title: SalGAN: Visual Saliency Prediction with Generative Adversarial Networks

abstract: We introduce SalGAN, a deep convolutional neural network for visual saliency prediction trained with adversarial examples. The first stage of the network consists of a generator model whose weights are learned by back-propagation computed from a binary cross entropy (BCE) loss over downsampled versions of the saliency maps. The resulting prediction is processed by a discriminator network trained to solve a binary classification task between the saliency maps generated by the generative stage and the ground truth ones. Our experiments show how adversarial training allows reaching state-of-the-art performance across different metrics when combined with a widely-used loss function like BCE. Our results can be reproduced with the source code and trained models available at https://imatge-upc.github. io/saliency-salgan-2017/.


title: Improving Controllability and Predictability of Interactive Recommendation Interfaces for Exploratory Search

abstract: In exploratory search, when a user directs a search engine using uncertain relevance feedback, usability problems regarding controllability and predictability may arise. One problem is that the user is often modelled as a passive source of relevance information, instead of an active entity trying to steer the system based on evolving information needs. This may cause the user to feel that the response of the system is inconsistent with her steering. Another problem arises due to the sheer size and complexity of the information space, and hence of the system, as it may be difficult for the user to anticipate the consequences of her actions in this complex environment. These problems can be mitigated by interpreting the user's actions as setting a goal for an optimization problem regarding the system state, instead of passive relevance feedback, and by allowing the user to see the predicted effects of an action before committing to it. In this paper, we present an implementation of these improvements in a visual user-controllable search interface. A user study involving exploratory search for scientific literature gives some indication on improvements in task performance, usability, perceived usefulness and user acceptance.


Building an Ontology of Cyber Security


title: Magnetic Tunnel Junction (MTJ) sensors for integrated circuits (IC) electric current measurement

abstract: We report on MgO Magnetic Tunnel Junction (MTJ) devices focusing on their potential application in the measurement of electrical current at the integrated circuit level. Single devices and full bridges have been specifically developed for this purpose. A sort of different designs regarding their geometry arrangement as well as the number of constitutive elements have been tested. Experimental characterization has been performed and results including impedance and sensitivity measurements are given.


title: Digital Asset Management with Distributed Permission over Blockchain and Attribute-Based Access Control

abstract: Digital asset management (DAM) has increasing benefits in booming global Internet economy, but it is still a great challenge for providing an effective way to manage, store, ingest, organize and retrieve digital asset. To do it, we present a new digital asset management platform, called DAM-Chain, with Transaction-based Access Control (TBAC) which integrates the distribution ABAC model and the blockchain technology. In this platform, the ABAC provides flexible and diverse authorization mechanisms for digital asset escrowed into blockchain while the blockchain's transactions serve as verifiable and traceable medium of access request procedure. We also present four types of transactions to describe the TBAC access control procedure, and provide the algorithms of these transactions corresponding to subject registration, object escrowing and publication, access request and grant. By maximizing the strengths of both ABAC and blockchain, this platform can support flexible and diverse permission management, as well as verifiable and transparent access authorization process in an open decentralized environment.


title: RTL Hardware IP Protection Using Key-Based Control and Data Flow Obfuscation

abstract: Recent trends of hardware intellectual property (IP) piracy and reverse engineering pose major business and security concerns to an IP-based system-on-chip (SoC) design flow. In this paper, we propose a Register Transfer Level (RTL) hardware IP protection technique based on low-overhead key-based obfuscation of control and data flow. The basic idea is to transform the RTL core into control and data flow graph (CDFG) and then integrate a well-obfuscated finite state machine (FSM) of special structure, referred as “Mode-Control FSM”, into the CDFG in a manner that normal functional behavior is enabled only after application of a specific input sequence. We provide formal analysis of the effectiveness of the proposed approach and present a simple metric to quantify the level of obfuscation. We also present an integrated design flow that implements the proposed obfuscation at low computational overhead. Simulation results for two open-source IP cores show that high levels of security is achievable at nominal area and power overheads under delay constraint.


title: The effect of live measles vaccines on serum vitamin A levels in healthy children

abstract: OBJECTIVE ::: Serum retinol levels have been shown to be depressed during measles infection. This study aims to demonstrate whether there is any decrease in serum vitamin A level following immunization with live viral vaccine and its relation with vaccine seroconversion in children with measles. Since many children receive measles vaccine alone or in combination with measles-mumps-rubella vaccine, we studied serum vitamin A levels and antibody levels in healthy, well-nourished children before and after immunization with monovalent and combined live attenuated measles vaccine. ::: ::: ::: METHODS ::: The first group included 21 healthy children between the ages of 9-11 months who received live measles (Schwarz) vaccine. There were also 21 healthy children (range 14-20 months of age) who received measles-mumps-rubella Trimovax (Pasteur Merieux) vaccine. All children were tested for serum vitamin A levels before vaccination, on days 9-14 and 30-42 following both vaccinations. Measles specific antibody levels were also measured on admission and 30-42 days following vaccinations. ::: ::: ::: RESULTS ::: In both vaccination groups, mean serum vitamin A levels reduced significantly on days 9-14, but increased slightly on days 30-42 in the measles-mumps-rubella vaccinated group (P < 0.05). The baseline and follow-up levels of mean serum vitamin A did not differ between seroconverted and nonseroconverted cases within the measles vaccinated group. ::: ::: ::: CONCLUSION ::: Serum vitamin A levels are reduced following vaccination with monovalent and combined live attenuated measles vaccines.


title: Palladium(II)-Catalyzed Direct Carboxylation of Alkenyl C–H Bonds with CO2

abstract: Pd-catalyzed direct carboxylation of alkenyl C–H bonds with carbon dioxide was realized for the first time. Treatment of 2-hydroxystyrenes and a catalytic amount of Pd(OAc)2 with Cs2CO3 under atmospheric pressure of CO2 afforded corresponding coumarins in good yield. Furthermore, isolation of the key alkenylpalladium intermediate via C–H bond cleavage was achieved. The reaction was proposed to undergo reversible nucleophilic addition of the alkenylpalladium intermediate to CO2.


title: A fast transient LDO based on dual loop FVF with high PSRR

abstract: In this work, the design of a dual loop flipped voltage follower (FVF) based low-dropout regulator (LDO) to achieve fast transient response is proposed for all-digital phase locked loop (ADPLL) in the RFID application. With the help of an additional reference generation loop in FVF LDO and the cascaded structure, high power supply rejection ratio (PSRR) is achievable. This design is fabricated in GF 40nm CMOS technology. The FVF LDO core only occupies small area of 0.036 mm2. This area also includes 80pF on chip capacitor. Without large off-chip capacitor, this LDO is suitable for system-on-chip (SoC) requirement. Post layout simulation shows that fast response of 45ns and high PSRR of −42dB through up to 10GHz frequency range.


title: A Condorcet Voting Theory Based AHP Approach for MCDM Problems

abstract: Analytical Hierarchical Process has been used as a useful methodology for multi-criteria decision making environments with substantial applications in recent years. But the weakness of the traditional AHP method lies in the use of subjective judgement based assessment and standardized scale for pairwise comparison matrix creation. The paper proposes a Condorcet Voting Theory based AHP method to solve multi criteria decision making problems where Analytical Hierarchy Process (AHP) is combined with Condorcet theory based preferential voting technique followed by a quantitative ratio method for framing the comparison matrix instead of the standard importance scale in traditional AHP approach. The consistency ratio (CR) is calculated for both the approaches to determine and compare the consistency of both the methods. The results reveal Condorcet – AHP method to be superior generating lower consistency ratio and more accurate ranking of the criterion for solving MCDM problems.


title: Keyword Search in Relational Databases: A Survey

abstract: The integration of DB and IR provides flexible ways for users to query information in the same platform [6, 2, 3, 7, 5, 28]. On one hand, the sophisticated DB facilities provided by RDBMSs assist users to query well-structured information using SQL. On the other hand, IR techniques allow users to search unstructured information using keywords based on scoring and ranking, and do not need users to understand any database schemas. We survey the developments on finding structural information among tuples in an RDB using an l-keyword query, Q, which is a set of keywords of size l, denoted as Q = {k1, k2, · · · , kl}. Here, an RDB is viewed as a data graph GD(V,E), where V represents a set of tuples, and E represents a set of edges between tuples. An edge exists between two tuples if at least there is a foreign key reference from one to the other. A tuple consists of attribute values and some of them are strings or full-text. The structural information to be returned for an l-keyword query is a set of connected structures, R, where a connected structure represents how the tuples, that contain the required keywords, are interconnected in a database GD. R can be either all trees or all subgraphs. When a function score(·) is given to score a structure, we can find the top-k structures instead of all structures in GD. Such a score(·) function can be based on either the text information maintained in tuples (node weights), or the connections among tuples (edge weights), or both. In Section 2, we focus on supporting keyword search in an RDBMS using SQL. Since this implies making use of the database schema information to issue SQL queries in order to find structures for an l-keyword query, it is called the schema-based approach. The two main steps in the schema-based approach are how to generate a set of SQL queries that can find all the structures among tuples in an RDB completely, and how to evaluate the generated set of SQL queries efficiently. Due to the nature of set operations used in SQL and the underneath relational algebra, a data graph GD is considered as an undirected graph by ignoring the direction of references between tuples, and therefore a returned structure is of undirected structure (either tree or subgraph). The existing algorithms use a parameter to control the maximum size of a structure allowed. Such a size control parameter limits the number of SQL queries to be executed. Otherwise, the number of SQL queries to be executed for finding all or even top-k structures is too large. The score(·) functions used to rank the structures are all based on the text information on tuples. In Section 3, we focus on supporting keyword search in an RDBMS from a different viewpoint, by materializing an RDB as a directed graph GD. Unlike an undirected graph, the fact that a tuple v can reach to another tuple u in a directed graph does not necessarily mean that the tuple v is reachable from u. In this context, a returned structure (either steiner tree, distinct rooted tree, r-radius steiner graph, or multi-center subgraph) is directed. Such direction handling provides users with more information on how the tuples are interconnected.


title: Sparse feature fidelity for image quality assessment

abstract: A quality metric called sparse feature fidelity (SFF) is proposed for full-reference image quality assessment (IQA). It is inspired by the fact that images are transformed into sparse representations by the primary visual cortex which is the most important part of the human visual system (HVS). The proposed method is based on sparse features that are acquired from a set of feature detectors called simple cell matrix which is trained on samples of natural images by a sparse coding algorithm. Then the SFF scores are obtained by a similarity measurement between the features of reference and distorted images. Moreover, two strategies are designed to simulate the properties of the visual perception: visual attention and visual threshold. Experimental results on four image databases show that SFF is more consistent with the subjective evaluations than the leading IQA methods.


Tachyon : Memory Throughput I / O for Cluster Computing Frameworks


title: Clinical relevance of contextual factors as triggers of placebo and nocebo effects in musculoskeletal pain

abstract: Placebo and nocebo effects are embodied psycho-neurobiological responses capable of modulating pain and producing changes at different neurobiological, body at perceptual and cognitive levels. These modifications are triggered by different contextual factors (CFs) presented in the therapeutic encounter between patient and healthcare providers, such as healing rituals and signs. The CFs directly impact on the quality of the therapeutic outcome: a positive context, that is a context characterized by the presence of positive CFs, can reduce pain by producing placebo effects, while a negative context, characterized by the presence of negative CFs, can aggravate pain by creating nocebo effects. Despite the increasing interest about this topic; the detailed study of CFs as triggers of placebo and nocebo effects is still lacked in the management of musculoskeletal pain.Increasing evidence suggest a relevant role of CFs in musculoskeletal pain management. CFs are a complex sets of internal, external or relational elements encompassing: patient's expectation, history, baseline characteristics; clinician's behavior, belief, verbal suggestions and therapeutic touch; positive therapeutic encounter, patient-centered approach and social learning; overt therapy, posology of intervention, modality of treatment administration; marketing features of treatment and health care setting. Different explanatory models such as classical conditioning and expectancy can explain how CFs trigger placebo and nocebo effects. CFs act through specific neural networks and neurotransmitters that were described as mediators of placebo and nocebo effects.Available findings suggest a relevant clinical role and impact of CFs. They should be integrated in the clinical reasoning to increase the number of treatment solutions, boosts their efficacy and improve the quality of the decision-making. From a clinical perspective, the mindful manipulation of CFs represents a useful opportunity to enrich a well-established therapy in therapeutic setting within the ethical border. From a translational perspective, there is a strong need of research studies on CFs close to routine and real-world clinical practice in order to underline the uncertainty of therapy action and help clinicians to implement knowledge in daily practice.


title: Combining Global Models for Parsing Universal Dependencies.

abstract: We describe our entry, C2L2, to the CoNLL 2017 shared task on parsing Universal Dependencies from raw text. Our system features an ensemble of three global parsing paradigms, one graph-based and two transition-based. Each model leverages character-level bidirectional LSTMs as lexical feature extractors to encode morphological information. Though relying on baseline tokenizers and focusing only on parsing, our system ranked second in the official end-to-end evaluation with a macro-average of ::: 75.00 LAS F1 score over 81 test treebanks. In addition, we had the top average performance on the four surprise languages and on the small treebank subset.


title: Analysis and design of triple-rotor axial-flux spoke-array vernier permanent magnet machines

abstract: This paper presents a triple-rotor axial-flux spoke-array vernier permanent magnet (TR-AFSAVPM) machine to enhance the performance of vernier machines. By cooperating spoke-array rotor and coil-wounded winding, power factor and torque density of the proposed machines are much improved and their copper utilization is reduced as well comparing with conventional vernier machines. Firstly, the machine structure and operation principles are introduced. After that, analytical equations of the machine back-EMF and torque are derived to reveal its features. Based on both quasi-3D finite element analysis (FEA) and 3D-FEA, its high torque density performance is verified and a set of optimized machine sizing specifications is ultimately settled. A fractional slot axial-flux permanent magnet machine and an axial-flux surface vernier permanent magnet machine are designed to compare with the proposed machine. Analysis results show that the TR-AFSAVPM machine has high power factor, viz. 0.97 and high torque density, viz. 24.2kNm/m3. A prototype has been designed and is being manufactured to validate the results.


title: Advancements and recent trends in emotion recognition using facial image analysis and machine learning models

abstract: As the demand for systems with human computer interaction grows, automated systems with human gesture and emotion recognition capabilities are the need of the hour. Emotions are understood by textual, vocal, and verbal expression data. Facial imagery also provides a constructive option to interpret and analyse human emotional issues. This paper describes the recent advancements in methods and techniques used to gauge the five primary emotions or moods frequently captured on images containing the human face: surprise, happiness, disgust, normality, drowsiness, through automated machinery. Looking at the recent developments in facial expression recognition techniques, the focus is on artificial neural networks and Support Vector Machine (SVM) in emotion classification. The technique first analyses the information conveyed by the facial regions of the eye and mouth into a merged new image and using it as an input to a feed forward neural network trained by back propagation. The second method showcases the use of Oriented Fast and Rotated (ORB) on a single frame of imagery to extract texture information, and the classification is completed using SVM. The special case of drowsiness detection systems using facial imagery by pattern classification, as automated drowsiness detection promises to play a revolutionary role in preventing road fatalities due to lethargic symptoms in drivers is also discussed.


title: The Relationship Between Intestinal Microbiota and the Central Nervous System in Normal Gastrointestinal Function and Disease

abstract: Although many people are aware of the communication that occurs between the gastrointestinal (GI) tract and the central nervous system, fewer know about the ability of the central nervous system to influence the microbiota or of the microbiota's influence on the brain and behavior. Within the GI tract, the microbiota have a mutually beneficial relationship with their host that maintains normal mucosal immune function, epithelial barrier integrity, motility, and nutrient absorption. Disruption of this relationship alters GI function and disease susceptibility. Animal studies suggest that perturbations of behavior, such as stress, can change the composition of the microbiota; these changes are associated with increased vulnerability to inflammatory stimuli in the GI tract. The mechanisms that underlie these alterations are likely to involve stress-induced changes in GI physiology that alter the habitat of enteric bacteria. Furthermore, experimental perturbation of the microbiota can alter behavior, and the behavior of germ-free mice differs from that of colonized mice. Gaining a better understanding of the relationship between behavior and the microbiota could provide insight into the pathogenesis of functional and inflammatory bowel disorders.


title: Scrum Abandonment in Distributed Teams: A Revelatory Case

abstract: The last decade has witnessed substantial growth in the adoption of both Agile and distributed software development. However, combining Agile practices, which emphasize regular informal communication, with geographically and temporally distributed sites, which hinder regular informal communication, presents numerous challenges. Proponents of Agile, especially the Scrum project management framework, have published several case studies of successful Scrum implementations in distributed environments. However, few empirical studies examine failed or abandoned Scrum implementations. Consequently, this paper presents a revelatory case study of a geographically and temporally distributed software development team that abandoned its attempted transition to Scrum. Two factors associated with the team’s decision to abandon Scrum are identified – degradation of Scrum practices due to distribution and the undermining of the ScrumMaster’s credibility. Based on this analysis the paper proposes that task/team familiarity, group cohesion and transactive memory may be combined to understand the relationship between geotemporal distribution, process and performance.


title: Design of Ku/Ka band down-converter front-end for digital broadcast satellite receivers

abstract: Different configurations and design techniques to achieve high performance Ku/Ka band down-converter front-end for digital broadcast satellite (DBS) receivers are presented. The individual blocks of low-noise amplifier (LNA), mixer, balun, and IF amplifier suitable for DBS applications are reviewed and discussed in details. Also, our recent progress in the fully-integrated low-noise blocks (LNBs) using low cost 0.18-µm SiGe BiCMOS technology for DBS down-converter is reported, including a Ku-band design with a low NF (< 5.8 dB) and a high conversion gain up to 47.7 dB, and also a Ka-band design achieving a NF < 6.6 dB with a conversion gain up to 49.4 dB. The output P1dB of the two designs are 5.8 dBm and 4.2 dBm, respectively.


title: Agent-Centric Risk Assessment: Accident Anticipation and Risky Region Localization

abstract: For survival, a living agent must have the ability to assess risk (1) by temporally anticipating accidents before they occur, and (2) by spatially localizing risky regions in the environment to move away from threats. In this paper, we take an agent-centric approach to study the accident anticipation and risky region localization tasks. We propose a novel soft-attention Recurrent Neural Network (RNN) which explicitly models both spatial and appearance-wise non-linear interaction between the agent triggering the event and another agent or static-region involved. In order to test our proposed method, we introduce the Epic Fail (EF) dataset consisting of 3000 viral videos capturing various accidents. In the experiments, we evaluate the risk assessment accuracy both in the temporal domain (accident anticipation) and spatial domain (risky region localization) on our EF dataset and the Street Accident (SA) dataset. Our method consistently outperforms other baselines on both datasets.


title: A Winner-Take-All Method for Training Sparse Convolutional Autoencoders

abstract: We explore combining the benefits of convolutional architectures and autoencoders for learning deep representations in an unsupervised manner. A major challenge is to achieve appropriate sparsity among hidden variables, since neighbouring variables in each feature map tend to be highly correlated and a suppression mechanism is therefore needed. Previously, deconvolutional networks and convolutional predictive sparse decomposition have been used to construct systems that have a recognition pathway and a data generation pathway that are trained so that they agree and so that the hidden representation is sparse. We take a more direct approach and describe a way to train convolutional autoencoders layer by layer, where in each layer sparsity is achieved using a winner-take-all activation function within each feature map. Learning is computationally efficient and we show that our method can be used to train shallow and deep convolutional autoencoders whose representations can be used to achieve classification rates on the MNIST, CIFAR-10 and NORB datasets that are competitive with the state of the art.


title: JoDroid: Adding Android Support to a Static Information Flow Control Tool

abstract: We present our ongoing work on the extension of our PDG-based information flow control tool Joana to handle Android applications. We elaborate on the challenges posed by android applications, outline what we have already done and discuss what we intend to do in the future.


Packet Leashes: A Defense against Wormhole Attacks in Wireless Networks


title: The neurobiological consequences of early stress and childhood maltreatment

abstract: Early severe stress and maltreatment produces a cascade of neurobiological events that have the potential to cause enduring changes in brain development. These changes occur on multiple levels, from neurohumoral (especially the hypothalamic–pituitary–adrenal {HPA} axis) to structural and functional. The major structural consequences of early stress include reduced size of the mid-portions of the corpus callosum and attenuated development of the left neocortex, hippocampus, and amygdala. Major functional consequences include increased electrical irritability in limbic structures and reduced functional activity of the cerebellar vermis. There are also gender differences in vulnerability and functional consequences. The neurobiological sequelae of early stress and maltreatment may play a significant role in the emergence of psychiatric disorders during development.


title: The effects of feedback on energy conservation: A meta-analysis.

abstract: Feedback has been studied as a strategy for promoting energy conservation for more than 30 years, with studies reporting widely varying results. Literature reviews have suggested that the effectiveness of feedback depends on both how and to whom it is provided; yet variations in both the type of feedback provided and the study methodology have made it difficult for conclusions to be drawn. The current article analyzes past theoretical and empirical research on both feedback and proenvironmental behavior to identify unresolved issues, and utilizes a meta-analysis of 42 feedback studies published between 1976 and 2010 to test a set of hypotheses about when and how feedback about energy usage is most effective. Results indicate that feedback is effective overall, r = .071, p < .001, but with significant variation in effects (r varied from -.080 to .480). Several treatment variables were found to moderate this relationship, including frequency, medium, comparison message, duration, and combination with other interventions (e.g., goal, incentive). Overall, results provide further evidence of feedback as a promising strategy to promote energy conservation and suggest areas in which future research should focus to explore how and for whom feedback is most effective.


title: A miniaturized electrolytic pump sensorized with a strain gauge based on thermoplastic nanocomposite for drug delivery systems

abstract: In this paper we present a miniature electrolytic pump sensorized with a novel strain sensor to be used as active component of a drug delivery system. It consists of an electrolytic solution reservoir where inert electrodes are immersed. By polarizing the electrodes, the electrolytic reaction is activated and the produced gases (i.e. oxygen and hydrogen) displace an elastic membrane delimiting the electrolytic solution reservoir. In order to measure and monitor the membrane displacement, and therefore the volume of drug ejected, a strain gauge sensor has been prepared using a conductive thermoplastic nanocomposite elastomer (CTPE). The sensor has been fixed on the deformable membrane. The conductive thermoplastic elastomer is a good candidate for this application because of its high sensitivity. Furthermore, the CTPE allows to customize the resistance of the device in order to obtain low power consumption.


Toward accurate molecular identification of species in complex environmental samples: testing the performance of sequence filtering and clustering methods


title: Use of airborne LiDAR and aerial photography in the estimation of individual tree heights in forestry

abstract: This paper describes the use of aerial photography and airborne LiDAR to estimate individual tree heights in forest stands. The advantages and disadvantages in the use of LiDAR systems are revised and a data fusion analysis with digital aerial photography is proposed. An example of the use of these techniques in a forested area in Scotland is presented. An algorithm has been developed to extract a high-resolution digital terrain model of the bare ground. This provided a tree canopy model as the difference between the laser first pulse and the model of the underlying terrain. Information about individual trees was obtained by image segmentation and classification. This analysis provided a good method of estimating individual tree canopies and heights.


title: The Moderating Effect of Organizational Learning Culture on Individual Motivation and ERP System Assimilation at Individual Level

abstract: This study contributes to the enterprise resource planning (ERP) system assimilation theory by providing an enriched understanding about how organizational learning culture influences end users’ usage degree in post-implementation stage. Empirical data collected from 141 ERP users showed that both intrinsic motivation and perceived usefulness has positive impact on individual assimilation level of ERP systems. We further observe significant moderating effect of organizational learning culture on the association between individual motivations and individual assimilation level of ERP technology. Our findings highlight the importance of organizational learning culture and provide some guidelines for the firms to facilitate appropriate organizational culture, so as to enhance the intrinsic and extrinsic motivators of ERP users, promote the employees’ fully utilization of the ERP software, and achieve business benefits with the assimilation of ERP systems. Theoretical and practical implications of the study are discussed along with its limitations.


title: Location based service for presence system using haversine method

abstract: Location-Based Services (LBS) stands for the provision of information about the position of a device or a user often offered as a service via various means of media. Haversine formula is the method used to calculate the distance from two different points. In this research, Haversine method applied on calculating the distance for presence system based on the distance of location. This system allows users in presenting using mobile applications in addition to facilitate the presenting of data preservation. Use of LBS technology to detect users in an area of allowable locations. This detection is done by looking at the closest point from the predetermined center point. Center point will store the coordinates and radius of the presence area. This research develops Haversine method for measurement and estimation of distance with limitation in localization. Implementation model includes signal measurements with wifi position and GPS values to identify the precise location for human presence system.


title: Process Approach for Information Systems in Health Care: A Systematic Review and PRISMA Method

abstract: Hospital Information System is the most fragile component of the health care system in the countries in development process. The modernization of the health care system does not integrate a rigid reflection on the installation of these tools into hospitals, which remain foreign with the strategies of these countries. The main objective of this research is to present a systematic review on the relationship between process approach and continuous improvement with the development and continuity of hospital information systems. Hospital Information System and its sustainability are key factors for the functioning of services in Hospital Institutions which requires principles respect of quality management as the process approach among others. The main question to be treated in this paper is "Which reasons have been given for the views that the process approach is or isn't helping to have and give a Hospital Information System in continuous development?", in order to contribute to the systematization of knowledge in this area, the main objective of this research is to present a systematic review on the relationship between process approach and continuous improvement with the development and continuity of hospital information systems. The systematic review methodology was the PRISMA Statement ®, the search granted to find 7735 based on defined key-words, and after a preliminary examination, according to the exclusion conditions and the eligibility criteria 20 papers were considered relevant to a more detailed study. After an analysis of all relevant documents we have tried to reveal the important gap, which we will try to explore more in future investigations.


title: Fixed-point algorithms for determinantal point processes

abstract: Determinantal point processes (DPPs) offer an elegant tool for encoding probabilities over subsets of a ground set. Discrete DPPs are parametrized by a positive semidefinite matrix (called the DPP kernel), and estimating this kernel is key to learning DPPs from observed data. We consider the task of learning the DPP kernel, and develop for it a surprisingly simple yet effective new algorithm. Our algorithm offers the following benefits over previous approaches: (a) it is much simpler; (b) it yields equally good and sometimes even better local maxima; and (c) it runs an order of magnitude faster on large problems. We present experimental results on both real and simulated data to illustrate the numerical performance of our technique.


title: Characterizing Geographic Variation in Well-Being Using Tweets

abstract: The language used in tweets from 1,300 different US counties was found to be predictive of the subjective well-being of people living in those counties as measured by representative surveys. Topics, sets of co-occurring words derived from the tweets using LDA, improved accuracy in predicting life satisfaction over and above standard demographic and socio-economic controls (age, gender, ethnicity, income, and education). The LDA topics provide a greater behavioural and conceptual resolution into life satisfaction than the broad socio-economic and demographic variables. For example, tied in with the psychological literature, words relating to outdoor activities, spiritual meaning, exercise, and good jobs correlate with increased life satisfaction, while words signifying disengagement like ’bored’ and ’tired’ show a negative association.


title: Convergence Analysis of Two-layer Neural Networks with ReLU Activation

abstract: In recent years, stochastic gradient descent (SGD) based techniques has become the standard tools for training neural networks. However, formal theoretical understanding of why SGD can train neural networks in practice is largely missing. In this paper, we make progress on understanding this mystery by providing a convergence analysis for SGD on a rich subset of two-layer feedforward networks with ReLU activations. This subset is characterized by a special structure called"identity mapping". We prove that, if input follows from Gaussian distribution, with standard $O(1/\sqrt{d})$ initialization of the weights, SGD converges to the global minimum in polynomial number of steps. Unlike normal vanilla networks, the"identity mapping"makes our network asymmetric and thus the global minimum is unique. To complement our theory, we are also able to show experimentally that multi-layer networks with this mapping have better performance compared with normal vanilla networks. Our convergence theorem differs from traditional non-convex optimization techniques. We show that SGD converges to optimal in"two phases": In phase I, the gradient points to the wrong direction, however, a potential function $g$ gradually decreases. Then in phase II, SGD enters a nice one point convex region and converges. We also show that the identity mapping is necessary for convergence, as it moves the initial point to a better place for optimization. Experiment verifies our claims.


title: Frugal ECC: efficient and versatile memory error protection through fine-grained compression

abstract: Because main memory is vulnerable to errors and failures, large-scale systems and critical servers utilize error checking and correcting (ECC) mechanisms to meet their reliability requirements. We propose a novel mechanism, Frugal ECC (FECC), that combines ECC with fine-grained compression to provide versatile protection that can be both stronger and lower overhead than current schemes, without sacrificing performance. FECC compresses main memory at cache-block granularity, using any left over space to store ECC information. Compressed data and its ECC information are then frequently read with a single access even without redundant memory chips; insufficiently compressed blocks require additional storage and accesses. As examples, we present chipkill-correct ECCs on a non-ECC DIMM with x4 chips and the first true chipkill-correct ECC for x8 devices using an ECC DIMM. FECC relies on a new Coverage-oriented-Compression that we developed specifically for the modest compression needs of ECC and for floating-point data.


title: Cross-sectional evaluation of thoracic lymphoma.

abstract: In patients who have lymphoma, the presence and distribution of thoracic involvement is important in both tumor staging and treatment. Thoracic involvement in Hodgkin lymphoma (HL) is more common than in non-Hodgkin lymphoma (NHL). In HL, mediastinal lymphadenopathy with contiguous spread is a hallmark, and lung parenchymal involvement at the initial presentation is almost always associated with mediastinal lymphadenopathy. NHL is more heterogeneous and generally presents at a more advanced stage than HL. Most often, mediastinal involvement occurs as a disseminated or recurrent form of extrathoracic lymphoma. Bulky mediastinal disease with compression of adjacent structures can occur, particularly with high-grade subtypes of NHL and isolated lung disease without mediastinal lymphadenopathy can occur in contrast to HL.


title: PANORAMA: A 3D Shape Descriptor Based on Panoramic Views for Unsupervised 3D Object Retrieval

abstract: We present a novel 3D shape descriptor that uses a set of panoramic views of a 3D object which describe the position and orientation of the object's surface in 3D space. We obtain a panoramic view of a 3D object by projecting it to the lateral surface of a cylinder parallel to one of its three principal axes and centered at the centroid of the object. The object is projected to three perpendicular cylinders, each one aligned with one of its principal axes in order to capture the global shape of the object. For each projection we compute the corresponding 2D Discrete Fourier Transform as well as 2D Discrete Wavelet Transform. We further increase the retrieval performance by employing a local (unsupervised) relevance feedback technique that shifts the descriptor of an object closer to its cluster centroid in feature space. The effectiveness of the proposed 3D object retrieval methodology is demonstrated via an extensive consistent evaluation in standard benchmarks that clearly shows better performance against state-of-the-art 3D object retrieval methods.


title: A visual motion detection circuit suggested by Drosophila connectomics

abstract: Animal behaviour arises from computations in neuronal circuits, but our understanding of these computations has been frustrated by the lack of detailed synaptic connection maps, or connectomes. For example, despite intensive investigations over half a century, the neuronal implementation of local motion detection in the insect visual system remains elusive. Here we develop a semi-automated pipeline using electron microscopy to reconstruct a connectome, containing 379 neurons and 8,637 chemical synaptic contacts, within the Drosophila optic medulla. By matching reconstructed neurons to examples from light microscopy, we assigned neurons to cell types and assembled a connectome of the repeating module of the medulla. Within this module, we identified cell types constituting a motion detection circuit, and showed that the connections onto individual motion-sensitive neurons in this circuit were consistent with their direction selectivity. Our results identify cellular targets for future functional investigations, and demonstrate that connectomes can provide key insights into neuronal computations.


title: Causal effects of violent sports video games on aggression: Is it competitiveness or violent content?

abstract: Three experiments examined the impact of excessive violence in sport video games on aggression-related variables. Participants played either a nonviolent simulation-based sports video game (baseball or football) or a matched excessively violent sports video game. Participants then completed measures assessing aggressive cognitions (Experiment 1), aggressive affect and attitudes towards violence in sports (Experiment 2), or aggressive behavior (Experiment 3). Playing an excessively violent sports video game increased aggressive affect, aggressive cognition, aggressive behavior, and attitudes towards violence in sports. Because all games were competitive, these findings indicate that violent content uniquely leads to increases in several aggression-related variables, as predicted by the General Aggression Model and related social–cognitive models.


title: User Interests Identification on Twitter Using a Hierarchical Knowledge Base

abstract: Twitter, due to its massive growth as a social networking platform, has been in focus for the analysis of its user generated content for personalization and recommendation tasks. A common challenge across these tasks is identifying user interests from tweets. Semantic enrichment of Twitter posts, to determine user interests, has been an active area of research in the recent past. These approaches typically use available public knowledge-bases (such as Wikipedia) to spot entities and create entity-based user profiles. However, exploitation of such knowledge-bases to create richer user profiles is yet to be explored. In this work, we leverage hierarchical relationships present in knowledge-bases to infer user interests expressed as a Hierarchical Interest Graph. We argue that the hierarchical semantics of concepts can enhance existing systems to personalize or recommend items based on a varied level of conceptual abstractness. We demonstrate the effectiveness of our approach through a user study which shows an average of approximately eight of the top ten weighted hierarchical interests in the graph being relevant to a user’s interests.


title: Hydrogen evolution by a photoelectrochemical cell based on a Cu 2 O-ZnO-[FeFe] hydrogenase electrode

abstract: Abstract A Cu2O-ZnO-hydrogenase photocathode possessed enzyme/semiconductor junction has been constructed by immobilizing a biological protein catalyst, hydrogenase-CrHydA1 enzyme on the ZnO protected Cu2O electrode. With light illumination, a photocurrent of 0.8 mA/cm2 at 0.15 V vs. RHE was obtained and hydrogen was successfully detected from the photocathode in photoelectrochemical measurements with Faradaic efficiency of ca. 1%. The construction as well as the stability of the system are also reported. The result shows that this biohybrid photocathode is capable of photocatalytic proton reduction under mild conditions.


The Implant Distal Jet.


title: Use of digital game based learning and gamification in secondary school science: The effect on student engagement, learning and gender difference

abstract: This study aimed to identify the impact of a game based learning (GBL) application using computer technologies on student engagement in secondary school science classrooms. The literature reveals that conventional Science teaching techniques (teacher-centered lecture and teaching), which foster rote learning among students, are one of the major concerns in Pakistan Education system. This leads to student disengagement in science lessons eventually resulting in student absenteeism and dropouts from the schools. This study consisted of five stages: (1) examining the impact of Digital Game-Based Learning (DGBL) and gamification on engagement, learning and gender difference, and literature related to using DGBL models for instructional design; (2) planning learning activities and developing a GBL application based on a specific content in Science; (3) conducting an intervention with a sample of 72 participants of 8th grade (aged 12---15) in a low cost private school of Pakistan following quasi-experimental research framework; (4) observing behaviour and emotions of the participants during science lessons; (5) conducting pre and post tests to assess the learning outcomes of participants followed by focus groups discussion. Analysis from Friedman test, Mann-Whitney U test, and Wilcoxon Signed Rank test show that the GBL application has a positive influence on student engagement. However, GBL application was not equally effective for all students since girls outperformed boys in terms of engagement and learning outcomes. This study gives insights into the development of better educational games to promote student learning.


Evaluation of RISC-V RTL with FPGA-Accelerated Simulation


title: Learning a Mahalanobis distance metric for data clustering and classification

abstract: Distance metric is a key issue in many machine learning algorithms. This paper considers a general problem of learning from pairwise constraints in the form of must-links and cannot-links. As one kind of side information, a must-link indicates the pair of the two data points must be in a same class, while a cannot-link indicates that the two data points must be in two different classes. Given must-link and cannot-link information, our goal is to learn a Mahalanobis distance metric. Under this metric, we hope the distances of point pairs in must-links are as small as possible and those of point pairs in cannot-links are as large as possible. This task is formulated as a constrained optimization problem, in which the global optimum can be obtained effectively and efficiently. Finally, some applications in data clustering, interactive natural image segmentation and face pose estimation are given in this paper. Experimental results illustrate the effectiveness of our algorithm.


title: A Study of Age and Gender seen through Mobile Phone Usage Patterns in Mexico

abstract: Mobile phone usage provides a wealth of information, which can be used to better understand the demographic structure of a population. In this paper we focus on the population of Mexican mobile phone users. Our first contribution is an observational study of mobile phone usage according to gender and age groups. We were able to detect significant differences in phone usage among different subgroups of the population. Our second contribution is to provide a novel methodology to predict demographic features (namely age and gender) of unlabeled users by leveraging individual calling patterns, as well as the structure of the communication graph. We provide details of the methodology and show experimental results on a real world dataset that involves millions of users.


Identifying the Provision of Choices in Privacy Policy Text.


title: Obstructive sleep apnoea treatment and fasting lipids: a comparative effectiveness study

abstract: Obstructive sleep apnoea (OSA) is associated with cardiovascular disease. Dyslipidaemia has been implicated as a mechanism linking OSA with atherosclerosis, but no consistent associations with lipids exist for OSA or positive airway pressure treatment. We assessed the relationships between fasting lipid levels and obesity and OSA severity, and explored the impact of positive airway pressure treatment on 2-year fasting lipid level changes. ::: ::: Analyses included moderate-to-severe OSA patients from the Icelandic Sleep Apnoea Cohort. Fasting morning lipids were analysed in 613 untreated participants not on lipid-lowering medications at baseline. Patients were then initiated on positive airway pressure and followed for 2 years. Sub-classification using propensity score quintiles, which aimed to replicate covariate balance associated with randomised trials and, therefore, minimise selection bias and allow causal inference, was used to design the treatment group comparisons. 199 positive airway pressure adherent patients and 118 non-users were identified. ::: ::: At baseline, obesity was positively correlated with triglycerides and negatively correlated with total cholesterol, and low-density and high-density lipoprotein cholesterol. A small correlation was observed between the apnoea/hypopnoea index and high-density lipoprotein cholesterol. No effect of positive airway pressure adherence on 2-year fasting lipid changes was observed. ::: ::: Results do not support the concept of changes in fasting lipids as a primary mechanism for the increased risk of atherosclerotic cardiovascular disease in OSA. ::: ::: Fasting lipid changes are unaffected by positive airway pressure and thus don’t increase cardiovascular risk


title: Trust, Satisfaction, and Online Repurchase Intention: The Moderating Role of Perceived Effectiveness of E-Commerce Institutional Mechanisms

abstract: The effects of e-commerce institutional mechanisms on trust and online purchase have traditionally been understood in the initial online purchase context. This study extends this literature by exploring the role of e-commerce institutional mechanisms in the online repurchase context. In doing so, it responds to the emerging call for understanding the institutional context under which customer trust operates in an e-commerce environment. Specifically, this study introduces a key moderator, perceived effectiveness of e-commerce institutional mechanisms (PEEIM), to the relationships between trust, satisfaction, and repurchase intention. Drawing on the theory of organizational trust, and based on a survey of 362 returning online customers, we find that PEEIM negatively moderates the relationship between trust in an online vendor and online customer repurchase intention, as it decreases the importance of trust to promoting repurchase behavior. We also find that PEEIM positively moderates the relationship between customer satisfaction and trust as it enhances the customer's reliance on past transaction experience with the vendor to reevaluate trust in the vendor. Consistent with the predictions made in the literature, PEEIM does not directly affect trust or repurchase intention. Academic and practical implications and future research directions are discussed.


title: Experience Replay for Continual Learning

abstract: Interacting with a complex world involves continual learning, in which tasks and data distributions change over time. A continual learning system should demonstrate both plasticity (acquisition of new knowledge) and stability (preservation of old knowledge). Catastrophic forgetting is the failure of stability, in which new experience overwrites previous experience. In the brain, replay of past experience is widely believed to reduce forgetting, yet it has been largely overlooked as a solution to forgetting in deep reinforcement learning. Here, we introduce CLEAR, a replay-based method that greatly reduces catastrophic forgetting in multi-task reinforcement learning. CLEAR leverages off-policy learning and behavioral cloning from replay to enhance stability, as well as on-policy learning to preserve plasticity. We show that CLEAR performs better than state-of-the-art deep learning techniques for mitigating forgetting, despite being significantly less complicated and not requiring any knowledge of the individual tasks being learned.


title: A review of Mobile Commerce consumers' behaviour research: consumer acceptance, loyalty and continuance (2000-2009)

abstract: This paper executed a comprehensive review on papers published between 2000 and 2009 about consumer behaviour in Mobile Commerce. We screened all the papers on important journals in Mobile Commerce and searched the ISI database for papers with related keywords. This yields a collection of 92 papers. The papers can be classified into three theme: consumer acceptance, loyalty and continuance. We made a comparative analysis in each theme from the aspects of the types of mobile commerce, the theoretical foundation, the research methods and research discoveries. Based on the analysis, suggestions for the future study are put forth in conclusion.


title: Biologically Inspired Media Quality Modeling

abstract: A successful quality model is indispensable in a rich variety of multimedia applications, e.g., image classification and video summarization. Conventional approaches have developed many features to assess media quality at both low-level and high-level. However, they cannot reflect the process of human visual cortex in media perception. It is generally accepted that an ideal quality model should be biologically plausible, i.e., capable of mimicking human gaze shifting as well as the complicated visual cognition. In this paper, we propose a biologically inspired quality model, focusing on interpreting how humans perceive visually and semantically important regions in an image (or a video clip). Particularly, we first extract local descriptors (graphlets in this work) from an image/frame. They are projected onto the perceptual space, which is built upon a set of low-level and high-level visual features. Then, an active learning algorithm is utilized to select graphlets that are both visually and semantically salient. The algorithm is based on the observation that each graphlet can be linearly reconstructed by its surrounding ones, and spatially nearer ones make a greater contribution. In this way, both the local and global geometric properties of an image/frame can be encoded in the selection process. These selected graphlets are linked into a so-called biological viewing path (BVP) to simulate human visual perception. Finally, the quality of an image or a video clip is predicted by a probabilistic model. Experiments shown that 1) the predicted BVPs are over 90% consistent with real human gaze shifting paths on average; and 2) our quality model outperforms many of its competitors remarkably.


title: Automatic Registration of Multi-Projector Domes Using a Single Uncalibrated Camera

abstract: In this paper we present a novel technique for easily calibrating multiple casually aligned projectors on spherical domes using a single uncalibrated camera. Using the prior knowledge of the display surface being a dome, we can estimate the camera intrinsic and extrinsic parameters and the projector to display surface correspondences automatically using a set of images. These images include the image of the dome itself and a projected pattern from each projector. Using these correspondences we can register images from the multiple projectors on the dome. Further, we can register displays which are not entirely visible in a single camera view using multiple pan and tilted views of an uncalibrated camera making our method suitable for displays of different size and resolution. We can register images from any arbitrary viewpoint making it appropriate for a single head-tracked user in a 3D visualization system. Also, we can use several cartographic mapping techniques to register images in a manner that is appropriate for multi-user visualization. ::: ::: Domes are known to produce a tremendous sense of immersion and presence in visualization systems. Yet, till date, there exists no easy way to register multiple projectors on a dome to create a high-resolution realistic visualizations. To the best of our knowledge, this is the first method that can achieve accurate geometric registration of multiple projectors on a dome simply and automatically using a single uncalibrated camera.


title: Shock–Acoustic Waves Generated during Rocket Launches and Earthquakes

abstract: Shock–acoustic waves generated during rocket launches and earthquakes are investigated by a method developed earlier for processing data from a global network of receivers of the GPS navigation system. Disturbances of the total electron content in the ionosphere accompanying the launches of the Proton, Soyuz, and Space Shuttle space vehicles from the Baikonur cosmodrome and Kennedy Space Center launch site in 1998–2000, as well as the earthquakes in Turkey on August 17 and November 12, 1999, were analyzed. It was shown that, regardless of the source type, the impulsive disturbance has the character of an N-wave with a period of 200–360 s and an amplitude exceeding background fluctuations under moderate geomagnetic conditions by a factor of 2–5 as a minimum. The elevation angle of the disturbance wave vector varies from 25° to 65°, and the phase velocity (900–1200 m/s) approaches the speed of sound at heights of the ionospheric F-region maximum. The source location corresponds to a segment of the booster trajectories at a distance of no less than 500–1000 km from the start position and to a flight altitude of no less than 100 km. In the case of earthquakes the source location approximately coincides with the epicenter.


Language Independent Named Entity Recognition Combining Morphological And Contextual Evidence


title: Voice Conversion Using Deep Neural Networks With Layer-Wise Generative Training

abstract: This paper presents a new spectral envelope conversion method using deep neural networks (DNNs). The conventional joint density Gaussian mixture model (JDGMM) based spectral conversion methods perform stably and effectively. However, the speech generated by these methods suffer severe quality degradation due to the following two factors: 1) inadequacy of JDGMM in modeling the distribution of spectral features as well as the non-linear mapping relationship between the source and target speakers, 2) spectral detail loss caused by the use of high-level spectral features such as mel-cepstra. Previously, we have proposed to use the mixture of restricted Boltzmann machines (MoRBM) and the mixture of Gaussian bidirectional associative memories (MoGBAM) to cope with these problems. In this paper, we propose to use a DNN to construct a global non-linear mapping relationship between the spectral envelopes of two speakers. The proposed DNN is generatively trained by cascading two RBMs, which model the distributions of spectral envelopes of source and target speakers respectively, using a Bernoulli BAM (BBAM). Therefore, the proposed training method takes the advantage of the strong modeling ability of RBMs in modeling the distribution of spectral envelopes and the superiority of BAMs in deriving the conditional distributions for conversion. Careful comparisons and analysis among the proposed method and some conventional methods are presented in this paper. The subjective results show that the proposed method can significantly improve the performance in terms of both similarity and naturalness compared to conventional methods.


title: Detecting Deceptive Reviews Using Generative Adversarial Networks

abstract: In the past few years, consumer review sites have become the main target of deceptive opinion spam, where fictitious opinions or reviews are deliberately written to sound authentic. Most of the existing work to detect the deceptive reviews focus on building supervised classifiers based on syntactic and lexical patterns of an opinion. With the successful use of Neural Networks on various classification applications, in this paper, we propose FakeGAN a system that for the first time augments and adopts Generative Adversarial Networks (GANs) for a text classification task, in particular, detecting deceptive reviews. Unlike standard GAN models which have a single Generator and Discriminator model, FakeGAN uses two discriminator models and one generative model. The generator is modeled as a stochastic policy agent in reinforcement learning (RL), and the discriminators use Monte Carlo search algorithm to estimate and pass the intermediate action-value as the RL reward to the generator. Providing the generator model with two discriminator models avoids the mod collapse issue by learning from both distributions of truthful and deceptive reviews. Indeed, our experiments show that using two discriminators provides FakeGAN high stability, which is a known issue for GAN architectures. While FakeGAN is built upon a semi-supervised classifier, known for less accuracy, our evaluation results on a dataset of TripAdvisor hotel reviews show the same performance in terms of accuracy as of the state-of-the-art approaches that apply supervised machine learning. These results indicate that GANs can be effective for text classification tasks. Specifically, FakeGAN is effective at detecting deceptive reviews.


title: Short-Text Topic Modeling via Non-negative Matrix Factorization Enriched with Local Word-Context Correlations

abstract: Being a prevalent form of social communications on the Internet, billions of short texts are generated everyday. Discovering knowledge from them has gained a lot of interest from both industry and academia. The short texts have a limited contextual information, and they are sparse, noisy and ambiguous, and hence, automatically learning topics from them remains an important challenge. To tackle this problem, in this paper, we propose a semantics-assisted non-negative matrix factorization (SeaNMF) model to discover topics for the short texts. It effectively incorporates the word-context semantic correlations into the model, where the semantic relationships between the words and their contexts are learned from the skip-gram view of the corpus. The SeaNMF model is solved using a block coordinate decent algorithm. We also develop a sparse variant of the SeaNMF model which can achieve a better model interpretability. Extensive quantitative evaluations on various real-world short text datasets demonstrate the superior performance of the proposed models over several other state-of-the-art methods in terms of topic coherence and classification accuracy. The qualitative semantic analysis demonstrates the interpretability of our models by discovering meaningful and consistent topics. With a simple formulation and the superior performance, SeaNMF can be an effective standard topic model for short texts.


title: Emotion Analysis Using Audio/Video, EMG and EEG: A Dataset and Comparison Study

abstract: This paper describes a study on automated emotion recognition using four different modalities – audio, video, electromyography (EMG), and electroencephalography (EEG). We collected a dataset using the 4 modalities as 12 human subjects expressed six different emotions or maintained a neutral expression. Three different aspects of emotion recognition were investigated: model selection, feature selection, and data selection. Both generative models (DBNs) and discriminative models (LSTMs) were applied to the four modalities, and from these analyses we conclude that LSTM is better for audio and video together with their corresponding sophisticated feature extractors (MFCC and CNN), whereas DBN is better for both EMG and EEG. By examining these signals at different stages (pre-speech, during-speech, and post-speech) of the current and following trials, we found that the most effective stages for emotion recognition from EEG occur after the emotion has been expressed, suggesting that the neural signals conveying an emotion are long-lasting.


title: Catalytic Z-selective olefin cross-metathesis for natural product synthesis

abstract: Alkenes are found in many biologically active molecules, and there are a large number of chemical transformations in which alkenes act as the reactants or products (or both) of the reaction. Many alkenes exist as either the E or the higher-energy Z stereoisomer. Catalytic procedures for the stereoselective formation of alkenes are valuable, yet methods enabling the synthesis of 1,2-disubstituted Z alkenes are scarce. Here we report catalytic Z-selective cross-metathesis reactions of terminal enol ethers, which have not been reported previously, and of allylic amides, used until now only in E-selective processes. The corresponding disubstituted alkenes are formed in up to >98% Z selectivity and 97% yield. These transformations, promoted by catalysts that contain the highly abundant and inexpensive metal molybdenum, are amenable to gram-scale operations. Use of reduced pressure is introduced as a simple and effective strategy for achieving high stereoselectivity. The utility of this method is demonstrated by its use in syntheses of an anti-oxidant plasmalogen phospholipid, found in electrically active tissues and implicated in Alzheimer's disease, and the potent immunostimulant KRN7000.


title: Supervised dimensionality reduction using mixture models

abstract: Given a classification problem, our goal is to find a low-dimensional linear transformation of the feature vectors which retains information needed to predict the class labels. We present a method based on maximum conditional likelihood estimation of mixture models. Use of mixture models allows us to approximate the distributions to any desired accuracy while use of conditional likelihood as the contrast function ensures that the selected subspace retains maximum possible mutual information between feature vectors and class labels. Classification experiments using Gaussian mixture components show that this method compares favorably to related dimension reduction techniques. Other distributions belonging to the exponential family can be used to reduce dimensions when data is of a special type, for example binary or integer valued data. We provide an EM-like algorithm for model estimation and present visualization experiments using Gaussian and Bernoulli mixture models.


title: New trust metric for the RPL routing protocol

abstract: Establishing trust relationships between routing nodes represents a vital security requirement to establish reliable routing processes that exclude infected or selfish nodes. In this paper, we propose a new security scheme for the Internet of things and mainly for the RPL (Routing Protocol for Low-power and Lossy Networks) called: Metric-based RPL Trustworthiness Scheme (MRTS). The primary aim is to enhance RPL security and deal with the trust inference problem. MRTS addresses trust issue during the construction and maintenance of routing paths from each node to the BR (Border Router). To handle this issue, we extend DIO (DODAG Information Object) message by introducing a new trust-based metric ERNT (Extended RPL Node Trustworthiness) and a new Objective Function TOF (Trust Objective Function). In fact, ERNT represents the trust values for each node within the network, and TOF demonstrates how ERNT is mapped to path cost. In MRTS all nodes collaborate to calculate ERNT by taking into account nodes' behavior including selfishness, energy, and honesty components. We implemented our scheme by extending the distributed Bellman-Ford algorithm. Evaluation results demonstrated that the new scheme improves the security of RPL.


Low oxygen concentrations affecting antioxidant activity and bioactive compounds in coloured rice.


title: Bayesian Biosurveillance of Disease Outbreaks

abstract: Early, reliable detection of disease outbreaks is a critical problem today. This paper reports an investigation of the use of causal Bayesian networks to model spatio-temporal patterns of a non-contagious disease (respiratory anthrax infection) in a population of people. The number of parameters in such a network can become enormous, if not carefully managed. Also, inference needs to be performed in real time as population data stream in. We describe techniques we have applied to address both the modeling and inference challenges. A key contribution of this paper is the explication of assumptions and techniques that are sufficient to allow the scaling of Bayesian network modeling and inference to millions of nodes for real-time surveillance applications. The results reported here provide a proof-of-concept that Bayesian networks can serve as the foundation of a system that effectively performs Bayesian biosurveillance of disease outbreaks.


title: Break it Down for Me: A Study in Automated Lyric Annotation

abstract: Comprehending lyrics, as found in songs and poems, can pose a challenge to human and machine readers alike. This motivates the need for systems that can understand the ambiguity and jargon found in such creative texts, and provide commentary to aid readers in reaching the correct interpretation. We introduce the task of automated lyric annotation (ALA). Like text simplification, a goal of ALA is to rephrase the original text in a more easily understandable manner. However, in ALA the system must often include additional information to clarify niche terminology and abstract concepts. To stimulate research on this task, we release a large collection of crowdsourced annotations for song lyrics. We analyze the performance of translation and retrieval models on this task, measuring performance with both automated and human evaluation. We find that each model captures a unique type of information important to the task.


title: Gait-based authentication using a wrist-worn device

abstract: Every individual has a distinctive way of walking. For this reason gait can be a key element of biometric techniques aimed at authenticating and/or identifying the user of a wearable device. This paper presents a lightweight method that uses the acceleration collected at the user's wrist for authentication purposes. The user's typical gait pattern is learned during the first period of use, then detection of anomalies in a set of acceleration-based features is used to understand if a new user, a possible impostor or a thief, is wearing the device. The method has been successfully evaluated with 15 volunteers, showing an Equal Error Rate of 2.9%. These results suggest that gait-based authentication with a wrist-worn device can be carried out with high accuracy levels. A comparison with a similar method executed on a pocket-worn device is also included.


title: A Performance Evaluation of Several Deep Neural Networks for Reverberant Speech Separation

abstract: In this paper, we compare different deep neural networks (DNN) in extracting speech signals from competing speakers in room environments, including the conventional fully-connected multilayer perception (MLP) network, convolutional neural network (CNN), recurrent neural network (RNN), and the recently proposed capsule network (CapsNet). Each DNN takes input of both spectral features and converted spatial features that are robust to position mismatch, and outputs the separation mask for target source estimation. In addition, a psychacoustically-motivated objective function is integrated in each DNN, which explores perceptual importance of each TF unit in the training process. Objective evaluations are performed on the separated sounds using the converged models, in terms of PESQ, SDR as well as STOI. Overall, all the implemented DNNs have greatly improved the quality and speech intelligibility of the embedded target source as compared to the original recordings. In particular, bidirectional RNN, either along the temporal direction or along the frequency bins, outperforms the other DNN structures with consistent improvement.


title: The monte carlo database system: Stochastic analysis close to the data

abstract: The application of stochastic models and analysis techniques to large datasets is now commonplace. Unfortunately, in practice this usually means extracting data from a database system into an external tool (such as SAS, R, Arena, or Matlab), and then running the analysis there. This extract-and-model paradigm is typically error-prone, slow, does not support fine-grained modeling, and discourages what-if and sensitivity analyses. In this article we describe MCDB, a database system that permits a wide spectrum of stochastic models to be used in conjunction with the data stored in a large database, without ever extracting the data. MCDB facilitates in-database execution of tasks such as risk assessment, prediction, and imputation of missing data, as well as management of errors due to data integration, information extraction, and privacy-preserving data anonymization. MCDB allows a user to define “random” relations whose contents are determined by stochastic models. The models can then be queried using standard SQL. Monte Carlo techniques are used to analyze the probability distribution of the result of an SQL query over random relations. Novel “tuple-bundle” processing techniques can effectively control the Monte Carlo overhead, as shown in our experiments.


Secure Smartphone Unlock : Robust Face Spoof Detection on Mobile


title: SUSAN—A New Approach to Low Level Image Processing

abstract: This paper describes a new approach to low level image processing; in particular, edge and corner detection and structure preserving noise reduction. ::: ::: Non-linear filtering is used to define which parts of the image are closely related to each individual pixel; each pixel has associated with it a local image region which is of similar brightness to that pixel. The new feature detectors are based on the minimization of this local image region, and the noise reduction method uses this region as the smoothing neighbourhood. The resulting methods are accurate, noise resistant and fast. ::: ::: Details of the new feature detectors and of the new noise reduction method are described, along with test results.


title: Heart rate variability interventions for concussion and rehabilitation

abstract: The study of Heart Rate Variability (HRV) has emerged as an essential component of cardiovascular health, as well as a physiological mechanism by which one can increase the interactive communication between the cardiac and the neurocognitive systems (i.e., the body and the brain). It is well-established that lack of heart rate variability implies cardiopathology, morbidity, reduced quality-of-life, and precipitous mortality. On the positive, optimal heart rate variability has been associated with good cardiovascular health, autonomic nervous system (ANS) control, emotional regulation, and enhanced neurocognitive processing. In addition to health benefits, optimal HRV has been shown to improve neurocognitive performance by enhancing focus, visual acuity and readiness, and by promoting emotional regulation needed for peak performance. In concussed athletes and soldiers, concussions not only alter brain connectivity, but also alter cardiac functioning and impair cardiovascular performance upon exertion. Altered sympathetic and parasympathetic balance in the ANS has been postulated as a critical factor in refractory Post Concussive Syndrome (PCS). This article will review both the pathological aspects of reduced heart rate variability on athletic performance, as well as the cardiovascular and cerebrovascular components of concussion and PCS. Additionally, this article will review interventions with HRV biofeedback (HRV BFB) training as a promising and underutilized treatment for sports and military-related concussion. Finally, this article will review research and promising case studies pertaining to use of HRV BFB for enhancement of cognition and performance, with applicability to concussion rehabilitation.


Machine Learning Classification over Encrypted Data


title: Autonomous tracking of vehicle rear lights and detection of brakes and turn signals

abstract: Automatic detection of vehicle alert signals is extremely critical in autonomous vehicle applications and collision avoidance systems, as these detection systems can help in the prevention of deadly and costly accidents. In this paper, we present a novel and lightweight algorithm that uses a Kalman filter and a codebook to achieve a high level of robustness. The algorithm is able to detect braking and turning signals of the vehicle in front both during the daytime and at night (daytime detection being a major advantage over current research), as well as correctly track a vehicle despite changing lanes or encountering periods of no or low-visibility of the vehicle in front. We demonstrate that the proposed algorithm is able to detect the signals accurately and reliably under different lighting conditions.


title: Epileptic seizure prediction and control

abstract: Epileptic seizures are manifestations of epilepsy, a serious brain dynamical disorder second only to strokes. Of the world's /spl sim/50 million people with epilepsy, fully 1/3 have seizures that are not controlled by anti-convulsant medication. The field of seizure prediction, in which engineering technologies are used to decode brain signals and search for precursors of impending epileptic seizures, holds great promise to elucidate the dynamical mechanisms underlying the disorder, as well as to enable implantable devices to intervene in time to treat epilepsy. There is currently an explosion of interest in this field in academic centers and medical industry with clinical trials underway to test potential prediction and intervention methodology and devices for Food and Drug Administration (FDA) approval. This invited paper presents an overview of the application of signal processing methodologies based upon the theory of nonlinear dynamics to the problem of seizure prediction. Broader application of these developments to a variety of systems requiring monitoring, forecasting and control is a natural outgrowth of this field.


title: Sensor network security: a survey

abstract: Wireless sensor networks (WSNs) use small nodes with constrained capabilities to sense, collect, and disseminate information in many types of applications. As sensor networks become wide-spread, security issues become a central concern, especially in mission-critical tasks. In this paper, we identify the threats and vulnerabilities to WSNs and summarize the defense methods based on the networking protocol layer analysis first. Then we give a holistic overview of security issues. These issues are divided into seven categories: cryptography, key management, attack detections and preventions, secure routing, secure location security, secure data fusion, and other security issues. Along the way we analyze the advantages and disadvantages of current secure schemes in each category. In addition, we also summarize the techniques and methods used in these categories, and point out the open research issues and directions in each area.


title: Symmetrical SURF and Its Applications to Vehicle Detection and Vehicle Make and Model Recognition

abstract: Speeded-Up Robust Features (SURF) is a robust and useful feature detector for various vision-based applications but it is unable to detect symmetrical objects. This paper proposes a new symmetrical SURF descriptor to enrich the power of SURF to detect all possible symmetrical matching pairs through a mirroring transformation. A vehicle make and model recognition (MMR) application is then adopted to prove the practicability and feasibility of the method. To detect vehicles from the road, the proposed symmetrical descriptor is first applied to determine the region of interest of each vehicle from the road without using any motion features. This scheme provides two advantages: there is no need for background subtraction and it is extremely efficient for real-time applications. Two MMR challenges, namely multiplicity and ambiguity problems, are then addressed. The multiplicity problem stems from one vehicle model often having different model shapes on the road. The ambiguity problem results from vehicles from different companies often sharing similar shapes. To address these two problems, a grid division scheme is proposed to separate a vehicle into several grids; different weak classifiers that are trained on these grids are then integrated to build a strong ensemble classifier. The histogram of gradient and SURF descriptors are adopted to train the weak classifiers through a support vector machine learning algorithm. Because of the rich representation power of the grid-based method and the high accuracy of vehicle detection, the ensemble classifier can accurately recognize each vehicle.


Examination of the QM Process: Making A Case for Transformative Professional Development Model


title: The Relaxation Schemes for Systems of Conservation Laws in Arbitrary Space Dimensions

abstract: We present a class of numerical schemes (called the relaxation schemes) for systems of conservation laws in several space dimensions. The idea is to use a local relaxation approximation. We construct a linear hyperbolic system with a stiff lower order term that approximates the original system with a small dissipative correction. The new system can be solved by underresolved stable numerical discretizations without using either Riemann solvers spatially or a nonlinear system of algebraic equations solvers temporally. Numerical results for 1-D and 2-D problems are presented. The second-order schemes are shown to be total variation diminishing (TVD) in the zero relaxation limit for scalar equations. ©1995 John Wiley & Sons, Inc.


title: Real-Time Detection of Anomalous Taxi Trajectories from GPS Traces

abstract: Trajectories obtained from GPS-enabled taxis grant us an opportunity to not only extract meaningful statistics, dynamics and behaviors about certain urban road users, but also to monitor adverse and/or malicious events. In this paper we focus on the problem of detecting anomalous routes by comparing against historically “normal” routes. We propose a real-time method, iBOAT, that is able to detect anomalous trajectories “on-the-fly”, as well as identify which parts of the trajectory are responsible for its anomalousness. We evaluate our method on a large dataset of taxi GPS logs and verify that it has excellent accuracy (AUC ≥ 0.99) and overcomes many of the shortcomings of other state-of-the-art methods.


title: Evaluating the Readability of Force Directed Graph Layouts: A Deep Learning Approach

abstract: Existing graph layout algorithms are usually not able to optimize all the aesthetic properties desired in a graph layout. To evaluate how well the desired visual features are reflected in a graph layout, many readability metrics have been proposed in the past decades. However, the calculation of these readability metrics often requires access to the node and edge coordinates and is usually computationally inefficient, especially for dense graphs. Importantly, when the node and edge coordinates are not accessible, it becomes impossible to evaluate the graph layouts quantitatively. In this paper, we present a novel deep learning-based approach to evaluate the readability of graph layouts by directly using graph images. A convolutional neural network architecture is proposed and trained on a benchmark dataset of graph images, which is composed of synthetically generated graphs and graphs created by sampling from real large networks. Multiple representative readability metrics (including edge crossing, node spread, and group overlap) are considered in the proposed approach. We quantitatively compare our approach to traditional methods and qualitatively evaluate our approach by showing usage scenarios and visualizing convolutional layers. This paper is a first step towards using deep learning based methods to quantitatively evaluate images from the visualization field.


Robotic Systems Fuzzy Logic Navigation and Obstacle Avoidance by a Mobile Robot in an Unknown Dynamic Environment Regular Paper


title: NautiLOD: A Formal Language for the Web of Data Graph

abstract: The Web of Linked Data is a huge graph of distributed and interlinked datasources fueled by structured information. This new environment calls for formal languages and tools to automatize navigation across datasources (nodes in such graph) and enable semantic-aware and Web-scale search mechanisms. In this article we introduce a declarative navigational language for the Web of Linked Data graph called N auti LOD. N auti LOD enables one to specify datasources via the intertwining of navigation and querying capabilities. It also features a mechanism to specify actions (e.g., send notification messages) that obtain their parameters from datasources reached during the navigation. We provide a formalization of the N auti LOD semantics, which captures both nodes and fragments of the Web of Linked Data. We present algorithms to implement such semantics and study their computational complexity. We discuss an implementation of the features of N auti LOD in a tool called swget, which exploits current Web technologies and protocols. We report on the evaluation of swget and its comparison with related work. Finally, we show the usefulness of capturing Web fragments by providing examples in different knowledge domains.


title: Explaining visible behaviour

abstract: This thesis presents a novel approach to the problem of behaviour modelling within computer vision. This technique is not based upon statistical measures of typicality, but upon building an understanding of the way people navigate towards a goal. Representing movement through the scene in terms of the known goals and obstacles and interpreting people's behaviour as representative of underlying intentions enables behaviour to be explained in terms of these previously defined goals. A family of related algorithms for performing this goal-directed analysis of behaviour are presented and evaluated, alongside a number of metrics for measuring how well the computed explanation matches the observed behaviour. These measurements can be interpreted as measurements of goal-directedness or intentionality. The system is evaluated using a novel methodology which involves comparing the algorithmic output with the performance of humans engaged in a visual surveillance task. An application of this technique is demonstrated within the visual surveillance domain, providing classification of behaviour patterns as explicable or inexplicable. The advantages of such an approach are multiple: it handles the presence of movable goals (for example, parked cars) with ease, and trajectories which have never before been presented to the system can be classified as explicable. The output of the system (for example �Agent n is heading towards goal m� with an associated score indicating how good this explanation is) are easily interpreted. The systems described in this thesis could also in principle be extended to handle richer varieties of scene, moving obstacles, and more complicated systems of goals.


title: The Effectiveness of the Game of Dice Task in Predicting At-Risk and Problem Gambling Among Adolescents: The Contribution of the Neural Networks

abstract: The Game of Dice Task (GDT; Brand et al. in Neuropsychology 19:267–277, 2005a; Psychiatry Res 133:91–99, 2005b) measures decision-making under objective risk conditions. Although disadvantageous decision-making has been shown in individuals with substance dependency, such as pathological dependency, any studies have been conducted with adolescents by using the GDT to investigate the relationship between the performance on the task and gambling behavior. Moreover, all the previous studies have considered only the GDT net score and not the single choices. In the current study, focusing on adolescents, we wanted to investigate the relationship between the sequence of the choices at the GDT and gambling behavior, measured with the SOGS-RA. To analyze the predictive power of the sequence of choices made in the GDT and problem gambling and gambling frequency, we used the Neural Networks (NNs), which are often used to find relationships between a series of input actions and the correspondent empirical outputs in order to discover behavioral patterns that may be predictive of at-risk behaviors. Results showed that neither a linear or a non-linear relationship could be detected between the GDT performance and the SOGS-RA classification both in terms of gambling problem severity and gambling frequency. Indeed, different training algorithms produced different performances of the NN on the training sets, but all of them showed a very low prediction capability on new samples. Thus, the performance at the GDT did not discriminate between adolescent gamblers with different and progressive levels of problematic gambling behavior and gambling frequency. Limitations and future studies are discussed.


title: Clinical efficacy of virtual reality for acute procedural pain management: A systematic review and meta-analysis

abstract: Background Acutely painful procedures are commonplace. Current approaches to pain most often involve pharmacotherapy, however, there is interest in virtual reality (VR) as a non-pharmacological alternative. A methodologically rigorous systematic review and meta-analysis is lacking. Methods Following PRISMA guidelines, we searched the Cochrane Library, Ovid MEDLINE, Embase, CINAHL, ERIC, NIHR Centre for Review and Dissemination, Proquest, the System for Information on Grey Literature in Europe and the WHO International Clinical Trials Registry Platform from inception to 5 November 2017. Included studies were randomised with an experimental trial design, included a non-VR control group and examined the efficacy of VR with regards to an acutely painful clinical intervention. Bias was assessed along Cochrane guidelines, with performance bias not assessed due to the non-blindable nature of VR. We extracted summary data for maximal pain score and used standard mean difference DerSimonian-Laird random-effects meta-analysis (RevMan 5.3). This review was prospectively registered (PROSPERO CRD42017058204). Findings Of the 12,450 studies identified, 20 studies were eligible for the systematic review. No trials reported in sufficient detail to judge their risk of bias, and 10 studies were at high risk of bias in at least one domain. 16 studies (9 randomised controlled trials, 7 crossover studies) examining 656 individuals were included in quantitative synthesis. Pain scales were heterogenous, but mostly employed 100-point scales. Across all trials, meta-analysis was suggestive of a -0.49 (95%CI -0.83 to -0.41, p = 0.006) standardised mean difference reduction in pain score with VR. However there was a high degree of statistical heterogeneity (χ2 p<0.001, I2 81%, 95%CI for I2 70–88%), driven by randomised studies, with substantial clinical heterogeneity. Conclusion These data suggest that VR may have a role in acutely painful procedures, however included studies were clinically and statistically heterogenous. Further research is required to validate findings, establish cost efficacy and optimal clinical settings for usage. Future trials should report in accordance with established guidelines.


title: A power modulating leg mechanism for monopedal hopping

abstract: New work in robotics targets the development of controllable agile motions such as leaping. In this work, we examine animal and robotic systems on the metric of jumping agility and find that animals can outperform the most agile robots by a factor of two. These specially adapted animals use a jumping strategy we term power modulation to generate more peak power for jumping than otherwise possible. A novel eight-bar revolute mechanism designed with a new linkage synthesis approach encodes the properties for power modulation as well as constraints which assure rotation-free jumping motion. We fabricate an 85 gram prototype and demonstrate that it can perform a range of jumps while constrained by a linear slide. The prototype can deliver 3.63 times more peak jumping power than the maximum its motor can produce. A simulation matched to the physical parameters of the prototype predicts that the robot can attain an agility exceeding that of the most agile animals if the actuator power is increased to 15W.


title: Operational amplifier with two-stage gain-boost

abstract: In this paper we present a three-stage fully differential operational amplifier in 120nm digital CMOS. To reach high gain gain-boosted cascodes in the first stage are used. The gain-boost amplifiers are realized as two-stage amplifiers with self cascodes. A DC gain of 108dB and an unity-gain frequency of 1.06GHz are achieved at 1.2V supply. The operational amplifier is appropriate for supply voltages from 1.5V down to 1.0V.


title: Self-compassion and intuitive eating in college women: Examining the contributions of distress tolerance and body image acceptance and action

abstract: Abstract Self-compassion has been linked to higher levels of psychological well-being. The current study evaluated whether this effect also extends to a more adaptive food intake process. More specifically, this study investigated the relationship between self-compassion and intuitive eating among 322 college women. In order to further clarify the nature of this relationship this research additionally examined the indirect effects of self-compassion on intuitive eating through the pathways of distress tolerance and body image acceptance and action using both parametric and non-parametric bootstrap resampling analytic procedures. Results based on responses to the self-report measures of the constructs of interest indicated that individual differences in body image acceptance and action (β = .31, p p = .94) helped explain the relationship between self-compassion and intuitive eating. This effect was retained in a subsequent model adjusted for body mass index (BMI) and self-esteem (β = .19, p


title: Physics-inspired adaptive fracture refinement

abstract: Physically based animation of detailed fracture effects is not only computationally expensive, but also difficult to implement due to numerical instability. In this paper, we propose a physics-inspired approach to enrich low-resolution fracture animation by realistic fracture details. Given a custom-designed material strength field, we adaptively refine a coarse fracture surface into a detailed one, based on a discrete gradient descent flow. Using the new fracture surface, we then generate a high-resolution fracture animation with details on both the fracture surface and the exterior surface. Our experiment shows that this approach is simple, fast, and friendly to user design and control. It can generate realistic fracture animations within a few seconds.


title: A Wireless Sensor Network for Smart Irrigation and Environmental Monitoring: A Position Article

abstract: Traditional instrumentation based on discrete and wired solutions, presents many difficulties to measure or to compute plant physiological responses due to the large geographical areas that may be involved. This paper describes an implementation of a wireless sensor network for agriculture low data-rate applications. This network includes several solar powered wireless acquisition stations used on soil moisture measurements in greenhouses and open field crops in order to improve irrigation efficiency. Soil moisture is sensed by using the dual-probe heat-pulse method. Climate parameters such as air and soil temperature, solar radiation and relative humidity are also measured. After acquisition, data is routed to a base station where it is stored, analyzed and used to perform local control tasks. The network protocol uses a combination of TDMA (Time Division Multiple Access) and CSMA (Carrier Sense Multiple Access) techniques to allow easy deployment and to provide plug-in capabilities to the in-range acquisition stations. The out-range units employ a zone routing protocol where data flow converges to the in-range units, which are also responsible for data aggregation. It is also discussed actual trends in miniaturization, and how each acquisition node is being replaced by a CMOS single-chip, with a built-in soil moisture micro-sensor with wireless capabilities. Following the plant instrumentation approach, specific solar powered microsystems provide a convenient way to acquire several important parameters from the plant themselves. However, as their size and cost decreases, effective power supplies become a larger problem.


title: The capital intensity of photovoltaics manufacturing: barrier to scale and opportunity for innovation

abstract: Using a bottom-up cost model, we assess the impact of initial factory capital expenditure (capex) on photovoltaic (PV) module minimum sustainable price (MSP) and industry-wide trends. We find capex to have two important impacts on PV manufacturing. First, capex strongly influences the per-unit MSP of a c-Si module: we calculate that the capex-related elements sum to 22% of MSP for an integrated wafer, cell, and module manufacturer. This fraction provides a significant opportunity to reduce MSP toward the U.S. DOE SunShot module price target through capex innovation. Second, a combination of high capex and low margins leads to a poor financial rate of return, which limits the growth rate of PV module manufacturing capacity. We quantify the capex of Czochralski-based crystalline silicon (c-Si) PV manufacturing, summing to 0.68 $/WaCap ($ per annual production capacity in watts, $year/W) from wafer to module and 1.01 $/WaCap from polysilicon to module. At a sustainable operating margin determined by the MSP methodology for our bottom-up scenario, we calculate the sustainable growth rate of PV manufacturing capacity to be ∼19% annually – below the historical trend of ∼50% annually. We conclude with a discussion of innovation opportunities to reduce the capex of PV manufacturing through both incremental and disruptive process innovation with c-Si, platform innovations, and financial approaches.