Query: Can we beat Hadamard multiplexing? Data driven design and analysis for computational imaging systems

Relevant passages:
Coded exposure deblurring: Optimized codes for PSF estimation and invertibility <=SEP=> We consider the problem of single image object motion deblurring from a static camera. It is well-known that deblurring of moving objects using a traditional camera is ill-posed, due to the loss of high spatial frequencies in the captured blurred image. A coded exposure camera modulates the integration pattern of light by opening and closing the shutter within the exposure time using a binary code. The code is chosen to make the resulting point spread function (PSF) invertible, for best deconvolution performance. However, for a successful deconvolution algorithm, PSF estimation is as important as PSF invertibility. We show that PSF estimation is easier if the resulting motion blur is smooth and the optimal code for PSF invertibility could worsen PSF estimation, since it leads to non-smooth blur. We show that both criterions of PSF invertibility and PSF estimation can be simultaneously met, albeit with a slight increase in the deconvolution noise. We propose design rules for a code to have good PSF estimation capability and outline two search criteria for finding the optimal code for a given length. We present theoretical analysis comparing the performance of the proposed code with the code optimized solely for PSF invertibility. We also show how to easily implement coded exposure on a consumer grade machine vision camera with no additional hardware. Real experimental results demonstrate the effectiveness of the proposed codes for motion deblurring.

Natural image statistics and neural representation. <=SEP=> It has long been assumed that sensory neurons are adapted, through both evolutionary and developmental processes, to the statistical properties of the signals to which they are exposed. Attneave (1954)Barlow (1961) proposed that information theory could provide a link between environmental statistics and neural responses through the concept of coding efficiency. Recent developments in statistical modeling, along with powerful computational tools, have enabled researchers to study more sophisticated statistical models for visual images, to validate these models empirically against large sets of data, and to begin experimentally testing the efficient coding hypothesis for both individual neurons and populations of neurons.

Blue Screen Matting <=SEP=> A classical problem of imaging—the matting problem—is separation of a non-rectangular foreground image from a (usually) rectangular background image—for example, in a film frame, extraction of an actor from a background scene to allow substitu tion of a different background. Of the several attacks on this diff cult and persistent problem, we discuss here only the special ca of separating a desired foreground image from a background of constant, or almost constant, backing color. This backing color has often been blue, so the problem, and its solution, have bee called blue screen matting . However, other backing colors, such as yellow or (increasingly) green, have also been used, so we o ten generalize to constant color matting . The mathematics of constant color matting is presented and proven to be unsolvable as generally practiced. This, of course, flies in the face of the fact that the technique is commonly used in film and video, so we demonstrate constraints on the general problem that lead to so tions, or at least significantly prune the search space of solution We shall also demonstrate that an algorithmic solution is possib by allowing the foreground object to be shot against two constant backing colors—in fact, against two completely arbitrary backin so long as they differ everywhere.

Image statistics and the perception of surface qualities <=SEP=> The world is full of surfaces, and by looking at them we can judge their material qualities. Properties such as colour or glossiness can help us decide whether a pancake is cooked, or a patch of pavement is icy. Most studies of surface appearance have emphasized textureless matte surfaces, but real-world surfaces, which may have gloss and complex mesostructure, are now receiving increased attention. Their appearance results from a complex interplay of illumination, reflectance and surface geometry, which are difficult to tease apart given an image. If there were simple image statistics that were diagnostic of surface properties it would be sensible to use them. Here we show that the skewness of the luminance histogram and the skewness of sub-band filter outputs are correlated with surface gloss and inversely correlated with surface albedo (diffuse reflectance). We find evidence that human observers use skewness, or a similar measure of histogram asymmetry, in making judgements about surfaces. When the image of a surface has positively skewed statistics, it tends to appear darker and glossier than a similar surface with lower skewness, and this is true whether the skewness is inherent to the original image or is introduced by digital manipulation. We also find a visual after-effect based on skewness: adaptation to patterns with skewed statistics can alter the apparent lightness and glossiness of surfaces that are subsequently viewed. We suggest that there are neural mechanisms sensitive to skewed statistics, and that their outputs can be used in estimating surface properties.

Non-local sparse models for image restoration <=SEP=> We propose in this paper to unify two different approaches to image restoration: On the one hand, learning a basis set (dictionary) adapted to sparse signal descriptions has proven to be very effective in image reconstruction and classification tasks. On the other hand, explicitly exploiting the self-similarities of natural images has led to the successful non-local means approach to image restoration. We propose simultaneous sparse coding as a framework for combining these two approaches in a natural manner. This is achieved by jointly decomposing groups of similar signals on subsets of the learned dictionary. Experimental results in image denoising and demosaicking tasks with synthetic and real noise show that the proposed method outperforms the state of the art, making it possible to effectively restore raw images from digital cameras at a reasonable speed and memory cost.

Hidden Markov model-based speech emotion recognition <=SEP=> In this contribution we introduce speech emotion recognition by use of continuous hidden Markov models. Two methods are propagated and compared throughout the paper. Within the first method a global statistics framework of an utterance is classified by Gaussian mixture models using derived features of the raw pitch and energy contour of the speech signal. A second method introduces increased temporal complexity applying continuous hidden Markov models considering several states using low-level instantaneous features instead of global statistics. The paper addresses the design of working recognition engines and results achieved with respect to the alluded alternatives. A speech corpus consisting of acted and spontaneous emotion samples in German and English language is described in detail. Both engines have been tested and trained using this equivalent speech corpus. Results in recognition of seven discrete emotions exceeded 86% recognition rate. As a basis of comparison the similar judgment of human deciders classifying the same corpus at 79.8% recognition rate was analyzed.

Fundamentals for Future Mobile-Health (mHealth): A Systematic Review of Mobile Phone and Web-Based Text Messaging in Mental Health <=SEP=> BACKGROUND
Mobile phone text messages (short message service, SMS) are used pervasively as a form of communication. Almost 100% of the population uses text messaging worldwide and this technology is being suggested as a promising tool in psychiatry. Text messages can be sent either from a classic mobile phone or a web-based application. Reviews are needed to better understand how text messaging can be used in mental health care and other fields of medicine.


OBJECTIVE
The objective of the study was to review the literature regarding the use of mobile phone text messaging in mental health care.


METHODS
We conducted a thorough literature review of studies involving text messaging in health care management. Searches included PubMed, PsycINFO, Cochrane, Scopus, Embase and Web of Science databases on May 25, 2015. Studies reporting the use of text messaging as a tool in managing patients with mental health disorders were included. Given the heterogeneity of studies, this review was summarized using a descriptive approach.


RESULTS
From 677 initial citations, 36 studies were included in the review. Text messaging was used in a wide range of mental health situations, notably substance abuse (31%), schizophrenia (22%), and affective disorders (17%). We identified four ways in which text messages were used: reminders (14%), information (17%), supportive messages (42%), and self-monitoring procedures (42%). Applications were sometimes combined.


CONCLUSIONS
We report growing interest in text messaging since 2006. Text messages have been proposed as a health care tool in a wide spectrum of psychiatric disorders including substance abuse, schizophrenia, affective disorders, and suicide prevention. Most papers described pilot studies, while some randomized clinical trials (RCTs) were also reported. Overall, a positive attitude toward text messages was reported. RCTs reported improved treatment adherence and symptom surveillance. Other positive points included an increase in appointment attendance and in satisfaction with management and health care services. Insight into message content, preventative strategies, and innovative approaches derived from the mental health field may be applicable in other medical specialties.

Remote Sensing for Crop Management <=SEP=> Scientists with the Agricultural Research Service (ARS) and various government agencies and private institutions have provided a great deal of fundamental information relating spectral reflectance and thermal emittance properties of soils and crops to their agronomic and biophysical characteristics. This knowledge has facilitated the development and use of various remote sensing methods for non-destructive monitoring of plant growth and development and for the detection of many environmental stresses which limit plant productivity. Coupled with rapid advances in computing and positionlocating technologies, remote sensing from ground-, air-, and space-based platforms is now capable of providing detailed spatial and temporal information on plant response to their local environment that is needed for site specific agricultural management approaches. This manuscript, which emphasizes contributions by ARS researchers, reviews the biophysical basis of remote sensing; examines approaches that have been developed, refined, and tested for management of water, nutrients, and pests in agricultural crops; and assesses the role of remote sensing in yield prediction. It concludes with a discussion of challenges facing remote sensing in the future. Introduction Agricultural production strategies have changed dramatically over the past decade. Many of these changes have been driven by economic decisions to reduce inputs and maximize profits and by environmental guidelines mandating more efficient and safer use of agricultural chemicals. However, growers now have a heightened sensitivity to concerns over the quality, nutritional value, and safety of agricultural products. They are selecting cultivars and adjusting planting dates to accommodate anticipated patterns in weather, e.g., El Niño or La Niña events (Jones et al., 2000). They are also relying on biotechnological innovations for suppressing pests, e.g., insect protected (Bt) and Roundup® ready crops (Monsanto Company, 2003). The possibility for selling carbon credits to industry is breathing new life into on-farm conservation tillage practices that enhance carbon sequestration (Robert, 2001). Perhaps the most significant change in agriculture during the past ten years is the shift towards precision, or sitespecific, crop management (National Research Council, 1997). Growers have long recognized within-field variability in potential productivity. Now, at the beginning of the 21st Century, they are seeking new ways to exploit that variability. In the process, they are discovering they need more information on soil and plant conditions than was required a decade ago. Not only does this information need to be accurate and consistent across their farm and from year to year, it must also be available at temporal and spatial scales that match rapidly evolving capabilities to vary cultural procedures, irrigations, and agrochemical inputs. A very large body of research spanning almost four decades has demonstrated that much of this required information is available remotely, via aircraftand satellitebased sensor systems. When combined with remarkable advances in Global Positioning System (GPS) receivers, microcomputers, geographic information systems (GIS), yield monitors, and enhanced crop simulation models, remote sensing technology has the potential to transform the ways that growers manage their lands and implement precision farming techniques. The objective of this paper is to review progress that has been made in remote sensing applications for crop management and, in particular, highlight the role that the USDA and its primary research agency, the Agricultural Research Service (ARS), has had in the movement. Of course, these advances have not been a singular effort by ARS (Pinter et al., 2003; p. 615 this issue). They have resulted from long-standing cooperation among a number of different agencies and institutions, all in pursuit of expanding remote sensing’s role in providing information for crop management. We will begin with some fundamental relationships between the electromagnetic spectrum and basic agronomic conditions and biophysical plant processes, and then present specific examples of remote sensing P H O T O G R A M M E T R I C E N G I N E E R I N G & R E M O T E S E N S I N G Photogrammetric Engineering & Remote Sensing Vol. 69, No. 6, June 2003, pp. 647–664. 0099-1112/03/6906–647$3.00/0 © 2003 American Society for Photogrammetry and Remote Sensing P.J. Pinter, Jr., is with the USDA, ARS, U.S. Water Conservation Laboratory, 4331 E. Broadway Rd., Phoenix, AZ 85040-8807 (ppinter@uswcl.ars.ag.gov). J.L. Hatfield is with the USDA, ARS, National Soil Tilth Research Laboratory, 2150 Pammel Dr., Ames/Ankeny, IA 50011-3120 (hatfield@nstl.gov). J.S. Schepers is with the USDA, ARS, Soil and Water Conservation Research Laboratory, 120 Keim Hall on the University of Nebraska E. Campus, Lincoln, NE 68583-0934 (jschepers1@unl.edu). E. M. Barnes was with the USDA, ARS, U.S. Water Conservation Laboratory, 4331 E. Broadway Rd., Phoenix, AZ 85040-8807; he is currently with Cotton Inc., 6399 Weston Parkway, Cary, NC 27513 (Ebarnes@cottoninc.com). M.S. Moran is with the USDA, ARS, Southwest Watershed Research Center, 200 E. Allen Rd., Tucson, AZ 85719-1596 (smoran@tucson.ars.ag.gov). C.S.T. Daughtry is with the USDA, ARS, Hydrology and Remote Sensing Laboratory, Bldg. 007, BARC-West, 10300 Baltimore Blvd., Beltsville, MD 20705-2350 (cdaughtry@ hydrolab.arsusda.gov). D.R. Upchurch is with the USDA, ARS, Cropping Systems Research Laboratory, 3810 4th Street, Lubbock, TX 79415 (dupchurch@lbk.ars.usda.gov). IPC_Grams_03-904 4/12/03 7:40 AM Page 1

Special Perforated Steel Plate Shear Walls with Reduced Beam Section Anchor Beams . I : Experimental Investigation <=SEP=> This paper presents results of an experimental investigation of specially detailed ductile perforated steel plate shear walls SPSWs designed to accommodate utility passage, and having anchor beams with reduced beam sections connections. Single-story, single-bay SPSW frames are subjected to quasi-static cyclic loading up to their maximum strength and displacement capacity. The tested specimens also had low yield strength steel infill panels. Two specimens make allowances for penetration of the panel by utilities. The first, having multiple holes specially laid out in the steel panel, also has the characteristic of reduced panel strength and stiffness compared to the corresponding SPSW having a solid panel. The second such specimen utilizes quarter-circle cutouts in the panel corners, which are reinforced to transfer the panel forces to the adjacent framing. A SPSW with solid panel is also tested for reference purposes. All specimens resisted an imposed input history of increasing displacements to a minimum drift of 3%. The perforated panel reduced the elastic stiffness and overall strength of the specimen by 15% as compared with the solid panel specimen. DOI: 10.1061/ ASCE 0733-9445 2009 135:3 211 CE Database subject headings: Steel plates; Beams; Anchors; Experimentation; Shear walls.

Automatic Building of Synthetic Voices from Audio Books <=SEP=> Current state-of-the-art text-to-speech systems produce intelligible speech but lack the prosody of natural utterances. Building better models of prosody involves development of prosodically rich speech databases. However, development of such speech databases requires a large amount of effort and time. An alternative is to exploit story style monologues (long speech files) in audio books. These monologues already encapsulate rich prosody including varied intonation contours, pitch accents and phrasing patterns. Thus, audio books act as excellent candidates for building prosodic models and natural sounding synthetic voices. The processing of such audio books poses several challenges including segmentation of long speech files, detection of mispronunciations, extraction and evaluation of representations of prosody. In this thesis, we address the issues of segmentation of long speech files, capturing prosodic phrasing patterns of a speaker, and conversion of speaker characteristics. Techniques developed to address these issues include – text-driven and speech-driven methods for segmentation of long speech files; an unsupervised algorithm for learning speaker-specific phrasing patterns and a voice conversion method by modeling target speaker characteristics. The major conclusions of this thesis are – • Audio books can be used for building synthetic voices. Segmentation of such long speech files can be accomplished without the need for a speech recognition system. • The prosodic phrasing patterns are specific to a speaker. These can be learnt and incorporated to improve the quality of synthetic voices. • Conversion of speaker characteristics can be achieved by modeling speaker-specific features of a target speaker. Finally, the techniques developed in this thesis enable prosody research by leveraging a large number of audio books available in the public domain.

System in wafer-level package technology with RDL-first process <=SEP=> We have developed a new system-in-package (SiP) called a “System in Wafer-Level Package” (SiWLP). It is fabricated using “RDL-first” technology for fan-out wafer-level-packages (FO-WLPs) and provides high chip-I/O density, design flexibility, and package miniaturization. We developed this SiWLP by using multilayer RDLs and evaluated its unique packaging processes. We achieved high-throughput fabrication by using die-to-wafer (D2W) bonding with fine-pitch reflow soldering and simultaneous molding/underfilling at the wafer level.

DropConnected neural network trained with diverse features for classifying heart sounds <=SEP=> A fully-connected, two-hidden-layer neural network trained by error backpropagation, and regularized with DropConnect is used to classify heart sounds as normal or abnormal. The heart sounds are segmented using an open-source algorithm based on a hidden semi-Markov model. Features are extracted from the heart sounds using a wavelet transform, mel-frequency cepstral coefficients, inter-beat properties, and signal complexity. Features are normalized by subtracting by their means and dividing by their standard deviations across the whole training set. Any feature which is not significantly different between normal and abnormal recordings in the training data is removed, as are highly-correlated features. The dimensionality of the features vector is reduced by projecting it onto its first 70 principal components. A 10 fold cross-validation study gives a mean classification score of 84.1% with a variance of 2.9%. The final score on the test data was 85.2%.

Review: IT-Dependent Strategic Initiatives and Sustained Competitive Advantage: A Review and Synthesis of the Literature <=SEP=> The role of information systems in the creation and appropriation of economic value has a long tradition of research, within which falls the literature on the sustainability of IT-dependent competitive advantage. In this article, we formally define the notion of IT-dependent strategic initiative and use Jane Webster was the accepting senior editor for this paper. Anitesh Barua was the associate editor. Jeanne Ross, Anandhi Bharadwaj, and Paul Pavlou served as reviewers. it to frame a review of the literature on the sustainability of competitive advantage rooted in information systems use. We offer a framework that articulates both the dynamic approach to ITdependent strategic advantage currently receiving attention in the literature and the underlying drivers of sustainability. This framework models how and why the characteristics of the IT-dependent strategic initiative enable sustained competitive advantage, and how the determinants of sustainability are developed and strengthened over time. Such explanation facilitates the pre-implementation analysis of planned initiatives by innovators, as well as the post-implementation evaluation of existing initiatives so as to identify the basis of their sustainability. In carrying out this study, we examined the interdisciplinary literature on strategic information systems. Using a structured methodology, we reviewed the titles and abstracts of 648 articles drawn from information systems, strategic management, and marketing literature. We then examined and individually coded a relevant subset of 117 articles. The literature has identified four barriers to erosion of competitive advantage for ITdependent strategic initiatives and has surfaced the structural determinants of their magnitude. Previous work has also begun to theorize about the process by which these barriers to erosion evolve over time. Our review reveals that signifiPiccoli & Ives/IT-Dependent Strategic Initiatives 748 MIS Quarterly Vol. 29 No. 4/December 2005 cant exploratory research and theoretical development have occurred in this area, but there is a paucity of research providing rigorous tests of theoretical propositions. Our work makes three principal contributions. First, it formalizes the definition of IT-dependent strategic initiative. Second, it organizes the extant interdisciplinary research around an integrative framework that should prove useful to both research and practice. This framework offers an explanation of how and why IT-dependent strategic initiatives contribute to sustained competitive advantage, and explains the process by which they evolve over time. Finally, our review and analysis of the literature offers the basis for future research directions.

Selecting Valuable Stock Using Genetic Algorithm <=SEP=> In this study, we utilize the genetic algorithm (GA) to select high quality stocks with investment value. Given the fundamental financial and price information of stocks trading, we attempt to use GA to identify stocks that are likely to outperform the market by having excess returns. To evaluate the efficiency of the GA for stock selection, the return of equally weighted portfolio formed by the stocks selected by GA is used as evaluation criterion. Experiment results reveal that the proposed GA for stock selection provides a very flexible and useful tool to assist the investors in selecting valuable stocks.

Model-Driven Artificial Intelligence for Online Network Optimization <=SEP=> Future 5G wireless networks will rely on agile and automated network management, where the usage of diverse resources must be jointly optimized with surgical accuracy. A number of key wireless network functionalities (e.g., traffic steering, energy savings) give rise to hard optimization problems. What is more, high spatio-temporal traffic variability coupled with the need to satisfy strict per slice/service SLAs in modern networks, suggest that these problems must be constantly (re-)solved, to maintain close-to-optimal performance. To this end, in this paper we propose the framework of Online Network Optimization (ONO), which seeks to maintain both agile and efficient control over time, using an arsenal of data-driven, adaptive, and AI-based techniques. Since the mathematical tools and the studied regimes vary widely among these methodologies, a theoretical comparison is often out of reach. Therefore, the important question “what is the right ONO technique?” remains open to date. In this paper, we discuss the pros and cons of each technique and further attempt a direct quantitative comparison for a specific use case, using real data. Our results suggest that carefully combining the insights of problem modeling with state-of-the-art AI techniques provides significant advantages at reasonable complexity.

Learning to Forget: Continual Prediction with LSTM <=SEP=> Long short-term memory (LSTM; Hochreiter & Schmidhuber, 1997) can solve numerous tasks not solvable by previous learning algorithms for recurrent neural networks (RNNs). We identify a weakness of LSTM networks processing continual input streams that are not a priori segmented into subsequences with explicitly marked ends at which the network's internal state could be reset. Without resets, the state may grow indefinitely and eventually cause the network to break down. Our remedy is a novel, adaptive forget gate that enables an LSTM cell to learn to reset itself at appropriate times, thus releasing internal resources. We review illustrative benchmark problems on which standard LSTM outperforms other RNN algorithms. All algorithms (including LSTM) fail to solve continual versions of these problems. LSTM with forget gates, however, easily solves them, and in an elegant way.

Analytic Performance Model of a Main-Memory Index Structure <=SEP=> Efficient evaluation of multi-dimensional range queries in a main-memory database is an important, but difficult task. State-of-the-art techniques rely on optimised sequential scans or tree-based structures. For range queries with small result sets, sequential scans exhibit poor asymptotic performance. Also, as the dimensionality of the data set increases, the performance of tree-based structures degenerates due to the curse of dimensionality. Recent literature proposed the Elf, a main-memory structure that is optimised for the case of such multi-dimensional low-selectivity queries. The Elf outperforms other state-ofthe-art methods in manually tuned scenarios. However, choosing an optimal parameter configuration for the Elf is vital, since for poor configurations, the search performance degrades rapidly. Consequently, further knowledge about the behaviour of the Elf in different configurations is required to achieve robust performance. In this thesis, we therefore propose a numerical cost model for the Elf. Like all main-memory index structures, the Elf response time is not dominated by disk accesses, refusing a straightforward analysis. Our model predicts the size and shape of the Elf region that is examined during search. We propose that the response time of a search is linear to the size of this region. Furthermore, we study the impact of skewed data distributions and correlations on the shape of the Elf. We find that they lead to behaviour that is accurately describable through simple reductions in attribute cardinality. Our experimental results indicate that for data sets of up to 15 dimensions, our cost model predicts the size of the examined Elf region with relative errors below 5%. Furthermore, we find that the size of the Elf region examined during search predicts the response time with an accuracy of 80 %.

Analyzing News Media Coverage to Acquire and Structure Tourism Knowledge <=SEP=> Destination image significantly influences a tourist’s decision-making process. The impact of news media coverage on destination image has attracted research attention and became particularly evident after catastrophic events such as the 2004 Indian Ocean earthquake that triggered a series of lethal tsunamis. Building upon previous research, this article analyzes the prevalence of tourism destinations among 162 international media sites. Term frequency captures the attention a destination receives—from a general and, after contextual filtering, from a tourism perspective. Calculating sentiment estimates positive and negative media influences on destination image at a given point in time. Identifying semantic associations with the names of countries and major cities, the results of co-occurrence analysis reveal the public profiles of destinations, and the impact of current events on media coverage. These results allow national tourism organizations to assess how their destination is covered by news media in general, and in a specific tourism context. To guide analysts and marketers in this assessment, an iterative analysis of semantic associations extracts tourism knowledge automatically, and represents this knowledge as ontological structures.

Beyond Cyberbullying: Self-Disclosure, Harm and Social Support on ASKfm <=SEP=> ASKfm is a social media platform popular among teens and young adults where users can interact anonymously or semi-anonymously. In this paper, we identify the modes of disclosure and interaction that occur on the site, and evaluate why users are motivated to post and interact on the site, despite its reputation for facilitating cyberbullying. Through topic modeling - supplemented with manual annotation - of a large dataset of ASKfm posts, we identify and classify the rich variety of discourse posted on ASKfm, including both positive and negative forms, providing insights into the why individuals continue to engage with the site. These findings are complemented by a survey of young adults (aged 18-20) ASKfm users, which provides additional insights into users' motivations and interaction patterns. We discuss how the affordances specific to platforms like ASKfm, including anonymity and visibility, might enable users to respond to cyberbullying in novel ways, engage in positive forms of self-disclosure, and gain social support on sensitive topics. We conclude with design recommendations that would highlight the positive interactions on the website and help diminish the repurcussions of the negative interactions.

Cognitive behavioural therapy for major psychiatric disorder: does it really work? A meta-analytical review of well-controlled trials. <=SEP=> BACKGROUND
Although cognitive behavioural therapy (CBT) is claimed to be effective in schizophrenia, major depression and bipolar disorder, there have been negative findings in well-conducted studies and meta-analyses have not fully considered the potential influence of blindness or the use of control interventions.


METHOD
We pooled data from published trials of CBT in schizophrenia, major depression and bipolar disorder that used controls for non-specific effects of intervention. Trials of effectiveness against relapse were also pooled, including those that compared CBT to treatment as usual (TAU). Blinding was examined as a moderating factor.


RESULTS
CBT was not effective in reducing symptoms in schizophrenia or in preventing relapse. CBT was effective in reducing symptoms in major depression, although the effect size was small, and in reducing relapse. CBT was ineffective in reducing relapse in bipolar disorder.


CONCLUSIONS
CBT is no better than non-specific control interventions in the treatment of schizophrenia and does not reduce relapse rates. It is effective in major depression but the size of the effect is small in treatment studies. On present evidence CBT is not an effective treatment strategy for prevention of relapse in bipolar disorder.

Attention-based Pyramid Aggregation Network for Visual Place Recognition <=SEP=> Visual place recognition is challenging in the urban environment and is usually viewed as a large scale image retrieval task. The intrinsic challenges in place recognition exist that the confusing objects such as cars and trees frequently occur in the complex urban scene, and buildings with repetitive structures may cause over-counting and the burstiness problem degrading the image representations. To address these problems, we present an Attention-based Pyramid Aggregation Network (APANet), which is trained in an end-to-end manner for place recognition. One main component of APANet, the spatial pyramid pooling, can effectively encode the multi-size buildings containing geo-information. The other one, the attention block, is adopted as a region evaluator for suppressing the confusing regional features while highlighting the discriminative ones. When testing, we further propose a simple yet effective PCA power whitening strategy, which significantly improves the widely used PCA whitening by reasonably limiting the impact of over-counting. Experimental evaluations demonstrate that the proposed APANet outperforms the state-of-the-art methods on two place recognition benchmarks, and generalizes well on standard image retrieval datasets.

Paving green passage for emergency vehicle in heavy traffic: Real-time motion planning under the connected and automated vehicles environment <=SEP=> This paper describes a real-time multi-vehicle motion planning (MVMP) algorithm for the emergency vehicle clearance task. To address the inherent limitations of human drivers in perception, communication, and cooperation, we require that the emergency vehicle and the surrounding normal vehicles are connected and automated vehicles (CAVs). The concerned MVMP task is to find cooperative trajectories such that the emergency vehicle can efficiently pass through the normal vehicles ahead. We use an optimal-control based formulation to describe the MVMP problem, which is centralized, straightforward, and complete. For the online solutions, the centralized MVMP formulation is converted into a multi-period and multi-stage version. Concretely, each period consists of two stages: the emergency vehicle and several normal CAVs ahead try to form a regularized platoon via acceleration or deceleration (stage 1); when a regularized platoon is formed, these vehicles act cooperatively to make way for the emergency vehicle until the emergency vehicle becomes the leader in this local platoon (stage 2). When one period finishes, the subsequent period begins immediately. This sequential process continues until the emergency vehicle finally passes through all the normal CAVs. The subproblem at stage 1 is extremely easy because nearly all the challenging nonlinearity gathers only in stage 2; typical solutions to the subproblem at stage 2 can be prepared offline, and then implemented online directly. Through this, our proposed MVMP algorithm avoids heavy online computations and thus runs in real time.

Simulating LTE Cellular Systems: An Open-Source Framework <=SEP=> Long-term evolution (LTE) represents an emerging and promising technology for providing broadband ubiquitous Internet access. For this reason, several research groups are trying to optimize its performance. Unfortunately, at present, to the best of our knowledge, no open-source simulation platforms, which the scientific community can use to evaluate the performance of the entire LTE system, are freely available. The lack of a common reference simulator does not help the work of researchers and poses limitations on the comparison of results claimed by different research groups. To bridge this gap, herein, the open-source framework LTE-Sim is presented to provide a complete performance verification of LTE networks. LTE-Sim has been conceived to simulate uplink and downlink scheduling strategies in multicell/multiuser environments, taking into account user mobility, radio resource optimization, frequency reuse techniques, the adaptive modulation and coding module, and other aspects that are very relevant to the industrial and scientific communities. The effectiveness of the proposed simulator has been tested and verified considering 1) the software scalability test, which analyzes both memory and simulation time requirements; and 2) the performance evaluation of a realistic LTE network providing a comparison among well-known scheduling strategies.

Diagnostically relevant facial gestalt information from ordinary photos <=SEP=> Craniofacial characteristics are highly informative for clinical geneticists when diagnosing genetic diseases. As a first step towards the high-throughput diagnosis of ultra-rare developmental diseases we introduce an automatic approach that implements recent developments in computer vision. This algorithm extracts phenotypic information from ordinary non-clinical photographs and, using machine learning, models human facial dysmorphisms in a multidimensional 'Clinical Face Phenotype Space'. The space locates patients in the context of known syndromes and thereby facilitates the generation of diagnostic hypotheses. Consequently, the approach will aid clinicians by greatly narrowing (by 27.6-fold) the search space of potential diagnoses for patients with suspected developmental disorders. Furthermore, this Clinical Face Phenotype Space allows the clustering of patients by phenotype even when no known syndrome diagnosis exists, thereby aiding disease identification. We demonstrate that this approach provides a novel method for inferring causative genetic variants from clinical sequencing data through functional genetic pathway comparisons.DOI: http://dx.doi.org/10.7554/eLife.02020.001.

2395-1621 Hand Gesture Recognition Using Flex Sensors <=SEP=> ARTICLE INFO In this system an electro-mechanical robot is designed and controlled using hand gesture in real time. The system is designed on microcontroller platform using Keil and MPLAB tools. Hand gesture recognition is done on the principle of resistance change sensed through flex sensor. These sensors are integrated in a hand gloves from which input to the system is given. The designed system is divided into two sections as transmitter and receiver. The transmitter section will be in hand gloves from which the data is sensed and processed through PIC16F7487 and send serially to the receiver section. RF technology is used to transmit the data at the receiver section at the frequency of 2.4 GHz. ARM 7 (LPC2148) processor is used to receive the data. Here from the received data, the character is predicted and matched with the closet character from which the character is identified and displayed on LCD. The various case studies is prepared for the designed system and tested in real time. The proposed system can be used for the various applications such as in unmanned machines, industries, handicapped personnel etc. Keywords— a Sensor gloves, flex sensors, PIC controller, ARM processor etc Article History Received:20 th September 2015 Received in revised form :

Wide-band microstrip-to-coplanar stripline/slotline transitions <=SEP=> Wide-band microstrip line to coplanar stripline (CPS) transitions are proposed. The transition consists of a multisection matching transformer and a quarter-wavelength radial stub for the impedance matching and field matching between the microstrip line and CPS, respectively. The proposed planar transition has the advantages of compact size, wide bandwidth, and straightforward design procedure. Several parameters are studied through simulations and experiments to derive some design guidelines. With the return loss of better than 14 dB, the 1- and 3-dB back-to-back insertion loss bandwidth can cover from 1.4 to 6.6 GHz (1 : 4.7) and from 1.1 to 10.5 GHz (1 : 9.6), respectively. In addition, the microstrip-to-CPS transition is extended to design a microstrip-to-slotline transition by tapering the CPS into a slotline. From 2.7 to 10.4 GHz (1 : 3.85), the back-to-back return loss is better than 15 dB and the insertion loss is less than 3 dB.

A survey of sparse matrix-vector multiplication performance on large matrices <=SEP=> One of the main sources of sparse matrices is the discretization of partial differential equations that govern continuumphysics phenomena such as fluid flow and transport, phase separation, mechanical deformation, electromagnetic wave propagation, and others. Recent advances in high-performance computing area have been enabling researchers to tackle increasingly larger problems leading to sparse linear systems with hundreds of millions to a few tens of billions of unknowns, e.g., [5, 6]. Iterative linear solvers are popular in large-scale computing as they consume less memory than direct solvers. Contrary to direct linear solvers, iterative solvers approach the solution gradually requiring the computation of sparse matrixvector (SpMV) products. The evaluation of SpMV products can emerge as a bottleneck for computational performance within the context of the simulation of large problems. In this work, we focus on a linear system arising from the discretization of the Cahn–Hilliard equation, which is a fourth order nonlinear parabolic partial differential equation that governs the separation of a two-component mixture into phases [3]. The underlying spatial discretization is performed using the discontinuous Galerkin method (e.g. [10]) and Newton’s method. A number of parallel algorithms and strategies have been evaluated in this work to accelerate the evaluation of SpMV products.

Scale Space Technique for Word Segmentation in Handwritten Documents <=SEP=> Indexing large archives of historical manuscripts, like the papers of George Washington, is required to allow rapid perusal by scholars and researchers who wish to consult the original manuscripts. Presently, such large archives are indexed manually. Since optical character recognition (OCR) works poorly with handwriting, a scheme based on matching word images called word spotting has been suggested previously for indexing such documents. The important steps in this scheme are segmentation of a document page into words and creation of lists containing instances of the same word by word image matching. We have developed a novel methodology for segmenting handwritten document images by analyzing the extent of “blobs” in a scale space representationof the image. We believe this is the first application of scale space to this problem. The algorithm has been applied to around 30 grey level images randomly picked from different sections of the George Washington corpus of 6,400 handwritten document images. An accuracy of 77 − 96 percent was observed with an average accuracy of around 87 percent. The algorithm works well in the presence of noise, shine through and other artifacts which may arise due aging and degradation of the page over a couple of centuries or through the man made processes of photocopying and scanning.

Mining and analyzing the enterprise knowledge graph <=SEP=> Today's enterprises hold ever-growing amounts of public data, stemming from different organizational systems, such as development environments, CRM systems, business intelligence systems, and enterprise social media. This data unlocks rich and diverse information about entities, people, terms, and the relationships among them. A lot of insight can be gained through analyzing this knowledge graph, both by individual employees and by the organization as a whole. In this talk, I will review recent work done by the Social Technologies & Analytics group at IBM Research-Haifa to mine these relationships, represent them in a generalized model, and use the model for different aims within the enterprise, including social search [5], expertise location [1], social recommendation [2, 3], and network analysis [4].

Exploring IOT Application Using Raspberry Pi <=SEP=> There are thousands of sensors in an industry with different usage, such as, pressure transmitters, flow meter, temperature transmitters, level transmitters, and so on. Wired networks are mainly used to transfer data to base station by connecting sensor. It brings advantage as it provides reliable and stable communication system for instruments and controls. However, the cost of cables necessary is very costly. Therefore, recently low cost wireless networks are strongly required by customers, for example, temporary instrument networks and/or some non-critical permanent sites which require low data rate and longer battery life. In client/server model, file server act as a parent’s node which allow multiple child node to connect with it. It is responsible for central storage and data management so that other computers enable to access the file under the same network. This article explores the use of Raspberry Pi to function as a server in which several laptops are connected to it to copy, store and delete the file over network. IT requires authentication for user login before granting access to the file to ensure data integrity and security. File server is widely used in many areas, for example in education for uploading study note into the serve and student immediate downloading it into their computer. Moreover this work also explores the use of Raspberry Pi B+ model and XBee (ZigBee module) to demonstrate wireless communication data transmission, proving the validity of usage as a mobile low-power wireless network communication. The main goal of the research is to explore the use of Raspberry Pi for client-server communication using various wireless communication scenario such as Wi-Fi and ZigBee. Index Terms – client/server model, Raspberry Pi, XBee.