title: The Multiscale Laplacian Graph Kernel

abstract: Many real world graphs, such as the graphs of molecules, exhibit structure at multiple different scales, but most existing kernels between graphs are either purely local or purely global in character. In contrast, by building a hierarchy of nested subgraphs, the Multiscale Laplacian Graph kernels (MLG kernels) that we define in this paper can account for structure at a range of different scales. At the heart of the MLG construction is another new graph kernel, called the Feature Space Laplacian Graph kernel (FLG kernel), which has the property that it can lift a base kernel defined on the vertices of two graphs to a kernel between the graphs. The MLG kernel applies such FLG kernels to subgraphs recursively. To make the MLG kernel computationally feasible, we also introduce a randomized projection procedure, similar to the Nystr\"om method, but for RKHS operators.


title: Single-Fed Low-Profile High-Gain Circularly Polarized Slotted Cavity Antenna Using a High-Order Mode

abstract: In this letter, a single-fed low-profile high-gain circularly polarized slotted cavity antenna using a high-order cavity mode, i.e., TE440 mode, is proposed. The proposed antenna has a simple structure that consists of a radiating linearly polarized slotted cavity antenna and a linear-to-circular polarization converter. An antenna prototype operating at WLAN 5.8-GHz band fabricated with low-cost standard printed circuit board (PCB) process is exemplified to validate the proposed concept. Measured results compared to their simulated counterparts are presented, and a good agreement between simulation and measurement is obtained.


title: Spherical hashing

abstract: Many binary code encoding schemes based on hashing have been actively studied recently, since they can provide efficient similarity search, especially nearest neighbor search, and compact data representations suitable for handling large scale image databases in many computer vision problems. Existing hashing techniques encode high-dimensional data points by using hyperplane-based hashing functions. In this paper we propose a novel hypersphere-based hashing function, spherical hashing, to map more spatially coherent data points into a binary code compared to hyperplane-based hashing functions. Furthermore, we propose a new binary code distance function, spherical Hamming distance, that is tailored to our hypersphere-based binary coding scheme, and design an efficient iterative optimization process to achieve balanced partitioning of data points for each hash function and independence between hashing functions. Our extensive experiments show that our spherical hashing technique significantly outperforms six state-of-the-art hashing techniques based on hyperplanes across various image benchmarks of sizes ranging from one to 75 million of GIST descriptors. The performance gains are consistent and large, up to 100% improvements. The excellent results confirm the unique merits of the proposed idea in using hyperspheres to encode proximity regions in high-dimensional spaces. Finally, our method is intuitive and easy to implement.


title: Reliability assessment of SiC power module stack based on thermo-structural analysis

abstract: Power semiconductor modules for power electronics converters are required to withstand significant temperature cycles in operation. Due to the availability of novel wide-bandgap semiconductors such as SiC, operation temperatures and corresponding temperature cycles of the power modules are intended to be increased. This can lead to severe lifetime limitation by fatigue failure mechanism induced by the CTE (coefficient of thermal expansion) mismatch of the material layers in the module. In this study a 3D finite element model (FEM) of an advanced power module concept was developed. The main material stack consists of a ceramic substrate, an Ag sintered SiC MOSFET and a sintered top plate that serves as a bond buffer to allow for Cu wire bonding. Stress simulations are performed for several thermal load cycles starting with an analysis of the initia bonding process during manufacture, and following a temperature cycling mission profile during operation. Plastic strain energy densities in the critical regions of the bond interfaces are computed and its development upon consecutives cycles are followed-up in the time domain. Based on these investigations, it is possible to propose design improvements that are effective in reducing thermo-mechanical stresses, and to increase fatigue life.


title: Client Motivation for Therapy Scale: A Measure of Intrinsic Motivation, Extrinsic Motivation, and Amotivation for Therapy

abstract: The purpose of this study was to examine the psychometric properties of a new measure of client motivation for therapy, the Client Motivation for Therapy Scale. This scale is designed to measure client's Intrinsic Motivation, four forms of regulation for Extrinsic Motivation (integrated, identified, introjected, and external regulation), and Amotivation for therapy. These subscales correspond to different forms of motivation identified by Deci and Ryan (1985) and fall along a self-determination continuum. An experimental version of the scale, along with related scales, was distributed to a total sample of 138 clients involved in therapy. The results supported the factor structure of the scale and revealed a satisfactory level of internal consistency. Correlations among the subscales revealed a simplex pattern that, in general, provides support for the self-determination continuum and the construct validity of the scale. Implications for research on client motivation for therapy are discussed.


title: Neuroimaging studies of mood disorders

abstract: Abstract Neuroimaging studies of major depression have identified neurophysiologic abnormalities in multiple areas of the orbital and medial prefrontal cortex, the amygdala, and related parts of the striatum and thalamus. Some of these abnormalities appear mood state–dependent and are located in regions where cerebral blood flow increases during normal and other pathologic emotional states. These neurophysiologic differences between depressives and control subjects may thus implicate areas where physiologic activity changes to mediate or respond to the emotional, behavioral, and cognitive manifestations of major depressive episodes. Other abnormalities persist following symptom remission, and are found in orbital and medial prefrontal cortex areas where postmortem studies demonstrate reductions in cortex volume and histopathologic changes in primary mood disorders. These areas appear to modulate emotional behavior and stress responses, based upon evidence from brain mapping, lesion analysis, and electrophysiologic studies of humans and/or experimental animals. Dysfunction involving these regions is thus hypothesized to play a role in the pathogenesis of depressive symptoms. Taken together, these findings implicate interconnected neural circuits in which pathologic patterns of neurotransmission may result in the emotional, motivational, cognitive, and behavioral manifestations of primary and secondary affective disorders.


title: Deep Learning for Mobile Multimedia: A Survey

abstract: Deep Learning (DL) has become a crucial technology for multimedia computing. It offers a powerful instrument to automatically produce high-level abstractions of complex multimedia data, which can be exploited in a number of applications, including object detection and recognition, speech-to- text, media retrieval, multimodal data analysis, and so on. The availability of affordable large-scale parallel processing architectures, and the sharing of effective open-source codes implementing the basic learning algorithms, caused a rapid diffusion of DL methodologies, bringing a number of new technologies and applications that outperform, in most cases, traditional machine learning technologies. In recent years, the possibility of implementing DL technologies on mobile devices has attracted significant attention. Thanks to this technology, portable devices may become smart objects capable of learning and acting. The path toward these exciting future scenarios, however, entangles a number of important research challenges. DL architectures and algorithms are hardly adapted to the storage and computation resources of a mobile device. Therefore, there is a need for new generations of mobile processors and chipsets, small footprint learning and inference algorithms, new models of collaborative and distributed processing, and a number of other fundamental building blocks. This survey reports the state of the art in this exciting research area, looking back to the evolution of neural networks, and arriving to the most recent results in terms of methodologies, technologies, and applications for mobile environments.


title: Detecting Large Reshare Cascades in Social Networks

abstract: Detecting large reshare cascades is an important problem in online social networks. There are a variety of attempts to model this problem, from using time series analysis methods to stochastic processes. Most of these approaches heavily depend on the underlying network features and use network information to detect the virality of cascades. In most cases, however, getting such detailed network information can be hard or even impossible. ::: ::: In contrast, in this paper, we propose SANSNET, a network-agnostic approach instead. Our method can be used to answer two important questions: (1) Will a cascade go viral? and (2) How early can we predict it? We use techniques from survival analysis to build a supervised classifier in the space of survival probabilities and show that the optimal decision boundary is a survival function. A notable feature of our approach is that it does not use any network-based features for the prediction tasks, making it very cheap to implement. Finally, we evaluate our approach on several real-life data sets, including popular social networks like Facebook and Twitter, on metrics like recall, F-measure and breakout coverage. We find that network agnostic SANSNET classifier outperforms several non-trivial competitors and baselines which utilize network information.


title: TrueNorth Ecosystem for Brain-Inspired Computing: Scalable Systems, Software, and Applications

abstract: This paper describes the hardware and software ecosystem encompassing the brain-inspired TrueNorth processor - a 70mW reconfigurable silicon chip with 1 million neurons, 256 million synapses, and 4096 parallel and distributed neural cores. For systems, we present a scale-out system loosely coupling 16 single-chip boards and a scale-up system tightly integrating 16 chips in a 4 × 4 configuration by exploiting TrueNorth's native tiling. For software, we present an end-to-end ecosystem consisting of a simulator, a programming language, an integrated programming environment, a library of algorithms and applications, firmware, tools for deep learning, a teaching curriculum, and cloud enablement. For the scale-up systems we summarize our approach to physical placement of neural network, to reduce intra- and inter-chip network traffic. The ecosystem is in use at over 30 universities and government/corporate labs. Our platform is a substrate for a spectrum of applications from mobile and embedded computing to cloud and supercomputers.


title: A Closed-Form Solution to Asymmetric Motion Profile Allowing Acceleration Manipulation

abstract: In this paper, we propose a new complete closed-form formula for asymmetric motion profiles of long, medium, and short distances, which enables easy manipulation of jerks in arrival time for effectively reducing the acceleration and thus the overshoot. The design parameter named jerk ratio is newly introduced to scale down the jerks during the deceleration period so that the velocity profile is in an asymmetric S-curve. Owing to the jerk ratio, the motion formulas are remarkably simplified in analytic forms for short, medium, and long distances. The effectiveness of the proposed approach will be illustrated with the sledge movement control in an optical data storage system by experiments.


title: Radiometric calibration from a single image

abstract: Photometric methods in computer vision require calibration of the camera's radiometric response, and previous works have addressed this problem using multiple registered images captured under different camera exposure settings. In many instances, such an image set is not available, so we propose a method that performs radiometric calibration from only a single image, based on measured RGB distributions at color edges. This technique automatically selects appropriate edge information for processing, and employs a Bayesian approach to compute the calibration. Extensive experimentation has shown that accurate calibration results can be obtained using only a single input image.


title: TF Boosted Trees: A scalable TensorFlow based framework for gradient boosting

abstract: TF Boosted Trees (TFBT) is a new open-sourced frame-work for the distributed training of gradient boosted trees. It is based on TensorFlow, and its distinguishing features include a novel architecture, automatic loss differentiation, layer-by-layer boosting that results in smaller ensembles and faster prediction, principled multi-class handling, and a number of regularization techniques to prevent overfitting.


Real-World Performance of Cryptographic Accumulators


title: Debunking the 100X GPU vs. CPU myth: an evaluation of throughput computing on CPU and GPU

abstract: Recent advances in computing have led to an explosion in the amount of data being generated. Processing the ever-growing data in a timely manner has made throughput computing an important aspect for emerging applications. Our analysis of a set of important throughput computing kernels shows that there is an ample amount of parallelism in these kernels which makes them suitable for today's multi-core CPUs and GPUs. In the past few years there have been many studies claiming GPUs deliver substantial speedups (between 10X and 1000X) over multi-core CPUs on these kernels. To understand where such large performance difference comes from, we perform a rigorous performance analysis and find that after applying optimizations appropriate for both CPUs and GPUs the performance gap between an Nvidia GTX280 processor and the Intel Core i7-960 processor narrows to only 2.5x on average. In this paper, we discuss optimization techniques for both CPU and GPU, analyze what architecture features contributed to performance differences between the two architectures, and recommend a set of architectural features which provide significant improvement in architectural efficiency for throughput kernels.


title: Real-Time Visualization System of Magnetic Field Utilizing Augmented Reality Technology for Education

abstract: In electromagnetics education, it is important for beginners, who start to learn electromagnetics, to give an illustration of magnetic field. In this paper we propose a new real-time visualization system. It can visualize a composite image of source materials and their generated magnetic field utilizing the Augmented Reality technique to the users. With this real-time visualization system, electromagnetics learners can observe the visualized magnetic field as a realistic magnetic distribution on real-time and the visualized field changes immediately they move the objects.


title: Probabilistic Recurrent State-Space Models

abstract: State-space models (SSMs) are a highly expressive model class for learning patterns in time series data and for system identification. Deterministic versions of SSMs (e.g. LSTMs) proved extremely successful in modeling complex time series data. Fully probabilistic SSMs, however, are often found hard to train, even for smaller problems. To overcome this limitation, we propose a novel model formulation and a scalable training algorithm based on doubly stochastic variational inference and Gaussian processes. In contrast to existing work, the proposed variational approximation allows one to fully capture the latent state temporal correlations. These correlations are the key to robust training. The effectiveness of the proposed PR-SSM is evaluated on a set of real-world benchmark datasets in comparison to state-of-the-art probabilistic model learning methods. Scalability and robustness are demonstrated on a high dimensional problem.


title: Ask Your Neurons: A Deep Learning Approach to Visual Question Answering

abstract: We address a question answering task on real-world images that is set up as a Visual Turing Test. By combining latest advances in image representation and natural language processing, we propose Ask Your Neurons, a scalable, jointly trained, end-to-end formulation to this problem. ::: In contrast to previous efforts, we are facing a multi-modal problem where the language output (answer) is conditioned on visual and natural language inputs (image and question). We provide additional insights into the problem by analyzing how much information is contained only in the language part for which we provide a new human baseline. To study human consensus, which is related to the ambiguities inherent in this challenging task, we propose two novel metrics and collect additional answers which extend the original DAQUAR dataset to DAQUAR-Consensus. ::: Moreover, we also extend our analysis to VQA, a large-scale question answering about images dataset, where we investigate some particular design choices and show the importance of stronger visual models. At the same time, we achieve strong performance of our model that still uses a global image representation. Finally, based on such analysis, we refine our Ask Your Neurons on DAQUAR, which also leads to a better performance on this challenging task.


title: Reimagining HCI: toward a more human-centered perspective

abstract: us to rethink our whole value frame concerning means and ends, and the place of technology within this frame. The ambit of HCI has expanded enormously since the field’s emergence in the early 1980s. Computing has changed significantly; mobile and ubiquitous communication networks span the globe, and technology has been integrated into all aspects of our daily lives. Computing is not simply for calculating, but rather is a medium through which we collaborate and interact with other people. The focus of HCI is not so much on human-computer interaction as it is on human activities mediated by computing [1]. Just as the original meaning of ACM (Association for Computing Machinery) has become dated, perhaps so too has the original meaning of HCI (humancomputer interaction). It is time for us to rethink how we approach issues of people and technology. In this article I explore how we might develop a more humancentered approach to computing. for the 21st century, centered on the exploration of new forms of living with and through technologies that give primacy to human actors, their values, and their activities. The area of concern is much broader than the simple “fit” between people and technology to improve productivity (as in the classic human factors mold); it encompasses a much more challenging territory that includes the goals and activities of people, their values, and the tools and environments that help shape their everyday lives. We have evermore sophisticated and complex technologies available to us in the home, at work, and on the go, yet in many cases, rather than augmenting our choices and capabilities, this plethora of new widgets and systems seems to confuse us—or even worse, disable us. (Surely there is something out of control when a term such as “IT disability” can be taken seriously in national research programs.) Solutions do not reside simply in ergonomic corrections to the interface, but instead require Some years ago, HCI researcher Panu Korhonen of Nokia outlined to me how HCI is changing, as follows: In the early days the Nokia HCI people were told “Please evaluate our user interface, and make it easy to use.” That gave way to “Please help us design this user interface so that it is easy to use.” That, in turn, led to a request: “Please help us find what the users really need so that we know how to design this user interface.” And now, the engineers are pleading with us: “Look at this area of


title: The impact of phlebotomy in nonalcoholic fatty liver disease: A prospective, randomized, controlled trial.

abstract: UNLABELLED ::: Iron is implicated in the pathogenesis of liver injury and insulin resistance (IR) and thus phlebotomy has been proposed as a treatment for nonalcoholic fatty liver disease (NAFLD). We performed a prospective 6-month randomized, controlled trial examining the impact of phlebotomy on the background of lifestyle advice in patients with NAFLD. Primary endpoints were hepatic steatosis (HS; quantified by magnetic resonance imaging) and liver injury (determined by alanine aminotransaminase [ALT] and cytokeratin-18 [CK-18]). Secondary endpoints included insulin resistance measured by the insulin sensitivity index (ISI) and homeostasis model of assessment (HOMA), and systemic lipid peroxidation determined by plasma F2-isoprostane levels. A total of 74 subjects were randomized (33 phlebotomy and 41 control). The phlebotomy group underwent a median (range) of 7 (1-19) venesection sessions and had a significantly greater reduction in ferritin levels over 6 months, compared to controls (-148 ± 114 vs. -38 ± 89 ng/mL; P < 0.001). At 6 months, there was no difference between phlebotomy and control groups in HS (17.7% vs. 15.5%; P = 0.4), serum ALT (36 vs. 46 IU/L; P = 0.4), or CK-18 levels (175 vs. 196 U/L; P = 0.9). Similarly, there was no difference in end-of-study ISI (2.5 vs. 2.7; P = 0.9), HOMA (3.2 vs. 3.2; P = 0.6), or F2-isoprostane levels (1,332 vs. 1,190 pmmol/L; P = 0.6) between phlebotomy and control groups. No differences in any endpoint were noted in patients with hyperferritinemia at baseline. Among patients undergoing phlebotomy, there was no correlation between number of phlebotomy sessions and change in HS, liver injury, or IR from baseline to end of study. ::: ::: ::: CONCLUSION ::: Reduction in ferritin by phlebotomy does not improve liver enzymes, hepatic fat, or IR in subjects with NAFLD.


title: Nasal bone hypoplasia in trisomy 21 at 15-22 weeks' gestation

abstract: Objective To investigate the potential value of ultrasound examination of the fetal profile for present/hypoplastic fetal nasal bone at 15–22 weeks’ gestation as a marker for trisomy 21. Methods This was an observational ultrasound study in 1046 singleton pregnancies undergoing amniocentesis for fetal karyotyping at 15–22 (median, 17) weeks’ gestation. Immediately before amniocentesis the fetal profile was examined to determine if the nasal bone was present or hypoplastic (absent or shorter than 2.5 mm). The incidence of nasal hypoplasia in the trisomy 21 and the chromosomally normal fetuses was determined and the likelihood ratio for trisomy 21 for nasal hypoplasia was calculated. Results All fetuses were successfully examined for the presence of the nasal bone. The nasal bone was hypoplastic in 21/34 (61.8%) fetuses with trisomy 21, in 12/982 (1.2%) chromosomally normal fetuses and in 1/30 (3.3%) fetuses with other chromosomal defects. In 3/21 (14.3%) trisomy 21 fetuses with nasal hypoplasia there were no other abnormal ultrasound findings. In the chromosomally normal group hypoplastic nasal bone was found in 0.5% of Caucasians and in 8.8% of AfroCaribbeans. The likelihood ratio for trisomy 21 for hypoplastic nasal bone was 50.5 (95% CI 27.1–92.7) and for present nasal bone it was 0.38 (95% CI 0.24–0.56). Conclusion Nasal bone hypoplasia at the 15–22-week scan is associated with a high risk for trisomy 21 and it is a highly sensitive and specific marker for this chromosomal abnormality. Copyright  2002 ISUOG. Published by John Wiley & Sons, Ltd.


title: Economic prospects for a new antimalarial drug

abstract: Abstract The market for antimalarial drugs consists of the 2·8 billion (2·8 × 10 9 ) people living in malaria endemic areas and about 20–30 million people, mainly Europeans and North Americans, who travel or live in malarious areas, and the wealthy elite of malarious developing countries. Some of the largest markets include China, India, and Indonesia with a total of 1·9 billion people exposed; Latin America, with 119 million; and sub-Saharan Africa with 400 million. An estimated 200 million clinical cases occur each year, with around 2 million deaths annually, primarily in African children. Antimalarial drugs are distributed through several different networks and are purchased by governments and private individuals. At present the world market is dominated by chloroquine by virtue of its safety, wide availability and low price. Approximately 20% of the total production of chloroquine was distributed through national control programmes and 80% through other channels; chloroquine is probably the second or third most widely consumed drug in the world. The global market for antimalarial drugs is likely to be of the order of US$100–120 million, with chloroquine making up about US$64–80 m, sulfadoxine/pyrimethamine about US$20 m, and other drugs making up US$10–20 m. Chloroquine is rapidly losing its effectiveness against the malaria parasite, and a safe, effective, cheap replacement is urgently needed. Another product which was found to be safe and effective against malaria, with a price per treatment held at or near the present price of chloroquine, could quickly replace chloroquine as the first-line treatment against malaria. However, most purchasers of chloroquine are very price sensitive, and governments in particular have to balance the number of treatments they can buy with a limited budget against the potentially greater effectiveness of a more expensive drug. Sales of the 2 newest widely available antimalarial drugs, mefloquine and halofantrine, are virtually limited to the travellers' market because of their high price. Since there is no cheap, safe, and effective alternative ready to take the place of chloroquine in the treatment of some 200 million cases of clinical malaria which occur each year, conditions are optimal for further development of promising compounds which might fulfill this major therapeutic gap. The rewards, both financial and in terms of public recognition, are likely to be great, and the risks of marketing such a product appear to be manageable.


title: Performance analysis of DCO-OFDM in VLC system

abstract: The performance of indoor visible light communication (VLC) systems using a direct current-biased optical orthogonal frequency division multiplexing (DCO-OFDM) scheme is investigated in this paper. The impact of nonlinearity of Light Emitting Diode (LED) and its beam angle on the VLC system performance is studied. We later analyze the effect of modulation order, number of subcarriers, signal scaling and biasing operation on the peak to average power ratio (PAPR) which is a major issue in OFDM based VLC system. Simulation results show that the bit error rate (BER) decreases as the degree of nonlinearity increases and a better BER can be achieved as the LED behavior approaches linear model. In addition, it is shown that reducing the QAM order or increasing the number of subcarriers may reduce the effect of the LED nonlinearity, thus improving the BER performance of the VLC system. Moreover, it is demonstrated that PAPR is higher for a large number of subcarriers and modulation order. Finally, the PAPR of the visible light OFDM system can significantly be reduced by employing the signal scaling combined with biasing operation.


title: L-Bench: An Android benchmark set for low-power mobile GPUs ☆

abstract: Abstract In recent years GPUs have become one of the most important components in mobile application processors (APs). Thus, performance measurement and analysis of mobile GPUs are crucial to mobile AP manufacturers, device manufacturers, graphics application programmers, and end users. However, it is hard to analyze mobile GPUs in depth via existing high-level (with frames per second) or low-level benchmarks (with a fill rate, ALU performance, etc.). To bridge the gap between the benchmarks, we present a novel Android benchmark set for low-power GPUs, called L-Bench. This benchmark set consists of mid-level micro-benchmarks implemented on OpenGL ES 3.1, which are carefully chosen for different workloads. By analyzing the results, this benchmark suite provides not only frames per second of each benchmark but also performance of each GPU subsystem (geometry units, ALUs, texture mapping units, raster operations pipelines, caches/memory units, and tessellators) and overall GPU performance. For experiments, we perform our benchmark suite on five representative mobile devices that have different mobile GPUs, after that, we describe comprehensive analysis of each GPU architecture.


title: Students' Perceptions of Online Learning: A Comparative Study

abstract: In search of better, more cost effective ways to deliver instruction and training, universities and corporations have expanded their use of e-learning. Although several studies suggest that online education and blended instruction (a “blend” of online and traditional approaches) can be as effective as traditional classroom models, few studies have focused on learner satisfaction with online instruction, particularly in the transition to online learning from traditional approaches. This study examines students’ perceptions of integrating online components in two undergraduate business courses where students completed online learning modules prior to class discussion. The results indicate that participants in an elective course rated the online modules significantly better than those in a required course. Overall, participants in the elective course rated the online modules marginally positive while those in the required course rated them marginally negative. These outcomes suggest that instructors should be selective in the way they integrate online units into traditional, classroom-delivered courses. This integration should be carefully planned based on learner characteristics, course content, and the learning context. For most participants of the study (83 percent), this was their first experience completing an online learning activity or module. In addition, the largest dissatisfaction factor reported among the participants was the time required to complete the online modules. Future research is encouraged to explore: (1) how previous experience with technology and online learning affects students’ attitudes towards and success with e-learning; and (2) the effects of interspersing online units that are considerably shorter in length into the traditional classroom model. This additional research can provide greater insight into which factors promote e-learning success.


title: Software Defined Radio Implementation of a QPSK Modulator/Demodulator in an Extensive Hardware Platform Based on FPGAs Xilinx ZYNQ

abstract: Software Defined Radio (SDR) technology enables wireless devices deployment with support to multiple interfaces in modulation formats and it has gained importance due to the current wireless standards proliferation. In order to activate these functionalities it is necessary to apply SDR inside a reconfigurable hardware such as the Field Programmable Gate Array (FPGA). In this study, design procedures developments are presented resulting in a Quadrature Phase Shift Keying (QPSK) modulator/demodulator based on a hardware architecture of the Xilinx FPGA Zynq family. Also in the modem’s conception it was employed the Xilinx Vivado-tool combined with Matlab, Simulink and development with ISE/EDK apparatuses. As for the reconfigurable hardware platform applied to this, it was utilized the ZedBoard along with the Analog Devices FMCCOMMS1 radio module. In addition, it is noteworthy to indicate that the entire software-hardware solution was initially carried out through a simulation that achieved the system scheme, including the hardware platform, before any hard implementation of the QPSK modem itself. The central goal in this investigation is to fully demonstrate how to construct a feasible radio system through software as close to the state of the art as possible.


title: Autism Spectrum Disorder: Does Neuroimaging Support the DSM-5 Proposal for a Symptom Dyad? A Systematic Review of Functional Magnetic Resonance Imaging and Diffusion Tensor Imaging Studies

abstract: A systematic review of 208 studies comprising functional magnetic resonance imaging and diffusion tensor imaging data in patients with ‘autism spectrum disorder’ (ASD) was conducted, in order to determine whether these data support the forthcoming DSM-5 proposal of a social communication and behavioral symptom dyad. Studies consistently reported abnormal function and structure of fronto-temporal and limbic networks with social and pragmatic language deficits, of temporo-parieto-occipital networks with syntactic–semantic language deficits, and of fronto-striato-cerebellar networks with repetitive behaviors and restricted interests in ASD patients. Therefore, this review partially supports the DSM-5 proposal for the ASD dyad.


title: A new similarity measure based on edge counting

abstract: In order to overcome this problem, we propose, in this paper, a new similarity measure giving realistic results and closer relations to reality for concepts not located in the same path. The paper presents a similarity measure that is suited for comparing concepts in ontology. Although finding similar concepts is a core task in the area of ontology alignment/merging [5][6]. The proposed measure can be adopted effectively in this field.


title: SOCIOECONOMIC STATUS MODIFIES HERITABILITY OF IQ IN YOUNG CHILDREN

abstract: Scores on the Wechsler Intelligence Scale for Children were analyzed in a sample of 7-year-old twins from the National Col- laborative Perinatal Project. A substantial proportion of the twins were raised in families living near or below the poverty level. Biometric analyses were conducted using models allowing for components attrib- utable to the additive effects of genotype, shared environment, and non- shared environment to interact with socioeconomic status (SES) measured as a continuous variable. Results demonstrate that the proportions of IQ variance attributable to genes and environment vary nonlinearly with SES. The models suggest that in impoverished families, 60% of the vari- ance in IQ is accounted for by the shared environment, and the contri- bution of genes is close to zero; in affluent families, the result is almost exactly the reverse.


title: Convolutional Pseudo-Prior for Structured Labeling

abstract: Current practice in convolutional neural networks (CNN) remains largely bottom-up and the role of top-down process in CNN for pattern analysis and visual inference is not very clear. In this paper, we propose a new method for structured labeling by developing convolutional pseudo-prior (ConvPP) on the ground-truth labels. Our method has several interesting properties: (1) compared with classical machine learning algorithms like CRFs and Structural SVM, ConvPP automatically learns rich convolutional kernels to capture both short- and long- range contexts; (2) compared with cascade classifiers like Auto-Context, ConvPP avoids the iterative steps of learning a series of discriminative classifiers and automatically learns contextual configurations; (3) compared with recent efforts combing CNN models with CRFs and RNNs, ConvPP learns convolution in the labeling space with much improved modeling capability and less manual specification; (4) compared with Bayesian models like MRFs, ConvPP capitalizes on the rich representation power of convolution by automatically learning priors built on convolutional filters. We accomplish our task using pseudo-likelihood approximation to the prior under a novel fixed-point network structure that facilitates an end-to-end learning process. We show state-of-the-art results on sequential labeling and image labeling benchmarks.


title: Apoidea: A Decentralized Peer-to-Peer Architecture for Crawling the World Wide Web

abstract: This paper describes a decentralized peer-to-peer model for building a Web crawler. Most of the current systems use a centralized client-server model, in which the crawl is done by one or more tightly coupled machines, but the distribution of the crawling jobs and the collection of crawled results are managed in a centralized system using a centralized URL repository. Centralized solutions are known to have problems like link congestion, being a single point of failure, and expensive administration. It requires both horizontal and vertical scalability solutions to manage Network File Systems (NFS) and load balancing DNS and HTTP requests.


title: A computational model for financial reporting fraud detection

abstract: A computational fraud detection model (CFDM) was proposed for detecting fraud in financial reporting. CFDM uses a quantitative approach on textual data. It incorporates techniques that use essentially all of information contained in the textual data for fraud detection. Extant work provides a foundation for detecting deception in high and low synchronicity computer-mediated communication (CMC). CFDM provides an analytical method that has the potential for automation. It was tested on the Management's Discussion and Analysis from 10-K filings and was able to distinguish fraudulent filings from non-fraudulent ones. CFDM can serve as a screening tool where deception is suspected.


title: CGNet: A Light-weight Context Guided Network for Semantic Segmentation

abstract: The demand of applying semantic segmentation model on mobile devices has been increasing rapidly. Current state-of-the-art networks have enormous amount of parameters hence unsuitable for mobile devices, while other small memory footprint models ignore the inherent characteristic of semantic segmentation. To tackle this problem, we propose a novel Context Guided Network (CGNet), which is a light-weight network for semantic segmentation on mobile devices. We first propose the Context Guided (CG) block, which learns the joint feature of both local feature and surrounding context, and further improves the joint feature with the global context. Based on the CG block, we develop Context Guided Network (CGNet), which captures contextual information in all stages of the network and is specially tailored for increasing segmentation accuracy. CGNet is also elaborately designed to reduce the number of parameters and save memory footprint. Under an equivalent number of parameters, the proposed CGNet significantly outperforms existing segmentation networks. Extensive experiments on Cityscapes and CamVid datasets verify the effectiveness of the proposed approach. Specifically, without any post-processing, CGNet achieves 64.8% mean IoU on Cityscapes with less than 0.5 M parameters, and has a frame-rate of 50 fps on one NVIDIA Tesla K80 card for 2048 $\times$ 1024 high-resolution images. The source code for the complete system are publicly available.


title: Gateway Redundancy Protocol

abstract: When there is a packet loss in given network, it cannot change the route all by itself. Administrator manually have to configure the given network and thereby provide access to the router which has efficient route to the client. By using Hot standby routing Protocol (HSRP), it automatically configures the router (also called auto redundancy) which is facing user traffic, with router which has highest priority after active router. It is enabled through HSRP. It reduces network congestion and enables soft operation. We use Open short path first (OSPF) protocol in this projectas it has higher convergence rate which leads to very less packet loss. Based on the IP address present in the IP packet header, OSPF transmits the packets. Our main agenda of the project is to reduce packet loss and provide efficient transport of information to the clients who expect more accuracy round the clock and also provide auto redundancy, low cost.


title: Cross-modal recipe retrieval: How to cook this dish?

abstract: In social media users like to share food pictures. One intelligent feature, potentially attractive to amateur chefs, is the recommendation of recipe along with food. Having this feature, unfortunately, is still technically challenging. First, the current technology in food recognition can only scale up to few hundreds of categories, which are yet to be practical for recognizing ten of thousands of food categories. Second, even one food category can have variants of recipes that differ in ingredient composition. Finding the best-match recipe requires knowledge of ingredients, which is a fine-grained recognition problem. In this paper, we consider the problem from the viewpoint of cross-modality analysis. Given a large number of image and recipe pairs acquired from the Internet, a joint space is learnt to locally capture the ingredient correspondence from images and recipes. As learning happens at the region level for image and ingredient level for recipe, the model has ability to generalize recognition to unseen food categories. Furthermore, the embedded multi-modal ingredient feature sheds light on the retrieval of best-match recipes. On an in-house dataset, our model can double the retrieval performance of DeViSE, a popular cross-modality model but not considering region information during learning.


title: The negative effects of open government data - investigating the dark side of open data

abstract: Reports and research appears to assume that the benefits of open data dominate open data's negative consequences. Moreover, much of the existing research discusses benefits and disadvantages on a high level without providing much detailed insight in the underlying processes. Yet many governments are reluctant to open their data, as they are afraid of possible negative consequences of opening data. The objective of this policy paper is to better understand the aspects of the dark side of open data and contributes to the literature by providing a more realistic perspective on open data. We conducted nineteen in depth interviews with public sector officials and data archivists and identified sixteen categories of negative effects. For the dark side inherent to open data efforts the research suggests that a context and dataset dependent decision-making model needs to be made weighing the benefits of open data on the one hand (e.g. creating transparency, the possibility to strengthen economic growth), and the risks and disadvantages of open data (e.g. violating privacy and possible misuse and misinterpretation of data) on the other hand.


title: Disorder-Specific Symptoms and Psychosocial Well-Being in Relation to No-Show Rates in Forensic ADHD Patients

abstract: ABSTRACT No-show rates in forensic psychiatry are related to higher recidivism risk and financial costs in mental health care, yet little is known about risk factors for high no-show rates. In this study, the extent to which disorder specific symptoms and psychosocial well-being are related to no-show rates in forensic patients with ADHD was examined. Sixty male patients with ADHD (M age = 35.9, SD = 8.6) who received treatment in a Dutch forensic outpatient center completed the Adult Self-Report on disorder-specific symptoms and general psychosocial well-being. Data on no-show rates and background characteristics were obtained via electronic patient files. Independent sample t-tests showed a trend in which patients with high no-show rates (15–45% missed appointments) had more ADHD symptoms compared to patients with low no-show rates (0–14.9% missed appointments). Furthermore, multivariate regression analyses showed that rule-breaking, externalizing problems and somatic problems were associated with higher no-show rates, whereas anxiety problems were associated with lower no-show rates. Results suggest that no-show rates in forensic patients with ADHD are related to specific psychopathological symptoms. This knowledge can be used to prevent no-show in forensic psychiatric treatment.


title: Fast Compact City Modeling for Navigation Pre-Visualization

abstract: Nowadays, GPS-based car navigation systems mainly use speech and aerial views of simplified road maps to guide drivers to their destination. However, drivers often experience difficulties in linking the simple 2D aerial map with the visual impression that they get from the real environment, which is inherently ground-level based. Therefore, supplying realistically textured 3D city models at ground-level proves very useful for pre-visualizing an upcoming traffic situation. Because this pre-visualization can be rendered from the expected future viewpoints of the driver, the latter will more easily understand the required maneuver. 3D city models can be reconstructed from the imagery recorded by surveying vehicles. The vastness of image material gathered by these vehicles, however, puts extreme demands on vision algorithms to ensure their practical usability. Algorithms need to be as fast as possible and should result in compact, memory efficient 3D city models for future ease of distribution and visualization. For the considered application, these are not contradictory demands. Simplified geometry assumptions can speed up vision algorithms while automatically guaranteeing compact geometry models. We present a novel city modeling framework which builds upon this philosophy to create 3D content at high speed which could allow for pre-visualization of any conceivable traffic situation by car navigation modules.


title: Associations between Online Friendship and Internet Addiction among Adolescents and Emerging Adults.

abstract: The past decades have witnessed a dramatic increase in the number of youths using the Internet, especially for communicating with peers. Online activity can widen and strengthen the social networks of adolescents and emerging adults (Subrahmanyam & Smahel, 2011), but it also increases the risk of Internet addiction. Using a framework derived from Griffiths (2000a), this study examined associations between online friendship and Internet addiction in a representative sample (n = 394) of Czech youths ages 12–26 years (M = 18.58). Three different approaches to friendship were identified: exclusively offline, face-to-face oriented, Internet oriented, on the basis of the relative percentages of online and offline associates in participants' friendship networks. The rate of Internet addiction did not differ by age or gender but was associated with communication styles, hours spent online, and friendship approaches. The study revealed that effects between Internet addiction and approaches to friendship may be reciprocal: Being oriented toward having more online friends, preferring online communication, and spending more time online were related to increased risk of Internet addiction; on the other hand, there is an alternative causal explanation that Internet addiction and preference for online communication conditions young people's tendency to seek friendship from people met online.


title: PSP-Based Scalable MOS Varactor Model

abstract: A physically based scalable model for MOS Varactors is presented. The model includes a PSP-based analytical surface potential charge formulation, MOS varactor specific gate current models, and physical geometry and process parameter based parasitic modeling. Key device performances of capacitance and quality factor Q are validated over voltage, frequency, and geometry, for several technologies. The model, implemented in Verilog-A, provides robust and accurate RF simulation of MOS varactors. A VCO design application is detailed.


End-To-End Multi-Modal Sensors Fusion System For Urban Automated Driving


title: Automating Construction of Machine Learning Models With Clinical Big Data: Proposal Rationale and Methods

abstract: Background: To improve health outcomes and cut health care costs, we often need to conduct prediction/classification using large clinical datasets (aka, clinical big data), for example, to identify high-risk patients for preventive interventions. Machine learning has been proposed as a key technology for doing this. Machine learning has won most data science competitions and could support many clinical activities, yet only 15% of hospitals use it for even limited purposes. Despite familiarity with data, health care researchers often lack machine learning expertise to directly use clinical big data, creating a hurdle in realizing value from their data. Health care researchers can work with data scientists with deep machine learning knowledge, but it takes time and effort for both parties to communicate effectively. Facing a shortage in the United States of data scientists and hiring competition from companies with deep pockets, health care systems have difficulty recruiting data scientists. Building and generalizing a machine learning model often requires hundreds to thousands of manual iterations by data scientists to select the following: (1) hyper-parameter values and complex algorithms that greatly affect model accuracy and (2) operators and periods for temporally aggregating clinical attributes (eg, whether a patient’s weight kept rising in the past year). This process becomes infeasible with limited budgets. Objective: This study’s goal is to enable health care researchers to directly use clinical big data, make machine learning feasible with limited budgets and data scientist resources, and realize value from data. Methods: This study will allow us to achieve the following: (1) finish developing the new software, Automated Machine Learning (Auto-ML), to automate model selection for machine learning with clinical big data and validate Auto-ML on seven benchmark modeling problems of clinical importance; (2) apply Auto-ML and novel methodology to two new modeling problems crucial for care management allocation and pilot one model with care managers; and (3) perform simulations to estimate the impact of adopting Auto-ML on US patient outcomes. Results: We are currently writing Auto-ML’s design document. We intend to finish our study by around the year 2022. Conclusions: Auto-ML will generalize to various clinical prediction/classification problems. With minimal help from data scientists, health care researchers can use Auto-ML to quickly build high-quality models. This will boost wider use of machine learning in health care and improve patient outcomes. [JMIR Res Protoc 2017;6(8):e175]


title: RNA targeted therapeutics for hematologic malignancies.

abstract: In a variety of experimental systems, antisense nucleic acids (ASNA) of various composition, including antisense oligodeoxynucleotides (ODN) and siRNA, have been shown to have the ability to variably perturb gene expression in a sequence specific manner. Pilot clinical studies from our group, and others, have demonstrated that gene silencing is a therapeutic strategy that is starting to make a real contribution to the treatment of various diseases. The development of this field, with specific reference to hematologic malignancies, is reviewed very briefly below.


A statistical approach for real-time robust background subtraction and shadow detection


title: Differentiable plasticity: training plastic neural networks with backpropagation

abstract: How can we build agents that keep learning from experience, quickly and efficiently, after their initial training? Here we take inspiration from the main mechanism of learning in biological brains: synaptic plasticity, carefully tuned by evolution to produce efficient lifelong learning. We show that plasticity, just like connection weights, can be optimized by gradient descent in large (millions of parameters) recurrent networks with Hebbian plastic connections. First, recurrent plastic networks with more than two million parameters can be trained to memorize and reconstruct sets of novel, high-dimensional 1000+ pixels natural images not seen during training. Crucially, traditional non-plastic recurrent networks fail to solve this task. Furthermore, trained plastic networks can also solve generic meta-learning tasks such as the Omniglot task, with competitive results and little parameter overhead. Finally, in reinforcement learning settings, plastic networks outperform a non-plastic equivalent in a maze exploration task. We conclude that differentiable plasticity may provide a powerful novel approach to the learning-to-learn problem.


title: Real-world Noisy Image Denoising: A New Benchmark

abstract: Most of previous image denoising methods focus on additive white Gaussian noise (AWGN). However,the real-world noisy image denoising problem with the advancing of the computer vision techiniques. In order to promote the study on this problem while implementing the concurrent real-world image denoising datasets, we construct a new benchmark dataset which contains comprehensive real-world noisy images of different natural scenes. These images are captured by different cameras under different camera settings. We evaluate the different denoising methods on our new dataset as well as previous datasets. Extensive experimental results demonstrate that the recently proposed methods designed specifically for realistic noise removal based on sparse or low rank theories achieve better denoising performance and are more robust than other competing methods, and the newly proposed dataset is more challenging. The constructed dataset of real photographs is publicly available at \url{this https URL} for researchers to investigate new real-world image denoising methods. We will add more analysis on the noise statistics in the real photographs of our new dataset in the next version of this article.


ExtRA: Extracting Prominent Review Aspects from Customer Feedback


title: On-Policy Dataset Synthesis for Learning Robot Grasping Policies Using Fully Convolutional Deep Networks

abstract: Rapid and reliable robot grasping for a diverse set of objects has applications from warehouse automation to home de-cluttering. One promising approach is to learn deep policies from synthetic training datasets of point clouds, grasps, and rewards sampled using analytic models with stochastic noise models for domain randomization. In this letter, we explore how the distribution of synthetic training examples affects the rate and reliability of the learned robot policy. We propose a synthetic data sampling distribution that combines grasps sampled from the policy action set with guiding samples from a robust grasping supervisor that has full state knowledge. We use this to train a robot policy based on a fully convolutional network architecture that evaluates millions of grasp candidates in 4-DOF (3-D position and planar orientation). Physical robot experiments suggest that a policy based on fully convolutional grasp quality CNNs (FC-GQ-CNNs) can plan grasps in 0.625 s, considering 5000x more grasps than our prior policy based on iterative grasp sampling and evaluation. This computational efficiency improves rate and reliability, achieving 296 mean picks per hour (MPPH) compared to 250 MPPH for iterative policies. Sensitivity experiments explore the effect of supervisor guidance level and granularity of the policy action space. Code, datasets, videos, and supplementary material can be found at http://berkeleyautomation.github.io/fcgqcnn .


title: 3D DRAM Design and Application to 3D Multicore Systems

abstract: From a system architecture perspective, 3D technology can satisfy the high memory bandwidth demands that future multicore/manycore architectures require. This article presents a 3D DRAM architecture design and the potential for using 3D DRAM stacking for both L2 cache and main memory in 3D multicore architecture.


title: Triangle detection based on windowed Hough Transform

abstract: A new algorithm based on windowed Hough Transform is proposed for triangle detection. A sliding window scans the image pixel by pixel and the Hough Transform is computed in the small region. Peaks of the Hough image which correspond to the line segments are then extracted. A triangle is detected when the three lines satisfy the certain conditions. Experimental results show that the arbitrary triangle can be detected and retrieved efficiently.


title: A survey of best practices for RNA-seq data analysis

abstract: RNA-sequencing (RNA-seq) has a wide variety of applications, but no single analysis pipeline can be used in all cases. We review all of the major steps in RNA-seq data analysis, including experimental design, quality control, read alignment, quantification of gene and transcript levels, visualization, differential gene expression, alternative splicing, functional analysis, gene fusion detection and eQTL mapping. We highlight the challenges associated with each step. We discuss the analysis of small RNAs and the integration of RNA-seq with other functional genomics techniques. Finally, we discuss the outlook for novel technologies that are changing the state of the art in transcriptomics.


title: Reproducibility of quantitative tractography methods applied to cerebral white matter

abstract: Tractography based on diffusion tensor imaging (DTI) allows visualization of white matter tracts. In this study, protocols to reconstruct eleven major white matter tracts are described. The protocols were refined by several iterations of intra- and inter-rater measurements and identification of sources of variability. Reproducibility of the established protocols was then tested by raters who did not have previous experience in tractography. The protocols were applied to a DTI database of adult normal subjects to study size, fractional anisotropy (FA), and T2 of individual white matter tracts. Distinctive features in FA and T2 were found for the corticospinal tract and callosal fibers. Hemispheric asymmetry was observed for the size of white matter tracts projecting to the temporal lobe. This protocol provides guidelines for reproducible DTI-based tract-specific quantification.


title: A novel control method in flux-weakening region for efficient operation of interior permanent magnet synchronous motor

abstract: This paper proposes a novel control method for an interior permanent magnet synchronous motor in the flux-weakening region. The proposed method consists of a maximum torque per flux (MTPF) control, and an efficient flux-weakening control under a light load. In order to simplify the MTPF control, a method for approximating the MTPF curve is proposed. In the flux-weakening control, more efficient control is applied by minimizing the armature current under a light load. In the proposed efficient flux-weakening control, the current command generation method, in which the armature current is adopted for the operation along the constant- voltage ellipse, is proposed. The validity of the proposed control method is verified based on simulation and experimental results.


title: Large-scale PV system based on the multiphase isolated DC/DC converter

abstract: The large-scale photovoltaic (PV) systems have already been reached 200 MW power level and they will continue to grow in size in the upcoming years. This trend will challenge the existing PV system architectures by requiring power converters with a higher power rating and a higher voltage level at the point of common coupling (PCC). The cascaded H-bridge (CHB) multilevel converter is one of the solutions which could deal with the aforementioned challenges. However, the topology based on the CHB converter faces the issue of leakage current that flows through the solar panel parasitic capacitance to ground which could damage the PV panels and pose safety problems. This paper proposes a multiphase isolated DC/DC converter for the CHB topology for a large-scale PV system which eliminates the leakage current issue. At the same time, the multiphase structure of the DC/DC converter helps to increase the power rating of the converter and to reduce the PV voltage and current ripples. A 0.54 MW rated seven-level CHB converter using multiphase isolated DC/DC converters has been modeled and simulated using MATLAB/Simulink and PLECS Blockset. Simulation results of different case studies are presented to evaluate the performance of the proposed PV system configuration.


title: A preliminary study of the effect of saturation and cross-magnetization on the inductances of a fractional-slot concentrated-wound interior PM synchronous machine

abstract: Accurate determination of the variation of inductances with respect to increase in current is necessary to characterize the behavior of a Permanent Magnet Synchronous Machine (PMSM) under saturation. This is even more crucial in the Fractional Slot Concentrated Wound Interior PM synchronous machine (FSCW IPMSM) because of increased spatial harmonics in the magneto-motive force (MMF) waveform. This paper attempts to determine the variation of inductances of a prototype FSCW IPMSM due to saturation and cross-magnetization effects. The variation of incremental and apparent inductances and their effect on voltage equations are investigated. The flux-linkage calculation method was used to calculate the incremental and apparent inductances for a wide range of operating points including constant torque and flux-weakening regions. The results of a Finite Element (FE) model which agree well with the experimental results were used to further investigate the inductance characteristics.


title: Transforming reactive auto-scaling into proactive auto-scaling

abstract: Elasticity is a key characteristic of cloud platforms enabling resource to be acquired on-demand in response to time-varying workloads. We introduce a new elasticity management framework that takes as input commonly used reactive rule-based scaling strategies but offers in return proactive auto-scaling. The elasticity framework combines reactive and predictive auto-scaling techniques, and we discuss the specification and performance of these individual components. We present a case study, based on real datasets, to demonstrate that our framework is capable of making appropriate auto-scaling decisions that can improve resource utilization compared to that obtained from a purely reactive approach.


title: Robotics to promote elementary education pre-service teachers' STEM engagement, learning, and teaching

abstract: We report a research project with a purpose of helping teachers learn how to design and implement science, technology, engineering, and mathematics (STEM) lessons using robotics. Specifically, pre-service teachers' STEM engagement, learning, and teaching via robotics were investigated in an elementary teacher preparation course. Data were collected from surveys, classroom observations, interviews, and lesson plans. Both quantitative and qualitative data analyses indicated that pre-service teachers engaged in robotics activities actively and mindfully. Their STEM engagement improved overall. Their emotional engagement (e.g., interest, enjoyment) in STEM significantly improved and in turn influenced their behavioral and cognitive engagement in STEM. Their lesson designs showed their STEM teaching was developing in productive directions although further work was needed. These findings suggest that robotics can be used as a technology in activities designed to enhance teachers' STEM engagement and teaching through improved attitudes toward STEM. Future research and teacher education recommendations are also presented. The preparation of elementary teachers to teach STEM via robotics was studied.Integrative STEM education was highlighted.The lens of engagement theory was used to examine teacher learning and performance.Productive struggles while learning to program were showcased.The potential of robotics to enhance teachers' STEM engagement and teaching is discussed.


title: Recurrent Spatial Transformer Networks

abstract: We integrate the recently proposed spatial transformer network (SPN) [Jaderberg et. al 2015] into a recurrent neural network (RNN) to form an RNN-SPN model. We use the RNN-SPN to classify digits in cluttered MNIST sequences. The proposed model achieves a single digit error of 1.5% compared to 2.9% for a convolutional networks and 2.0% for convolutional networks with SPN layers. The SPN outputs a zoomed, rotated and skewed version of the input image. We investigate different down-sampling factors (ratio of pixel in input and output) for the SPN and show that the RNN-SPN model is able to down-sample the input images without deteriorating performance. The down-sampling in RNN-SPN can be thought of as adaptive down-sampling that minimizes the information loss in the regions of interest. We attribute the superior performance of the RNN-SPN to the fact that it can attend to a sequence of regions of interest.


title: Discovery of significant parameters in kidney dialysis data sets by K-means algorithm

abstract: The contributing factors for kidney dialysis such as creatinine, sodium, urea plays an important role in deciding the survival prediction of the patients as well as the need for undergoing kidney transplantation. Several attempts have been made to derive automated decision making procedure for earlier prediction. This preliminary study investigates the importance of clustering technique for identifying the influence of kidney dialysis parameters. A simple K-means algorithm is used to elicit knowledge about the interaction between many of these measured parameters and patient survival. The clustering procedure predicts the survival period of the patients who is undergoing the dialysis procedure.


title: Patient utilities in ankylosing spondylitis and the association with other outcome measures.

abstract: OBJECTIVE ::: To compare in patients with ankylosing spondylitis (AS) utilities derived by rating scale and standard gamble method, to relate these values to other outcome measures, and to assess the sensitivity to change of utilities relative to changes in other outcomes. ::: ::: ::: METHODS ::: Patients with AS were randomly allocated to either weekly sessions of supervised group physical therapy for a period of 9 months or daily exercises at home. Analysis was restricted to the 59 patients who completed the Maastricht Utility Measurement Questionnaire (MUMQ) at baseline and after 9 months' followup and who were seen by the same interviewer. Reliability was assessed by intraclass correlation coefficient and change scores for marker states of disease. Construct validity was evaluated by correlation and multiple regression of baseline values with a variety of disease outcomes (pain and stiffness, patient's and physician's global assessment, Sickness Impact Profile, Health Assessment Questionnaire for the Spondyloarthropathies, Arthritis Impact Measurement Scale, functional, articular, and enthesis indices and spinal mobility measures). Sensitivity to change was assessed against changes in these outcome measures at followup. ::: ::: ::: RESULTS ::: The test-retest intraclass correlation coefficients for patient utilities were 0.95 (rating scale) and 0.79 (standard gamble), and for the marker state of mild disease 0.70 (rating scale) and 0.77 (standard gamble). A multiple regression analysis with the baseline rating scale or standard gamble utilities as dependent variable showed that patient's global assessment explained 59 and 11% of the total variance respectively. By multiple regression analysis 10% of the variance of change in rating scale utilities was explained by changes of patient's global assessment. In contrast, variance in change in standard gamble utilities was not explained by changes in other disease outcomes. ::: ::: ::: CONCLUSION ::: Findings obtained by rating scale and standard gamble differ considerably. Standard gamble utilities seem to address different aspects of health status than do rating scale utilities and more traditional outcomes. Utility measurement is sensitive to the method chosen to elicit patient well being.


title: Discovering epistatic feature interactions from neural network models of regulatory DNA sequences

abstract: Motivation Transcription factors bind regulatory DNA sequences in a combinatorial manner to modulate gene expression. Deep neural networks (DNNs) can learn the cis‐regulatory grammars encoded in regulatory DNA sequences associated with transcription factor binding and chromatin accessibility. Several feature attribution methods have been developed for estimating the predictive importance of individual features (nucleotides or motifs) in any input DNA sequence to its associated output prediction from a DNN model. However, these methods do not reveal higher‐order feature interactions encoded by the models. Results We present a new method called Deep Feature Interaction Maps (DFIM) to efficiently estimate interactions between all pairs of features in any input DNA sequence. DFIM accurately identifies ground truth motif interactions embedded in simulated regulatory DNA sequences. DFIM identifies synergistic interactions between GATA1 and TAL1 motifs from in vivo TF binding models. DFIM reveals epistatic interactions involving nucleotides flanking the core motif of the Cbf1 TF in yeast from in vitro TF binding models. We also apply DFIM to regulatory sequence models of in vivo chromatin accessibility to reveal interactions between regulatory genetic variants and proximal motifs of target TFs as validated by TF binding quantitative trait loci. Our approach makes significant strides in improving the interpretability of deep learning models for genomics. Availability and implementation Code is available at: https://github.com/kundajelab/dfim. Supplementary information Supplementary data are available at Bioinformatics online.


title: Challenges Facing the Industrial Implementation of Fog Computing

abstract: Recently, the industries converge to the integration of the industry 4.0 paradigm to keep responding to the variable market demands. This integration is realized by the adoption of several components of the industry 4.0 such as IoT, Big Data and Cloud Computing, etc. Several difficulties concerning the integration of data management were encountered during first level of Industry 4.0 integration because of the unexpected quantity of data generated by IoT devices. The Fog computing can be considered as a new component of Industry 4.0 to resolve this kind of problem. However its implementation in the industrial field faces several challenges from different natures. This paper explains the role of Fog Computing solution to enhance the Cloud layer (distribution, low latency, real-time,. . . ) and studies its ability to be implemented in manufacturing systems. The Fog Manufacturing is introduced as the new industrial Fog vision. The challenges preventing the Fog Manufacturing implementation are studied and the links between each other are justified. A future use case is described to carry out the solutions given to satisfy the Fog Manufacturing challenges.


title: Teleoperation with inverse dynamics control for PHANToM Omni haptic device

abstract: In this paper, a teleoperation system using inverse dynamic control is implemented with two PHANToM Omni haptic devices connected in a master-slave configuration. The inverse dynamic control is designed to linearize the nonlinear system and achieve motion tracking between the two devices. This paper provides the kinematic model and dynamic equation of PHANToM Omni haptic device used for constructing the inverse dynamic controller. Experimental results demonstrate the improved trajectory tracking performance of the teleoperated system with an inverse dynamic controller when compared with a conventional PID controller.


title: ET-Net: A Generic Edge-aTtention Guidance Network for Medical Image Segmentation

abstract: Segmentation is a fundamental task in medical image analysis. However, most existing methods focus on primary region extraction and ignore edge information, which is useful for obtaining accurate segmentation. In this paper, we propose a generic medical segmentation method, called Edge-aTtention guidance Network (ET-Net), which embeds edge-attention representations to guide the segmentation network. Specifically, an edge guidance module is utilized to learn the edge-attention representations in the early encoding layers, which are then transferred to the multi-scale decoding layers, fused using a weighted aggregation module. The experimental results on four segmentation tasks (i.e., optic disc/cup and vessel segmentation in retinal images, and lung segmentation in chest X-Ray and CT images) demonstrate that preserving edge-attention representations contributes to the final segmentation accuracy, and our proposed method outperforms current state-of-the-art segmentation methods. The source code of our method is available at this https URL.


title: A contextual collaborative approach for app usage forecasting

abstract: Fine-grained long-term forecasting enables many emerging recommendation applications such as forecasting the usage amounts of various apps to guide future investments, and forecasting users' seasonal demands for a certain commodity to find potential repeat buyers. For these applications, there often exists certain homogeneity in terms of similar users and items (e.g., apps), which also correlates with various contexts like users' spatial movements and physical environments. Most existing works only focus on predicting the upcoming situation such as the next used app or next online purchase, without considering the long-term temporal co-evolution of items and contexts and the homogeneity among all dimensions. In this paper, we propose a contextual collaborative forecasting (CCF) model to address the above issues. The model integrates contextual collaborative filtering with time series analysis, and simultaneously captures various components of temporal patterns, including trend, seasonality, and stationarity. The approach models the temporal homogeneity of similar users, items, and contexts. We evaluate the model on a large real-world app usage dataset, which validates that CCF outperforms state-of-the-art methods in terms of both accuracy and efficiency for long-term app usage forecasting.


Total Quality Management 9 Total Quality Management Concepts , Evolution and Acceptability in Developing Economies


title: Measuring the informativeness of a retrieval process

abstract: Evaluation of information retrieval systems should be based on measures of the information provided by the retrieval process, “informativeness” measures which take into account the interactive and full-text nature of present-day systems and the different types of questions which are asked of them. Desirable properties for an informativeness measure are developed, including context sensitivity, user centrality, and logarithmic response. A hypergraph-based framework for measuring the informativeness of a retrieval process is presented and a measure developed which satisfies the desired properties. The measure is compared to previously developed information measures and illustrated via an application.


title: Exploiting moral wriggle room : Behavior inconsistent with a preference for fair outcomes

abstract: Subjects in economic experiments often behave altruistically. This is interpreted as reflecting a concern for equitable monetary outcomes. We show that a considerable proportion of such fair behavior may not be driven by a desire to implement equitable outcomes. To do so, we first demonstrate a high frequency of fair behavior in a situation similar to the dictator game, but then show that such behavior decreases substantially when the connection between choices and outcomes is obfuscated. Specifically, we show that in a binary version of a dictator game, a majority of subjects choose equitably. We then show that subjects playing the same game behave more selfishly when the recipients' payoffs are uncertain, even if this uncertainty can be costlessly resolved. We also find that when either of two subjects can sacrifice to implement an equitable outcome, but neither can ensure inequity, selfishness prevails. Finally, we also find more selfish behavior when an unfair outcome could plausibly have resulted from an external factor rather than just the dictator's choice. Our results indicate that apparently fair behavior may reflect motivations other than simply a preference for fair outcomes.


title: Factors associated with human small aggressive non small cell lung cancer.

abstract: BACKGROUND ::: Some non-small cell lung cancers (NSCLC) progress to distant lymph nodes or metastasize while relatively small. Such small aggressive NSCLCs (SA-NSCLC) are no longer resectable with curative intent, carry a grave prognosis, and may involve unique biological pathways. This is a study of factors associated with SA-NSCLC. ::: ::: ::: METHODS ::: A nested case-case study was embedded in the National Cancer Institute's Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial. SA-NSCLC cases had stage T1, N3, and/or M1 NSCLC (n = 48) and non-SA-NSCLC cases had T2 to T3, N0 to N2, and M0 NSCLC (n = 329). Associations were assessed by multiple logistic regression. ::: ::: ::: RESULTS ::: SA-NSCLCs were associated with younger age at diagnosis [odds ratio (OR)(>or=65 versus <65), 0.44; 95% confidence interval (95% CI), 0.22-0.88], female gender, family history of lung cancer, and the interaction gender*family history of lung cancer and were inversely associated with ibuprofen use (OR(yes versus no), 0.29; 95% CI, 0.11-0.76). The ORs for associating gender (women versus men) with SA-NSCLC in those with and without a family history of lung cancer were 11.76 (95% CI, 2.00-69.22) and 1.86 (95% CI, 0.88-3.96), respectively. These associations held adjusted for histology and time from screening to diagnosis and when alternative controls were assessed. ::: ::: ::: CONCLUSION ::: SA-NSCLC was associated with female gender, especially in those with a family history of lung cancer. If these exploratory findings, which are subject to bias, are validated as causal, elucidation of the genetic and female factors involved may improve understanding of cancer progression and lead to preventions and therapies. Ibuprofen may inhibit lung cancer progression.


Multiracial Facial Golden Ratio and Evaluation of Facial Appearance


title: The Stratosphere platform for big data analytics

abstract: We present Stratosphere, an open-source software stack for parallel data analysis. Stratosphere brings together a unique set of features that allow the expressive, easy, and efficient programming of analytical applications at very large scale. Stratosphere's features include "in situ" data processing, a declarative query language, treatment of user-defined functions as first-class citizens, automatic program parallelization and optimization, support for iterative programs, and a scalable and efficient execution engine. Stratosphere covers a variety of "Big Data" use cases, such as data warehousing, information extraction and integration, data cleansing, graph analysis, and statistical analysis applications. In this paper, we present the overall system architecture design decisions, introduce Stratosphere through example queries, and then dive into the internal workings of the system's components that relate to extensibility, programming model, optimization, and query execution. We experimentally compare Stratosphere against popular open-source alternatives, and we conclude with a research outlook for the next years.


title: Inflatable antenna for CubeSat: A new spherical design for increased X-band gain

abstract: Interplanetary1 CubeSats and small satellites have potential to provide means to explore space and to perform science in a more affordable way. As the goals for these spacecraft become more ambitious in space exploration, the communication systems currently implemented will need to be improved to support those missions. One of the bottlenecks is the antennas' size, due to the close relation between antenna gain and dimensions. Hence, a possible solution is to develop inflatable antennas which can be packaged efficiently, occupying a small amount of space, and they can provide, once deployed, large dish dimension and correspondent gain. A prototype of a 1 m inflatable antenna for X-Band has been developed in a joint effort between JPL and ASU. After initial photogrammetry tests and radiation tests, it was discovered that the design was not able to meet the required gain. As a result, a new design, based on a spherical inflatable membrane, is proposed. This new design will allow reaching a more stable inflatable surface, hence improving the electromagnetic performance. This paper will detail the principle challenges in developing this new antenna focusing on: design, EM analysis, fabrication and tests.


title: Recurrent common peroneal palsy in association with the Ehlers-Danlos syndrome. A case report.

abstract: We report a patient with type III Ehlers-Danlos syndrome, presenting with recurrent footdrop, who had electrophysiologic evidence of a conduction block of the common peroneal nerve followed by complete resolution. We feel that this case supports the possible link between Ehlers-Danlos syndrome and multiple pressure sensitive neuropathy (tomaculous neuropathy), alluded to by Schady and Ochoa (1984).


title: Horizontal Eye Movements Studied With the On-Line Computer

abstract: An on-line computer has been used to study the dynamic behavior of horizontal eye movements. The computer was used to present to the subject quasi-random target movements in a horizontal plane and to record and store his responses for processing. Averaging techniques were used extensively and increased the repeatability of the data markedly.


title: Location based Applications for Mobile Augmented Reality

abstract: In this work we investigate building indoor location based applications for a mobile augmented reality system. We believe that augmented reality is a natural interface to visualize spacial information such as position or direction of locations and objects for location based applications that process and present information based on the user's position in the real world. To enable such applications we construct an indoor tracking system that covers a substantial part of a building. It is based on visual tracking of fiducial markers enhanced with an inertial sensor for fast rotational updates. To scale such a system to a whole building we introduce a space partitioning scheme to reuse fiducial markers throughout the environment. Finally we demonstrate two location based applications built upon this facility, an indoor navigation aid and a library search application.


title: Fast discrete curvlet transforms

abstract: This paper describes two digital implementations of a new mathematical transform, namely, the second generation curvelet transform in two and three dimensions. The first digital transformation is based on unequally spaced fast Fourier transforms, while the second is based on the wrapping of specially selected Fourier samples. The two implementations essentially differ by the choice of spatial grid used to translate curvelets at each scale and angle. Both digital transformations return a table of digital curvelet coefficients indexed by a scale parameter, an orientation parameter, and a spatial location parameter. And both implementations are fast in the sense that they run in O(n^2 log n) flops for n by n Cartesian arrays; in addition, they are also invertible, with rapid inversion algorithms of about the same complexity. Our digital transformations improve upon earlier implementations—based upon the first generation of curvelets—in the sense that they are conceptually simpler, faster, and far less redundant. The software CurveLab, which implements both transforms presented in this paper, is available at http://www.curvelet.org.


Change Management in Adult Educational Organizations: A Slovenian Case Study


title: Prediction of Surface Texture Characteristics in Turning of FRPs Using ANN

abstract: The objective of the present study is to develop an artificial neural network (ANN) in order to predict surface texture characteristics for the turning performance of a fiber reinforced polymer (FRP) composite. Full factorial design of experiments was designed and conducted. The process parameters considered in the experiments were cutting speed and feed rate, whilst the depth of cut has been held constant. The corresponding surface texture parameters that have been studied are the Ra and Rt. A feed forward back propagation neural network was fitted on the experimental results. It was found that accurate predictions of performance can be achieved through the feed forward back propagation (FFBP) neural network developed for the surface texture parameters.


title: Simulation of modern Traffic Lights Control Systems using the open source Traffic Simulation SUMO

abstract: Within the project “OIS” (optical information systems) new traffic control mechanisms had to be invented and tested. One of the most important topics was to optimize the flow over a junction using information from the OIS sensors which can not be measured using normal sensors such as induct loops. For this purpose, an “agentbased” traffic lights logic algorithm was used, which uses the length of a jam in front of a traffic light as input. As we had no possibility to test the traffic lights control within the reality, the improvement of the flow throughput of such junctions was shown using the open source traffic Simulation “SUMO” (Simulation of Urban MObility) [1, 2]. ::: This publication describes the algorithm itself and how it was embedded within the simulation. Furthermore, the simulation results are given.


title: Beyond Linguistic Equivalence. An Empirical Study of Translation Evaluation in a Translation Learner Corpus

abstract: The realisation that fully automatic translation in many settings is still far from producing output that is equal or superior to human translation has lead to an intense interest in translation evaluation in the MT community. However, research in this field, by now, has not only largely ignored the tremendous amount of relevant knowledge available in a closely related discipline, namely translation studies, but also failed to provide a deeper understanding of the nature of "translation errors" and "translation quality". This paper presents an empirical take on the latter concept, translation quality, by comparing human and automatic evaluations of learner translations in the KOPTE corpus. We will show that translation studies provide sophisticated concepts for translation quality estimation and error annotation. Moreover, by applying well-established MT evaluation scores, namely BLEU and Meteor, to KOPTE learner translations that were graded by a human expert, we hope to shed light on properties (and potential shortcomings) of these scores. 1 Translation quality assessment In recent years, researchers in the field of MT evaluation have proposed a large variety of methods for assessing the quality of automatically produced translations. Approaches range from fully automatic quality scoring to efforts aimed at the development of "human" evaluation scores that try to exploit the (often tacit) linguistic knowledge of human evaluators. The criteria according to which quality is estimated often include adequacy, the degree of meaning preservation, and fluency, target language correctness (Callison-Burch et al., 2007). The goals of both "human" evaluation and fully automatic quality scoring are manifold and cover system optimisation as well as benchmarking and comparison. In translation studies, the scientific (and prescientific) discussion on how to assess the quality of human translations has been going on for centuries. In recent years, the development of appropriate concepts and tools has become even more vital to the discipline due to the pressing needs of the language industry. However, different from the belief, typical to MT, that the "goodness" of a translation can be scored on the basis of linguistic criteria alone, the notion of "translation quality", in translation studies, has assumed a multi-faceted shape, distancing itself from a simple strive for equivalence and embracing concepts such as functional, stylistic and pragmatic appropriateness as well as textual coherence. In this section, we provide an overview over approaches to translation quality assessment developed in MT and translation studies to specify how "quality" is being defined in both fields and which methods and features are used. Due to the amount of available literature, this overview is necessarily incomplete, but still insightful with respect to differences and commonalities between MT and human translation evaluation.


title: More Accurate Question Answering on Freebase

abstract: Real-world factoid or list questions often have a simple structure, yet are hard to match to facts in a given knowledge base due to high representational and linguistic variability. For example, to answer "who is the ceo of apple" on Freebase requires a match to an abstract "leadership" entity with three relations "role", "organization" and "person", and two other entities "apple inc" and "managing director". Recent years have seen a surge of research activity on learning-based solutions for this method. We further advance the state of the art by adopting learning-to-rank methodology and by fully addressing the inherent entity recognition problem, which was neglected in recent works. We evaluate our system, called Aqqu, on two standard benchmarks, Free917 and WebQuestions, improving the previous best result for each benchmark considerably. These two benchmarks exhibit quite different challenges, and many of the existing approaches were evaluated (and work well) only for one of them. We also consider efficiency aspects and take care that all questions can be answered interactively (that is, within a second). Materials for full reproducibility are available on our website: http://ad.informatik.uni-freiburg.de/publications.


title: Experimental analysis of Weight-Compensated Weighted Centroid Localization algorithm based on RSSI

abstract: This paper presents an experimental analysis of WCWCL-RSSI (Weight-Compensated Weighted Centroid Localization algorithm based on Received Signal Strength Indicator (RSSI)) in an urban environment. In order to collect the data necessary for localization purposes, measurements were performed in the GSM (Global System for Mobile Communications) network in Belgrade (the central zones of city). The results of the work provide a valuable insight into performance of this method, compared to traditional CL (Centroid Localization) and WCL (Weighted Centroid Localization) methods in an urban environment.


title: Walkable school neighborhoods are not playable neighborhoods.

abstract: The objectives were to determine whether: (1) playability features differed across walkable and non-walkable school neighborhoods, and (2) physical activity differed in children living in walkable and non-walkable school neighborhoods. A total of 3912 grade 6-8 students from 132 school neighborhoods were studied. There was more developed park space in high walkability neighborhoods than low walkability neighborhoods. Other playability features were more preferable in the low (e.g., undeveloped treed and water areas) and moderate (e.g., physical disorder/esthetics) walkability neighborhoods. Children from low walkability neighborhoods were more likely to engage in free-time physical activity outside of school and to achieve recommended levels of moderate-to-vigorous physical activity than were children from high walkability neighborhoods.


title: Filling large holes in LiDAR data by inpainting depth gradients

abstract: We introduce a technique to fill large holes in LiDAR data sets. We combine concepts from patch-based image inpainting and gradient-domain image editing to simultaneously fill both texture and structure in a LiDAR scan. We discuss the problems with directly inpainting a depth image, and present a solution to this problem based on inpainting the depth gradients. Once the inpainted depth gradients are obtained, we use an image reconstruction technique to obtain the final 3D scene structure. We present several real-world examples of this technique with excellent results.


title: i-JEN: visual interactive Malaysia crime news retrieval system

abstract: Supporting crime news investigation involves a mechanism to help monitor the current and past status of criminal events. We believe this could be well facilitated by focusing on the user interfaces and the event crime model aspects. In this paper we discuss on a development of Visual Interactive Malaysia Crime News Retrieval System (i-JEN) and describe the approach, user studies and planned, the system architecture and future plan. Our main objectives are to construct crime-based event; investigate the use of crimebased event in improving the classification and clustering; develop an interactive crime news retrieval system; visualize crime news in an effective and interactive way; integrate them into a usable and robust system and evaluate the usability and system performance. The system will serve as a news monitoring system which aims to automatically organize, retrieve and present the crime news in such a way as to support an effective monitoring, searching, and browsing for the target users groups of general public, news analysts and policemen or crime investigators. The study will contribute to the better understanding of the crime data consumption in the Malaysian context as well as the developed system with the visualisation features to address crime data and the eventual goal of combating the crimes.


title: A feasibility study of high order volumetric texture features for computer aided diagnosis of polyps via CT colonography

abstract: Differentiating pathological stages, i.e., hyperplastic (H), tubular adenoma (Ta), tubulovillous adenoma (Va) and adenocarcinoma (A), of detected colon lesions is a main task for computer aided diagnosis (CADx) of polyps for computed tomography colonography (CTC). In this paper, we propose a virtual pathological model of differentiating the polyp types based on the Haralick texture analysis model, which computes various correlations of the image density distribution inside each polyp volume. Our model explores the utility of texture features from higher order differentiations or amplification, i.e., gradient and curvature, of the image density distribution, mimicking the amplification in pathology. The first set of texture features is extracted from the gradient of the image density distribution. The second set of texture features is derived from the curvature of the image density distribution. The gain of these two sets of newly developed higher order texture features was measured using the area under the receiver operating characteristic (ROC) curve (AUC) from a database of 124 lesions (polyps and masses, confirmed by both optical colonoscopy (OC) and CTC). Support vector machine (SVM) is employed for classification. The gain by the two sets new features over the original Haralick texture model is noticeable, i.e., by 15% of improvement of the average AUC by including first set and second set of new texture features for group HvsRest and 11% for group H&TavsRest than the basic Haralick texture features.


title: Evaluation of JavaScript frameworks : Why should you use them?

abstract: JavaScript is today’s most common client-side programming language for the web. Choosing the most adapted framework for each and every project can optimize the development process and increase profits. Developers often lack knowledge about what to prioritize when choosing a framework and very little previous research regarding this subject area is available. This thesis investigates two JavaScript client-side frameworks and answers the questions of why you should use a framework when developing applications. This thesis also investigates why a company should develop a new framework, when equivalent open-source alternatives are available. To be able to answer the questions interviews with developers were conducted. The results are that using a framework can optimize the development process regarding time and complexity, but learning a new framework can be difficult. In rare cases it is a great idea to develop a new framework for a certain project, but it is often not worth it. Developers also seem to choose experience of using a framework over performance.


title: A large-scale spiking neural network accelerator for FPGA systems

abstract: Spiking neural networks (SNN) aim to mimic membrane potential dynamics of biological neurons. They have been used widely in neuromorphic applications and neuroscience modeling studies. We design a parallel SNN accelerator for producing large-scale cortical simulation targeting an off-the-shelf Field-Programmable Gate Array (FPGA)-based system. The accelerator parallelizes synaptic processing with run time proportional to the firing rate of the network. Using only one FPGA, this accelerator is estimated to support simulation of 64K neurons 2.5 times real-time, and achieves a spike delivery rate which is at least 1.4 times faster than a recent GPU accelerator with a benchmark toroidal network.


title: Multiprocess 3D printing for increasing component functionality

abstract: Layer-by-layer deposition of materials to manufacture parts-better known as three-dimensional (3D) printing or additive manufacturing-has been flourishing as a fabrication process in the past several years and now can create complex geometries for use as models, assembly fixtures, and production molds. Increasing interest has focused on the use of this technology for direct manufacturing of production parts; however, it remains generally limited to single-material fabrication, which can limit the end-use functionality of the fabricated structures. The next generation of 3D printing will entail not only the integration of dissimilar materials but the embedding of active components in order to deliver functionality that was not possible previously. Examples could include arbitrarily shaped electronics with integrated microfluidic thermal management and intelligent prostheses custom-fit to the anatomy of a specific patient. We review the state of the art in multiprocess (or hybrid) 3D printing, in which complementary processes, both novel and traditional, are combined to advance the future of manufacturing.