title: Technical section: A controlled clothoid spline

abstract: A clothoid has the property that its curvature varies linearly with arclength. This is a useful feature for the path of a vehicle whose turning radius is controlled as a linear function of the distance travelled. Highways, railways and the paths of car-like robots may be composed of straight line segments, clothoid segments and circular arcs. Control polylines are used in computer aided design and computer aided geometric design applications to guide composite curves during the design phase. This article examines the use of a control polyline to guide a curve composed of segments of clothoids, straight lines, and circular arcs.


title: The Construction of Synergetic Development System of Product Innovation and Process Innovation in Manufacturing Enterprises

abstract: The process of technological innovation in enterprises is complex system engineering. Aiming at the problems about the synergetic development of product innovation and process innovation in the interior of manufacturing enterprises in the process of technological innovation, this paper constructs a synergetic development system of product innovation and process innovation in manufacturing enterprises (In this paper, the system `synergetic development system of product innovation and process innovation in manufacturing enterprises' is abbreviated to `SDS') from the angle of system science. And the paper deeply analyses system elements, system structure, system function and external environment etc. In SDS, system elements mainly include product innovation subsystem and process innovation subsystem; system structure mainly includes invested resources, system main body, coordination mechanism and its characteristics; system function mainly includes integration function, learning function, feedback function, risk down function and value increment function etc; system environment includes legal environment, economic environment, technical environment and cultural environment. The aim is to promote better the synergetic development of product innovation and process innovation, and improve technological independence innovation capability and core competitiveness of manufacturing enterprises.


title: A novel broadband AMC surface for lowering the height of planar antennas

abstract: A novel broadband artificial magnetic conductor (AMC) surface is proposed for lowering the height of planar antennas. By replacing the conventional perfect electric conductor (PEC) ground plane with the AMC surface, the height of planar antenna can be significantly reduced. Three planar antennas for LTE700/GSM900 base stations are investigated. The antenna height is lowered from 0.263λ 0.83GHz to 0.083λ 0.83GHz , (where λ 0.83GHz is the wavelength in free space at the frequency 0.83 GHz). The height-lowered planar antennas maintain a wide bandwidth of, e.g., 38% for a linearly polarized antenna, 35% for a dual-polarized antenna, and 46% for a circularly polarized antenna. All these planar antennas above the AMC surface have a gain of higher than 7 dBi.


Word Meaning and Commonsense Metaphysics


title: The SEMAINE Database: Annotated Multimodal Records of Emotionally Colored Conversations between a Person and a Limited Agent

abstract: SEMAINE has created a large audiovisual database as a part of an iterative approach to building Sensitive Artificial Listener (SAL) agents that can engage a person in a sustained, emotionally colored conversation. Data used to build the agents came from interactions between users and an "operator” simulating a SAL agent, in different configurations: Solid SAL (designed so that operators displayed an appropriate nonverbal behavior) and Semi-automatic SAL (designed so that users' experience approximated interacting with a machine). We then recorded user interactions with the developed system, Automatic SAL, comparing the most communicatively competent version to versions with reduced nonverbal skills. High quality recording was provided by five high-resolution, high-framerate cameras, and four microphones, recorded synchronously. Recordings total 150 participants, for a total of 959 conversations with individual SAL characters, lasting approximately 5 minutes each. Solid SAL recordings are transcribed and extensively annotated: 6-8 raters per clip traced five affective dimensions and 27 associated categories. Other scenarios are labeled on the same pattern, but less fully. Additional information includes FACS annotation on selected extracts, identification of laughs, nods, and shakes, and measures of user engagement with the automatic system. The material is available through a web-accessible database.


title: Changes in Continental Freshwater Discharge from 1948 to 2004

abstract: Abstract A new dataset of historical monthly streamflow at the farthest downstream stations for the world’s 925 largest ocean-reaching rivers has been created for community use. Available new gauge records are added to a network of gauges that covers ∼80 × 106 km2 or ∼80% of global ocean-draining land areas and accounts for about 73% of global total runoff. For most of the large rivers, the record for 1948–2004 is fairly complete. Data gaps in the records are filled through linear regression using streamflow simulated by a land surface model [Community Land Model, version 3 (CLM3)] forced with observed precipitation and other atmospheric forcings that are significantly (and often strongly) correlated with the observed streamflow for most rivers. Compared with previous studies, the new dataset has improved homogeneity and enables more reliable assessments of decadal and long-term changes in continental freshwater discharge into the oceans. The model-simulated runoff ratio over drainage areas with and witho...


title: Induction heating appliance with a mobile double-coil inductor

abstract: An induction heating appliance designed to uniformly heat up metallic plates is studied in this paper. It consists of one planar inductor with two concentric coils attached to a mechanism, which allows moving the inductor under the plate while heating. This system is a possible solution for the growing concept of flexible induction cooking hobs, improving their performance in flexibility and in thermal distribution in the pans. With different combinations of motion and the selective activation of the inductor coils, any pan can be uniformly heated regardless its size or position on the hob. In this paper, we develop a thermal model to analyze the temperature distribution obtained in the pans for each diameter and strategy used. The model is solved using finite differences, and it is validated with experimental measurements. From the calculations, the best strategy for each pan diameter is obtained.


Heuristic search methods and cellular automata modelling for layout design


title: Cloud-based data stream processing

abstract: In this tutorial we present the results of recent research about the cloud enablement of data streaming systems. We illustrate, based on both industrial as well as academic prototypes, new emerging uses cases and research trends. Specifically, we focus on novel approaches for (1) scalability and (2) fault tolerance in large scale distributed streaming systems. In general, new fault tolerance mechanisms strive to be more robust and at the same time introduce less overhead. Novel load balancing approaches focus on elastic scaling over hundreds of instances based on the data and query workload. Finally, we present open challenges for the next generation of cloud-based data stream processing engines.


title: A hybrid bug triage algorithm for developer recommendation

abstract: With a great number of software applications that have been developed, software maintenance has become an important and challenging task, particularly due to the increasing scale of software projects. Even if developers can create and update bug reports in bug repositories to support software maintenance, a large software project receives a large number of bug reports each day. For reducing the workload of developers, many researchers and software engineers have begun recommending appropriate developers to fix bugs. This process is called bug triage and is a hot research topic for software maintenance. In this paper, we propose a hybrid bug triage algorithm, combining a probability model and an experience model to rank all candidate developers for fixing a new bug. For this study, we adopted the smoothed Unigram Model (UM) instead of the traditional Vector Space Model (VSM) to search similar bug reports. In the probability model, we used a social network to analyze the probability of fixing a new bug for a candidate developer. We first proposed to add a new feature (the number of re-opened bugs) in order to get the fixing probability. In the experience model, we considered the number of fixed bugs and fixing cost for each candidate developer as the estimate factor. In addition, we introduced a new concept, activity factor, to better model developers' experience. We performed the experiments on two large-scale, open source projects. The results show that our method can effectively recommend the best developer for fixing bugs.


title: Gradient Descent Converges to Minimizers

abstract: We show that gradient descent converges to a local minimizer, almost surely with random initialization. This is proved by applying the Stable Manifold Theorem from dynamical systems theory.


title: A Phase Ib study evaluating MNRP1685A, a fully human anti-NRP1 monoclonal antibody, in combination with bevacizumab and paclitaxel in patients with advanced solid tumors

abstract: PURPOSE ::: MNRP1685A is a human monoclonal antibody that blocks binding of vascular endothelial growth factor (VEGF), VEGF-B, and placental growth factor 2 to neuropilin-1 resulting in vessel immaturity and VEGF dependency. The safety of combining MNRP1685A with bevacizumab, with or without paclitaxel, was examined. ::: ::: ::: METHODS ::: Patients with advanced solid tumors received escalating doses of MNRP1685A (7.5, 15, 24, and 36 mg/kg) with bevacizumab 15 mg/kg every 3 weeks in Arm A (n = 14). Arm B (n = 10) dosing consisted of MNRP1685A (12 and 16 mg/kg) with bevacizumab 10 mg/kg (every 2 weeks) and paclitaxel 90 mg/m(2) (weekly, 3 of 4 weeks). Objectives were to determine safety, pharmacokinetics, pharmacodynamics, and the maximum tolerated dose of MNRP1685A. ::: ::: ::: RESULTS ::: Infusion reactions (88 %) and transient thrombocytopenia (67 %) represent the most frequent study drug-related adverse events (AEs). Drug-related Grade 2 or 3 proteinuria occurred in 13 patients (54 %). Additional study drug-related AEs occurring in >20 % of patients included neutropenia, alopecia, dysphonia, fatigue, and nausea. Neutropenia occurred only in Arm B. Grade ≥3 study drug-related AEs in ≥3 patients included neutropenia (Arm B), proteinuria, and thrombocytopenia. Two confirmed and three unconfirmed partial responses were observed. ::: ::: ::: CONCLUSIONS ::: The safety profiles were consistent with the single-agent profiles of all study drugs. However, a higher than expected rate of clinically significant proteinuria was observed that does not support further testing of MNRP1685A in combination with bevacizumab.


title: The Relationship Between Mobile Phone Use, Metacognitive Awareness and Academic Achievement

abstract: Mobile phones are getting smarter and the usage through university students becoming more popular. University students using mobile phones for talking, for texting message, for Internet search, for listening music, watching videos, playing games, using social media etc... Mobile phones are not accessory any more, they are integrated like our clothes. There are studies examining the effects of using mobile phones frequently. The aim of this study was to determine the relationship between mobile phone usage, satisfaction with life, academic achievement and metacognitive awareness. Metacognitive awareness inventory used to measure the awareness. Total GPA was used to measure the academic achievement and the mobile phone using time, the number of text messaging and callings are used for indicating the mobile phone usage. 250 total university students attended voluntarily to the study. The study carried out in private university in the southeast region of Turkey. The results indicated the usage of mobile phones are very frequent through university students. Many of the students are using mobile phones heavily. There was a positive relationship between mobile phone usage and academic achievement, also between mobile phone usage and metacognitive awareness.


Skills and Characteristics of Successful Cybersecurity Advocates.


title: A Review on Processing Big Data

abstract: Big Data has become a known buzzword to public at large which represents huge amount of data in terabytes. Processing such huge data is not possible with conventional environments. Therefore new environments came into existence with data centers and distributed file systems. Hadoop is a well known framework for processing big data using its new programming model known as MapReduce. However, later on many improvements came into existence with the help of other frameworks such as Sailfish, Haloop and AROM besides many other frameworks. In this paper we focused on the three important frameworks such as Sailfish, Haloop and AROM in terms of their performance and characteristics such as Velocity, Volume and Variety. This paper provides insights into big data processing that can help reader to have fundamental knowledge about Big Data, processing it and advantages derived from big data.


title: Social media as a catalyst for online deliberation? Exploring the affordances of Facebook and YouTube for political expression

abstract: This manuscript aims to assess the potential of social media as a channel to foster democratic deliberation. It does this by examining whether the types of discussions that citizens maintain in two of the most used social media channels managed by the White House - Facebook and YouTube - meet the necessary conditions for deliberative democracy. For this purpose 7230 messages were analyzed and assessed in terms of indicators developed to evaluate online discourse derived from the work of Habermas. By contrasting social media channels that differ in the affordances of identifiability and networked information access (two traditional predictors of online deliberation), we seek to contribute a deeper understanding of social media and its impact on deliberation. Drawing on the social identification/deindividuation (SIDE) model of computer mediated communication and network theories, we predict that political discussions in Facebook will present a more egalitarian distribution of comments between discussants and higher level of politeness in their messages. Consistent with our theoretical framework, results confirm that Facebook expands the flow of information to other networks and enables more symmetrical conversations among users, whereas politeness is lower in the more anonymous and deindividuated YouTube.


title: TADA: Trend Alignment with Dual-Attention Multi-task Recurrent Neural Networks for Sales Prediction

abstract: As a common strategy in sales-supply chains, the prediction of sales volume offers precious information for companies to achieve a healthy balance between supply and demand. In practice, the sales prediction task is formulated as a time series prediction problem which aims to predict the future sales volume for different products with the observation of various influential factors (e.g., brand, season, discount, etc.) and corresponding historical sales records. However, with the development of contemporary commercial markets, the dynamic interaction between influential factors with different semantic meanings becomes more subtle, causing challenges in fully capturing dependencies among these variables. Besides, though seeking similar trends from the history benefits the accuracy for the prediction of upcoming sales, existing methods hardly suit sales prediction tasks because the trends in sales time series are more irregular and complex. Hence, we gain insights from the encoder-decoder recurrent neural network (RNN) structure, and propose a novel framework named TADA to carry out trend alignment with dualattention, multi-task RNNs for sales prediction. In TADA, we innovatively divide the influential factors into internal feature and external feature, which are jointly modelled by a multi-task RNN encoder. In the decoding stage, TADA utilizes two attention mechanisms to compensate for the unknown states of influential factors in the future and adaptively align the upcoming trend with relevant historical trends to ensure precise sales prediction. Experimental results on two real-world datasets comprehensively show the superiority of TADA in sales prediction tasks against other state-of-the-art competitors.


title: Improvement of Radar Signal Phase Extraction using All Phase FFT Spectrum Analysis

abstract: In this article, the design of an ultra-wideband (UWB) microstrip patch antenna is presented, analyzed and manufactured. The antenna consists of modified circular-shaped patch antenna and semicircular finite ground plane. The UWB is achieved by introducing a staircase shaped slot to the radiating element and a rectangular slot to the ground plane. The proposed antenna has a small size of 23×28 mm2 and operates over the frequency band between 3.0–14.6 GHz.


title: On Analyzing User Topic-Specific Platform Preferences Across Multiple Social Media Sites

abstract: Topic modeling has traditionally been studied for single text collections and applied to social media data represented in the form of text documents. With the emergence of many social media platforms, users find themselves using different social media for posting content and for social interaction. While many topics may be shared across social media platforms, users typically show preferences of certain social media platform(s) over others for certain topics. Such platform preferences may even be found at the individual level. To model social media topics as well as platform preferences of users, we propose a new topic model known as MultiPlatform-LDA (MultiLDA). Instead of just merging all posts from different social media platforms into a single text collection, MultiLDA keeps one text collection for each social media platform but allowing these platforms to share a common set of topics. MultiLDA further learns the user-specific platform preferences for each topic. We evaluate MultiLDA against TwitterLDA, the state-of-the-art method for social media content modeling, on two aspects: (i) the effectiveness in modeling topics across social media platforms, and (ii) the ability to predict platform choices for each post. We conduct experiments on three real-world datasets from Twitter, Instagram and Tumblr sharing a set of common users. Our experiments results show that the MultiLDA outperforms in both topic modeling and platform choice prediction tasks. We also show empirically that among the three social media platforms, "Daily matters" and "Relationship matters" are dominant topics in Twitter, "Social gathering", "Outing" and "Fashion" are dominant topics in Instagram, and "Music", "Entertainment" and "Fashion" are dominant topics in Tumblr.


title: Neural Mechanisms of Stress Resilience and Vulnerability

abstract: Exposure to stressful events can be differently perceived by individuals and can have persistent sequelae depending on the level of stress resilience or vulnerability of each person. The neural processes that underlie such clinically and socially important differences reside in the anatomical, functional, and molecular connectivity of the brain. Recent work has provided novel insight into some of the involved biological mechanisms that promises to help prevent and treat stress-related disorders. In this review, we focus on causal and mechanistic evidence implicating altered functions and connectivity of the neuroendocrine system, and of hippocampal, cortical, reward, and serotonergic circuits in the establishment and the maintenance of stress resilience and vulnerability. We also touch upon recent findings suggesting a role for epigenetic mechanisms and neurogenesis in these processes and briefly discuss promising avenues of future investigation.


title: Labelling Topics using Unsupervised Graph-based Methods

abstract: This paper introduces an unsupervised graph-based method that selects textual labels for automatically generated topics. Our approach uses the topic keywords to query a search engine and generate a graph from the words contained in the results. PageRank is then used to weigh the words in the graph and score the candidate labels. The state-of-the-art method for this task is supervised (Lau et al., 2011). Evaluation on a standard data set shows that the performance of our approach is consistently superior to previously reported methods.


title: Internet-Based and Other Computerized Psychological Treatments for Adult Depression: A Meta-Analysis

abstract: Computerized and, more recently, Internet-based treatments for depression have been developed and tested in controlled trials. The aim of this meta-analysis was to summarize the effects of these treatments and investigate characteristics of studies that may be related to the effects. In particular, the authors were interested in the role of personal support when completing a computerized treatment. Following a literature search and coding, the authors included 12 studies, with a total of 2446 participants. Ten of the 12 studies were delivered via the Internet. The mean effect size of the 15 comparisons between Internet-based and other computerized psychological treatments vs. control groups at posttest was d = 0.41 (95% confidence interval [CI]: 0.29–0.54). However, this estimate was moderated by a significant difference between supported (d = 0.61; 95% CI: 0.45–0.77) and unsupported (d = 0.25; 95% CI: 0.14–0.35) treatments. The authors conclude that although more studies are needed, Internet and other co...


title: Fluency-Guided Cross-Lingual Image Captioning

abstract: Image captioning has so far been explored mostly in English, as most available datasets are in this language. However, the application of image captioning should not be restricted by language. Only few studies have been conducted for image captioning in a cross-lingual setting. Different from these works that manually build a dataset for a target language, we aim to learn a cross-lingual captioning model fully from machine-translated sentences. To conquer the lack of fluency in the translated sentences, we propose in this paper a fluency-guided learning framework. The framework comprises a module to automatically estimate the fluency of the sentences and another module to utilize the estimated fluency scores to effectively train an image captioning model for the target language. As experiments on two bilingual (English-Chinese) datasets show, our approach improves both fluency and relevance of the generated captions in Chinese, but without using any manually written sentences from the target language.


title: Your AP knows how you move: fine-grained device motion recognition through WiFi

abstract: Recent WiFi standards use Channel State Information (CSI) feedback for better MIMO and rate adaptation. CSI provides detailed information about current channel conditions for different subcarriers and spatial streams. In this paper, we show that CSI feedback from a client to the AP can be used to recognize different fine-grained motions of the client. We find that CSI can not only identify if the client is in motion or not, but also classify different types of motions. To this end, we propose APsense, a framework that uses CSI to estimate the sensor patterns of the client. It is observed that client's sensor (e.g. accelerometer) values are correlated to CSI values available at the AP. We show that using simple machine learning classifiers, APsense can classify different motions with accuracy as high as 90%.


title: Magnetic Metamaterial Superlens for Increased Range Wireless Power Transfer

abstract: The ability to wirelessly power electrical devices is becoming of greater urgency as a component of energy conservation and sustainability efforts. Due to health and safety concerns, most wireless power transfer (WPT) schemes utilize very low frequency, quasi-static, magnetic fields; power transfer occurs via magneto-inductive (MI) coupling between conducting loops serving as transmitter and receiver. At the “long range” regime – referring to distances larger than the diameter of the largest loop – WPT efficiency in free space falls off as (1/d)6; power loss quickly approaches 100% and limits practical implementations of WPT to relatively tight distances between power source and device. A “superlens”, however, can concentrate the magnetic near fields of a source. Here, we demonstrate the impact of a magnetic metamaterial (MM) superlens on long-range near-field WPT, quantitatively confirming in simulation and measurement at 13–16 MHz the conditions under which the superlens can enhance power transfer efficiency compared to the lens-less free-space system.


title: Surface Plasmon Resonance Imaging Sensors: A Review

abstract: Surface plasmon resonance (SPR) imaging sensors realize label-free, real-time, highly sensitive, quantitative, high-throughput biological interaction monitoring and the binding profiles from multi-analytes further provide the binding kinetic parameters between different biomolecules. In the past two decades, SPR imaging sensors found rapid increasing applications in fundamental biological studies, medical diagnostics, drug discovery, food safety, precision measurement, and environmental monitoring. In this paper, we review the recent advances of SPR imaging sensor technology towards high-throughput multi-analyte screening. Finally, we describe our multiplex spectral-phase SPR imaging biosensor for high-throughput biosensing applications.


title: Interobserver Reliability of the Total Body Score System for Quantifying Human Decomposition.

abstract: Several authors have tested the accuracy of the Total Body Score (TBS) method for quantifying decomposition, but none have examined the reliability of the method as a scoring system by testing interobserver error rates. Sixteen participants used the TBS system to score 59 observation packets including photographs and written descriptions of 13 human cadavers in different stages of decomposition (postmortem interval: 2-186 days). Data analysis used a two-way random model intraclass correlation in SPSS (v. 17.0). The TBS method showed "almost perfect" agreement between observers, with average absolute correlation coefficients of 0.990 and average consistency correlation coefficients of 0.991. While the TBS method may have sources of error, scoring reliability is not one of them. Individual component scores were examined, and the influences of education and experience levels were investigated. Overall, the trunk component scores were the least concordant. Suggestions are made to improve the reliability of the TBS method.


title: Actuating Fibers: Design and Applications

abstract: Actuators are devices capable of moving or controlling objects and systems by applying mechanical force on them. Among all kinds of actuators with different shapes, fibrous ones deserve particular attention. In spite of their apparent simplicity, actuating fibers allow for very complex actuation behavior. This review discusses different approaches for the design of actuating fibers, and their advantages and disadvantages. We also discuss the prospects for the design of fibers with advanced architectures and complex actuation behavior.


title: Safe Policy Learning from Observations

abstract: In this paper, we consider the problem of learning a policy by observing numerous non-expert agents. Our goal is to extract a policy that, with high-confidence, acts better than the average agents' performance. Such a setting is important for real-world problems where expert data is scarce but non-expert data can easily be obtained, e.g. by crowdsourcing. Our approach is to pose this problem as safe policy improvement in Reinforcement Learning. First, we evaluate an average behavior policy and approximate its value function. Then, we develop a stochastic policy improvement algorithm, termed Rerouted Behavior Improvement (RBI), that safely improves the average behavior. The primary advantages of RBI over current safe learning methods are its stability in the presence of value estimation errors and the elimination of a policy search process. We demonstrate these advantages in a Taxi grid-world domain and in four games from the Atari learning environment.


title: Highly efficient 5G linear power amplifiers (PA) design challenges

abstract: The incoming 5G revolution will dramatically increase the design complexity for handsets and communication infrastructures, demanding the RFIC and ASIC chipsets designers, network and system components vendors and telecom operators to provide viable 5G E2E (End-to-End) products and solutions before A.D. 2020. The broadband modulation bandwidth for RF transmitters (i.e., 250 MHz to above 1 GHz), stringent linearity, and power efficiency requirements at the cm-Wave/mm-Wave 5G frequencies will make it particularly challenging for highly efficient linear 5G power amplifier (PA) design. We will briefly address two major design challenges and solutions for 5G PA design here, namely the device technology choices and efficiency enhancement techniques.


title: Learning Longer Memory in Recurrent Neural Networks

abstract: Recurrent neural network is a powerful model that learns temporal patterns in sequential data. For a long time, it was believed that recurrent networks are difficult to train using simple optimizers, such as stochastic gradient descent, due to the so-called vanishing gradient problem. In this paper, we show that learning longer term patterns in real data, such as in natural language, is perfectly possible using gradient descent. This is achieved by using a slight structural modification of the simple recurrent neural network architecture. We encourage some of the hidden units to change their state slowly by making part of the recurrent weight matrix close to identity, thus forming kind of a longer term memory. We evaluate our model in language modeling experiments, where we obtain similar performance to the much more complex Long Short Term Memory (LSTM) networks (Hochreiter & Schmidhuber, 1997).


title: E-LEARNING TOOLS FOR TEACHING AND LEARNING, - II part –

abstract: In this second part of the paper is description of some Asynchronous tools of Virtual Learning Environment Tools used in education, knowing that in the previous part was presented some Synchronous tools.Since the tools provided by VLEs are very diverse, teachers must get to know them well in order to be able to use them efficiently in the process of teaching, learning and evaluation. When a teacher chooses to use e-learning tools, s/he should have in mind both the type of the course in which they can be applied and the students’ needs and abilities.


Activity monitoring: noticing interesting changes in behavior


title: Continuous monitoring of distance-based outliers over data streams

abstract: Anomaly detection is considered an important data mining task, aiming at the discovery of elements (also known as outliers) that show significant diversion from the expected case. More specifically, given a set of objects the problem is to return the suspicious objects that deviate significantly from the typical behavior. As in the case of clustering, the application of different criteria lead to different definitions for an outlier. In this work, we focus on distance-based outliers: an object x is an outlier if there are less than k objects lying at distance at most R from x. The problem offers significant challenges when a stream-based environment is considered, where data arrive continuously and outliers must be detected on-the-fly. There are a few research works studying the problem of continuous outlier detection. However, none of these proposals meets the requirements of modern stream-based applications for the following reasons: (i) they demand a significant storage overhead, (ii) their efficiency is limited and (iii) they lack flexibility. In this work, we propose new algorithms for continuous outlier monitoring in data streams, based on sliding windows. Our techniques are able to reduce the required storage overhead, run faster than previously proposed techniques and offer significant flexibility. Experiments performed on real-life as well as synthetic data sets verify our theoretical study.


title: Floating-point sparse matrix-vector multiply for FPGAs

abstract: Large, high density FPGAs with high local distributed memory bandwidth surpass the peak floating-point performance of high-end, general-purpose processors. Microprocessors do not deliver near their peak floating-point performance on efficient algorithms that use the Sparse Matrix-Vector Multiply (SMVM) kernel. In fact, it is not uncommon for microprocessors to yield only 10--20% of their peak floating-point performance when computing SMVM. We develop and analyze a scalable SMVM implementation on modern FPGAs and show that it can sustain high throughput, near peak, floating-point performance. For benchmark matrices from the Matrix Market Suite we project 1.5 double precision Gflops/FPGA for a single Virtex II 6000-4 and 12 double precision Gflops for 16 Virtex IIs (750Mflops/FPGA).


title: Learning a Hierarchical Compositional Shape Vocabulary for Multi-class Object Representation

abstract: Hierarchies allow feature sharing between objects at multiple levels of representation, can code exponential variability in a very compact way and enable fast inference. This makes them potentially suitable for learning and recognizing a higher number of object classes. However, the success of the hierarchical approaches so far has been hindered by the use of hand-crafted features or predetermined grouping rules. This paper presents a novel framework for learning a hierarchical compositional shape vocabulary for representing multiple object classes. The approach takes simple contour fragments and learns their frequent spatial configurations. These are recursively combined into increasingly more complex and class-specific shape compositions, each exerting a high degree of shape variability. At the top-level of the vocabulary, the compositions are sufficiently large and complex to represent the whole shapes of the objects. We learn the vocabulary layer after layer, by gradually increasing the size of the window of analysis and reducing the spatial resolution at which the shape configurations are learned. The lower layers are learned jointly on images of all classes, whereas the higher layers of the vocabulary are learned incrementally, by presenting the algorithm with one object class after another. The experimental results show that the learned multi-class object representation scales favorably with the number of object classes and achieves a state-of-the-art detection performance at both, faster inference as well as shorter training times.


Algorithms for asymptotic extrapolation ∗


title: An Analysis of the Switching Performance and Robustness of Power MOSFETs Body Diodes: A Technology Evaluation

abstract: The tradeoff between the switching energy and electro-thermal robustness is explored for 1.2-kV SiC MOSFET, silicon power MOSFET, and 900-V CoolMOS body diodes at different temperatures. The maximum forward current for dynamic avalanche breakdown is decreased with increasing supply voltage and temperature for all technologies. The CoolMOS exhibited the largest latch-up current followed by the SiC MOSFET and silicon power MOSFET; however, when expressed as current density, the SiC MOSFET comes first followed by the CoolMOS and silicon power MOSFET. For the CoolMOS, the alternating p and n pillars of the superjunctions in the drift region suppress BJT latch-up during reverse recovery by minimizing lateral currents and providing low-resistance paths for carriers. Hence, the temperature dependence of the latch-up current for CoolMOS was the lowest. The switching energy of the CoolMOS body diode is the largest because of its superjunction architecture which means the drift region have higher doping, hence more reverse charge. In spite of having a higher thermal resistance, the SiC MOSFET has approximately the same latch-up current while exhibiting the lowest switching energy because of the least reverse charge. The silicon power MOSFET exhibits intermediate performance on switching energy with lowest dynamic latching current.


title: Trajectory Data Mining: An Overview

abstract: The advances in location-acquisition and mobile computing techniques have generated massive spatial trajectory data, which represent the mobility of a diversity of moving objects, such as people, vehicles, and animals. Many techniques have been proposed for processing, managing, and mining trajectory data in the past decade, fostering a broad range of applications. In this article, we conduct a systematic survey on the major research into trajectory data mining, providing a panorama of the field as well as the scope of its research topics. Following a road map from the derivation of trajectory data, to trajectory data preprocessing, to trajectory data management, and to a variety of mining tasks (such as trajectory pattern mining, outlier detection, and trajectory classification), the survey explores the connections, correlations, and differences among these existing techniques. This survey also introduces the methods that transform trajectories into other data formats, such as graphs, matrices, and tensors, to which more data mining and machine learning techniques can be applied. Finally, some public trajectory datasets are presented. This survey can help shape the field of trajectory data mining, providing a quick understanding of this field to the community.


title: Location Fingerprinting With Bluetooth Low Energy Beacons

abstract: The complexity of indoor radio propagation has resulted in location-awareness being derived from empirical fingerprinting techniques, where positioning is performed via a previously-constructed radio map, usually of WiFi signals. The recent introduction of the Bluetooth Low Energy (BLE) radio protocol provides new opportunities for indoor location. It supports portable battery-powered beacons that can be easily distributed at low cost, giving it distinct advantages over WiFi. However, its differing use of the radio band brings new challenges too. In this work, we provide a detailed study of BLE fingerprinting using 19 beacons distributed around a $\sim\! 600\ \mbox{m}^2$ testbed to position a consumer device. We demonstrate the high susceptibility of BLE to fast fading, show how to mitigate this, and quantify the true power cost of continuous BLE scanning. We further investigate the choice of key parameters in a BLE positioning system, including beacon density, transmit power, and transmit frequency. We also provide quantitative comparison with WiFi fingerprinting. Our results show advantages to the use of BLE beacons for positioning. For one-shot (push-to-fix) positioning we achieve $30\ \mbox{m}^2$ ), compared to $100\ \mbox{m}^2$ ) and < 8.5 m for an established WiFi network in the same area.


title: An Efficient Image Encryption Technique using Chaotic Logistic Map and RC4 Stream Cipher

abstract: In recent years, because of the frequent flow of digital images across the world over the transmission media, image encryption become one of the most substantial topics. In this paper, we propose a new image encryption method based on the well-known Chaotic Logistic Map (CLM) and the Rivest Cipher 4 (RC4) encryption methods. Here, we use the secret key, and the CLM to produce a one-dimensional array of different numbers. Then the RC4 algorithms used to make some sort of random shuffling (relying on the contents of the array created by the CLM) to the array that is created by the RC4 first algorithm. After that, the second algorithm of RC4 used inside a loop to change the value of each color (using the resultant array of the first RC4 algorithm) of a pixel until all the pixels of the image be changed. And by doing that we have produced a cipher image that is completely different and does not reveal any information of the plain image, also we proved that it is really a key sensitive encryption that can't be decrypted easily by using brute force or any other kind of attacks.


title: Design of optimized reversible binary adder/subtractor and BCD adder

abstract: Reversible logic has gained the interest of many researchers due to its applicability in emerging low power technologies such as Quantum computing, QCA, optical computing etc., Adders/Subtractors are basic design components of any processor. Optimized design of these adders results in efficient processors. In this work we propose optimized Binary adders/subtractors and BCD adders. The adders/subtractors designed in this work are optimized for Quantum cost and Delay. We also propose a generic design of n-bit adders and subtractors. In this work, we explore the use of Negative control lines for detecting overflow logic of BCD adder which considerably reduces Quantum cost, delay and gate count which result in high speed BCD adder with optimized area which give way to lot of scope in the field of reversible computing in near future.


title: Scholarly Data Mining: Making Sense of Scientific Literature

abstract: During the last decade the amount of scientific information available on-line increased at an unprecedented rate and this situation is unlikely to change. As a consequence, nowadays researchers are overwhelmed by an enormous and continuously growing number of publications to consider when they perform research activities like the exploration of advances in specific topics, peer reviewing, writing and evaluation of proposals. Natural Language Processing technology plays a key role in enabling intelligent access to the content of scientific publications. By mining the contents of scientific papers, for example, rich scientific knowledge bases can be built, thus supporting more effective information discovery and question answering approaches. Moreover, text summarization technology can help condense long papers to their essential contents so as to speed up the selection of scientific articles of interest or to assist in the manual or automatic generation of state of the art reports. Paraphrase and textual entailment techniques can contribute to the identification of relations across different scientific textual sources, thus, for instance, identifying implicit links between publications. This tutorial provides an overview of approaches to the extraction of knowledge from scientific literature, including the in-depth analysis of the structure of the scientific articles, their semantic interpretation, content extraction, summarization, and visualization.


title: SkeletonNet: Mining Deep Part Features for 3-D Action Recognition

abstract: This letter presents SkeletonNet, a deep learning framework for skeleton-based 3-D action recognition. Given a skeleton sequence, the spatial structure of the skeleton joints in each frame and the temporal information between multiple frames are two important factors for action recognition. We first extract body-part-based features from each frame of the skeleton sequence. Compared to the original coordinates of the skeleton joints, the proposed features are translation, rotation, and scale invariant. To learn robust temporal information, instead of treating the features of all frames as a time series, we transform the features into images and feed them to the proposed deep learning network, which contains two parts: one to extract general features from the input images, while the other to generate a discriminative and compact representation for action recognition. The proposed method is tested on the SBU kinect interaction dataset, the CMU dataset, and the large-scale NTU RGB+D dataset and achieves state-of-the-art performance.


title: Representation Sharing for Prolog

abstract: Representation sharing can reduce the memory footprint of a program by sharing one representation between duplicate terms. The most common implementation of representation sharing in functional programming systems is known as hash-consing. In the context of Prolog, representation sharing has been given little attention. Some current techniques that deal with representation sharing are reviewed. The new contributions are 1 an easy implementation of input sharing for findall/3; and 2 a description of a sharer module that introduces representation sharing at runtime. Their realization is shown in the context of the Warren Abstract Machine WAM as implemented by hProlog. Both can be adapted to any WAM-like Prolog implementation. The sharer works independently of the garbage collector, but it can be made to cooperate with the garbage collector. Benchmark results show that the sharer has a cost comparable to the heap garbage collector, that its effectiveness is highly application-dependent, and that its policy must be tuned to the collector.


Bayesian Back-Propagation.


title: Concurrent and Distributed CloudSim Simulations

abstract: Cloud Computing researches involve a tremendous amount of entities such as users, applications, and virtual machines. Due to the limited access and often variable availability of such resources, researchers have their prototypes tested against the simulation environments, opposed to the real cloud environments. Existing cloud simulation environments such as CloudSim and EmuSim are executed sequentially, where a more advanced cloud simulation tool could be created extending them, leveraging the latest technologies as well as the availability of multi-core computers and the clusters in the research laboratories. This research seeks to develop Cloud2Sim, a concurrent and distributed cloud simulator, extending CloudSim while exploiting the features provided by Hazel cast, Infinispan and Hibernate Search to distribute the storage and execution of the simulation.


title: Geodesic Regression and the Theory of Least Squares on Riemannian Manifolds

abstract: This paper develops the theory of geodesic regression and least-squares estimation on Riemannian manifolds. Geodesic regression is a method for finding the relationship between a real-valued independent variable and a manifold-valued dependent random variable, where this relationship is modeled as a geodesic curve on the manifold. Least-squares estimation is formulated intrinsically as a minimization of the sum-of-squared geodesic distances of the data to the estimated model. Geodesic regression is a direct generalization of linear regression to the manifold setting, and it provides a simple parameterization of the estimated relationship as an initial point and velocity, analogous to the intercept and slope. A nonparametric permutation test for determining the significance of the trend is also given. For the case of symmetric spaces, two main theoretical results are established. First, conditions for existence and uniqueness of the least-squares problem are provided. Second, a maximum likelihood criteria is developed for a suitable definition of Gaussian errors on the manifold. While the method can be generally applied to data on any manifold, specific examples are given for a set of synthetically generated rotation data and an application to analyzing shape changes in the corpus callosum due to age.


title: A Brain-Machine Interface Instructed by Direct Intracortical Microstimulation

abstract: Brain-machine interfaces (BMIs) establish direct communication between the brain and artificial actuators. As such, they hold considerable promise for restoring mobility and communication in patients suffering from severe body paralysis. To achieve this end, future BMIs must also provide a means for delivering sensory signals from the actuators back to the brain. Prosthetic sensation is needed so that neuroprostheses can be better perceived and controlled. Here we show that a direct intracortical input can be added to a BMI to instruct rhesus monkeys in choosing the direction of reaching movements generated by the BMI. Somatosensory instructions were provided to two monkeys operating the BMI using either: (a) vibrotactile stimulation of the monkey's hands or (b) multi-channel intracortical microstimulation (ICMS) delivered to the primary somatosensory cortex (S1) in one monkey and posterior parietal cortex (PP) in the other. Stimulus delivery was contingent on the position of the computer cursor: the monkey placed it in the center of the screen to receive machine-brain recursive input. After 2 weeks of training, the same level of proficiency in utilizing somatosensory information was achieved with ICMS of S1 as with the stimulus delivered to the hand skin. ICMS of PP was not effective. These results indicate that direct, bi-directional communication between the brain and neuroprosthetic devices can be achieved through the combination of chronic multi-electrode recording and microstimulation of S1. We propose that in the future, bidirectional BMIs incorporating ICMS may become an effective paradigm for sensorizing neuroprosthetic devices.


title: Espresso: Efficient Forward Propagation for Binary Deep Neural Networks

abstract: There are many applications scenarios for which the computational performance and memory footprint of the prediction phase of Deep Neural Networks (DNNs) need to be optimized. Binary Deep Neural Networks (BDNNs) have been shown to be an effective way of achieving this objective. In this paper, we show how Convolutional Neural Networks (CNNs) can be implemented using binary representations. Espresso is a compact, yet powerful library written in C/CUDA that features all the functionalities required for the forward propagation of CNNs, in a binary file less than 400KB, without any external dependencies. Although it is mainly designed to take advantage of massive GPU parallelism, Espresso also provides an equivalent CPU implementation for CNNs. Espresso provides special convolutional and dense layers for BCNNs, leveraging bit-packing and bit-wise computations for efficient execution. These techniques provide a speed-up of matrix-multiplication routines, and at the same time, reduce memory usage when storing parameters and activations. We experimentally show that Espresso is significantly faster than existing implementations of optimized binary neural networks (~ 2 orders of magnitude). Espresso is released under the Apache 2.0 license and is available at http://github.com/organization/project.


title: Environmental influence in the brain, human welfare and mental health

abstract: Environmental influences affect the brain and mental health and often are social or have social components, even the more complex societal or area-level exposures. This Review discusses the neural correlates of adverse and protective social influences and argues that innovative methods may provide ecologically more valid insights in social neuroscience.


title: RadialGAN: Leveraging multiple datasets to improve target-specific predictive models using Generative Adversarial Networks

abstract: Training complex machine learning models for prediction often requires a large amount of data that is not always readily available. Leveraging these external datasets from related but different sources is therefore an important task if good predictive models are to be built for deployment in settings where data can be rare. In this paper we propose a novel approach to the problem in which we use multiple GAN architectures to learn to translate from one dataset to another, thereby allowing us to effectively enlarge the target dataset, and therefore learn better predictive models than if we simply used the target dataset. We show the utility of such an approach, demonstrating that our method improves the prediction performance on the target domain over using just the target dataset and also show that our framework outperforms several other benchmarks on a collection of real-world medical datasets.


title: Global Convergence of Langevin Dynamics Based Algorithms for Nonconvex Optimization

abstract: We present a unified framework to analyze the global convergence of Langevin dynamics based algorithms for nonconvex finite-sum optimization with $n$ component functions. At the core of our analysis is a direct analysis of the ergodicity of the numerical approximations to Langevin dynamics, which leads to faster convergence rates. Specifically, we show that gradient Langevin dynamics (GLD) and stochastic gradient Langevin dynamics (SGLD) converge to the almost minimizer within $\tilde O\big(nd/(\lambda\epsilon) \big)$ and $\tilde O\big(d^7/(\lambda^5\epsilon^5) \big)$ stochastic gradient evaluations respectively, where $d$ is the problem dimension, and $\lambda$ is the spectral gap of the Markov chain generated by GLD. Both of the results improve upon the best known gradient complexity results. Furthermore, for the first time we prove the global convergence guarantee for variance reduced stochastic gradient Langevin dynamics (VR-SGLD) to the almost minimizer after $\tilde O\big(\sqrt{n}d^5/(\lambda^4\epsilon^{5/2})\big)$ stochastic gradient evaluations, which outperforms the gradient complexities of GLD and SGLD in a wide regime. Our theoretical analyses shed some light on using Langevin dynamics based algorithms for nonconvex optimization with provable guarantees.


title: Advance Persistent Threat: Botnet

abstract: Growth of internet era and corporate sector dealings communication online has introduced crucial security challenges in cyber space. Statistics of recent large scale attacks defined new class of threat to online world, advanced persistent threat (APT) able to impact national security and economic stability of any country. From all APTs, botnet is one of the well-articulated and stealthy attacks to perform cybercrime. Botnet owners and their criminal organizations are continuously developing innovative ways to infect new targets into their networks and exploit them. The concept of botnet refers collection of compromised computers (bots) infected by automated software robots, that interact to accomplish some distributed task which run without human intervention for illegal purposes. They are mostly malicious in nature and allow cyber criminals to control the infected machines remotely without the victim's knowledge. They use various techniques, communication protocols and topologies in different stages of their lifecycle; also specifically they can upgrade their methods at any time. Botnet is global in nature and their target is to steal or destroy valuable information from organizations as well as individuals. In this paper we present real world botnet (APTs) survey.


title: RaptorX-Property: a web server for protein structure property prediction

abstract: RaptorX Property (http://raptorx2.uchicago.edu/StructurePropertyPred/predict/) is a web server predicting structure property of a protein sequence without using any templates. It outperforms other servers, especially for proteins without close homologs in PDB or with very sparse sequence profile (i.e. carries little evolutionary information). This server employs a powerful in-house deep learning model DeepCNF (Deep Convolutional Neural Fields) to predict secondary structure (SS), solvent accessibility (ACC) and disorder regions (DISO). DeepCNF not only models complex sequence–structure relationship by a deep hierarchical architecture, but also interdependency between adjacent property labels. Our experimental results show that, tested on CASP10, CASP11 and the other benchmarks, this server can obtain ∼84% Q3 accuracy for 3-state SS, ∼72% Q8 accuracy for 8-state SS, ∼66% Q3 accuracy for 3-state solvent accessibility, and ∼0.89 area under the ROC curve (AUC) for disorder prediction.


title: The Practical Impact of Ontologies on Biomedical Informatics

abstract: Objectives: To examine recent research work in the development and evaluation of controlled biomedical terminologies – especially, the representation of structured, controlled definitional knowledge about the terms themselves; such terminologies are often referred to as „ontologies“. Methods: A review of the published literature using PubMed, as well as full-text searches of recent Medinfo and American Medical Informatics Association (AMIA) Symposia proceedings, searching for the terms “ontology” and “ontologies” and for articles discussing specific, prominent ontological work. Results: We summaries the ontologic aspects of twelve current terminology projects: Galen, the Unified Medical Language System (UMLS), the Medical Entities Dictionary (MED), SNOMED-CT, LOINC, the Foundational Model of Anatomy (FMA), the Gene Ontology (GO), ISO Reference Terminology Model for Nursing Diagnosis, NDF-RT, RxNorm, the NCI Thesaurus, and DOLCE+. We discuss the origins, domain, and ontologic representation of each of these and attempt to summarize the impact that each has had on terminologic work and biomedical applications. We also note the contributions of the Protege tool to many of these efforts. Conclusion: Terminologic research and development have advanced significantly in the past 20 years, especially since the recent orientation toward controlled biomedical ontologies. This work has had significant impact on the development of terminologies themselves, their acceptance and dissemination as standards, and their use in supporting biomedical information systems. Haux R, Kulikowski C, editors. IMIA Yearbook of Medical Informatics 2006. Methods Inf Med 2006; 45 Suppl 1: S124-35.


title: Software maintenance and evolution: a roadmap

abstract: The production of new management approaches to evolution, leading to understanding of the relationships between technology and business. How can software be designed so that it can easily be evolved? More effective tools and methods for program comprehension for both code and data A better formalism and conceptualisation of 'maintainability'; how do we measure it? The development of a service-based model of software, to replace a product view. better


title: Issues in Using Function Approximation for Reinforcement Learning

abstract: Reinforcement learning techniques address the problem of learning to select actions in unknown, dynamic environments. It is widely acknowledged that to be of use in complex domains, reinforcement learning techniques must be combined with generalizing function approximation methods such as artificial neural networks. Little, however, is understood about the theoretical properties of such combinations, and many researchers have encountered failures in practice. In this paper we identify a prime source of such failures—namely, a systematic overestimation of utility values. Using Watkins’ Q-Learning [18] as an example, we give a theoretical account of the phenomenon, deriving conditions under which one may expected it to cause learning to fail. Employing some of the most popular function approximators, we present experimental results which support the theoretical findings.


title: Adaptive Markov Random Field Approach for Classification of Hyperspectral Imagery

abstract: An adaptive Markov random field (MRF) approach is proposed for classification of hyperspectral imagery in this letter. The main feature of the proposed method is the introduction of a relative homogeneity index for each pixel and the use of this index to determine an appropriate weighting coefficient for the spatial contribution in the MRF classification. In this way, overcorrection of spatially high variation areas can be avoided. Support vector machines are implemented for improved class modeling and better estimate of spectral contribution to this approach. Experimental results of a synthetic hyperspectral data set and a real hyperspectral image demonstrate that the proposed method works better on both homogeneous regions and class boundaries with improved classification accuracy.


title: Aspect-based opinion mining from product reviews

abstract: "What other people think" has always been an important piece of information for most of us during the decision-making process. Today people tend to make their opinions available to other people via the Internet. As a result, the Web has become an excellent source of consumer opinions. There are now numerous Web resources containing such opinions, e.g., product reviews forums, discussion groups, and blogs. But, it is really difficult for a customer to read all of the reviews and make an informed decision on whether to purchase the product. It is also difficult for the manufacturer of the product to keep track and manage customer opinions. Also, focusing on just user ratings (stars) is not a sufficient source of information for a user or the manufacturer to make decisions. Therefore, mining online reviews (opinion mining) has emerged as an interesting new research direction. Extracting aspects and the corresponding ratings is an important challenge in opinion mining. An aspect is an attribute or component of a product, e.g. 'zoom' for a digital camera. A rating is an intended interpretation of the user satisfaction in terms of numerical values. Reviewers usually express the rating of an aspect by a set of sentiments, e.g. 'great zoom'. In this tutorial we cover opinion mining in online product reviews with the focus on aspect-based opinion mining. This problem is a key task in the area of opinion mining and has attracted a lot of researchers in the information retrieval community recently. Several opinion related information retrieval tasks can benefit from the results of aspect-based opinion mining and therefore it is considered as a fundamental problem. This tutorial covers not only general opinion mining and retrieval tasks, but also state-of-the-art methods, challenges, applications, and also future research directions of aspect-based opinion mining.


Toward A Dynamic View of Organizational Ambidexterity : Promoting a Sense of Balance and Contingency


title: Multi-Source Domain Adaptation Using Approximate Label Matching

abstract: Domain adaptation, and transfer learning more generally, seeks to remedy the problem created when training and testing datasets are generated by different distributions. In this work, we introduce a new unsupervised domain adaptation algorithm for when there are multiple sources available to a learner. Our technique assigns a rough labeling on the target samples, then uses it to learn a transformation that aligns the two datasets before final classification. In this article we give a convenient implementation of our method, show several experiments using it, and compare it to other methods commonly used in the field.


title: Employing transaction aggregation strategy to detect credit card fraud

abstract: Credit card fraud costs consumers and the financial industry billions of dollars annually. However, there is a dearth of published literature on credit card fraud detection. In this study we employed transaction aggregation strategy to detect credit card fraud. We aggregated transactions to capture consumer buying behavior prior to each transaction and used these aggregations for model estimation to identify fraudulent transactions. We use real-life data of credit card transactions from an international credit card operation for transaction aggregation and model estimation.


Preference learning in recommender systems


title: Characterization on the Passivation Stability of HF Aqueous Solution Treated Silicon Surfaces for HIT Solar Cell Application by the Effective Minority Carrier Lifetime Measurement

abstract: The traditional passivation processes, standard RCA cleaning and 1% HF aqueous solution immersion, were introduced to passivate the surfaces of p-type Si(100) wafers for heterojunction with intrinsic thin-layer (HIT) solar cell application. The passivation stability was checked by monitoring the time decay of the effective minority carrier lifetime of the Si wafer in air via the microwave photoconductive decay (μPCD) method. The results show that the passivation effect is greatly dependent on the initial surface morphology of ::: the Si wafer. During the subsequent exposure in air, the obtained effective minority carrier lifetime decays rapidly, corresponding to that the Si surface states increase greatly. Such decay occurs more severely on the textured surface than on the polished one, which gives a time limitation to the subsequent processes in the HIT solar cell fabrication. So, such passivation processes need further improvement. The results also prove that the effective ::: minority carrier lifetime measurement can be adopted as an efficient and convenient method to check the passivation stability of the Si surface treated by wet-chemical methods.


title: The “R.A.R.E.” Technique (Reverse and Repositioning Effect): The Renaissance of the Aging Face and Neck

abstract: Considering the fixed points of the face (Fig. 1), and in light of the fact that gravity is one of the main factors involved in aging, a new alternative concept in cosmetic surgery is discussed in this paper. In our approach, rejuvenation of the face and neck involves two completely separate procedures. The whole face must be treated " homothetically", with an upward (vertical) and deep (subperiosteal) approach, to preserve facial proportions and distances, thus preserving the original facial identity. The facial portion of our rejuvenation surgery becomes a single " en bloc" and "closed" procedure, correcting the sagging tissue in the lateral sector, between the fixed zones which must be preserved. The Malaris portion of the Orbicularis Oculi Muscle, (through its strong connections with the skin and the malar fat) has become the "key tool" of the rejuvenation of the whole face. Then, neck surgery becomes a completely distinct procedure, and is to be performed in an oblique/horizontal direction. We now seek to preserve the very firmly attached neck zones, which are the attachment of the posterior border of the fibrous platysma onto the S.C.M. (Sterno-Cleido-Mastoidien muscle). This will permit a more conservative and less aggressive neck surgery, without any sub-platysmal dissection. Over 200 RARE procedures have been performed during almost four years. Improvement in terms of facial rejuvenation is dramatic and the technique is quite safe and predictable. The only possible difficulty involves the patient's temporary initial concern about early postoperative appearance.


title: Vehicle trajectory prediction based on motion model and maneuver recognition

abstract: Predicting other traffic participants trajectories is a crucial task for an autonomous vehicle, in order to avoid collisions on its planned trajectory. It is also necessary for many Advanced Driver Assistance Systems, where the ego-vehicle's trajectory has to be predicted too. Even if trajectory prediction is not a deterministic task, it is possible to point out the most likely trajectory. This paper presents a new trajectory prediction method which combines a trajectory prediction based on Constant Yaw Rate and Acceleration motion model and a trajectory prediction based on maneuver recognition. It takes benefit on the accuracy of both predictions respectively a short-term and long-term. The defined Maneuver Recognition Module selects the current maneuver from a predefined set by comparing the center lines of the road's lanes to a local curvilinear model of the path of the vehicle. The overall approach was tested on prerecorded human real driving data and results show that the Maneuver Recognition Module has a high success rate and that the final trajectory prediction has a better accuracy.


title: Characterization of Cement Paste in Fresh State Using Electrical Resistivity Technique

abstract: The impedance spectroscopy technique was used to monitor the electrical resistivity of various cement paste mixtures during first 2 hours after mixing before the setting time. The conductivity and pH of the pore solutions extracted from fresh cement paste mixtures was also determined. The conductivity of pore solution extracted from select hardened paste mixtures cured in sealed moisture condition was also obtained. These data were used to investigate the various influential parameters such as (1) the effect of time on the electrical resistivity, (2) the effect of supplementary cementitious materials and chemical admixtures on the electrical resistivity, (3) the effect of w/c ratio on the electrical resistivity, (4) tortuosity and the relationship between electrical resistivity of paste and pore solution and (5) correlation between the pH and electrical conductivity of pore solution. In addition, a numerical model was proposed to estimate the conductivity of pore solution in fresh state.


title: Convolutional Kernel Networks

abstract: An important goal in visual recognition is to devise image representations that are invariant to particular transformations. In this paper, we address this goal with a new type of convolutional neural network (CNN) whose invariance is encoded by a reproducing kernel. Unlike traditional approaches where neural networks are learned either to represent data or for solving a classification task, our network learns to approximate the kernel feature map on training data. ::: ::: Such an approach enjoys several benefits over classical ones. First, by teaching CNNs to be invariant, we obtain simple network architectures that achieve a similar accuracy to more complex ones, while being easy to train and robust to overfitting. Second, we bridge a gap between the neural network literature and kernels, which are natural tools to model invariance. We evaluate our methodology on visual recognition tasks where CNNs have proven to perform well, e.g., digit recognition with the MNIST dataset, and the more challenging CIFAR-10 and STL-10 datasets, where our accuracy is competitive with the state of the art.


A Survey about the Usage of Semantic Technologies for the Description of Robotic Components and Capabilities


title: Study on the feasibility of LoRaWAN for smart city applications

abstract: Recent research on Low Power Wide Area Network (LPWAN) technologies which provide the capability of serving massive low power devices simultaneously has been very attractive. The LoRaWAN standard is one of the most successful developments. Commercial pilots are seen in many countries around the world. However, the feasibility of large scale deployments, for example, for smart city applications need to be further investigated. This paper provides a comprehensive case study of LoRaWAN to show the feasibility, scalability, and reliability of LoRaWAN in realistic simulated scenarios, from both technical and economic perspectives. We develop a Matlab based LoRaWAN simulator to offer a software approach of performance evaluation. A practical LoRaWAN network covering Greater London area is implemented. Its performance is evaluated based on two typical city monitoring applications. We further present an economic analysis and develop business models for such networks, in order to provide a guideline for commercial network operators, IoT vendors, and city planners to investigate future deployments of LoRaWAN for smart city applications.


title: Children and computers: New technology - Old concerns

abstract: Computer technology has ushered in a new era of mass media, bringing with it great promise and great concerns about the effect on children’s development and wellbeing. Although we tend to see these issues as being new, similar promises and concerns have accompanied each new wave of media technology throughout the past century: films in the early 1900s, radio in the 1920s, and television in the 1940s. With the introduction of each of these technologies, proponents touted the educational benefits for children, while opponents voiced fears about exposure to inappropriate commercial, sexual, and violent content. This article places current studies on children and computers in a historical context, noting the recurrent themes and patterns in media research during the twentieth century. Initial research concerning each innovation has tended to focus on issues of access and the amount of time children were spending with the new medium. As use of the technology became more prevalent, research shifted to issues related to content and its effects on children. Current research on children’s use of computers is again following this pattern. But the increased level of interactivity now possible with computer games and with the communication features of the Internet has heightened both the promise of greatly enriched learning and the concerns related to increased risk of harm. As a result, research on the effects of exposure to various types of content has taken on a new sense of urgency. The authors conclude that to help inform and sustain the creation of more quality content for children, further research is needed on the effects of media on children, and new partnerships must be forged between industry, academia, and advocacy groups.


title: Design of a quasi-passive knee exoskeleton to assist running

abstract: In this paper we describe the design and preliminary evaluation of an energetically-autonomous powered knee exoskeleton to facilitate running. The device consists of a knee brace in which a motorized mechanism actively places and removes a spring in parallel with the knee joint. This mechanism is controlled such that the spring is in parallel with the knee joint from approximately heel-strike to toe-off, and is removed from this state during the swing phase of running. In this way, the spring is intended to store energy at heel-strike which is then released when the heel leaves the ground, reducing the effort required by the quadriceps to exert this energy, thereby reducing the metabolic cost of running.


title: Lightweight modular staging: a pragmatic approach to runtime code generation and compiled DSLs

abstract: Software engineering demands generality and abstraction, performance demands specialization and concretization. Generative programming can provide both, but the effort required to develop high-quality program generators likely offsets their benefits, even if a multi-stage programming language is used. We present lightweight modular staging, a library-based multi-stage programming approach that breaks with the tradition of syntactic quasi-quotation and instead uses only types to distinguish between binding times. Through extensive use of component technology, lightweight modular staging makes an optimizing compiler framework available at the library level, allowing programmers to tightly integrate domain-specific abstractions and optimizations into the generation process. We argue that lightweight modular staging enables a form of language virtualization, i.e. allows to go from a pure-library embedded language to one that is practically equivalent to a stand-alone implementation with only modest effort.


Nudging problematic smartphone use to a lower level


title: Wireless Sensor Networks for Water Quality Monitoring: A Case of Zambia

abstract: Water quality monitoring is a very important undertaking that would ensure safe and clean water being delivered to the end users. Water quality monitoring is essential in controlling physical, chemical and biological characteristics of water. To ensure a complete and an all inclusive monitoring of the underground water and surface water, data sampling need to be carried out at many different locations within and without the study phenomenon. The current water quality monitoring methods in Zambia are mostly sparse and manual. This paper proposes the use of Wireless Sensor Networks to monitor water quality using cheap, effective and efficient sensors that have the ability to sense, process and transmit the sensed data.


title: Retrograde amnesia and memory consolidation: a neurobiological perspective

abstract: Abstract The fact that information acquired before the onset of amnesia can be lost (retrograde amnesia) has fascinated psychologists, biologists, and clinicians for over 100 years. Studies of retrograde amnesia have led to the concept of memory consolidation, whereby medial temporal lobe structures direct the gradual establishment of memory representations in neocortex. Recent theoretical accounts have inspired a simple neural network model that produces behavior consistent with experimental data and makes these ideas about memory consolidation more concrete. Recent physiological and anatomical findings provide important information about how memory consolidation might actually occur.


title: GPGPU-Aided 3D Staggered-grid Finite-difference Seismic Wave Modeling

abstract: Finite difference is a simple, fast and effective numerical method for seismic wave modeling, and has been widely used in forward waveform inversion and reverse time migration. However, intensive calculation of three-dimensional seismic forward modeling has been restricting the industrial application of 3D pre-stack reverse time migration and inversion. Aiming at this problem, in this paper, a parallelized 3D Staggered-grid Finite-difference has been developed using General-purpose computing on the graphics processing unit (GPGPU), namely G-3DFD, since the emergence of graphic processing units (GPU) as an effective alternative to traditional general purpose processors has become increasingly capable in accelerating large-scale scientific computing. We analyze three-dimensional staggered grid finite difference method for the implementation on GPU, making possible the industrial application of 3D pre-stack reverse time migration and inversion. Experiments show that G-3DFD has dramatically improved the runtime performance 88 times on modern GPGPU platforms comparing to the original CPU implementation methods.


title: Removing ring artifacts in CBCT images via generative adversarial networks with unidirectional relative total variation loss

abstract: Cone beam computed tomography (CBCT) is an important tool for clinical diagnosis and many industrial applications. However, ring artifacts usually appear in CBCT images, due to device responding inconsistence. This paper designs a generative adversarial network (GAN) to remove ring artifacts and meanwhile to retain important texture details in CBCT images. This method firstly transforms ring artifacts in Cartesian coordinates to stripe artifacts in polar coordinates, which is very helpful for removing ring artifacts. Then, we design a new loss function for GAN, including three parts: unidirectional relative total variation loss, perceptual loss and adversarial loss. Further, inspired by super-resolution generative adversarial networks, we use very deep residual networks for both generator and discriminator. Experimental results show that the proposed method is more effective for ring artifacts removal, compared to our baseline and some traditional methods.


title: Exploiting Wikipedia as External Knowledge for Named Entity Recognition

abstract: We explore the use of Wikipedia as external knowledge to improve named entity recognition (NER). Our method retrieves the corresponding Wikipedia entry for each candidate word sequence and extracts a category label from the first sentence of the entry, which can be thought of as a definition part. These category labels are used as features in a CRF-based NE tagger. We demonstrate using the CoNLL 2003 dataset that the Wikipedia category labels extracted by such a simple method actually improve the accuracy of NER.


title: Brain Substrates of Infant–Mother Attachment: Contributions of Opioids, Oxytocin, and Norepinephrine

abstract: NELSON, E. E., PANKSEPP, J.Brain substrates of infant‐mother attachment: contributions of opioids, oxytocin, and norepinephrine . NEUROSCI BIOBEHAV REV22(3) 437‐452, 1998.—The aim of this paper is to review recent work concerning the psychobiological substrates of social bonding, focusing on the literature attributed to opioids, oxytocin and norepinephrine in rats. Existing evidence and thinking about the biological foundations of attachment in young mammalian species and the neurobiology of several other affiliative behaviors including maternal behavior, sexual behavior and social memory is reviewed. We postulate the existence of social motivation circuitry which is common to all mammals and consistent across development. Oxytocin, vasopressin, endogenous opioids and catecholamines appear to participate in a wide variety of affiliative behaviors and are likely to be important components in this circuitry. It is proposed that these same neurochemical and neuroanatomical patterns will emerge as key substrates in the neurobiology of infant attachments to their caregivers. q 1998 Elsevier Science Ltd. All rights reserved.


title: Predicting Important Objects for Egocentric Video Summarization

abstract: We present a video summarization approach for egocentric or "wearable" camera data. Given hours of video, the proposed method produces a compact storyboard summary of the camera wearer's day. In contrast to traditional keyframe selection techniques, the resulting summary focuses on the most important objects and people with which the camera wearer interacts. To accomplish this, we develop region cues indicative of high-level saliency in egocentric video--such as the nearness to hands, gaze, and frequency of occurrence--and learn a regressor to predict the relative importance of any new region based on these cues. Using these predictions and a simple form of temporal event detection, our method selects frames for the storyboard that reflect the key object-driven happenings. We adjust the compactness of the final summary given either an importance selection criterion or a length budget; for the latter, we design an efficient dynamic programming solution that accounts for importance, visual uniqueness, and temporal displacement. Critically, the approach is neither camera-wearer-specific nor object-specific; that means the learned importance metric need not be trained for a given user or context, and it can predict the importance of objects and people that have never been seen previously. Our results on two egocentric video datasets show the method's promise relative to existing techniques for saliency and summarization.


title: Seed coating reduces respiration losses and affects sugar metabolism during germination and early seedling growth in cereals

abstract: Seed germination and the successful establishment of young seedlings is an important aspect of plant life. Seed coats are used to improve stand establishment and early seedling vigour. Seedlings growing from hydro-absorber coated barley, rye and wheat with coat-shares greater than 75% of the average seed have been shown to promote better seedling growth compared with those seedlings growing from uncoated seeds. We investigated how and why these seedlings performed better by analysing the proportion of grain reserves mobilised for growth and respiration as well as how both sucrose and glucose available in the embryo translated into seedling growth in the presence or absence of seed coats containing hydro-absorber gel. We found that mobilisation efficiency was higher, resulting in higher biomass in these cereals when they were coated. The relationship between sucrose and glucose available to the seedling as well as its correlation with early seedling growth indicate a switch in the enzymatic cleavage of embryonic sucrose from invertase to sucrose synthase. This in turn indicates that in coated seeds, embryonic tissue must be hypoxic leading to a more efficient use of glucose and thus reduced respiration losses during germination.


title: Collaborative TCP sequence number inference attack: how to crack sequence number under a second

abstract: In this study, we discover a new class of unknown side channels --- "sequence-number-dependent" host packet counters --- that exist in Linux/Android and BSD/Mac OS to enable TCP sequence number inference attacks. It allows a piece of unprivileged on-device malware to collaborate with an off-path attacker to infer the TCP sequence numbers used between a client and a server, leading to TCP injection and hijacking attacks. We show that the inference takes, in common cases, under a second to complete and is quick enough for attackers to inject malicious Javascripts into live Facebook sessions and to perform malicious actions on behalf of a victim user. Since supporting unprivileged access to global packet counters is an intentional design choice, we believe our findings provide important lessons and offer insights on future system and network design.


title: Image feature descriptor based on shape salience points

abstract: Abstract The work presented in this article aims at shape feature extraction and description. In this paper, we propose a shape-based image retrieval technique using salience points to describe shapes. The saliences of a shape are defined as the higher curvature points along the shape contour. The technique presented here consists of: a salience point detector; a salience representation using angular relative position and curvature value analyzed from a multi-scale perspective; and a matching algorithm considering local and global features to calculate the dissimilarity. The proposed technique is robust to noise and presents good performance when dealing with shapes of different classes but visually similar. The experiments were made in order to illustrate the performance of the proposed technique, and the results show the good performance of our method when compared with other shape-based methods in literature.


title: A recursive branch-and-bound algorithm for the rectangular guillotine strip packing problem

abstract: A heuristic recursive algorithm for the two-dimensional rectangular strip packing problem is presented. It is based on a recursive structure combined with branch-and-bound techniques. Several lengths are tried to determine the minimal plate length to hold all the items. Initially the plate is taken as a block. For the current block considered, the algorithm selects an item, puts it at the bottom-left corner of the block, and divides the unoccupied region into two smaller blocks with an orthogonal cut. The dividing cut is vertical if the block width is equal to the plate width; otherwise it is horizontal. Both lower and upper bounds are used to prune unpromising branches. The computational results on a class of benchmark problems indicate that the algorithm performs better than several recently published algorithms.


title: Wavelet-based Machine Learning Techniques for ECG Signal Analysis

abstract: Machine learning of ECG is a core component in any of the ECG-based healthcare informatics system. Since the ECG is a nonlinear signal, the subtle changes in its amplitude and duration are not well manifested in time and frequency domains. Therefore, in this chapter, we introduce a machine-learning approach to screen arrhythmia from normal sinus rhythm from the ECG. The methodology consists of R-point detection using the Pan-Tompkins algorithm, discrete wavelet transform (DWT) decomposition, sub-band principal component analysis (PCA), statistical validation of features, and subsequent pattern classification. The k-fold cross validation is used in order to reduce the bias in choosing training and testing sets for classification. The average accuracy of classification is used as a benchmark for comparison. Different classifiers used are Gaussian mixture model (GMM), error back propagation neural network (EBPNN), and support vector machine (SVM). The DWT basis functions used are Daubechies-4, Daubechies-6, Daubechies-8, Symlet-2, Symlet-4, Symlet-6, Symlet-8, Coiflet-2, and Coiflet-5. An attempt is made to exploit the energy compaction in the wavelet sub-bands to yield higher classification accuracy. Results indicate that the Symlet-2 wavelet basis function provides the highest accuracy in classification. Among the classifiers, SVM yields the highest classification accuracy, whereas EBPNN yields a higher accuracy than GMM. The use of other time frequency representations using different time frequency kernels as a future direction is also observed. The developed machine-learning approach can be used in a web-based telemedicine system, which can be used in remote monitoring of patients in many healthcare informatics systems.


title: CSF Biomarkers for Alzheimer's Disease Diagnosis

abstract: Alzheimer's disease (AD) is the most common form of dementia that affects several million people worldwide. The major neuropathological hallmarks of AD are the presence of extracellular amyloid plaques that are composed of Abeta40 and Abeta42 and intracellular neurofibrillary tangles (NFT), which is composed of hyperphosphorylated protein Tau. While the amyloid plaques and NFT could define the disease progression involving neuronal loss and dysfunction, significant cognitive decline occurs before their appearance. Although significant advances in neuroimaging techniques provide the structure and physiology of brain of AD cases, the biomarker studies based on cerebrospinal fluid (CSF) and plasma represent the most direct and convenient means to study the disease progression. Biomarkers are useful in detecting the preclinical as well as symptomatic stages of AD. In this paper, we discuss the recent advancements of various biomarkers with particular emphasis on CSF biomarkers for monitoring the early development of AD before significant cognitive dysfunction.


title: Splicebuster: A new blind image splicing detector

abstract: We propose a new feature-based algorithm to detect image splicings without any prior information. Local features are computed from the co-occurrence of image residuals and used to extract synthetic feature parameters. Splicing and host images are assumed to be characterized by different parameters. These are learned by the image itself through the expectation-maximization algorithm together with the segmentation in genuine and spliced parts. A supervised version of the algorithm is also proposed. Preliminary results on a wide range of test images are very encouraging, showing that a limited-size, but meaningful, learning set may be sufficient for reliable splicing localization.


title: Image forgery detection

abstract: We are undoubtedly living in an age where we are exposed to a remarkable array of visual imagery. While we may have historically had confidence in the integrity of this imagery, today's digital technology has begun to erode this trust. From the tabloid magazines to the fashion industry and in mainstream media outlets, scientific journals, political campaigns, courtrooms, and the photo hoaxes that land in our e-mail in-boxes, doctored photographs are appearing with a growing frequency and sophistication. Over the past five years, the field of digital forensics has emerged to help restore some trust to digital images. The author reviews the state of the art in this new and exciting field.


title: Beginner's luck: a language for property-based generators

abstract: Property-based random testing a la QuickCheck requires building efficient generators for well-distributed random data satisfying complex logical predicates, but writing these generators can be difficult and error prone. We propose a domain-specific language in which generators are conveniently expressed by decorating predicates with lightweight annotations to control both the distribution of generated values and the amount of constraint solving that happens before each variable is instantiated. This language, called Luck, makes generators easier to write, read, and maintain. We give Luck a formal semantics and prove several fundamental properties, including the soundness and completeness of random generation with respect to a standard predicate semantics. We evaluate Luck on common examples from the property-based testing literature and on two significant case studies, showing that it can be used in complex domains with comparable bug-finding effectiveness and a significant reduction in testing code size compared to handwritten generators.


title: Evaluation of Cannabinoid and Terpenoid Content: Cannabis Flower Compared to Supercritical CO2 Concentrate

abstract: A recent cannabis use survey revealed that 60% of cannabis users rely on smelling the flower to select their cannabis. Olfactory indicators in plants include volatile compounds, principally represented by the terpenoid fraction. Currently, medicinal- and adult-use cannabis is marketed in the United States with relatively little differentiation between products other than by a common name, association with a species type, and Δ-9 tetrahydrocannabinol/cannabidiol potency. Because of this practice, how terpenoid compositions may change during an extraction process is widely overlooked. Here we report on a comparative study of terpenoid and cannabinoid potencies of flower and supercritical fluid CO2 (SC-CO2) extract from six cannabis chemovars grown in Washington State. To enable this comparison, we employed a validated high-performance liquid chromatography/diode array detector methodology for quantification of seven cannabinoids and developed an internal gas chromatography-mass spectrometry method for quantification of 42 terpenes. The relative potencies of terpenoids and cannabinoids in flower versus concentrate were significantly different. Cannabinoid potency increased by factors of 3.2 for Δ-9 tetrahydrocannabinol and 4.0 for cannabidiol in concentrates compared to flower. Monoterpenes were lost in the extraction process; a ketone increased by 2.2; an ether by 2.7; monoterpene alcohols by 5.3, 7 and 9.4; and sesquiterpenes by 5.1, 4.2, 7.7, and 8.9. Our results demonstrate that the product of SC-CO2 extraction may have a significantly different chemotypic fingerprint from that of cannabis flower. These results highlight the need for more complete characterization of cannabis and associated products, beyond cannabinoid content, in order to further understand health-related consequences of inhaling or ingesting concentrated forms.


title: Detection of heart conditions using HRV processor in Matlab simulink

abstract: This paper describes a model for processing ECG signal for analysis of various heart conditions like apnea, tachyarrhythmia etc. and heart rate variability (HRV). The HRV processor has been designed in Simulink. HRV is utilized as a quantitative indicator of the Autonomic Nervous System (ANS) and cardiovascular system. The ECG test system empowers us to study normal and abnormal ECG waveforms without really utilizing ECG machines. In this work the QRS complex which is the main component of ECG signal is used to calculate HR which carries information about HRV. Further the model designed in Simulink can be implemented on FPGA.


title: The Analysis of Image Contrast: From Quality Assessment to Automatic Enhancement

abstract: Proper contrast change can improve the perceptual quality of most images, but it has largely been overlooked in the current research of image quality assessment (IQA). To fill this void, we in this paper first report a new large dedicated contrast-changed image database (CCID2014), which includes 655 images and associated subjective ratings recorded from 22 inexperienced observers. We then present a novel reduced-reference image quality metric for contrast change (RIQMC) using phase congruency and statistics information of the image histogram. Validation of the proposed model is conducted on contrast related CCID2014, TID2008, CSIQ and TID2013 databases, and results justify the superiority and efficiency of RIQMC over a majority of classical and state-of-the-art IQA methods. Furthermore, we combine aforesaid subjective and objective assessments to derive the RIQMC based Optimal HIstogram Mapping (ROHIM) for automatic contrast enhancement, which is shown to outperform recently developed enhancement technologies.


title: Shorter Decentralized Attribute-Based Encryption via Extended Dual System Groups

abstract: Decentralized attribute-based encryption (ABE) is a special form of multiauthority ABE systems, in which no central authority and global coordination are required other than creating the common reference parameters. In this paper, we propose a new decentralized ABE in prime-order groups by using extended dual system groups. We formulate some assumptions used to prove the security of our scheme. Our proposed scheme is fully secure under the standard -Lin assumption in random oracle model and can support any monotone access structures. Compared with existing fully secure decentralized ABE systems, our construction has shorter ciphertexts and secret keys. Moreover, fast decryption is achieved in our system, in which ciphertexts can be decrypted with a constant number of pairings.


title: A 10-MHz 2–800-mA 0.5–1.5-V 90% Peak Efficiency Time-Based Buck Converter With Seamless Transition Between PWM/PFM Modes

abstract: Time-based controllers are well suited for implementing both single- and multi-phase wide bandwidth high switching frequency pulsewidth modulation (PWM)-based dc–dc converters. They also consume very little quiescent current but their light load efficiency is severely degraded by switching losses. We explore pulse frequency modulation (PFM) that is commonly used to improve light load efficiency in voltage-mode controllers and extend its operation to time-based controllers. To maintain high efficiency even in the presence of dynamic load variations, we present techniques to perform automatic and seamless switching between PWM/PFM modes. Fabricated in a 65-nm CMOS, the prototype buck converter using the time-based PWM/PFM control achieves 90% peak efficiency and >80% efficiency over a load current range of 2–800 mA. Output voltage changes by less than 40 mV during PWM to PFM transitions.