title: Mindfulness-Based Interventions for the Treatment of Substance and Behavioral Addictions: A Systematic Review

abstract: Background: Emotion (dys)regulation as well as the interventions for improving these difficulties have received growing attention in the literature. The aim of the present paper was to systematically review current evidence on the clinical changes and benefits related to mindfulness-based interventions (MBIs) in both substance and behavioral addictions. Method: A literature search was conducted using Cochrane, PubMed and Web of Science. Fifty-four randomized controlled trials published in English since 2009 to April 2017 were included into a narrative synthesis. Results: MBIs were applied in a wide range of addictions, including substance use disorders (from smoking to alcohol, among others) and behavioral addictions (namely gambling disorder). These treatments were successful for reducing dependence, craving and other addiction-related symptoms by also improving mood state and emotion dysregulation. Among the most commonly used MBIs approaches were: Mindfulness-Based Relapse Prevention (MBRP), Mindfulness Training for Smokers (MTS) or Mindfulness-Oriented Recovery Enhancement (MORE) and the most frequent control group in the included studies was Treatment as Usual (TAU). The most effective approach was the combination de MBIs with TAU or other active treatment. However, there is a lack of studies showing the maintenance of the effect over time. Therfore, studies with longer follow-ups are needed. Conclusions: The revised literature shows support for the effectiveness of the mindfulness-based interventions. Future research should focus on longer follow-up assessments as well as on adolescence and young population, as they are a vulnerable population for developing problems associated with alcohol, drugs or other addictions. Key words: mindfulness, addiction, behavioral addiction, substance use, gambling.


title: Multilingualism (But Not Always Bilingualism) Delays the Onset of Alzheimer Disease: Evidence From a Bilingual Community

abstract: A recent paper by Bialystok et al in Neuropsychologia (vol. 45, pgs. 459 to 464) suggested that early bilingualism produced a statistically significant 4.1-year delay in onset of memory loss symptoms in older individuals with Alzheimer disease, possibly reflecting an increase in the cognitive reserve of these individuals. That study focused on multilingual elderly patients of whom 90% were immigrants. Our memory clinic, in Montreal Canada, has the advantage of having a large set of individuals who are either multilingual immigrants to Canada, or who are nonimmigrants but raised in both official languages of Canada— French and English. We thus attempted to replicate the above findings using a larger cohort in a different setting. We examined age at diagnosis of Alzheimer disease and age at symptom onset for all unilingual versus multilingual participants, and then for those who were nonimmigrant English/French bilinguals. Overall, we found a small but significant protective effect of more than 2 languages spoken, but we found no significant benefit in bilinguals overall in relation to age at diagnosis or age at symptom onset. However, in the immigrant group, the results mirrored those of Bialystok et al with 2 or more languages delaying the diagnosis of Alzheimer disease by almost 5 years. A trend toward the same effect was also seen in nonimmigrants whose first language was French. In contrast, in nonimmigrants whose first language was English, no such effect was found. These results are discussed in relation to the earlier findings and the theory of cognitive reserve.


title: An Architecture Combining Convolutional Neural Network (CNN) and Support Vector Machine (SVM) for Image Classification

abstract: Convolutional neural networks (CNNs) are similar to "ordinary" neural networks in the sense that they are made up of hidden layers consisting of neurons with "learnable" parameters. These neurons receive inputs, performs a dot product, and then follows it with a non-linearity. The whole network expresses the mapping between raw image pixels and their class scores. Conventionally, the Softmax function is the classifier used at the last layer of this network. However, there have been studies (Alalshekmubarak and Smith, 2013; Agarap, 2017; Tang, 2013) conducted to challenge this norm. The cited studies introduce the usage of linear support vector machine (SVM) in an artificial neural network architecture. This project is yet another take on the subject, and is inspired by (Tang, 2013). Empirical data has shown that the CNN-SVM model was able to achieve a test accuracy of ~99.04% using the MNIST dataset (LeCun, Cortes, and Burges, 2010). On the other hand, the CNN-Softmax was able to achieve a test accuracy of ~99.23% using the same dataset. Both models were also tested on the recently-published Fashion-MNIST dataset (Xiao, Rasul, and Vollgraf, 2017), which is suppose to be a more difficult image classification dataset than MNIST (Zalandoresearch, 2017). This proved to be the case as CNN-SVM reached a test accuracy of ~90.72%, while the CNN-Softmax reached a test accuracy of ~91.86%. The said results may be improved if data preprocessing techniques were employed on the datasets, and if the base CNN model was a relatively more sophisticated than the one used in this study.


title: Voltage-Balancing Method for Modular Multilevel Converters Switched at Grid Frequency

abstract: The modular multilevel converter (MMC) becomes attractive for high-voltage and high-power applications due to its high modularity, availability, and power quality. The voltage balance issue of capacitors is very important in the MMC, and balancing of the capacitor voltage is increasingly difficult as the switching frequency is reduced. In this paper, a voltage-balancing method is proposed for the MMC switched at grid frequency with reduced losses and does not rely on the arm current. By assigning the low-frequency pulses with different pulsewidths, the capacitor charge transfer in the MMC can be controlled for keeping the capacitor voltage balancing in the MMC. Simulations and experimental studies of the MMC are conducted, and the results confirm the effectiveness of the proposed capacitor voltage-balancing method.


title: Context-aware query classification

abstract: Understanding users'search intent expressed through their search queries is crucial to Web search and online advertisement. Web query classification (QC) has been widely studied for this purpose. Most previous QC algorithms classify individual queries without considering their context information. However, as exemplified by the well-known example on query "jaguar", many Web queries are short and ambiguous, whose real meanings are uncertain without the context information. In this paper, we incorporate context information into the problem of query classification by using conditional random field (CRF) models. In our approach, we use neighboring queries and their corresponding clicked URLs (Web pages) in search sessions as the context information. We perform extensive experiments on real world search logs and validate the effectiveness and effciency of our approach. We show that we can improve the F1 score by 52% as compared to other state-of-the-art baselines.


title: Investigating the problem of IDS false alarms: An experimental study using Snort

abstract: IDS can play a vital role in the overall security infrastructure, as one last defence against attacks after secure network architecture design, secure program design and firewalls [1]. Although IDS technology has become an essential part of corporate network architecture, the art of detecting intrusions is still far from perfect. A significant problem is that of false alarms, which correspond to legitimate activity that has been mistakenly classed as malicious by the IDS. Recognising the real alarms from the huge volume of alarms is a complicated and time-consuming task. Therefore, reducing false alarms is a serious problem in ensuring IDS efficiency and usability [2]. A common technique for reducing the false alarm rate is by performing a tuning procedure. This can be done by adapting the set of signatures to the specific environment and disabling the signatures that are not related to it [8], based on the fact that some vulnerabilities exist in a particular OS platform only. However, although this can offer a means of reducing the number of false alarms, the procedure can also increase the risk of missing noteworthy incidents. Therefore, the tuning process is actually a trade-off between reducing false alarms and maintaining the security level. This often leaves administrators with the difficulty of determining a proper balance between an ideal detection rate and the possibility of having false alarms. Furthermore, tuning requires a thorough examination of the environment by qualified IT personnel, and requires frequently updating to keep up with the flow of new vulnerabilities or threats discovered [26].


title: Modeling, design and control of bidirectional DC-DC converter using state-space average model

abstract: Modeling, design and control of a DC-DC converter using state-space averaging approached are presented. A proposed DC-DC converter topology is based on two half-bridge DC-DC converters which control the power between the battery, energy storage system (ESS) of an ultra-capacitor, and an output load resistance. Two well-known control schemes based on AC equivalent circuit modeling are used to design converter control system. The average voltage mode control is used for the boost converter operation and the two-loop average current mode control for the bidirectional operation. Control schemes are modeled and designed, the performance and responses were analyzed using Matlab/Simulink. The simulation results show that the proposed control system is capable to regulate efficiently the power flow between two DC sources.


title: A low cost wireless sensor node for building monitoring

abstract: In this paper a medium-high performance wireless sensor node based on low-cost Micro Electro-Mechanical Systems (MEMS) accelerometers for Structural Health Monitoring (SHM) applications is proposed. The objective of the work is the estimation of the performances of the low cost MEMS system for SHM with a possible application on a pervasive permanent monitoring of buildings in seismic prone regions often affected by earthquakes. The work aims at improving the sensing technology involved in the dynamic identification and SHM, with the intent to reduce the overall cost of acquisition's system and to make faster the tests procedure. Preliminary experimental tests performed to compare the performance of the proposed system with those of a typical high-performance and expensive wired system, are presented and discussed.


title: A textual-based technique for Smell Detection

abstract: In this paper, we present TACO (Textual Analysis for Code Smell Detection), a technique that exploits textual analysis to detect a family of smells of different nature and different levels of granularity. We run TACO on 10 open source projects, comparing its performance with existing smell detectors purely based on structural information extracted from code components. The analysis of the results indicates that TACO's precision ranges between 67% and 77%, while its recall ranges between 72% and 84%. Also, TACO often outperforms alternative structural approaches confirming, once again, the usefulness of information that can be derived from the textual part of code components.


title: Computational intelligence approaches for malignant melanoma detection and diagnosis

abstract: Malignant melanoma is reported to be the deadliest of skin cancers. Therefore, early diagnosis is crucial for reducing of melanoma-related deaths. Medical Informatics uses the computer technology such as Computer Aided Diagnosis (CAD) for melanoma diagnostic. This paper presents computational intelligence approaches namely, Artificial Neural Network (ANN) and Adaptive-Network-based Fuzzy Inference System (ANFIS). The dermoscopy images are taken from Dermatology Information System (DermIS) and DermQuest, image enhancement is achieved by various pre-processing approaches. The extracted features are based on Discrete Wavelet Transform (DWT), and Principle Component Analysis (PCA) is used to take the eigenvalue as features. These features become the input to the various classification approaches such as: ANN and ANFIS to classify the lesions as malignant or benign. The results show the rate of accuracy for ANFIS is 95.18%, while ANN gives higher rate of accuracy about 98.8%. Moreover; the results obtained are compared with other approaches. The comparative results indicated that the proposed feature extraction and classification approaches are more accurate than other approaches in this field of melanoma diagnosis.


title: Proximodistal Heterogeneity of Hippocampal CA3 Pyramidal Neuron Intrinsic Properties, Connectivity, and Reactivation during Memory Recall

abstract: Summary The hippocampal CA3 region is classically viewed as a homogeneous autoassociative network critical for associative memory and pattern completion. However, recent evidence has demonstrated a striking heterogeneity along the transverse, or proximodistal, axis of CA3 in spatial encoding and memory. Here we report the presence of striking proximodistal gradients in intrinsic membrane properties and synaptic connectivity for dorsal CA3. A decreasing gradient of mossy fiber synaptic strength along the proximodistal axis is mirrored by an increasing gradient of direct synaptic excitation from entorhinal cortex. Furthermore, we uncovered a nonuniform pattern of reactivation of fear memory traces, with the most robust reactivation during memory retrieval occurring in mid-CA3 (CA3b), the region showing the strongest net recurrent excitation. Our results suggest that heterogeneity in both intrinsic properties and synaptic connectivity may contribute to the distinct spatial encoding and behavioral role of CA3 subregions along the proximodistal axis.


title: Combining search-based procedural content generation and social gaming in the petalz video game

abstract: Search-based procedural content generation methods allow video games to introduce new content continually, thereby engaging the player for a longer time while reducing the burden on developers. However, games so far have not explored the potential economic value of unique evolved artifacts. Building on this insight, this paper presents for the first time a Facebook game called Petalz in which players can share flowers they breed themselves with other players through a global marketplace. In particular, the market in this social game allows players to set the price of their evolved aesthetically-pleasing flowers in virtual currency. Furthermore, the transaction in which one player buys seeds from another creates a new social element that links the players in the transaction. The combination of unique user-generated content and social gaming in Petalz facilitates meaningful collaboration between users, positively influences the dynamics of the game, and opens new possibilities in digital entertainment.


title: Agronomy for sustainable agriculture. A review

abstract: Sustainability rests on the principle that we must meet the needs of the present without compromising the ability of future generations to meet their own needs. Starving people in poor nations, obesity in rich nations, increasing food prices, on-going climate changes, increasing fuel and transportation costs, flaws of the global market, worldwide pesticide pollution, pest adaptation and resistance, loss of soil fertility and organic carbon, soil erosion, decreasing biodiversity, desertification, and so on. Despite unprecedented advances in sciences allowing us to visit planets and disclose subatomic particles, serious terrestrial issues about food show clearly that conventional agriculture is no longer suited to feeding humans and preserving ecosystems. Sustainable agriculture is an alternative for solving fundamental and applied issues related to food production in an ecological way (Lal (2008) Agron. Sustain. Dev. 28, 57–64.). While conventional agriculture is driven almost solely by productivity and profit, sustainable agriculture integrates biological, chemical, physical, ecological, economic and social sciences in a comprehensive way to develop new farming practices that are safe and do not degrade our environment. To address current agronomical issues and to promote worldwide discussions and cooperation we implemented sharp changes at the journal Agronomy for Sustainable Development from 2003 to 2006. Here we report (1) the results of the renovation of the journal and (2) a short overview of current concepts of agronomical research for sustainable agriculture. Considered for a long time as a soft, side science, agronomy is rising fast as a central science because current issues are about food, and humans eat food. This report is the introductory article of the book Sustainable Agriculture, volume 1, published by EDP Sciences and Springer (Lichtfouse et al. (2009) Sustainable Agriculture, Vol. 1, Springer, EDP Sciences, in press).


title: A Survey of Zero-Shot Learning: Settings, Methods, and Applications

abstract: Most machine-learning methods focus on classifying instances whose classes have already been seen in training. In practice, many applications require classifying instances whose classes have not been seen previously. Zero-shot learning is a powerful and promising learning paradigm, in which the classes covered by training instances and the classes we aim to classify are disjoint. In this paper, we provide a comprehensive survey of zero-shot learning. First of all, we provide an overview of zero-shot learning. According to the data utilized in model optimization, we classify zero-shot learning into three learning settings. Second, we describe different semantic spaces adopted in existing zero-shot learning works. Third, we categorize existing zero-shot learning methods and introduce representative methods under each category. Fourth, we discuss different applications of zero-shot learning. Finally, we highlight promising future research directions of zero-shot learning.


title: Theory and Implementation of RF-Input Outphasing Power Amplification

abstract: Conventional outphasing power amplifier systems require both a radio frequency (RF) carrier input and a separate baseband input to synthesize a modulated RF output. This work presents an RF-input/RF-output outphasing power amplifier that directly amplifies a modulated RF input, eliminating the need for multiple costly IQ modulators and baseband signal component separation as in previous outphasing systems. An RF signal decomposition network directly synthesizes the phase- and amplitude-modulated signals used to drive the branch power amplifiers (PAs). With this approach, a modulated RF signal including zero-crossings can be applied to the single RF input port of the outphasing RF amplifier system. The proposed technique is demonstrated at 2.14 GHz in a four-way lossless outphasing amplifier with transmission-line power combiner. The RF decomposition network is implemented using a transmission-line resistance compression network with nonlinear loads designed to provide the necessary amplitude and phase decomposition. The resulting proof-of-concept outphasing power amplifier has a peak CW output power of 93 W, peak drain efficiency of 70%, and performance on par with a previously-demonstrated outphasing and power combining system requiring four IQ modulators and a digital signal component separator.


Improving palliative care: the need for change.


title: Agile Maturity Model: Oxymoron or the Next Level of Understanding

abstract: From the agile camp you can hear someone to say that CMMI is the big American waterfall model monster, and is outright contra productive to agile methods. From the CMMI camp you can hear someone to say that agile methods is hackers from hell that uses the agile paradigm to enjoy anarchy with no rules. You can also hear some say that agile works the best in CMMI level 5 companies. The context of the dilemma however is slightly awkward. CMMI describes characteristics of good development practices, and agile is a lifecycle concept. So from a meta point of view they can easily co-exist. We would like to state that they do, and that you need both to support the best development performance. Starting in December 2011 three surveys were launched to get an idea about what could an agile maturity model deliver and what might be its added value. 67 Participants from several agile or/and CMMI® related LinkedIn Groups contributed to the survey. This article explains the survey results and proposes further research topics and harmonization actions.


title: Helping consumers know themselves

abstract: Firms sometimes know more about a consumer's expected usage than the consumer herself. We explore the consequences of this reversal in the information asymmetry. We analyze the consequences of making consumers more informed about themselves. While making consumers more informed decreases their expenditure conditional on a given set of prices, equilibrium prices may increase, offsetting the direct benefit of information. We discuss theoretical and practical issues surrounding so-called RECAP regulation that would require firms to provide each consumer with information about her own usage of the firm's product.


title: Measurement of leg length discrepancy after total hip arthroplasty. The reliability of a plain radiographic method compared to CT-scanogram

abstract: OBJECTIVE ::: To measure the interobserver reliability and intraobserver reproducibility of post total hip arthroplasty (THA) leg length discrepancy (LLD) measurement on radiographs as well as to evaluate its accuracy by comparing it with LLD measurement on computed tomographic scanogram (CT-scanogram). ::: ::: ::: MATERIALS AND METHODS ::: In this prospective study, postoperative LLD measurements in ten THA patients were made by four observers on anteroposterior radiographs of the pelvis (inter-teardrop line to the tip of lesser trochanter) and compared to LLD measurements made on CT-scanogram scout views of the lower limb. Two observers repeated the LLD measurements on radiographs 8 weeks after the first measurements. The interobserver reliability of the LLD measurement on plain radiographs was evaluated by comparing the measurements of the four observers and the intraobserver reproducibility by comparing the two repeated measurements made by the two observers. ::: ::: ::: RESULTS ::: We found excellent interobserver reliability (mean ICC 0.83) and intraobserver reproducibility (ICC 0.90 and 0.88) of the LLD measurements on plain radiographs. There was a moderate to excellent agreement, but with wide variation of measurements among the four observers, when plain radiographic measurement was compared with CT-scanogram (ICC 0.58, 0.60, 0.71, and 0.82). ::: ::: ::: CONCLUSION ::: Despite the excellent interobserver reliability and intraobserver reproducibility of LLD measurement on radiographs, clinicians should be aware of its limited accuracy when compared to CT-scanogram.


title: Metaldehyde poisoning of dogs.

abstract: SIR, — Our practice has seen an increasing incidence of slug bait poisoning in our canine patients, possibly due to the particularly wet summer, which has suited the slugs and snails rather better than us. ::: ::: My colleagues and I have discovered, only recently, that such poisoning incidents should be


title: A Theoretical Analysis of Noisy Sparse Subspace Clustering on Dimensionality-Reduced Data

abstract: Subspace clustering is the problem of partitioning unlabeled data points into a number of clusters so that data points within one cluster lie approximately on a low-dimensional linear subspace . In many practical scenarios, the dimensionality of data points to be clustered is compressed due to the constraints of measurement, computation, or privacy. In this paper, we study the theoretical properties of a popular subspace clustering algorithm named sparse subspace clustering (SSC) and establish formal success conditions of SSC on dimensionality-reduced data. Our analysis applies to the most general fully deterministic model, where both underlying subspaces and data points within each subspace are deterministically positioned, and also a wide range of dimensionality reduction techniques (e.g., Gaussian random projection, uniform subsampling, and sketching) that fall into a subspace embedding framework. Finally, we apply our analysis to a differentially private SSC algorithm and established both privacy and utility guarantees of the proposed method.


title: An investigation of augmenting speaker representations to improve speaker normalisation for DNN-based speech recognition

abstract: The conventional short-term interval features used by the Deep Neural Networks (DNNs) lack the ability to learn longer term information. This poses a challenge for training a speaker-independent (SI) DNN since the short-term features do not provide sufficient information for the DNN to estimate the real robust factors of speaker-level variations. The key to this problem is to obtain a sufficiently robust and informative speaker representation. This paper compares several speaker representations. Firstly, a DNN speaker classifier is used to extract the bottleneck features as the speaker representation, called the Bottleneck Speaker Vector (BSV). To further improve the robustness of this representation, a first-order Bottleneck Speaker Super Vector (BSSV) is also proposed, where the BSV is expanded into a super vector space by incorporating the phoneme posterior probabilities. Finally, a more fine-grain speaker representation based on the FMLLR-shifted features is examined. The experimental results on the WSJ0 and WSJ1 datasets show that the proposed speaker representations are useful in normalising the speaker effects for robust DNN-based automatic speech recognition. The best performance is achieved by augmenting both the BSSV and the FMLLR-shifted representations, yielding 10.0% – 15.3% relatively performance gains over the SI DNN baseline.


title: Design and implementation of real time vehicle monitoring, tracking and controlling system

abstract: Currently almost of the public having an own vehicle, theft is happening on parking and sometimes driving insecurity places. The safe of vehicles is extremely essential for public vehicles. It is necessary to design a advanced vehicle monitoring and tracking system for monitoring the vehicle location and speed at real time. The proposed system works on GPS/GSM SIM900A module which include all the two things namely GPS GSM. The GPS current location of a vehicle and the GSM is used to send the alert message to the vehicle's owner mobile. The proposed system would place inside the vehicle whose position is to be determined on the web page and monitored at real time. If the driver drives the vehicle on the wrong path then the alert message will be sent from the proposed system to the vehicle's owner mobile and also speakers alert driven using driver. If the vehicle's speed goes beyond the specified value of the speed, then also the warning message will be sent from system to the owner mobile. The proposed system also took care of the traveller's safety by using LPG Gas leakage sensor MQ6 and temperature sensor DS18B20.


title: Effects of rain fading in 5G millimeter wavelength mesh networks

abstract: Millimeter wave point to point links have been used as backhaul links for cellular networks for decades. These have always been carefully planned so that their link budgets include a reserve for rain fading. Fast and low cost deployment of 5G backhaul networks may be enabled by a system of millimeter wavelength point to point links where the network is not carefully planned; instead the nodes are semi-randomly deployed and afterwards organized into a network. While this concept has many benefits, rain fading may cause some links to fail, thereby threatening the connectivity of the network. This paper presents the effects of rain fading in such millimeter wavelength mesh networks. The issue is investigated with simulations, and with the use of rain fading measurement data covering a span of five years. Our main result quantifies the probability of network failure due to rain fading in millimeter wave flexible backhaul mesh networks of future 5G systems.


title: Workplace bullying among nurses and their related factors in Japan: a cross-sectional survey.

abstract: AIMS AND OBJECTIVES ::: To explore the association between workplace bullying and workplace environment factors among nurses in Japan. ::: ::: ::: BACKGROUND ::: Workplace bullying among nurses is increasing globally and occurs more frequently than among other professions. However, there is little information on the impact of workplace environment factors on nurse bullying in Japan. ::: ::: ::: DESIGN ::: A cross-sectional survey using a self-administered questionnaire. ::: ::: ::: METHODS ::: Participants were 1152 nurses recruited at seminars or training courses outside of their workplaces in Tokyo. Workplace bullying was measured using the Negative Acts Questionnaire-Revised. Participants were considered to have been 'bullied' if they reported experiencing at least one negative act on a daily or weekly basis. Workplace environment factors were measured using the Practice Environment Scale of the Nursing Work Index, which comprises five domains: nurse participation in hospital affairs; nursing foundations for quality of care; nurse manager ability, leadership and support of nurses; staffing and resource adequacy; and collegial nurse-physician relationships. ::: ::: ::: RESULTS ::: A total of 898 (78·0%) questionnaires were returned, of which 825 (71·6%) were analysed. Altogether, 153 (18·5%) nurses were considered 'bullied.' The three most frequent negative acts reported as occurring on a weekly or daily basis were 'someone withholding information which affects your performance' (6·7%), 'being exposed to an unmanageable workload' (4·4%) and 'being shouted at or being the target of spontaneous anger (or rage)' (3·6%). Logistic regression analysis indicated that 'bullied' were associated with low scores on two work environment domains: nurse manager ability, leadership and support of nurses and staffing and resource adequacy. ::: ::: ::: CONCLUSIONS ::: Effective nurse manager leadership and support as well as appropriate staffing management may positively influence workplace bullying among nurses in Japan. Authentic leadership styles and allowing nurses to easily request days off might also be important. ::: ::: ::: RELEVANCE TO CLINICAL PRACTICE ::: Nurse managers' leadership may influence nurses' workplace bullying.


title: Active suspension controller using MPC based on a full-car model with preview information

abstract: Active suspension systems in vehicles provide the possibility to overcome the trade-off between ride comfort and handling characteristics of passive systems. Active suspensions have even greater potential if preview information of the oncoming road height profile is available. This paper presents a control strategy for vehicles with active suspensions and available preview information, which has to be obtained by a sensor mounted in front of the car. The controller is based on a full-car model. To incorporate all hard constraints on control variables, a Model Predictive Control (MPC) design scheme is applied. Since wheel dynamics are not within the bandwidth of energy-saving, low bandwidth active suspension systems, the controller is based on the idea, that only vehicle body dynamics and not wheel dynamics should explicitly considered in controller design. Accordingly the cost function of the controller considers only the minimization of vehicle body accelerations and hence the improvement of ride comfort. It is shown, that all the same, handling characteristics are also improved.


Fully Distributed Representation


title: Who Makes Trends? Understanding Demographic Biases in Crowdsourced Recommendations

abstract: Users of social media sites like Facebook and Twitter rely on crowdsourced content recommendation systems (e.g., Trending Topics) to retrieve important and useful information. Contents selected for recommendation indirectly give the initial users who promoted (by liking or posting) the content an opportunity to propagate their messages to a wider audience. Hence, it is important to understand the demographics of people who make a content worthy of recommendation, and explore whether they are representative of the media site's overall population. In this work, using extensive data collected from Twitter, we make the first attempt to quantify and explore the demographic biases in the crowdsourced recommendations. Our analysis, focusing on the selection of trending topics, finds that a large fraction of trends are promoted by crowds whose demographics are significantly different from the overall Twitter population. More worryingly, we find that certain demographic groups are systematically under-represented among the promoters of the trending topics. To make the demographic biases in Twitter trends more transparent, we developed and deployed a Web-based service 'Who-Makes-Trends' at this http URL.


title: Review of pricing models for grid & cloud computing

abstract: Distributed system resources have become prevalent in ICT departments to lessen the burden of huge expenses incurred by very expensive storage computer systems. Add to this the continuous introduction and ever-growing evolution of simple to complex applications, the demand to access huge quantities of data, intensive computations, powerful simulations, maintaining and offering system resources and middleware infrastructure services the need to do all of this at an affordable and reasonable price is crucial. Distributed grid and cloud computing resources are currently considered to be one of the best technology options to provide this. They have many similar features and functions, and both of them are classed as distributed systems. They are capable of offering unaffordable resources and services at a reasonable price in a mass marketplace. The big question is: what is a reasonable price? How is pricing modeled and on what kind of economic principles is it based? Much of the issues surrounding these questions are very complex in themselves. This paper provides a comparative review of grid and cloud computing economic and pricing models from which appropriate tariffs and charging models can be chosen to meet particular business objectives. The actual choice depends on many other factors like enterprise regulations, tax laws, service level agreements and return on investments, are very important but outside the scope of this paper. In this paper we give the basic core principles and a comparative review of the latest and most appropriate economic and pricing models applicable to grid and cloud computing in order to propose better models for the future.


The Transformation of Scientific Communication: AModel for 2020


Large-Scale Online Expectation Maximization with Spark Streaming


title: Relaxation search: a simple way of managing optional clauses

abstract: A number of problems involve managing a set of optional clauses. For example, the soft clauses in a MAXSAT formula are optional--they can be falsified for a cost. Similarly, when computing a Minimum Correction Set for an unsatisfiable formula, all clauses are optional--some can be falsified in order to satisfy the remaining. In both of these cases the task is to find a subset of the optional clauses that achieves some criteria, and whose removal leaves a satisfiable formula. Relaxation search is a simple method of using a standard SAT solver to solve this task. Relaxation search is easy to implement, sometimes requiring only a simple modification of the variable selection heuristic in the SAT solver; it offers considerable flexibility and control over the order in which subsets of optional clauses are examined; and it automatically exploits clause learning to exchange information between the two phases of finding a suitable subset of optional clauses and checking if their removal yields satisfiability. We demonstrate how relaxation search can be used to solve MAXSAT and to compute Minimum Correction Sets. In both cases relaxation search is able to achieve state-of-the-art performance and solve some instances other solvers are not able to solve.


title: Approximate clustering via core-sets

abstract: In this paper, we show that for several clustering problems one can extract a small set of points, so that using those core-sets enable us to perform approximate clustering efficiently. The surprising property of those core-sets is that their size is independent of the dimension.Using those, we present a (1+ e)-approximation algorithms for the k-center clustering and k-median clustering problems in Euclidean space. The running time of the new algorithms has linear or near linear dependency on the number of points and the dimension, and exponential dependency on 1/e and k. As such, our results are a substantial improvement over what was previously known.We also present some other clustering results including (1+ e)-approximate 1-cylinder clustering, and k-center clustering with outliers.


title: Design of a general autonomy payload for low-cost AUV R&D

abstract: Advances in computer hardware and sensing technologies have enabled the design and development of low-cost autonomous underwater vehicles (AUVs). The reduced cost of new platforms such as the Bluefin SandShark present the opportunity for more frequent and high-risk AUV-based experiments and reduce the barrier to entry for multi-vehicle operations. The smaller size of these new vehicles compared to AUV platforms typically used for ocean exploration also makes deployment and operations possible in accessible smaller bodies of water such as lakes and rivers. However, the limited payload space and power availability of these platforms constrains the types of sensors that may be installed, demanding a compromise between operating range, mission duration, and sensor use. This paper describes the design of a standard payload for low-cost AUVs based on these constraints, and the implementation of that payload for autonomy, acoustic and environmental research in stand-alone configuration and on a Bluefin SandShark AUV.


title: A Generic Approach to Challenge Modeling for the Procedural Creation of Video Game Levels

abstract: This paper presents an approach to automatic video game level design consisting of a computational model of player enjoyment and a generative system based on evolutionary computing. The model estimates the entertainment value of game levels according to the presence of “rhythm groups,” which are defined as alternating periods of high and low challenge. The generative system represents a novel combination of genetic algorithms (GAs) and constraint satisfaction (CS) methods and uses the model as a fitness function for the generation of fun levels for two different games. This top-down approach improves upon typical bottom-up techniques in providing semantically meaningful parameters such as difficulty and player skill, in giving human designers considerable control over the output of the generative system, and in offering the ability to create levels for different types of games.


title: Stretchable Interconnects for Elastic Electronic Surfaces

abstract: Elastic electronic surfaces will integrate stiff thin film devices onto compliant polymer substrates. These surfaces may be stretched once or many times, by up to tens of percent strain. One way to make such an elastic electronic surface is to distribute rigid subcircuit islands over the polymer surface, and then fabricate active devices on the islands. These islands need to be interconnected with stretchable metallization. We describe stretchable interconnects made of stripes of thin gold film patterned on elastomeric membranes. These membranes can be stretched by up to twice their initial length and maintain electrical conduction. We review the fabrication of these conductors, present their electrical and mechanical properties, and summarize our model for their extreme stretchability. Using such stretchable interconnects, we made the first elastic circuit, an inverter of thin film transistors. The circuit remains functional when stretched and relaxed by 12% strain.


title: Glycyrrhetinic acid-modified chitosan nanoparticles enhanced the effect of 5-fluorouracil in murine liver cancer model via regulatory T-cells

abstract: Modified chitosan nanoparticles are a promising platform for drug, such as 5-fluorouracil (5-FU), gene, and vaccine delivery. Here, we used chitosan and hepatoma cell-specific binding molecule glycyrrhetinic acid (GA) to synthesize glycyrrhetinic acid-modified chitosan (GA-CTS). The synthetic product was confirmed by infrared spectroscopy and hydrogen nuclear magnetic resonance. By combining GA-CTS and 5-FU, we obtained a GA-CTS/5-FU nanoparticle, with a particle size of 193.7 nm, drug loading of 1.56%, and a polydispersity index of 0.003. The GA-CTS/5-FU nanoparticle provided a sustained-release system comprising three distinct phases of quick, steady, and slow release. In vitro data indicated that it had a dose- and time-dependent anticancer effect. The effective drug exposure time against hepatic cancer cells was increased in comparison with that observed with 5-FU. In vivo studies on an orthotropic liver cancer mouse model demonstrated that GA-CTS/5-FU significantly inhibited cancer cell proliferation, resulting in increased survival time. The antitumor mechanisms for GA-CTS/5-FU nanoparticle were possibly associated with an increased expression of regulatory T-cells, decreased expression of cytotoxic T-cell and natural killer cells, and reduced levels of interleukin-2 and interferon gamma.


Negotiating Masculinity in J . D . Salinger ’ s Catcher in the Rye


title: Learning Discriminative Binary Codes for Large-scale Cross-modal Retrieval

abstract: Hashing based methods have attracted considerable attention for efficient cross-modal retrieval on large-scale multimedia data. The core problem of cross-modal hashing is how to learn compact binary codes that construct the underlying correlations between heterogeneous features from different modalities. A majority of recent approaches aim at learning hash functions to preserve the pairwise similarities defined by given class labels. However, these methods fail to explicitly explore the discriminative property of class labels during hash function learning. In addition, they usually discard the discrete constraints imposed on the to-be-learned binary codes, and compromise to solve a relaxed problem with quantization to obtain the approximate binary solution. Therefore, the binary codes generated by these methods are suboptimal and less discriminative to different classes. To overcome these drawbacks, we propose a novel cross-modal hashing method, termed discrete cross-modal hashing (DCH), which directly learns discriminative binary codes while retaining the discrete constraints. Specifically, DCH learns modality-specific hash functions for generating unified binary codes, and these binary codes are viewed as representative features for discriminative classification with class labels. An effective discrete optimization algorithm is developed for DCH to jointly learn the modality-specific hash function and the unified binary codes. Extensive experiments on three benchmark data sets highlight the superiority of DCH under various cross-modal scenarios and show its state-of-the-art performance.


title: Creating patents on the new technology using analogy-based patent mining

abstract: Patents on the new technology-a technology not yet commercialized and in an early stage of its life cycle-give firms many benefits. However, existing methods are inadequate because of dependencies on customers and physical prototypes. And there is lack of systems, focused on a problem identification process or an inter-technological comparison. In this research, to remedy existing limitations, analogy-based patent mining system is suggested. The system is developed based on an assumption that similar problems would occur in technologies that have similar properties or functions. So, the system is focused on identification of a Problem Solved Concept (PSC), which describes what problem is solved in the patent. At the first part of the system, the mature technology-a technology relatively matured than the new technology-is described with a property and a function; one of the property or the function should be similar to which of the new technology considered. And the system extract PSCs, construct patent map, and evaluate PSCs utilizing patents on the new and the mature technologies. As a result, the PSCs with high opportunities are revealed and patents related to the PSCs are examined. Then users of this system select some patents as resources for analogy. The system is tested by a case study of wireless charger technology. For the case study, 352 patents on wireless router technology and 227 patents on wireless charger technology are used. At the final, patents related to 'handoff', showed a high opportunity score and one of the patents is introduced to show the possibility of patent creation through analogy.


title: On the Development of an Integrated CMOS-Based UWB Tunable-Pulse Transmit Module

abstract: A novel low-cost low-power fully integrated tunable transmit module composed of a tunable CMOS monocycle pulse generator and compact uniplanar antenna was designed, built, and tested for ultra-wideband (UWB) impulse systems. The CMOS tunable pulse generator integrates a tuning delay circuit, square-wave generator, impulse-forming circuit, and pulse-shaping circuit in a single chip using a standard low-cost 0.25-mum CMOS process. It can generate a monocycle pulse and Gaussian-type impulse (without the pulse-shaping circuitry) signals with tunable pulse duration. A compact uniplanar UWB antenna was also developed and integrated directly with the CMOS pulse generator chip to form the complete integrated tunable UWB transmit module. Measured results show that the CMOS tunable pulse generator can produce a 0.3-0.6-V peak-to-peak monocycle pulse with 140-350-ps tunable pulse duration and a 0.5-1.3-V peak-to-peak impulse signal with 100-300-ps tunable pulse-duration, and the uniplanar antenna has less than a 18-dB return loss and is suitable for transmitting/receiving UWB time-domain impulse signals covering the entire UWB bandwidth of 3.1-10.6 GHz. Good agreement between measured and calculated performance is also achieved. The UWB transmit module was experimentally characterized and its performance is verified. This UWB module finds applications in various time-domain UWB systems including wireless communications and radar


title: Integrated Optic Disc and Cup Segmentation with Deep Learning

abstract: Glaucoma is a widespread ocular disorder leading to irreversible loss of vision. Therefore, there is a pressing need for cost-effective screening, such that preventive measures can be taken. This can be achieved with an accurate segmentation of the optic disc and cup from retinal images to obtain the cup-to-disc ratio. We describe a comprehensive solution based on applying convolutional neural networks to feature exaggerated inputs emphasizing disc pallor without blood vessel obstruction, as well as the degree of vessel kinking. The produced raw probability maps then undergo a robust refinement procedure that takes into account prior knowledge about retinal structures. Analysis of these probability maps further allows us to obtain a confidence estimate on the correctness of the segmentation, which can be used to direct the most challenging cases for manual inspection. Tests on two large real-world databases, including the publicly-available MESSIDOR collection, demonstrate the effectiveness of our proposed system.


title: File System Logging Versus Clustering: A Performance Comparison

abstract: The Log-structured File System (LFS), introduced in 1991 [8], has received much attention for its potential order-of-magnitude improvement in file system performance. Early research results [9] showed that small file performance could scale with processor speed and that cleaning costs could be kept low, allowing LFS to write at an effective bandwidth of 62 to 83% of the maximum. Later work showed that the presence of synchronous disk operations could degrade performance by as much as 62% and that cleaning overhead could become prohibitive in transaction processing workloads, reducing performance by as much as 40% [10]. The same work showed that the addition of clustered reads and writes in the Berkeley Fast File System [6] (FFS) made it competitive with LFS in large-file handling and software development environments as approximated by the Andrew benchmark [4]. ::: ::: These seemingly inconsistent results have caused confusion in the file system research community. This paper presents a detailed performance comparison of the 4.4BSD Log-structured File System and the 4.4BSD Fast File System. Ignoring cleaner overhead, our results show that the order-of-magnitude improvement in performance claimed for LFS applies only to meta-data intensive activities, specifically the creation of files one-kilobyte or less and deletion of files 64 kilobytes or less. ::: ::: For small files, both systems provide comparable read performance, but LFS offers superior performance on writes. For large files (one megabyte and larger), the performance of the two file systems is comparable. When FFS is tuned for writing, its large-file write performance is approximately 15% better than LFS, but its read performance is 25% worse. When FFS is optimized for reading, its large-file read and write performance is comparable to LFS. ::: ::: Both LFS and FFS can suffer performance degradation, due to cleaning and disk fragmentation respectively. We find that active FFS file systems function at approximately 85-95% of their maximum performance after two to three years. We examine LFS cleaner performance in a transaction processing environment and find that cleaner overhead reduces LFS performance by more than 33% when the disk is 50% full.


title: Parallel architecture for DNA sequence inexact matching with Burrows-Wheeler Transform

abstract: The Burrows-Wheeler Transform (BWT) based methodology seems ideally suited for DNA sequence alignment due to its high speed and low space complexity. Despite being efficient in exact matching, the application of BWT in inexact matching still has problems due to the excessive backtracking process. This paper presents a hardware architecture for the BWT-based inexact sequence mapping algorithm using the Field Programmable Gate Array (FPGA). The proposed design can handle up to two errors, including mismatches and gaps. The original recursive algorithm implementation is dealt with using hierarchical tables, and is then parallelized to a large extension through a dual-base extension method. Extensive performance evaluations for the proposed architecture have been conducted using both Virtex 6 and Virtex 7 FPGAs. This design is considerably faster than a direct implementation. When compared with the popular software evaluation tool BWA, our architecture can achieve the same match quality tolerating up to two errors. In an execution speed comparison with the BWA aln process, our design outperforms a range of CPU platforms with multiple threads under the same configuration conditions.


title: Collaborative Quantization for Cross-Modal Similarity Search

abstract: Cross-modal similarity search is a problem about designing a search system supporting querying across content modalities, e.g., using an image to search for texts or using a text to search for images. This paper presents a compact coding solution for efficient search, with a focus on the quantization approach which has already shown the superior performance over the hashing solutions in the single-modal similarity search. We propose a cross-modal quantization approach, which is among the early attempts to introduce quantization into cross-modal search. The major contribution lies in jointly learning the quantizers for both modalities through aligning the quantized representations for each pair of image and text belonging to a document. In addition, our approach simultaneously learns the common space for both modalities in which quantization is conducted to enable efficient and effective search using the Euclidean distance computed in the common space with fast distance table lookup. Experimental results compared with several competitive algorithms over three benchmark datasets demonstrate that the proposed approach achieves the state-of-the-art performance.


title: Complete reverse-engineering of AES-like block ciphers by SCARE and FIRE attacks

abstract: Despite Kerckhoffs's principle, proprietary or otherwise secret cryptographic algorithms are still used in real life. For security and efficiency reasons a common design practice simply modifies some parameters of widely used and well studied encryption standards. In this paper, we investigate the feasibility of reverse engineering the secret specifications of an AES-like block cipher by a FIRE attack based on Ineffective Fault Analysis (IFA) or by SCARE techniques based on two models of collision power analysis. In the considered fault or observational models, we demonstrate that an adversary who does not know the secret key can recover the full set of secret parameters of an AES-like software implementation and, in some models, even if it is protected by common Boolean masking and shuffling of independent operations. We thereby intend to demonstrate that protecting the implementation of such AES-like function is not an option even if its specifications are not public.


title: Structure of Sad1-UNC84 Homology (SUN) Domain Defines Features of Molecular Bridge in Nuclear Envelope

abstract: The SUN (Sad1-UNC-84 homology) domain is conserved in a number of nuclear envelope proteins involved in nuclear migration, meiotic telomere tethering, and antiviral responses. The LINC (linker of nucleoskeleton and cytoskeleton) complex, formed by the SUN and the nesprin proteins at the nuclear envelope, serves as a mechanical linkage across the nuclear envelope. Here we report the crystal structure of the SUN2 protein SUN domain, which reveals a homotrimer. The SUN domain is sufficient to mediate binding to the KASH (Klarsicht, ANC-1, and Syne homology) domain of nesprin 2, and the regions involved in the interaction have been identified. Binding of the SUN domain to the KASH domain is abolished by deletion of a region important for trimerization or by point mutations associated with nuclear migration failure. We propose a model of the LINC complex, where the SUN and the KASH domains form a higher ordered oligomeric network in the nuclear envelope. These findings provide the structural basis for understanding the function and the regulation of the LINC complex.


title: Nanoseconds Switching for High Voltage Circuit Using Avalanche Transistors

abstract: The avalanche transistor is suitable for switching high voltage in kilovolts region. In this paper, a simple switching circuit consists of avalanche transistor is demonstrated. Sixteen of ZTX 415 avalanche transistors were used to switch a high voltage circuit up to 4.5 kV. A PIC16F84 microcontroller was utilized as control unit to generate input trigger. The result shows that the developed circuit was able to switch applied voltage up-to 4.5 kV with an average falling time is 2.89 ns.


title: The Effect of Strength Training on Performance in Endurance Athletes

abstract: Economy, velocity/power at maximal oxygen uptake (vV˙O2max/wV˙O2max\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ {\text {v}} \dot{V}{\text{O}}_{2 \, \hbox{max} } /{\text w}\dot{V}{\text{O}}_{2 \, \hbox{max} } $$\end{document}) and endurance-specific muscle power tests (i.e. maximal anaerobic running velocity; vMART), are now thought to be the best performance predictors in elite endurance athletes. In addition to cardiovascular function, these key performance indicators are believed to be partly dictated by the neuromuscular system. One technique to improve neuromuscular efficiency in athletes is through strength training. The aim of this systematic review was to search the body of scientific literature for original research investigating the effect of strength training on performance indicators in well-trained endurance athletes—specifically economy, vV˙O2max/wV˙O2max\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ {\text{v}}\dot{V}{\text{O}}_{2 \, \hbox{max} } /\,{\text{w}}\dot{V}{\text{O}}_{2 \, \hbox{max} } $$\end{document} and muscle power (vMART). A search was performed using the MEDLINE, PubMed, ScienceDirect, SPORTDiscus and Web of Science search engines. Twenty-six studies met the inclusion criteria (athletes had to be trained endurance athletes with ≥6 months endurance training, training ≥6 h per week OR V˙O2max\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ \dot{V}{\text{O}}_{2 \, \hbox{max} } $$\end{document} ≥50 mL/min/kg, the strength interventions had to be ≥5 weeks in duration, and control groups used). All studies were reviewed using the PEDro scale. The results showed that strength training improved time-trial performance, economy, vV˙O2max/wV˙O2max\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ {\text{v}}\dot{V}{\text{O}}_{2 \, \hbox{max} } /{\text{w}}\dot{V}{\text{O}}_{2 \, \hbox{max} } $$\end{document} and vMART in competitive endurance athletes. The present research available supports the addition of strength training in an endurance athlete’s programme for improved economy, vV˙O2max/wV˙O2max\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ {\text{v}}\dot{V}{\text{O}}_{2 \, \hbox{max} } /{\text{w}}\dot{V}{\text{O}}_{2 \, \hbox{max} } $$\end{document}, muscle power and performance. However, it is evident that further research is needed. Future investigations should include valid strength assessments (i.e. squats, jump squats, drop jumps) through a range of velocities (maximal-strength ↔ strength-speed ↔ speed-strength ↔ reactive-strength), and administer appropriate strength programmes (exercise, load and velocity prescription) over a long-term intervention period (>6 months) for optimal transfer to performance.


title: Investigating the Effect of Information Systems Factors on Trust in E-Government Initiative Adoption in Palestinian Public Sector

abstract: The study conducted a primary investigation into the relationship between information system and trust in e-government followed by a secondary one into the relationship between trust in e-government and intention to use. It followed a quantitative approach of data collection through the use of questionnaire survey. The findings revealed that information quality, system quality, service quality, perceived usefulness, perceived ease of use, and security- privacy contributed positively to trust in e-government while the latter impacted intention to use. Moreover, the study's findings supported the rationale behind the IS theories (TAM and IS Success Model) and security-privacy were proven to illustrate trust in e-government adoption initiative. The findings theoretical and practical implications and limitation and suggestions for future research were discussed towards the end of the study.


title: Games of Friends: a game-theoretical approach for link prediction in online social networks

abstract: Online Social Networks (OSN) have enriched the social lives of millions of users. Discovering new friends in the social network is valuable both for the user and for the health of OSN since users with more friends engage longer and more often with the site. The simplest way to formalize friendship recommendation is to cast the problem as a link prediction problem in the social graph. In this work we introduce a game-theoretical approach based on the Graph Transduction Game. It scales with ease beyond 13 million of users and was tested on a real world data from Tuenti OSN. We utilize the social graph and several other graphs that naturally arise in Tuenti such as the wall-to-wall post graph. We compare our approach to standard local measures and demonstrate a significant performance benefit in terms of mean average precision and reciprocal rank.


title: MEMS-based hemispherical resonator gyroscopes

abstract: This paper introduces a fabrication technique that uses planar MEMS micromachining processes to produce hemispherical resonating shells for gyroscopes. The hemispheres exhibit a quality factor in excess of 20,000 with resonant frequencies in the range of 20 kHz for the 4-node wineglass mode. The fabrication process enables production of almost perfect hemispheres (less than 1% asphericity near the pedestal) with an average surface roughness of 5nm. The high degree of sphericity contains the relative frequency mismatch Δf/f between the two degenerate modes to 0.02%. Simplicity of the fabrication process and the successful testing of the drive/sense mechanism in the resonator make it a good candidate for use as gyroscopes.


title: Effect of non-cooperative nodes in mobile DTNs

abstract: When applying delay-tolerant networking concepts to communication in mobile ad-hoc networks formed between mobile users, a general assumption is that users are willing to share own resources to support communication between others. However, we cannot assume that all users are altruistic in their behavior; instead, we have to deal with users who only make a limited or no contribution to the mobile community. Nodes not participating in communication only reduce the effective node density, but do not consume resources. Others act as sources and sinks but perform only limited or no forwarding and thus may impact the overall network performance. When considering routing in mobile DTNs, such selfish nodes have to be considered. We introduce two types of selfish nodes and evaluate their impact on message delivery performance for different routing protocols by means of simulation in different synthetic mobility models and with real-world traces. We find that their impact can be surprisingly low in our scenarios, suggesting that DTN communication can be quite robust against selfishness and that controlled non-cooperative behavior may be a suitable way to overcome resource limitations, such as battery depletion.


title: Airline Passenger Profiling Based on Fuzzy Deep Machine Learning

abstract: Passenger profiling plays a vital part of commercial aviation security, but classical methods become very inefficient in handling the rapidly increasing amounts of electronic records. This paper proposes a deep learning approach to passenger profiling. The center of our approach is a Pythagorean fuzzy deep Boltzmann machine (PFDBM), whose parameters are expressed by Pythagorean fuzzy numbers such that each neuron can learn how a feature affects the production of the correct output from both the positive and negative sides. We propose a hybrid algorithm combining a gradient-based method and an evolutionary algorithm for training the PFDBM. Based on the novel learning model, we develop a deep neural network (DNN) for classifying normal passengers and potential attackers, and further develop an integrated DNN for identifying group attackers whose individual features are insufficient to reveal the abnormality. Experiments on data sets from Air China show that our approach provides much higher learning ability and classification accuracy than existing profilers. It is expected that the fuzzy deep learning approach can be adapted for a variety of complex pattern analysis tasks.


title: Semi-Global Weighted Least Squares in Image Filtering

abstract: Solving the global method of Weighted Least Squares (WLS) model in image filtering is both time- and memory-consuming. In this paper, we present an alternative approximation in a time- and memory- efficient manner which is denoted as Semi-Global Weighed Least Squares (SG-WLS). Instead of solving a large linear system, we propose to iteratively solve a sequence of subsystems which are one-dimensional WLS models. Although each subsystem is one-dimensional, it can take two-dimensional neighborhood information into account due to the proposed special neighborhood construction. We show such a desirable property makes our SG-WLS achieve close performance to the original two-dimensional WLS model but with much less time and memory cost. While previous related methods mainly focus on the 4-connected/8-connected neighborhood system, our SG-WLS can handle a more general and larger neighborhood system thanks to the proposed fast solution. We show such a generalization can achieve better performance than the 4-connected/8-connected neighborhood system in some applications. Our SG-WLS is $\sim20$ times faster than the WLS model. For an image of $M\times N$, the memory cost of SG-WLS is at most at the magnitude of $max\{\frac{1}{M}, \frac{1}{N}\}$ of that of the WLS model. We show the effectiveness and efficiency of our SG-WLS in a range of applications.


title: Synergy, Redundancy and Common Information

abstract: We consider the problem of decomposing the total mutual information conveyed by a pair of predictor random variables about a target random variable into redundant, unique and synergistic contributions. We focus on the relationship between "redundant information" and the more familiar information-theoretic notions of "common information". Our main contribution is an impossibility result. We show that for independent predictor random variables, any common information based measure of redundancy cannot induce a nonnegative decomposition of the total mutual information. Interestingly, this entails that any reasonable measure of redundant information cannot be derived by optimization over a single random variable.


title: Similar researcher search in academic environments

abstract: Entity search is an emerging IR and NLP task that involves the retrieval of entities of a specific type in response to a query. We address the similar researcher search" or the "researcher recommendation" problem, an instance of similar entity search" for the academic domain. In response to a researcher name' query, the goal of a researcher recommender system is to output the list of researchers that have similar expertise as that of the queried researcher. We propose models for computing similarity between researchers based on expertise profiles extracted from their publications and academic homepages. We provide results of our models for the recommendation task on two publicly-available datasets. To the best of our knowledge, we are the first to address content-based researcher recommendation in an academic setting and demonstrate it for Computer Science via our system, ScholarSearch.


title: Design and natural science research on information technology

abstract: Research in IT must address the design tasks faced by practitioners. Real problems must be properly conceptualized and represented, appropriate techniques for their solution must be constructed, and solutions must be implemented and evaluated using appropriate criteria. If significant progress is to be made, IT research must also develop an understanding of how and why IT systems work or do not work. Such an understanding must tie together natural laws governing IT systems with natural laws governing the environments in which they operate. This paper presents a two dimensional framework for research in information technology. The first dimension is based on broad types of design and natural science research activities: build, evaluate, theorize, and justify. The second dimension is based on broad types of outputs produced by design research: representational constructs, models, methods, and instantiations. We argue that both design science and natural science activities are needed to insure that IT research is both relevant and effective.


title: Polarization-Reconfigurable Magnetoelectric Dipole Antenna for 5G Wi-Fi

abstract: A novel polarization-reconfigurable aperture-coupled magnetoelectric (ME) dipole antenna is presented. By electronically controlling the state of switches between square patches and an additional strip of the ME dipole, the antenna is capable of switching the polarization among one linearly polarized (LP) and two orthogonal circularly polarized states. A fully functional prototype is developed and tested, demonstrating the antenna with an overlapped bandwidth of 16% from 5.07 to 5.95 GHz for both $S$ 11 ≤ –10 dB and axial ratio (AR) ≤ 3 dB. A measured gain of approximately 8.2 dBi and a radiation efficiency of approximately 85% over the overlapped band are obtained for all polarization states. Moreover, due to advantages of the ME dipole, the proposed design is able to realize stable radiation patterns with both cross-polarization and back-radiation levels lower than –13 dB. With all these features, the proposed design is very attractive for 5G Wi-Fi applications.


title: Incorporating Feedback into Tree-based Anomaly Detection

abstract: Anomaly detectors are often used to produce a ranked list of statistical anomalies, which are examined by human analysts in order to extract the actual anomalies of interest. Unfortunately, in realworld applications, this process can be exceedingly difficult for the analyst since a large fraction of high-ranking anomalies are false positives and not interesting from the application perspective. In this paper, we aim to make the analyst's job easier by allowing for analyst feedback during the investigation process. Ideally, the feedback influences the ranking of the anomaly detector in a way that reduces the number of false positives that must be examined before discovering the anomalies of interest. In particular, we introduce a novel technique for incorporating simple binary feedback into tree-based anomaly detectors. We focus on the Isolation Forest algorithm as a representative tree-based anomaly detector, and show that we can significantly improve its performance by incorporating feedback, when compared with the baseline algorithm that does not incorporate feedback. Our technique is simple and scales well as the size of the data increases, which makes it suitable for interactive discovery of anomalies in large datasets.


title: Unifying Linear Dimensionality Reduction

abstract: Linear dimensionality reduction methods are a cornerstone of analyzing high dimensional data, due to their simple geometric interpretations and typically attractive computational properties. These methods capture many data features of interest, such as covariance, dynamical structure, correlation between data sets, input-output relationships, and margin between data classes. Methods have been developed with a variety of names and motivations in many fields, and perhaps as a result the connections between all these methods have not been highlighted. Here we survey methods from this disparate literature as optimization programs over matrix manifolds. We discuss principal component analysis, factor analysis, linear multidimensional scaling, Fisher's linear discriminant analysis, canonical correlations analysis, maximum autocorrelation factors, slow feature analysis, sufficient dimensionality reduction, undercomplete independent component analysis, linear regression, distance metric learning, and more. This optimization framework gives insight to some rarely discussed shortcomings of well-known methods, such as the suboptimality of certain eigenvector solutions. Modern techniques for optimization over matrix manifolds enable a generic linear dimensionality reduction solver, which accepts as input data and an objective to be optimized, and returns, as output, an optimal low-dimensional projection of the data. This simple optimization framework further allows straightforward generalizations and novel variants of classical methods, which we demonstrate here by creating an orthogonal-projection canonical correlations analysis. More broadly, this survey and generic solver suggest that linear dimensionality reduction can move toward becoming a blackbox, objective-agnostic numerical technology.


title: Clustering Social Networks Using Distance-Preserving Subgraphs

abstract: Cluster analysis describes the division of a dataset into subsets of related objects, which are usually disjoint. There is considerable variety among the different types of clustering algorithms. Some of these clustering algorithms represent the dataset as a graph, and use graph-based properties to generate the clusters. However, many graph properties have not been explored as the basis for a clustering algorithm. In graph theory, a subgraph of a graph is distance-preserving if the distances (lengths of shortest paths) between every pair of vertices in the subgraph are the same as the corresponding distances in the original graph. In this paper, we consider the question of finding proper distance-preserving subgraphs, and the problem of partitioning a simple graph into an arbitrary number of distance-preserving subgraphs for clustering purposes. We also present a clustering algorithm called DP-Cluster, based on the notion of distance-preserving subgraphs. One area of research that makes considerable use of graph theory is the analysis of social networks. For this reason we evaluate the performance of DP-Cluster on two real-world social network datasets.


title: An Efficient Algorithm for the Incremental Updation of Association Rules in Large Databases

abstract: Efficient discovery of association rules in large databases is a well studied problem and several approaches have been proposed. However, it is non trivial to maintain the association rules current when the database is updated since, such updates could invalidate existing rules or introduce new rules. In this paper, we propose an incremental updating technique based on negative borders, for the maintenance of association rules when new transaction data is added to or deleted from a transaction database. An important feature of our algorithm is that it requires a full scan (exactly one) of the whole database only if the database update causes the negative border of the set of large itemsets to expand.


title: A Comprehensive View on Urban Spatial Structure: Urban Density Patterns of German City Regions

abstract: Urban density must be considered a key concept in the description of a city’s urban spatial structure. Countless studies have provided evidence of a close relationship between built density and activity densities, on the one hand, and urban environmental conditions or social practices, on the other hand. However, despite the concept’s common use in urban research, urban density is a rather fuzzy and highly complex concept that is accompanied by a confusing variety of indicators and measurement approaches. To date, an internationally-accepted standard for the implementation of density indicators that permits a robust comparison of different countries, regions or cities is widely missing. This paper discusses the analytical opportunities that recent remote sensing data offer in regard to an objective and transparent measurement of built density patterns of city regions. It furthermore clarifies the interrelations between built and activity densities. We apply our approach to four German city regions to demonstrate the analytical capacity of spatially-refined density indicators for the purposes of comparative urban research at a regional scale. In so doing, we contribute to a more encompassing and robust understanding of the urban density concept when analyzing regional morphology.


title: Digital CMOS neuromorphic processor design featuring unsupervised online learning

abstract: The compute-intensive and power-efficient brain has been a source of inspiration for a broad range of neural networks to solve recognition and classification tasks. Compared to the supervised deep neural networks (DNNs) that have been very successful on well-defined labeled datasets, bio-plausible spiking neural networks (SNNs) with unsupervised learning rules could be well-suited for training and learning representations from the massive amount of unlabeled data. To design dense and low-power hardware for such unsupervised SNNs, we employ digital CMOS circuits for neuromorphic processors, which can exploit transistor scaling and dynamic voltage scaling to the utmost. As exemplary works, we present two neuromorphic processor designs. First, a 45nm neuromorphic chip is designed for a small-scale network of spiking neurons. Through tight integration of memory (64k SRAM synapses) and computation (256 digital neurons), the chip demonstrates on-chip learning on pattern recognition tasks down to 0.53V supply. Secondly, a 65nm neuromorphic processor that performs unsupervised on-line spike-clustering for brain sensing applications is implemented with 1.2k digital neurons and 4.7k latch-based synapses. The processor exhibits a power consumption of 9.3µW/ch at 0.3V supply. Synapse hardware precision, efficient synapse memory array access, overfitting, and voltage scaling will be discussed for dense and power-efficient on-chip learning for CMOS spiking neural networks.


title: Generate modern style Chinese poems based on common sense and evolutionary computation

abstract: Writing a poem is considered to be one of intelligent tasks that can be conducted by humans and that demand high competence on creativity, literary knowledge and common sense. This paper proposes an automated framework that can generate modern Chinese style poems under arbitrary topics. It is based on common sense knowledgebase ConceptNet and an evolutionary algorithm to search for a “best scored” poem. We design lexicon patterns and word segmentation sentence templates to compose lexicons into a modern style Chinese five-character verse. It turns out that after cleaning up ConceptNet noisy relations, the evolutionary algorithm can generate poems within 200 generations that make sense both syntactically and semantically under many different topics.


title: How HTTP/2 pushes the web: An empirical study of HTTP/2 server push

abstract: The simplicity of HTTP made it the default building block for desktop and mobile apps, yet it suffers from inherent inefficiencies in the modern web. HTTP/2 was designed to address these inefficiencies and its adoption remarks a major protocol shift in the Internet. Despite this relevance, its Internet-wide adoption remains unknown. Especially, the adoption and use of server push — advertised as a key feature to further reduce page load times — is completely unexplored. To answer both questions, we provide large-scale measurements of the HTTP/2 adoption and usage of server push in the wild, probing the entire IPv4 address space and the complete set of .com/.net/.org domains. We find 5.38M HTTP/2 enabled domains hosted by only few infrastructures driving this adoption. While we find the overall HTTP/2 adoption to increase, only few hundred domains utilize server push. We examine pushed content, push strategies and identify the use of currently undocumented push strategies. Moreover, we discover large sources of overheads through server push for reoccurring page visits. By measuring page load times, we show that while push can speed up webpages, it also can slow them down — motivating the need for optimized push strategies.


title: EFFICIENT EXTERNAL SORTING ON FLASH MEMORY EMBEDDED DEVICES

abstract: Many embedded system applications involve storing and querying large datasets. Existing research in this area has focused on adapting and applying conventional database algorithms to embedded devices. Algorithms designed for processing queries on embedded devices must be able to execute given the small amount of available memory and energy constraints. Most embedded devices use flash memory to store large amounts of data. Flash memory has unique performance characteristics that can be exploited to improve algorithm performance. In this paper, we describe the Flash MinSort external sorting algorithm that uses an index, generated at runtime, to take advantage of fast random reads in flash memory. This algorithm adapts to the amount of memory available and performs best in applications where sort keys are clustered. Experimental results show that Flash MinSort is two to ten times faster than previous approaches for small memory sizes where external merge sort is not executable.


title: All-electric ships—A review of the present state of the art

abstract: Modern navies are exploring ways to power all-electric ships (AES), enabling ships' loads to be powered from the same electrical source as that of the propulsion system, thus avoiding a separate generation system for these loads. AES are envisioned to be equipped with a variety of high power electronic components, pulsed loads like electromagnetic guns and electromagnetic aircraft launchers, machines and cables made of high temperature superconductors that improve efficiency, and highly intelligent controls for adaptability and reconfiguration. This paper presents a review of present state-of-the-art in the field of AES covering all the technological aspects.


title: Memory consolidation, retrograde amnesia and the hippocampal complex

abstract: Abstract Results from recent studies of retrograde amnesia following damage to the hippocampal complex of human and non-human subjects have shown that retrograde amnesia is extensive and can encompass much of a subject's lifetime; the degree of loss may depend upon the type of memory assessed. These and other findings suggest that the hippocampal formation and related structures are involved in certain forms of memory (e.g. autobiographical episodic and spatial memory) for as long as they exist and contribute to the transformation and stabilization of other forms of memory stored elsewhere in the brain.


title: Hate Lingo: A Target-based Linguistic Analysis of Hate Speech in Social Media

abstract: While social media empowers freedom of expression and individual voices, it also enables anti-social behavior, online harassment, cyberbullying, and hate speech. In this paper, we deepen our understanding of online hate speech by focusing on a largely neglected but crucial aspect of hate speech -- its target: either"directed"towards a specific person or entity, or"generalized"towards a group of people sharing a common protected characteristic. We perform the first linguistic and psycholinguistic analysis of these two forms of hate speech and reveal the presence of interesting markers that distinguish these types of hate speech. Our analysis reveals that Directed hate speech, in addition to being more personal and directed, is more informal, angrier, and often explicitly attacks the target (via name calling) with fewer analytic words and more words suggesting authority and influence. Generalized hate speech, on the other hand, is dominated by religious hate, is characterized by the use of lethal words such as murder, exterminate, and kill; and quantity words such as million and many. Altogether, our work provides a data-driven analysis of the nuances of online-hate speech that enables not only a deepened understanding of hate speech and its social implications but also its detection.


title: Variable On-Time (VOT)-Controlled Critical Conduction Mode Buck PFC Converter for High-Input AC/DC HB-LED Lighting Applications

abstract: For high input voltage (>;264 Vac) ac/dc applications, a buck power factor correction (PFC) converter is a good choice because of its low output voltage, high efficiency, lifetime improvement, and cost reduction by using a low voltage rating (<;200 V) electrolytic capacitor. However, due to the inherent dead angle of the input current, the harmonics of the buck PFC converter are high, which limits its application in lighting systems. In order to make the buck PFC converter meet the harmonics requirements (IEC61000-3-2, Class C) in lighting applications, this paper proposes a variable on-time controller for a critical conduction mode (CRM) buck PFC front-end converter for isolated high-brightness LED applications. By feedforwarding the input voltage and regulating the on-time of the switch, the high-order harmonics can be reduced to meet the lighting system limitations. Experimental results obtained on a 150-W CRM buck front-end PFC prototype show that the efficiency of buck PFC exceeds 96% during the entire line input range (250-530 Vac) at full load, and the current harmonics content can meet the harmonic requirements.


title: Micro-Doppler signatures for intelligent human gait recognition using a UWB impulse radar

abstract: A UWB impulse Doppler radar has been developed recently to detect the micro-Doppler signatures of human motions. The system RF front-end, wideband antenna array and high speed data acquisition module are introduced in details. The developed radar is used to detect the Doppler features of arms swing, which has unique characteristics compared to movements of other body components, for intelligent human gait recognition. Several experiments, including human marching and walking, with either one-arm or two-arm swings, have been performed. Promising experimental results have been achieved from both high resolution range profiles and Doppler spectrogram. The developed radar is also capable of detecting vibrations of respiration and heartbeat for stationary human targets buried in collapse for search and rescue operations.


title: Semi-Stochastic Gradient Descent Methods

abstract: In this paper we study the problem of minimizing the average of a large number of smooth convex loss functions. We propose a new method, S2GD (Semi-Stochastic Gradient Descent), which runs for one or several epochs in each of which a single full gradient and a random number of stochastic gradients is computed, following a geometric law. For strongly convex objectives, the method converges linearly. The total work needed for the method to output an epsilon-accurate solution in expectation, measured in the number of passes over data, is proportional to the condition number of the problem and inversely proportional to the number of functions forming the average. This is achieved by running the method with number of stochastic gradient evaluations per epoch proportional to conditioning of the problem. The SVRG method of Johnson and Zhang arises as a special case. To illustrate our theoretical results, S2GD only needs the workload equivalent to about 2.1 full gradient evaluations to find a 10e-6 accurate solution for a problem with 10e9 functions and a condition number of 10e3.


title: Designing Serious Game Interventions for Individuals with Autism

abstract: The design of “Serious games” that use game components (e.g., storyline, long-term goals, rewards) to create engaging learning experiences has increased in recent years. We examine of the core principles of serious game design and examine the current use of these principles in computer-based interventions for individuals with autism. Participants who undergo these computer-based interventions often show little evidence of the ability to generalize such learning to novel, everyday social communicative interactions. This lack of generalized learning may result, in part, from the limited use of fundamental elements of serious game design that are known to maximize learning. We suggest that future computer-based interventions should consider the full range of serious game design principles that promote generalization of learning.


title: ElliFit: An unconstrained, non-iterative, least squares based geometric Ellipse Fitting method

abstract: A novel ellipse fitting method which is selective for digital and noisy elliptic curves is proposed in this paper. The method aims at fitting an ellipse only when the data points are highly likely belong to an ellipse. This is achieved using the geometric distances of the ellipse from the data points. The proposed method models the non-linear problem of ellipse fitting as a combination of two operators, with one being linear, numerically stable, and easily invertible, while the other being non-linear but unique and easily invertible operator. As a consequence, the proposed ellipse fitting method has several salient properties like unconstrained, stable, non-iterative, and computationally inexpensive. The efficacy of the method is compared against six contemporary and recent algorithms based on the least squares formulation using five experiments of diverse practical challenges, like digitization, incomplete ellipses, and Gaussian noise (up to 30%). Three of the experiments comprise of a total of 44,400 ellipses (positive test data) while the other two are tested on 320,000 non-elliptic conics (negative test data). The results show that the proposed method is quite selective to elliptic shapes only and provides accurate fitting results, indicating potential application in medical, robotics, object detection, and other image processing industrial applications.


title: Comparative study for feature selection algorithms in intrusion detection system

abstract: The Intrusion Detection System (IDS) deals with the huge amount of network data that includes redundant and irrelevant features causing slow training and testing procedure, higher resource usage and poor detection ratio. Feature selection is a vital preprocessing step in intrusion detection. Hence, feature selec-tion is an essential issue in intrusion detection and need to be addressed by selec-ting the appropriate feature selection algorithm. A major challenge to select the optimal feature selection methods can precisely calculate the relevance of fea-tures to the detection process and the redundancy among features. In this paper, we study the concepts and algorithms used for feature selection algorithms in the IDS. We conclude this paper by identifying the best feature selection algorithm to select the important and useful features from the network dataset.


title: Uses and Gratifications Theory in the 21st Century

abstract: Some mass communications scholars have contended that uses and gratifications is not a rigorous social science theory. In this article, I argue just the opposite, and any attempt to speculate on the future direction of mass communication theory must seriously include the uses and gratifications approach. In this article, I assert that the emergence of computer-mediated communication has revived the significance of uses and gratifications. In fact, uses and gratifications has always provided a cutting-edge theoretical approach in the initial stages of each new mass communications medium: newspapers, radio and television, and now the Internet. Although scientists are likely to continue using traditional tools and typologies to answer questions about media use, we must also be prepared to expand our current theoretical models of uses and gratifications. Contemporary and future models must include concepts such as interactivity, demassification, hypertextuality, and asynchroneity. Researchers must also be willing to explore interpersonal and qualitative aspects of mediated communication in a more holistic methodology.


title: An evaluation framework for auto-conversion of 2D to 3D video streaming using depth profile and pipelining technique in handheld cellular devices

abstract: The main objective of this paper is to propose a Reliable and Speedy Communication for Upstream Emergency (RESCUE) framework to create 3D remote sensing videos. The proposed framework uses low memory space on mobile devices with the help of efficient pipelining processes. Many methods for converting 2D videos to 3D have been proposed wherein developers convert high-quality 2D to 3D. We propose a method to apply the pre-classification process on either training on dataset or heuristics based on the quality of streaming attributes. The evaluation framework automatically changes remote sensing videos from 2D to 3D by using depth profile with method filter, segmentation and sharpening transformation of the videos. The traditional Hough transformation algorithm is not suitable for hardware implementation and saliency is based on the colour histogram to support slow motion object of frames which leads to inordinate delay in converting 2D to 3D. Therefore, the RESCUE framework, with its extended vanishing point and line algorithm, is applied to provide suitable solutions and overcome the drawbacks of existing techniques. The framework is a lightweight process when compared with JAVRAE, and it utilizes limited phone memory and consumes less processing time.


title: Low-Profile, Wideband Dual-Polarized Antenna With High Isolation and Low Cross Polarization

abstract: A wideband dual-polarized antenna with high isolation and low cross polarization is presented. A pair of orthogonal planar printed dipoles, with a reflector ground plane, are used to obtain the two linear polarizations. Slots cut at the dipole's corners and shorted connections between the orthogonal dipoles are used to improve the isolation and cross polarization. Measured results show that the antenna achieves a VSWR <; 1.5 from 575-722 MHz (22.7% bandwidth). The isolation between the two polarizations is better than 35 dB, and the cross-pol level is 30 dB below the co-pol level over the entire band. The overall height of the antenna is 80 mm, which is about 0.15λ at 575 MHz.


title: Deterministic Replay of System's Execution with Multi-target QEMU Simulator for Dynamic Analysis and Reverse Debugging

abstract: Deterministic replay is a technology that allows capturing the execution of a running virtual machine for later replay. It is useful for debugging failures that are not easily reproduced, capturing execution traces, dynamic analysis of the software, and so on. This paper describes the design and implementation of full-system logging and deterministic replay mechanisms in QEMU simulator for several target platforms.


title: Double-Balanced Gilbert Mixer with Current Bleeding for RF Front-End Using 0.13µm SiGe BiCMOS Technology

abstract: This paper presents the design of a differential double-balanced Gilbert mixer in 0.13 um SiGe BiCMOS technology. A current-bleeding injection technique is adopted to increase the bias current at the driver stage without causing overvoltage headroom at the differential pair stage. This mechanism improves the performance in terms of conversion gain, linearity and noise figure. The proposed mixer achieves 10.7 dB conversion gain, 15 dB noise figure, -1.67 dBm 1-dB compression point, and 5 dBm IIP3. The designed double balanced Gilbert mixer with current bleeding is part of an integrated RF front-end for full duplex radio applications in the 2.4 GHz band and occupies an area of 0.1002 × 0.0748 mm2 excluding the pads.


title: The design and development of a gloveless endoskeletal prosthetic hand.

abstract: Current prosthetic hands, although functional, have the potential of being improved significantly. We report here the design and development of a novel prosthetic hand that is lighter in weight, less expensive, and more functional than current hands. The new prosthesis features an endoskeleton embedded in self-skinning foam that provides a realistic look and feel and obviates the need for a separate cosmetic glove. The voluntary-closing mechanism offers variable grip strength. Placement of joints at three locations (metacarpophalangeal and proximal and distal interphalangeal) within each of four fingers affords realistic finger movement. High-strength synthetic cable attached to the distal phalanx of each finger is used to effect flexion. A multiposition passive thumb provides both precision and power grips. The new prosthesis can securely grasp objects with various shapes and sizes. Compared to current hands, weight has been reduced by approximately 50%, and cable excursion required for full finger flexion by more than 50%. The new endoskeletal prosthesis requires approximately 12-24% less force input to grasp a variety of everyday objects, largely due to its adaptive grip. Production cost estimates reveal the new prosthesis to be significantly less expensive than current prosthetic hands.


title: The Ritvo Autism Asperger Diagnostic Scale-Revised (RAADS-R): A Scale to Assist the Diagnosis of Autism Spectrum Disorder in Adults: An International Validation Study

abstract: The Ritvo Autism Asperger Diagnostic Scale-Revised (RAADS-R) is a valid and reliable instrument to assist the diagnosis of adults with Autism Spectrum Disorders (ASD). The 80-question scale was administered to 779 subjects (201 ASD and 578 comparisons). All ASD subjects met inclusion criteria: DSM-IV-TR, ADI/ADOS diagnoses and standardized IQ testing. Mean scores for each of the questions and total mean ASD vs. the comparison groups’ scores were significantly different (p < .0001). Concurrent validity with Constantino Social Responsiveness Scale-Adult = 95.59%. Sensitivity = 97%, specificity = 100%, test–retest reliability r = .987. Cronbach alpha coefficients for the subscales and 4 derived factors were good. We conclude that the RAADS-R is a useful adjunct diagnostic tool for adults with ASD.