title: Using Rule-Based Labels for Weak Supervised Learning: A ChemNet for Transferable Chemical Property Prediction

abstract: With access to large datasets, deep neural networks (DNN) have achieved human-level accuracy in image and speech recognition tasks. However, in chemistry data is inherently small and fragmented. In this work, we develop an approach of using rule-based knowledge for training ChemNet, a transferable and generalizable deep neural network for chemical property prediction that learns in a weak-supervised manner from large unlabeled chemical databases. When coupled with transfer learning approaches to predict other smaller datasets for chemical properties that it was not originally trained on, we show that ChemNet's accuracy outperforms contemporary DNN models that were trained using conventional supervised learning. Furthermore, we demonstrate that the ChemNet pre-training approach is equally effective on both CNN (Chemception) and RNN (SMILES2vec) models, indicating that this approach is network architecture agnostic and is effective across multiple data modalities. Our results indicate a pre-trained ChemNet that incorporates chemistry domain knowledge and enables the development of generalizable neural networks for more accurate prediction of novel chemical properties.


title: A survey on multiple unmanned vehicles formation control and coordination: Normal and fault situations

abstract: This paper provides a review on the strategies and methodologies developed in recent years for trajectory/path following, coordination and unified group behavior of a team of unmanned vehicles in terms of application and categorization. The unmanned vehicles being studied have a common mission to maintain group formation and reach their target destinations in either known or unknown environments. The ability for a group of vehicles to follow individual paths is the first critical step in achieving group coordination and originates from path following employing a single vehicle. Once this technique is refined then various algorithm constructs can be explored in order to create efficient and harmonious group coordination, which is based on their originality on whether they are employed in a centralized or decentralized system. In this paper, survey and analysis on the various multi-vehicle applications in formation operation and categorizations of existing works are provided based on over 140 published literatures since 1986. A few challenges and future works are also recognized.


title: THE INFORMATION CAPACITY OF THE HUMAN MOTOR SYSTEM IN CONTROLLING THE AMPLITUDE OF MOVEMENT 1

abstract: Information theory has recently been employed to specify more precisely than has hitherto been possible man's capacity in certain sensory, perceptual, and perceptual-motor functions (5, 10, 13, 15, 17, 18). The experiments reported in the present paper extend the theory to the human motor system. The applicability of only the basic concepts, amount of information, noise, channel capacity, and rate of information transmission, will be examined at this time. General familiarity with these concepts as formulated by recent writers (4, 11,20, 22) is assumed. Strictly speaking, we cannot study man's motor system at the behavioral level in isolation from its associated sensory mechanisms. We can only analyze the behavior of the entire receptor-neural-effector system. However, by asking 51 to make rapid and uniform responses that have been highly overlearned, and by holding all relevant stimulus conditions constant with the exception of those resulting from 5"s own movements, we can create an experimental situation in which it is reasonable to assume that performance is limited primarily by the capacity of the motor system. The motor system in the present case is defined as including the visual and proprioceptive feedback loops that permit S to monitor his own activity. The information capacity of the motor system is specified by its ability to produce consistently one class of movement from among several alternative movement classes. The greater the number of alternative classes, the greater is the information capacity of a particular type of response. Since measurable aspects of motor responses, such as their force, direction, and amplitude, are continuous variables, their information capacity is limited only by the amount of statistical variability, or noise, that is characteristic of repeated efforts to produce the same response. The information capacity of the motor Editor's Note. This article is a reprint of an original work published in 1954 in the Journal of Experimental Psychology, 47, 381391.


title: Multifacet's general execution-driven multiprocessor simulator (GEMS) toolset

abstract: The Wisconsin Multifacet Project has created a simulation toolset to characterize and evaluate the performance of multiprocessor hardware systems commonly used as database and web servers. We leverage an existing full-system functional simulation infrastructure (Simics [14]) as the basis around which to build a set of timing simulator modules for modeling the timing of the memory system and microprocessors. This simulator infrastructure enables us to run architectural experiments using a suite of scaled-down commercial workloads [3]. To enable other researchers to more easily perform such research, we have released these timing simulator modules as the Multifacet General Execution-driven Multiprocessor Simulator (GEMS) Toolset, release 1.0, under GNU GPL [9].


title: Social and News Media Enable Estimation of Epidemiological Patterns Early in the 2010 Haitian Cholera Outbreak

abstract: During infectious disease outbreaks, data collected through health institutions and official reporting structures may not be available for weeks, hindering early epidemiologic assessment. By contrast, data from informal media are typically available in near real-time and could provide earlier estimates of epidemic dynamics. We assessed correlation of volume of cholera-related HealthMap news media reports, Twitter postings, and government cholera cases reported in the first 100 days of the 2010 Haitian cholera outbreak. Trends in volume of informal sources significantly correlated in time with official case data and was available up to 2 weeks earlier. Estimates of the reproductive number ranged from 1.54 to 6.89 (informal sources) and 1.27 to 3.72 (official sources) during the initial outbreak growth period, and 1.04 to 1.51 (informal) and 1.06 to 1.73 (official) when Hurricane Tomas afflicted Haiti. Informal data can be used complementarily with official data in an outbreak setting to get timely estimates of disease dynamics.


title: Text authorship identified using the dynamics of word co-occurrence networks

abstract: Automatic identification of authorship in disputed documents has benefited from complex network theory as this approach does not require human expertise or detailed semantic knowledge. Networks modeling entire books can be used to discriminate texts from different sources and understand network growth mechanisms, but only a few studies have probed the suitability of networks in modeling small chunks of text to grasp stylistic features. In this study, we introduce a methodology based on the dynamics of word co-occurrence networks representing written texts to classify a corpus of 80 texts by 8 authors. The texts were divided into sections with equal number of linguistic tokens, from which time series were created for 12 topological metrics. Since 73% of all series were stationary (ARIMA(p, 0, q)) and the remaining were integrable of first order (ARIMA(p, 1, q)), probability distributions could be obtained for the global network metrics. The metrics exhibit bell-shaped non-Gaussian distributions, and therefore distribution moments were used as learning attributes. With an optimized supervised learning procedure based on a nonlinear transformation performed by Isomap, 71 out of 80 texts were correctly classified using the K-nearest neighbors algorithm, i.e. a remarkable 88.75% author matching success rate was achieved. Hence, purely dynamic fluctuations in network metrics can characterize authorship, thus paving the way for a robust description of large texts in terms of small evolving networks.


title: EEG spectral analysis of human cognitive workload study

abstract: The paper presents an experiment performed in order to confirm the hypothesis that the level of human cognitive workload can be studied by spectral analysis. The experiment contained intervals ensuring high cognitive load. They were displayed alternately with relaxing breaks. The spectral analysis covered changes in EEG bands including the alpha/theta ratio.


title: Resource and Instance Hour Minimization for Deadline Constrained DAG Applications Using Computer Clouds

abstract: In this paper, we address the resource and virtual machine instance hour minimization problem for directed-acyclic-graph-based deadline constrained applications deployed on computer clouds. The allocated resources and instance hours on computer clouds must: (1) guarantee the satisfaction of a deadline constrained application’s end-to-end deadline; (2) ensure that the number of virtual machine (VM) instances allocated to the application is minimized; (3) under the allocated number of VM instances, determine application execution schedule that minimizes the application’s makespan; and (4) under the decided application execution schedule, determine a VM operation schedule, i.e., when a VM should be turned on or off, that minimizes total VM instance hours needed to execute the application. We first give lower and upper bounds for the number of VM instances needed to guarantee the satisfaction of a deadline constrained application’s end-to-end deadline. Based on the bounds, we develop a heuristic algorithm called minimal slack time and minimal distance (MSMD) algorithm that finds the minimum number of VM instances needed to guarantee the application’s deadline and schedules tasks on the allocated VM instances so that the application’s makespan is minimized. Once the application execution schedule and the number of VM instances needed are determined, the proposed VM instance hour minimization (IHM) algorithm is applied to further reduce the instance hours needed by VMs to complete the application’s execution. Our experimental results show that the MSMD algorithm can guarantee applications’ end-to-end deadlines with less resources than the HEFT [32], MOHEFT [16], DBUS [9], QoS-base [40] and Auto-Scaling [25] heuristic scheduling algorithms in the literature. Furthermore, under allocated resources, the MSMD algorithm can, on average, reduce an application’s makespan by 3.4 percent of its deadline. In addition, with the IHM algorithm we can effectively reduce the application’s execution instance hours compared with when IHM is not applied.


title: Customer event history for churn prediction: How long is long enough?

abstract: The key question of this study is: How long should customer event history be for customer churn prediction? While most studies in predictive churn modeling aim to improve models by data augmentation or algorithm improvement, this study focuses on a another dimension: time window optimization with respect to predictive performance. This paper first presents a formalization of the time window selection strategy, along with a literature review. Next, using logistic regression, classification trees and bagging in combination with classification trees, this study analyzes the improvement in churn-model performance by extending customer event history from one to sixteen years. The results show that, after the fifth additional year, predictive performance is only marginally increased, meaning that the company in this study can discard 69% of its data with almost no decrease in predictive performance. The practical implication is that analysts can substantially decrease data-related burdens, such as data storage, preparation and analysis. This is particularly valuable in times of big data when decreasing computational complexity is paramount.


title: Guide-bound structures of an RNA-targeting A-cleaving CRISPR–Cas13a enzyme

abstract: CRISPR adaptive immune systems protect bacteria from infections by deploying CRISPR RNA (crRNA)-guided enzymes to recognize and cut foreign nucleic acids. Type VI-A CRISPR-Cas systems include the Cas13a enzyme, an RNA-activated RNase capable of crRNA processing and single-stranded RNA degradation upon target-transcript binding. Here we present the 2.0-Å resolution crystal structure of a crRNA-bound Lachnospiraceae bacterium Cas13a (LbaCas13a), representing a recently discovered Cas13a enzyme subtype. This structure and accompanying biochemical experiments define the Cas13a catalytic residues that are directly responsible for crRNA maturation. In addition, the orientation of the foreign-derived target-RNA-specifying sequence in the protein interior explains the conformational gating of Cas13a nuclease activation. These results describe how Cas13a enzymes generate functional crRNAs and how catalytic activity is blocked before target-RNA recognition, with implications for both bacterial immunity and diagnostic applications.


title: Camera to map alignment for accurate low-cost lane-level scene interpretation

abstract: To be able to predict the evolution of the driving context, estimate the expected risks and plan future behavior alternatives, it is crucial to know where traffic participants can go and where they will most likely go. Consequently, for future Advanced Driver Assistance Systems (ADAS), precise, lane-accurate localization of the ego- as well as the other vehicles is a key technology. The proposed standard solutions to lane-accurate localization are usually based on expensive detailed 3D maps and precise absolute position estimation sensors. To the contrary, in this paper we propose to improve the localization of scene items based on state-of-the-art map data, combined with a coarse and cheap position estimation as e.g. provided by standard GNSS. From the map data, we infer the structure of the contextual road geometry, and align it with the road view(s) as provided by a front camera. This results in an improved relative positioning of the sensed items on the map structures, allowing a better scene interpretation. The alignment occurs by best-match search based on a feature comparison between the real road view from the camera and virtually generated road views based on the map considering different assumed ego-vehicle positions. On standard road scenes, we validate the approach, and show that it can be used as a cheap means to support intelligent ADAS and improve localization.


title: The Cloud Adoption Toolkit: Addressing the Challenges of Cloud Adoption in Enterprise

abstract: Cloud computing promises a radical shift in the provisioning of computing resource within the enterprise. This paper: i) describes the challenges that decision makers face when attempting to determine the feasibility of the adoption of cloud computing in their organisations; ii) illustrates a lack of existing work to address the feasibility challenges of cloud adoption in the enterprise; iii) introduces the Cloud Adoption Toolkit that provides a framework to support decision makers in identifying their concerns, and matching these concerns to appropriate tools/techniques that can be used to address them. The paper adopts a position paper methodology such that case study evidence is provided, where available, to support claims. We conclude that the Cloud Adoption Toolkit, whilst still under development, shows signs that it is a useful tool for decision makers as it helps address the feasibility challenges of cloud adoption in the enterprise.


title: A self-sustaining unmanned aerial vehicle routing protocol for smart farming

abstract: Increasing agricultural productivity has been a long quest for farmers and only a few can achieve it. One major factor that hinders them to achieve such goal is the lack of proper agricultural monitoring technique. Recent advancement in technology has enabled the integration of sensor networks and traditional farming, resulting in effective monitoring through smart farming. However, there exists a hefty investment in equipment and infrastructure installation throughout the coverage area. We design two routing approaches, called Location-agnostic (LA) and Location-specific (LS) protocols, to facilitate the self-sustaining agricultural monitoring platform, requiring no infrastructure installation, comprises of Unmanned Aerial Vehicle (UAV) with solar energy harvesting and wireless power transfer capability. The LA protocol does not require location information of monitoring stations to be visited prior to the flight, and is useful for dynamic environment. The LS protocol relies on the complete view of the topology prior to the flight and is suitable for static environment. These protocols determine the optimal UAV routing path from a set of monitoring stations under various conditions. Through a combination of simulation and experimentation studies, we demonstrate significant energy efficiency and coverage area improvement over the classical routing protocol.


title: Equivariance Through Parameter-Sharing

abstract: We propose to study equivariance in deep neural networks through parameter symmetries. In particular, given a group G that acts discretely on the input and output of a standard neural network layer ϕW : ℝM → ℝN, we show that ϕW is equivariant with respect to G-action iff G explains the symmetries of the network parameters W. Inspired by this observation, we then propose two parameter-sharing schemes to induce the desirable symmetry on W. Our procedure for tying the parameters achieves G-equivariance and, under some conditions on the action of G, it guarantees sensitivity to all other permutation groups outside G.


title: A rule based approach for NLP based query processing

abstract: Information is playing an important role in our lives. One of the major sources of information is databases. Databases and database technology are having major impact on the growing use of computers. In order to retrieve information from a database, one needs to formulate a query in such way that the computer will understand and produce the desired output. Generally, query processing is handled by the Structured Query Language (SQL). But the non IT people cannot be able to write SQL queries as they may not be aware of the SQL as well as structure of the database. So there is a need for non-expert users to query the databases in their natural language instead of working with the values of the attributes. This paper proposes an approach for accessing the database easily using natural language without having any knowledge about the query language. The approach is a rule based approach. The obvious advantage is that it makes a great promise for computer interfaces easier for the use of general people. Because of this, people will be able to communicate to the computer in their own language instead of learning a specialized language or commands. In order to test our approach in an actual computer environment, we have developed a prototype system. We obtained promising results using our system.


Study of Various Mutation Operators in Genetic Algorithms


title: How far are we from solving the 2D&3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks)

abstract: This paper investigates how far a very deep neural network is from attaining close to saturating performance on existing 2D and 3D face alignment datasets. To this end, we make the following 5 contributions: (a) we construct, for the first time, a very strong baseline by combining a state-of-the-art architecture for landmark localization with a state-of-the-art residual block, train it on a very large yet synthetically expanded 2D facial landmark dataset and finally evaluate it on all other 2D facial landmark datasets. (b) We create a guided by 2D landmarks network which converts 2D landmark annotations to 3D and unifies all existing datasets, leading to the creation of LS3D-W, the largest and most challenging 3D facial landmark dataset to date ~230,000 images. (c) Following that, we train a neural network for 3D face alignment and evaluate it on the newly introduced LS3D-W. (d) We further look into the effect of all"traditional"factors affecting face alignment performance like large pose, initialization and resolution, and introduce a"new"one, namely the size of the network. (e) We show that both 2D and 3D face alignment networks achieve performance of remarkable accuracy which is probably close to saturating the datasets used. Training and testing code as well as the dataset can be downloaded from https://www.adrianbulat.com/face-alignment/


title: Word Spotting in the Wild

abstract: We present a method for spotting words in the wild, i.e., in real images taken in unconstrained environments. Text found in the wild has a surprising range of difficulty. At one end of the spectrum, Optical Character Recognition (OCR) applied to scanned pages of well formatted printed text is one of the most successful applications of computer vision to date. At the other extreme lie visual CAPTCHAs - text that is constructed explicitly to fool computer vision algorithms. Both tasks involve recognizing text, yet one is nearly solved while the other remains extremely challenging. In this work, we argue that the appearance of words in the wild spans this range of difficulties and propose a new word recognition approach based on state-of-the-art methods from generic object recognition, in which we consider object categories to be the words themselves. We compare performance of leading OCR engines - one open source and one proprietary - with our new approach on the ICDAR Robust Reading data set and a new word spotting data set we introduce in this paper: the Street View Text data set. We show improvements of up to 16% on the data sets, demonstrating the feasibility of a new approach to a seemingly old problem.


title: A Heterogeneous Accelerated Matrix Multiplication: OpenCL + APU + GPU+ Fast Matrix Multiply

abstract: As users and developers, we are witnessing the opening of a new computing scenario: the introduction of hybrid processors into a single die, such as an accelerated processing unit (APU) processor, and the plug-and-play of additional graphics processing units (GPUs) onto a single motherboard. These APU processors provide multiple symmetric cores with their memory hierarchies and an integrated GPU. Moreover, these processors are designed to work with external GPUs that can push the peak performance towards the TeraFLOPS boundary. We present a case study for the development of dense Matrix Multiplication (MM) codes for matrix sizes up to 19K 19K, thus using all of the above computational engines, and an achievable peak performance of 200 GFLOPS for, literally, a madeat-home built. We present the results of our experience, the quirks, the pitfalls, the achieved performance, and the achievable peak performance.


title: Visibility estimation and joint inpainting of lidar depth maps

abstract: This paper presents a novel variational image inpainting method to solve the problem of generating, from 3-D lidar measures, a dense depth map coherent with a given color image, tackling visibility issues. When projecting the lidar point cloud onto the image plane, we generally obtain a sparse depth map, due to undersampling. Moreover, lidar and image sensor positions generally differ during acquisition, such that depth values referring to objects that are hidden from the image view point might appear with a naive projection. The proposed algorithm estimates the complete depth map, while simultaneously detecting and excluding those hidden points. It consists in a primal-dual optimization method, where a coupled total variation regularization term is included to match the depth and image gradients and a visibility indicator handles the selection of visible points. Tests with real data prove the effectiveness of the proposed strategy.


title: Cochlear implantation in children and adults in Switzerland

abstract: The cochlear implant (CI) is one of the most successful neural prostheses developed to date. It offers artificial hearing to individuals with profound sensorineural hearing loss and with insufficient benefit from conventional hearing aids. The first implants available some 30 years ago provided a limited sensation of sound. The benefit for users of these early systems was mostly a facilitation of lip-reading based communication rather than an understanding of speech. Considerable progress has been made since then. Modern, multichannel implant systems feature complex speech processing strategies, high stimulation rates and multiple sites of stimulation in the cochlea. Equipped with such a state-of-the-art system, the majority of recipients today can communicate orally without visual cues and can even use the telephone. The impact of CIs on deaf individuals and on the deaf community has thus been exceptional. To date, more than 300,000 patients worldwide have received CIs. In Switzerland, the first implantation was performed in 1977 and, as of 2012, over 2,000 systems have been implanted with a current rate of around 150 CIs per year. The primary purpose of this article is to provide a contemporary overview of cochlear implantation, emphasising the situation in Switzerland.


title: Design patterns for gamification of work

abstract: Gamification of work provides a chance of rising employees' engagement, improving their attitude towards work, and, in turn, increasing their productivity. As the problems faced when applying gamification to various work environments are similar, so are the solutions. Unexpectedly, even though a number of reusable gamification elements, techniques or patterns were described in the literature, due to the way they are presented, they offer little guidance for their use by a work gamification designer. This work aims to address this gap by providing a concise description format for gamification design patterns, their practical classification, and definitions of 21 patterns that can be used for designing gamification of work.


title: TRUST -BASED APPROACH IN WIRELESS SENSOR NETWORKS USING AN AGENT TO EACH CLUSTER

abstract: Detection of malicious node in the neighborhood with minimal infrastructure and computations is a requirement. The existing models require more computation, storage, and complex security calculations. These models are inefficient in wireless sensor networks due to their resource limitations. Therefore, an agent-based approach that maintains the node’s current status is proposed in this research. In agent-based approach detection is possible through maintaining the ratings of each node. The ratings of a node will be done through the ratio of packet forwarded by packets received. Further, the ratings can be done using the E-commerce models. In E-commerce models, each node votes the successive node depending upon the ratio of packet forwarded by packets received. The update ratings will be done through Sporas formula or Molina’s formula or with a combination of both models. Further, the proposed agent-based framework uses reputation of a node through neighbouring nodes as part of trust calculation. The simulations were presented to calculate the trust of a node.


Ebola Virus Epidemiology, Transmission, and Evolution during Seven Months in Sierra Leone


title: To be recorded” analysis in reverse-mode automatic differentiation

abstract: The automatic generation of adjoints of mathematical models that are implemented as computer programs is receiving increased attention in the scientific and engineering communities. Reverse-mode automatic differentiation is of particular interest for large-scale optimization problems. It allows the computation of gradients at a small constant multiple of the cost for evaluating the objective function itself, independent of the number of input parameters. Source-to-source transformation tools apply simple differentiation rules to generate adjoint codes based on the adjoint version of every statement. In order to guarantee correctness, certain values that are computed and overwritten in the original program must be made available in the adjoint program. For their determination we introduce a static data-flow analysis called "to be recorded" analysis. Possible overestimation of this set must be kept minimal to get efficient adjoint codes. This efficiency is essential for the applicability of source-to-source transformation tools to real-world applications.


title: Hand Gesture Recognition for Deaf People Interfacing

abstract: In this paper, an approach for deaf-people interfacing using computer vision is presented. The recognition of alphabetic static signs of the Spanish Sign Language is addressed. The proposed approach combines a number of norms to evaluate the distance of the current sign, to the sign models stored in a database (a dictionary). This solution leads to a largely selective criterion. The method is simple enough to provide real-time recognition, and works suitably for most letters.


title: Cache Privacy in Named-Data Networking

abstract: Content-Centric Networking (CCN) is an alternative to host-centric networking exemplified by today's Internet. CCN emphasizes content distribution by making content directly addressable. Named-Data Networking (NDN) is an example of CCN being considered as a candidate next-generation Internet architecture. One key NDN feature is router-side content caching that optimizes bandwidth consumption, reduces congestion and provides fast fetching for popular content. Unfortunately, the same feature is also detrimental to privacy of both consumers and producers of content. As we show in this paper, simple and difficult-to-detect timing attacks can exploit NDN routers as "oracles" and allow the adversary to learn whether a nearby consumer recently requested certain content. Similarly, probing attacks that target adjacent content producers can be used to discover whether certain content has been recently fetched. After analyzing the scope and feasibility of such attacks, we propose and evaluate some efficient countermeasures that offer quantifiable privacy guarantees while retaining key features of NDN.


title: A Survey of Energy-Efficient Techniques for 5G Networks and Challenges Ahead

abstract: After about a decade of intense research, spurred by both economic and operational considerations, and by environmental concerns, energy efficiency has now become a key pillar in the design of communication networks. With the advent of the fifth generation of wireless networks, with millions more base stations and billions of connected devices, the need for energy-efficient system design and operation will be even more compelling. This survey provides an overview of energy-efficient wireless communications, reviews seminal and recent contribution to the state-of-the-art, including the papers published in this special issue, and discusses the most relevant research challenges to be addressed in the future.


title: Understanding design considerations for adaptive user interfaces for accessible pointing with older and younger adults

abstract: The Internet has become an important tool for many aspects of modern life. However, some individuals may experience challenges accessing the Internet if they have difficulty accurately controlling a pointing device. These pointing problems can vary in severity and frequency and may be caused by a motor impairment, limited hand dexterity, or fatigue. We believe that adaptive user interfaces (AUIs) offer a unique solution to detect and accommodate an individual's current pointing behavior. While past work has explored the implementation of detecting pointing challenges and designing adaptations, little has been done to understand how this population would like to interact with this software. In this paper, we present the results from participatory design sessions with older and younger adults that highlight their preferences for novel AUIs. We found diverse preferences and attitudes towards how AUIs notify a user of their current performance, and how they should adapt to pointing behavior. We present these diverse opinions as three personas that can be applied to future design of any AUI.


title: Critical care of the morbidly obese.

abstract: Abstract This article describes the special needs of the critically ill morbidly obese, with a focus on the care of the postoperative obesity surgery patient. The technique of surgery is described elsewhere in this journal. Details of nursing care complicated by obesity are discussed. Environment of care modifications required for routine care of the morbidly obese in critical care are detailed. Pharmacokinetic factors to be considered are reviewed. A case study is presented to coalesce concepts presented.


A Survey of Maneuvering Target Tracking — Part II : Ballistic Target Models


title: A survey of logical models for OLAP databases

abstract: In this paper, we present different proposals for multidimensional data cubes, which are the basic logical model for OLAP applications. We have grouped the work in the field in two categories: commercial tools (presented along with terminology and standards) and academic efforts. We further divide the academic efforts in two subcategories: the relational model extensions and the cube-oriented approaches. Finally, we attempt a comparative analysis of the various efforts.


title: From speech to letters - using a novel neural network architecture for grapheme based ASR

abstract: Main-stream Automatic Speech Recognition systems are based on modelling acoustic sub-word units such as phonemes. Phonemisation dictionaries and language model based decoding techniques are applied to transform the phoneme hypothesis into orthographic transcriptions. Direct modelling of graphemes as sub-word units using HMM has not been successful. We investigate a novel ASR approach using Bidirectional Long Short-Term Memory Recurrent Neural Networks and Connectionist Temporal Classification, which is capable of transcribing graphemes directly and yields results highly competitive with phoneme transcription. In design of such a grapheme based speech recognition system phonemisation dictionaries are no longer required. All that is needed is text transcribed on the sentence level, which greatly simplifies the training procedure. The novel approach is evaluated extensively on the Wall Street Journal 1 corpus.


title: Identifying dynamic model parameters of a BLDC motor

abstract: Abstract An off-line identification method founded on the least-squares approximation technique and a closed-loop disturbance observer is applied for identifying the parameters of a BLDC motor model. No special configuration of the motor is required besides the availability of experimental data for back-EMF, phase currents, rotor position, and rotor speed. This method is used to identify the back-EMF harmonics and mechanical parameters, where the mechanical parameters refer to cogging-torque, viscous friction coefficient, and Coulomb friction coefficient. The proposed identification method is theoretically investigated, and the method’s effectiveness is proved by experimental results performed on a low-power BLDC motor.


title: Formal methods and software engineering for DL. Security, safety and productivity for DL systems development

abstract: Deep Learning (DL) techniques are now widespread and being integrated into many important systems. Their classification and recognition abilities ensure their relevance for multiple application domains. As machine-learning that relies on training instead of algorithm programming, they offer a high degree of productivity. But they can be vulnerable to attacks and the verification of their correctness is only just emerging as a scientific and engineering possibility. This paper is a major update of a previously-published survey, attempting to cover all recent publications in this area. It also covers an even more recent trend, namely the design of domain-specific languages for producing and training neural nets.


title: A survey of vertical handover decision algorithms in Fourth Generation heterogeneous wireless networks

abstract: Vertical handover decision (VHD) algorithms are essential components of the architecture of the forthcoming Fourth Generation (4G) heterogeneous wireless networks. These algorithms need to be designed to provide the required Quality of Service (QoS) to a wide range of applications while allowing seamless roaming among a multitude of access network technologies. In this paper, we present a comprehensive survey of the VHD algorithms designed to satisfy these requirements. To offer a systematic comparison, we categorize the algorithms into four groups based on the main handover decision criterion used. Also, to evaluate tradeoffs between their complexity of implementation and efficiency, we discuss three representative VHD algorithms in each group.


Evaluation of Chatbot . . .


title: Evaluating the Usefulness and the Ease of Use of a Web-based Inspection Data Collection Tool

abstract: Software tools can only be effective if users accept them. Investigating user acceptance requires a model explaining people's attitudes and behaviour as well as reliable and valid measurement instruments. This paper applies Davis' (1989) technology acceptance model (TAM) and its questionnaire-based measurement instrument for evaluating a newly developed Web-based inspection data collection tool (WIPS) from a user's perspective. TAM postulates that two particular user beliefs, usefulness and ease of use, predict tool usage and, thus, tool acceptance quite well. To investigate this and to assess the usefulness and ease of use of WIPS, we performed an experiment with computer science students as subjects. Our experimental results confirmed the reliability and the validity of the TAM and its questionnaire. Moreover WIPS received high ratings for both TAM determinants. These results, together with the fact that usefulness was correlated to self-predicted future usage, explain why most of our subjects preferred WIPS over paper-based forms for inspection data collection. The reliability and validity of the TAM questionnaire is not limited to inspection tools, but applies to the user acceptance evaluation of tools and techniques in general. We demonstrate this with data from an experiment we performed in an industrial setting with professional software developers to evaluate different inspection techniques.


title: The interaction of the explicit and the implicit in skill learning: A dual-process approach

abstract: This article explicates the interaction between implicit and explicit processes in skill learning, in contrast to the tendency of researchers to study each type in isolation. It highlights various effects of the interaction on learning (including synergy effects). The authors argue for an integrated model of skill learning that takes into account both implicit and explicit processes. Moreover, they argue for a bottom-up approach (first learning implicit knowledge and then explicit knowledge) in the integrated model. A variety of qualitative data can be accounted for by the approach. A computational model, CLARION, is then used to simulate a range of quantitative data. The results demonstrate the plausibility of the model, which provides a new perspective on skill learning.


title: Real time testing and validation of Smart Grid devices and algorithms

abstract: With ongoing efforts to upgrade the traditional electric power system into a smart grid, emphasis is on the integration of state-of-the-art computer based online monitoring and control tools along with advanced communication technology. However, testing and validation of these devices and algorithms are required before implementation in a physical grid. New testing and validation method needs to be developed in a simulated environment in a lab. This paper discusses some of the applications of the “smart grid test bed” developed at the “Smart Grid Development and Research Investigation Lab (SGDRIL)” at Washington State University. The specific applications discussed in this paper include synchrophasor device testing, microgrid reconfiguration, voltage stability and vulnerability analysis.


title: Drift Time Measurement in the ATLAS Liquid Argon Electromagnetic Calorimeter using Cosmic Muons

abstract: The ionization signals in the liquid argon of the ATLAS electromagnetic calorimeter are studied in detail using cosmic muons. In particular, the drift time of the ionization electrons is measured and used to assess the intrinsic uniformity of the calorimeter gaps and estimate its impact on the constant term of the energy resolution. The drift times of electrons in the cells of the second layer of the calorimeter are uniform at the level of 1.3% in the barrel and 2.8% in the endcaps. This leads to an estimated contribution to the constant term of $(0.29^{+0.05}_{-0.04})$% in the barrel and $(0.54^{+0.06}_{-0.04})$% in the endcaps. The same data are used to measure the drift velocity of ionization electrons in liquid argon, which is found to be 4.61±0.07 mm/μs at 88.5 K and 1 kV/mm.


title: Spatial damping identification in the frequency domain—A theoretical and experimental comparison

abstract: Abstract This paper deals with spatial damping identification methods. In contrast to the commonly used damping methods (modal, proportional) the spatial damping information improves structural models with a known location of the damping sources. The Lee–Kim, Chen–Ju–Tsuei, Fritzen IV and local equation of motion methods were theoretically and experimentally compared. Experimentally, the spatial damping identification was tested against: modal and spatial incompleteness, differences in viscous and hysteretic damping models, the performance of identification methods and the effect of damping treatments. It was found that for a structure with a known equation of motion (beam, plate) the local equation of motion method is more efficient and gives a more precise location of the damping. Full frequency response function (FRF) matrix methods can also identify the spatial damping, but are more demanding because the numerical and measurement effort increases with n 2 , where n is the number of measurement points and, consequently, the size of the FRF matrix.


title: A review of cell assemblies

abstract: Since the cell assembly (CA) was hypothesised, it has gained substantial support and is believed to be the neural basis of psychological concepts. A CA is a relatively small set of connected neurons, that through neural firing can sustain activation without stimulus from outside the CA, and is formed by learning. Extensive evidence from multiple single unit recording and other techniques provides support for the existence of CAs that have these properties, and that their neurons also spike with some degree of synchrony. Since the evidence is so broad and deep, the review concludes that CAs are all but certain. A model of CAs is introduced that is informal, but is broad enough to include, e.g. synfire chains, without including, e.g. holographic reduced representation. CAs are found in most cortical areas and in some sub-cortical areas, they are involved in psychological tasks including categorisation, short-term memory and long-term memory, and are central to other tasks including working memory. There is currently insufficient evidence to conclude that CAs are the neural basis of all concepts. A range of models have been used to simulate CA behaviour including associative memory and more process- oriented tasks such as natural language parsing. Questions involving CAs, e.g. memory persistence, CAs' complex interactions with brain waves and learning, remain unanswered. CA research involves a wide range of disciplines including biology and psychology, and this paper reviews literature directly related to the CA, providing a basis of discussion for this interdisciplinary community on this important topic. Hopefully, this discussion will lead to more formal and accurate models of CAs that are better linked to neuropsychological data.


title: Multi agent simulation for decision making in warehouse management

abstract: The paper presents an agent-based simulation as a tool for decision making about automatic warehouses management. The proposed multi-agent system is going to be used in a real environment within a project developed with a company working on logistics. More in details, we have developed a simulation framework in order to study problems, constraints and performance issues of the truck unload operations. We aim to optimize the suitable number of Automated Guided Vehicles (AGVs) used for unloading containers arrived to the warehouse. This is a critical issue since an AGV is a costly resource and an augment in number does not necessarily correspond to an improved unloading speed. The experiment performed with our simulated environment allows us also to evaluate the impact of other elements to the performance.


title: Design and optimization of decoupled concentric and coplanar coils for WPT systems

abstract: This paper presents the analysis and design of two decoupled, concentric and coplanar PCB coils. Decoupled coils could be used as two independent receivers sensing the same field and not interfering with each other. In order to nullify the mutual inductance, one of the coils is split into two segments and the segments are wound in different directions, while the second coil is inserted in between those two segments. Since wireless power transfer systems operate at high frequencies, the coil efficiency suffers mainly due to the losses caused by skin and proximity effects. Optimizing a coil to reduce the AC conduction losses and increase the inductance can help improve system efficiency. One of the methods presented in the paper involves splitting the coil turns into multiple parallel traces to reduce the losses due to proximity or current crowding effect. The design methodology includes the optimization of coils to achieve a high quality factor and find an arrangement where two circular spiral coils are coaxial, coplanar and decoupled at the same time. The proposed method is verified through simulations and experiments on a selected PCB design.


title: Understanding the predictive power of social media

abstract: Purpose – The purpose of this paper is to consolidate existing knowledge and provide a deeper understanding of the use of social media (SM) data for predictions in various areas, such as disease outbreaks, product sales, stock market volatility and elections outcome predictions. Design/methodology/approach – The scientific literature was systematically reviewed to identify relevant empirical studies. These studies were analysed and synthesized in the form of a proposed conceptual framework, which was thereafter applied to further analyse this literature, hence gaining new insights into the field. Findings – The proposed framework reveals that all relevant studies can be decomposed into a small number of steps, and different approaches can be followed in each step. The application of the framework resulted in interesting findings. For example, most studies support SM predictive power, however, more than one-third of these studies infer predictive power without employing predictive analytics. In addition, a...


A Conceptual Model of Service Innovation: The Case of Academic Libraries


title: PyMVPA: a Python Toolbox for Multivariate Pattern Analysis of fMRI Data

abstract: Decoding patterns of neural activity onto cognitive states is one of the central goals of functional brain imaging. Standard univariate fMRI analysis methods, which correlate cognitive and perceptual function with the blood oxygenation-level dependent (BOLD) signal, have proven successful in identifying anatomical regions based on signal increases during cognitive and perceptual tasks. Recently, researchers have begun to explore new multivariate techniques that have proven to be more flexible, more reliable, and more sensitive than standard univariate analysis. Drawing on the field of statistical learning theory, these new classifier-based analysis techniques possess explanatory power that could provide new insights into the functional properties of the brain. However, unlike the wealth of software packages for univariate analyses, there are few packages that facilitate multivariate pattern classification analyses of fMRI data. Here we introduce a Python-based, cross-platform, and open-source software toolbox, called PyMVPA, for the application of classifier-based analysis techniques to fMRI datasets. PyMVPA makes use of Python’s ability to access libraries written in a large variety of programming languages and computing environments to interface with the wealth of existing machine learning packages. We present the framework in this paper and provide illustrative examples on its usage, features, and programmability.


title: Ithemal: Accurate, Portable and Fast Basic Block Throughput Estimation using Deep Neural Networks

abstract: Statically estimating the number of processor clock cycles it takes to execute a basic block of assembly instructions in steady state (throughput) is important for compiler backend optimizations such as register allocation, instruction selection and instruction scheduling. This is complicated specially in modern x86-64 Complex Instruction Set Computer (CISC) machines with sophisticated processor microarchitectures. Traditionally, compiler writers invest time experimenting and referring to processor manuals to analytically model modern processors with incomplete specifications. This is tedious, error prone and should be done for each processor generation. We present Ithemal, the first automatically learnt estimator to statically predict throughput of a set of basic block instructions using machine learning. Ithemal uses a novel Directed Acyclic Graph-Recurrent Neural Network (DAG-RNN) based data-driven approach for throughput estimation. We show that Ithemal is accurate than state-of-the-art hand written tools used in compiler backends and static machine code analyzers. In particular, our model has a worst case average error of 10.53% on actual throughput values when compared to best case average errors of 19.57% for the LLVM scheduler (llvm-mca) and 22.51% for IACA, Intel's machine code analyzer when compared on three different microarchitectures, while predicting throughput values at a faster rate than aforementioned tools. We also show that Ithemal is portable, learning throughput estimation for Intel Nehalem, Haswell and Skylake microarchitectures without requiring changes to its structure.


title: Interaction between dialog structure and coreference resolution

abstract: Determining the coreference of entity mentions in a discourse is a key part of the interpretation process for advanced spoken dialog applications. In this paper, we present the most comprehensive system for statistical coreference resolution in dialog to date. We also compare the impact of two contrasting theories of dialog structure (the stack model and the cache model) on the performance of statistical coreference resolution, and show that the stack model outperforms the cache model.


High Speed Architecture for Galois/Counter Mode of Operation (GCM)


title: Variance Reduction for Faster Non-Convex Optimization

abstract: We consider the fundamental problem in non-convex optimization of efficiently reaching a stationary point. In contrast to the convex case, in the long history of this basic problem, the only known theoretical results on first-order non-convex optimization remain to be full gradient descent that converges in $O(1/\varepsilon)$ iterations for smooth objectives, and stochastic gradient descent that converges in $O(1/\varepsilon^2)$ iterations for objectives that are sum of smooth functions. We provide the first improvement in this line of research. Our result is based on the variance reduction trick recently introduced to convex optimization, as well as a brand new analysis of variance reduction that is suitable for non-convex optimization. For objectives that are sum of smooth functions, our first-order minibatch stochastic method converges with an $O(1/\varepsilon)$ rate, and is faster than full gradient descent by $\Omega(n^{1/3})$. We demonstrate the effectiveness of our methods on empirical risk minimizations with non-convex loss functions and training neural nets.


title: On speaker adaptation of long short-term memory recurrent neural networks.

abstract: Long Short-Term Memory (LSTM) is a recurrent neural network (RNN) architecture specializing in modeling long-range temporal dynamics. On acoustic modeling tasks, LSTM-RNNs have shown better performance than DNNs and conventional RNNs. In this paper, we conduct an extensive study on speaker adaptation of LSTM-RNNs. Speaker adaptation helps to reduce the mismatch between acoustic models and testing speakers. We have two main goals for this study. First, on a benchmark dataset, the existing DNN adaptation techniques are evaluated on the adaptation of LSTM-RNNs. We observe that LSTMRNNs can be effectively adapted by using speaker-adaptive (SA) front-end, or by inserting speaker-dependent (SD) layers. Second, we propose two adaptation approaches that implement the SD-layer-insertion idea specifically for LSTM-RNNs. Using these approaches, speaker adaptation improves word error rates by 3-4% relative over a strong LSTM-RNN baseline. This improvement is enlarged to 6-7% if we exploit SA features for further adaptation.


title: Technical Note On the Handling in Decision Tree of Continuous-Valued Attributes Generation

abstract: We present a result applicable to classification learning algorithms that generate decision trees or rules using the information entropy minimization heuristic for discretizing continuous-valued attributes. The result serves to give a better understanding of the entropy measure, to point out that the behavior of the information entropy heuristic possesses desirable properties that justify its usage in a formal sense, and to improve the efficiency of evaluating continuous-valued attributes for cut value selection. Along with the formal proof, we present em- pirical results that demonstrate the theoretically expected reduction in evaluation effort for training data sets from real-world domains.


title: Finding Dynamic Dense Subgraphs

abstract: Online social networks are often defined by considering interactions of entities at an aggregate level. For example, a call graph is formed among individuals who have called each other at least once; or at least k times. Similarly, in social-media platforms, we consider implicit social networks among users who have interacted in some way, e.g., have made a conversation, have commented to the content of each other, and so on. Such definitions have been used widely in the literature and they have offered significant insights regarding the structure of social networks. However, it is obvious that they suffer from a severe limitation: They neglect the precise time that interactions among the network entities occur. ::: In this article, we consider interaction networks, where the data description contains not only information about the underlying topology of the social network, but also the exact time instances that network entities interact. In an interaction network, an edge is associated with a timestamp, and multiple edges may occur for the same pair of entities. Consequently, interaction networks offer a more fine-grained representation, which can be leveraged to reveal otherwise hidden dynamic phenomena. ::: In the setting of interaction networks, we study the problem of discovering dynamic dense subgraphs whose edges occur in short time intervals. We view such subgraphs as fingerprints of dynamic activity occurring within network communities. Such communities represent groups of individuals who interact with each other in specific time instances, for example, a group of employees who work on a project and whose interaction intensifies before certain project milestones. We prove that the problem we define is NP-hard, and we provide efficient algorithms by adapting techniques for finding dense subgraphs. We also show how to speed-up the proposed methods by exploiting concavity properties of our objective function and by the means of fractional programming. We perform extensive evaluation of the proposed methods on synthetic and real datasets, which demonstrates the validity of our approach and shows that our algorithms can be used to obtain high-quality results.


title: Conceptual mathematics: a first introduction to categories

abstract: In the last 60 years, the use of the notion of category has led to a remarkable unification and simplification of mathematics. Conceptual Mathematics, Second Edition, introduces the concept of 'category' for the learning, development, and use of mathematics, to both beginning students and general readers, and to practicing mathematical scientists. The treatment does not presuppose knowledge of specific fields, but rather develops, from basic definitions, such elementary categories as discrete dynamical systems and directed graphs; the fundamental ideas are then illuminated by examples in these categories.


title: Modular switched capacitor voltage multiplier topology for pulsed power supply

abstract: Along with rapid advancement of power semiconductors, voltage multipliers have introduced new series of pulsed power generators. In this paper, based on conventional voltage multiplier and by using power electronics switches a new topology in high voltage pulsed power application is proposed. This topology is a modular circuit that can generate a high output voltage from a relatively low input voltage with fast rise time and adjustable frequency, pulse width, and voltage levels using a series connection of switched capacitor cells. A comparative analysis is carried out to show the advantages of proposed topology. Experimental and simulation results are presented to confirm the analysis.


title: Simple model for downstream variation of median sediment size in Chilean rivers

abstract: A simple theory is developed to account for the observed downstream variation of the median sediment size in Chilean rivers based on a reach-wise equilibrium sediment transport concept. The theory makes use of a bedload transport equation linked with a resistance equation to estimate the median sediment diameter as a function of channel slope, with the flow Reynolds number and bedload concentration as parameters. Both, Meyer-Peter and Muller’s and Ackers and White’s formulas are used alternatively as bedload equations. A Manning-Strickler type of formulation is used as a resistance relationship. The resulting model is validated against field data corresponding to 150 rivers in Central Chile, covering slopes in the range of 0.04–8.61%, with median sediment size in the range of 0.3–250 mm. Despite the simplicity of the present theory and the somewhat bold assumptions made in its derivation, the estimated variation of the median sediment size with channel slope follows the same trend as the field data. Most of the scatter of these data falls within the theoretical limits given by the estimated range of values of the parameters of the model.


title: A Low-Cost Sensor Network for Real-Time Monitoring and Contamination Detection in Drinking Water Distribution Systems

abstract: This paper presents a low cost and holistic approach to the water quality monitoring problem for drinking water distribution systems as well as for consumer sites. Our approach is based on the development of low cost sensor nodes for real time and in-pipe monitoring and assessment of water quality on the fly. The main sensor node consists of several in-pipe electrochemical and optical sensors and emphasis is given on low cost, lightweight implementation, and reliable long time operation. Such implementation is suitable for large scale deployments enabling a sensor network approach for providing spatiotemporally rich data to water consumers, water companies, and authorities. Extensive literature and market research are performed to identify low cost sensors that can reliably monitor several parameters, which can be used to infer the water quality. Based on selected parameters, a sensor array is developed along with several microsystems for analog signal conditioning, processing, logging, and remote presentation of data. Finally, algorithms for fusing online multisensor measurements at local level are developed to assess the water contamination risk. Experiments are performed to evaluate and validate these algorithms on intentional contamination events of various concentrations of escherichia coli bacteria and heavy metals (arsenic). Experimental results indicate that this inexpensive system is capable of detecting these high impact contaminants at fairly low concentrations. The results demonstrate that this system satisfies the online, in-pipe, low deployment-operation cost, and good detection accuracy criteria of an ideal early warning system.


Acoustic group feature selection using wrapper method for automatic eating condition recognition.


title: A LSTM based framework for handling multiclass imbalance in DGA botnet detection

abstract: In recent years, botnets have become a major threat on the Internet. Most sophisticated bots use Domain Generation Algorithms (DGA) to pseudo-randomly generate a large number of domains and select a subset in order to communicate with Command and Control (C&C) server. The basic aim is to avoid blacklisting, sinkholing and evade the security systems. Long Short-Term Memory network (LSTM) provides a mean to combat this botnet type. It operates on raw domains and is amenable to immediate applications. LSTM is however prone to multiclass imbalance problem, which becomes even more significant in DGA malware detection. This is due the fact that many DGA classes have a very little support in the training dataset. This paper presents a novel LSTM.MI algorithm to combine both binary and multiclass classification models, where the original LSTM is adapted to be cost-sensitive. The cost items are introduced into backpropagation learning procedure to take into account the identification importance among classes. Experiments are carried out on a real-world collected dataset. They demonstrate that LSTM.MI provides an improvement of at least 7% in terms of macro-averaging recall and precision as compared to the original LSTM and other state-of-the-art cost-sensitive methods. It is also able to preserve the high accuracy on non-DGA generated class (0.9849 F1-score), while helping recognize 5 additional bot families.


title: SnapNETS: Automatic Segmentation of Network Sequences with Node Labels

abstract: Given a sequence of snapshots of flu propagating over a population network, can we find a segmentation when the patterns of the disease spread change, possibly due to interventions? In this paper, we study the problem of segmenting graph sequences with labeled nodes. Memes on the Twitter network, diseases over a contact network, movie-cascades over a social network, etc. are all graph sequences with labeled nodes. Most related work is on plain graphs (and hence ignore the label dynamics) or fix parameters or require much feature engineering. Instead, we propose SnapNETS, to automatically find segmentations of such graph sequences, with different characteristics of nodes of each label in adjacent segments. It satisfies all the desired properties (being parameter-free, comprehensive and scalable) by leveraging a principled, multi-level, flexible framework which maps the problem to a path optimization problem over a weighted DAG. Extensive experiments on several diverse real datasets show that it finds cut points matching ground-truth or meaningful external signals outperforming non-trivial baselines. We also show that SnapNETS scales near-linearly with the size of the input.


title: Comparison of rhizobacterial community composition in soil suppressive or conducive to tobacco black root rot disease

abstract: Work on soils suppressive to Thielaviopsis basicola-mediated tobacco black root rot has focused on antagonistic pseudomonads to date. The role of non-Pseudomonas rhizosphere populations has been neglected, and whether they differ in black root rot-suppressive versus -conducive soils is unknown. To assess this possibility, tobacco was grown in a suppressive and a conducive soil of similar physicochemical properties, and rhizobacterial community composition was compared using a 16S rRNA taxonomic microarray. The microarray contains 1033 probes and targets 19 bacterial phyla. Among them, 398 probes were designed for Proteobacteria, Firmicutes, Actinomycetes, Cyanobacteria and Bacteroidetes genera/species known to include strains relevant for plant protection or plant growth promotion. Hierarchical clustering as well as principal component analysis of microarray data discriminated clearly between black root rot-suppressive and -conducive soils. In contrast, T. basicola inoculation had no impact on rhizobacterial community composition. In addition to fluorescent Pseudomonas, the taxa Azospirillum, Gluconacetobacter, Burkholderia, Comamonas and Sphingomonadaceae, which are known to comprise strains with plant-beneficial properties, were more prevalent in the suppressive soil. Mycobacterium, Bradyrhizobium, Rhodobacteraceae, Rhodospirillum and others were more prevalent in the conducive soil. For selected taxa, microarray results were largely corroborated by quantitative PCR and cloning/sequencing. In conclusion, this work identified novel bacterial taxa that could serve as indicators of disease suppressiveness in soil-quality assessments, and it extends the range of bacterial taxa hypothesized to participate in black root rot suppression.


Can Your Smartphone Infer Your Mood ?


title: The advantages of using traditional Chinese medicine as an adjunctive therapy in the whole course of cancer treatment instead of only terminal stage of cancer

abstract: Recent studies indicate that Traditional Chinese medicine (TCM) can play an important role in the whole course of cancer treatment such as recovery stages of post-operative, radiotherapy or chemotherapy stages instead of only terminal stage of cancer. In this review, we have summarized current evidence for using TCM as adjuvant cancer treatment in different stages of cancer lesions. Some TCMs (e.g., TJ-41, Liu-jun-zi-tang, PHY906, Coumarin, and Aescine) are capable of improving the post-operative symptoms such as fatigue, pain, appetite, diarrhea, nausea, vomiting, and lymphedema. Some TCMs (e.g., Ginseng, Huang-Qi, BanZhiLian, TJ-48, Huachansu injection, Shenqi fuzheng injection, and Kanglaite injection) in combination with chemo- or radio-therapy are capable of enhancing the efficacy of and diminishing the side effects and complications caused by chemo- and radiotherapy. Taken together, they have great advantages in terms of suppressing tumor progression, relieving surgery complications, increasing the sensitivity of chemo- and radio- therapeutics, improving an organism's immune system function, and lessening the damage caused by surgery, chemo- or radio-therapeutics. They have significant effects on relieving breast cancer-related lymphedema, reducing cancer-related fatigue and pain, improving radiation pneumonitis and gastrointestinal side effects, protecting liver function, and even ameliorating bone marrow suppression. This review of those medicines should contribute to an understanding of Chinese herbal medicines as an adjunctive therapy in the whole course of cancer treatment instead of only terminal stage of cancer, by providing useful information for development of more effective anti-cancer drugs and making more patients "survival with cancer" for a long time.


title: Jordanian nurses' job satisfaction, patients' satisfaction and quality of nursing care

abstract: PURPOSE ::: To study nurses' job satisfaction, patients' satisfaction, and quality of nursing care in a Jordanian educational hospital. ::: ::: ::: DESIGN ::: A descriptive cross-sectional comparative design was used. The total populations at the educational hospital where the study was conducted were: 200 nurses (response rate 60%), 510 patients (response rate 49%), and 26 head nurses (NHs) (response rate 92%). Mueller/McCloskey Satisfaction Scale (MMSS) 1990; Eriksen's (1988) scale of The Satisfaction with Nursing Care; and Quality of Nursing Care Questionnaire-Head Nurse of Safford & Schlotfeldt (1960) were used to measure the phenomena of interest. ::: ::: ::: FINDINGS ::: Nurses were 'neither satisfied nor dissatisfied' in their jobs, nurses who work in wards reported a slightly better job satisfaction than nurses who work in critical care units. Patients reported that they were 'moderately' satisfied, and head nurses reported that nurses 'usually (practically)' provide a high of quality of nursing care. There were no significant differences between critical care units and wards in regard to patients' satisfaction and quality of nursing care. ::: ::: ::: CONCLUSIONS ::: Jordanian nurses' job satisfaction is on the borderline, which arguably requires more interventions. Patients' satisfaction and quality of nursing care have to be enhanced to reach the levels of 'very satisfied' and 'always' consecutively.


title: Encoder-decoder with focus-mechanism for sequence labelling based spoken language understanding

abstract: This paper investigates the framework of encoder-decoder with attention for sequence labelling based spoken language understanding. We introduce Bidirectional Long Short Term Memory - Long Short Term Memory networks (BLSTM-LSTM) as the encoder-decoder model to fully utilize the power of deep learning. In the sequence labelling task, the input and output sequences are aligned word by word, while the attention mechanism cannot provide the exact alignment. To address this limitation, we propose a novel focus mechanism for encoder-decoder framework. Experiments on the standard ATIS dataset showed that BLSTM-LSTM with focus mechanism defined the new state-of-the-art by outperforming standard BLSTM and attention based encoder-decoder. Further experiments also show that the proposed model is more robust to speech recognition errors.


title: IEEE 802.15.6-based Prototype System for WBAN: Design and Implementation

abstract: Various current trends such as ever growing population and accelerated aging effects have effectively promoted the growth of Wireless Body Area Network (WBAN). Being the specialized standard of WBAN, the burgeoning IEEE 802.15.6 is on its way to refinement and perfection. Since experiments are the foundation of research in WBAN that reproduce system operating patterns, yet existing platforms in WBAN are mainly simulations that could not highly conform to reality with strict requirements to some degree. Thus, a reliable and efficient system based on the IEEE 802.15.6, named WBAN prototype system, is proposed and implemented in this paper. The prototype system is ascertained to be valid, authentic and efficient via analyses of scenario tests. Running in circumstances much closer to reality, the system obtains experimental results that meet rigid requirements preferably. Furthermore, based on the valid platform we created, optimizations of two parameters in the IEEE 802.15.6 by actual measurements are derived. We obtain an optimum number of transmission retries and relationship of payload length within 30 with FER, making a trade-off between waste of resource and high packet loss rate.


title: Quality improvement methodologies: PDCA cycle, RADAR matrix, DMAIC and DFSS

abstract: Purpose: of this paper is to introduce the reader to the characteristics of PDCA tool and Six Sigma (DMAIC, DFSS) techniques and EFQM Excellence Model (RADAR matrix), which are possible to use for the continuous quality improvement of products, processes and services in organizations. Design/methodology/approach: We compared the main characteristics of the presented methodologies aiming to show the main prerequisites, differences, strengths and limits in their application. Findings: Depending on the purpose every organization will have to find a proper way and a combination of methodologies in its implementation process. The PDCA cycle is a well known fundamental concept of continuous-improvement processes, RADAR matrix provides a structured approach assessing the organizational performance, DMAIC is a systematic, and fact based approach providing framework of results-oriented project management, DFSS is a systematic approach to new products or processes design focusing on prevent activities. Research limitations/implications: This paper provides general information and observations on four presented methodologies. Further research could be done towards more detailed study of characteristics and positive effects of these methodologies. Practical implications: The paper presents condensed presentation of main characteristics, strengths and limitations of presented methodologies. Our findings could be used as solid information for management decisions about the introduction of various quality programmes. Originality/value: We compared four methodologies and showed their main characteristics and differences. We showed that some methodologies are more simple and therefore easily to understand and introduce (e.g. PDCA cycle). On the contrary Six Sigma and EFQM Excellence model are more complex and demanding methodologies and therefore need more time and resources for their proper implementation.


title: Brain Intelligence: Go Beyond Artificial Intelligence

abstract: Artificial intelligence (AI) is an important technology that supports daily social life and economic activities. It contributes greatly to the sustainable growth of Japan's economy and solves various social problems. In recent years, AI has attracted attention as a key for growth in developed countries such as Europe and the United States and developing countries such as China and India. The attention has been focused mainly on developing new artificial intelligence information communication technology (ICT) and robot technology (RT). Although recently developed AI technology certainly excels in extracting certain patterns, there are many limitations. Most ICT models are overly dependent on big data, lack a self-idea function, and are complicated. In this paper, rather than merely developing next-generation artificial intelligence technology, we aim to develop a new concept of general-purpose intelligence cognition technology called "Beyond AI". Specifically, we plan to develop an intelligent learning model called "Brain Intelligence (BI)" that generates new ideas about events without having experienced them by using artificial life with an imagine function. We will also conduct demonstrations of the developed BI intelligence learning model on automatic driving, precision medical care, and industrial robots.


title: Commute Times for a Directed Graph using an Asymmetric Laplacian

abstract: The expected commute times for a strongly connected directed graph are related to an asymmetric Laplacian matrix as a direct extension to similar well known formulas for undirected graphs. We show the close relationships between the asymmetric Laplacian and the so-called Fundamental matrix. We give bounds for the commute times in terms of the stationary probabilities for a random walk over the graph together with the asymmetric Laplacian and show how this can be approximated by a symmetrized Laplacian derived from a related weighted undirected graph.


title: Pediatric obesity.

abstract: Childhood obesity is a tremendous burden for children, their families, and society. Obesity prevention remains the ultimate goal but rapid development and deployment of effective nonsurgical treatment options is not currently achievable given the complexity of this disease. Surgical options for adolescent obesity have been proven to be safe and effective and should be offered. The development of stratified protocols of increasing intensity should be individualized for patients based on their disease severity and risk factors. These protocols should be offered in multidisciplinary, cooperative clinical trials to critically evaluate and develop optimal treatment strategies for morbid obesity.


title: A hand-held photometric stereo camera for 3-D modeling

abstract: This paper presents a simple yet practical 3-D modeling method for recovering surface shape and reflectance from a set of images. We attach a point light source to a hand-held camera to add a photometric constraint to the multi-view stereo problem. Using the photometric constraint, we simultaneously solve for shape, surface normal, and reflectance. Unlike prior approaches, we formulate the problem using realistic assumptions of a near light source, non-Lambertian surfaces, perspective camera model, and the presence of ambient lighting. The effectiveness of the proposed method is verified using simulated and real-world scenes.


title: Handwritten Bangla Compound character recognition: Potential challenges and probable solution.

abstract: A novel technique is presented here for recognition of handwritten compound characters of Bangla alphabet. It advocates for incrementally expanding the number of learned character classes from more frequently occurred to less frequently occurred ones. The work is preceded by a survey for finding the frequencies of occurrences of all Bangla characters in the standard literature. One important finding of the survey is that only 4.27 percent of characters in a standard text piece are on average compound characters. Out of the 160 compound character classes, characters of 55 classes constitute 90 percent of the compound characters occurring on average in a standard text piece. For the time being, handwritten characters from these classes are considered here. The average recognition rate, as observed under this work, is 84.67 percent after 3 fold cross validation of results. It is more or less comparable with the performance reported in another related work[3]. The work presented here can be considered as an important step for the development of OCR for handwritten Bangla characters, including complex shaped compound characters.


title: Hybris: Robust Hybrid Cloud Storage

abstract: Besides well-known benefits, commodity cloud storage also raises concerns that include security, reliability, and consistency. We present Hybris key-value store, the first robust hybrid cloud storage system, aiming at addressing these concerns leveraging both private and public cloud resources. Hybris robustly replicates metadata on trusted private premises (private cloud), separately from data, which are dispersed (using replication or erasure coding) across multiple untrusted public clouds. Hybris maintains metadata stored on private premises at the order of few dozens of bytes per key, avoiding the scalability bottleneck at the private cloud. In turn, the hybrid design allows Hybris to efficiently and robustly tolerate cloud outages but also potential malice in clouds without overhead. Namely, to tolerate up to f malicious clouds, in the common case of the Hybris variant with data replication, writes replicate data across f+1 clouds, whereas reads involve a single cloud. In the worst case, only up to f additional clouds are used. This is considerably better than earlier multi-cloud storage systems that required costly 3f+1 clouds to mask f potentially malicious clouds. Finally, Hybris leverages strong metadata consistency to guarantee to Hybris applications strong data consistency without any modifications to the eventually consistent public clouds. We implemented Hybris in Java and evaluated it using a series of micro and macro-benchmarks. Our results show that Hybris significantly outperforms comparable multi-cloud storage systems and approaches the performance of bare-bone commodity public cloud storage.


title: A novel shortest path query algorithm

abstract: The shortest path query is one of the hot issues in graph research. In view of the low query efficiency and poor scalability caused by the long time of the construction of index and the large scale of index in the existing methods, we propose an associated index strategy based on the single branch path vertices, which is to construct the associated index for the vertex of single branch path vertices and construct the 2-hop label index for the other vertices in order to reduce the size and construction time of index by reducing the number of redundant data storage and graph traversal. Then we propose the corresponding shortest path query algorithm based on the single branch path vertices associated index. And then, we introduce the concept of core vertex for the construction of the 2-hop label index, which can further reduce the number of graph traversal and improve the efficiency of index construction. And we apply it to the shortest path query algorithm for the large graphs. Finally, according to test on the 12 real datasets, we verify the high efficiency of the method proposed in this paper compared with the existing methods from the following aspects, such as the index construction time, the index size and the shortest path query time.


title: Perinatal Parenting Stress, Anxiety, and Depression Outcomes in First-Time Mothers and Fathers: A 3- to 6-Months Postpartum Follow-Up Study

abstract: Objective: Although the proven link between parenting stress, postnatal depression and anxiety, no study has investigated such relationship among fathers and mothers. The specific aims of this study were: 1) to investigate whether there were any differences between first-time fathers’ and mothers’ postnatal parenting stress, anxiety and depression symptoms and to see their evolution between 3 and 6 months after the child’s birth; 2) to explore how one’s own parenting stress and anxiety levels and the anxiety-depressive symptoms of the partner contribute to parental postnatal depression. Method: The sample was composed by 362 parents – 181 couples - (mothers’ MAge = 35.03, SD = 4.7; fathers’ MAge = 37.9, SD = 5.6) of a healthy baby. At three (T1) and six months (T2) postpartum, both parents filled out, in a counterbalanced order, the Parenting Stress Index-Short Form, the Edinburgh Postnatal Depression Scale and the State-Trait Anxiety Inventory. Results: Analyses showed that, compared to fathers, mothers reported higher scores on postpartum anxiety, depression and parenting stress. Scores on all measures for both mothers and fathers decreased from T1 to T2. However, path analyses suggested that the persistence of both maternal and paternal postnatal depression were influenced directly by one’s own levels of anxiety and parenting stress as well as by the presence of partner’s depression. Discussion: The study pointed out the relevant impact and effects of both maternal and paternal stress, anxiety and depression symptoms during the transition to parenthood. Therefore, in order to provide efficacious, targeted, early interventions, perinatal screening should always be directed to both parents.


title: Learning physics through play in an augmented reality environment

abstract: The Learning Physics through Play Project (LPP) engaged 6–8 year old students (n = 43) in a series of scientific investigations of Newtonian force and motion including a series of augmented reality activities. We outline the two design principles behind the LPP curriculum: 1) the use of socio-dramatic, embodied play in the form of participatory modeling to support inquiry; and 2) progressive symbolization within rich semiotic ecologies to help students construct meaning. We then present pre- and post-test results to show that young students were able to develop a conceptual understanding of force, net force, friction and two-dimensional motion after participating in the LPP curriculum. Finally, we present two case studies that illustrate the design principles in action. Taken together the cases show some of the strengths and challenges associated with using augmented reality, embodied play, and a student invented semiotic ecology for scientific inquiry.


title: DLANet: A manifold-learning-based discriminative feature learning network for scene classification

abstract: Abstract This paper presents Discriminative Locality Alignment Network (DLANet), a novel manifold-learning-based discriminative learnable feature, for wild scene classification. Based on a convolutional structure, DLANet learns the filters of multiple layers by applying DLA and exploits the block-wise histograms of the binary codes of feature maps to generate the local descriptors. A DLA layer maximizes the margin between the inter-class patches and minimizes the distance of the intra-class patches in the local region. In particular, we construct a two-layer DLANet by stacking two DLA layers and a feature layer. It is followed by a popular framework of scene classification, which combines Locality-constrained Linear Coding–Spatial Pyramid Matching (LLC–SPM) and linear Support Vector Machine (SVM). We evaluate DLANet on NYU Depth V1, Scene-15 and MIT Indoor-67. Experiments show that DLANet performs well on depth image. It outperforms the carefully tuned features, including SIFT and is also competitive to the other reported methods.


title: A Fast and Scalable Payment Network with Bitcoin Duplex Micropayment Channels

abstract: Bitcoin does not scale, because its synchronization mechanism, the blockchain, limits the maximum rate of transactions the network can process. However, using off-blockchain transactions it is possible to create long-lived channels over which an arbitrary number of transfers can be processed locally between two users, without any burden to the Bitcoin network. These channels may form a network of payment service providers PSPs. Payments can be routed between any two users in real time, without any confirmation delay. In this work we present a protocol for duplex micropayment channels, which guarantees end-to-end security and allow instant transfers, laying the foundation of the PSP network.


title: Implicit Cognition and Addiction: A Tool for Explaining Paradoxical Behavior

abstract: Research on implicit cognition and addiction has expanded greatly during the past decade. This research area provides new ways to understand why people engage in behaviors that they know are harmful or counterproductive in the long run. Implicit cognition takes a different view from traditional cognitive approaches to addiction by assuming that behavior is often not a result of a reflective decision that takes into account the pros and cons known by the individual. Instead of a cognitive algebra integrating many cognitions relevant to choice, implicit cognition assumes that the influential cognitions are the ones that are spontaneously activated during critical decision points. This selective review highlights many of the consistent findings supporting predictive effects of implicit cognition on substance use and abuse in adolescents and adults; reveals a recent integration with dual-process models; outlines the rapid evolution of different measurement tools; and introduces new routes for intervention.


title: Recovery Principles in MySQL Cluster 5.1

abstract: MySQL Cluster is a parallel main memory database. It is using the normal MySQL software with a new storage engine NDB Cluster. MySQL Cluster 5.1 has been adapted to also handle fields on disk. In this work a number of recovery principles of MySQL Cluster had to be adapted to handle very large data sizes. The article presents an efficient algorithm for synchronizing a starting node with very large data sets. It provides reasons for the unorthodox choice of a no-steal algorithm in the buffer manager. It also presents the algorithm to change the data.


title: Anaesthetic efficacy of lidocaine/clonidine for inferior alveolar nerve block in patients with irreversible pulpitis

abstract: Aims ::: This prospective, randomized, double-blind study aimed to compare the efficacy of lidocaine with epinephrine versus lidocaine with clonidine for inferior alveolar nerve block (IANB) and hemodynamic stability (heart rate, systolic blood pressure, diastolic blood pressure and mean arterial pressure) in patients with irreversible pulpitis. ::: ::: Methodology ::: One hundred patients with irreversible pulpitis in mandibular molar teeth randomly received 1.8 mL of 2% lidocaine with clonidine (15 μg mL−1) or 1.8 mL of 2% lidocaine with epinephrine (12.5 μg mL−1), using a conventional IANB technique. Endodontic access cavities were prepared 15 min after solution deposition, and all patients were required to have profound lip numbness. Success was defined as no or mild pain (visual analog scale recording) upon endodontic access cavity preparation or initial canal instrumentation. The hemodynamic parameters were measured before, during and 5, 10 and 30 min after administration. Finally, the collected data were subjected to independent t-test, chi-square and Fisher's exact test using spss software ver.20 at a significant level of 0.05. ::: ::: Results ::: The success rates for IANB using lidocaine with epinephrine and lidocaine with clonidine solutions were 29% and 59%, respectively. The clonidine group exhibited a significantly higher success rate (P < 0.05). Five minutes after drug administration, systolic blood pressure and heart rate significantly increased in the lidocaine with epinephrine group and insignificantly decreased in lidocaine with clonidine group. ::: ::: Conclusion ::: For mandibular molars with irreversible pulpitis, addition of clonidine to lidocaine improved the success rate of IANB compared to a standard lidocaine/epinephrine solution.


title: Circulator integrated in low temperature co-fired ceramics technology

abstract: The front-end circuitry of transceiver modules is slowly being updated from GaAs-based MMICs to Gallium-Nitride. Especially GaN power amplifiers and TR switches, but also low-noise amplifiers, offer significant performance improvement over GaAs components. Therefore it is interesting to also explore the possible advantages of a GaN mixer to complete a fully GaN-based front-end. In this paper the design-experiment and measurement results of a double-balanced image-rejectmixer MMIC in 0.25 μm AlGaN/GaN technology are presented. This design features an integrated LO amplifier and active IF balun. The measured conversion loss is less than 8 dB from 6 to 12 GHz, at 0 dBm LO power. © 2014 European Microwave Association.


Graph Prediction in Movie Graphs


Low Cost Interconnected Architecture for the Hardware Spiking Neural Networks


Inflammatory bowel disease epidemiology in São Paulo State, Brazil


title: A Survey on Latest DoS Attacks:Classificationand Defense Mechanisms

abstract: Distributed Denial of Service (DDoS) is defined as an attack in which mutiple compromised systems are made to attack a single target to make the services unavailable foe legitimate users.It is an attack designed to render a computer or network incapable of providing normal services. DDoS attack uses many compromised intermediate systems, known as botnets which are remotely controlled by an attacker to launch these attacks. DDOS attack basically results in the situation where an entity cannot perform an action for which it is authenticated. This usually means that a legitimate node on the network is unable to reach another node or their performance is degraded. The high interruption and severance caused by DDoS is really posing an immense threat to entire internet world today. Any compromiseto computing, communication and server resources such as sockets, CPU, memory, disk/database bandwidth, I/O bandwidth, router processing etc. for collaborative environment would surely endanger the entire application. It becomes necessary for researchers and developers to understand behaviour of DDoSattack because it affects the target network with little or no advance warning. Hence developing advanced intrusion detection and prevention systems for preventing, detecting, and responding to DDOS attack is a critical need for cyber space. Our rigorous survey study presented in this paper describes a platform for the study of evolution of DDoS attacks and their defense mechanisms.


title: From Bitcoin to cybersecurity: A comparative study of blockchain application and security issues

abstract: With the accelerated iteration of technological innovation, blockchain has rapidly become one of the hottest Internet technologies in recent years. As a decentralized and distributed data management solution, blockchain has restored the definition of trust by the embedded cryptography and consensus mechanism, thus providing security, anonymity and data integrity without the need of any third party. But there still exists some technical challenges and limitations in blockchain. This paper has conducted a systematic research on current blockchain application in cybersecurity. In order to solve the security issues, the paper analyzes the advantages that blockchain has brought to cybersecurity and summarizes current research and application of blockchain in cybersecurity related areas. Through in-depth analysis and summary of the existing work, the paper summarizes four major security issues of blockchain and performs a more granular analysis of each problem. Adopting an attribute-based encryption method, the paper also puts forward an enhanced access control strategy.


title: A simple and accurate method to predict offset voltage in dynamic comparators

abstract: In a dynamic comparator, it's always challenging to analytically predict the input offset voltage due to the existence of the internal positive feedback and transient process. In this paper, a simple method is presented to accurately estimate input offset voltages caused by process variations in dynamic comparators. The "Lewis-Gray" comparator implemented in TSMC0.25mum process is applied as an example to verify the effectiveness of the analytical method. Based on the SPICE level 1 model, the method shows good agreements with Monte Carlo transient simulation based on the sophisticated BSEVI3V3 model. The analytical results allow the circuit designers to fully explore the tradeoffs in comparator design, such as offset voltage, area and speed. To illustrate the potential, the analytical method was used to re-size the "Lewis-Gray" structure to reduce its random offset while maintaining a constant total area. After the optimization, input offset voltage has been reduced by 41% compared with its original sizing.


title: Video2vec: Learning semantic spatio-temporal embeddings for video representation

abstract: We propose to learn semantic spatio-temporal embeddings for videos to support high-level video analysis. The first step of the proposed embedding employs a deep architecture consisting of two channels of convolutional neural networks (capturing appearance and local motion) followed by their corresponding Gated Recurrent Unit encoders for capturing longer-term temporal structure of the CNN features. The resultant spatio-temporal representation (a vector) is used to learn a mapping via a multilayer perceptron to the word2vec semantic embedding space, leading to a semantic interpretation of the video vector that supports high-level analysis. We demonstrate the usefulness and effectiveness of this new video representation by experiments on action recognition, zero-shot video classification, and “word-to-video” retrieval, using the UCF-101 dataset.


title: Cascaded face alignment via intimacy definition feature

abstract: Recent years have witnessed the emerging popularity of regression-based face aligners, which directly learn mappings between facial appearance and shape-increment manifolds. We propose a random-forest based, cascaded regression model for face alignment by using a locally lightweight feature, namely intimacy definition feature. This feature is more discriminative than the pose-indexed feature, more efficient than the histogram of oriented gradients feature and the scale-invariant feature transform feature, and more compact than the local binary feature (LBF). Experimental validation of our algorithm shows that our approach achieves state-of-the-art performance when testing on some challenging datasets. Compared with the LBF-based algorithm, our method achieves about twice the speed, 20% improvement in terms of alignment accuracy and saves an order of magnitude on memory requirement.


title: Geometry Coding and VRML

abstract: The virtual-reality modeling language (VRML) is rapidly becoming the standard file format for transmitting three-dimensional (3-D) virtual worlds across the Internet. Static and dynamic descriptions of 3-D objects, multimedia content, and a variety of hyperlinks can be represented in VRML files. Both VRML browsers and authoring tools for the creations of VRML files are widely available for several different platforms. In this paper, we describe the topologically assisted geometric compression technology included in our proposal for the VRML compressed binary format. This technology produces significant reduction of file sizes and, subsequently, of the time required for transmission of such filed across the Internet. Compression ratios of 50:1 or more are achieved for large models. The proposal also includes a binary encoding to create compact, rapidly parsable binary VRML files. The proposal is currently being evaluated by the Compressed Binary Format Working Group of the VRML consortium as a possible extension of the VRML standard. In the topologically assisted compression scheme, a polyhedron is represented using two interlocking trees: a spanning tree of vertices and a spanning tree of triangles. The connectivity information represented in other compact schemes, such as triangular strips and generalized triangular meshes, can be directly derived from this representation. Connectivity information for large models is compressed with storage requirements approaching one bit per triangle. A variable-length, optionally lossy compression technique is used for vertex positions, normals, colors, and texture coordinates. The format supports all VRML property binding conventions.


title: Improvements to the design of the 0-180/spl deg/ rat race coupler and its application to the design of balanced mixers with high LO to RF isolation

abstract: An improved rat-race coupler is described which, in the ideal case, has a pair of ports with infinite isolation between them at all frequencies. This improved rat-race coupler is useful in such applications as the design of balanced mixers with high LO to RF isolation.