title: Clinical performance and failure modes of pulpless teeth restored with posts: a systematic review.

abstract: The aim of this systematic review was to compare the clinical performance and failure modes of teeth restored with intra-radicular retainers. A search was performed on PubMed/Medline, Central and ClinicalTrials databases for randomized clinical trials comparing clinical behavior and failures of at least two types of retainers. From 341 detected papers, 16 were selected for full-text analysis, of which 9 met the eligibility criteria. A manual search added 2 more studies, totalizing 11 studies that were included in this review. Evaluated retainers were fiber (prefabricated and customized) and metal (prefabricated and cast) posts, and follow-up ranged from 6 months to 10 years. Most studies showed good clinical behavior for evaluated intra-radicular retainers. Reported survival rates varied from 71 to 100% for fiber posts and 50 to 97.1% for metal posts. Studies found no difference in the survival among different metal posts and most studies found no difference between fiber and metal posts. Two studies also showed that remaining dentine height, number of walls and ferrule increased the longevity of the restored teeth. Failures of fiber posts were mainly due to post loss of retention, while metal post failures were mostly related to root fracture, post fracture and crown and/or post loss of retention. In conclusion, metal and fiber posts present similar clinical behavior at short to medium term follow-up. Remaining dental structure and ferrule increase the survival of restored pulpless teeth. Studies with longer follow-up are needed.


title: Depression, quality of life and breast cancer: a review of the literature

abstract: Depression is misdiagnosed and undertreated among breast cancer population. Risk factors for depression in the 5 years after diagnosis are related more to the patient rather than to the disease or its treatment. The breast cancer stage (early and advanced) is not statistically significant in terms of rates of psychosocial distress except for recurrence. Risk factors of depression might impair quality of life such as fatigue, past history or recent episode of depression after the onset of cancer, cognitive attitudes of helplessness/hopelessness, resignation. Body image impairment from mastectomy and sexuality aftermath generates higher rates of mood disorders. The link between increased risk of breast cancer and depression is controversial among the literature. Some studies suggest a protective factor, others find a relation between stress, immunity and cancer occurrence or even mortality. Breast cancer survivors report a higher prevalence of mild to moderate depression with a lower quality of life in all areas except for family functioning. Treatment of depression in breast cancer women improves their quality of life and may increase longevity. Antidepressant medications remain the cornerstone of depression treatment. The hypothetical link between their prescription and increased breast cancer risk is not supported by literature's data.


title: Image Segmentation Using Local Variation and Edge-Weighted Centroidal Voronoi Tessellations

abstract: The classic centroidal Voronoi tessellation (CVT) model and its generalizations work quite well at extracting uniformly colored objects, but often fail to handle images with distinct color distribution or strong inhomogeneous intensity. To resolve this problem within the CVT methodology, in this paper we incorporate the information of local variation of colors/intensities and the length of boundaries into the energy functional and develop a new model called the Local Variation and Edge-Weighted Centroidal Voronoi Tessellation (LVEWCVT) for image segmentation. Its mathematical formulation and practical implementations are also discussed and given. We test the LVEWCVT method on various type of segments and also compare it with several state-of-art algorithms using extensive segmentation examples, the results demonstrate excellent performance and competence of the proposed method.


title: Circuit ORAM: On Tightness of the Goldreich-Ostrovsky Lower Bound

abstract: We propose a new tree-based ORAM scheme called Circuit ORAM. Circuit ORAM makes both theoretical and practical contributions. From a theoretical perspective, Circuit ORAM shows that the well-known Goldreich-Ostrovsky logarithmic ORAM lower bound is tight under certain parameter ranges, for several performance metrics. Therefore, we are the first to give an answer to a theoretical challenge that remained open for the past twenty-seven years. Second, Circuit ORAM earns its name because it achieves (almost) optimal circuit size both in theory and in practice for realistic choices of block sizes. We demonstrate compelling practical performance and show that Circuit ORAM is an ideal candidate for secure multi-party computation applications.


title: Synthesis and characterization of a new polymer–drug conjugate with pH-induced activity

abstract: Abstract A well-defined, stimuli-responsive tetrapolymer with pH-responsive characteristics and targeting specificity has been synthesized by radical copolymerization of methacrylic acid, N -(2-hydroxypropyl)methacrylamide, methacryloyl glycylglycyl sulfamethoxazole, and N -(methacryloyl)glycylglycine 4-nitrophenyl ester. The structure and properties of tetrapolymer were investigated by NMR, FT-IR, UV–visible absorption, TEM and gel permeation chromatography. Incorporation of maleimide linker into tetrapolymer facilitates its conjugation with antibody fragments, as demonstrated by the solid-phase immunoassay experiments. The TEM image shows that tetrapolymer had self-assembled a spherical micelle with a diameter ranging from 50 to 150 nm. Altering the pH of the solution leads to a different extent of aggregation at pH 6.5–3.5, responding in accordance with the properties associated with the extracellular environment of solid tumors and endocytosis. Furthermore, fluorescence spectroscopy indicated a critical micelle concentration (CMC) of 1 mg/mL. Because of the solvation and ionization effects, the tetrapolymer showed considerably enhanced antibacterial activities against Escherichia coli in the presence of DMSO and the antibacterial activity increased with decreasing pH value.


title: Kinetic and thermal energy harvesters for implantable medical devices and biomedical autonomous sensors

abstract: Implantable medical devices usually require a battery to operate and this can represent a severe restriction. In most cases, the implantable medical devices must be surgically replaced because of the dead batteries; therefore, the longevity of the whole implantable medical device is determined by the battery lifespan. For this reason, researchers have been studying energy harvesting techniques from the human body in order to obtain batteryless implantable medical devices. The human body is a rich source of energy and this energy can be harvested from body heat, breathing, arm motion, leg motion or the motion of other body parts produced during walking or any other activity. In particular, the main human-body energy sources are kinetic energy and thermal energy. This paper reviews the state-of-art in kinetic and thermoelectric energy harvesters for powering implantable medical devices. Kinetic energy harvesters are based on electromagnetic, electrostatic and piezoelectric conversion. The different energy harvesters are analyzed highlighting their sizes, energy or power they produce and their relative applications. As they must be implanted, energy harvesting devices must be limited in size, typically about 1 cm 3 . The available energy depends on human-body positions; therefore, some positions are more advantageous than others. For example, favorable positions for piezoelectric harvesters are hip, knee and ankle where forces are significant. The energy harvesters here reported produce a power between 6 nW and 7.2 mW; these values are comparable with the supply requirements of the most common implantable medical devices; this demonstrates that energy harvesting techniques is a valid solution to design batteryless implantable medical devices.


title: Imaging with terahertz waves

abstract: We present what is to our knowledge the first imaging system based on optoelectronic terahertz time-domain spectroscopy. Terahertz time-domain waveforms are downconverted from the terahertz to the kilohertz frequency range, and the waveform for each pixel is frequency analyzed in real time with a digital signal processor to extract compositional information at that point. We demonstrate applications to package inspection and chemical content mapping in biological objects.


title: Learning for Biomedical Information Extraction: Methodological Review of Recent Advances

abstract: Biomedical information extraction (BioIE) is important to many applications, including clinical decision support, integrative biology, and pharmacovigilance, and therefore it has been an active research. Unlike existing reviews covering a holistic view on BioIE, this review focuses on mainly recent advances in learning based approaches, by systematically summarizing them into different aspects of methodological development. In addition, we dive into open information extraction and deep learning, two emerging and influential techniques and envision next generation of BioIE.


title: A Wideband GAAS 6-Bit True-Time Delay MMIC Employing On-Chip Digital Drivers

abstract: This paper describes a 2-20 GHz 6-bit True-Time Delay. A total equivalent electrical length in air of 43.5 mm (145 ps) is achieved over a 2-20 GHz bandwidth. Digital drivers and a serial-to-parallel converter are integrated on the same MMIC. The ED02AH 0.2 ?m PHEMT process from OMMIC is used. The time delay elements are realised using constant-R networks. The three smallest bits make use of a self-switched version of the constant-R networks while the 3 largest bits use a topology with single-pole double-throw (SPDT) switches and constant-R networks in the delay path. Measurement results for a typical chip are presented.


title: Change-Point Detection in Time-Series Data by Direct Density-Ratio Estimation

abstract: An electronic device of the type including a thin film body having a superposed electrode has short circuit defects therein passivated by a conversion process in which the electrical resistivity of the electrode material is increased proximate the defect regions. Conversion is accomplished by exposing the electrode material to a conversion reagent and activating the reagent proximate the defect regions. The process may be utilized for a variety of differently configured devices, and may be readily adapted for use in a roll-to-roll device fabrication process.


title: A One-Time Password Scheme with QR-Code Based on Mobile Phone

abstract: User authentication is one of the fundamental procedures to ensure secure communications and share system resources over an insecure public network channel. Especially, the purpose of the one-time password is to make it more difficult to gain unauthorized access to restricted resources. Instead of using the password file as conventional authentication systems, many researchers have devoted to implement various one-time password schemes using smart cards, time-synchronized token or short message service in order to reduce the risk of tampering and maintenance cost. However, these schemes are impractical because of the far from ubiquitous hardware devices or the infrastructure requirements. To remedy these weaknesses, the attraction of the QR code technique can be introduced into our one-time password authentication protocol. Not the same as before, the proposed scheme based on QR code not only eliminates the usage of the password verification table, but also is a cost effective solution since most internet users already have mobile phones.


title: High-Performance Low-Power Carry Speculative Addition With Variable Latency

abstract: Adders are one of the most critical arithmetic circuits in a system and their throughput affects the overall performance of the system. Traditional n-bit adders provide accurate results, but the lower bound of their critical path delay is $\Omega {(\log ~n)}$ . To achieve a critical path delay lower than $\Omega {(\log ~n)}$ , many approximate adders have been proposed. These approximate adders decrease the critical path delay and improve the speed by sacrificing computation accuracy or predicting the computation results. This paper proposes a high-performance low-power carry speculative adder (CSPA). This adder separates the carry generator and sum generator. Only one sum generator is used in a block adder to reduce the critical path delay and area overhead. In addition, to generate 100% accurate results, error detection and recovery circuits are added to the proposed CSPA to construct a variable-latency carry speculative adder (VLCSPA). Instead of recalculating all results, the error detection and recovery circuits find and correct the block adder that generates incorrect partial sum bits, reducing power consumption. The experimental results show that the proposed CSPA achieves a 26.59% delay reduction, a 14.06% area reduction, and a 19.03% power consumption reduction compared to the corresponding values for an existing speculative carry-select adder. The experimental results also show the proposed CSPA can be used to improve image denoising results as well.


title: Fast head modeling for animation

abstract: Abstract This paper describes an efficient method to make individual faces for animation from several possible inputs. We present a method to reconstruct a three-dimensional (3D) facial model for animation from two orthogonal pictures taken from front and side views, or from range data obtained from any available resources. It is based on extracting features on a face in a semiautomatic way and modifying a generic model with detected feature points. Then fine modifications follow if range data is available. Automatic texture mapping is employed using an image composed from the two images. The reconstructed 3D-face can be animated immediately with given expression parameters. Several faces by obtained one methodology applied to different input data to get a final animatable face are illustrated.


Guessing Meaning From Word Sounds of Unfamiliar Languages: A Cross-Cultural Sound Symbolism Study


title: Artificial bee colony programming for symbolic regression

abstract: Artificial bee colony algorithm simulating the intelligent foraging behavior of honey bee swarms is one of the most popular swarm based optimization algorithms. It has been introduced in 2005 and applied in several fields to solve different problems up to date. In this paper, an artificial bee colony algorithm, called as Artificial Bee Colony Programming (ABCP), is described for the first time as a new method on symbolic regression which is a very important practical problem. Symbolic regression is a process of obtaining a mathematical model using given finite sampling of values of independent variables and associated values of dependent variables. In this work, a set of symbolic regression benchmark problems are solved using artificial bee colony programming and then its performance is compared with the very well-known method evolving computer programs, genetic programming. The simulation results indicate that the proposed method is very feasible and robust on the considered test problems of symbolic regression.


LEARNING ANALYTICS APPLIED TO CURRICULUM ANALYSIS


title: Pixel-wise Segmentation of Street with Neural Networks

abstract: Pixel-wise street segmentation of photographs taken from a drivers perspective is important for self-driving cars and can also support other object recognition tasks. A framework called SST was developed to examine the accuracy and execution time of different neural networks. The best neural network achieved an $F_1$-score of 89.5% with a simple feedforward neural network which trained to solve a regression task.


title: Planar transmit-array antenna and plano-convex lens antenna for 77-GHz automotive radar

abstract: Two types of antenna lens antenna, namely, a planar dual-layered transmit-array antenna and a plano-convex lens antenna, are developed for 77-GHz automotive radar systems. Both the antennas are excited by four substrate-integrated-waveguide (SIW) slot antennas as the primary feeds. The SIW feeding slot antennas are linearly positioned with identical spacing and shifted their positions accordingly to generate four beams with specific beam-coverage and higher gain. The transmit-array antenna exhibits a combined beam-coverage of ± 15° with the realized gain greater than 18.5 dBi at 76.5 GHz. The plano-convex lens antenna achieves 24-dBi gain and a combined beam-coverage of ±12o at 77 GHz. Both antennas are experimentally validated.


title: FANET Application Scenarios and Mobility Models

abstract: FANETs are a promising solution for application scenarios involving UAVs. There are many mobility models that can be used to reproduce the behavior of the mobile nodes in an ad-hoc network, but some of these cannot simulate the realistic motion of UAVs. In this paper we list the available mobility models and try to understand which ones should be adopted for different FANET application scenarios, discussing their pros and cons.


title: Soccer League Competition Algorithm, a New Method for Solving Systems of Nonlinear Equations

abstract: This paper introduces ::: Soccer League Competition (SLC) algorithm as a new optimization technique for ::: solving nonlinear systems of equations. Fundamental ideas of the method are ::: inspired from soccer leagues and based on the competitions among teams and ::: players. Like other meta-heuristic methods, the proposed technique starts with ::: an initial population. Population individuals called players are in two types: fixed players ::: and substitutes that all together form some teams. The competition among teams ::: to take the possession of the top ranked positions in the league table and the ::: internal competitions between players in each team for personal improvements ::: results in the convergence of population individuals to the global optimum. ::: Results of applying the proposed algorithm in solving nonlinear systems of ::: equations demonstrate that SLC converges to the answer more accurately and rapidly ::: in comparison with other Meta-heuristic and Newton-type methods.


title: Control of Prosthetic Hands via the Peripheral Nervous System

abstract: This paper intends to provide a critical review of the literature on the technological issues on control and sensorization of hand prostheses interfacing with the Peripheral Nervous System (i.e. PNS), and their experimental validation on amputees. The study opens with an in-depth analysis of control solutions and sensorization features of research and commercially available prosthetic hands. Pros and cons of adopted technologies, signal processing techniques and motion control solutions are investigated. Special emphasis is then dedicated to the recent studies on the restoration of tactile perception in amputees through neural interfaces. The paper finally proposes a number of suggestions for designing the prosthetic system able to re-establish a bidirectional communication with the PNS and foster the prosthesis natural control.


title: An Introduction to Musical Metacreation

abstract: Musical metacreation (MuMe), also known as musical computational creativity, is a subfield of computational creativity that focuses on endowing machines with the ability to achieve creative musical tasks, such as composition, interpretation, improvisation, accompaniment, mixing, etc. It covers all dimensions of the theory and practice of computational generative music systems, ranging from purely artistic approaches to purely scientific ones, inclusive of discourses relevant to this topic from the humanities. MuMe systems range from purely generative ones to a variety of interactive systems, such as those for computer-assisted composition and computer-assisted sound design. In order to better appreciate the many dimensions of this interdisciplinary domain and see how it overlaps and differs from research in computer music, this introduction provides a general entry point. After defining and introducing the domain, its context, and some of its terminology, we reflect on some challenges and opportunities for the field as a whole.


title: Decomposing Generalization: Models of Generic, Habitual, and Episodic Statements

abstract: We present a novel semantic framework for modeling linguistic expressions of generalization---generic, habitual, and episodic statements---as combinations of simple, real-valued referential properties of predicates and their arguments. We use this framework to construct a dataset covering the entirety of the Universal Dependencies English Web Treebank. We use this dataset to probe the efficacy of type-level and token-level information---including hand-engineered features and static (GloVe) and contextual (ELMo) word embeddings---for predicting expressions of generalization. Data and code are available at decomp.io.


title: Influence of Hysteretic Behavior on Equivalent Period and Damping of Structural Systems

abstract: Relations for the equivalent period \iT\Deq\N and equivalent damping ratio ζ\Deq\N for the equivalent linear system approach for seismic design are proposed. These relations are based on optimal \iT\Deq\N and ζeq values derived from extensive time-history analyses of single-degree-of-freedom systems with six types of hysteretic behavior and a period range of 0.1 to 1.5 s. The proposed relations are more general (in their applicability to a wide range of hysteretic systems) and have stronger ties to physical behavior compared to existing empirical methods for evaluating \iT\Deq\N and ζ\Deq\N. The proposed relations address differences in equivalent stiffness resulting from different hysteretic behavior. Simulations show that the optimal \iT\Deq\N, associated with the stiffness of the partial load-displacement cycles of a hysteretic system, is the highest for the slip system (with a zero unloading/reloading stiffness) and the lowest for the bilinear elastic system (with an elastic unloading/reloading stiffness). The optimal ζ\Deq\N, associated with the energy dissipation capacity of a hysteretic system, is the highest for the elastoplastic system and the lowest for the bilinear elastic system.


Expressive Collaborative Music Performance via Machine Learning


title: Design and Analysis of Stacked Power Amplifier in Series-Input and Series-Output Configuration

abstract: The stacked-device power-combining technique is a proven method to increase the output power and load impedance of a power amplifier (PA) simultaneously. The series-input configuration is physically realizable for multicell stacked device configuration in monolithic circuits. The series-input and series-output stack configuration is rigorously analyzed and proven to increase both the input impedance and output impedance simultaneously, easing the matching circuit designs in high PAs. The effects of asymmetry of the input feed and amplifier cells due to distributed effects and process variation on the performance of the stack amplifier are discussed. A four-cell HBT amplifier operating at 5-6 GHz is demonstrated to validate the circuit concept.


title: Polar codes for channel and source coding

abstract: The two central topics of information theory are the compression and the transmission of data. Shannon, in his seminal work, formalized both these problems and determined their fundamental limits. Since then the main goal of coding theory has been to find practical schemes that approach these limits. Polar codes, recently invented by Arikan, are the first "practical" codes that are known to achieve the capacity for a large class of channels. Their code construction is based on a phenomenon called "channel polarization". The encoding as well as the decoding operation of polar codes can be implemented with O(N log N) complexity, where N is the blocklength of the code. We show that polar codes are suitable not only for channel coding but also achieve optimal performance for several other important problems in information theory. The first problem we consider is lossy source compression. We construct polar codes that asymptotically approach Shannon's rate-distortion bound for a large class of sources. We achieve this performance by designing polar codes according to the "test channel", which naturally appears in Shannon's formulation of the rate-distortion function. The encoding operation combines the successive cancellation algorithm of Arikan with a crucial new ingredient called "randomized rounding". As for channel coding, both the encoding as well as the decoding operation can be implemented with O(N log N) complexity. This is the first known "practical" scheme that approaches the optimal rate-distortion trade-off. We also construct polar codes that achieve the optimal performance for the Wyner-Ziv and the Gelfand-Pinsker problems. Both these problems can be tackled using "nested" codes and polar codes are naturally suited for this purpose. We further show that polar codes achieve the capacity of asymmetric channels, multi-terminal scenarios like multiple access channels, and degraded broadcast channels. For each of these problems, our constructions are the first known "practical" schemes that approach the optimal performance. The original polar codes of Arikan achieve a block error probability decaying exponentially in the square root of the block length. For source coding, the gap between the achieved distortion and the limiting distortion also vanishes exponentially in the square root of the blocklength. We explore other polar-like code constructions with better rates of decay. With this generalization, we show that close to exponential decays can be obtained for both channel and source coding. The new constructions mimic the recursive construction of Arikan and, hence, they inherit the same encoding and decoding complexity. We also propose algorithms based on message-passing to improve the finite length performance of polar codes. In the final two chapters of this thesis we address two important problems in graphical models related to communications. The first problem is in the area of low-density parity-check codes (LDPC). For practical lengths, LDPC codes using message-passing decoding are still the codes to beat. The current analysis, using density evolution, evaluates the performance of these algorithms on a tree. The tree assumption corresponds to using an infinite length code. But in practice, the codes are of finite length. We analyze the message-passing algorithms for this scenario. The absence of tree assumption introduces correlations between various messages. We show that despite this correlation, the prediction of the tree analysis is accurate. The second problem we consider is related to code division multiple access (CDMA) communication using random spreading. The current analysis mainly focuses on the information theoretic limits, i.e., using Gaussian input distribution. However in practice we use modulation schemes like binary phase-shift keying (BPSK), which is far from being Gaussian. The effects of the modulation scheme cannot be analyzed using traditional tools which are based on spectrum of large random matrices. We follow a new approach using tools developed for random spin systems in statistical mechanics. We prove a tight upper bound on the capacity of the system when the user input is BPSK. We also show that the capacity depends only on the power of the spreading sequences and is independent of their exact distribution.


title: The new blocs on the block: using community forums to foster new neighbourhoods

abstract: Research has consistently shown that online tools increase social capital. In the context of neighbourhoods Hampton and Wellman have shown that in newly developed areas residents effectively used mailing lists to connect with each other, circulate information, and ask for help. The research question of whether similar findings would hold in the larger context of a city for a long period of time is still open. To tackle this research question, we have gathered the complete dataset of the most popular neighbourhood online forum in Dublin. In this dataset, we have people sharing a common purpose (blocs) who live in the same neighbourhood and interact online to ask for help, engage in local activities, and, more generally, have a better understanding of their physical community. Our analysis highlights the particularly concentrated usage in newly established developments where a pre-existing community may be absent. Additionally, these communications provide a valuable resource to understand local issues relevant to the community.


title: What Makes Mathematics Manipulatives Effective? Lessons from Cognitive Science and Montessori Education.

abstract: Manipulatives are ubiquitous in early childhood classrooms; yet, findings regarding their efficacy for learning mathematics concepts are inconsistent. In this article, we present four general principles that have emerged from cognitive science about ways to ensure that manipulatives promote learning when used with young children. We also describe how Montessori instruction offers a concrete example of the application of these principles in practice, which may, in turn, explain the high levels of mathematics achievement among children who attend Montessori programs during early childhood. The general principles and concrete examples presented in this article should help early childhood programs maximize the benefits of using manipulatives for developmentally appropriate mathematics instruction.


title: Representing Meaning with a Combination of Logical Form and Vectors.

abstract: NLP tasks differ in the semantic information they require, and at this time no single semantic representation fulfills all requirements. Logic-based representations characterize sentence structure, but do not capture the graded aspect of meaning. Distributional models give graded similarity ratings for words and phrases, but do not adequately capture overall sentence structure. So it has been argued that the two are complementary. In this paper, we adopt a hybrid approach that combines logic-based and distributional semantics through probabilistic logic inference in Markov Logic Networks (MLNs). We focus on textual entailment (RTE), a task that can utilize the strengths of both representations. Our system is three components, 1) parsing and task representation, where input RTE problems are represented in probabilistic logic. This is quite different from representing them in standard first-order logic. 2) knowledge base construction in the form of weighted inference rules from different sources like WordNet, paraphrase collections, and lexical and phrasal distributional rules generated on the fly. We use a variant of Robinson resolution to determine the necessary inference rules. More sources can easily be added by mapping them to logical rules; our system learns a resource-specific weight that counteract scaling differences between resources. 3) inference, where we show how to solve the inference problems efficiently. In this paper we focus on the SICK dataset, and we achieve a state-of-the-art result. Our system handles overall sentence structure and phenomena like negation in the logic, then uses our Robinson resolution variant to query distributional systems about words and short phrases. Therefor, we use our system to evaluate distributional lexical entailment approaches. We also publish the set of rules queried from the SICK dataset, which can be a good resource to evaluate them.


title: Hi-Lo tech games: crafting, coding and collaboration of augmented board games by high school youth

abstract: Most research on game making activities for learning has focused on programming screen-based designs. Only recently has research begun to include the design of tangible interfaces; connecting on-screen programming with hands-on crafting. In this paper, we examine the potential of a workshop that combines the high and low of technology with game design in which teams of high school youth crafted, coded and collaborated on their own augmented board games to highlight intersections between learning programming and making, and creating across digital and tangible modalities. We focused our analysis of students' projects, interactions, and reflections on how young designers conceptualized the integration of screen and board game elements, realized computational concepts and practices in their board game designs and augmentations, and reflected on their game design experience connecting crafting and coding. In the discussion, we review how the expansion of game making activities can create new opportunities for interaction design and research.


title: Spatial Memory Streaming

abstract: Prior research indicates that there is much spatial variation in applications' memory access patterns. Modern memory systems, however, use small fixed-size cache blocks and as such cannot exploit the variation. Increasing the block size would not only prohibitively increase pin and interconnect bandwidth demands, but also increase the likelihood of false sharing in shared-memory multiprocessors. In this paper, we show that memory accesses in commercial workloads often exhibit repetitive layouts that span large memory regions (e.g., several kB), and these accesses recur in patterns that are predictable through codebased correlation. We propose Spatial Memory Streaming, a practical on-chip hardware technique that identifies codecorrelated spatial access patterns and streams predicted blocks to the primary cache ahead of demand misses. Using cycle-accurate full-system multiprocessor simulation of commercial and scientific applications, we demonstrate that Spatial Memory Streaming can on average predict 58% of L1 and 65% of off-chip misses, for a mean performance improvement of 37% and at best 307%.


title: BinaryRelax: A Relaxation Approach For Training Deep Neural Networks With Quantized Weights

abstract: We propose BinaryRelax, a simple two-phase algorithm, for training deep neural networks with quantized weights. The set constraint that characterizes the quantization of weights is not imposed until the late stage of training, and a sequence of \emph{pseudo} quantized weights is maintained. Specifically, we relax the hard constraint into a continuous regularizer via Moreau envelope, which turns out to be the squared Euclidean distance to the set of quantized weights. The pseudo quantized weights are obtained by linearly interpolating between the float weights and their quantizations. A continuation strategy is adopted to push the weights towards the quantized state by gradually increasing the regularization parameter. In the second phase, exact quantization scheme with a small learning rate is invoked to guarantee fully quantized weights. We test BinaryRelax on the benchmark CIFAR and ImageNet color image datasets to demonstrate the superiority of the relaxed quantization approach and the improved accuracy over the state-of-the-art training methods. Finally, we prove the convergence of BinaryRelax under an approximate orthogonality condition.


title: A Mechanically Beam-Steered Phased Array Antenna for Power-Harvesting Applications [Antenna Applications Corner]

abstract: Beam-steered antennas are highly desirable for radio-frequency (RF)- power-harvesting applications. By changing the direction of its main beam, maximum power can be harvested without physically rotating the antenna. This is specifically favorable to power-harvesting applications where the harvesting unit needs to be placed on a planar surface, cannot be rotated, and the available volume is constrained. We present a 2 X 2 beam-steered phased array antenna for RF-power-harvesting applications. The designed antenna array achieves a continuous beam-steering angle of ?3? in the E-plane. Beam steering is realized by mechanically shifting the position of the feeding point. The antenna does not utilize any active elements; thereby, the maximum amount of power can be delivered into the harvesting circuitry. A small and wideband patch antenna element has also been designed and incorporated in the array, which reduces its overall size and enables wide scanning angle impedance matching. At the operating frequency of 2.4 GHz, the size of the array is 0.96 X 0.96 X 0.056 ?. The measured peak gain is 11.3 dBi, and bandwidth is 12.9%.


title: Parallel sparse matrix-vector and matrix-transpose-vector multiplication using compressed sparse blocks

abstract: This paper introduces a storage format for sparse matrices, called compressed sparse blocks (CSB), which allows both Ax and A,x to be computed efficiently in parallel, where A is an n×n sparse matrix with nnzen nonzeros and x is a dense n-vector. Our algorithms use Θ(nnz) work (serial running time) and Θ(√nlgn) span (critical-path length), yielding a parallelism of Θ(nnz/√nlgn), which is amply high for virtually any large matrix. The storage requirement for CSB is the same as that for the more-standard compressed-sparse-rows (CSR) format, for which computing Ax in parallel is easy but A,x is difficult. Benchmark results indicate that on one processor, the CSB algorithms for Ax and A,x run just as fast as the CSR algorithm for Ax, but the CSB algorithms also scale up linearly with processors until limited by off-chip memory bandwidth.


title: A Scalable Complex Pattern Mining Framework for Global Settlement Mapping

abstract: Human settlements manifest as complex spatial patterns in very high-resolution (VHR) satellite remote sensing images. Widely used pixel and object-based methods are incapable of capturing these complex patterns. Recently developed multiple instance learning algorithms showed to be very effective in mapping different types of human settlements. However, multiple instance learning approaches are computationally expensive and do not scale for global scale problems using big VHR imagery data. In this paper, we extend the Gaussian Multiple Instance (GMIL) learning by simplifying the model assumptions. Experimental evaluation shows that this method is computationally more efficient while maintaining similar accuracy as the GMIL algorithm.


title: Automatic fault location for data structures

abstract: Specification-based data structure verification is a powerful debugging technique. In this work we combine specification-based data structure verification with automatic detection of faulty program statements that corrupt data structures. The user specifies the consistency constraints for dynamic data structures as relationships among the nodes of a memory graph. Our system detects constraint violations to identify corrupted data structures during program execution and then automatically locates faulty code responsible for data structure corruption. Our approach offers two main advantages: (1) a highly precise automatic fault location method, and (2) a simple specification language. We employ incremental constraint checking for time efficient constraint matching and fault location. On average, while Tarantula statistical debugging technique narrows the fault to 10 statements, our technique narrows it to ≈ 4 statements..


title: Secure and flexible data sharing via ciphertext retrieval for cloud computing

abstract: Cloud-based data storage is attracting more and more users, yet there is no well-recognized solution that satisfies their requirements for both secure storage and flexible sharing. Many researchers have proposed different schemes, but none of existing schemes accounts for security of cloud data, flexibility of sharing, computing cost, and other factors at the same time. In this paper, we propose a secure and flexible data sharing scheme for cloud computing. Combining homomorphic encryption and a TF-IDF based vector space model, we apply ciphertext retrieval in cloud data sharing for the first time. Adapt to the distributed nature of cloud computing, we propose a parallel homomorphic encryption algorithm based on MapReduce. In addition, we also adopt CP-ABE in our scheme to offer fine-grained access control according to appropriate access structures. We have conducted several experiments on a Hadoop cloud platform to evaluate the proposed scheme. The results show that our data sharing scheme has the advantages of higher security, flexibility, and efficiency with less computation cost.


title: On the State of NoSQL Benchmarks

abstract: The proliferation of Big Data systems and namely NoSQL databases has resulted in a tremendous heterogeneity in its offerings. It has become increasingly difficult to compare and select the most optimal NoSQL storage technology. Current benchmark efforts, such as the Yahoo! Cloud Serving Benchmark (YCSB), evaluate simple read and write operations on a primary key. However, while YCSB has become the de-facto benchmark solution for practitioners and NoSQL vendors, it is lacking in capabilities to extensively evaluate specific NoSQL solutions. In this paper, we present a systematic survey of current NoSQL benchmarks, in which we identify a clear gap in benchmarking more advanced workloads (e.g. nested document search) for features specific to NoSQL database families (e.g. document stores). Secondly, based on our survey, we discuss the strengths and weaknesses of the different benchmark design approaches, and argue in favor of a benchmark suite that targets specific families of NoSQL databases yet still allows overall comparison of databases in terms of their commonalities.


title: Multipath Routing in Mobile Ad Hoc Networks: Issues and Challenges

abstract: Mobile ad hoc networks (MANETs) consist of a collection of wireless mobile nodes which dynamically exchange data among themselves without the reliance on a fixed base station or a wired backbone network. MANET nodes are typically distinguished by their limited power, processing, and memory resources as well as high degree of mobility. In such networks, the wireless mobile nodes may dynamically enter the network as well as leave the network. Due to the limited transmission range of wireless network nodes, multiple hops are usually needed for a node to exchange information with any other node in the network. Thus routing is a crucial issue to the design of a MANET. In this paper, we specifically examine the issues of multipath routing in MANETs. Multipath routing allows the establishment of multiple paths between a single source and single destination node. It is typically proposed in order to increase the reliability of data transmission (i.e., fault tolerance) or to provide load balancing. Load balancing is of especial importance in MANETs because of the limited bandwidth between the nodes. We also discuss the application of multipath routing to support application constraints such as reliability, load-balancing, energy-conservation, and Quality-of-Service (QoS).


title: TV white spaces, I presume?: the quest for TVWS in Malawi and Zambia

abstract: TV White Spaces (TVWS) technology and regulation has the potential to make connectivity both technically and economically feasible in rural Africa where affordable access remains a challenge. The superior propagation characteristics of TVWS technology make it particularly well suited to connecting remote communities. Evidence collected from our measurements in Malawi and Zambia suggest that most UHF spectrum is already available both in urban as well as in rural areas and could be used to provide Internet connectivity. In this paper we present the findings of a TVWS spectrum measurement initiative in Malawi and Zambia. We introduce an open hardware device that geo-tags spectrum measurements and saves the results on a micro SD card. The device can also be used to record the use of spectrum over long periods of time.


title: Automatic OpenCL work-group size selection for multicore CPUs

abstract: In this paper, we address the effect of the work-group size on the performance of OpenCL kernels. We propose a profiling-based algorithm that finds a good work-group size, in terms of performance, for the target multicore CPU architecture. Our algorithm reduces misses in the private L1 data cache and achieves load balancing between cores. It exploits the polyhedral model to estimate the working-set size and the number of cache misses for a parameterized work-group size of the OpenCL kernel. Based on the profiling information, it heuristically searches the space of parameterized work-group sizes. Our virtually-extended index space helps to increase the probability to find a better work-group size. We implement our work-group size selection algorithm as a development tool that consists of a code generator and a search library. The code generator extracts the polytope of each memory reference from the kernel code and generates a function that simplifies polytopes using the run-time information and invokes search library routines. The search library calculates the working-set size using the polytopes and finds a proper work-group size. We evaluate our approach using 31 OpenCL kernels on four different multicore CPUs. We compare its accuracy and search time to those of an exhaustive search method. Experimental results show that our tool is, on average, 1566 times faster than the exhaustive search and selects a work-group size whose performance is the same as or comparable to that of the exhaustive search.


title: On the (In-)Accuracy of GPS Measures of Smartphones: A Study of Running Tracking Applications

abstract: Sports tracking applications are increasingly available on the market, and research has recently picked up this topic. Tracking a user's running track and providing feedback on the performance are among the key features of such applications. However, little attention has been paid to the accuracy of the applications' localization measurements. In evaluating the nine currently most popular running applications, we found tremendous differences in the GPS measurements. Besides this finding, our study contributes to the scientific knowledge base by qualifying the findings of previous studies concerning accuracy with smartphones' GPS components.


title: Progressive search space reduction for human pose estimation

abstract: The objective of this paper is to estimate 2D human pose as a spatial configuration of body parts in TV and movie video shots. Such video material is uncontrolled and extremely challenging. We propose an approach that progressively reduces the search space for body parts, to greatly improve the chances that pose estimation will succeed. This involves two contributions: (i) a generic detector using a weak model of pose to substantially reduce the full pose search space; and (ii) employing 'grabcut' initialized on detected regions proposed by the weak model, to further prune the search space. Moreover, we also propose (Hi) an integrated spatio- temporal model covering multiple frames to refine pose estimates from individual frames, with inference using belief propagation. The method is fully automatic and self-initializing, and explains the spatio-temporal volume covered by a person moving in a shot, by soft-labeling every pixel as belonging to a particular body part or to the background. We demonstrate upper-body pose estimation by an extensive evaluation over 70000 frames from four episodes of the TV series Buffy the vampire slayer, and present an application to full- body action recognition on the Weizmann dataset.


title: The adoption of hyped technologies: a qualitative study

abstract: The introduction of new consumer technology is often greeted with declarations that the way people conduct their lives will be changed instantly. In some cases, this might create hype surrounding a specific technology. This article investigates the adoption of hyped technology, a special case that is absent in the adoption literature. The study employs a consumer research perspective, specifically the theory of consumption values (TCV), to understand the underlying motives for adopting the technology. In its original form, TCV entails five values that influence consumer behavior: functional, social, epistemic, emotional and conditional. The values catch the intrinsic and extrinsic motives influencing behavior. Using a qualitative approach that includes three focus groups and 60 one-on-one interviews, the results of the study show that emotional, epistemic and social values influence the adoption of hyped technologies. Contrary to expectations, functional value, which is similar to the widely used information system constructs of perceived usefulness and relative advantage, has little impact on the adoption of technologies that are surrounded with significant hype. Using the findings of the study, this article proposes a model for investigating and understanding the adoption of hyped technologies. This article contributes to the literature by (1) focusing on the phenomenon of hyped technology, (2) introducing TCV, a consumer research-based theoretical framework, to enhance the understanding of technology adoption, and (3) proposing a parsimonious model explaining the adoption of hyped technology.


title: Kernel Null Space Methods for Novelty Detection

abstract: Detecting samples from previously unknown classes is a crucial task in object recognition, especially when dealing with real-world applications where the closed-world assumption does not hold. We present how to apply a null space method for novelty detection, which maps all training samples of one class to a single point. Beside the possibility of modeling a single class, we are able to treat multiple known classes jointly and to detect novelties for a set of classes with a single model. In contrast to modeling the support of each known class individually, our approach makes use of a projection in a joint subspace where training samples of all known classes have zero intra-class variance. This subspace is called the null space of the training data. To decide about novelty of a test sample, our null space approach allows for solely relying on a distance measure instead of performing density estimation directly. Therefore, we derive a simple yet powerful method for multi-class novelty detection, an important problem not studied sufficiently so far. Our novelty detection approach is assessed in comprehensive multi-class experiments using the publicly available datasets Caltech-256 and Image Net. The analysis reveals that our null space approach is perfectly suited for multi-class novelty detection since it outperforms all other methods.


title: Serum survival and plasmid possession by strains of Salmonella enteritidis, Salm. typhimurium and Salm. virchow

abstract: Strains of Salmonella enteritidis, Salm. typhimurium and Salm. virchow, carrying different numbers of plasmids, were examined for the ability to multiply in sera. Viable counts were performed to monitor the kinetics of growth of bacteria when in human, chicken and turkey sera. The presence of plasmids in Salm. enteritidis, Salm. typhimurium and Salm. virchow reduced considerably the ability of strains of these serotypes to multiply in serum. SDS-PAGE was used to show that growth of Salm. enteritidis in serum did not involve changes in outer membrane proteins or lipopolysaccharide. It was concluded that the carriage of plasmids may be disadvantageous for the survival in serum of certain common salmonella serotypes.


title: Security Enhancements in EMV Protocol for NFC Mobile Payment

abstract: Today, by integrating Near Field Communication (NFC) technology in smartphones, bank cards and payment terminals, a purchase transaction can be executed immediately without any physical contact, without entering a PIN code or a signature. Europay Mastercard Visa (EMV) is the standard dedicated for securing contactless-NFC payment transactions. However, it does not ensure two main security proprieties: (1) the authentication of the payment terminal to the client's payment device, (2) the confidentiality of personal banking data. In this paper, we first of all detail EMV standard and its security vulnerabilities. Then, we propose a solution that enhances the EMV protocol by adding a new security layer aiming to solve EMV weaknesses. We formally check the correctness of the proposal using a security verification tool called Scyther.


title: Detection of emotions during learning with AutoTutor

abstract: Detection of Emotions during Learning with AutoTutor Art Graesser (a-graesser@memphis.edu) Amy Witherspoon (awthrspn@mail.psyc.memphis.edu) Department of Psychology, 202 Psychology Building Memphis. TN 38152 USA Department of Psychology, 202 Psychology Building Memphis. TN 38152 USA Bethany McDaniel (btmcdanl@memphis.edu) Department of Psychology, 202 Psychology Building Memphis. TN 38152 USA Sidney D’Mello (sdmello@memphis.edu) Institute for Intelligent Systems, 365 Innovation Drive Memphis, TN 38152 USA Patrick Chipman (pchipman@memphis.edu) Department of Psychology, 202 Psychology Building Memphis. TN 38152 USA Barry Gholson (b.gholson@memphis.edu) Department of Psychology, 202 Psychology Building Memphis. TN 38152 USA between cognition and emotions, they do not directly explain and predict the sort of emotions that occur during complex learning, such as attempts to master physics, biology, or computer literacy. Researchers in many different fields are familiar with Ekman’s work on the detection of emotions from facial expressions (Ekman & Friesen, 1978). However, the emotions that Ekman intensely investigated (e.g., sadness, happiness, anger, fear, disgust, surprise) have minimal relevance to learning per se (Kort et al., 2001). The pervasive affective states during complex learning include confusion, frustration, boredom, flow/engagement, interest, and being stuck (Craig, Graesser, Sullins, & Gholson, 2004; Csikszentmihalyi, 1990). There are a number of ways in which tutors (and other types of learning environments) might adaptively respond to the learner’s affective states in the course of enhancing learning (D’Mello, Craig, Sullins, & Graesser, in press; Graesser et al., 2005; Lepper & Woolverton, 2002). If the learner is frustrated, for example, the tutor can give hints to advance the learner in constructing knowledge or can make supportive empathetic comments to enhance motivation. If the learner is bored, the tutor needs to present more engaging or challenging problems for the learner to work on. The tutor would probably want to lay low and stay out the learner’s way when the learner is in a state of flow (Csikszentmihaly, 1990), i.e., when the learner is so deeply engaged in learning the material that time and fatigue disappear. The flow experience is believed to occur when the learning rate is high and the learner has achieved a high level of mastery at the region of proximal learning (Metcalfe & Kornell, 2005). The affective state of confusion is particularly interesting because it is believed to play an important role in learning (Graesser et al., 2005; Guhe et al., 2004) and has a large correlation with learning gains (Craig et al., 2004). Confusion is diagnostic of cognitive disequilibrium, a state that occurs when learners face obstacles to goals, contradictions, incongruities, anomalies, uncertainty, and salient contrasts (Festinger, 1957; Graesser, Lu, Olde, Pye- Cooper, & Whitten, 2005; Graesser & Olde, 2003; Piaget, Abstract The relationship between emotions and learning was investigated by tracking the affective states that college students experienced while interacting with AutoTutor, an intelligent tutoring system with conversational dialogue. An emotionally responsive tutor would presumably facilitate learning, but this would only occur if learner emotions can be accurately identified. After a learning session with AutoTutor, the affective states of the learner were classified by the learner, a peer, and judges trained on Ekman’s Facial Action Coding system. The classification of the trained judges was more reliable and matched the learners much better than the low scores of untrained peers. This result suggests that peer tutors may be limited in detecting the affective states of peer learners. Classification accuracy was poor at constant intervals of polling (every 20 seconds) but much higher when individuals declared that an affect state had been experienced. Keywords: Emotion; Instruction and teaching; Human computer interaction; AutoTutor; Affective states Introduction Connections between complex learning and emotions have received increasing attention in the fields of psychology (Carver, 2004; Deci & Ryan, 2002; Dweck, 2002), education (Lepper & Henderlong, 2000; Linnenbrink & Pintrich, 2004; Meyer & Turner, 2002), neuroscience (Damasio, 2003), and computer science (Kort, Reilly, & Picard, 2001; Picard, 1997). A deep understanding of such affect-learning connections is needed in order to design engaging educational artifacts that range from responsive intelligent tutoring systems on technical material (DeVicente & Pain, 2002; Graesser, Person, Lu, Jeon, & McDaniel, 2005; Guhe, Gray, Schoelles, & Ji, 2004; Litman & Silliman, 2004) to entertaining media and games (Conati, 2002; Gee, 2003; Vorderer, 2003). There have been several theories that link cognition and affect very generally (Bower, 1981; Mandler, 1984; Ortony, Clore, & Collins, 1988; Russell, 2003; Stein & Levine, 1991). While these theories convey general links


title: Impact of emergency medical helicopter transport directly to a university hospital trauma center on mortality of severe blunt trauma patients until discharge

abstract: The benefits of transporting severely injured patients by helicopter remain controversial. This study aimed to analyze the impact on mortality of helicopter compared to ground transport directly from the scene to a University hospital trauma center. The French Intensive Care Research for Severe Trauma cohort study enrolled 2,703 patients with severe blunt trauma requiring admission to University hospital intensive care units within 72 hours. Pre-hospital and hospital clinical data, including the mode of transport, (helicopter (HMICU) versus ground (GMICU), both with medical teams), were recorded. The analysis was restricted to patients admitted directly from the scene to a University hospital trauma center. The main endpoint was mortality until ICU discharge. Of the 1,958 patients analyzed, 74% were transported by GMICU, 26% by HMICU. Median injury severity score (ISS) was 26 (interquartile range (IQR) 19 to 34) for HMICU patients and 25 (IQR 18 to 34) for GMICU patients. Compared to GMICU, HMICU patients had a higher median time frame before hospital admission and were more intensively treated in the pre-hospital phase. Crude mortality until hospital discharge was the same regardless of pre-hospital mode of transport. After adjustment for initial status, the risk of death was significantly lower (odds ratio (OR): 0.68, 95% confidence interval (CI) 0.47 to 0.98, P = 0.035) for HMICU compared with GMICU. This result did not change after further adjustment for ISS and overall surgical procedures. This study suggests a beneficial impact of helicopter transport on mortality in severe blunt trauma. Whether this association could be due to better management in the pre-hospital phase needs to be more thoroughly assessed.


title: Hi YouTube!: personality impressions and verbal content in social video

abstract: Despite the evidence that social video conveys rich human personality information, research investigating the automatic prediction of personality impressions in vlogging has shown that, amongst the Big-Five traits, automatic nonverbal behavioral cues are useful to predict mainly the Extraversion trait. This finding, also reported in other conversational settings, indicates that personality information may be coded in other behavioral dimensions like the verbal channel, which has been less studied in multimodal interaction research. In this paper, we address the task of predicting personality impressions from vloggers based on what they say in their YouTube videos. First, we use manual transcripts of vlogs and verbal content analysis techniques to understand the ability of verbal content for the prediction of crowdsourced Big-Five personality impressions. Second, we explore the feasibility of a fully-automatic framework in which transcripts are obtained using automatic speech recognition (ASR). Our results show that the analysis of error-free verbal content is useful to predict four of the Big-Five traits, three of them better than using nonverbal cues, and that the errors caused by the ASR system decrease the performance significantly.


title: Project management and communication software selection using the weighted regularized Hasse method

abstract: This paper addresses the problem of selecting the most appropriate project management and communication software for a project having specific requirements. A four-stage procedure featuring the weighted regularized Hasse method is used to compare and rank the candidate tools. The ranking of the tools takes into consideration the importance of the functional and non-functional features of the project management systems with their respective weights based on the results of a questionnaire conducted among members of a dispersed international project team.


title: Deep transformation learning for face recognition in the unconstrained scene

abstract: Because human pose variations cannot be controlled in unconstrained scene, it is frequently hard to capture frontal face image. This is why either face recognition rate is low, or face image cannot be recognized at all. To tackle the problem, this paper proposes deep transformation learning to extract the pose-robust feature within one model; it includes feature transformation and joint supervision of softmax loss and pose loss. Specifically, the feature transformation is designed to learn the transformation from different poses. The pose loss is designed to simultaneously learn the feature center of different poses and keep intra-pose relationships. The extracted deep features tend to be more pose-robust and discriminative. Experimental results also confirm the performances to be valid on several important face recognition benchmarks, including Labeled Faces in the Wild, IARPA Janus Benchmark A.


title: A fuzzy methodology for innovation management measurement

abstract: Purpose ::: ::: ::: ::: ::: Innovation has been recognized as one of the main sources of competitive advantage for organizations and nations. The purpose of this study is to present an innovation management measurement approach by applying fuzzy techniques to small- and medium-sized manufacturing enterprises. ::: ::: ::: ::: ::: Design/methodology/approach ::: ::: ::: ::: ::: This study presents a survey focusing on seven innovation measurement areas: innovation strategy, knowledge management, project management, portfolio management, internal enablers, organization and structure and external enablers. A total of 91 small- and medium-sized enterprises (SMEs) located in the city of Morelia, Mexico, participated in the study. Furthermore, the data collected were analyzed under a multi-criteria decision-making approach using the theory of expertons and the induced generalized ordered weighted averaging operator. ::: ::: ::: ::: ::: Findings ::: ::: ::: ::: ::: The results show that the most valued areas are innovation strategy, organization and structure, knowledge management and project management. Furthermore, portfolio management, external drivers and internal drivers are the areas with the lowest valuations. ::: ::: ::: ::: ::: Originality/value ::: ::: ::: ::: ::: This paper presents an original methodological structure based on an expertise process designed to achieve well-founded results from uncertain and subjective opinions directly from the managers of the surveyed SMEs.


title: Time-space network based exact models for periodical monitoring routing problem

abstract: Unmanned Air Vehicles (UAVs) have been used cooperatively with Unattended Ground Sensors (UGSs) to monitor and identify intruders on a road network system. The current practice includes four phases. In the patrol phase, UAVs visits UGSs periodically to detect the possible alarms. Once an alarm is detected, the second isolation phase is activated to determine the region of the intruders on the network. Delivery/ classification and response are the two following phases, which need more human involvements. This work focuses on the first patrol phase to determine the optimal patrol route visiting the UGSs periodically with minimal weighted revisit time interval. Since the objective focuses on a single UGS with worst performance (longest revisit time) and there are large number of alternative optimal solutions, a secondary objective is considered to minimize the duration of the total reiterative tour. Two mixed integer programming models are considered on a time-space discretized network. Numerical experiments show the nontrivial solutions on a small network. The second model improves the computational performance from the first model. The quality of the solutions are evaluated by upper and lower bounds.


title: Curl-noise for procedural fluid flow

abstract: Procedural methods for animating turbulent fluid are often preferred over simulation, both for speed and for the degree of animator control. We offer an extremely simple approach to efficiently generating turbulent velocity fields based on Perlin noise, with a formula that is exactly incompressible (necessary for the characteristic look of everyday fluids), exactly respects solid boundaries (not allowing fluid to flow through arbitrarily-specified surfaces), and whose amplitude can be modulated in space as desired. In addition, we demonstrate how to combine this with procedural primitives for flow around moving rigid objects, vortices, etc.


title: An Intervention for Sensory Difficulties in Children with Autism: A Randomized Trial

abstract: This study evaluated a manualized intervention for sensory difficulties for children with autism, ages 4–8 years, using a randomized trial design. Diagnosis of autism was confirmed using gold standard measures. Results show that the children in the treatment group (n = 17) who received 30 sessions of the occupational therapy intervention scored significantly higher (p = 0.003, d = 1.2) on Goal Attainment Scales (primary outcome), and also scored significantly better on measures of caregiver assistance in self-care (p = 0.008 d = 0.9) and socialization (p = 0.04, d = 0.7) than the Usual Care control group (n = 15). The study shows high rigor in its measurement of treatment fidelity and use of a manualized protocol, and provides support for the use of this intervention for children with autism. Findings are discussed in terms of their implications for practice and future research.


title: Electric load forecasting using an artificial neural network

abstract: An artificial neural network (ANN) approach is presented for electric load forecasting. The ANN is used to learn the relationship among past, current and future temperatures and loads. In order to provide the forecasted load, the ANN interpolates among the load and temperature data in a training data set. The average absolute errors of the 1 h and 24 h-ahead forecasts in tests on actual utility data are shown to be 1.40% and 2.06%, respectively. This compares with an average error of 4.22% for 24 h ahead forecasts with a currently used forecasting technique applied to the same data. >


title: Towards a Distributed Large-Scale Dynamic Graph Data Store

abstract: In many graph applications, the structure of the graph changes dynamically over time and may require real time analysis. However, constructing a large graph is expensive, and most studies for large graphs have not focused on a dynamic graph data structure, but rather a static one. To address this issue, we propose DegAwareRHH, a high performance dynamic graph data store designed for scaling out to store large, scale-free graphs by leveraging compact hash tables with high data locality. We extend DegAwareRHH for multiple processes and distributed memory, and perform dynamic graph construction on large scale-free graphs using emerging 'Big Data HPC' systems such as the Catalyst cluster at LLNL. We demonstrate that DegAwareRHH processes a request stream 206.5x faster than a state-of-the-art shared-memory dynamic graph processing framework, when both implementations use 24 threads/processes to construct a graph with 1 billion edge insertion requests and 54 million edge deletion requests. DegAwareRHH also achieves a processing rate of over 2 billion edge insertion requests per second using 128 compute nodes to construct a large-scale web graph, containing 128 billion edges, the largest open-source real graph dataset to our knowledge.


title: A Direct Matrix Synthesis for In-Line Filters With Transmission Zeros Generated by Frequency-Variant Couplings

abstract: A direct matrix approach is presented for the first time to synthesize high selectivity in-line topology filters where multiple transmission zeros are generated and independently controlled by a set of frequency-variant couplings. As the resultant network only involves resonators cascaded one by one without any auxiliary elements (such as cross-coupled or extracted-pole structures), this paper provides the best synthesis solution in configuration simplicity for narrowband filters. Considering both the couplings and capacitances of a traditional low-pass prototype, a generalized transformation on the admittance matrix is introduced as the basis of the synthesis, which allows more than one cross-coupling to be annihilated in a single step, while generating a frequency-variant coupling simultaneously. It is then shown that the in-line topology as well as some other unique topologies can be determined by applying a specific sequence of the transformations. For the validation, a group of examples with synthesis as well as experimental results are demonstrated.


title: Fabrication of 20.19% Efficient Single-Crystalline Silicon Solar Cell with Inverted Pyramid Microstructure

abstract: This paper reports inverted pyramid microstructure-based single-crystalline silicon (sc-Si) solar cell with a conversion efficiency up to 20.19% in standard size of 156.75 × 156.75 mm2. The inverted pyramid microstructures were fabricated jointly by metal-assisted chemical etching process (MACE) with ultra-low concentration of silver ions and optimized alkaline anisotropic texturing process. And the inverted pyramid sizes were controlled by changing the parameters in both MACE and alkaline anisotropic texturing. Regarding passivation efficiency, the textured sc-Si with normal reflectivity of 9.2% and inverted pyramid size of 1 μm was used to fabricate solar cells. The best batch of solar cells showed a 0.19% higher of conversion efficiency and a 0.22 mA cm−2 improvement in short-circuit current density, and the excellent photoelectric property surpasses that of the same structure solar cell reported before. This technology shows great potential to be an alternative for large-scale production of high efficient sc-Si solar cells in the future.


title: Atlas-Based Auto-segmentation of Head and Neck CT Images

abstract: Treatment planning for high precision radiotherapy of head and neck (H&N) cancer patients requires accurate delineation of many structures and lymph node regions. Manual contouring is tedious and suffers from large inter- and intra-rater variability. To reduce manual labor, we have developed a fully automated, atlas-based method for H&N CT image segmentation that employs a novel hierarchical atlas registration approach. This registration strategy makes use of object shape information in the atlas to help improve the registration efficiency and robustness while still being able to account for large inter-subject shape differences. Validation results showed that our method provides accurate segmentation for many structures despite difficulties presented by real clinical data. Comparison of two different atlas selection strategies is also reported.


title: Investigation of additional AC losses in tooth-coil winding PMSM with high electrical frequency

abstract: Stator winding structure and its manufacturing in permanent magnet synchronous machines (PMSM) is the vital part of the whole machine design. It is important to follow particular procedures in order to keep the winding losses at the acceptable level, which include DC resistance loss and additional losses due to alternative magnetic flux within the area of the stator winding. The winding transposition or using of Litz wire, which is effective way of proximity loss suppression, sometimes cannot be implemented due to challenges in assembling or cost aspects (especially in low cost PMSM solutions). In this case it is important to know consequences of using straight (non-twisting) windings in PMSMs with relatively high electrical frequency. This article studies impact of the number of stator winding parallel strands on the additional AC losses distribution in the slot where this winding is located. Also, thermal condition (which impacts the life time of the insulation) of the winding is analyzed with different number of parallel strands. Current and temperature distributions in the slot conductors were attained by finite element method (FEM). The total losses caused by additional AC losses in the winding were compared with the measurements results.


title: Frequency analysis of a ZVS parallel quasi resonant inverter for a solar based induction heating system

abstract: This paper proposes the design of an efficient zero voltage switching parallel quasi resonant inverter for small scale industrial and domestic applications using solar energy as the dc input and induction heating technology. The resonant inverter is designed using PSIM software and the designed model is verified using the PROTEUS software which operates for a frequency around 20KHz under ZVS condition for different equivalent heating workpiece resistance. The resonant frequency can be varied over a range by altering the duty cycle values. The detail design of the resonant inverter using PSIM and PROTEUS along with the simulation results of ZVS condition and data analysis are presented in this paper.


title: Comprison of canny and sobel edge detection in MRI images

abstract: Feature extraction approach in medical magnetic resonance imaging (MRI) is very important in order to perform diagnostic image analysis [1]. Edge detection is one of the way to extract more information from magnetic resonance images. Edge detection reduces the amount of data and filters out useless information, while protecting the important structural properties in an image [2]. In this paper, we compare Sobel and Canny edge detection method. In order to compare between them, one slice of MRI image tested with both method. Both method of the edge detection operators are implemented with convolution masks. Sobel method with 3x3 masks while canny used adjustable mask. Those masks will determine the quality of the edge. Edges areas represent a strong intensity contrast which is darker or brighter. Keyword: MRI, Edge detection, Canny method


title: Decomposing Consumer Health Questions

abstract: This paper presents a method for decomposing long, complex consumer health questions. Our approach largely decomposes questions using their syntactic structure, recognizing independent questions embedded in clauses, as well as coordinations and exemplifying phrases. Additionally, we identify elements specific to disease-related consumer health questions, such as the focus disease and background information. To achieve this, our approach combines rank-and-filter machine learning methods with rule-based methods. Our results demonstrate significant improvements over the heuristic methods typically employed for question decomposition that rely only on the syntactic parse tree.


title: Patient and community experiences of tuberculosis diagnosis and care within a community-based intervention in Ethiopia: a qualitative study

abstract: The Ethiopian TB control programme relies on passive case finding of TB cases. The predominantly rural-based population in Ethiopia has limited access to health facilities creating barriers to TB services. An intervention package aimed to bring TB diagnosis and treatment services closer to communities has been implemented through partnership with health extension workers (HEWs). They undertook advocacy, communication and social mobilization (ACSM) activities, identified symptomatic individuals, collected sputum, prepared smears and fixed slides at community level. Field supervisors supported HEWs by delivering smeared slides to the laboratory, feeding back results to the HEWs and following up smear-negative cases. Patients diagnosed with TB initiated treatment in the community, they were supported by supervisors and HEWs through the local health post. Case notification increased from 64 to 127/100,000 population/year. This qualitative study assessed community members’ treatment seeking behaviour and their perceptions of the intervention. In-depth interviews (n=36) were undertaken with participants in six districts. Participants were clients of the community-based intervention, currently on TB treatment or those screened negative for TB. Transcripts were translated to English and a thematic analytical framework was developed guided by the different steps symptomatic individuals take within the intervention package. Coding was done and queries run using NVivo software. Prior to the intervention many patients with chronic cough did not access TB services. Participants described difficulties they faced in accessing district level health facilities that required travel outside their communities. Giving sputum samples and receiving results from within their home communities was appreciated by all participants. The intervention had a high level of acceptability; particularly clear benefits emerged for poor women and men and those too weak to travel. Some participants appeared to prefer a diagnosis of TB, this is likely because receiving a negative smear microscopy result brought further uncertainty and necessitated seeking further investigation. There is evidence rural populations with high levels of poverty, and in particular women, are at high risk of unmet health needs and undiagnosed TB. Embedding TB services within communities was an acceptable approach for vulnerable groups experiencing poor access to health facilities. In the Ethiopian context this approach can facilitate early diagnosis and improve treatment outcomes.


title: Genomic Security (Lest We Forget)

abstract: Genomic privacy has attracted much attention from the research community, because its risks are unique and breaches can lead to terrifying leakage of sensitive information. The less-explored topic of genomic security must address threats of digitized genomes being altered, which can have dire consequences in medical or legal settings.


title: Requirements engineering and Business/IT alignment: Lessons Learned

abstract: This paper presents a review on the Business/IT alignment with the goal of exploring how requirements engineering can help model business environment and how business environment can help perform requirements engineering tasks in order to develop a system which meets business expectations. It discusses ten lessons learned from the Business/IT alignment research area in the context of requirements engineering (RE). It is intended to be useful to researchers and graduate students who consider doing research into alignment using requirements engineering approaches, and also to requirements engineers who are interested in deriving system requirements from business environment. Challenges to the value of alignment research, the role of requirements engineering research in alignment, and recommendations for RE researchers in order to highlight future RE research directions in Business/IT alignment domain are also presented.


title: A methodology for learning, analyzing, and mitigating social influence bias in recommender systems

abstract: The seminal 2003 paper by Cosley, Lab, Albert, Konstan, and Reidl, demonstrated the susceptibility of recommender systems to rating biases. To facilitate browsing and selection, almost all recommender systems display average ratings before accepting ratings from users which has been shown to bias ratings. This effect is called Social Inuence Bias (SIB); the tendency to conform to the perceived \norm" in a community. We propose a methodology to 1) learn, 2) analyze, and 3) mitigate the effect of SIB in recommender systems. In the Learning phase, we build a baseline dataset by allowing users to rate twice: before and after seeing the average rating. In the Analysis phase, we apply a new non-parametric significance test based on the Wilcoxon statistic to test whether the data is consistent with SIB. If significant, we propose a Mitigation phase using polynomial regression and the Bayesian Information Criterion (BIC) to predict unbiased ratings. We evaluate our approach on a dataset of 9390 ratings from the California Report Card (CRC), a rating-based system designed to encourage political engagement. We found statistically significant evidence of SIB. Mitigating models were able to predict changed ratings with a normalized RMSE of 12.8% and reduce bias by 76.3%. The CRC, our data, and experimental code are available at: http://californiareportcard.org/data/


title: High-quality antidepressant discovery by understanding stress hormone physiology.

abstract: Compensating the consequences of impaired corticosteroid receptor signaling is a novel strategy to discover better antidepressants. The prevailing drugs gradually improve stress hormone regulation along with ameliorating psychopathology. The current understanding of how neuropeptides, such as corticotropin-releasing hormone (CRH) and vasopressin (AVP), drive cortisol secretion via corticotrophin has paved the way for CRH- and AVP-receptor antagonists. As alternative strategies, the blockade of corticosteroid receptors or inhibition of cortisol synthesis has emerged. All these strategies are not yet fully clinically developed, but preliminary data from basic and clinical research strongly underscore that such strategies may lead to innovative treatment modalities.


title: An automatic mobile-health based approach for EEG epileptic seizures detection

abstract: We developed a new mobile-based approach to automatically detect seizures.We developed and implemented a set of suitable algorithms to prepare EEG signals.We developed and implemented two new algorithms to automate features selection.We implemented two algorithms to acquire and display signals and analysis results.Our approach is experimentally evaluated using the standard clinical database. In this article, we develop a comprehensive mobile-based approach, which is able to perform the essential processes needed to automatically analyze and detect epileptic seizures using the information contained in electroencephalography (EEG) signals. We first develop and implement an appropriate combination of different algorithms that resample, smooth, remove artifacts, and constantly and adaptively segment signals to prepare them for further processing. We then improve and fully implement a large variety of features introduced in the literature of epileptic seizures detection. We also select the relevant features to reduce a feature vector space and improve the classification process by developing two automated filter and wrapper selection algorithms. We thoroughly compare between these selection algorithms in terms of redundant features, execution time and classification accuracy through three experiments. We subsequently exploit the selected features as input to a machine learning classifier to detect epileptic seizure states in a reasonable time. We experimentally and theoretically evaluate the scalability of the whole algorithm respectively on patients' data available in standard clinical database and on 500 EEG recordings including 500 seizures. Having efficient and scabble algorithm, we develop two extra algorithms to dynamically acquire and transmit EEG signals from wireless sensors attached to patients and to visualize on mobile devices the obtained processing and analysis results. We finally integrate all our algorithms together along with an android mobile application to implement an effective mobile-based EEG monitoring system where its accuracy is tested on live EEG data.


title: Auction mechanisms for network resource allocation

abstract: In the autonomic Internet of the future, auction mechanisms arise as key methods for realizing efficient resource allocation. The major asset of auctions is their obliviousness to node utilities, which renders them capable of achieving a desired resource allocation regime without knowledge of the utility functions of involved entities. Auctions can aid in addressing major research challenges in such autonomic settings, such as the need to cope with diverse and conflicting interests of network entities, the need to carry out resource allocation in a decentralized manner, the requirement for matching dynamic spatiotemporal patterns of demand and supply, and the need to operate under limited or no network state and utility information. In this survey paper, we delineate the main trends and challenges associated with auction design. We start from first principles auction design for maximum auctioneer revenue or maximum allocation efficiency for one or multiple indivisible items and for divisible resources. We gradually move to more composite models, those of position auctions for Internet advertisements and those arising in spectrum sharing in cognitive radio networks. We argue that some directions worth pursuing are: (i) the design of advanced auction models that capture multi-level interaction of involved entities, (ii) the employment of double auctions for multiple seller and buyer interaction, and (iii) the design of decentralized negotiation and resource trading mechanisms.


title: Enhancing visual perception of shape through tactile glances

abstract: Object shape information is an important parameter in robot grasping tasks. However, it may be difficult to obtain accurate models of novel objects due to incomplete and noisy sensory measurements. In addition, object shape may change due to frequent interaction with the object (cereal boxes, etc). In this paper, we present a probabilistic approach for learning object models based on visual and tactile perception through physical interaction with an object. Our robot explores unknown objects by touching them strategically at parts that are uncertain in terms of shape. The robot starts by using only visual features to form an initial hypothesis about the object shape, then gradually adds tactile measurements to refine the object model. Our experiments involve ten objects of varying shapes and sizes in a real setup. The results show that our method is capable of choosing a small number of touches to construct object models similar to real object shapes and to determine similarities among acquired models.


title: Transforming homo economicus into homo ludens: A field experiment on gamification in a utilitarian peer-to-peer trading service

abstract: Abstract During recent years, the addition of game mechanisms to non-game services has gained a relatively large amount of attention. Popular discussion connects gamification to successful marketing and increased profitability through higher customer engagement, however, there is a dearth of empirical studies that confirm such expectations. This paper reports the results of a field experiment, which gamifies a utilitarian peer-to-peer trading service by implementing the game mechanism of badges that users can earn from a variety of tasks. There were 3234 users who were randomly assigned to treatment groups and subjected to different versions of the badge system in a 2 × 2 design. The results show that the mere implementation of gamification mechanisms does not automatically lead to significant increases in use activity in the studied utilitarian service, however, those users who actively monitored their own badges and those of others in the study showed increased user activity.


title: applications: State-of-the art and future research directions

abstract: Map matching algorithms integrate positioning data with spatial road network data to identify the correct link on which a vehicle is traveling and to determine the location of a vehicle on a link. A map matching algorithm could be used as a key component to improve the navigation functions of intelligent transport systems (ITS) that require locational data of a relatively high quality. The required horizontal positioning accuracy of such ITS applications is in the range of 1m to 40m (95%). A number of map matching algorithms have been developed by researchers around the world using different techniques such as topological analysis of spatial road network data, probabilistic theory, Kalman filter, fuzzy logic, and belief theory. The performance of these algorithms has improved over the years due to the application of advanced techniques in the map matching processes and to the improvements of the quality of both positioning data and spatial road network data over time. However, these algorithms are not always capable of supporting the navigation functions that require a high positioning accuracy, specifically within dense urban areas. This suggests that further improvements to map matching techniques are essential. Therefore, it is very important that the constraints and limitations of existing map matching algorithms are identified to facilitate further enhancement of the algorithms. The objectives of this paper are then to uncover the constraints and limitations of current map matching algorithms by an in-depth literature review and to suggest possible solutions where feasible for further improvements. This paper also reports the potential impacts of the forthcoming European Galileo system and the EGNOS (European Geostationary Overlay Service) on the performance of map matching algorithms.


title: Mining the network value of customers

abstract: One of the major applications of data mining is in helping companies determine which potential customers to market to. If the expected profit from a customer is greater than the cost of marketing to her, the marketing action for that customer is executed. So far, work in this area has considered only the intrinsic value of the customer (i.e, the expected profit from sales to her). We propose to model also the customer's network value: the expected profit from sales to other customers she may influence to buy, the customers those may influence, and so on recursively. Instead of viewing a market as a set of independent entities, we view it as a social network and model it as a Markov random field. We show the advantages of this approach using a social network mined from a collaborative filtering database. Marketing that exploits the network value of customers---also known as viral marketing---can be extremely effective, but is still a black art. Our work can be viewed as a step towards providing a more solid foundation for it, taking advantage of the availability of large relevant databases.


Aggressive, Tense or Shy? Identifying Personality Traits from Crowd Videos


title: Daytime Preceding Vehicle Brake Light Detection Using Monocular Vision

abstract: Advanced vehicle safety is a recently emerging issue appealed from the explosive population of car owners. Increasing driver assistance systems have been developed for warning drivers of potential hazards by analyzing the surroundings with sensors and/or cameras. Issuing vehicle deceleration and potential collision, brake lights are particularly important warning signals, allowing of no neglect. In this paper, we propose a vision-based daytime brake light detection system using a driving video recorder, which tends to be widespread used. At daytime, visual features, motions, and appearances of vehicles are highly visible. However, brake lights, on the contrary, are hard to notice due to low contrast between the brakes lights and environments. Without the significant characteristic of light scattering as at night, the proposed system extracts preceding vehicles with taillight symmetry verification, and then integrates both luminance and radial symmetry features to detect brake lights. A detection refinement process using temporal information is also employed for miss recovery. Experiments are conducted on a test data set collected by front-mounted driving video recorders, and the results verify that the proposed system can effectively detect brake lights at daytime, showing its good feasibility in real-world environments.


title: Compositional Learning for Human Object Interaction

abstract: The world of human-object interactions is rich. While generally we sit on chairs and sofas, if need be we can even sit on TVs or top of shelves. In recent years, there has been progress in modeling actions and human-object interactions. However, most of these approaches require lots of data. It is not clear if the learned representations of actions are generalizable to new categories. In this paper, we explore the problem of zero-shot learning of human-object interactions. Given limited verb-noun interactions in training data, we want to learn a model than can work even on unseen combinations. To deal with this problem, In this paper, we propose a novel method using external knowledge graph and graph convolutional networks which learns how to compose classifiers for verb-noun pairs. We also provide benchmarks on several dataset for zero-shot learning including both image and video. We hope our method, dataset and baselines will facilitate future research in this direction.


title: SOD-MTGAN: Small Object Detection via Multi-Task Generative Adversarial Network

abstract: Object detection is a fundamental and important problem in computer vision. Although impressive results have been achieved on large/medium sized objects in large-scale detection benchmarks (e.g. the COCO dataset), the performance on small objects is far from satisfactory. The reason is that small objects lack sufficient detailed appearance information, which can distinguish them from the background or similar objects. To deal with the small object detection problem, we propose an end-to-end multi-task generative adversarial network (MTGAN). In the MTGAN, the generator is a super-resolution network, which can up-sample small blurred images into fine-scale ones and recover detailed information for more accurate detection. The discriminator is a multi-task network, which describes each super-resolved image patch with a real/fake score, object category scores, and bounding box regression offsets. Furthermore, to make the generator recover more details for easier detection, the classification and regression losses in the discriminator are back-propagated into the generator during training. Extensive experiments on the challenging COCO dataset demonstrate the effectiveness of the proposed method in restoring a clear super-resolved image from a blurred small one, and show that the detection performance, especially for small sized objects, improves over state-of-the-art methods.


title: An effective pose invariant face recognition system with the aid of ABC optimized ANFIS

abstract: Utilizing a stocked up database of faces, Face recognition recognizes or verifies one or else more individuals in the specified still or else video images of a scene. This is owing to its abundant vital applications in security, human–computer interactions, authentication, and surveillance. A method intended for efficacy face recognition system on the video sequence is splashed by this paper. Deducting its intricacies is the salient cause of this paper. In the proposed posture invariant face recognition technique, first database video clip is separated into variant frames. Preprocessing is executed for every frame for removing the noise utilizing Gaussian filtering. The Face is identified from the preprocessed image by utilizing Viola–Jones algorithm. Then effective features are excerpted from detected face. Next, the excerpted feature value will present as input to train for adaptive neuro-fuzzy inference system (ANFIS) classifier. ANFIS parameters are optimized through artificial bee colony ABC algorithm to derive prompt high acknowledgment throughout the training. Likely the query image’s features are employed for verifying the suggested recognition system performance. The technique will be achieved on the working platform in MATLAB in addition the outcome will be examined and matched with the prevailing techniques for elucidating the suggested video face recognition method’s performance.


Visual and nondestructive evaluation inspection of live gas mains using the Explorer™ family of pipe robots


title: Deep Reinforcement Learning for Robotic Manipulation-The state of the art

abstract: The focus of this work is to enumerate the various approaches and algorithms that center around application of reinforcement learning in robotic ma- ]]nipulation tasks. Earlier methods utilized specialized policy representations and human demonstrations to constrict the policy. Such methods worked well with continuous state and policy space of robots but failed to come up with generalized policies. Subsequently, high dimensional non-linear function approximators like neural networks have been used to learn policies from scratch. Several novel and recent approaches have also embedded control policy with efficient perceptual representation using deep learning. This has led to the emergence of a new branch of dynamic robot control system called deep r inforcement learning(DRL). This work embodies a survey of the most recent algorithms, architectures and their implementations in simulations and real world robotic platforms. The gamut of DRL architectures are partitioned into two different branches namely, discrete action space algorithms(DAS) and continuous action space algorithms(CAS). Further, the CAS algorithms are divided into stochastic continuous action space(SCAS) and deterministic continuous action space(DCAS) algorithms. Along with elucidating an organ- isation of the DRL algorithms this work also manifests some of the state of the art applications of these approaches in robotic manipulation tasks.