title: Interaction Forces of Soft Fiber Reinforced Bending Actuators

abstract: Soft-bending actuators are inherently compliant, compact, and lightweight. They are preferable candidates over rigid actuators for robotic applications ranging from physical human interaction to delicate object manipulation. However, characterizing and predicting their behaviors are challenging due to the material nonlinearities and the complex motions they can produce. This paper investigates a soft-bending actuator design that uses a single air chamber and fiber reinforcements. Additionally, the actuator design incorporates a sensing layer to enable real-time bending angle measurement for analysis and control. In order to study the bending and force exertion characteristics when interacting with the environment, a quasi-static analytical model is developed based on the bending moments generated from the applied internal pressure and stretches of the soft materials. Comparatively, a finite-element method model is created for the same actuator design. Both the analytical model and the finite-element model are used in the fiber reinforcement analysis and the validation experiments with fabricated actuators. The experimental results demonstrate that the analytical model captures the relationships of supplied air pressure, actuator bending angle, and interaction force at the actuator tip. Moreover, it is shown that an off-the-shelf bend angle sensor integrated to the actuator in this study could provide real-time force estimation, thus eliminating the need for a force sensor.


title: Data integration from open internet sources and network detection to combat underage sex trafficking

abstract: Victims of sex trafficking are compelled into sexual exploitation by the use of coercion, deception or fraud, and often find themselves forced into a situation of imprisonment and slavery. This form of human trafficking is a serious felony and law enforcement agents have active ongoing work to combat such crimes, with especial focus on under-age sexual trafficking. Sexual exploitation from traffickers can be often masked as services such as classified ads and escort and massage services (EMS) on the Internet. In this paper, we describe the prototype of a law enforcement support system that aims to make daily extractions of online data, filter relevant information, find hidden patterns and display relevant leads to law enforcement agents. The system uses information retrieval and integration, natural language processing, image analysis, and data linking techniques to allow various forms of relevant information visualization for supporting the combat of sex trafficking. It has been used by law enforcement agencies on specific occasions, and is being developed to suit certain operational needs.


title: Area Classification Is Not a Copy-and-Paste Process: Performing Reliable Hazardous-Area-Classification Studies

abstract: When designing electrical installations in an industry that processes flammable products, it is necessary to know where the classified locations are. These locations are identified in drawings to direct the specification of adequate electrical and electronic equipment. This article shows why the figures' copy-and-paste practice does not guarantee a cost-effective installation. It also presents comparisons between gas dispersion models, aiming to help professionals perform reliable hazardous area-classification studies.


title: Guaranteeing persistent feasibility of model predictive motion planning for autonomous vehicles

abstract: Model predictive control (MFC) approach is prone to loss of feasibility due to the limited prediction horizon for decision making. For autonomous vehicle motion planning, many of detected obstacles, which are beyond the prediction horizon, cannot be considered in the instantaneous decisions, and late consideration of them may cause infeasibility. The conditions that guarantee persistent feasibility of a model predictive motion planning scheme are studied in this paper. Maintaining the system's states in a control invariant set of the system guarantees the persistent feasibility of the corresponding MPC scheme. Therefore, the persistent feasibility concern can be expressed as the problem of computing an effective control invariant set of the system and maintaining the system states inside it. In this paper, two approaches are presented to compute control invariant sets for the motion planning problem, the linearization-convexification approach and the brute-force search approach. The control invariant sets calculated via these two approaches are numerically analyzed and compared.


title: Mobile applications in tourism: the future of the tourism industry?

abstract: Purpose ::: ::: ::: ::: ::: The purpose of this paper is to extend the unified theory of acceptance and use of technology with psychological constructs, namely, perceived playfulness, mobile innovativeness in information technology and perceived expressiveness (PEX) to examine on the consumers’ intention to adopt mobile applications (m-apps) as another mean in purchasing tourism-related product and services via their mobile devices. The study also included gender as moderating variable. ::: ::: ::: ::: ::: Design/methodology/approach ::: ::: ::: ::: ::: Partial least square-structural equation modeling approach was adopted to test the research framework using 474 valid respondents. ::: ::: ::: ::: ::: Findings ::: ::: ::: ::: ::: The results demonstrated that only performance expectancy and PEX are non-significant with the intention to adopt. In addition, gender was found to have a moderating effect between social influence and intention. ::: ::: ::: ::: ::: Practical implications ::: ::: ::: ::: ::: The study has several useful implications for researchers, m-apps developers, travel-related organizations and even government agencies when rendering m-apps services and disseminating information to their potential consumers. ::: ::: ::: ::: ::: Originality/value ::: ::: ::: ::: ::: The study contributes to the growing literature on m-apps in tourism from a developing country’s perspectives.


title: Super resolution for multiview mixed resolution images in transform-domain with optimal weight

abstract: Mixed resolution framework has been applied in stereoscopic video coding system to reduce data-size and bit rate. Recently, Super Resolution (SR) problem in mixed resolution format has drawn a lot of attention in order to provide the users with high quality 3D visual experience. In this paper we present a novel SR scheme, which not only borrows the high frequency content from neighboring full resolution frame, but also makes use of all high resolution (HR) warped images to enhance low resolution (LR) view image. The correlation between the target LR view and auxiliary HR views determines the weight of these high frequency components, which will be merged into LR frame to construct the output HR image. The DCT is used for frequency decomposition and frequency integration. Experimental results show that the proposed method achieves higher performance in both subjective and objective evaluations.


title: Mobile edge computing-enabled channel-aware video streaming for 4G LTE

abstract: Ever increasing numbers of users are surfing the net via an LTE access network. It has been predicted that three quarters (75%) of the world's mobile data traffic will be video-based by 2020. As the video space grows, media companies have been using adaptive bitrate technology for many years. The MPEG-DASH is the widely used HTTP-based adaptive streaming solution based on TCP. To address the bandwidth utilization issue, in this paper we investigate the ETSI Mobile Edge Computing (MEC) Intelligent Video Acceleration Service mechanism to improve video transmission over 4G LTE. Specifically, with the radio information provided by the MEC server, the video server can adaptively transmit just one copy of suitable bitrate data based on the throughput. We use OpenAirInterface (OAI), a 4G compliance emulator, as our testbed to implement and verify our proposed MEC-based adaptive video transmission mechanism. We design and implement a “filter” module in the Medium Access Control (MAC) layer over the OAI platform, which can reference Channel Quality Indicator (CQI) as a parameter to decide which bitrate type of data should be passed to the MAC Packet Scheduler for real resource allocation. By using constant-bitrate as our control groups, the testbed indicates that, consistent with our expectations, our mechanism can significantly benefit from lower latency with a reasonable throughput.


title: Enhancing Performance of a Horizontal Axis Tidal Turbine using Adaptive Blades

abstract: Free stream tidal turbines are a source of growing interest in the marine renewable energy field. Some designs use variable pitch blade control devices in order to maximize the efficiency of the turbines; however these are complex to design, construct and maintain under the severe load conditions sub sea devices experience. Adaptive materials have been used in the wind industry to create bend-twist coupled blades in an effort to bring turbine efficiency ever closer to the Betz limit, and increase annual energy capture. This work encompasses a feasibility study, focusing on hydrodynamic performance calculations, wherein the blade geometry is artificially reconfigured as a function of flow velocity. The pressure distribution over the blade is also analysed. The concept of a passively adaptive tidal turbine blade is shown to increase annual energy capture, reduce blade loading and delay cavitation inception.


title: The Detection of Disease Clustering and a Generalized Regression Approach

abstract: The problem of identifying subtle time-space clustering of disease, as may be occurring in leukemia, is described and reviewed. Published approaches, generally associated with studies of leukemia, not dependent on knowledge of the underlying population for their validity, are directed towards identifying clustering by establishing a relationship between the temporal and the spatial separations for the n ( n - 1)/2 possible pairs which can be formed from the n observed cases of disease. Here it is proposed that statistical power can be improved by applying a reciprocal transform to these separations. While a permutational approach can give valid probability levels for any observed association, for reasons of practicability, it is suggested that the observed association be tested relative to its permutational variance. Formulas and computational procedures for doing so are given. ::: ::: While the distance measures between points represent symmetric relationships subject to mathematical and geometric regularities, the variance formula developed is appropriate for arbitrary relationships. Simplified procedures are given for the case of symmetric and skew-symmetric relationships. The general procedure is indicated as being potentially useful in other situations as, for example, the study of interpersonal relationships. Viewing the procedure as a regression approach, the possibility for extending it to nonlinear and multivariate situations is suggested. ::: ::: Other aspects of the problem and of the procedure developed are discussed. ::: ::: Similarly, pure temporal clustering can be identified by a study of incidence rates in periods of widespread epidemics. In point of fact, many epidemics of communicable diseases are somewhat local in nature and so these do actually constitute temporal-spatial clusters. For leukemia and similar diseases in which cases seem to arise substantially at random rather than as clear-cut epidemics, it is necessary to devise sensitive and efficient procedures for detecting any nonrandom component of disease occurrence. ::: ::: Various ingenious procedures which statisticians have developed for the detection of disease clustering are reviewed here. These procedures can be generalized so as to increase their statistical validity and efficiency. The technic to be given below for imparting statistical validity to the procedures already in vogue can be viewed as a generalized form of regression with possible useful application to problems arising in quite different contexts.


CHANGES IN EEC ALPHA FREQUENCY AND EVOKED RESPONSE LATENCY DURING SOLITARY CONFINEMENT J


The Proactive Insights Engine: Process Mining meets Machine Learning and Artificial Intelligence


Learning Deep Semantic Embeddings for Cross-Modal Retrieval.


title: Effect of UBM and BCB layers on the thermo-mechanical reliability of wafer level chip scale package (WLCSP)

abstract: Cracking of the silicon chip of a wafer level chip scale package (WLCSP) is encountered during a thermal cycle test (TCT). This paper attempts to examine the failure mechanism. Both numerical and experimental efforts were devoted to investigate the problem. A series of finite element models with different combinations of material properties and geometric configurations were developed. The results showed that both the under bump metallization (UBM) and the dielectric layer Benzocyclobuten (BCB) contributed significantly to the stress level induced inside the silicon chip. In addition, solder ball pull tests were performed. The silicon cratering failure mode was reproduced which confirmed the failure mechanism as proposed by the finite element analysis. The effects of all relevant constituent materials on the chip are discussed in detail. Suggestions for the product design improvement are provided at the end of the paper.


title: Flavonol glycosides from Montcalm dark red kidney bean: implications for the genetics of seed coat color in Phaseolus vulgaris L.

abstract: Three flavonol glycosides were isolated and identified from the commercial dark red kidney bean (Phaseolus vulgaris L.) cultivar Montcalm. In order of highest to lowest concentration these compounds were 3',4',5,7-tetrahydroxyflavonol 3-O-beta-D-glucopyranosyl (2-->1) O-beta-D-xylopyranoside (compound 1), quercetin 3-O-beta-D-glucopyranoside (compound 2), and kaempferol 3-O-beta-D-glucopyranoside (compound 3). Compound 1 is a flavonol glycoside that has not been reported before in P. vulgaris L. These three flavonol glycosides were yellow compounds that do not contribute to the garnet red color of Montcalm seed coats. Red-colored compounds which tested positive for proanthocyanidins are most likely responsible for the red seed coat color of Montcalm. Previous work on the chemistry of the compounds produced from the multi-allelic seed coat gene series C-C(r)()-c(u) indicated that neither anthocyanins nor flavonol glycosides were detected from seed coat extracts in the presence of the c(u)() locus. However, the seed coat color genotype of Montcalm is c(u) J g B v rk(d) and three flavonol glycosides were found. Technological advances such as modern HPLC analysis of seed coat extracts may allow for detection of small amounts of compounds which previously could not be seen using paper chromatography. Alternatively, the change of the Rk allele to rk(d) may allow for the synthesis of flavonol glycosides in the presence of c(u).


title: A Compact Broadband Water Patch Antenna

abstract: A novel compact broadband water dense patch antenna with relatively thick air layer is introduced. The distilled water with high permittivity is located on the top of the low-loss, low-permittivity supporting substrate to provide an electric wall boundary. The dense water patch antenna is excited with cavity mode, reducing the impact of dielectric loss of the water on the antenna efficiency. The designs of loading the distilled water and T-shaped shorting sheet are applied for size reduction. The wide bandwidth is attributed to the coupling L-shaped probe, proper size of the coupled T-shaped shorting sheet, and thick air layer. As a result, the dimensions of the water patch are only 0.146 λ0 × 0.078 λ0 × 0.056 λ0. The proposed antenna has a high radiation up to 70% over the lower frequency band of 4G mobile communication from 690 to 960 MHz. Good agreements are achieved between the measured results and the simulated results.


title: Low Power Flash ADC #1 M.Subba Reddy, #2

abstract: In this project, a new design for a low power CMOS flash Analog-to-Digital Converter (ADC) is proposed. A 6-bit flash ADC, with a maximum acquisition speed of 1 GHz, is implemented in a 1.2 V analog supply voltage. Microwind simulation results for the proposed flash ADC verifying the analytical results are also given. It shows that the proposed 6-bit flash ADC consumes about 72 mW in a commercial 90 nm CMOS process. The new design offers lower number of comparators and lower power consumption compared with the traditional flash ADC.


title: Internet of Things for Industrial Automation -- Challenges and Technical Solutions

abstract: Using internet of things (IoT) to connect things, service, and people for intelligent operations has been discussed and deployed in many industry domains such as smart city, smart energy, healthcare, food and water tracking, logistics and retail, and transportation. However, scarce information is available for IoT usage in industrial automation domain for reliable and collaborative automation with respect to e.g., enabling scalable collaboration between heterogeneous devices and systems, offering predictable and fault-tolerant real-time closed-loop control, and inclusion of intelligent service features from edge devices to the cloud. In this paper, we will clarify the specific quality attribute constraints within industrial automation, present specific industrial IoT challenges due to these constraints, and discuss the potentials of utilizing some technical solutions to cope with these challenges.


title: Estimation of blood pressure levels from reflective Photoplethysmograph using smart phones

abstract: As part of preventive healthcare, there is a need to regularly monitor blood pressure (BP) of cardiac patients and elderly people. Mobile Healthcare, measuring human vitals like heart rate, Spo2 and blood pressure with smart phones using the Photoplethysmography technique is becoming widely popular. But, for estimating the BP, multiple smart phone sensors or additional hardware is required, which causes uneasiness for patients to use it, individually. In this paper, we present a methodology to estimate the systolic and diastolic BP levels by only using PPG signals captured with smart phones, which adds to the affordability, usability and portability of the system. Initially, a training model (Linear Regression Model or SVM Model) for various known levels of BP is created using a set of PPG features. This model is later used to estimate the BP levels from the features of the newly captured PPG signals. Experiments are performed on benchmark hospital dataset and data captured from smart phones in our lab. Results indicate that by additionally adding information of height, weight and age play a vital role in increasing the accuracy of the estimation of BP levels.


title: Abdominal obesity and metabolic syndrome: exercise as medicine?

abstract: Background ::: Metabolic syndrome is defined as a cluster of at least three out of five clinical risk factors: abdominal (visceral) obesity, hypertension, elevated serum triglycerides, low serum high-density lipoprotein (HDL) and insulin resistance. It is estimated to affect over 20% of the global adult population. Abdominal (visceral) obesity is thought to be the predominant risk factor for metabolic syndrome and as predictions estimate that 50% of adults will be classified as obese by 2030 it is likely that metabolic syndrome will be a significant problem for health services and a drain on health economies.Evidence shows that regular and consistent exercise reduces abdominal obesity and results in favourable changes in body composition. It has therefore been suggested that exercise is a medicine in its own right and should be prescribed as such. ::: ::: ::: Purpose of this review ::: This review provides a summary of the current evidence on the pathophysiology of dysfunctional adipose tissue (adiposopathy). It describes the relationship of adiposopathy to metabolic syndrome and how exercise may mediate these processes, and evaluates current evidence on the clinical efficacy of exercise in the management of abdominal obesity. The review also discusses the type and dose of exercise needed for optimal improvements in health status in relation to the available evidence and considers the difficulty in achieving adherence to exercise programmes. ::: ::: ::: Conclusion ::: There is moderate evidence supporting the use of programmes of exercise to reverse metabolic syndrome although at present the optimal dose and type of exercise is unknown. The main challenge for health care professionals is how to motivate individuals to participate and adherence to programmes of exercise used prophylactically and as a treatment for metabolic syndrome.


title: Bot detection: Will focusing on recall cause overall performance deterioration?

abstract: Social bots are an effective tool in the arsenal of malicious actors who manipulate discussions on social media. Bots help spread misinformation, promote political propaganda, and inflate the popularity of users and content. Hence, it is necessary to differentiate bot accounts and human users. There are several bot detection methods that approach this problem. Conventional methods either focus on precision regardless of the overall performance or optimize overall performance, say \(F_1\), without monitoring its effect on precision or recall. Focusing on precision means that those users marked as bots are more likely than not bots but a large portion of the bots could remain undetected. From a user’s perspective, however, it is more desirable to have less interaction with bots, even if it would incur a loss in precision. This can be achieved by a detection method with higher recall. A trivial, but useless, solution for high recall is to classify every account (human or bot) as bot, hence, resulting in poor overall performance.


Towards Automatic Understanding of `Virtual Pointing’ in Interaction


A Performance and Scalability Analysis of Actor Message Passing and Migration in SALSA Lite


OBDA Beyond Relational DBs: A Study for MongoDB


Unsupervised Learning of Visual Representations using Videos


title: QR Code Recognition based on Principal Components Analysis Method

abstract: QR (Quick Response) code recognition systems (based on computer vision) have always been challenging to be accurately devised due to two main constraints: (1) QR code recognition system must be able to localize QR codes from an acquired image even in case of unfavorable conditions (illumination variations, perspective distortions) and (2) The system must be adapted to embedded system platforms in terms of processing complexity and resources requirement. Most of the earlier proposed QR code recognition systems implemented complex feature descriptors such as (Harris features, Hough transform which aim at extracting QR code pattern features and subsequently estimating their positions. This process is reinforced by pattern classifiers e.g. (Random forests, SVM) which are used to remove false detected patterns. Those approaches are very computationally expensive. Thus, they are not able to be run in real-time systems. ::: In this paper, a streamlined QR code recognition approach is proposed to be efficiently operable in systems characterized by a limited performance. The evoked approach is conducted as follows: the captured image is segmented in order to reduce searching space and extract the regions of interest. Afterwards a horizontal and vertical scans are performed to localize preliminarily QR code patterns, followed by Principal Component Analysis (PCA) method which allows removing false positives. Thereafter, the remaining patterns are assembled according to a constraint so as to localize the corresponding QR codes. Experimental results show that the incorporation of PCA decreases notably the processing time and increase QR code recognition accuracy (96%).


title: Circuit Modeling for Rectangular Printed Disc Monopole Antenna with Slot for UWB System

abstract: In this paper, a circuit model is done for rectangular printed disc monopole antenna with slot for ultra wideband application. For this circuit modeling, patch cavity model consists of series resistor and inductor which represent the surface resistance and surface inductance with the combination of LC circuit as the resonant circuit is introduced. Moreover, a slot is etched on the patch antenna and represented by a series LC circuit for its circuit model. The result is then repeated with modification done on the slot and also the circuit model. Finally return loss result for both antenna structure and circuit model were compared and discussed.


title: PWM Inverters Using Split-Wound Coupled Inductors

abstract: The number of voltage levels available in PWM voltage source inverters can be increased by using an inverter leg with a split-wound coupled inductor and interleaved PWM switching of the upper and lower switches. The magnetizing inductance of the split-wound inductor filters the PWM-cycle instantaneous voltage differences between the upper and lower switches. The windings of the same inductor present a high quality 3-level PWM voltage at the inverter output terminals, with the winding leakage inductance effectively in series with this voltage. Since dc-rail short circuits are not a serious issue, dead-time PWM signal delays can be minimized. The overall quality of the output PWM waveforms are improved with higher fundamental frequencies and fundamental voltages possible, and using higher PWM frequencies and lower common mode voltage stresses. The application of this technique in 3-phase inverters is emphasized. Simulation and experimental results are used to illustrate the operation of the proposed inverter structure.


title: Smoke rings from smoke

abstract: We give an algorithm which extracts vortex filaments ("smoke rings") from a given 3D velocity field. Given a filament strength h > 0, an optimal number of vortex filaments, together with their extent and placement, is given by the zero set of a complex valued function over the domain. This function is the global minimizer of a quadratic energy based on a Schrödinger operator. Computationally this amounts to finding the eigenvector belonging to the smallest eigenvalue of a Laplacian type sparse matrix. ::: Turning traditional vector field representations of flows, for example, on a regular grid, into a corresponding set of vortex filaments is useful for visualization, analysis of measured flows, hybrid simulation methods, and sparse representations. To demonstrate our method we give examples from each of these.


title: Toward Completeness in Concept Extraction and Classification

abstract: Many algorithms extract terms from text together with some kind of taxonomic classification (is-a) link. However, the general approaches used today, and specifically the methods of evaluating results, exhibit serious shortcomings. Harvesting without focusing on a specific conceptual area may deliver large numbers of terms, but they are scattered over an immense concept space, making Recall judgments impossible. Regarding Precision, simply judging the correctness of terms and their individual classification links may provide high scores, but this doesn't help with the eventual assembly of terms into a single coherent taxonomy. Furthermore, since there is no correct and complete gold standard to measure against, most work invents some ad hoc evaluation measure. We present an algorithm that is more precise and complete than previous ones for identifying from web text just those concepts 'below' a given seed term. Comparing the results to WordNet, we find that the algorithm misses terms, but also that it learns many new terms not in WordNet, and that it classifies them in ways acceptable to humans but different from WordNet.


title: Open pattern matching for C++

abstract: Pattern matching is an abstraction mechanism that can greatly simplify source code. We present functional-style pattern matching for C++ implemented as a library, called Mach71. All the patterns are user-definable, can be stored in variables, passed among functions, and allow the use of class hierarchies. As an example, we implement common patterns used in functional languages. ::: Our approach to pattern matching is based on compile-time composition of pattern objects through concepts. This is superior (in terms of performance and expressiveness) to approaches based on run-time composition of polymorphic pattern objects. In particular, our solution allows mapping functional code based on pattern matching directly into C++ and produces code that is only a few percent slower than hand-optimized C++ code. ::: The library uses an efficient type switch construct, further extending it to multiple scrutinees and general patterns. We compare the performance of pattern matching to that of double dispatch and open multi-methods in C++.


title: Trustworthy and protected data collection for event detection using networked sensing systems

abstract: Data collection in wireless networked sensing systems (WNSS) is usually not reliable due to sensor faults and/or security attacks. This makes detection of an event (e.g., structural damage) through data aggregation unreliable. In this paper, we propose a trustworthy and protected data collection (TPDC) framework for event detection in WNSS. This framework facilitates reliable data for aggregation at clusters of WNSS. The key idea of TPDC is to allow a cluster head to check whether or not the transmitted data is trustworthy (i.e., unaltered estimated at the sensor node level) and protected (i.e., received without alteration after the transmission) before aggregating the data at a cluster. For the trustworthy data, we propose an algorithm to make sure that transmitted data is unaltered. For the protected data, we present a truth discovery approach, whose goal is to infer truthful facts from unreliable sensor data. Through simulations, we demonstrate that the collected data in TPDC is trustworthy and protected that can make aggregation for event detection reliable.


title: Thwarting Fake OSN Accounts by Predicting their Victims

abstract: Traditional defense mechanisms for fighting against automated fake accounts in online social networks are victim-agnostic. Even though victims of fake accounts play an important role in the viability of subsequent attacks, there is no work on utilizing this insight to improve the status quo. In this position paper, we take the first step and propose to incorporate predictions about victims of unknown fakes into the workflows of existing defense mechanisms. In particular, we investigated how such an integration could lead to more robust fake account defense mechanisms. We also used real-world datasets from Facebook and Tuenti to evaluate the feasibility of predicting victims of fake accounts using supervised machine learning.


title: Experimental Validation of a Simple System for Through-the-Wall Inverse Scattering

abstract: In this letter, we propose a system for through-wall imaging based on a bistatic setup and a two-step inversion procedure. Since the adopted configuration is simple and low cost but only delivers a limited amount of data, the data-processing stage is tackled in a stepwise fashion. First, the targets are detected from the estimation of the surface impedance along the wall and then the area behind the identified portion of the wall is imaged by means of a linearized microwave-tomographic algorithm. The overall device is described, and the results of a proof-of-concept experimental validation are reported.


title: Part-of-Speech Relevance Weights for Learning Word Embeddings

abstract: This paper proposes a model to learn word embeddings with weighted contexts based on part-of-speech (POS) relevance weights. POS is a fundamental element in natural language. However, state-of-the-art word embedding models fail to consider it. This paper proposes to use position-dependent POS relevance weighting matrices to model the inherent syntactic relationship among words within a context window. We utilize the POS relevance weights to model each word-context pairs during the word embedding training process. The model proposed in this paper paper jointly optimizes word vectors and the POS relevance matrices. Experiments conducted on popular word analogy and word similarity tasks all demonstrated the effectiveness of the proposed method.


title: A 0.26-nJ/node, 400-kHz Tx Driving, Filtered Fully Differential Readout IC With Parasitic RC Time Delay Reduction Technique for 65-in $169 \times 97$ Capacitive-Type Touch Screen Panel

abstract: This paper presents a readout method and circuit for large, capacitive-type touch-screen panels (TSPs). Despite the considerable amount of RC time delay of large-area TSPs, the proposed readout method with a receiver (Rx) input series-capacitor improves the settling speed of signals transferred from the transmitter (Tx) to the Rx, by reducing the RC time delay. Combined with the Rx input series-capacitor, a capacitive-input fully differential filtered charge integrator effectively cancels out the display noise and reduces the self-noise in capacitive-type TSPs. The proposed Rx circuit was implemented using 0.35- $\mu \text{m}$ CMOS. Using a 65-in metal-mesh TSP with 169 Tx and 97 Rx electrodes mounted on a liquid-crystal display for testing, the proposed readout method achieved 65% reduction of signal settling time within an accuracy of $\geq 3\tau $ for the longest signal path of the TSP. Using the RC time delay reduction technique, the Tx driving frequency could be boosted by as much as 400 kHz, and the measured signal-to-noise ratio of 43.5 dB was obtained for finger touch at a 120-Hz scan rate, resulting in a figure-of-merit of 0.26 nJ/node, while the overall power consumption was 76 mW.


title: Citing for high impact

abstract: The question of citation behavior has always intrigued scientists from various disciplines. While general citation patterns have been widely studied in the literature we develop the notion of citation projection graphs by investigating the citations among the publications that a given paper cites. We investigate how patterns of citations vary between various scientific disciplines and how such patterns reflect the scientific impact of the paper. We find that idiosyncratic citation patterns are characteristic for low impact papers; while narrow, discipline-focused citation patterns are common for medium impact papers. Our results show that crossing-community, or bridging citation patters are high risk and high reward since such patterns are characteristic for both low and high impact papers. Last, we observe that recently citation networks are trending toward more bridging and interdisciplinary forms.


title: Learning Recurrent Binary/Ternary Weights

abstract: Recurrent neural networks (RNNs) have shown excellent performance in processing sequence data. However, they are both complex and memory intensive due to their recursive nature. These limitations make RNNs difficult to embed on mobile devices requiring real-time processes with limited hardware resources. To address the above issues, we introduce a method that can learn binary and ternary weights during the training phase to facilitate hardware implementations of RNNs. As a result, using this approach replaces all multiply-accumulate operations by simple accumulations, bringing significant benefits to custom hardware in terms of silicon area and power consumption. On the software side, we evaluate the performance (in terms of accuracy) of our method using long short-term memories (LSTMs) on various sequential models including sequence classification and language modeling. We demonstrate that our method achieves competitive results on the aforementioned tasks while using binary/ternary weights during the runtime. On the hardware side, we present custom hardware for accelerating the recurrent computations of LSTMs with binary/ternary weights. Ultimately, we show that LSTMs with binary/ternary weights can achieve up to 12x memory saving and 10x inference speedup compared to the full-precision implementation on an ASIC platform.


title: Cloud-Network Disaster Recovery against Cascading Failures

abstract: Abstract-Cloud computing uses cloud networks (CNs) that integrate and virtualize computing servers and communication networks. In a CN, virtual machines (VMs) are interconnected through virtual networks (VNs) provisioned over a physical optical network. A disaster event is a serious threat to cloud computing infrastructure, not only for CN disconnections caused by multiple infrastructure failures, but by subsequent and unpredictable CN disconnections induced by cascading failures. Studies on disaster protection for CNs suggest large pre-provisioning of additional capacity before a possible disaster, with limited protection for later cascading failures. In this work, we propose an adaptive and cascading- failure-aware CN disaster recovery scheme that (re-)acts after the disaster, and uses risk modeling to reduce the capacity required for the recovery and minimize the post-disaster disconnection of CNs. Major power grid outages could cause cascading failures on cloud infrastructure operation. Thus, in this study, propagation patterns of power grid failures are used to estimate the location of cascading failures. Simulation results based on human-made disasters, e.g., weapon of mass destruction (WMD) attacks, show that our approach can lead to significant reduction in the risk of CN disconnections due to cascading failures, while reducing up to 50% of the capacity re-provisioning required for the recovery.


title: Ideology and Brand Consumption

abstract: Do mundane daily choices, such as what brands people buy in a supermarket, reflect aspects of values and ideologies? This article presents a large-scale field study performed to determine whether traits associated with a conservative ideology, as measured by voting behavior and religiosity, are manifested in consumers’ routine, seemingly inconsequential product choices. Our analysis of market shares for a variety of frequently purchased products shows that both of these measures of conservatism are associated with a systematic preference for established national brands (as opposed to their generic substitutes) and with a lower propensity to buy newly launched products. These tendencies correspond with other psychological traits associated with a conservative ideology, such as preference for tradition and the status quo, avoidance of ambiguity and uncertainty, and skepticism about new experiences.


title: KB-LDA: Jointly Learning a Knowledge Base of Hierarchy, Relations, and Facts

abstract: Many existing knowledge bases (KBs), including Freebase, Yago, and NELL, rely on a fixed ontology, given as an input to the system, which defines the data to be cataloged in the KB, i.e., a hierarchy of categories and relations between them. The system then extracts facts that match the predefined ontology. We propose an unsupervised model that jointly learns a latent ontological structure of an input corpus, and identifies facts from the corpus that match the learned structure. Our approach combines mixed membership stochastic block models and topic models to infer a structure by jointly modeling text, a latent concept hierarchy, and latent semantic relationships among the entities mentioned in the text. As a case study, we apply the model to a corpus of Web documents from the software domain, and evaluate the accuracy of the various components of the learned ontology.


title: Cupid: Cluster-Based Exploration of Geometry Generators with Parallel Coordinates and Radial Trees

abstract: Geometry generators are commonly used in video games and evaluation systems for computer vision to create geometric shapes such as terrains, vegetation or airplanes. The parameters of the generator are often sampled automatically which can lead to many similar or unwanted geometric shapes. In this paper, we propose a novel visual exploration approach that combines the abstract parameter space of the geometry generator with the resulting 3D shapes in a composite visualization. Similar geometric shapes are first grouped using hierarchical clustering and then nested within an illustrative parallel coordinates visualization. This helps the user to study the sensitivity of the generator with respect to its parameter space and to identify invalid parameter settings. Starting from a compact overview representation, the user can iteratively drill-down into local shape differences by clicking on the respective clusters. Additionally, a linked radial tree gives an overview of the cluster hierarchy and enables the user to manually split or merge clusters. We evaluate our approach by exploring the parameter space of a cup generator and provide feedback from domain experts.


title: Optimal speed control of DC motor using linear quadratic regulator and model predictive control

abstract: The objective of this paper is to control the angular speed in a model of a DC motor using different control strategies like Model Predictive Control and Linear Quadratic Regulator for comparison purpose. Model Predictive Control approach provides online & offline computation of the optimization law by Multi Parametric Quadratic Programming. The controllers are designed based on the optimal control theory. Performance of these controllers has been verified through simulation results using MATLAB/SIMULINK software.


title: Deep Active Learning with a Neural Architecture Search

abstract: We consider active learning of deep neural networks. Most active learning works in this context have focused on studying effective querying mechanisms and assumed that an appropriate network architecture is a priori known for the problem at hand. We challenge this assumption and propose a novel active strategy whereby the learning algorithm searches for effective architectures on the fly, while actively learning. We apply our strategy using three known querying techniques (softmax response, MC-dropout, and coresets) and show that the proposed approach overwhelmingly outperforms active learning using fixed architectures.


title: Spectral Curvature Clustering (SCC)

abstract: This paper presents novel techniques for improving the performance of a multi-way spectral clustering framework (Govindu in Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), vol. 1, pp. 1150---1157, 2005; Chen and Lerman, 2007, preprint in the supplementary webpage) for segmenting affine subspaces. Specifically, it suggests an iterative sampling procedure to improve the uniform sampling strategy, an automatic scheme of inferring the tuning parameter from the data, a precise initialization procedure for K-means, as well as a simple strategy for isolating outliers. The resulting algorithm, Spectral Curvature Clustering (SCC), requires only linear storage and takes linear running time in the size of the data. It is supported by theory which both justifies its successful performance and guides our practical choices. We compare it with other existing methods on a few artificial instances of affine subspaces. Application of the algorithm to several real-world problems is also discussed.


title: Designing optimal spatial filters for single-trial EEG classification in a movement task

abstract: Abstract We devised spatial filters for multi-channel EEG that lead to signals which discriminate optimally between two conditions. We demonstrate the effectiveness of this method by classifying single-trial EEGs, recorded during preparation for movements of the left or right index finger or the right foot. The classification rates for 3 subjects were 94, 90 and 84%, respectively. The filters are estimated from a set of multi-channel EEG data by the method of Common Spatial Patterns, and reflect the selective activation of cortical areas. By construction, we obtain an automatic weighting of electrodes according to their importance for the classification task. Computationally, this method is parallel by nature, and demands only the evaluation of scalar products. Therefore, it is well suited for on-line data processing. The recognition rates obtained with this relatively simple method are as good as, or higher than those obtained previously with other methods. The high recognition rates and the method's procedural and computational simplicity make it a particularly promising method for an EEG-based brain–computer interface.


title: Assessing bike sharing business model

abstract: Bike-sharing program continues to spread widely around the world, offering many benefits, i.e. increased accessibility, inexpensiveness, a positive environmental impact, improved public health and increased mobility. Until 2017, there are more than 1.173 cities around the world has implemented bike-sharing programs and with active bikers has reached around 1.9 million. The fast growth of bike sharing program globally, unfortunately, is not followed with its trend in Indonesia. Bike sharing idea is still new and hardly can be found in Indonesian cities. With the status of eighth largest of world economy in 2017, the bike sharing business is expected to grow along with global trend. The article examined the pilot project of bike sharing in one of well-known state universities in Indonesia to illustrate the feasibility of bike sharing business. The article applied mobile application with cloud computing paradigm and business model canvas (BMC) to illustrate the feasibility of bike sharing business. The outcome of the paper is expected to be used as reference for growing bike sharing business in Indonesia.


title: Kinetic study of the extraction of copper(ii) by di(2-ethylhexyl)phosphoric acid in a Lewis-type cell

abstract: Abstract The rate of extraction of copper from aqueous nitrate media by di(2-ethylhexyl)phosphoric acid (D2EHPA) has been studied both theoretically and experimentally. A kinetic equation involving the diffusional or mass transfer resistance term was developed. The mass transfer resistance was found to have a significant influence on the overall rate of the extraction system. It could be inferred that a second intermediate complex was formed by the reaction of CuR i + with extractant in the organic bulk phase and also that the intrinsic chemical reaction was controlled by this step. A similarity between the Szyszkowski equation and the “interface saturation model” could be observed in the evaluation of the interfacial population of extractant.


title: Semantic Structure and Interpretability of Word Embeddings

abstract: Dense word embeddings, which encode meanings of words to low-dimensional vector spaces, have become very popular in natural language processing (NLP) research due to their state-of-the-art performances in many NLP tasks. Word embeddings are substantially successful in capturing semantic relations among words, so a meaningful semantic structure must be present in the respective vector spaces. However, in many cases, this semantic structure is broadly and heterogeneously distributed across the embedding dimensions making interpretation of dimensions a big challenge. In this study, we propose a statistical method to uncover the underlying latent semantic structure in the dense word embeddings. To perform our analysis, we introduce a new dataset (SEMCAT) that contains more than 6500 words semantically grouped under 110 categories. We further propose a method to quantify the interpretability of the word embeddings. The proposed method is a practical alternative to the classical word intrusion test that requires human intervention.


title: Efficient topic-based unsupervised name disambiguation

abstract: Name ambiguity is a special case of identity uncertainty where one person can be referenced by multiple name variations in different situations or even share the same name with other people. In this paper, we focus on the problem of disambiguating person names within web pages and scientific documents. We present an efficient and effective two-stage approach to disambiguate names. In the first stage, two novel topic-based models are proposed by extending two hierarchical Bayesian text models, namely Probabilistic Latent Semantic Analysis (PLSA) and Latent Dirichlet Allocation (LDA). Our models explicitly introduce a new variable for persons and learn the distribution of topics with regard to persons and words. After learning an initial model, the topic distributions are treated as feature sets and names are disambiguated by leveraging a hierarchical agglomerative clustering method. Experiments on web data and scientific documents from CiteSeer indicate that our approach consistently outperforms other unsupervised learning methods such as spectral clustering and DBSCAN clustering and could be extended to other research fields. We empirically addressed the issue of scalability by disambiguating authors in over 750,000 papers from the entire CiteSeer dataset.


title: Bilingual Correspondence Recursive Autoencoder for Statistical Machine Translation

abstract: Learning semantic representations and tree structures of bilingual phrases is beneficial for statistical machine translation. In this paper, we propose a new neural network model called Bilingual Correspondence Recursive Autoencoder (BCorrRAE) to model bilingual phrases in translation. We incorporate word alignments into BCorrRAE to allow it freely access bilingual constraints at different levels. BCorrRAE minimizes a joint objective on the combination of a recursive autoencoder reconstruction error, a structural alignment consistency error and a crosslingual reconstruction error so as to not only generate alignment-consistent phrase structures, but also capture different levels of semantic relations within bilingual phrases. In order to examine the effectiveness of BCorrRAE, we incorporate both semantic and structural similarity features built on bilingual phrase representations and tree structures learned by BCorrRAE into a state-of-the-art SMT system. Experiments on NIST Chinese-English test sets show that our model achieves a substantial improvement of up to 1.55 BLEU points over the baseline.


title: CRITICAL SUCCESS FACTORS FOR DATA WAREHOUSING: A CLASSIC ANSWER TO A MODERN QUESTION

abstract: Businesses, operating in an increasingly complex world, are pressured by globalization, customer demand, government regulations, market conditions, and competition. Executives must have the right data to make strategic, tactical, and operational decisions. Data warehouses, when implemented correctly, promise to meet this need by integrating and organizing key operational data in a form that is consistent, reliable, timely, and readily available, wherever and whenever needed. Unfortunately, 40% to 50% of data warehouse initiatives end in costly failure. The search for root causes converged on not understanding the user’s business problems. Rockart’s classic Critical Success Factor (CSF) method offers a proven way to move from high-level need to the lower-level data that support management decisions. While this paper summarizes the research on data warehousing implementation CSFs, its primary focus is to recommend Rockart’s CSF method as a means to address the fundamental challenge of building an effective data warehouse; namely understanding the user’s business problems and providing concise, dependable, data, information, and knowledge, about current trends and changes. The outcomes may be useful to business leaders and information technologists as they plan and implement data warehousing (DW) solutions.


title: Show and Tell: Lessons learned from the 2015 MSCOCO Image Captioning Challenge

abstract: Automatically describing the content of an image is a fundamental problem in artificial intelligence that connects computer vision and natural language processing. In this paper, we present a generative model based on a deep recurrent architecture that combines recent advances in computer vision and machine translation and that can be used to generate natural sentences describing an image. The model is trained to maximize the likelihood of the target description sentence given the training image. Experiments on several datasets show the accuracy of the model and the fluency of the language it learns solely from image descriptions. Our model is often quite accurate, which we verify both qualitatively and quantitatively. Finally, given the recent surge of interest in this task, a competition was organized in 2015 using the newly released COCO dataset. We describe and analyze the various improvements we applied to our own baseline and show the resulting performance in the competition, which we won ex-aequo with a team from Microsoft Research.


title: Race, Gender, and Partnership in the Patient-Physician Relationship

abstract: ContextMany studies have documented race and gender differences in health care ::: received by patients. However, few studies have related differences in the ::: quality of interpersonal care to patient and physician race and gender.ObjectiveTo describe how the race/ethnicity and gender of patients and physicians ::: are associated with physicians' participatory decision-making (PDM) styles.Design, Setting, and ParticipantsTelephone survey conducted between November 1996 and June 1998 of 1816 ::: adults aged 18 to 65 years (mean age, 41 years) who had recently attended ::: 1 of 32 primary care practices associated with a large mixed-model managed ::: care organization in an urban setting. Sixty-six percent of patients surveyed ::: were female, 43% were white, and 45% were African American. The physician ::: sample (n=64) was 63% male, with 56% white, and 25% African American.Main Outcome MeasurePatients' ratings of their physicians' PDM style on a 100-point scale.ResultsAfrican American patients rated their visits as significantly less participatory ::: than whites in models adjusting for patient age, gender, education, marital ::: status, health status, and length of the patient-physician relationship (mean ::: [SE] PDM score, 58.0 [1.2] vs 60.6 [3.3]; P=.03). ::: Ratings of minority and white physicians did not differ with respect to PDM ::: style (adjusted mean [SE] PDM score for African Americans, 59.2 [1.7] vs whites, ::: 61.7 [3.1]; P=.13). Patients in race-concordant relationships ::: with their physicians rated their visits as significantly more participatory ::: than patients in race-discordant relationships (difference [SE], 2.6 [1.1]; P=.02). Patients of female physicians had more participatory ::: visits (adjusted mean [SE] PDM score for female, 62.4 [1.3] vs male, 59.5 ::: [3.1]; P=.03), but gender concordance between physicians ::: and patients was not significantly related to PDM score (unadjusted mean [SE] ::: PDM score, 76.0 [1.0] for concordant vs 74.5 [0.9] for discordant; P=.12). Patient satisfaction was highly associated with PDM score within ::: all race/ethnicity groups.ConclusionsOur data suggest that African American patients rate their visits with ::: physicians as less participatory than whites. However, patients seeing physicians ::: of their own race rate their physicians' decision-making styles as more participatory. ::: Improving cross-cultural communication between primary care physicians and ::: patients and providing patients with access to a diverse group of physicians ::: may lead to more patient involvement in care, higher levels of patient satisfaction, ::: and better health outcomes.


title: High performances of shielded LTCC vertical transitions from DC up to 50 GHz

abstract: This paper reports on research on two generic shielded vertical transitions in low-temperature cofired ceramic technology. These interconnections are simulated and optimized by three-dimensional electromagnetic simulations. The first circuit, a coplanar waveguide (CPW) or microstrip-to-stripline transition, presents great experimental performances from dc up to 50 GHz, and the second, a CPW-to-waveguide transition, is defined for Q-band applications.


title: BIRDS AT AIRPORTS

abstract: Birds have been a hazard to aircraft from the first powered flight. During the early days of aviation, when aircraft flew at slow speeds, birds had little difficulty in getting out of the way. Bird strikes were infrequent and damage was mainly confined to cracked windshields. The likelihood of the loss of aircraft and/or human lives was remote. With the development and introduction of jet aircraft, bird strikes became a serious hazard and costly problem. Faster speeds mean birds have less time to react to approaching aircraft. The force gener-


title: Analysing the connectivity and communication of suicidal users on twitter

abstract: We investigate the characteristics of the authors of Tweets containing suicidal intent or thinking, through the analysis of their online social network relationships and interactions.Results show a high degree of reciprocal connectivity between the authors of suicidal content when compared to other studies of Twitter users, suggesting a tightly-coupled virtual community.Analysis of the retweet graph identified bridge nodes and hub nodes connecting users posting suicidal ideation with users who were not, suggesting a potential for information cascade and risk of possible 'contagion'.Retweet graphs of suicidal content exhibit an average shortest path similar to that of a large comparison network, demonstrating large scale information propagation in small-scale networks. In this paper we aim to understand the connectivity and communication characteristics of Twitter users who post content subsequently classified by human annotators as containing possible suicidal intent or thinking, commonly referred to as suicidal ideation. We achieve this understanding by analysing the characteristics of their social networks. Starting from a set of human annotated Tweets we retrieved the authors' followers and friends lists, and identified users who retweeted the suicidal content. We subsequently built the social network graphs. Our results show a high degree of reciprocal connectivity between the authors of suicidal content when compared to other studies of Twitter users, suggesting a tightly-coupled virtual community. In addition, an analysis of the retweet graph has identified bridge nodes and hub nodes connecting users posting suicidal ideation with users who were not, thus suggesting a potential for information cascade and risk of a possible contagion effect. This is particularly emphasised by considering the combined graph merging friendship and retweeting links.


Genetics and antisocial behavior: substance versus sound bytes.


title: Convolutional Neural Networks as a Computational Model for the Underlying Processes of Aesthetics Perception

abstract: Understanding the underlying processes of aesthetic perception is one of the ultimate goals in empirical aesthetics. While deep learning and convolutional neural networks (CNN) already arrived in the area of aesthetic rating of art and photographs, only little attempts have been made to apply CNNs as the underlying model for aesthetic perception. The information processing architecture of CNNs shows a strong match with the visual processing pipeline in the human visual system. Thus, it seems reasonable to exploit such models to gain better insight into the universal processes that drives aesthetic perception. This work shows first results supporting this claim by analyzing already known common statistical properties of visual art, like sparsity and self-similarity, with the help of CNNs. We report about observed differences in the responses of individual layers between art and non-art images, both in forward and backward (simulation) processing, that might open new directions of research in empirical aesthetics.


title: In Teacher We Trust: Learning Compressed Models for Pedestrian Detection

abstract: Deep convolutional neural networks continue to advance the state-of-the-art in many domains as they grow bigger and more complex. It has been observed that many of the parameters of a large network are redundant, allowing for the possibility of learning a smaller network that mimics the outputs of the large network through a process called Knowledge Distillation. We show, however, that standard Knowledge Distillation is not effective for learning small models for the task of pedestrian detection. To improve this process, we introduce a higher-dimensional hint layer to increase information flow. We also estimate the variance in the outputs of the large network and propose a loss function to incorporate this uncertainty. Finally, we attempt to boost the complexity of the small network without increasing its size by using as input hand-designed features that have been demonstrated to be effective for pedestrian detection. We succeed in training a model that contains $400\times$ fewer parameters than the large network while outperforming AlexNet on the Caltech Pedestrian Dataset.


title: Precise Positioning of Pneumatic Artificial Muscle Systems

abstract: Pneumatic artificial muscles (PAMs) currently possess a high power-to-weight ratio, a high power-to-volume ratio, and a high degree of safety. They have therefore been applied to many power assist devices and positioning mechanisms such as bionic robots, welfare devices, and parallel manipulators. However, the significant nonlinear characteristics of PAM mechanisms limit their positioning accuracies. The accuracies are generally lower than 5 μm, which preclude the PAM from precision systems. Nevertheless, enhancing the positioning accuracy is desired to extend the application fields of PAMs. This study aims to clarify a practical controller design method to achieve the precise positioning of PAM systems. As the first step of this research, a linear motion mechanism with a pair of McKibben PAMs was constructed and a conventional dynamic model for this system is introduced. The dynamic model is used to explain the basic characteristics of the PAM mechanism and discuss the necessary characteristics for precise positioning. Then open-loop step and sinusoidal responses of the PAM mechanism were examined by experimental and simulated results. Next, for precise positioning, the practical controller design procedure is discussed and determined based on the measured open-loop responses. The proposed controller design procedure can be easily implemented into PAM mechanisms without an exact dynamic model. The positioning performance of such a system was experimentally evaluated. The experimental results show that although the positioning accuracy depends on the target position, the positioning error is lower than 1 μm even in the worst case and the positioning resolution can be set to 0.5 μm.


title: Representation Learning for Retinal Vasculature Embeddings.

abstract: The retinal vasculature imaged with fundus photography has the potential of encoding precious information for image-based retinal biomarkers, however, progress in their development is slow due to the need of defining vasculature morphology variables a priori and developing algorithms specific to these variables. In this paper, we introduce a novel approach to learn a general descriptor (or embedding) that captures the vasculature morphology in a numerically compact vector with minimal feature engineering. The vasculature embedding is computed by leveraging the internal representation of a new encoder-enhanced fully convolutional neural network, trained end-to-end with the raw pixels and manually segmented vessels. This approach effectively transfers the vasculature patterns learned by the network into a general purpose vasculature embedding vector. Using Messidor and Messidor-2, two publicly available datasets, we test the vasculature embeddings on two tasks: (1) an image retrieval task, which retrieved similar images according to their vasculature; (2) a diabetic retinopathy classification task, where we show how the vasculature embeddings improve the classification of an algorithm based on microaneurysms detection by 0.04 AUC on average.


title: Two patients with EP300 mutations and facial dysmorphism different from the classic Rubinstein-Taybi syndrome.

abstract: Rubinstein-Taybi syndrome (RTS) is characterized by mental retardation, broad thumbs and great toes and a recognizable craniofacial phenotype. Causative mutations have been described in the CREBBP and EP300 genes. Here we present a 19-year-old woman and an unrelated 3-year-old boy, both with broad thumbs and halluces, but with facial aspects distinct from those of typical RTS. The woman had a marked learning disability, but no mental retardation. We identified a de novo c.7100delC mutation in EP300 (which predicts p.P2366RfsX35) in the woman and an apparently de novo c.638delG mutation in the boy, which predicts p.G213EfsX6. Mutations in EP300 are a known but rare cause of RTS. Only five other patients have been reported. We propose that individuals with EP300 mutations may exhibit a slightly different phenotype compared to individuals with CREBBP mutations, with milder cognitive impairment, more pronounced microcephaly, absent or mild downslanting of palpebral fissures, distinct arched eyebrows, and greater degree of retrognathia.


title: Cognitive behavioral therapy of negative symptoms.

abstract: Negative symptoms account for much of the functional disability associated with schizophrenia and often persist despite pharmacological treatment. Cognitive behavioral therapy (CBT) is a promising adjunctive psychotherapy for negative symptoms. The treatment is based on a cognitive formulation in which negative symptoms arise and are maintained by dysfunctional beliefs that are a reaction to the neurocognitive impairment and discouraging life events frequently experienced by individuals with schizophrenia. This article outlines recent innovations in tailoring CBT for negative symptoms and functioning, including the use of a strong goal-oriented recovery approach, in-session exercises designed to disconfirm dysfunctional beliefs, and adaptations to circumvent neurocognitive and engagement difficulties. A case illustration is provided. © 2009 Wiley Periodicals, Inc. J Clin Psychol: In Session 65:1–16, 2009.


title: Passive Radar Detection With Noisy Reference Channel Using Principal Subspace Similarity

abstract: Traditional passive radar detectors compute cross correlation of the raw data in the reference and surveillance channels. However, there is no optimality guarantee for this detector in the presence of a noisy reference. Here, we develop a new detector that utilizes a test statistic based on the cross correlation of the principal left singular vectors of the reference and surveillance signal-plus-noise matrices. This detector offers better performance by exploiting the inherent low-rank structure when the transmitted signals are a weighted periodic summation of several identical waveforms (amplitude and phase modulation), as is the case with commercial digital illuminators as well as noncooperative radar. We consider a scintillating target. We provide analytical detection performance guarantees establishing signal-to-noise ratio thresholds above which the proposed detection statistic reliably discriminates, in an asymptotic sense, the signal versus no-signal hypothesis. We validate these results using extensive numerical simulations. We demonstrate the “near constant false alarm rate (CFAR)” behavior of the proposed detector with respect to a fixed, SNR-independent threshold and contrast that with the need to adjust the detection threshold in an SNR-dependent manner to maintain CFAR for other detectors found in the literature. Extensions of the proposed detector for settings applicable to orthogonal frequency division multiplexing (OFDM), adaptive radar are discussed.


title: Prevalence and factors associated with depression among medical students in Cameroon: a cross-sectional study

abstract: BackgroundDepression is an important contributor to the global burden disease that affects people of communities all over the world. With high level of demands in academics and psychosocial pressure, medical students during their course of training tend to become depressed, leading to problems later in professional life and compromising patient care. In Cameroon, there is lack of data on the prevalence of depression and its impact on medical students. To determine the prevalence and predisposing factors associated with depression among medical students in Cameroon (preclinical and clinical). We also evaluated the impact of depression on self-reported academic performance.MethodsA cross sectional study was carried out in all 4 state medical schools in 4 different regions from December 2015 to January 2016. Diagnosis of depression, major depression and its associated factors were assessed using the 9-Item-Patient Health Questionnaire (PHQ-9) and a structured questionnaire respectively. We included 618 medical students (response rate: 90.4%).ResultsAbout a third of them (30.6%, 95% CI: 22.8–36.7) were found to have major depressive disorder (PHQ Score ≥ 10). With regards to the severity of depression, 214 (34.6%), 163 (26.4%), 21 (3.4%), and 5 (0.80%) students were classified as having mild, moderate, moderately severe and severe depression respectively. The presence of a chronic disease (OR: 3.70, 95% CI: 1.72–7.94, p = 0.001), major life events (OR: 2.17, 95%CI: 1.32–3.58, P = 0.002), female gender (OR: 1.59, 95% CI: 1.06–2.37, p = 0.024) and being a student at the clinical level (OR: 4.26, 95% CI: 2.71–6.71, p < 0.001) were independently associated with depression. There was no association between depression and self-reported academic performance, (OR: 1.2, 95% CI: 0.9–1.7, p = 0.080).ConclusionThe prevalence of major depressive disorders among medical students in Cameroon is high and is associated with the presence chronic disease, major life events, female gender and being a student at the clinical level. So we recommend clinicians attending to medical students with demographic features suggestive of greater risk of depression, to make an in depth investigation on the possible presence of depression. Despite this high prevalence of major depression among medical students, it was not associated with self-reported academic performance.


Hierarchical Clustering Experiments for Application to Audio Event Detection


Analysis Enhanced Particle-based Flow Visualization.


ℓp-norm multiple kernel learning


title: A CMOS transconductance-C filter technique for very high frequencies

abstract: CMOS circuits for integrated analog filters at very high frequencies, based on transconductance-C integrators, are presented. First a differential transconductance element based on CMOS inverters is described. With this circuit a linear, tunable integrator for very-high-frequency integrated filters can be made. This integrator has good linearity properties and nondominant poles in the gigahertz range owing to the absence of internal nodes. The integrator has a tunable DC gain, resulting in a controllable integrator quality factor. Experimental results of a VHF CMOS transconductance-C low-pass filter realized in a 3- mu m CMOS process are given. Both the cutoff frequency and the quality factors can be tuned. The cutoff frequency was tuned from 22 to 98 MHz and the measured filter response is very close to the ideal response of the passive prototype filter. Furthermore, a novel circuit for automatically tuning the quality factors of integrated filters built with these transconductors is described. >


Integrated Optimization Model for Production Planning and Scheduling with Logistics Constraints


title: Distortion-Aware Query-by-Example for Environmental Sounds

abstract: There has been much recent progress in the technical infrastructure necessary to continuously characterize and archive all sounds that occur within a given space or human life. Efficient and intuitive access, however, remains a considerable challenge. In other domains, i.e., melody retrieval, query-by-example (QBE) has found considerable success in accessing music that matches a specific query. We propose an extension of the QBE paradigm to the broad class of natural and environmental sounds. These sounds occur frequently in continuous recordings, and are often difficult for humans to imitate. We utilize a probabilistic QBE scheme that is flexible in the presence of time, level, and scale distortions along with a clustering approach to efficiently organize and retrieve the archived audio. Experiments on a test database demonstrate accurate retrieval of archived sounds, whose relevance to example queries is determined by human users.


title: Preparation and characterization of high uniformity zinc oxide nanosheets

abstract: This paper reports a synthesis of zinc oxide (ZnO) nanosheets by hydrothermal method. ZnO nanosheets on Al substrate were generated by hydrothermal synthesis with zinc nitrate hexahydrate (Zn(NO3)2·6H2O) and hexamethylenetetramine (HMT) as a research system, which were controlled conditions of the reaction. The energy dispersive spectroscopy (EDS), scanning electron microscope (SEM) and transmission electron microscopy (TEM) images were achieved to determine the characterization of ZnO nanosheets. The diameter of ZnO nanofilm was from 0.5 to 1 μm, and its thickness ranged from 30 to 50 nm.


title: Learning to Play in a Day: Faster Deep Reinforcement Learning by Optimality Tightening

abstract: We propose a novel training algorithm for reinforcement learning which combines the strength of deep Q-learning with a constrained optimization approach to tighten optimality and encourage faster reward propagation. Our novel technique makes deep reinforcement learning more practical by drastically reducing the training time. We evaluate the performance of our approach on the 49 games of the challenging Arcade Learning Environment, and report significant improvements in both training time and accuracy.


title: A Fast and Simple Randomized Parallel Algorithm for the Maximal Independent Set Problem

abstract: Abstract A simple parallel randomized algorithm to find a maximal independent set in a graph G = ( V , E ) on n vertices is presented. Its expected running time on a concurrent-read concurrent-write PRAM with O (| E | d max ) processors is O (log n ), where d max denotes the maximum degree. On an exclusive-read exclusive-write PRAM with O (| E |) processors the algorithm runs in O (log 2 n ). Previously, an O (log 4 n ) deterministic algorithm was given by Karp and Wigderson for the EREW-PRAM model. This was recently (independently of our work) improved to O (log 2 n ) by M. Luby. In both cases randomized algorithms depending on pairwise independent choices were turned into deterministic algorithms. We comment on how randomized combinatorial algorithms whose analysis only depends on d -wise rather than fully independent random choices (for some constant d ) can be converted into deterministic algorithms. We apply a technique due to A. Joffe (1974) and obtain deterministic construction in fast parallel time of various combinatorial objects whose existence follows from probabilistic arguments.


title: Secure and usable authentication on mobile devices

abstract: Mobile devices contain a multitude of sensitive data and provide access to even more data as well as services somewhere on the Internet. Even if only temporarily in the hands of non-entitled persons, privacy is at stake. Authentication protects against unauthorized usage. Today's operating systems of mobile devices offer authentication mechanisms. However, they are either vulnerable in some situations or not user friendly enough to be widely adopted. In this paper we suggest a novel authentication system which meets both the requirements of security and usability. For that purpose, we have analyzed existing authentication methods as well as targeting attacks. The resulting Android application SecureLock is a generic authentication system, which offers PIN and password, but also a property-based authentication method by means of NFC tags, and a novel image-based method called GesturePuzzle. The application has been evaluated and compared with other approaches for security and usability.


title: A pattern mining framework for inter-wafer abnormality analysis

abstract: This work presents three pattern mining methodologies for inter-wafer abnormality analysis. Given a large population of wafers, the first methodology identifies wafers with abnormal patterns based on a test or a group of tests. Given a wafer of interest, the second methodology searches for a test perspective that reveals the abnormality of the wafer. Given a particular pattern of interest, the third methodology implements a monitor to detect wafers containing similar patterns. This paper discusses key elements for implementing each of the methodologies and demonstrates their usefulness based on experiments applied to a high-quality SoC product line.


Moving Target Defense for Distributed Systems


A Review on Fault Diagnosis of Induction Motor using Artificial Neural Networks


title: Compact and high repetitive pulsed power modulator based on semiconductor switches

abstract: This paper describes the design of a compact and highly repetitive pulsed power modulator based on semiconductor switches. Output specifications of the proposed modulator are as follows: variable output pulse voltage, 1-10 kV; width, 1-10 μs; pulse repetition rate (PRR), 1-50,000 pps; and average output power, 10 kW. The proposed solid-state pulsed power modulator has two main parts: an IGBT stack that hasa gate drive circuit for the pulse output with a variable pulse width and repetition rate, and a capacitor charger that has a controllable output voltage. To connect all the storage capacitors in a series for high voltage pulses, a simple and reliable IGBT stack structure was proposed based on the Marx generator. In addition, the gate driver circuit, which supplies power and signals to all the IGBTs simultaneously, was introduced. This providesa superior protection function against the arc and the short. To charge the storage capacitors, a novel 10 kW (10 kV, 1 A) high voltage capacitor charger with the combined advantage of a series-loaded resonant converter and a ZVS (zero-voltage-switching) full-bridge pulse width modulation (PWM) converter was proposed and designed especially for the solid-state pulsed power modulator. Theexperiment results verified that the proposed scheme and structure can be used effectively for a high voltage pulsed power modulator that requires variable voltage, repetition rate, and pulse width, depending on the process of the applications.


Design methodology for hybrid (VTOL + Fixed Wing) unmanned aerial vehicles


title: Multi-perspective Process Mining

abstract: Process mining methods analyze an organization’s processes by using process execution data. During the handling of a process instance data about the execution of activities is recorded. Process mining uses such data to gain insights about the real execution of processes. In this thesis, we address research challenges in which a multi-perspective view on processes is needed and that look beyond the control-flow perspective, which defines the sequence of activities of a process. We consider problems in which multiple interacting process perspectives — in particular control-flow, data, resources, time, and functions — are considered together. The contributed methods span several types of process mining: two are concerned with conformance checking, two are process discovery techniques, and one is a decision mining method. All methods have been implemented, evaluated, and applied in the context of four case studies.


RSA & Public Key Cryptography in FPGAs


title: Attitudes towards following meat, vegetarian and vegan diets: an examination of the role of ambivalence

abstract: Vegetarianism within the U.K. is growing in popularity, with the current estimate of 7% of the population eating a vegetarian diet. This study examined differences between the attitudes and beliefs of four dietary groups (meat eaters, meat avoiders, vegetarians and vegans) and the extent to which attitudes influenced intentions to follow each diet. In addition, the role of attitudinal ambivalence as a moderator variable was examined. Completed questionnaires were obtained from 111 respondents (25 meat eaters, 26 meat avoiders, 34 vegetarians, 26 vegans). In general, predictions were supported, in that respondents displayed most positive attitudes and beliefs towards their own diets, and most negative attitudes and beliefs towards the diet most different form their own. Regression analyses showed that, as predicted by the Theory of Planned Behaviour, attitudes, subjective norm and perceived behavioural control were significant predictors of intention to follow each diet (apart from the vegetarian diet, where subjective norm was non-significant). In each case, attitudinal ambivalence was found to moderate the attitude-intention relationship, such that attitudes were found to be stronger predictors at lower levels of ambivalence. The results not only highlight the extent to which such alternative diets are an interesting focus for psychological research, but also lend further support to the argument that ambivalence in an important influence on attitude strength.


title: Bat algorithm: Recent advances

abstract: The bat algorithm (BA) is a nature-inspired algo-rithm, which has recently been applied in many applications. BA can deal with both continuous optimization and discrete optimization problems. The literature has expanded significantly in the past few years, this paper provides a timely review of the latest developments. We also highlight some topics for further research.


title: ‘Primitive intelligence’ in the auditory cortex

abstract: Abstract The everyday auditory environment consists of multiple simultaneously active sources with overlapping temporal and spectral acoustic properties. Despite the seemingly chaotic composite signal impinging on our ears, the resulting perception is of an orderly ‘auditory scene' that is organized according to sources and auditory events, allowing us to select messages easily, recognize familiar sound patterns, and distinguish deviant or novel ones. Recent data suggest that these perceptual achievements are mainly based on processes of a cognitive nature (‘sensory intelligence') in the auditory cortex. Even higher cognitive processes than previously thought, such as those that organize the auditory input, extract the common invariant patterns shared by a number of acoustically varying sounds, or anticipate the auditory events of the immediate future, occur at the level of sensory cortex (even when attention is not directed towards the sensory input).


title: Power transformer thermal analysis by using an advanced coupled 3D heat transfer and fluid flow FEM model

abstract: Abstract Thermal performance in oil-immersed power transformers is governed by the flow of oil, acting both as an electrical insulator and a medium for the transfer of heat generated in the core and windings toward the tank and the surrounding air. This paper presents the development of an advanced three-dimensional (3D) finite element model for the coupled solution of heat transfer and fluid flow equations governing transformer thermal performance. The main advantages of the proposed method are: (i) no need to predefine the convection coefficients at the interfaces between the active part/tank walls and the circulating oil, (ii) detailed representation of specific transformer parts that play an important role in the accurate representation of oil flow and heat dissipation (such as winding cooling ducts and corrugated tank panels) through an automated design process, enhancing the model accuracy with the least possible computational effort and (iii) accurate definition of the transformer heat sources (core and windings loss). The proposed methodology provides an integrated tool for thermal simulation, able to predict detailed thermal distribution in a specific transformer, without requiring prior knowledge of nodal temperature or temperature gradient values.


title: Analysis of Single-Phase SPWM Inverter

abstract: This project deals with studying the basic theory of a Sinusoidal Pulse Width Modulated Inverter (SPWM), its Simulink modeling, estimating various designing parameters and various instabilities. The project will be commenced by a basic understanding of the circuitry of the SPWM Inverter, the components used in its design and the reason for choosing such components in this circuitry. After this, it will be attempted to simulate a model circuit on any simulating software e.g. MATLAB and analyze the output waveforms for various values of the elements used in the circuit and hence study the system response and instabilities.


title: Development of Genbu : Active wheel passive joint articulated mobile robot

abstract: Snake-like robots have good properties to move on rough terrain. Their low-height bodies and articulated structures are proper to go into narrow space and to climb over obstacles. Thus, many applications of articulated mobile robots are expected (e.g. rescue robots). One of the types of snake-like robots named 'Genbu' consists of active wheels and passive jointed multi bodies. We classified this type of snake-like robots as active wheel passive joint articulated mobile robot. Although Genbu cannot go over wide gaps, they have high durability because their joints are not activated with gear-head motors. So, Genbu has high mobility on rough terrain at high-speed. Moreover the long articulated structure is proper to climb over obstacles because of size effect. For example, Genbu 2 equipped with 220mm diameter wheels can climb 300mm vertical wall. These characteristics are proper to use as rescue robots or planetary rovers. However these applications require semi-autonomous robots because the operator cannot approach the robots. The control method of this type of snake-like robots has not been established yet. In order to establish the control method, detecting the posture of the robot is very important. Nevertheless, Genbu 2 was difficult to detect the posture because of the elastic body join is. In addition, Genbu 2 had some problems for practical use (e.g. motor drivers are not inside). In this study, Genbu 3 was newly developed to solve these problems. In order to accomplish these purposes, the passive rigid joints for detecting the posture, and torque sensor with float differential mechanism are newly developed for Genbu 3. In this paper the mechanism of Genbu 3 and basic running experiments of Genbu 3 are described.


title: An Electronically Tunable Linear or Nonlinear MOS Resistor

abstract: We present a bidirectional MOS resistor circuit that is electronically tunable and has zero dc offset. For a given I-V characteristic, the circuit senses the source-to-drain potential across an MOS device and automatically generates an appropriate bias for the gate terminal to implement the characteristic via negative feedback. We show that the I-V characteristic of the resistor can be designed to be linear, compressive or expansive by using appropriate translinear current mode circuits for the feedback biasing. Our technique does not require the MOS transistor to operate in the triode region and is valid in both weak and strong inversion. Experimental results from a CMOS process show that a square-root, linear, or square resistor can be implemented as examples of our topology. The linear version was tunable over a resistance range of 1 MOmega-100 GOmega in our particular implementation and exhibited proportional-to-absolute temperature (PTAT) behavior. The measured excess noise of the resistor agrees with theoretical predictions.


title: Multiple-supplier inventory models in supply chain management: A review

abstract: This paper reviews inventory models with multiple supply options and discusses their contribution to supply chain management. After discussing strategic aspects of supplier competition and the role of operational flexibility in global sourcing, inventory models which use several suppliers in order to avoid or reduce the effects of shortage situations are outlined. Further, related inventory problems from the fields of reverse logistics and multi-echelon systems are presented. Finally, issues for future research and a synthesis of available supply chain management and multiple supplier inventory models are discussed. r 2002 Elsevier Science B.V. All rights reserved.


title: Clustering of Electricity Consumption Behavior Dynamics Toward Big Data Applications

abstract: In a competitive retail market, large volumes of smart meter data provide opportunities for load serving entities to enhance their knowledge of customers’ electricity consumption behaviors via load profiling. Instead of focusing on the shape of the load curves, this paper proposes a novel approach for clustering of electricity consumption behavior dynamics, where “dynamics” refer to transitions and relations between consumption behaviors, or rather consumption levels, in adjacent periods. First, for each individual customer, symbolic aggregate approximation is performed to reduce the scale of the data set, and time-based Markov model is applied to model the dynamic of electricity consumption, transforming the large data set of load curves to several state transition matrixes. Second, a clustering technique by fast search and find of density peaks (CFSFDP) is primarily carried out to obtain the typical dynamics of consumption behavior, with the difference between any two consumption patterns measured by the Kullback-Liebler distance, and to classify the customers into several clusters. To tackle the challenges of big data, the CFSFDP technique is integrated into a divide-and-conquer approach toward big data applications. A numerical case verifies the effectiveness of the proposed models and approaches.


title: Very Short-Term Nonparametric Probabilistic Forecasting of Renewable Energy Generation— With Application to Solar Energy

abstract: Due to the inherent uncertainty involved in renewable energy forecasting, uncertainty quantification is a key input to maintain acceptable levels of reliability and profitability in power system operation. A proposal is formulated and evaluated here for the case of solar power generation, when only power and meteorological measurements are available, without sky-imaging and information about cloud passages. Our empirical investigation reveals that the distribution of forecast errors do not follow any of the common parametric densities. This therefore motivates the proposal of a nonparametric approach to generate very short-term predictive densities, i.e., for lead times between a few minutes to one hour ahead, with fast frequency updates. We rely on an Extreme Learning Machine (ELM) as a fast regression model, trained in varied ways to obtain both point and quantile forecasts of solar power generation. Four probabilistic methods are implemented as benchmarks. Rival approaches are evaluated based on a number of test cases for two solar power generation sites in different climatic regions, allowing us to show that our approach results in generation of skilful and reliable probabilistic forecasts in a computationally efficient manner.


title: Network steganography and steganalysis - a concise review

abstract: Network steganography is an ever-growing research topic as the idea of hiding information attracts many researchers. It is a rapidly evolving topic with suggestions and proposals how one can exploit a network protocol to enable a secret communication, and how it can be detected and prevented. This paper reviews some of the notable network steganography and steganalysis techniques and comments on their novelties and shortcomings. Finally, the paper discusses the greater need for research on network steganalysis in comparison to network steganography.


title: Use your head: tangible windows for 3D information spaces in a tabletop environment

abstract: Tangible Windows are a novel concept for interacting with virtual 3D information spaces in a workbench-like multi-display environment. They allow for performing common 3D interaction tasks in a more accessible manner by combining principles of tangible interaction, head-coupled perspective, and multi-touch techniques. Tangible Windows unify the interaction and representation space in a single device. They either act as physical peepholes into a virtual 3D world or as physical containers for parts of that world and are well-suited for the collaborative exploration and manipulation of such information spaces. One important feature of Tangible Windows is that the use of obtrusive hardware, such as HMDs, is strictly avoided. Instead, lightweight paper-based displays are used. We present different techniques for canonical 3D interaction tasks such as viewport control or object selection and manipulation, based on the combination of independent input modalities. We tested these techniques on a self-developed prototype system and received promising early user feedback.


title: Gut Microbiota in Health and Disease

abstract: Gut microbiota is an assortment of microorganisms inhabiting the length and width of the mammalian gastrointestinal tract. The composition of this microbial community is host specific, evolving throughout an individual's lifetime and susceptible to both exogenous and endogenous modifications. Recent renewed interest in the structure and function of this “organ” has illuminated its central position in health and disease. The microbiota is intimately involved in numerous aspects of normal host physiology, from nutritional status to behavior and stress response. Additionally, they can be a central or a contributing cause of many diseases, affecting both near and far organ systems. The overall balance in the composition of the gut microbial community, as well as the presence or absence of key species capable of effecting specific responses, is important in ensuring homeostasis or lack thereof at the intestinal mucosa and beyond. The mechanisms through which microbiota exerts its beneficial or detrimental infl...


title: Workers’ motivation: the italian case of cooperative credit banks

abstract: The role of the cooperative credit banks in the European financial system is growing, particularly during the current period of financial crisis. Nevertheless, these cooperative banks have not received a great deal of attention from scholars. This lack of attention has resulted from two factors: i) the lack of empirical data and ii) the fact that the organizational structures and multiple goals of these cooperative banks are “generally more difficult to understand than the corporate governance of the commercial banks with their more easily interpretable and single goal of profit maximizing”(Groeneveld, 2011). This paper contributes to the understanding of Italian cooperative credit banks (Bccs) and their activity by describing their main characteristics and by providing a comparison with different cooperative bank models. Second, it analyzes the job satisfaction of Italian Bccs’ employees, which is a crucial factor because human resources, along with the Bccs’ unique structural elements, facilitate long-term relationships with the local communities where Bccs are located.


title: Optogenetic investigation of neural circuits underlying brain disease in animal models

abstract: Optogenetic tools have provided a new way to establish causal relationships between brain activity and behaviour in health and disease. Although no animal model captures human disease precisely, behaviours that recapitulate disease symptoms may be elicited and modulated by optogenetic methods, including behaviours that are relevant to anxiety, fear, depression, addiction, autism and parkinsonism. The rapid proliferation of optogenetic reagents together with the swift advancement of strategies for implementation has created new opportunities for causal and precise dissection of the circuits underlying brain diseases in animal models.


title: Extreme gigantomastia in pregnancy: case report and review of literature

abstract: We present an extreme case of Gigantomastia in pregnancy in a 24-year old woman, gravida 2, in a 28 weeks' of gestation, with a total breast weight of 33 kg, complicated by infection, ulcerations and subsequent hemorrhage. Thorough laboratory analyses did not reveal any hint as to the cause of this enormous breast enlargement. Gynecological examinations and ultrasound revealed a viable, progressive normal fetus. The severity of the problem is further emphasized by the patients' breathing problems and even big difficulty in standing and walking. We performed bilateral simple mastectomy as a life-saving procedure to prevent fatal complications. The procedure finished without any complications or large amount of blood loss. There are less than 100 cases of gravid gigantomastia reported, but never to such extreme breast weight. Etiology remains uncertain, and controversy exists in therapeutic modality. According to the literature the most reliable conservative treatment is bromocriptine therapy, but if the condition progresses surgical intervention, in the form of reduction mammoplasty or simple mastectomy, is the treatment of choice.


Cataloguing Non Functional Requirements as Softgoal Networks


title: Visibility culling using hierarchical occlusion maps

abstract: We present hierarchical occlusion maps (HOM) for visibility culling on complex models with high depth complexity. The culling algorithm uses an object space bounding volume hierarchy and a hierarchy of image space occlusion maps. Occlusion maps represent the aggregate of projections of the occluders onto the image plane. For each frame, the algorithm selects a small set of objects from the modelas occludersand renders them to form an initial occlusion map, from which a hierarchy of occlusion maps is built. The occlusion maps are used to cull away a portion of the model not visible from the current viewpoint. The algorithm is applicable to all models and makes no assumptions about the size, shape, or type of occluders. It supports approximate culling in which small holes in or among occluders can be ignored. The algorithm has been implemented on current graphics systems and has been applied to large models composed of hundreds of thousands of polygons. In practice, it achieves significant speedup in interactive walkthroughs of models with high depth complexity. CR