistic efficacy of adapalene 0.1%-benzoyl peroxide 2.5% in the treatment of 3855 acne vulgaris patients.

abstract: The adapalene-benzoyl peroxide (adapalene-BPO) combination gel is efficacious and safe in the treatment of acne vulgaris. We aimed in this pooled analysis to determine whether the adapalene-BPO combination demonstrates synergistic efficacy. Data were pooled and analyzed from three double-blind controlled studies, in which patients were randomized to receive adapalene-BPO, adapalene, BPO or vehicle once daily for 12 weeks. Efficacy assessments included percent reduction in lesion counts and Investigator's Global Assessment (IGA) success. Benefit of each treatment relative to vehicle was calculated by subtracting the vehicle result from the efficacy result. Synergy was defined as the benefit of the combination greater than the sum of benefits of adapalene and BPO monotherapies. Adapalene-BPO was significantly more efficacious than its monotherapies in decreasing lesion counts as early as week 1 and throughout the study (p < 0.05). Synergy in total lesion count reduction was observed up to week 8, contributing 48.7% of the benefit of adapalene-BPO relative to vehicle in decreasing total lesions at week 1. Synergy of the combination in IGA success was observed at weeks 1, 4, 8 and 12. In conclusion, the fixed-dose adapalene-BPO combination gel provides synergistic and significantly greater efficacy than its monotherapies in the treatment of acne vulgaris.


title: Centralized"big science"communities more likely generate non-replicable results

abstract: Growing concern that most published results, including those widely agreed upon, may be false are rarely examined against rapidly expanding research production. Replications have only occurred on small scales due to prohibitive expense and limited professional incentive. We introduce a novel, high-throughput replication strategy aligning 51,292 published claims about drug-gene interactions with high-throughput experiments performed through the NIH LINCS L1000 program. We show (1) that unique claims replicate 19% more frequently than at random, while those widely agreed upon replicate 45% more frequently, manifesting collective correction mechanisms in science; but (2) centralized scientific communities perpetuate claims that are less likely to replicate even if widely agreed upon, demonstrating how centralized, overlapping collaborations weaken collective understanding. Decentralized research communities involve more independent teams and use more diverse methodologies, generating the most robust, replicable results. Our findings highlight the importance of science policies that foster decentralized collaboration to promote robust scientific advance.


title: Factorial LDA: Sparse Multi-Dimensional Text Models

abstract: Latent variable models can be enriched with a multi-dimensional structure to consider the many latent factors in a text corpus, such as topic, author perspective and sentiment. We introduce factorial LDA, a multi-dimensional model in which a document is influenced by K different factors, and each word token depends on a K-dimensional vector of latent variables. Our model incorporates structured word priors and learns a sparse product of factors. Experiments on research abstracts show that our model can learn latent factors such as research topic, scientific discipline, and focus (methods vs. applications). Our modeling improvements reduce test perplexity and improve human interpretability of the discovered factors.


title: A performance comparison of contemporary DRAM architectures

abstract: In response to the growing gap between memory access time and processor speed, DRAM manufacturers have created several new DRAM architectures. This paper presents a simulation-based performance study of a representative group, each evaluated in a small system organization. These small-system organizations correspond to workstation-class computers and use on the order of 10 DRAM chips. The study covers Fast Page Mode, Extended Data Out, Synchronous, Enhanced Synchronous, Synchronous Link, Rambus, and Direct Rambus designs. Our simulations reveal several things: (a) current advanced DRAM technologies are attacking the memory bandwidth problem but not the latency problem; (b) bus transmission speed will soon become a primary factor limiting memory-system performance; (c) the post-L2 address stream still contains significant locality, though it varies from application to application; and (d) as we move to wider buses, row access time becomes more prominent, making it important to investigate techniques to exploit the available locality to decrease access time.


title: Detecting Social Bots on Twitter: A Literature Review

abstract: Due to the exponential growth in the popularity of online social networks (OSNs), such as Twitter and Facebook, the number of machine accounts that are designed to mimic human users has increased. Social bots accounts (Sybils) have become more sophisticated and deceptive in their efforts to replicate the behaviors of normal accounts. As such, there is a distinct need for the research community to develop technologies that can detect social bots. This paper presents a review of the recent techniques that have emerged that are designed to differentiate between social bot account and human accounts. We limit the analysis to the detection of social bots on the Twitter social media platform. We review the various detection schemes that are currently in use and examine common aspects such as the classifier, datasets, and selected features employed. We also compare the evaluation techniques that are employed to validate the classifiers. Finally, we highlight the challenges that remain in the domain of social bot detection and consider future directions for research efforts that are designed to address this problem.


title: Aligning architecture knowledge management with Scrum

abstract: Combining agile project management approaches such as Scrum and software architecture related work has been found problematic. In this paper, we propose several models to align architecture knowledge management with Scrum, based on different architecture work practices in Scrum identified in the industry. The proposed models exploit an architectural knowledge base with a capability to generate architectural documents, and a decision-based architecture evaluation method.


title: Contextual Multi-Scale Region Convolutional 3D Network for Activity Detection

abstract: Activity detection is a fundamental problem in computer vision. Detecting activities of different temporal scales is particularly challenging. In this paper, we propose the contextual multi-scale region convolutional 3D network (CMS-RC3D) for activity detection. To deal with the inherent temporal scale variability of activity instances, the temporal feature pyramid is used to represent activities of different temporal scales. On each level of the temporal feature pyramid, an activity proposal detector and an activity classifier are learned to detect activities of specific temporal scales. Temporal contextual information is fused into activity classifiers for better recognition. More importantly, the entire model at all levels can be trained end-to-end. Our CMS-RC3D detector can deal with activities at all temporal scale ranges with only a single pass through the backbone network. We test our detector on two public activity detection benchmarks, THUMOS14 and ActivityNet. Extensive experiments show that the proposed CMS-RC3D detector outperforms state-of-the-art methods on THUMOS14 by a substantial margin and achieves comparable results on ActivityNet despite using a shallow feature extractor.


title: Analysis of Hotel Guest Satisfaction Ratings and Reviews: An Application in Singapore

abstract: The hotel industry in Singapore is an important part of the hospitality and tourism infrastructure and a strategic part of Singapore’s growth story. Hotels are primarily viewed as a service industry with intangible areas of guest experience and service levels. The research objective of this paper is to better understand the hotel guest satisfaction and the areas that hotel management can change, in order to get better results. For this purpose, an analysis of hotel guest satisfaction ratings based on attributes such as Location, Sleep quality, Rooms, Service quality, Value for money and Cleanliness was performed. Further, text analysis of customer reviews was also performed to better understand the positive and negative sentiments of hotel guests. We focused on identifying the attributes that differentiate one hotel from another, and then using these attribute insights to make recommendation to hotel management, on how they can improve their operations, guest satisfaction and generally differentiate themselves from their competition. Data from an online website, Trip Advisor, was used to analyse and compare customer ratings and reviews on five hotels. Statistical data analysis techniques were used to identify the key attributes that are most important in choosing hotels and are critical to focus on in order to ensure guest satisfaction expectations are met. Based on text analytics, the key results from this study indicated that hotel guests look for a good room and a hotel with a pool and good service. Based on the ratings analysis, the most important attributes for guest satisfaction turned out to be Rooms, Value for money and Location.


title: Enzymatic approaches in paper industry for pulp refining and biofilm control

abstract: The use of enzymes has a high potential in the pulp and paper industry to improve the economics of the paper production process and to achieve, at the same time, a reduced environmental impact. Specific enzymes contribute to reduce the amount of chemicals and energy required for the modification of fibers and helps to prevent the formation or development of biofilms. This review is aimed at presenting the latest progresses made in the application of enzymes as refining aids and biofilm control agents.


title: Stochastic Learning on Imbalanced Data: Determinantal Point Processes for Mini-batch Diversification.

abstract: We study a mini-batch diversification scheme for stochastic gradient descent (SGD). While classical SGD relies on uniformly sampling data points to form a mini-batch, we propose a non-uniform sampling scheme based on the Determinantal Point Process (DPP). The DPP relies on a similarity measure between data points and gives low probabilities to mini-batches which contain redundant data, and higher probabilities to mini-batches with more diverse data. This simultaneously balances the data and leads to stochastic gradients with lower variance. We term this approach Balanced Mini-batch SGD (BM-SGD). We show that regular SGD and stratified sampling emerge as special cases. Furthermore, BM-SGD can be considered a generalization of stratified sampling to cases where no discrete features exist to bin the data into groups. We show experimentally that our method results more interpretable and diverse features in unsupervised setups, and in better classification accuracies in supervised setups.


Photorealistic Object Reconstruction Using Voxel Coloring and Adjusted Image Orientations, American Society for Photogrammetry and Remote


title: Stochastic Joint Radio and Computational Resource Management for Multi-User Mobile-Edge Computing Systems

abstract: Mobile-edge computing (MEC) has recently emerged as a prominent technology to liberate mobile devices from computationally intensive workloads, by offloading them to the proximate MEC server. To make offloading effective, the radio and computational resources need to be dynamically managed, to cope with the time-varying computation demands and wireless fading channels. In this paper, we develop an online joint radio and computational resource management algorithm for multi-user MEC systems, with the objective of minimizing the long-term average weighted sum power consumption of the mobile devices and the MEC server, subject to a task buffer stability constraint. Specifically, at each time slot, the optimal CPU-cycle frequencies of the mobile devices are obtained in closed forms, and the optimal transmit power and bandwidth allocation for computation offloading are determined with the Gauss-Seidel method ; while for the MEC server, both the optimal frequencies of the CPU cores and the optimal MEC server scheduling decision are derived in closed forms. Besides, a delay-improved mechanism is proposed to reduce the execution delay. Rigorous performance analysis is conducted for the proposed algorithm and its delay-improved version, indicating that the weighted sum power consumption and execution delay obey an $\left [{O\left ({1 / V}\right), O\left ({V}\right) }\right ]$ tradeoff with $V$ as a control parameter. Simulation results are provided to validate the theoretical analysis and demonstrate the impacts of various parameters.


title: The Cysteine Protease Activity of the Major Dust Mite Allergen Der P 1 Selectively Enhances the Immunoglobulin E Antibody Response

abstract: The house dust mite Dermatophagoides pteronyssinus allergen Der p 1 is the most immunodominant allergen involved in the expression of dust mite–specific immunoglobulin (Ig)E–mediated hypersensitivity. The reason for this potent IgE-eliciting property of Der p 1 remains unknown, but there is mounting in vitro evidence linking the allergenicity of Der p 1 to its cysteine protease activity. Here we demonstrate for the first time that immunization of mice with proteolytically active Der p 1 results in a significant enhancement in total IgE and Der p 1–specific IgE synthesis compared with animals immunized with Der p 1 that was irreversibly blocked with the cysteine protease inhibitor E-64. We conclude that the proteolytic activity of Der p 1 is a major contributor to its allergenicity.


title: Variation in adaptive traits among and within Spanish and European populations of Castanea sativa: selection of trees for timber production

abstract: Phenological, growth, form, and survival traits were assessed in 3-, 4-, 7-, 8-, and 12-year-old seedlings in two provenance–progeny tests of sweet chestnut (Castanea sativa Miller). One trial included six Spanish populations, with two subpopulations corresponding to wild and orchard trees distinguished within each population, and 77 open-pollinated families. The second trial encompassed six European populations and 156 open-pollinated families. Differences between populations were highly significant for phenological, growth, and form traits, and changes in the ranking of some populations were found for growth traits over the years of the study. Significant differences in survival were reported for the first time. Observed geographic variation among chestnut populations confirmed that drought is an important factor in determining traits related to growth and phenology. Significant differences among families were detected for all traits except for survival. Estimated individual heritabilities ranged from 0.21 to 0.43 for flushing, 0.16 to 0.37 for growth traits and straightness, 0.12 to 0.17 for bud set, and 0.08 to 0.24 for apical dominance. Significant, moderate genetic correlations were observed between growth and form traits, apical dominance and straightness, and flushing and bud set. On the basis of volume and form traits, the best trees of the best families within the best-adapted populations were selected for their inclusion in main and elite Galician breeding populations.


title: Location-Based Web Service QoS Prediction via Preference Propagation for Improving Cold Start Problem

abstract: With the popularity of service-oriented architecture, many web systems have been developed in form of composite services. Since the performance of these composite services highly depends on Quality of Service (QoS) of employed atomic web services, it is important to predict the QoS values of atomic web services with high accuracy. Although collaborative filtering based approaches have recently been proposed to predict the web service QoS values, they mostly face a cold start problem which causes unreliable prediction due to the highly sparse historical data, newly introduced users and web services. Furthermore, existing work only considers the case of newly introduced users. In this paper, we propose a Location-based Matrix Factorization technique via Preference Propagation (LMF-PP) to improve the cold start problem in web service QoS prediction domain. LMF-PP exploits the location information of entities (i.e., Users and web services) and employs the preference propagation to make the accurate QoS prediction even for the newly introduced entities and in the small amount of data (i.e., Highly sparse matrix). The performance of LMF-PP is compared with that of existing approaches on a real world dataset. The experimental results show that LMF-PP can outperform the existing approaches in not only a cold start environment but also a warm start environment.


title: Osteoblast Sensitivity to Topographical and Chemical Features of Titanium

abstract: The titanium-osteoblast-interaction can be influenced both by surface roughness and by chemical modifications. We have ascertained that a positively charged titanium surface boosts osteoblast cells adhesion due to their negatively charged cellular hyaluronan coat. In current experiments, chemical surface modifications were combined with different topographies. Titanium disks of technical purity were modified (i) in their roughness by polishing (P), machining (M) and corundum blasting (CB), and (ii) by subsequently chemical functionalization by a thin film (d≤0.1 µm) of microwave plasma polymerized allylamine (PPAAm). In addition, collagen I was immobilized on PPAAm via the bifunctional linker polyethylene glycol diacid or glutar dialdehyde, respectively. The cell shape and material's contact of human osteoblasts was analyzed by FE-SEM and time dependent cell adhesion measured by flow cytometry. The cell dynamic of the adhesion component vinculin was observed in living cells. Amino-functionalization (PPAAm) considerably enhances the adhesion of osteoblasts in combination with topographical features, which was in contrast to collagen modified surfaces. PPAAm allows the cells to literally melt into the groove structure of the titanium. The bone cells lie over a large area and very close to the surface, so that the edges of the cells can hardly be distinguished from the structure of the surface. The combinatory effect of topography and plasma modification could improve bonding of the implant to the bone tissue.


Pan F, He P, Liu C, Li T, Murray A, Zheng D. Variation of the Korotkoff stethoscope sounds during blood pressure measurement: Analysis using a convolutional neural network. IEEE Journal of Biomedical and Health Informatics


title: Volume xx (200y), Number z, pp. 1–14 Full Wave Modeling of Light Propagation and Reflection

abstract: The propagation and reflection of electromagnetic waves in a three-dimensional environment is simulated, and realistic images are produced using the resulting light distributions and reflectance functions. A finite difference time domain method is employed to advance the electric and magnetic fields in a scene. Surfaces containing wavelength scaled structures are created, the interaction of the electromagnetic waves with these nano-structured materials is calculated, and the sub-surface interference and diffraction effects are modelled. The result is a reflectance function with wavelength composition and spatial distribution properties that could not have been predicted using classic computer graphic ray tracing approaches. The techniques are employed to reproduce demonstrations of simple interference and diffraction effects, and to create computer-generated pictures of a Morpho butterfly.


title: MiningZinc: A declarative framework for constraint-based mining

abstract: Abstract We introduce MiningZinc, a declarative framework for constraint-based data mining. MiningZinc consists of two key components: a language component and an execution mechanism. First, the MiningZinc language allows for high-level and natural modeling of mining problems, so that MiningZinc models are similar to the mathematical definitions used in the literature. It is inspired by the Zinc family of languages and systems and supports user-defined constraints and functions. Secondly, the MiningZinc execution mechanism specifies how to compute solutions for the models. It is solver independent and supports both standard constraint solvers and specialized data mining systems. The high-level problem specification is first translated into a normalized constraint language (FlatZinc). Rewrite rules are then used to add redundant constraints or solve subproblems using specialized data mining algorithms or generic constraint programming solvers. Given a model, different execution strategies are automatically extracted that correspond to different sequences of algorithms to run. Optimized data mining algorithms, specialized processing routines and generic solvers can all be automatically combined. Thus, the MiningZinc language allows one to model constraint-based itemset mining problems in a solver independent way, and its execution mechanism can automatically chain different algorithms and solvers. This leads to a unique combination of declarative modeling with high-performance solving.


Emotion Intensity Prediction with Affective Norms , Automatically Extended Resources and Deep Learning


title: The Mobile MAV concept for Mars Sample Return

abstract: Mars Sample Return (MSR) is the highest priority planetary science objective as identified in the Planetary Science Decadal Survey. To return a sample to Earth would require the accomplishment of several critical steps in the collection and transfer of the sample from the surface of Mars to the surface of the Earth. While there are many architectures and approaches to returning a sample of Martian surface material to the Earth, the current architecture includes the following series of missions: (1) a sample caching mission which would scientifically select, acquire and cache the sampled material on the surface of Mars, (2) a sample retrieval mission which would collect the sample from the surface and place it into Mars orbit, and (3) a Sample return orbiter which would rendezvous with the sample and return it to Earth. A fourth element of the architecture, the Mars Returned Sample Handling facility (MRSH) would be developed to receive the returned sample and provide for the sample analysis and curation. The Mobile MAV concept is an approach to the sample retrieval and launch element of the architecture. The design concept that was extant during the Decadal Survey studies featured a stationary platform lander, which would land a small fetch rover to retrieve the sample cache (collected by the previous mission), and the Mars Ascent Vehicle (MAV), which would inject the sample cache into Mars orbit. MSL's success in demonstrating a new Entry, Descent, and Landing architecture, which placed the rover directly on the surface in a mobility touchdown configuration, motivates a closer assessment of a MSL class rover which is capable of supporting the MAV and retrieving the cached samples. This architecture eliminates the need for a return trip to a stationary lander, which would be required of a fetch rover in the Decadal Survey mission architecture, significantly reducing the required surface mission duration and traverse requirements.


title: Automatic Parking Space Detection System

abstract: Searching a suitable parking space in populated metropolitan city is extremely difficult for drivers. Serious traffic congestion may occur due to unavailable parking space. Automatic smart parking system is emerging field and attracted computer vision researchers to contribute in this arena of technology. In this paper, we have presented a vision based smart parking framework to assist the drivers in efficiently finding suitable parking slot and reserve it. Initially, we have segmented the parking area into blocks using calibration. Then, classify each block to identify car and intimate the driver about the status of parking either reserved or free. Potentially, the performance accuracy of recommended system is higher than state of the art hardware solutions, validating the supremacy of the proposed framework.


title: 230 Rakesh Kumar, Dr. Ravinder Khanna Natural Language Engineering: The Study of Word Sense Disambiguation in Punjabi

abstract: Word Sense Disambiguation (WSD) is an important part of Machine Readable Dictionary (MRD) which is extensively used in Expert System/Intelligent Systems. All languages have multiple meanings of words or phrases depending on the context of their usage. WSD draws the correct (intended) meaning using a database called Machine Readable Dictionary (MRD). Some rudimentary designs of MRD have been made for some European Languages. In this paper a preliminary attempt has been made towards the formulation and design of MRD in Punjabi Language using modified Lesk Algorithm which uses a simple method for relating the appropriate word sense relative to set of dictionary meanings of the word or phrase.


Ontological Semantics


title: 3D model retrieval using Bag-of-View-Words

abstract: The view-based 3D model descriptors, which represent a 3D model using its projected views, have limitations on viewpoints sampling and computational cost. This paper proposes a new 3D model descriptor, called the Bag-of-View-Words (BoVW) descriptor, which describes a 3D model by measuring the occurrences of its projected views. An adaptive clustering method is applied to reduce the redundancy of the projected views of each 3D model. A 3D model is represented by a multi-resolution histogram, which is combined by several BoVW descriptors at different levels. The codebook is obtained by unsupervised learning. We also propose a new pyramid matching method for 3D model comparison. Experimental results demonstrated that our method outperforms several existing 3D model descriptors in respect of retrieval precision and computational cost.


title: Taxonomy and Survey of Collaborative Intrusion Detection

abstract: The dependency of our society on networked computers has become frightening: In the economy, all-digital networks have turned from facilitators to drivers; as cyber-physical systems are coming of age, computer networks are now becoming the central nervous systems of our physical world—even of highly critical infrastructures such as the power grid. At the same time, the 24s7 availability and correct functioning of networked computers has become much more threatened: The number of sophisticated and highly tailored attacks on IT systems has significantly increased. Intrusion Detection Systems (IDSs) are a key component of the corresponding defense measures; they have been extensively studied and utilized in the past. Since conventional IDSs are not scalable to big company networks and beyond, nor to massively parallel attacks, Collaborative IDSs (CIDSs) have emerged. They consist of several monitoring components that collect and exchange data. Depending on the specific CIDS architecture, central or distributed analysis components mine the gathered data to identify attacks. Resulting alerts are correlated among multiple monitors in order to create a holistic view of the network monitored. This article first determines relevant requirements for CIDSs; it then differentiates distinct building blocks as a basis for introducing a CIDS design space and for discussing it with respect to requirements. Based on this design space, attacks that evade CIDSs and attacks on the availability of the CIDSs themselves are discussed. The entire framework of requirements, building blocks, and attacks as introduced is then used for a comprehensive analysis of the state of the art in collaborative intrusion detection, including a detailed survey and comparison of specific CIDS approaches.


title: Internet parenting styles and the impact on Internet use of primary school children

abstract: Next to available data about actual Internet use of young children at home, most research especially focuses on the threats and opportunities about active Internet usage. Limited empirical research focuses on the role and impact of parents in this context. In the present study, Internet parenting styles are defined and operationalized to study the impact on actual Internet usage of children at home. Two dimensions are distinguished in Internet parenting styles: parental control and parental warmth. Based on a survey, involving 533 parents from children in primary schools, this Internet usage was studied from the perspective of Internet parenting styles. Results point at high Internet access at home. As to the parenting styles, we observe a dominance of the authoritative parenting style (59.4%). The styles differ when controlling for parent gender, educational background and age. Parenting styles are also linked to level of parent Internet usage, Internet attitude and Internet experience. Parenting styles also significantly affect child Internet usage. The highest child usage level is perceived when parents adopt a permissive parenting style; the lowest level is observed when parents adopt an authoritarian Internet parenting style. The variables Internet parenting style, parent Internet behavior, and parent educational background significantly predict Internet usage of children at home (R^2=.44). Theoretical and practical implications are discussed and directions for future research.


title: Fix-it: An extensible code auto-fix component in Review Bot

abstract: Coding standard violations, defect patterns and non-conformance to best practices are abundant in checked-in source code. This often leads to unmaintainable code and potential bugs in later stages of software life cycle. It is important to detect and correct these issues early in the development cycle, when it is less expensive to fix. Even though static analysis techniques such as tool-assisted code review are effective in addressing this problem, there is significant amount of human effort involved in identifying the source code issues and fixing it. Review Bot is a tool designed to reduce the human effort and improve the quality in code reviews by generating automatic reviews using static analysis output. In this paper, we propose an extension to Review Bot- addition of a component called Fix-it for the auto-correction of various source code issues using Abstract Syntax Tree (AST) transformations. Fix-it uses built-in fixes to automatically fix various issues reported by the auto-reviewer component in Review Bot, thereby reducing the human effort to greater extent. Fix-it is designed to be highly extensible-users can add support for the detection of new defect patterns using XPath or XQuery and provide fixes for it based on AST transformations written in a high-level programming language. It allows the user to treat the AST as a DOM tree and run XQuery UPDATE expressions to perform AST transformations as part of a fix. Fix-it also includes a designer application which enables Review Bot administrators to design new defect patterns and fixes. The developer feedback on a stand-alone prototype indicates the possibility of significant human effort reduction in code reviews using Fix-it.


Determining Gains Acquired from Word Embedding Quantitatively Using Discrete Distribution Clustering


title: Using phishing experiments and scenario-based surveys to understand security behaviours in practice

abstract: Purpose – The purpose of the study was threefold: to understand security behaviours in practice by investigating factors that may cause an individual to comply with a request posed by a perpetrator; to investigate if adding information about the victim to an attack increases the probability of the attack being successful; and, finally, to investigate if there is a correlation between self-reported and observed behaviour. Design/methodology/approach – Factors for investigation were identified based on a review of existing literature. Data were collected through a scenario-based survey, phishing experiments, journals and follow-up interviews in three organisations. Findings – The results from the experiment revealed that the degree of target information in an attack increased the likelihood that an organisational employee falls victim to an actual attack. Further, an individual’s trust and risk behaviour significantly affected the actual behaviour during the phishing experiment. Computer experience at work,...


title: A Short Linearly Homomorphic Proxy Signature Scheme

abstract: Linearly homomorphic signature schemes allow the performance of linear computations on authenticated data. They are important primitives for many applications, such as electronic voting, smart grids, electronic health records, and so on. Proxy signature schemes allow an original signer to delegate his/her signing power to a proxy signer, so that the proxy signer can sign on behalf of the original signer. Therefore, a signature scheme offering both of the above signatures’ properties is very desirable. In this paper, we construct the first linearly homomorphic proxy signature scheme, so the proxy signer can produce a linearly homomorphic signature on behalf of the original signer. The scheme is provably secure in the random oracle model. Moreover, the length of signature is short and constant. Linearly homomorphic proxy signature scheme can be used in applications, such as electronic business and cloud computing.


Author Profiling with Word+Character Neural Attention Network


title: Wind Turbine Condition Monitoring: State-of-the-Art Review, New Trends, and Future Challenges

abstract: As the demand for wind energy continues to grow at exponential rates, reducing operation and maintenance (OM) costs and improving reliability have become top priorities in wind turbine (WT) maintenance strategies. In addition to the development of more highly evolved WT designs intended to improve availability, the application of reliable and cost-effective condition-monitoring (CM) techniques offers an efficient approach to achieve this goal. This paper provides a general review and classification of wind turbine condition monitoring (WTCM) methods and techniques with a focus on trends and future challenges. After highlighting the relevant CM, diagnosis, and maintenance analysis, this work outlines the relationship between these concepts and related theories, and examines new trends and future challenges in the WTCM industry. Interesting insights from this research are used to point out strengths and weaknesses in today’s WTCM industry and define research priorities needed for the industry to meet the challenges in wind industry technological evolution and market growth.


title: Working Paper 1/2003Stochastic models underlying Croston’s method for

abstract: Croston's method is widely used to predict inventory demand when it is intermittent. However, it is an ad hoc method with no properly formulated underlying stochastic model. In this paper, we explore possible models underlying Croston's method and three related methods, and we show that any underlying model will be inconsistent with the properties of intermittent demand data. However, we find that the point forecasts and prediction intervals based on such underlying models may still be useful. Copyright © 2005 John Wiley & Sons, Ltd.


title: An Empirical Study of Leader Ethical Values, Transformational and Transactional Leadership, and Follower Attitudes Toward Corporate Social Responsibility

abstract: Several leadership and ethics scholars suggest that the transformational leadership process is predicated on a divergent set of ethical values compared to transactional leadership. Theoretical accounts declare that deontological ethics should be associated with transformational leadership while transactional leadership is likely related to teleological ethics. However, very little empirical research supports these claims. Furthermore, despite calls for increasing attention as to how leaders influence their followers’ perceptions of the importance of ethics and corporate social responsibility (CSR) for organizational effectiveness, no empirical study to date has assessed the comparative impact of transformational and transactional leadership styles on follower CSR attitudes. Data from 122 organizational leaders and 458 of their followers indicated that leader deontological ethical values (altruism, universal rights, Kantian principles, etc.) were strongly associated with follower ratings of transformational leadership, while leader teleological ethical values (utilitarianism) were related to follower ratings of transactional leadership. As predicted, only transformational leadership was associated with follower beliefs in the stakeholder view of CSR. Implications for the study and practice of ethical leadership, future research directions, and management education are discussed.


title: Saline instillation before tracheal suctioning decreases the incidence of ventilator-associated pneumonia*

abstract: OBJECTIVES ::: To compare the incidence of ventilator-associated pneumonia (VAP) with or without isotonic saline instillation before tracheal suctioning. As a secondary objective, we compared the incidence of endotracheal tube occlusion and atelectasis. ::: ::: ::: DESIGN ::: Randomized clinical trial. ::: ::: ::: SETTING AND PATIENTS ::: The study was conducted in a medical surgical intensive care unit of an oncologic hospital. We selected consecutive patients needing mechanical ventilation for >72 hrs. Patients were allocated into two groups: a saline group that received instillation of 8 mL of saline before tracheal suctioning and a control group which did not. VAP was diagnosed based on clinical suspicion and confirmed by bronchoalveolar lavage quantitative culture. The incidence of atelectasis on daily chest radiography and endotracheal tube occlusions were recorded. The sample size was calculated to a power of 80% and a type I error probability of 5%. ::: ::: ::: MEASUREMENTS AND MAIN RESULTS ::: One hundred thirty patients were assigned to the saline group and 132 to the control group. The baseline demographic variables were similar between groups. The rate of clinically suspected VAP was similar in both groups. The incidence of microbiological proven VAP was significantly lower in the saline group (23.5% x 10.8%; p = 0.008) (incidence density/1.000 days of ventilation 21.22 x 9.62; p < 0.01). Using the Kaplan-Meier curve analysis, the proportion of patients remaining without VAP was higher in the saline group (p = 0.02, log-rank test). The relative risk reduction of VAP in the saline instillation group was 54% (95% confidence interval, 18%-74%) and the number needed to treat was eight (95% confidence interval, 5-27). The incidence of atelectases and endotracheal tube occlusion were similar between groups. ::: ::: ::: CONCLUSIONS ::: Instillation of isotonic saline before tracheal suctioning decreases the incidence of microbiological proven VAP.


Performance Evaluations of Hard-Switching Interleaved DC / DC Boost Converter with New Generation Silicon Carbide MOSFETs


title: To stylize or not to stylize?: the effect of shape and material stylization on the perception of computer-generated faces

abstract: Virtual characters contribute strongly to the entire visuals of 3D animated films. However, designing believable characters remains a challenging task. Artists rely on stylization to increase appeal or expressivity, exaggerating or softening specific features. In this paper we analyze two of the most influential factors that define how a character looks: shape and material. With the help of artists, we design a set of carefully crafted stimuli consisting of different stylization levels for both parameters, and analyze how different combinations affect the perceived realism, appeal, eeriness, and familiarity of the characters. Moreover, we additionally investigate how this affects the perceived intensity of different facial expressions (sadness, anger, happiness, and surprise). Our experiments reveal that shape is the dominant factor when rating realism and expression intensity, while material is the key component for appeal. Furthermore our results show that realism alone is a bad predictor for appeal, eeriness, or attractiveness.


title: Presenting system uncertainty in automotive UIs for supporting trust calibration in autonomous driving

abstract: To investigate the impact of visualizing car uncertainty on drivers' trust during an automated driving scenario, a simulator study was conducted. A between-group design experiment with 59 Swedish drivers was carried out where a continuous representation of the uncertainty of the car's ability to autonomously drive during snow conditions was displayed to one of the groups, whereas omitted for the control group. The results show that, on average, the group of drivers who were provided with the uncertainty representation took control of the car faster when needed, while they were, at the same time, the ones who spent more time looking at other things than on the road ahead. Thus, drivers provided with the uncertainty information could, to a higher degree, perform tasks other than driving without compromising with driving safety. The analysis of trust shows that the participants who were provided with the uncertainty information trusted the automated system less than those who did not receive such information, which indicates a more proper trust calibration than in the control group.


title: Pedestrian detection in crowded scenes

abstract: In this paper, we address the problem of detecting pedestrians in crowded real-world scenes with severe overlaps. Our basic premise is that this problem is too difficult for any type of model or feature alone. Instead, we present an algorithm that integrates evidence in multiple iterations and from different sources. The core part of our method is the combination of local and global cues via probabilistic top-down segmentation. Altogether, this approach allows examining and comparing object hypotheses with high precision down to the pixel level. Qualitative and quantitative results on a large data set confirm that our method is able to reliably detect pedestrians in crowded scenes, even when they overlap and partially occlude each other. In addition, the flexible nature of our approach allows it to operate on very small training sets.


title: Digital Footprinting: Uncovering Tourists with User-Generated Content

abstract: Novel methods and tools are being developed to explore the significance of the new types of user-related spatiotemporal data. This approach helps uncover the presence and movements of tourists from cell phone network data and the georeferenced photos they generate. A city's visitors have many ways of leaving voluntary or involuntary electronic trails: prior to their visits, tourists generate server log entries when they consult digital maps or travel Web sites; during their visit, they leave traces on wireless networks whenever they use their mobile phones; and after their visit, they might add online reviews and photos. Broadly speaking then, there are two types of footprint: active and passive. Passive tracks are left through interaction with an infrastructure, such as a mobile phone network, that produces entries in locational logs; active prints come from the users themselves when they expose locational data in photos, messages, and sensor measurements. In this article, we consider two types of digital traces from Rome, Italy: georeferenced photos made publicly available on the photo-sharing Web site Flickr and aggregate records of wireless network events generated by mobile phone users making calls and sending text messages on the Telecom Italia Mobile (TIM) system.


title: Robust Lexical Features for Improved Neural Network Named-Entity Recognition

abstract: Neural network approaches to Named-Entity Recognition reduce the need for carefully hand-crafted features. While some features do remain in state-of-the-art systems, lexical features have been mostly discarded, with the exception of gazetteers. In this work, we show that this is unfair: lexical features are actually quite useful. We propose to embed words and entity types into a low-dimensional vector space we train from annotated data produced by distant supervision thanks to Wikipedia. From this, we compute - offline - a feature vector representing each word. When used with a vanilla recurrent neural network model, this representation yields substantial improvements. We establish a new state-of-the-art F1 score of 87.95 on ONTONOTES 5.0, while matching state-of-the-art performance with a F1 score of 91.73 on the over-studied CONLL-2003 dataset.


title: A Hierarchical Recurrent Neural Network for Symbolic Melody Generation

abstract: In recent years, neural networks have been used to generate symbolic melodies. However, the long-term structure in the melody has posed great difficulty for designing a good model. In this paper, we present a hierarchical recurrent neural network for melody generation, which consists of three Long-Short-Term-Memory (LSTM) subnetworks working in a coarse-to-fine manner along time. Specifically, the three subnetworks generate bar profiles, beat profiles and notes in turn, and the output of the high-level subnetworks are fed into the low-level subnetworks, serving as guidance for generating the finer time-scale melody components in low-level subnetworks. Two human behavior experiments demonstrate the advantage of this structure over the single-layer LSTM which attempts to learn all hidden structures in melodies. Compared with the state-of-the-art models MidiNet and MusicVAE, the hierarchical recurrent neural network produces better melodies evaluated by humans.


title: Rectenna Application of Miniaturized Implantable Antenna Design for Triple-Band Biotelemetry Communication

abstract: A novel antenna design that effectively covers three bands (the medical implant communications service (MICS) band at 402 MHz, and the industrial, scientific, and medical (ISM) band at 433 MHz and 2.45 GHz) using a π-shaped radiator with a stacked and spiral structure is described. The antenna has compact size of 254 mm 3 (10 mm by 10 mm by 2.54 mm). The proposed design is effective for triple-band biotelemetry with data telemetry (402 MHz), wireless powering transmission (433 MHz), and wake-up controller (2.45 GHz). An experimental prototype of the compact stacked rectenna was fabricated on a Roger 3210 substrate. This antenna was used in a rectenna (rectifying antenna) for 433 MHz wireless powering transmission, and provided a conversion efficiency of 86% when 11 dBm microwave power was received at 433 MHz with a 5 kΩ load. The optimal antenna was fabricated and tested in a minced front leg of pork. The simulated and measured bandwidths were 86 MHz and 113 MHz in the MICS band, and 60 MHz and 70 MHz in the ISM band, respectively.


title: Jointly Learning to Extract and Compress

abstract: We learn a joint model of sentence extraction and compression for multi-document summarization. Our model scores candidate summaries according to a combined linear model whose features factor over (1) the n-gram types in the summary and (2) the compressions used. We train the model using a margin-based objective whose loss captures end summary quality. Because of the exponentially large set of candidate summaries, we use a cutting-plane algorithm to incrementally detect and add active constraints efficiently. Inference in our model can be cast as an ILP and thereby solved in reasonable time; we also present a fast approximation scheme which achieves similar performance. Our jointly extracted and compressed summaries outperform both unlearned baselines and our learned extraction-only system on both ROUGE and Pyramid, without a drop in judged linguistic quality. We achieve the highest published ROUGE results to date on the TAC 2008 data set.


title: Learning to Adapt Structured Output Space for Semantic Segmentation

abstract: Convolutional neural network-based approaches for semantic segmentation rely on supervision with pixel-level ground truth, but may not generalize well to unseen image domains. As the labeling process is tedious and labor intensive, developing algorithms that can adapt source ground truth labels to the target domain is of great interest. In this paper, we propose an adversarial learning method for domain adaptation in the context of semantic segmentation. Considering semantic segmentations as structured outputs that contain spatial similarities between the source and target domains, we adopt adversarial learning in the output space. To further enhance the adapted model, we construct a multi-level adversarial network to effectively perform output space domain adaptation at different feature levels. Extensive experiments and ablation study are conducted under various domain adaptation settings, including synthetic-to-real and cross-city scenarios. We show that the proposed method performs favorably against the state-of-the-art methods in terms of accuracy and visual quality.


title: When Worlds Collide: Integrating Different Counterfactual Assumptions in Fairness

abstract: Machine learning is now being used to make crucial decisions about people's lives. For nearly all of these decisions there is a risk that individuals of a certain race, gender, sexual orientation, or any other subpopulation are unfairly discriminated against. Our recent method has demonstrated how to use techniques from counterfactual inference to make predictions fair across different subpopulations. This method requires that one provides the causal model that generated the data at hand. In general, validating all causal implications of the model is not possible without further assumptions. Hence, it is desirable to integrate competing causal models to provide counterfactually fair decisions, regardless of which causal "world" is the correct one. In this paper, we show how it is possible to make predictions that are approximately fair with respect to multiple possible causal models at once, thus mitigating the problem of exact causal specification. We frame the goal of learning a fair classifier as an optimization problem with fairness constraints entailed by competing causal explanations. We show how this optimization problem can be efficiently solved using gradient-based methods. We demonstrate the flexibility of our model on two real-world fair classification problems. We show that our model can seamlessly balance fairness in multiple worlds with prediction accuracy.


title: Chronic disease and its impact: The adolescent's perspective

abstract: Abstract Although evidence suggests that adolescents with chronic illness are at a greater risk for psychosocial disability, little is known about the adolescent's perception of the impact of the disease on his or her day-to-day life. Standardized measures of coping strategies, mastery, self-efficacy, social support, depression, and a semistructured interview on everyday difficulties were administered to matched groups (sex and age) of 31 adolescents with cystic fibrosis, 31 adolescents with diabetes, and 31 healthy controls. No differences were found between control and adolescents with a chronic disease responses on the standardized measures. The semistructured interview, however, revealed that the adolescent's perception of his or her physical health and the reaction of other family members to the illness were important sources of stress. These findings suggest that, in general, adolescents with a chronic illness cope effectively with their disability but that parents and clinicians must be sensitive to the adolescents' feelings and concerns regarding their health and its impact on the family.


title: A 65nm logic technology featuring 35nm gate lengths, enhanced channel strain, 8 Cu interconnect layers, low-k ILD and 0.57 /spl mu/m/sup 2/ SRAM cell

abstract: A 65nm generation logic technology with 1.2nm physical gate oxide, 35nm gate length, enhanced channel strain, NiSi, 8 layers of Cu interconnect, and low-k ILD for dense high performance logic is presented. Transistor gate length is scaled down to 35nm while not scaling the gate oxide as a means to improve performance and reduce power. Increased NMOS and PMOS drive currents are achieved by enhanced channel strain and junction engineering. 193nm lithography along with APSM mask technology is used on critical layers to provide aggressive design rules and a 6-T SRAM cell size of 0.57/spl mu/m/sup 2/. Process yield, performance and reliability are demonstrated on a 70 Mbit SRAM test vehicle with >0.5 billion transistors.