title: A low-cost compliant 7-DOF robotic manipulator

abstract: We present the design of a new low-cost series-elastic robotic arm. The arm is unique in that it achieves reasonable performance for the envisioned tasks (backlash-free, sub-3mm repeatability, moves at 1.5m/s, 2kg payload) but with a significantly lower parts cost than comparable manipulators. The paper explores the design decisions and tradeoffs made in achieving this combination of price and performance. A new, human-safe design is also described: the arm uses stepper motors with a series-elastic transmission for the proximal four degrees of freedom (DOF), and non-series-elastic robotics servos for the distal three DOF. Tradeoffs of the design are discussed, especially in the areas of human safety and control bandwidth. The arm is used to demonstrate pancake cooking (pouring batter, flipping pancakes), using the intrinsic compliance of the arm to aid in interaction with objects.


title: Resilience of Energy Infrastructure and Services: Modeling, Data Analytics and Metrics

abstract: Large scale power failures induced by severe weather have become frequent and damaging in recent years, causing millions of people to be without electricity service for days. Although the power industry has been battling weather-induced failures for years, it is largely unknown how resilient the energy infrastructure and services really are to severe weather disruptions. What fundamental issues govern the resilience? Can advanced approaches such as modeling and data analytics help industry to go beyond empirical methods? This paper discusses the research to date and open issues related to these questions. The focus is on identifying fundamental challenges and advanced approaches for quantifying resilience. In particular, a first aspect of this problem is how to model large-scale failures, recoveries and impacts, involving the infrastructure, service providers, customers, and weather. A second aspect is how to identify generic vulnerability (i.e., non-resilience) in the infrastructure and services through large-scale data analytics. And, a third is to understand what resilience metrics are needed and how to develop them.


title: Parallel Networks: Synthetic Biology and Artificial Intelligence

abstract: Synthetic biology has emerged as an important technology for engineering cells to behave in controllable and predictable ways. The promise of this modern technology is dependent on our understanding of cellular complexity to allow us to engineer cells with novel function. In this regard, the fields of computer science and synthetic biology are critical for accelerating both our understanding of biological systems, and our ability to quantitatively engineer cells. Thus, advances in biology and biotechnology are arising at the intersection of computer science and synthetic biology approaches. This review seeks to introduce the field of synthetic biology to the computer science community, and to ignite a curiosity and interest in fostering a unique synergy for possible collaborations between synthetic biologists and computer scientists.


title: A fusion-based enhancing method for weakly illuminated images

abstract: We propose a straightforward and efficient fusion-based method for enhancing weakly illumination images that uses several mature image processing techniques. First, we employ an illumination estimating algorithm based on morphological closing to decompose an observed image into a reflectance image and an illumination image. We then derive two inputs that represent luminance-improved and contrast-enhanced versions of the first decomposed illumination using the sigmoid function and adaptive histogram equalization. Designing two weights based on these inputs, we produce an adjusted illumination by fusing the derived inputs with the corresponding weights in a multi-scale fashion. Through a proper weighting and fusion strategy, we blend the advantages of different techniques to produce the adjusted illumination. The final enhanced image is obtained by compensating the adjusted illumination back to the reflectance. Through this synthesis, the enhanced image represents a trade-off among detail enhancement, local contrast improvement and preserving the natural feel of the image. In the proposed fusion-based framework, images under different weak illumination conditions such as backlighting, non-uniform illumination and nighttime can be enhanced. HighlightsA fusion-based method for enhancing various weakly illuminated images is proposed.The proposed method requires only one input to obtain the enhanced image.Different mature image processing techniques can be blended in our framework.Our method has an efficient computation time for practical applications.


Automatic Chemical Design with Molecular Graph Variational Autoencoders


title: Value Based BTB Indexing for indirect jump prediction

abstract: History-based branch direction predictors for conditional branches are shown to be highly accurate. Indirect branches however, are hard to predict as they may have multiple targets corresponding to a single indirect branch instruction. We propose the Value Based BTB Indexing (VBBI), a correlation-based target address prediction scheme for indirect jump instructions. For each static hard-to-predict indirect jump instruction, the compiler identifies a ‘hint instruction’, whose output value strongly correlates with the target address of the indirect jump instruction. At run time, multiple target addresses of the indirect jump instruction are stored and subsequently accessed from the BTB at different indices computed using the jump instruction PC and the hint instruction output values. In case the hint instruction has not finished its execution when the jump instruction is fetched, a second and more accurate target address prediction is made when the hint instruction output is available, thus reducing the jump misprediction penalty. We compare our design to the regular BTB design and the best previously proposed indirect jump predictor, the tagged target cache (TTC). Our evaluation shows that the VBBI scheme improves the indirect jump target prediction accuracy by 48% and 18%, compared with the baseline BTB and TTC designs, respectively. This results in average performance improvement of 16.4% over the baseline BTB scheme, and 13% improvement over the TTC predictor. Out of this performance improvement 2% is contributed by target prediction overriding which is accurate 96% of the time.


title: Adopting Abstract Images for Semantic Scene Understanding

abstract: Relating visual information to its linguistic semantic meaning remains an open and challenging area of research. The semantic meaning of images depends on the presence of objects, their attributes and their relations to other objects. But precisely characterizing this dependence requires extracting complex visual information from an image, which is in general a difficult and yet unsolved problem. In this paper, we propose studying semantic information in abstract images created from collections of clip art. Abstract images provide several advantages over real images. They allow for the direct study of how to infer high-level semantic information, since they remove the reliance on noisy low-level object, attribute and relation detectors, or the tedious hand-labeling of real images. Importantly, abstract images also allow the ability to generate sets of semantically similar scenes. Finding analogous sets of real images that are semantically similar would be nearly impossible. We create 1,002 sets of 10 semantically similar abstract images with corresponding written descriptions. We thoroughly analyze this dataset to discover semantically important features, the relations of words to visual features and methods for measuring semantic similarity. Finally, we study the relation between the saliency and memorability of objects and their semantic importance.


title: Improving Named Entity Recognition for Morphologically Rich Languages Using Word Embeddings

abstract: In this paper, we addressed the Named Entity Recognition (NER) problem for morphologically rich languages by employing a semi-supervised learning approach based on neural networks. We adopted a fast unsupervised method for learning continuous vector representations of words, and used these representations along with language independent features to develop a NER system. We evaluated our system for the highly inflectional Turkish and Czech languages. We improved the state-of-the-art F-score obtained for Turkish without using gazetteers by 2.26% and for Czech by 1.53%. Unlike the previous state-of-the-art systems developed for these languages, our system does not make use of any language dependent features. Therefore, we believe it can easily be applied to other morphologically rich languages.


title: Survey of Australians using cannabis for medical purposes

abstract: BackgroundThe New South Wales State Government recently proposed a trial of the medical use of cannabis. Australians who currently use cannabis medicinally do so illegally and without assurances of quality control. Given the dearth of local information on this issue, this study explored the experiences of medical cannabis users.MethodsAustralian adults who had used cannabis for medical purposes were recruited using media stories. A total of 147 respondents were screened by phone and anonymous questionnaires were mailed, to be returned by postage paid envelope.ResultsData were available for 128 participants. Long term and regular medical cannabis use was frequently reported for multiple medical conditions including chronic pain (57%), depression (56%), arthritis (35%), persistent nausea (27%) and weight loss (26%). Cannabis was perceived to provide "great relief" overall (86%), and substantial relief of specific symptoms such as pain, nausea and insomnia. It was also typically perceived as superior to other medications in terms of undesirable effects, and the extent of relief provided. However, nearly one half (41%) experienced conditions or symptoms that were not helped by its use. The most prevalent concerns related to its illegality. Participants reported strong support for their use from clinicians and family. There was almost universal interest (89%) in participating in a clinical trial of medical cannabis, and strong support (79%) for investigating alternative delivery methods.ConclusionAustralian medical cannabis users are risking legal ramifications, but consistent with users elsewhere, claim moderate to substantial benefits from its use in the management of their medical condition. In addition to strong public support, medical cannabis users show strong interest in clinical cannabis research, including the investigation of alternative delivery methods.


title: Cloud-Based Utility Service Framework for Trust Negotiations Using Federated Identity Management

abstract: Utility based cloud services can efficiently provide various supportive services to different service providers. Trust negotiations with federated identity management are vital for preserving privacy in open systems such as distributed collaborative systems. However, due to the large amounts of server based communications involved in trust negotiations scalability issues prove to be less cumbersome when offloaded on to the cloud as a utility service. In this view, we propose trust based federated identity management as a cloud based utility service. The main component of this model is the trust establishment between the cloud service provider and the identity providers. We propose novel trust metrics based on the potential vulnerability to be attacked, the available security enforcements and a novel cost metric based on policy dependencies to rank the cooperativeness of identity providers. Practical use of these trust metrics is demonstrated by analyses using simulated data sets, attack history data: published by MIT Lincoln laboratory, real-life attacks and vulnerabilities extracted from Common Vulnerabilities and Exposures (CVE) repository and fuzzy rule based evaluations. The results of the evaluations imply the significance of the proposed trust model to support cloud based utility services to ensure reliable trust negotiations using federated identity management.


title: Unsupervised feature learning for urban sound classification

abstract: Recent studies have demonstrated the potential of unsupervised feature learning for sound classification. In this paper we further explore the application of the spherical k-means algorithm for feature learning from audio signals, here in the domain of urban sound classification. Spherical k-means is a relatively simple technique that has recently been shown to be competitive with other more complex and time consuming approaches. We study how different parts of the processing pipeline influence performance, taking into account the specificities of the urban sonic environment. We evaluate our approach on the largest public dataset of urban sound sources available for research, and compare it to a baseline system based on MFCCs. We show that feature learning can outperform the baseline approach by configuring it to capture the temporal dynamics of urban sources. The results are complemented with error analysis and some proposals for future research.


title: Combining Explicit and Implicit Semantic Similarity Information for Word Embeddings

abstract: In this paper, we propose a new framework that combines both explicit and implicit semantic similarity information for training word embeddings. While the former determines the similarity degree between two words explicitly, the latter reflects word similarities implicitly through contextual and relational similarity. We also propose a novel concept called relative similarity in vocabulary, which deliberately utilizes explicit semantic similarity information (word's definition in particular) for word embeddings. We conduct experimental studies on various word similarity and word categorization datasets. The results show that our framework compares favorably to a number of state-of-the-art approaches for word embeddings.


title: Supporting Communication and Coordination in Collaborative Sensemaking

abstract: When people work together to analyze a data set, they need to organize their findings, hypotheses, and evidence, share that information with their collaborators, and coordinate activities amongst team members. Sharing externalizations (recorded information such as notes) could increase awareness and assist with team communication and coordination. However, we currently know little about how to provide tool support for this sort of sharing. We explore how linked common work (LCW) can be employed within a `collaborative thinking space', to facilitate synchronous collaborative sensemaking activities in Visual Analytics (VA). Collaborative thinking spaces provide an environment for analysts to record, organize, share and connect externalizations. Our tool, CLIP, extends earlier thinking spaces by integrating LCW features that reveal relationships between collaborators' findings. We conducted a user study comparing CLIP to a baseline version without LCW. Results demonstrated that LCW significantly improved analytic outcomes at a collaborative intelligence task. Groups using CLIP were also able to more effectively coordinate their work, and held more discussion of their findings and hypotheses. LCW enabled them to maintain awareness of each other's activities and findings and link those findings to their own work, preventing disruptive oral awareness notifications.


title: Black-box constructions of two-party protocols from one-way functions

abstract: We exhibit constructions of the following two-party cryptographic protocols given only black-box access to a one-way function: ::: constant-round zero-knowledge arguments (of knowledge) for any language in NP; ::: constant-round trapdoor commitment schemes; ::: constant-round parallel coin-tossing. ::: ::: Previous constructions either require stronger computational assumptions (e.g. collision-resistant hash functions), non-black-box access to a one-way function, or a super-constant number of rounds. As an immediate corollary, we obtain a constant-round black-box construction of secure two-party computation protocols starting from only semi-honest oblivious transfer. In addition, by combining our techniques with recent constructions of concurrent zero-knowledge and non-malleable primitives, we obtain black-box constructions of concurrent zero-knowledge arguments for NP and non-malleable commitments starting from only one-way functions.


title: Multi-task learning for Joint Language Understanding and Dialogue State Tracking

abstract: This paper presents a novel approach for multi-task learning of language understanding (LU) and dialogue state tracking (DST) in task-oriented dialogue systems. Multi-task training enables the sharing of the neural network layers responsible for encoding the user utterance for both LU and DST and improves performance while reducing the number of network parameters. In our proposed framework, DST operates on a set of candidate values for each slot that has been mentioned so far. These candidate sets are generated using LU slot annotations for the current user utterance, dialogue acts corresponding to the preceding system utterance and the dialogue state estimated for the previous turn, enabling DST to handle slots with a large or unbounded set of possible values and deal with slot values not seen during training. Furthermore, to bridge the gap between training and inference, we investigate the use of scheduled sampling on LU output for the current user utterance as well as the DST output for the preceding turn.


title: MAP Inference for Bayesian Inverse Reinforcement Learning

abstract: The difficulty in inverse reinforcement learning (IRL) arises in choosing the best reward function since there are typically an infinite number of reward functions that yield the given behaviour data as optimal. Using a Bayesian framework, we address this challenge by using the maximum a posteriori (MAP) estimation for the reward function, and show that most of the previous IRL algorithms can be modeled into our framework. We also present a gradient method for the MAP estimation based on the (sub)differentiability of the posterior distribution. We show the effectiveness of our approach by comparing the performance of the proposed method to those of the previous algorithms.


title: The social structure of free and open source software development

abstract: Metaphors, such as the Cathedral and Bazaar, used to describe the organization of FLOSS projects typically place them in sharp contrast to proprietary development by emphasizing FLOSS’s distinctive social and communications structures. But what do we really know about the communication patterns of FLOSS projects? How generalizable are the projects that have been studied? Is there consistency across FLOSS projects? Questioning the assumption of distinctiveness is important because practitioner–advocates from within the FLOSS community rely on features of social structure to describe and account for some of the advantages of FLOSS production. ::: ::: To address this question, we examined 120 project teams from SourceForge, representing a wide range of FLOSS project types, for their communications centralization as revealed in the interactions in the bug tracking system. We found that FLOSS development teams vary widely in their communications centralization, from projects completely centered on one developer to projects that are highly decentralized and exhibit a distributed pattern of conversation between developers and active users. ::: ::: We suggest, therefore, that it is wrong to assume that FLOSS projects are distinguished by a particular social structure merely because they are FLOSS. Our findings suggest that FLOSS projects might have to work hard to achieve the expected development advantages which have been assumed to flow from "going open." In addition, the variation in communications structure across projects means that communications centralization is useful for comparisons between FLOSS teams. We found that larger FLOSS teams tend to have more decentralized communication patterns, a finding that suggests interesting avenues for further research examining, for example, the relationship between communications structure and code modularity.


title: Nonlinear model predictive control using a recurrent neural network

abstract: As linear model predictive control (MPC) becomes a standard technology, nonlinear MPC (NMPC) approach is debuting both in academia and industry. In this paper, the NMPC problem is formulated as a convex quadratic programming problem based on nonlinear model prediction and linearization. A recurrent neural network for NMPC is then applied for solving the quadratic programming problem. The proposed network is globally convergent to the optimal solution of the NMPC problem. Simulation results are presented to show the effectiveness and performance of the neural network approach.


title: Critical success factors in online education

abstract: The Internet is a major technological advancement reshaping not only our society but also that of universities worldwide. In light of this, universities have to capitalise on the Internet for teaching, and one progressive development of this is the use of online delivery methods. This paper draws upon the results of a survey conducted amongst students enrolled in one online management course at an Australian university. Three critical success factors in online delivery are identified: technology, the instructor and the previous use of the technology from a student’s perspective. We also argue that the lecturer will continue to play a central role in online education, albeit his or her role will become one of a learning catalyst and knowledge navigator.


title: A Family of Isolated Buck-Boost Converters Based on Semiactive Rectifiers for High-Output Voltage Applications

abstract: A systematic method for developing isolated buck-boost (IBB) converters is proposed in this paper, and single-stage power conversion, soft-switching operation, and high-efficiency performance can be achieved with the proposed family of converters. On the basis of a nonisolated two-switch buck-boost converter, the proposed IBB converters are generated by replacing the dc buck-cell and boost-cell in the non-IBB converter with the ac buck-cell and boost-cell, respectively. Furthermore, a family of semiactive rectifiers (SARs) is proposed to serve as the secondary rectification circuit for the IBB converters, which helps to extend the converter voltage gain and reduce the voltage stresses on the devices in the rectification circuit. Hence, the efficiency is improved by employing a transformer with a smaller turns ratio and reduced parasitic parameters, by using low-voltage rating MOSFETs and diodes with better switching and conduction performances. A full-bridge IBB converter is proposed and analyzed in detail as an example. The phase-shift modulation strategy is applied to the full-bridge IBB converter to achieve IBB conversion. Moreover, soft-switching performance of all active switches and diodes can be achieved over a wide load and voltage range by the proposed converter and control strategy. A 380-V-output prototype is fabricated to verify the effectiveness of the proposed family of IBB converters, the SARs, and the control strategies.


BioKB - Text mining and semantic technologies for the biomedical content discovery


title: A Motivational Theory of Life-Span Development

abstract: This article had four goals. First, the authors identified a set of general challenges and questions that a life-span theory of development should address. Second, they presented a comprehensive account of their Motivational Theory of Life-Span Development. They integrated the model of optimization in primary and secondary control and the action-phase model of developmental regulation with their original life-span theory of control to present a comprehensive theory of development. Third, they reviewed the relevant empirical literature testing key propositions of the Motivational Theory of Life-Span Development. Finally, because the conceptual reach of their theory goes far beyond the current empirical base, they pointed out areas that deserve further and more focused empirical inquiry.


title: Pedestrian detection with dilated convolution, region proposal network and boosted decision trees

abstract: With the rapid development of driverless cars, pedestrian detection has been a canonical instance of object detection. Although recent deep learning detectors such as RPN+BF and MS-CNN have shown excellent performance for pedestrian detection, they have limited success for detecting pedestrian, and the importance of final feature receptive field has been awared by previous leading deep learning pedestrian detectors. Applying the dilated convolution to the feature learning of pedestrian detection, we constructed a pedestrian detection framework along with the region proposal network and boosted decision trees. Pipeline of our proposed framework can be briefly generalized as follows: firstly, the fine-tuned RPN with specified aspect ratio is used to get boxes and scores. Secondly, the designed dilated convolution feature extraction model is used to get features. As different dilation factors provide different receptive field scales, we concat the features of different layers with the dilated convolutional features to get the final features. Finally, the candidate boxes are sent to the boosted decision trees to be classified using the scores and features. We evaluated our method on the Caltech Pedestrian Detection Benchmark. Comparing with other state-of-the-art detection methods, the proposed framework with dilated convolution has better performance.


title: A systematic study of automated program repair: fixing 55 out of 105 bugs for $8 each

abstract: There are more bugs in real-world programs than human programmers can realistically address. This paper evaluates two research questions: ``What fraction of bugs can be repaired automatically?'' and ``How much does it cost to repair a bug automatically?'' In previous work, we presented GenProg, which uses genetic programming to repair defects in off-the-shelf C programs. To answer these questions, we: (1) propose novel algorithmic improvements to GenProg that allow it to scale to large programs and find repairs 68% more often, (2) exploit GenProg's inherent parallelism using cloud computing resources to provide grounded, human-competitive cost measurements, and (3) generate a large, indicative benchmark set to use for systematic evaluations. We evaluate GenProg on 105 defects from 8 open-source programs totaling 5.1 million lines of code and involving 10,193 test cases. GenProg automatically repairs 55 of those 105 defects. To our knowledge, this evaluation is the largest available of its kind, and is often two orders of magnitude larger than previous work in terms of code or test suite size or defect count. Public cloud computing prices allow our 105 runs to be reproduced for $403; a successful repair completes in 96 minutes and costs $7.32, on average.


title: Agnostic Estimation of Mean and Covariance

abstract: We consider the problem of estimating the mean and covariance of a distribution from iid samples in $\mathbb{R}^n$, in the presence of an $\eta$ fraction of malicious noise; this is in contrast to much recent work where the noise itself is assumed to be from a distribution of known type. The agnostic problem includes many interesting special cases, e.g., learning the parameters of a single Gaussian (or finding the best-fit Gaussian) when $\eta$ fraction of data is adversarially corrupted, agnostically learning a mixture of Gaussians, agnostic ICA, etc. We present polynomial-time algorithms to estimate the mean and covariance with error guarantees in terms of information-theoretic lower bounds. As a corollary, we also obtain an agnostic algorithm for Singular Value Decomposition.


title: Bottom-up/top-down image parsing by attribute graph grammar

abstract: In this paper, we present an attribute graph grammar for image parsing on scenes with man-made objects, such as buildings, hallways, kitchens, and living moms. We choose one class of primitives - 3D planar rectangles projected on images and six graph grammar production rules. Each production rule not only expands a node into its components, but also includes a number of equations that constrain the attributes of a parent node and those of its children. Thus our graph grammar is context sensitive. The grammar rules are used recursively to produce a large number of objects and patterns in images and thus the whole graph grammar is a type of generative model. The inference algorithm integrates bottom-up rectangle detection which activates top-down prediction using the grammar rules. The final results are validated in a Bayesian framework. The output of the inference is a hierarchical parsing graph with objects, surfaces, rectangles, and their spatial relations. In the inference, the acceptance of a grammar rule means recognition of an object, and actions are taken to pass the attributes between a node and its parent through the constraint equations associated with this production rule. When an attribute is passed from a child node to a parent node, it is called bottom-up, and the opposite is called top-down


title: The Mapillary Vistas Dataset for Semantic Understanding of Street Scenes

abstract: The Mapillary Vistas Dataset is a novel, large-scale street-level image dataset containing 25000 high-resolution images annotated into 66 object categories with additional, instance-specific labels for 37 classes. Annotation is performed in a dense and fine-grained style by using polygons for delineating individual objects. Our dataset is 5× larger than the total amount of fine annotations for Cityscapes and contains images from all around the world, captured at various conditions regarding weather, season and daytime. Images come from different imaging devices (mobile phones, tablets, action cameras, professional capturing rigs) and differently experienced photographers. In such a way, our dataset has been designed and compiled to cover diversity, richness of detail and geographic extent. As default benchmark tasks, we define semantic image segmentation and instance-specific image segmentation, aiming to significantly further the development of state-of-the-art methods for visual road-scene understanding.


title: Walk and Learn: Facial Attribute Representation Learning from Egocentric Video and Contextual Data

abstract: The way people look in terms of facial attributes (ethnicity, hair color, facial hair, etc.) and the clothes or accessories they wear (sunglasses, hat, hoodies, etc.) is highly dependent on geo-location and weather condition, respectively. This work explores, for the first time, the use of this contextual information, as people with wearable cameras walk across different neighborhoods of a city, in order to learn a rich feature representation for facial attribute classification, without the costly manual annotation required by previous methods. By tracking the faces of casual walkers on more than 40 hours of egocentric video, we are able to cover tens of thousands of different identities and automatically extract nearly 5 million pairs of images connected by or from different face tracks, along with their weather and location context, under pose and lighting variations. These image pairs are then fed into a deep network that preserves similarity of images connected by the same track, in order to capture identity-related attribute features, and optimizes for location and weather prediction to capture additional facial attribute features. Finally, the network is fine-tuned with manually annotated samples. We perform an extensive experimental analysis on wearable data and two standard benchmark datasets based on web images (LFWA and CelebA). Our method outperforms by a large margin a network trained from scratch. Moreover, even without using manually annotated identity labels for pre-training as in previous methods, our approach achieves results that are better than the state of the art.


title: AATOS – A Configurable Tool for Automatic Annotation

abstract: This paper presents an automatic annotation tool AATOS for providing documents with semantic annotations. The tool links entities found from the texts to ontologies defined by the user. The application is highly configurable and can be used with different natural language Finnish texts. The application was developed as a part of the WarSampo (http://seco.cs.aalto.fi/projects/sotasampo/en/) and Semantic Finlex (http://seco.cs.aalto.fi/projects/lawlod/en/) projects and tested using Kansa Taisteli magazine articles and consolidated Finnish legislation of Semantic Finlex. The quality of the automatic annotation was evaluated by measuring precision and recall against existing manual annotations. The results showed that the quality of the input text, as well as the selection and configuration of the ontologies impacted the results.


title: The Rag GTPases Bind Raptor and Mediate Amino Acid Signaling to mTORC1

abstract: The multiprotein mTORC1 protein kinase complex is the central component of a pathway that promotes growth in response to insulin, energy levels, and amino acids and is deregulated in common cancers. We find that the Rag proteins--a family of four related small guanosine triphosphatases (GTPases)--interact with mTORC1 in an amino acid-sensitive manner and are necessary for the activation of the mTORC1 pathway by amino acids. A Rag mutant that is constitutively bound to guanosine triphosphate interacted strongly with mTORC1, and its expression within cells made the mTORC1 pathway resistant to amino acid deprivation. Conversely, expression of a guanosine diphosphate-bound Rag mutant prevented stimulation of mTORC1 by amino acids. The Rag proteins do not directly stimulate the kinase activity of mTORC1, but, like amino acids, promote the intracellular localization of mTOR to a compartment that also contains its activator Rheb.


title: Simulating Our LifeSteps by Example

abstract: During the past few decades, a number of effective methods for indexing, query processing, and knowledge discovery in moving object databases have been proposed. An interesting research direction that has recently emerged handles semantics of movement instead of raw spatio-temporal data. Semantic annotations, such as “stop,” “move,” “at home,” “shopping,” “driving,” and so on, are either declared by the users (e.g., through social network apps) or automatically inferred by some annotation method and are typically presented as textual counterparts along with spatial and temporal information of raw trajectories. It is natural to argue that such “spatio-temporal-textual” sequences, called semantic trajectories, form a realistic representation model of the complex everyday life (hence, mobility) of individuals. Towards handling semantic trajectories of moving objects in Semantic Mobility Databases, the lack of real datasets leads to the need to design realistic simulators. In the context of the above discussion, the goal of this work is to realistically simulate the mobility life of a large-scale population of moving objects in an urban environment. Two simulator variations are presented: the core Hermoupolis simulator is parametric driven (i.e., user-defined parameters tune every movement aspect), whereas the expansion of the former, called Hermoupolisby-example, follows the generate-by-example paradigm and is self-tuned by looking inside a real small (sample) dataset. We stress test our proposal and demonstrate its novel characteristics with respect to related work.


title: Improvement of QoS Routing Method Using Ant Colony Algorithm and Fuzzy Logic in Ad hoc Networks

abstract: The ad hoc network is a system of network elements that combine to form a network requiring little or no planning. This may not be feasible as nodes can enter and leave the network. In such networks, each node can receive the packet (host) and the packet sender (router) to act. The goal of routing is finding paths that meet the needs of the network and effectively use network resources. This paper presents a method for QoS routing in ad hoc networks based on ant colony optimization (ACO) algorithm and fuzzy logic. The advantages of this method flexibility and routing are based on several criteria. The results show that the proposed method in comparison with the algorithm IACA has better performance, higher efficiency and greater throughput. Therefore, the combination of ant algorithm with Fuzzy Logic due to its simplicity fuzzy computing is appropriate for QoS routing.


title: Symmetric piecewise planar object reconstruction from a single image

abstract: Recovering 3D geometry from a single view of an object is an important and challenging problem in computer vision. Previous methods mainly focus on one specific class of objects without large topological changes, such as cars, faces, or human bodies. In this paper, we propose a novel single view reconstruction algorithm for symmetric piece-wise planar objects that are not restricted to some object classes. Symmetry is ubiquitous in manmade and natural objects and provides rich information for 3D reconstruction. Given a single view of a symmetric piecewise planar object, we first find out all the symmetric line pairs. The geometric properties of symmetric objects are used to narrow down the searching space. Then, based on the symmetric lines, a depth map is recovered through a Markov random field. Experimental results show that our algorithm can efficiently recover the 3D shapes of different objects with significant topological variations.


title: Glass interposer integrated dual-band millimeter wave TGV antenna for inter-/intra chip and board communications

abstract: A dual-band and dual-mode millimeter wave through glass via (TGV) antenna integrated in a glass interposer layer has been designed for wireless inter-/intra chip and board communications. A disc loaded TGV antenna is configured as a directional patch mode for out-of-plane communications at 62 GHz (WiGig), where the disc is a main radiating element, while the antenna operates as an omni-directional monopole mode for in-plane communications at 77 GHz (W-band), where the vertical via is a main radiating element. As the glass interposer layer serves as an antenna hosting material as well as an electromagnetic wave channel medium for wireless communications, low dielectric loss glass substrates with a loss tangent of 0.0036 are adopted.


title: Insights into the Active Site of Coproheme Decarboxylase from Listeria monocytogenes

abstract: Coproheme decarboxylases (ChdC) catalyze the hydrogen peroxide-mediated conversion of coproheme to heme b. This work compares the structure and function of wild-type (WT) coproheme decarboxylase from Listeria monocytogenes and its M149A, Q187A, and M149A/Q187A mutants. The UV–vis, resonance Raman, and electron paramagnetic resonance spectroscopies clearly show that the ferric form of the WT protein is a pentacoordinate quantum mechanically mixed-spin state, which is very unusual in biological systems. Exchange of the Met149 residue to Ala dramatically alters the heme coordination, which becomes a 6-coordinate low spin species with the amide nitrogen atom of the Q187 residue bound to the heme iron. The interaction between M149 and propionyl 2 is found to play an important role in keeping the Q187 residue correctly positioned for closure of the distal cavity. This is confirmed by the observation that in the M149A variant two CO conformers are present corresponding to open (A0) and closed (A1) conformations....


title: Bidirectional Attentive Fusion with Context Gating for Dense Video Captioning

abstract: Dense video captioning is a newly emerging task that aims at both localizing and describing all events in a video. We identify and tackle two challenges on this task, namely, (1) how to utilize both past and future contexts for accurate event proposal predictions, and (2) how to construct informative input to the decoder for generating natural event descriptions. First, previous works predominantly generate temporal event proposals in the forward direction, which neglects future video context. We propose a bidirectional proposal method that effectively exploits both past and future contexts to make proposal predictions. Second, different events ending at (nearly) the same time are indistinguishable in the previous works, resulting in the same captions. We solve this problem by representing each event with an attentive fusion of hidden states from the proposal module and video contents (e.g., C3D features). We further propose a novel context gating mechanism to balance the contributions from the current event and its surrounding contexts dynamically. We empirically show that our attentively fused event representation is superior to the proposal hidden states or video contents alone. By coupling proposal and captioning modules into one unified framework, our model outperforms the state-of-the-arts on the ActivityNet Captions dataset with a relative gain of over 100% (Meteor score increases from 4.82 to 9.65).


title: Simple real-time QRS detector with the MaMeMi filter

abstract: Abstract Detection of QRS complexes in ECG signals is required to determine heart rate, and it is an important step in the study of cardiac disorders. ECG signals are usually affected by noise of low and high frequency. To improve the accuracy of QRS detectors several methods have been proposed to filter out the noise and detect the characteristic pattern of QRS complex. Most of the existing methods are at a disadvantage from relatively high computational complexity or high resource needs making them less optimized for its implementation on portable embedded systems, wearable devices or ultra-low power chips. We present a new method to detect the QRS signal in a simple way with minimal computational cost and resource needs using a novel non-linear filter.


title: Multiple Feature Learning for Hyperspectral Image Classification

abstract: Hyperspectral image classification has been an active topic of research in recent years. In the past, many different types of features have been extracted (using both linear and nonlinear strategies) for classification problems. On the one hand, some approaches have exploited the original spectral information or other features linearly derived from such information in order to have classes which are linearly separable. On the other hand, other techniques have exploited features obtained through nonlinear transformations intended to reduce data dimensionality, to better model the inherent nonlinearity of the original data (e.g., kernels) or to adequately exploit the spatial information contained in the scene (e.g., using morphological analysis). Special attention has been given to techniques able to exploit a single kind of features, such as composite kernel learning or multiple kernel learning, developed in order to deal with multiple kernels. However, few approaches have been designed to integrate multiple types of features extracted from both linear and nonlinear transformations. In this paper, we develop a new framework for the classification of hyperspectral scenes that pursues the combination of multiple features. The ultimate goal of the proposed framework is to be able to cope with linear and nonlinear class boundaries present in the data, thus following the two main mixing models considered for hyperspectral data interpretation. An important characteristic of the presented approach is that it does not require any regularization parameters to control the weights of considered features so that different types of features can be efficiently exploited and integrated in a collaborative and flexible way. Our experimental results, conducted using a variety of input features and hyperspectral scenes, indicate that the proposed framework for multiple feature learning provides state-of-the-art classification results without significantly increasing computational complexity.


title: Assessment of Kinematics and Electromyography Following Arthroscopic Single-Tendon Rotator Cuff Repair

abstract: Abstract Background The increasing demand for rotator cuff (RC) repair patients to return to work as soon as they are physically able has led to exploration of when this is feasible. Current guidelines from our orthopedic surgery clinic recommend a return to work at 9 weeks postoperation. To more fully define capacity to return to work, the current study was conducted using a unique series of quantitative tools. To date, no study has combined 3-dimensional (3D) motion analysis with electromyography (EMG) assessment during activities of daily living (ADLs), including desk tasks, and commonly prescribed rehabilitation exercise. Objective To apply a quantitative, validated upper extremity model to assess the kinematics and muscle activity of the shoulder following repair of the supraspinatus RC tendon compared to that in healthy shoulders. Design A prospective, cross-sectional comparison study. Setting All participants were evaluated during a single session at the Medical College of Wisconsin Department of Orthopaedic Surgery's Motion Analysis Laboratory. Participants Ten participants who were 9-12 weeks post–operative repair of a supraspinatus RC tendon tear and 10 participants with healthy shoulders (HS) were evaluated. Methods All participants were evaluated with 3D motion analysis using a validated upper extremity model and synchronized EMG. Data from the 2 groups were compared using multivariate Hotelling T 2 tests with post hoc analyses based on Welch t -tests. Main Outcome Measurements Participants' thoracic and thoracohumeral joint kinematics, temporal-spatial parameters, and RC muscle activity were measured by applying a quantitative upper extremity model during 10 ADLs and 3 rehabilitation exercises. These included tasks of hair combing, drinking, writing, computer mouse use, typing, calling, reaching to back pocket, pushing a door open, pulling a door closed, external rotation, internal rotation, and rowing. Results There were significant differences of the thoracohumeral joint motion in only a few of the tested tasks: comb maximal flexion angle ( P = .004), pull door internal/external rotation range of motion ( P = .020), reach abduction/adduction range of motion ( P = .001), reach flexion/extension range of motion ( P = .001), reach extension minimal angle ( P = .025), active external rotation maximal angle ( P = .012), and active external rotation minimal angle ( P = .004). The thorax showed significantly different kinematics of maximal flexion angle during the call ( P = .011), mouse ( P = .007), and drink tasks ( P = .005) between the 2 groups. The EMG data analysis showed significantly increased subscapularis activity in the RC repair group during active external rotation. Conclusions Although limited abduction was expected due to repair of the supraspinatus tendon, only a single ADL (reaching to back pocket) had a significantly reduced abduction range of motion. Thoracic motion was shown to be used as a compensatory strategy during seated ADLs. Less flexion of the thorax may create passive shoulder flexion at the thoracohumeral joint in efforts to avoid active flexion. The RC repair group participants were able to accomplish the ADLs within the same time frame and through thoracohumeral joint kinematics similar to those in the healthy shoulder group participants. In summary, this study presents a quantification of the effects of RC repair and rehabilitation on the ability to perform ADLs. It may also point to a need for increased rehabilitation focus on either regaining external rotation strength or range of motion following RC repair to enhance recovery and return to the workforce. Level of Evidence III


title: Seismic Waveform Classification and First-Break Picking Using Convolution Neural Networks

abstract: Regardless of successful applications of the convolutional neural networks (CNNs) in different fields, its application to seismic waveform classification and first-break (FB) picking has not been explored yet. This letter investigates the application of CNNs for classifying time-space waveforms from seismic shot gathers and picking FBs of both direct wave and refracted wave. We use representative subimage samples with two types of labeled waveform classification to supervise CNNs training. The goal is to obtain the optimal weights and biases in CNNs, which are solved by minimizing the error between predicted and target label classification. The trained CNNs can be utilized to automatically extract a set of time-space attributes or features from any subimage in shot gathers. These attributes are subsequently inputted to the trained fully connected layer of CNNs to output two values between 0 and 1. Based on the two-element outputs, a discriminant score function is defined to provide a single indication for classifying input waveforms. The FB is then located from the calculated score maps by sequentially using a threshold, the first local minimum rule of every trace and a median filter. Finally, we adopt synthetic and real shot data examples to demonstrate the effectiveness of CNNs-based waveform classification and FB picking. The results illustrate that CNN is an efficient automatic data-driven classifier and picker.


title: A low-power Adaboost-based object detection processor using Haar-like features

abstract: This paper presents an architecture of a low-power real-time object detection processor using Adaboost with Haar-Like features. We employ a register array based architecture, and introduce two architectural-level power optimization techniques; signal gating domain for integral image extraction, and low-power integral image update. The power efficiency of our proposed architecture including nine classifiers is estimated to be 0.64mW/fps when handling VGA(640 × 480) 70fps video.


title: Fast, strong and compliant pneumatic actuation for dexterous tendon-driven hands

abstract: We describe a pneumatic actuation system for dexterous robotic hands. It was motivated by our desire to improve the ShadowHand system, yet it is quite universal and indeed we are already using it with a second robotic hand we have developed. Our actuation system allows us to move the ShadowHand skeleton faster than a human hand (70 msec limit-to-limit movement, 30 msec overall reflex latency), generate sufficient forces (40 N at each finger tendon, 125N at each wrist tendon), and achieve high compliance on the mechanism level (6 grams of external force at the fingertip displaces the finger when the system is powered.) This combination of speed, force and compliance is a prerequisite for dexterous manipulation, yet it has never before been achieved with a tendon-driven system, let alone a system with 24 degrees of freedom and 40 tendons.


Active Perception


title: PLC based DC drive control using Modbus RTU communication for selected applications of sugar mill

abstract: DC Drives are serving in industries with their efficient motion control ability. These Drives can operate in Local Mode as well as in Overriding Control mode. In Local Mode the Drive is controlled by its control panel where as in Overriding Control mode, it can be controlled remotely by a Programmable Logic Controller (PLC). This PLC based control of Drive is essential for process control in industries like Sugar Mill. Further, this control can be employed using any of the industrial communication protocols which reduce the wiring complexity. Modbus Remote Terminal Unit (RTU) is one such protocol which is open, robust and widely used in industries because of its simplicity. Therefore the same protocol has been implemented in the presented work. This paper depicts initially about configuring a DC Drive (DCS800 of ABB) for Modbus RTU communication and a preliminary test to verify the proper working of intermediate components and cables by communicating the Drive with PC using Modscan32 software. Later, the PLC and Drive communication details and development of ladder program are presented. Further, the results obtained from testing PLC-Drive communication considering the two applications of Sugar Mill are put forth. These ensure the successful implementation of PLC-Drive communication. The applications considered are; speed raise and lower of a DC motor by PLC and secondly, achieving a speed pattern (speed versus time graph) required by a process of Centrifugal Machine in Sugar Mill.


title: Obstacle detection for self-driving cars using only monocular cameras and wheel odometry

abstract: Mapping the environment is crucial to enable path planning and obstacle avoidance for self-driving vehicles and other robots. In this paper, we concentrate on ground-based vehicles and present an approach which extracts static obstacles from depth maps computed out of multiple consecutive images. In contrast to existing approaches, our system does not require accurate visual inertial odometry estimation but solely relies on the readily available wheel odometry. To handle the resulting higher pose uncertainty, our system fuses obstacle detections over time and between cameras to estimate the free and occupied space around the vehicle. Using monocular fisheye cameras, we are able to cover a wider field of view and detect obstacles closer to the car, which are often not within the standard field of view of a classical binocular stereo camera setup. Our quantitative analysis shows that our system is accurate enough for navigation purposes of self-driving cars and runs in real-time.


title: Feasibility of patterned vertical CNT for dry electrode sensing of physiological parameters

abstract: Dry electrodes for impedimetric sensing of physiological parameters (such as ECG, EEG, and GSR) promise the ability for long duration monitoring. This paper describes the feasibility of a novel dry electrode interfacing using Patterned Vertical Carbon Nanotube (pvCNT) for physiological parameter sensing. The electrodes were fabricated on circular discs (ϕ = 10 mm) stainless steel substrate. Multiwalled electrically conductive carbon nanotubes were grown in pattered pillar formation of 100 μm squared with 50, 100, 200 and 500μm spacing. The heights of the pillars were between 1 to 1.5 mm. A comparative test with commercial ECG electrodes shows that pvCNT has lower electrical impedance, stable impedance over very long time, and comparable signal capture in vitro. Long duration study shows minimal degradation of impedance over 2 days period. The results demonstrate the feasibility of using pvCNT dry electrodes for physiological parameter sensing.


Identity Aging: Eicient Blockchain Consensus


title: Automatic Enforcement of Data Use Policies with DataLawyer

abstract: Data has value and is increasingly being exchanged for commercial and research purposes. Data, however, is typically accompanied by terms of use, which limit how it can be used. To date, there are only a few, ad-hoc methods to enforce these terms. We propose DataLawyer, a new system to formally specify usage policies and check them automatically at query runtime in a relational database management system (DBMS). We develop a new model to specify policies compactly and precisely. We introduce novel algorithms to efficiently evaluate policies that can cut policy-checking overheads to only a few percent of the total query runtime. We implement DataLawyer and evaluate it on a real database from the health-care domain.


title: A Note on the Reduction of Two-Way Automata to One-Way Automata

abstract: We describe a new elementary reduction of two-way automata to one-way automata. The reduction is based on the subset construction rather than on crossing sequence analysis.


title: A compact planar Rogowski coil current sensor for active current balancing of parallel-connected Silicon Carbide MOSFETs

abstract: In this paper, a compact planar current sensor is developed to be used in active current balancing applications for parallel-connected Silicon Carbide (SiC) MOSFETs. The designed Rogowski coil allows non-intrusive current measurement with low profile, compact size, and high bandwidth. The sensor circuit design extends both lower and higher cutoff frequency of the sensor, and allows a continuous measurement of current waveforms that contain a DC component. The simulated bandwidth of the proposed current sensor is 2.66 Hz-100 MHz. The measured switching waveforms in the experiment are comparable to a 120 MHz commercial current probe.


title: A mechanics-based model for simulation and control of flexible needle insertion in soft tissue

abstract: In needle-based medical procedures, beveled-tip flexible needles are steered inside soft tissue with the aim of reaching pre-defined target locations. The efficiency of needle-based interventions depends on accurate control of the needle tip. This paper presents a comprehensive mechanics-based model for simulation of planar needle insertion in soft tissue. The proposed model for needle deflection is based on beam theory, works in real-time, and accepts the insertion velocity as an input that can later be used as a control command for needle steering. The model takes into account the effects of tissue deformation, needle-tissue friction, tissue cutting force, and needle bevel angle on needle deflection. Using a robot that inserts a flexible needle into a phantom tissue, various experiments are conducted to separately identify different subsets of the model parameters. The validity of the proposed model is verified by comparing the simulation results to the empirical data. The results demonstrate the accuracy of the proposed model in predicting the needle tip deflection for different insertion velocities.


title: On the performance of ad hoc networks with beamforming antennas

abstract: Beamforming antennas have the potential to provide a fundamental breakthrough in ad hoc network capacity. We present a broad-based examination of this potential, focusing on exploiting the longer ranges as well as the reduced interference that beamforming antennas can provide. We consider a number of enhancements to a convectional ad hoc network system, and evaluation the impact of each enhancement using simulation. Such enhancements include "aggressive" and "conservative" channel access models for beamforming antennas, link power control, and directional neighbor discovery. Our simulations are based on detailed modeling on detailed modeling of steered as well as swiched beams using antenna patterns of varying gains, and a realistic radio and propagation model. For the scenarios studied, our results show that beamforming can yield a 28% to 118% (depending upon the density) improvement in throughput, and up to a factor-of-28 reduction in delay. Our study also tells us which mechanisms are likely to be more effective and under what conditions, which in turn identifies areas where future research is neede


title: Bacterial Cellulose Production and its Industrial Applications

abstract: Microbial cellulose has proven to be a remarkably versatile biomaterial and can be used in wide variety of applied scientific endeavors, such as paper product, electronics and biomedical devices. In fact, biomedical devices recently have gained a significant attention owing to increase in tissue-engineering products for both wound care and the regeneration of demand or diseased organs. So, microbial cellulose production, characterization and influence of additives during its production are important to understand the microbial pathway and create new application.


title: A Unified Construction of the Glushkov, Follow, and Antimirov Automata

abstract: A number of different techniques have been introduced in the last few decades to create e-free automata representing regular expressions such as the Glushkov automata, follow automata, or Antimirov automata. This paper presents a simple and unified view of all these construction methods both for unweighted and weighted regular expressions. It describes simpler algorithms with time complexities at least as favorable as that of the best previously known techniques, and provides a concise proof of their correctness. Our algorithms are all based on two standard automata operations: epsilon-removal and minimization. This contrasts with the multitude of complicated and special-purpose techniques previously described in the literature, and makes it straightforward to generalize these algorithms to the weighted case. In particular, we extend the definition and construction of follow automata to the case of weighted regular expressions over a closed semiring and present the first algorithm to compute weighted Antimirov automata.


title: Analysis of political discourse on twitter in the context of the 2016 US presidential elections

abstract: Abstract Social media now plays a pivotal role in electoral campaigns. Rapid dissemination of information through platforms such as Twitter has enabled politicians to broadcast their message to a wide audience. In this paper, we investigated the sentiment of tweets by the two main presidential candidates, Hillary Clinton and Donald Trump, along with almost 2.9 million tweets by Twitter users during the 2016 US Presidential Elections. We analyzed these short texts to evaluate how accurately Twitter represented the public opinion and real world events of significance related with the elections. We also analyzed the behavior of over a million distinct Twitter users to identify whether the platform was used to share original opinions and to interact with other users or whether few opinions were repeated over and over again with little inter-user dialogue. Finally, we wanted to assess the sentiment of tweets by both candidates and their impact on the election related discourse on Twitter. Some of our findings included the discovery that little original content was created by users and Twitter was primarily used for rebroadcasting already present opinions in the form of retweets with little communication between users. Also of significance was the finding that sentiment and topics expressed on Twitter can be a good proxy of public opinion and important election related events. Moreover, we found that Donald Trump offered a more optimistic and positive campaign message than Hillary Clinton and enjoyed better sentiment when mentioned in messages by Twitter users.


Team UKNLP: Detecting ADRs, Classifying Medication Intake Messages, and Normalizing ADR Mentions on Twitter


title: A Novel RBF Tuning Strategy for Global Surrogate Modeling

abstract: In this paper, a research is performed on the tuning strategy for the shape parameter of the RBF to improve accuracy. The shape parameter is an influential factor for the accuracy but the configuration is commonly based on experience. An algorithm is designed to optimize the shape parameter. Three high-order mathematical benchmarks are used to compare the common RBF and the optimized RBF. This paper shows that the optimized RBF is more accurate for various test functions.


title: A comprehensive approach to horizontal class fragmentation in a Distributed Object Based System

abstract: Optimal application performance on a Distributed Object Based System (DOBS) requires class fragmentation and the development of allocation schemes to place fragments at distributed sites so data transfer is minimized. Fragmentation enhances application performance by reducing the amount of irrelevant data accessed and the amount of data transferred unnecessarily between distributed sites. Algorithms for effecting horizontal and vertical fragmentation ofrelations exist, but fragmentation techniques for class objects in a distributed object based system are yet to appear in the literature. This paper first reviews a taxonomy of the fragmentation problem in a distributed object base. The paper then contributes by presenting a comprehensive set of algorithms for horizontally fragmenting the four realizable class models on the taxonomy. The fundamental approach is top-down, where the entity of fragmentation is the class object. Our approach consists of first generating primary horizontal fragments of a class based on only applications accessing this class, and secondly generating derived horizontal fragments of the class arising from primary fragments of its subclasses, its complex attributes (contained classes), and/or its complex methods classes. Finally, we combine the sets of primary and derived fragments of each class to produce the best possible fragments. Thus, these algorithms account for inheritance and class composition hierarchies as well as method nesting among objects, and are shown to be polynomial time.


title: Image segmentation by iterative parallel region growing with applications to data compression and image analysis

abstract: An iterative parallel segmentation algorithm, which avoids the problem of dependency on the order in which portions of the image are processed by performing the globally best merges first, is presented. The segmentation approach and two implementations of the approach on a massively parallel processor (MPP) are discussed. Application of the segmentation approach to data compression and image analysis is described, and results of the application are given for a Landsat thematic mapper image. >


title: Docker container based analytics at IoT edge Video analytics usecase

abstract: The number of internet connected devices is growing rapidly. It is getting difficult to gather and process the huge volume of the data generated from these devices. Edge computing paradigm is evolving that helps to process the data locally close to the source. This paper highlights using docker container based analytics services at the edge for data processing. Feasibility is studied for setting up deep learning framework on Raspberry Pi for real-time analysis of video feed from the surveillance camera. Performance benchmarking is also done to figure out overhead of containerization.


title: The Eucalyptus Open-Source Cloud-Computing System

abstract: Cloud computing systems fundamentally provide access to large pools of data and computational resources through a variety of interfaces similar in spirit to existing grid and HPC resource management and programming systems. These types of systems offer a new programming target for scalable application developers and have gained popularity over the past few years. However, most cloud computing systems in operation today are proprietary, rely upon infrastructure that is invisible to the research community, or are not explicitly designed to be instrumented and modified by systems researchers. In this work, we present Eucalyptus -- an open-source software framework for cloud computing that implements what is commonly referred to as Infrastructure as a Service (IaaS); systems that give users the ability to run and control entire virtual machine instances deployed across a variety physical resources. We outline the basic principles of the Eucalyptus design, detail important operational aspects of the system, and discuss architectural trade-offs that we have made in order to allow Eucalyptus to be portable, modular and simple to use on infrastructure commonly found within academic settings. Finally, we provide evidence that Eucalyptus enables users familiar with existing Grid and HPC systems to explore new cloud computing functionality while maintaining access to existing, familiar application development software and Grid middle-ware.


title: Stress, memory and the amygdala

abstract: Stressful events often leave strong memories. Roozendaal and colleagues discuss how stress hormones and neurotransmitters acting in the amygdala mediate this phenomenon at the behavioural and synaptic level, and describe how stress-induced remodelling of amygdala neurons might underlie anxiety.


abstract: Development of the space industry has led to a diversity of specialized electronics-units implemented on space system. However, this provoked new problems such as increased system's complexity and the size. As a solution to the problem, the Integrated Modular Avionics (IMA) architecture and ARINC 653 standard have been suggested. This paper introduces a Linux-based memory efficient ARINC 653 partition scheduler, which employs fixed-priority preemptive scheduling algorithm for partitions scheduling. The implemented partition scheduler has small memory footprint for each partition and produces low partition switching overhead. The prototype was executed on a LEON4 processor, which is the European Space Agency (ESA) Next Generation Multicore Processor (NGMP) in the space sector. In evaluation, we analyzed execution trace, memory footprint and scheduling overhead.


title: Trace clustering based on conserved patterns : towards achieving better process models

abstract: Process mining refers to the extraction of process models from event logs. Real-life processes tend to be less structured and more flexible. Traditional process mining algorithms have problems dealing with such unstructured processes and generate “spaghetti-like” process models that are hard to comprehend. An approach to overcome this is to cluster process instances such that each of the resulting clusters correspond to coherent sets of process instances that can each be adequately represented by a process model. In this paper, we present multiple feature sets based on conserved patterns and show that the proposed feature sets have a better performance than contemporary approaches. We evaluate the goodness of the formed clusters using established fitness and comprehensibility metrics defined in the context of process mining. The proposed approach is able to generate clusters such that the process models mined from the clustered traces show a high degree of fitness and comprehensibility. Further, the proposed feature sets can be easily discovered in linear time making it amenable to real-time analysis of large data sets.


title: A D-band compact rat-race coupler using novel phase inverter in standard CMOS process

abstract: This paper describes a D-band on-chip rat-race coupler using a novel phase inverter and developed in a standard CMOS process. The simple structural phase inverter which is based on the characteristic impedance and phase difference analysis has demonstrated its potential for wideband and compact millimeter-wave frequency range applications. The developed rat-race coupler using the proposed phase inverter, features a 40% size reduction and over 60% bandwidth improvement compared to conventional structures. In the complete D-band range, measured amplitude and phase imbalances for the coupler are within 0.5 dB and 15°, respectively. The port-to-port isolation is better than 25 dB in the D-band range with the highest value of 41 dB at 118 GHz. The insertion loss achieves 1.2 ± 0.3 dB and the coral size of the coupler is only 290 × 162 µm2. The proposed coupler has a huge potential for integration in single-chip systems over 100 GHz.


title: Deep features to classify skin lesions

abstract: Diagnosing an unknown skin lesion is the first step to determine appropriate treatment. We demonstrate that a linear classifier, trained on features extracted from a convolutional neural network pretrained on natural images, distinguishes among up to ten skin lesions with a higher accuracy than previously published state-of-the-art results on the same dataset. Further, in contrast to competing works, our approach requires no lesion segmentations nor complex preprocessing. We gain consistent additional improvements to accuracy using a per image normalization, a fully convolutional network to extract multi-scale features, and by pooling over an augmented feature space. Compared to state-of-the-art, our proposed approach achieves a favourable accuracy of 85.8% over 5-classes (compared to 75.1%) with noticeable improvements in accuracy for underrepresented classes (e.g., 60% compared to 15.6%). Over the entire 10-class dataset of 1300 images captured from a standard (non-dermoscopic) camera, our method achieves an accuracy of 81.8% outperforming the 67% accuracy previously reported.


title: Solutions to the Failures and Limitations of Mamdani Fuzzy Inference in Ship Navigation

abstract: This paper proposes a methodology for overcoming Mamdani-type inference failures on a fuzzy-logic-based decision-making process applied to collision avoidance in ship navigation. The fuzzy inference failures are observed in three distinct situations: 1) intersected contradictory decision boundaries; 2) an improper transition region between the inference boundaries of nonintersected contradictory decisions; and 3) contradictory decision accumulation under multiple obstacle scenarios. The solutions for overcoming these fuzzy inference failures and their limitations are also discussed in this paper. The proposed solutions consist of insertion of smooth transition regions, determination of the proper size of the smooth transition regions, and use of multilevel decision/action formulations. Furthermore, this paper analyzes a decision-making process for ship navigation, derives input and output fuzzy membership functions (FMFs), formulates an if–then -rule-based fuzzy inference system (FIS), and presents simulation results that support recovery from rule inference failures in several contradictory decision boundary conditions.


title: On early stopping in gradient descent learning

abstract: In this paper we study a family of gradient descent algorithms to approximate the regression function from reproducing kernel Hilbert spaces (RKHSs), the family being characterized by a polynomial decreasing rate of step sizes (or learning rate). By solving a bias-variance trade-off we obtain an early stopping rule and some probabilistic upper bounds for the convergence of the algorithms. We also discuss the implication of these results in the context of classification where some fast convergence rates can be achieved for plug-in classifiers. Some connections are addressed with Boosting, Landweber iterations, and the online learning algorithms as stochastic approximations of the gradient descent method.


title: Analyzing Organizational Structure based on 7s model of McKinsey

abstract: The aim of this descriptive-survey research was to investigate and analyze the organizational structure of Qeshm free zone based on 7 S of McKinsey. The research population included managers and experts of Qeshm free zone. Simple random sampling was used to select research population, and the number of population was decided according to Cohcaran formula, which was 84. The research was conducted in the form of questionnaires. The reliability and validity of the questions were tested by alpha Cronbach (0.848) and the use of experienced professors and experts respectively. To analyze research data Kolmogorov-Smirinov, T-test and Freedman test were used. The results of the research indicated that organizational structure based on 7-S McKinsey in Qeshm free zone is unfavorable, with common value, clerks and structure having the worst conditions. According to the research findings, it is recommended that managers of Qeshm free zone pay specific attention to internal environment of organization and ways to improve it.


title: Fuzzy arithmetic based reliability allocation approach during early design and development

abstract: A fuzzy arithmetic based reliability allocation method at early system design stage.Linguistic variables are used to assess the ratings of allocation factors in an intuitive and easy manner.Use of trapezoidal fuzzy division linear programming method to reduce the fuzziness.More flexible to combine diversified opinions of multiple domain experts. During early design and development stages, every engineering system has to meet its specific reliability goals. The target reliability of the system is achieved through application of an effective reliability apportionment technique to its subsystems. There are various traditional methods exist to perform the reliability allocation based on engineering factors that are assessed in a subjective manner. The conventional reliability allocation approach requires the assessment of factors like complexity, cost, and maintenance. This may not be realistic in real applications if they are assessed in a crisp manner by the domain experts of their varied expertise and background.In this paper, we treat allocation factors as fuzzy numbers, which are evaluated in fuzzy linguistic terms. As a result, fuzzy proportionality factor scales are proposed for the subsystems. In order to accomplish fuzzy division to evaluate the fuzzy proportionality factor, an approximation method based on linear programming for trapezoidal fuzzy numbers is also proposed in this paper. For the evaluation of weighting factors from fuzzy proportionality factors, centroid method of defuzzification is being employed. The allocated reliability of each subsystem is computed with the help of weighting factor thereafter. An example is provided to illustrate the potential application of the proposed fuzzy based reliability allocation approach.


Faith and revivalism in a Nordic Romani community: Pentecostalism amongst the Kaale Roma of Sweden and Finland by David Thurfjell (review)


title: Characteristics of YouTube network traffic at a campus network - Measurements, models, and implications

abstract: User-Generated Content has become very popular since new web services such as YouTube allow for the distribution of user-produced media content. YouTube-like services are different from existing traditional VoD services in that the service provider has only limited control over the creation of new content. We analyze how content distribution in YouTube is realized and then conduct a measurement study of YouTube traffic in a large university campus network. Based on these measurements, we analyzed the duration and the data rate of streaming sessions, the popularity of videos, and access patterns for video clips from the clients in the campus network. The analysis of the traffic shows that trace statistics are relatively stable over short-term periods while long-term trends can be observed. We demonstrate how synthetic traces can be generated from the measured traces and show how these synthetic traces can be used as inputs to trace-driven simulations. We also analyze the benefits of alternative distribution infrastructures to improve the performance of a YouTube-like VoD service. The results of these simulations show that P2P-based distribution and proxy caching can reduce network traffic significantly and allow for faster access to video clips.


title: The critical role of microgrids in transition to a smarter grid: A technical review

abstract: Recent economic incentives have resulted in the proliferation of electric vehicles (EVs). Microgrids are a promising solution to the problem of large-scale EV integration into the electric power system. Furthermore, advances in energy storage technologies have rendered grid energy storage economically viable. In this paper, we review the literature on microgrids, focusing on their role in the integration of EVs, and energy storage into the electricity grid. Commercial ventures in the development of Energy Management Systems are also reviewed. As microgrids become more prevalent, scenarios where multiple microgrids operate in a coordinated fashion may arise. We also review the literature on operation and control of multiple microgrids.


title: Changing the Competitive Landscape: Continuous Innovation Through IT-Enabled Knowledge Capabilities

abstract: We theoretically and empirically investigate the relationship between information technology (IT) and firm innovation. Invoking absorptive capacity (ACAP) theory, we introduce and develop the concepts of three types of IT-enabled knowledge capabilities. Firm innovation is examined through two observable innovation outcomes: patents, and new product and service introductions. These innovation outcomes are often labeled as competitive actions aggressively undertaken by firms to gain market share or to achieve profitability. We use secondary data about IT-enabled knowledge capabilities and innovation outcomes of 110 firms. Our data results provide strong support for our main assertion that knowledge capabilities that are enhanced through the use of IT contribute to firm innovation. The study's findings suggest that the three types of IT-enabled knowledge capabilities have differential effects on firm innovation. This study substantially contributes to the information systems (IS) research, methodology, and practice in multiple ways.


title: SIMD-Scan: Ultra Fast in-Memory Table Scan using on-Chip Vector Processing Units

abstract: The availability of huge system memory, even on standard servers, generated a lot of interest in main memory database engines. In data warehouse systems, highly compressed column-oriented data structures are quite prominent. In order to scale with the data volume and the system load, many of these systems are highly distributed with a shared-nothing approach. The fundamental principle of all systems is a full table scan over one or multiple compressed columns. Recent research proposed different techniques to speedup table scans like intelligent compression or using an additional hardware such as graphic cards or FPGAs. In this paper, we show that utilizing the embedded Vector Processing Units (VPUs) found in standard superscalar processors can speed up the performance of mainmemory full table scan by factors. This is achieved without changing the hardware architecture and thereby without additional power consumption. Moreover, as on-chip VPUs directly access the system's RAM, no additional costly copy operations are needed for using the new SIMD-scan approach in standard main memory database engines. Therefore, we propose this scan approach to be used as the standard scan operator for compressed column-oriented main memory storage. We then discuss how well our solution scales with the number of processor cores; consequently, to what degree it can be applied in multi-threaded environments. To verify the feasibility of our approach, we implemented the proposed techniques on a modern Intel multi-core processor using Intel® Streaming SIMD Extensions (Intel® SSE). In addition, we integrated the new SIMD-scan approach into SAP® Netweaver® Business Warehouse Accelerator. We conclude with describing the performance benefits of using our approach for processing and scanning compressed data using VPUs in column-oriented main memory database systems.


title: On the security of public key protocols

abstract: Recently the use of public key encryption to provide secure network communication has received considerable attention. Such public key systems are usually effective against passive eavesdroppers, who merely tap the lines and try to decipher the message. It has been pointed out, however, that an improperly designed protocol could be vulnerable to an active saboteur, one who may impersonate another user or alter the message being transmitted. Several models are formulated in which the security of protocols can be discussed precisely. Algorithms and characterizations that can be used to determine protocol security in these models are given.


title: A Minutiae-Based Fingerprint Matching Algorithm Using Phase Correlation

abstract: Minutiae-based method is the most popular approach in fingerprint matching. However, most existing methods need to search for the best correspondence of minutiae pairs or use reference points (core and delta points) to estimate the alignment parameters. The problem of lost minutiae or spurious minutiae always occurs during the minutiae detection process. Hence, the corresponding pairs or reference points may not be found under this condition. This paper proposes a new minutiae-based fingerprint matching algorithm using phase correlation. We define a new representation called Minutiae Direction Map (MDM). First, we convert minutiae sets into 2D image spaces. Then the transformation parameters are calculated using phase correlation between two MDMs to align two fingerprints to be matched. The similarity of two fingerprints is determined by the distance between two minutiae sets. Our approach does not need to search for the corresponding minutiae pairs. Experimental results show that the proposed approach performed well in matching fingerprint minutiae sets, which greatly improved the economy of storage space.


title: A Novel Positioning and Orientation System Based on Three-Axis Magnetic Coils

abstract: The positioning and orientation system consists of three-axis generating coils and three-axis sensor coils in quasi-static magnetic field. The three-axis generating coils are fixed orthogonally and excited by the alternating current (AC) signals with different frequencies. They create the magnetic field that is equivalent to that generated by three orthogonal magnetic dipoles. Using the amplitude and phase information of the sensing signals in the sensor coils, the position and orientation parameters of the sensor coils can be computed by using an appropriate algorithm. In this paper, a novel algorithm is proposed to determine the position of the sensor coil object by some equations directly, so that its position and orientation parameters can be calculated much easier and faster. Based on this method, a system with support circuitry is designed with some special signal acquisition and sampling methods. Especially, a signal extraction (function fitting) method is proposed to pick up the coupling AC signal magnitude of the sensor coils, which simplifies the hardware circuitry and improves the signal acquisition accuracy. The simulation and real experimental results show that the system works satisfactorily with good accuracy.


title: Rectification for any epipolar geometry

abstract: This paper proposes a new approach to rectification, a process whereby a stereo pair of images is re-sampled so as to make imposing the two view geometric constraint simple. This is normally performed by applying a single linear transformation per image, a method which has the drawback that some camera motions produce rectified images which are heavily distorted or unbounded. More recent attempts have used nonlinear transformations to allow any camera motion, but as a consequence distort images so that matching features no longer look the same in both images. This work provides a hybrid linear/nonlinear method that greatly reduces this problem, and simplifies the technique. The technique also provides further improvements by selecting the rectifying transformation so as to minimise perspective effects between the images.


title: Aclar discs: a versatile substrate for routine high-pressure freezing of mammalian cell monolayers.

abstract: High-pressure freezing avoids the artefacts induced by conventional chemical fixation, and, in combination with freeze-substitution and plastic embedding, is a reliable method for the ultrastructural analysis of mammalian cell monolayers. In order to high-pressure freeze mammalian cell monolayers, cells have to be seeded on a suitable substrate. Unfortunately, electron microscopy analysis is often hampered by poor cell growth, changes in cell morphology induced by the cell substrate or cell loss during processing. We report a method to culture, high-pressure freeze, freeze-substitute and plastic embed mammalian cell monolayers. The method is based on the use of Aclar, a copolymer film with properties very similar to those of tissue culture plastic. We show that Aclar discs support the normal growth and morphology of a wide variety of mammalian cell types, and form an ideal starting point for high-pressure freezing, freeze-substitution and plastic embedding. We present a complete protocol, which, because of its simplicity and reproducibility, provides a method suitable for the routine analysis of mammalian cell monolayers by electron microscopy and tomography.


title: Protective effects of ursolic acid in an experimental model of liver fibrosis through Nrf2/ARE pathway

abstract: Summary Aim Liver fibrosis is a reversible wound-healing response that occurs following liver injury. In this study, we aimed to investigate the possible protective effects of ursolic acid in liver fibrosis induced by carbon tetrachloride (CCl 4 ). Methods ICR mice were randomly divided into six groups (Group 1: normal; Group 2: CCl 4 -treated group; Group 3: CCl 4 plus ursolic acid 25 mg/kg group; Group 4: CCl 4 plus ursolic acid 50 mg/kg group; Group 5: CCl 4 plus colchicine 1 mg/kg group; Group 6: ursolic acid 50 mg/kg group). Mice were administered with CCl 4 (2 mL of CCl 4 in olive oil (1:1, v/v) per kg body weight twice weekly) by intraperitoneal injection and oral injection of colchicine (1 mg/kg) or ursolic acid (25, 50 mg/kg) daily. After six weeks, serum aminotransferase activity, hepatic reactive oxygen species (ROS) production, thiobarbituric acid reactive substances (TBARS), antioxidase (SOD, CAT, GPx) activity and histopathological analysis were performed. The levels of nuclear factor E2-related factor 2 (Nrf2), NAD(P)H: quinone oxidoreductase-1 (NQO1), glutathione S-transferase (GST) and heme oxygenase-1 (HO-1), tumor necrosis factor-alpha (TNF-α), prostaglandin E2 (PGE2) and inducible nitric oxide synthase (iNOS), Bcl-2 and caspase-3 were measured. Results Ursolic acid significantly prevented CCl 4 -induced hepatotoxicity and fibrosis, indicated by both diagnostic indicators and histopathological analysis. CCl 4 -induced profound elevations of oxidative stress, inflammation and apoptosis in liver were suppressed by ursolic acid. Conclusions These results suggest that ursolic acid has the hepatoprotective actions. The inhibition of CCl 4 -induced liver fibrosis, inflammation and apoptosis by ursolic acid is due at least in part to its ability to modulate the Nrf2/ARE signalling pathway.


title: The potential of Internet of m-health Things “m-IoT” for non-invasive glucose level sensing

abstract: An amalgamated concept of Internet of m-health Things (m-IoT) has been introduced recently and defined as a new concept that matches the functionalities of m-health and IoT for a new and innovative future (4G health) applications. It is well know that diabetes is a major chronic disease problem worldwide with major economic and social impact. To-date there have not been any studies that address the potential of m-IoT for non-invasive glucose level sensing with advanced opto-physiological assessment technique and diabetes management. In this paper we address the potential benefits of using m-IoT in non-invasive glucose level sensing and the potential m-IoT based architecture for diabetes management. We expect to achieve intelligent identification and management in a heterogeneous connectivity environment from the mobile healthcare perspective. Furthermore this technology will enable new communication connectivity routes between mobile patients and care services through innovative IP based networking architectures.


title: Architecture and control of a high energy density buffer for power pulsation decoupling in grid-interfaced applications

abstract: In this paper we propose a series-stacked partial power processing architecture and the associated control scheme for double-line-frequency power pulsation decoupling in single phase inverter/rectifier applications. The proposed architecture exploits series stacking of the energy storage capacitors and a buffer converter across the DC bus such that the buffer converter only experiences low voltage stress and processes only a fraction of the total buffer power, while still maintaining a close to ripple free DC bus voltage. Such arrangement results in improved efficiency and higher energy density compared to conventional active decoupling solutions. A control scheme is proposed to exploit the small remaining bus ripple to compensate the power loss in the buffer converter and balance the power cycle of the architecture. A 2 kW hardware prototype has been built to demonstrate the benefit of the proposed solution. The hardware prototype achieves significant volume reduction compared to the conventional passive decoupling solution. An energy density of 611 W/in3 (by component volume) for the hardware prototype and an efficiency above 97.9% across a wide range of power and voltage levels has been experimentally verified.


title: Biomass-derived syngas fermentation into biofuels: Opportunities and challenges.

abstract: The conversion of biomass-derived synthesis gas (or syngas in brief) into biofuels by microbial catalysts (such as Clostridium ljungdahlii, Clostridium autoethanogenum, Acetobacterium woodii, Clostridium carboxidivorans and Peptostreptococcus productus) has gained considerable attention as a promising alternative for biofuel production in the recent past. The utilization of the whole biomass, including lignin, irrespective of biomass quality, the elimination of complex pre-treatment steps and costly enzymes, a higher specificity of biocatalysts, an independence of the H(2):CO ratio for bioconversion, bioreactor operation at ambient conditions, and no issue of noble metal poisoning are among the major advantages of this process. Poor mass transfer properties of the gaseous substrates (mainly CO and H(2)) and low ethanol yield of biocatalysts are the biggest challenges preventing the commercialization of syngas fermentation technology. This paper critically reviews the existing literature in biomass-derived syngas fermentation into biofuels, specifically, different biocatalysts, factors affecting syngas fermentation, and mass transfer. The paper also outlines the major challenges of syngas fermentation, key performance index and technology road map, and discusses the further research needs.


title: New Concepts and Perspectives on Micro-Rotorcraft and Small Autonomous Rotary-Wing Vehicles

abstract: Abstract : This paper summarizes ongoing work concerning micro-rotorcraft (MRC)--i.e., rotary-wing micro air vehicles (MAV)-- research and development. Technology trends involving microelectronic miniaturization, vehicle autonomy systems, electric propulsion and power electronics are contributing to an ongoing revolution in MAV and MRC aerial vehicle concepts and applications. New vehicle configurations are being developed, as well as old concepts being reassessed, for MAV and MRC vehicles.


title: A bank customer credit evaluation based on the decision tree and the simulated annealing algorithm

abstract: C4.5 is a learning algorithm that adopts local search strategy, and it cannot obtain the best decision rules. On the other hand, the simulated annealing algorithm is a globally optimized algorithm and it avoids the drawbacks of C4.5. This paper proposes a new credit evaluation method based on decision tree and simulated annealing algorithm. The experimental results demonstrate that the proposed method is effective.


title: Conditional proxy re-encryption secure against chosen-ciphertext attack

abstract: In a proxy re-encryption (PRE) system [4], a proxy, authorized by Alice, can convert a ciphertext for Alice into a ciphertext for Bob without seeing the underlying plaintext. PRE has found many practical applications requiring delegation. However, it is inadequate to handle scenarios where a fine-grained delegation is demanded. To overcome the limitation of existing PRE systems, we introduce the notion of conditional proxy re-encryption (C-PRE), whereby only ci-phertext satisfying a specific condition set by Alice can be transformed by the proxy and then decrypted by Bob. We formalize its security model and propose an efficient C-PRE scheme, whose chosen-ciphertext security is proven under the 3-quotient bilinear Diffie-Hellman assumption. We further extend the construction to allow multiple conditions with a slightly higher overhead.


title: A High-Performance Hierarchical Ring On-Chip Interconnect with Low-Cost Routers

abstract: Energy consumption of routers in commonly used mesh-based on-chip networks for chip multiprocessors is an increasingly important concern: these routers consist of a crossbar and complex control logic and can require significant buffers, hence high energy and area consumption. In contrast, an alternative design uses ring-based networks to connect network nodes with small and simple routers. Rings have been used in recent commercial designs, and are well-suited to smaller core counts. However, rings do not scale as efficiently as meshes. In this paper, we propose an energy-efficient yet high performance alternative to traditional mesh-based and ringbased on-chip networks. We aim to attain the scalability of meshes with the router simplicity and efficiency of rings. Our design is a hierarchical ring topology which consists of small local rings connected via one or more global ring. Routing between rings is accomplished using bridge routers that have minimal buffering, and use deflection in place of buffered flow control for simplicity. We comprehensively explore new issues in the design of such a topology, including the design of the routers, livelock freedom, energy, performance and scalability. We propose new router microarchitectures and show that these routers are significantly simpler and more area and energy efficient than both buffered and bufferless mesh based routers. We develop new mechanisms to preserve livelock-free routing in our topology and router design. Our evaluations compare our proposal to a traditional ring network and conventional buffered and bufferless mesh based networks, showing that our proposal reduces average network power by 52.4% (30.4%) and router area footprint by 70.5% from a buffered mesh in 16-node (64-node) configurations, while also improving system performance by 0.6% (5.0%).