title: The Inevitable Application of Big Data to Health Care

abstract: THE AMOUNT OF DATA BEING DIGITALLY COLLECTED AND stored is vast and expanding rapidly. As a result, the science of data management and analysis is also advancing to enable organizations to convert this vast resource into information and knowledge that helps them achieve their objectives. Computer scientists have invented the term big data to describe this evolving technology. Big data has been successfully used in astronomy (eg, the Sloan Digital Sky Survey of telescopic information), retail sales (eg, Walmart’s expansive number of transactions), search engines (eg, Google’s customization of individual searches based on previous web data), and politics (eg, a campaign’s focus of political advertisements on people most likely to support their candidate based on web searches). In this Viewpoint, we discuss the application of big data to health care, using an economic framework to highlight the opportunities it will offer and the roadblocks to implementation. We suggest that leveraging the collection of patient and practitioner data could be an important way to improve quality and efficiency of health care delivery. Widespread uptake of electronic health records (EHRs) has generated massive data sets. A survey by the American Hospital Association showed that adoption of EHRs has doubled from 2009 to 2011, partly a result of funding provided by the Health Information Technology for Economic and Clinical Health Act of 2009. Most EHRs now contain quantitative data (eg, laboratory values), qualitative data (eg, text-based documents and demographics), and transactional data (eg, a record of medication delivery). However, much of this rich data set is currently perceived as a byproduct of health care delivery, rather than a central asset to improve its efficiency. The transition of data from refuse to riches has been key in the big data revolution of other industries. Advances in analytic techniques in the computer sciences, especially in machine learning, have been a major catalyst for dealing with these large information sets. These analytic techniques are in contrast to traditional statistical methods (derived from the social and physical sciences), which are largely not useful for analysis of unstructured data such as text-based documents that do not fit into relational tables. One estimate suggests that 80% of business-related data exist in an unstructured format. The same could probably be said for health care data, a large proportion of which is text-based. In contrast to most consumer service industries, medicine adopted a practice of generating evidence from experimental (randomized trials) and quasi-experimental studies to inform patients and clinicians. The evidence-based movement is founded on the belief that scientific inquiry is superior to expert opinion and testimonials. In this way, medicine was ahead of many other industries in terms of recognizing the value of data and information guiding rational decision making. However, health care has lagged in uptake of newer techniques to leverage the rich information contained in EHRs. There are 4 ways big data may advance the economic mission of health care delivery by improving quality and efficiency. First, big data may greatly expand the capacity to generate new knowledge. The cost of answering many clinical questions prospectively, and even retrospectively, by collecting structured data is prohibitive. Analyzing the unstructured data contained within EHRs using computational techniques (eg, natural language processing to extract medical concepts from free-text documents) permits finer data acquisition in an automated fashion. For instance, automated identification within EHRs using natural language processing was superior in detecting postoperative complications compared with patient safety indicators based on discharge coding. Big data offers the potential to create an observational evidence base for clinical questions that would otherwise not be possible and may be especially helpful with issues of generalizability. The latter issue limits the application of conclusions derived from randomized trials performed on a narrow spectrum of participants to patients who exhibit very different characteristics. Second, big data may help with knowledge dissemination. Most physicians struggle to stay current with the latest evidence guiding clinical practice. The digitization of medical literature has greatly improved access; however, the sheer


title: Homography flow for dense correspondences

abstract: We present a unified framework for dense correspondence estimation, called Homography flow, to handle large photometric and geometric deformations in an efficient manner. Our algorithm is inspired by recent successes of the sparse to dense framework. The main intuition is that dense flows located in same plane can be represented as a single geometric transform. Tailored to dense correspondence task, the Homography flow differs from previous methods in the flow domain clustering and the trilateral interpolation. By estimating and propagating sparsely estimated transforms, dense flow field is estimated with very low computation time. The Homography flow highly improves the performance of dense correspondences, especially in flow discontinuous area. Experimental results on challenging image pairs show that our approach suppresses the state-of-the-art algorithms in both accuracy and computation time.


title: A big storm in a small body: seasonal changes in body mass, hormone concentrations and leukocyte profile in the little auk (Alle alle)

abstract: Birds undergo pronounced physiological changes during the reproduction, which may be linked to their parental efforts. Examining these changes may supply information about the birds’ energy expenditure during the particular phases of breeding and help to understand birds’ decisions about their subsequent parental investments. In this study, we measured a number of variables, i.e. body mass (scaled mass index, SMI), baseline corticosterone (CORT) and prolactin (PRL) concentrations and leucocyte profile [basically heterophils-to-lymphocytes (H/L) ratio], during the prelaying, incubation and chick rearing periods in 191 little auks (Alle alle), small Arctic seabirds. We expected the changes in the physiological variables to reflect the energy demands, i.e. the highest levels during chick rearing, moderate ones during incubation and the lowest ones prior to egg laying. Unexpectedly, we found SMI to be the highest during the incubation period, whereas lower and similar to each other values were recorded during the prelaying and chick rearing periods. Also, CORT unexpectedly peaked in the prelaying period, declined during incubation and remained at the incubation level during the chick rearing period. In accordance with our expectations, the PRL concentration was five times higher during the incubation than the chick rearing period and H/L ratio followed the pattern of the CORT changes. Consequently, there is no straightforward interpretation of the observed patterns of changes. Evidently, there are factors other than parental efforts per se that account for the dynamics of physiological changes. We found no sex differences in any of the variables investigated, which indicates that male and female parental investments are very similar.


title: Demand Response Optimization for Smart Home Scheduling Under Real-Time Pricing

abstract: Demand response (DR) is very important in the future smart grid, aiming to encourage consumers to reduce their demand during peak load hours. However, if binary decision variables are needed to specify start-up time of a particular appliance, the resulting mixed integer combinatorial problem is in general difficult to solve. In this paper, we study a versatile convex programming (CP) DR optimization framework for the automatic load management of various household appliances in a smart home. In particular, an L1 regularization technique is proposed to deal with schedule-based appliances (SAs), for which their on/off statuses are governed by binary decision variables. By relaxing these variables from integer to continuous values, the problem is reformulated as a new CP problem with an additional L1 regularization term in the objective. This allows us to transform the original mixed integer problem into a standard CP problem. Its major advantage is that the overall DR optimization problem remains to be convex and therefore the solution can be found efficiently. Moreover, a wide variety of appliances with different characteristics can be flexibly incorporated. Simulation result shows that the energy scheduling of SAs and other appliances can be determined simultaneously using the proposed CP formulation.


title: Comparing transforming growth factor beta-2 and fibronectin as pleurodesing agents

abstract: Background: We have recently demonstrated that transforming growth factor beta-2 (TGF-β2) can produce effective pleurodesis. Whether this effect can be reproduced by the use of its downstream proteins is not known. This study compared the effectiveness of TGF-β2 and fibronectin in inducing pleurodesis in rabbits. ::: ::: ::: ::: Methodology: New Zealand white rabbits (1.5–2.0 kg) were given 1.7 μg of TGF-β2 (n = 5) or 2.0 mg of cellular fibronectin (n = 4) intrapleurally via a chest tube. The induced pleural fluid was collected and analyzed. The rabbits were sacrificed after 14 days. The pleurodesis was graded macroscopically from 1 (none) to 8 (symphysis > 50%). ::: ::: ::: ::: Results: All rabbits in the TGF-β2 group developed effective pleurodesis while none in the fibronectin group had scores > 2 (pleurodesis scores 7.0 ± 0.6 vs 1.3 ± 0.3, P < 0.001). Rabbits that received TGF-β2 produced large amounts of pleural fluid initially (< 4 days). Microscopically, the pleura of rabbits in the TGF-β2 group showed prominent spindle cell proliferation and collagen deposition, but no significant inflammation or mesothelial proliferation. Pleural tissues of rabbits in the fibronectin group had occasional thin collagen deposits only. The intrapleural administration of 2.0 mg of fibronectin, a downstream product of TGF-β, did not induce effective pleurodesis, as did the intrapleural administration of TGF-β2. ::: ::: ::: ::: Conclusions: The present study suggests that the mechanism by which TGF-β2 induces pleurodesis is not predominantly dependent on the production of fibronectin.


title: Repeat operation for nodular recurrent hepatocellular carcinoma within the cirrhotic liver remnant: A comparison with transcatheter arterial chemoembolization

abstract: Management of recurrent hepatocellular carcinoma in a cirrhotic liver remnant is a difficult but challenging problem. To investigate the difference in survival between treatment by repeat resection and treatment by transcatheter arterial chemoembolization (TAE), a retrospective controlled study was conducted. Four patients with nodular recurrence received limited second operations which included right hepatic segmentectomy (2 patients), left lateral segmentectomy (1 patient), and subsegmental wedge resection (1 patient). Eight matched patients received a total of 16 repeated sessions of chemoembolizations. Complications of the TAE group consisted of gastrointestinal bleeding (2 patients), acute pancreatitis (1 patient), and acute cholecystitis (1 patient). No complication developed in the resection group. The 4 patients undergoing a second operation have survived 21, 26, 34, and 54 months after repeat surgery. Seven (87.5%) of the 8 patients receiving TAE died 4 to 11 months after TAE. The resection group survived significantly longer than the TAE group (p<0.01). Our results suggest that it is more advisable to perform a second operation than to undertake chemoembolizations for patients with cirrhosis and nodular recurrent hepatocellular carcinoma with acceptable functional liver reserve.


title: Toonsynth: example-based synthesis of hand-colored cartoon animations

abstract: ToonSynth allows artists to transfer appearance and motion stylization from a few hand-colored exemplars into new target animations specified by skeletal motion.


title: A FUZZY TRAFFIC CONTROLLER WITH MEMBERSHIP FUNCTIONS GENERATED BY GENETIC ALGORITHMS

abstract: In this paper, a fuzzy traffic controller that uses genetic algorithms is presented. In order to find near optimal fuzzy membership functions , the traffic simulations are performed by using genetic algorithms.


title: A general graph-based model for recommendation in event-based social networks

abstract: Event-based social networks (EBSNs), such as Meetup and Plancast, which offer platforms for users to plan, arrange, and publish events, have gained increasing popularity and rapid growth. EBSNs capture not only the online social relationship, but also the offline interactions from offline events. They contain rich heterogeneous information, including multiple types of entities, such as users, events, groups and tags, and their interaction relations. Three recommendation tasks, namely recommending groups to users, recommending tags to groups, and recommending events to users, have been explored in three separate studies. However, none of the proposed methods can handle all the three recommendation tasks. In this paper, we propose a general graph-based model, called HeteRS, to solve the three recommendation problems on EBSNs in one framework. Our method models the rich information with a heterogeneous graph and considers the recommendation problem as a query-dependent node proximity problem. To address the challenging issue of weighting the influences between different types of entities, we propose a learning scheme to set the influence weights between different types of entities. Experimental results on two real-world datasets demonstrate that our proposed method significantly outperforms the state-of-the-art methods for all the three recommendation tasks, and the learned influence weights help understanding user behaviors.


ALL Digital Phase-Locked Loop (ADPLL): A Survey


title: CULTURAL ADAPTATION IN CROSS BORDER E-COMMERCE: A STUDY OF GERMAN COMPANIES

abstract: The standardization vs. adaptation debate in international marketing is still ongoing, yet, in the online realm the discussion is only just emerging. While practitioners are excited to jump on the online communication and commerce bandwagon, empirical research on the issue of online standardization vs. adaptation is still relatively limited and mostly concerns US firms. This paper explores 100 German companies' domestic, U.S., U.K. and Latin American websites and employs a cultural value analysis. We build on Hofstede's and Hall's cultural framework. Findings suggest that cultural value depiction is not very strong in the relevant markets, thus a certain degree of 'cultural alienation' takes place. It is suggested that to engage better with their customer and reach better cultural congruency companies need to work harder on developing culturally adapted websites.


title: Toward curricular guidelines for cybersecurity

abstract: This session reports on a workshop convened by the ACM Education Board with funding by the US National Science Foundation and invites discussion from the community on the workshop findings. The topic, curricular directions for cybersecurity, is one that resonates in many departments considering how best to prepare graduates to face the challenges of security issues in employment and future research. The session will include presentation of the workshop context and conclusions, but will be open to participant discussion. This will be the first public presentation of the results of the workshop and the first opportunity for significant response.


title: Raccoon: A Novel Network I/O Allocation Framework for Workload-Aware VM Scheduling in Virtual Environments

abstract: We present a network I/O allocation framework, called Raccoon , for workload-aware VM scheduling algorithm to facilitate hybrid I/O workloads in virtual environments. Raccoon combines the strengths of paravirtual I/O and SR-IOV techniques to not only minimize the network latency, but also optimize the bandwidth utilization for workload-aware VM scheduling. In Raccoon , a limited number of VFs in SR-IOV are granted to I/O-intensive VMs while the paravirtual Network Interface Cards (vNICs) are allocated to other non-I/O-intensive VMs as the default resources. With this design, Raccoon provides latency reduction and bandwidth guarantee under the premise that I/O-intensive VMs will always be granted the VFs to facilitate their I/O operations. The types of workloads in each VM are identified at runtime by modified XenMon . By leveraging the ACPI Hotplug technique, Raccoon can adaptively plugin and plugout the SR-IOV VFs upon the changes of VM requirements so that an efficient I/O workload-aware VM scheduling algorithm can be implemented based on the bonding driver technique. The experimental results reveal that Raccoon can combine the benefits of para-virtual I/O and SR-IOV techniques to improve the overall performance of virtualized platforms with VMs that have diverse I/O workloads.


title: SWAT: A Programmable, In-Memory, Distributed, High-Performance Computing Platform

abstract: The field of data analytics is currently going through a renaissance as a result of ever-increasing dataset sizes, the value of the models that can be trained from those datasets, and a surge in flexible, distributed programming models. In particular, the Apache Hadoop and Spark programming systems, as well as their supporting projects (e.g. HDFS, SparkSQL), have greatly simplified the analysis and transformation of datasets whose size exceeds the capacity of a single machine. While these programming models facilitate the use of distributed systems to analyze large datasets, they have been plagued by performance issues. The I/O performance bottlenecks of Hadoop are partially responsible for the creation of Spark. Performance bottlenecks in Spark due to the JVM object model, garbage collection, interpreted/managed execution, and other abstraction layers are responsible for the creation of additional optimization layers, such as Project Tungsten. Indeed, the Project Tungsten issue tracker states that the "majority of Spark workloads are not bottlenecked by I/O or network, but rather CPU and memory". In this work, we address the CPU and memory performance bottlenecks that exist in Apache Spark by accelerating user-written computational kernels using accelerators. We refer to our approach as Spark With Accelerated Tasks (SWAT). SWAT is an accelerated data analytics (ADA) framework that enables programmers to natively execute Spark applications on high performance hardware platforms with co-processors, while continuing to write their applications in a JVM-based language like Java or Scala. Runtime code generation creates OpenCL kernels from JVM bytecode, which are then executed on OpenCL accelerators. In our work we emphasize 1) full compatibility with a modern, existing, and accepted data analytics platform, 2) an asynchronous, event-driven, and resource-aware runtime, 3) multi-GPU memory management and caching, and 4) ease-of-use and programmability. Our performance evaluation demonstrates up to 3.24x overall application speedup relative to Spark across six machine learning benchmarks, with a detailed investigation of these performance improvements.


title: An Intra-Panel Interface With Clock-Embedded Differential Signaling for TFT-LCD Systems

abstract: In this paper, an intra-panel interface with a clock embedded differential signaling for TFT-LCD systems is proposed. The proposed interface reduces the number of signal lines between the timing controller and the column drivers in a TFT-LCD panel by adopting the embedded clock scheme. The protocol of the proposed interface provides a delay-locked loop (DLL)-based clock recovery scheme for the receiver. The timing controller and the column driver integrated with the proposed interface are fabricated in 0.13- μm CMOS process technology and 0.18-μm high voltage CMOS process technology, respectively. The proposed interface is verified on a 47-inch Full High-Definition (FHD) (1920RGBt1080) TFT-LCD panel with 8-bit RGB and 120-Hz driving technology. The maximum data rate per differential pair was measured to be as high as 2.0 Gb/s in a wafer test.


title: DILoS: A dynamic integrated load manager and scheduler for continuous queries

abstract: In a Data Stream Management System (DSMS), continuous queries (CQs) registered by different applications inherently have different levels of importance (i.e., quality of service (QoS) and quality of data (QoD) requirements). Moreover, the shift to cloud services and the growing need for monitoring applications will inevitably lead to the establishment of data stream management cloud services. In such a multi-tenant environment, it is expected that the different applications/users will demand different QoS and QoD requirements for their CQs. In this work, we are proposing an approach that exploits the synergy between scheduling and load shedding to effectively handle different ranks of CQ classes, each associated with different QoS and QoD requirements. The proposed approach was implemented and evaluated within AQSIOS, our DSMS prototype.


title: Joint resource allocation and caching placement for network slicing in fog radio access networks

abstract: To satisfy diverse use cases and business models in fifth generation (5G) wireless communication, network slicing in fog radio access networks (F-RANs) is proposed, which provides a cost efficient networking in a convenient way. However, the resource management for network slices is challenging, especially when the edge caching is utilized to alleviate the fronthaul burden and reduce the delay. In this paper, we investigate the joint sub-carrier allocation and caching placement for two network slices, and an average delay optimization problem for one slice with user data rate guarantee for the other slice is formulated, which can be solved by a two-step iterative algorithm. The core idea is to optimize decoupled variables iteratively via Hungarian method, linear integer programming, and geometric programming(GP) with the help of decomposition. Simulation results reveal a fast convergence speed and a near-optimal performance of the proposed algorithm.


title: An Overview of NLIDB Approaches and Implementation for Airline Reservation System

abstract: Relational databases are queried using database query languages such as SQL. Natural language interfaces to databases (NLIDB) are systems that translate a natural language sentence into a database query. In this modern techno-crazy world, as more and more laymen access various systems and applications through their smart phones and tablets, the need for Natural Language Interfaces (NLIs) has increased manifold. The challenges in Natural language Query processing are interpreting the sentence correctly, removal of various ambiguity and mapping to the appropriate context. Natural language access problem is actually composed of two stages Linguistic processing and Database processing. NLIDB techniques encompass a wide variety of approaches. The approaches include traditional methods such as Pattern Matching, Syntactic Parsing and Semantic Grammar to modern systems such as Intermediate Query Generation, Machine Learning and Ontologies. In this report, various approaches to build NLIDB systems have been analyzed and compared along with their advantages, disadvantages and application areas. Also, a natural language interface to a flight reservation system has been implemented comprising of flight and booking inquiry systems. General Terms Natural Language Processing (NLP), Natural Language Interface to Databases (NLIDB)


title: Learning A Task-Specific Deep Architecture For Clustering

abstract: While sparse coding-based clustering methods have shown to be successful, their bottlenecks in both efficiency and scalability limit the practical usage. In recent years, deep learning has been proved to be a highly effective, efficient and scalable feature learning tool. In this paper, we propose to emulate the sparse coding-based clustering pipeline in the context of deep learning, leading to a carefully crafted deep model benefiting from both. A feed-forward network structure, named TAGnet, is constructed based on a graph-regularized sparse coding algorithm. It is then trained with task-specific loss functions from end to end. We discover that connecting deep learning to sparse coding benefits not only the model performance, but also its initialization and interpretation. Moreover, by introducing auxiliary clustering tasks to the intermediate feature hierarchy, we formulate DTAGnet and obtain a further performance boost. Extensive experiments demonstrate that the proposed model gains remarkable margins over several state-of-the-art methods.


title: Predicting long-term growth in students' mathematics achievement: the unique contributions of motivation and cognitive strategies.

abstract: This research examined how motivation (perceived control, intrinsic motivation, and extrinsic motivation), cognitive learning strategies (deep and surface strategies), and intelligence jointly predict long-term growth in students' mathematics achievement over 5 years. Using longitudinal data from six annual waves (Grades 5 through 10; Mage = 11.7 years at baseline; N = 3,530), latent growth curve modeling was employed to analyze growth in achievement. Results showed that the initial level of achievement was strongly related to intelligence, with motivation and cognitive strategies explaining additional variance. In contrast, intelligence had no relation with the growth of achievement over years, whereas motivation and learning strategies were predictors of growth. These findings highlight the importance of motivation and learning strategies in facilitating adolescents' development of mathematical competencies.


title: Trajectory Generation based on Rational Bezier Curves as Clothoids

abstract: This paper explains a fast overtaking algorithm for IVHS based on clothoidal trajectories. In spite of the fact that clothoidal trajectory generation requires complicated computation of Fresnel integrals, in this paper, an on-line clothoidal path is obtained just by scaling a general parametric curve. This is based on a general offline approximation of the Fresnel integrals into rational Bezier curves (RBC) which are later used in the generation of on-line clothoidal paths. In addition, in order to obtain a fast algorithm, a new methodology to approximate the Fresnel integrals into RBC is proposed. This approach guarantees that the resulting curve has the same behavior than the clothoid. In particular for overtaking maneuvers, a clothoidal path composed of two equal elementary paths is generated. Each one constructed joining two piecewise identical clothoids. A 3D simulation environment has been used for testing the trajectory generation algorithm described in the paper. As can be seen, the obstacle avoidance module generates free-collision intermediate positions which are reached by clothoidal paths.


title: Visualizations for an Explainable Planning Agent

abstract: In this paper, we report on the visualization capabilities of an Explainable AI Planning (XAIP) agent that can support human in the loop decision making. Imposing transparency and explainability requirements on such agents is especially important in order to establish trust and common ground with the end-to-end automated planning system. Visualizing the agent's internal decision-making processes is a crucial step towards achieving this. This may include externalizing the "brain" of the agent -- starting from its sensory inputs, to progressively higher order decisions made by it in order to drive its planning components. We also show how the planner can bootstrap on the latest techniques in explainable planning to cast plan visualization as a plan explanation problem, and thus provide concise model-based visualization of its plans. We demonstrate these functionalities in the context of the automated planning components of a smart assistant in an instrumented meeting space.


title: Harmonisation of knowledge management - comparing 160 KM frameworks around the globe

abstract: Purpose – The purpose of this paper is to look at how knowledge management (KM) has entered into a new phase where consolidation and harmonisation of concepts is required. Some first standards have been published in Europe and Australia in order to foster a common understanding of terms and concepts. The aim of this study was to analyse KM frameworks from research and practice regarding their model elements and try to discover differences and correspondences.Design/methodology/approach – A total of 160 KM frameworks from science, practice, associations and standardization bodies have been collected worldwide. These frameworks have been analysed regarding the use and understanding of the term knowledge, the terms used to describe the knowledge process activities and the factors influencing the success of knowledge management. Quantitative and qualitative content analysis methods have been applied.Findings – The result shows that despite the wide range of terms used in the KM frameworks an underlying consen...


title: Time series clustering and classification by the autoregressive metric

abstract: The statistical properties of the autoregressive (AR) distance between ARIMA processes are investigated. In particular, the asymptotic distribution of the squared AR distance and an approximation which is computationally efficient are derived. Moreover, the problem of time series clustering and classification is discussed and the performance of the AR distance is illustrated by means of some empirical applications.


title: Fisheye lens calibration using virtual grid.

abstract: We present herein a technique to calibrate fisheye lenses using cross diffractive optical elements. The setup generated a robust and accurate virtual calibration grid, and the calibration was performed by rotating the camera around two axes. We propose a comparison of three fisheye mathematical models and an evaluation of the number of images in the calibration process. The comparison of our experimental data according to the 3D calibration object results showed that our technique is efficient and reliable.


title: Understanding ride-on-demand service: Demand and dynamic pricing

abstract: Emerging ride-on-demand services (e.g., Uber or Uber-like) are vying to penetrate into the market of traditional taxi service, and they are ubiquitous in the nature, by using smart mobile devices like on-car GPS and mobile phone. These ubiquitous services are also beneficial for the environment by increasing the utilization of cars and improving travel efficiency. Through collaboration with a leading service provider in China, we are able to collect vast amount of accurate data and analyze the nature of the demand and dynamic pricing mechanisms that match the supply with demand. We consider the analysis as an important step towards making the ubiquitous service more efficient and beneficial to the sustainability of future smart cities. We collect datasets of passengers' orders and payment information, and focus on the analysis of demand and dynamic pricing. In demand analysis, we discuss its general characteristics, passenger grouping and demand clustering; in dynamic pricing analysis, we discuss the pattern and determination of dynamic pricing multipliers. Our findings pave the way for future study on system optimization, dynamic pricing and policy considerations.


CFD Analysis of the Blood Flow in Left Coronary Bifurcation with Variable Angulation


title: Toward Multimodal Image-to-Image Translation

abstract: Many image-to-image translation problems are ambiguous, as a single input image may correspond to multiple possible outputs. In this work, we aim to model a \emph{distribution} of possible outputs in a conditional generative modeling setting. The ambiguity of the mapping is distilled in a low-dimensional latent vector, which can be randomly sampled at test time. A generator learns to map the given input, combined with this latent code, to the output. We explicitly encourage the connection between output and the latent code to be invertible. This helps prevent a many-to-one mapping from the latent code to the output during training, also known as the problem of mode collapse, and produces more diverse results. We explore several variants of this approach by employing different training objectives, network architectures, and methods of injecting the latent code. Our proposed method encourages bijective consistency between the latent encoding and output modes. We present a systematic comparison of our method and other variants on both perceptual realism and diversity.


title: Adaptive Execution of Compiled Queries

abstract: Compiling queries to machine code is a very efficient way for executing queries. One often overlooked problem with compilation is the time it takes to generate machine code. Even with fast compilation frameworks like LLVM, generating machine code for complex queries often takes hundreds of milliseconds. Such durations can be a major disadvantage for workloads that execute many complex, but quick queries. To solve this problem, we propose an adaptive execution framework, which dynamically switches from interpretation to compilation. We also propose a fast bytecode interpreter for LLVM, which can execute queries without costly translation to machine code and dramatically reduces the query latency. Adaptive execution is fine-grained, and can execute code paths of the same query using different execution modes. Our evaluation shows that this approach achieves optimal performance in a wide variety of settings—low latency for small data sets and maximum throughput for large data sizes.


Stock Prediction Using Twitter Sentiment Analysis


title: Effectiveness of tizanidine in neuropathic pain: an open-label study.

abstract: The purpose of this research trial is to assess the effectiveness and tolerability of tizanidine in neuropathic pain. In an open-label study, patients with neuropathic pain received 1 to 4 mg of tizanidine once daily for 7 days, followed by weekly dose escalation of 2 to 8 mg to his/her effective or maximum tolerated dose or a maximum of 36 mg over an 8-week period. Treatment effects were assessed, using average weekly pain scores as well as biweekly scores for patient global assessment of pain relief, the neuropathic pain scale, and wisconsin brief pain inventory. Frequency and severity of adverse events were examined also. Twenty-three patients were enrolled. The mean average weekly pain score at baseline was 6.9, which decreased by 1.7 points at the end of week 8 to 5.2 (p < or =.01). A total of 15 patients (68%) reported that their pain relief was improved or much improved with tizanidine therapy, and 2 of these patients became completely pain-free. The following neuropathic pain qualities were significantly lower at week 8 compared with baseline: intense, sharp, hot, dull, cold, sensitive, unpleasant, and deep pain. There was a significant decline in pain quantity and interference of pain on quality of life from baseline to week 8. The mean effective or maximum tolerated dose was 23 mg/day (range 6 to 36 mg/day). Side effects consisted primarily of dizziness/lightheadedness (52%), drowsiness (48%), fatigue/weakness (43%), dry mouth (39%), gastrointestinal upset (30%), and sleep difficulty (22%). One patient developed significant elevation in liver function tests (LFTS) With symptoms at week 4. Tizanidine therapy was discontinued. LFTS returned to normal in 3 weeks. Tizanidine might be an effective treatment for neuropathic pain, offering an alternative for patients poorly responsive to other medications. A larger, randomized placebo-controlled trial is recommended. In addition, comparative studies with alternative agents should be sought.


title: Electronic sensing technologies for autonomous ground vehicles: A review

abstract: Autonomous ground vehicles have evolved a lot in the past decade. Several prototypes have been presented and tested in different conditions. A major challenge for such vehicles is the fact that they depend on advanced sensing technologies to perceive the environment and make real-time decisions. The complexity of this task typically requires multiple sensing devices, which is both a technical challenge as well as a limiting factor for autonomous vehicles evolution to mass-production. In this paper we review the current main sensing technologies for autonomous ground vehicles, their suitability and maturity status, as well as the way they have been used in prototype cars. This analysis gives a perspective of their evolution so far, as well as an indication of possible future trends.


title: A robust slip estimation method for skid-steered mobile robots

abstract: This paper presents a robust slip estimation method for skid-steered mobile robots when they traverse over rough terrain. An optical flow-based visual sensor looking down the terrain surface is employed to recover motion of a mobile robot by tracking features selected from the terrain surface. The motion states of the mobile robot are initially estimated by the visual sensor, however, the estimates are prone to noise and uncertainty which degrades the accuracy and robustness of estimation. To cope with the noise and uncertainty from the visual sensor, a sliding mode observer (SMO) based on the kinematics model of the skid-steered mobile robot is delicately designed to simultaneously estimate slip parameters. The SMO scheme can give more accurate estimates than the extended Kalman filter (EKF) when the slip of the mobile robot has significant changes at abrupt steering. The complete slip estimation method is independent of terrain parameters and robust in the presence of noise and uncertainty. Experimental results show that the method has confident potential for slip estimation of skid-steered mobile robots.


title: Learning to detect, localize and recognize many text objects in document images from few examples

abstract: The current trend in object detection and localization is to learn predictions with high capacity deep neural networks trained on a very large amount of annotated data and using a high amount of processing power. In this work, we particularly target the detection of text in document images and we propose a new neural model which directly predicts object coordinates. The particularity of our contribution lies in the local computations of predictions with a new form of local parameter sharing which keeps the overall amount of trainable parameters low. Key components of the model are spatial 2D-LSTM recurrent layers which convey contextual information between the regions of the image. We show that this model is more powerful than the state of the art in applications where training data are not as abundant as in the classical configuration of natural images and Imagenet/Pascal-VOC tasks. The proposed model also facilitates the detection of many objects in a single image and can deal with inputs of variable sizes without resizing. To enhance the localization precision of the coordinate regressor, we limit the amount of information produced by the local model components and propose two different regression strategies: (i) separately predict lower-left and upper-right corners of each object bounding box, followed by combinatorial pairing; (ii) only predict the left side of the objects and estimate the right position jointly with text recognition. These strategies lead to good full-page text recognition results in heterogeneous documents. Experiments have been performed on a document analysis task, the localization of the text lines in the Maurdor dataset.


title: OpenLitterMap.com – Open Data on Plastic Pollution with Blockchain Rewards (Littercoin)

abstract: OpenLitterMap rewards users with Littercoin for producing open data on litter. Open data on the geospatial characteristics of litter provide means of invoking and evaluating responses to plastic pollution. OpenLitterMap currently works as a web app on all devices with native mobile apps in development. The stack includes the integration of the Laravel PHP Framework on the backend; Vue for frontend reactivity; NativeScript-Vue for mobile apps; Bulma for CSS; Leaflet for web-mapping; Turf.js for geospatial analysis; the Ethereum Blockchain for tokenization; Stripe; ChartJS; AWS; and more. Anywhere from a single cigarette butt to the contents of an entire beach or street clean can be logged in a single geotagged photo. Alternatively, a simple index may be used if litter is incalculable. The open data includes an increasing 100+ pre-defined types of litter; 20+ corporate brands; verification status; coordinates; timestamp; phone model; the latest OpenStreetMap address at each location; and the litter presence as a Boolean. To date, 100% of all submitted data (~ 8200 photos, ~ 28,000 litter from over 150 contributors) has been manually verified which is being used to develop machine learning algorithms.


title: Who to Ask: An Intelligent Fashion Consultant

abstract: Humankind has always been in pursuit of fashion. Nevertheless, people are often troubled by collocating clothes, e.g., tops, bottoms, shoes, and accessories, from numerous fashion items in their closets. Moreover, it may be expensive and inconvenient to employ a fashion stylist. In this paper, we present Stile, an end-to-end intelligent fashion consultant system, to generate stylish outfits for given items. Unlike previous systems, our framework considers the global compatibility of fashion items in the outfit and models the dependencies among items in a fixed order via a bidirectional LSTM. Therefore, it can guarantee that items in the same outfit should share a similar style and neither redundant nor missing items exist in the resulting outfit for essential categories. The demonstration shows that our proposed system provides people with a practical and convenient solution to find natural and proper fashion outfits.


title: Discovering Signals from Web Sources to Predict Cyber Attacks

abstract: Cyber attacks are growing in frequency and severity. Over the past year alone we have witnessed massive data breaches that stole personal information of millions of people and wide-scale ransomware attacks that paralyzed critical infrastructure of several countries. Combating the rising cyber threat calls for a multi-pronged strategy, which includes predicting when these attacks will occur. The intuition driving our approach is this: during the planning and preparation stages, hackers leave digital traces of their activities on both the surface web and dark web in the form of discussions on platforms like hacker forums, social media, blogs and the like. These data provide predictive signals that allow anticipating cyber attacks. In this paper, we describe machine learning techniques based on deep neural networks and autoregressive time series models that leverage external signals from publicly available Web sources to forecast cyber attacks. Performance of our framework across ground truth data over real-world forecasting tasks shows that our methods yield a significant lift or increase of F1 for the top signals on predicted cyber attacks. Our results suggest that, when deployed, our system will be able to provide an effective line of defense against various types of targeted cyber attacks.


The INTERSPEECH 2017 computational paralinguistics challenge: Addressee, cold & snoring


title: A W-band InGaAs PIN-MMIC digital phase-shifter using a switched transmission-line structure

abstract: This paper describes the design and fabrication of a W-band InGaAs PIN-diode MMIC 4-bit phase shifter based on a switched delay line. In order to achieve low insertion loss and good phase shifting characteristics at W-band, the topology based on a switched delay-line is employed using a thin-film microstrip line structure. The fabricated phase shifter has demonstrated good performances such as an insertion loss less than 12.7 dB at a frequency range of 81 to 85 GHz with an intrinsic chip size of 1.93 × 0.80 mm2. To our knowledge, this is the first InGaAs PIN MMIC digital phase shifter demonstrated up to W-band.


title: HyTER: Meaning-Equivalent Semantics for Translation Evaluation

abstract: It is common knowledge that translation is an ambiguous, 1-to-n mapping process, but to date, our community has produced no empirical estimates of this ambiguity. We have developed an annotation tool that enables us to create representations that compactly encode an exponential number of correct translations for a sentence. Our findings show that naturally occurring sentences have billions of translations. Having access to such large sets of meaning-equivalent translations enables us to develop a new metric, HyTER, for translation accuracy. We show that our metric provides better estimates of machine and human translation accuracy than alternative evaluation metrics.


title: Evaluation and Efficiency Comparison of Front End AC-DC Plug-in Hybrid Charger Topologies

abstract: As a key component of a plug-in hybrid electric vehicle (PHEV) charger system, the front-end ac-dc converter must achieve high efficiency and power density. This paper presents a topology survey evaluating topologies for use in front end ac-dc converters for PHEV battery chargers. The topology survey is focused on several boost power factor corrected converters, which offer high efficiency, high power factor, high density, and low cost. Experimental results are presented and interpreted for five prototype converters, converting universal ac input voltage to 400 V dc. The results demonstrate that the phase shifted semi-bridgeless PFC boost converter is ideally suited for automotive level I residential charging applications in North America, where the typical supply is limited to 120 V and 1.44 kVA or 1.92 kVA. For automotive level II residential charging applications in North America and Europe the bridgeless interleaved PFC boost converter is an ideal topology candidate for typical supplies of 240 V, with power levels of 3.3 kW, 5 kW, and 6.6 kW.


title: Who Can Catch a Liar ?

abstract: The ability to detect lying was evaluated in 509 people including law-enforcement personnel, such as members of the US. Secret Service, Central Intelligence Agency, Federal Bureau of Investigation, National Security Agency, Drug Enforcement Agency, California police and judges, as well as psychiatrists, college students, and working adults. A videotape showed 10 people who were either lying or telling the truth in describing their feelings. Only the Secret Service performed better than chance, and they were significantly more accurate than all of the other groups. When occupational group was disregarded, it was found that those who were accurate apparently used different behavioral clues and had different skills than those who were inaccurate.


title: Sensorless assistive torque design for a lower extremity exoskeleton

abstract: For improving mobility of the elderly, it is desirable to use a lower limb exoskeleton to enhance strength and endurance for their daily life activities. This paper presents a novel design of assist torque generation and compliant motion of a knee joint for an exoskeleton robot. Based on a sensorless approach, a method is suggested to estimate the external torque and generate the assistance torque of the attached DC motor. The developed lower limb exoskeleton joint provides assistive torque in order to comply with motion of the user. In comparison with existing lower limb exoskeletons, the proposed exoskeleton robot can execute various training programs by adjusting the assisting torque with less sensors and reduced hardware complexity. Experimental results on a knee exoskeleton validate the proposed method. Desirable assistive torque can be generated to be compliant with a swing motion.


title: Evolutionary Processes of Diversification in a Model Island Archipelago

abstract: Long celebrated for its spectacular landscapes and strikingly high levels of endemic biodiversity, the Philippines has been studied intensively by biogeographers for two centuries. Concentration of so many endemic land vertebrates into a small area and shared patterns of distribution in many unrelated forms has inspired a search for common mechanisms of production, partitioning, and maintenance of life in the archipelago. In this review, we (a) characterize an ongoing renaissance of species discovery, (b) discuss the changing way biogeographers conceive of the archipelago, (c) review the role molecular phylogenetic studies play in understanding the evolutionary history of Philippine vertebrates, and (d) describe how a 25-year Pleistocene island connectivity paradigm continues to provide some explanatory power, but has been augmented by increased understanding of the archipelago's geological history and ecological gradients. Finally, we (e) review new insights provided by studies of adaptive versus nonadap...


title: NIRMAL: Automatic identification of software relevant tweets leveraging language model

abstract: Twitter is one of the most widely used social media platforms today. It enables users to share and view short 140-character messages called “tweets”. About 284 million active users generate close to 500 million tweets per day. Such rapid generation of user generated content in large magnitudes results in the problem of information overload. Users who are interested in information related to a particular domain have limited means to filter out irrelevant tweets and tend to get lost in the huge amount of data they encounter. A recent study by Singer et al. found that software developers use Twitter to stay aware of industry trends, to learn from others, and to network with other developers. However, Singer et al. also reported that developers often find Twitter streams to contain too much noise which is a barrier to the adoption of Twitter. In this paper, to help developers cope with noise, we propose a novel approach named NIRMAL, which automatically identifies software relevant tweets from a collection or stream of tweets. Our approach is based on language modeling which learns a statistical model based on a training corpus (i.e., set of documents). We make use of a subset of posts from StackOverflow, a programming question and answer site, as a training corpus to learn a language model. A corpus of tweets was then used to test the effectiveness of the trained language model. The tweets were sorted based on the rank the model assigned to each of the individual tweets. The top 200 tweets were then manually analyzed to verify whether they are software related or not, and then an accuracy score was calculated. The results show that decent accuracy scores can be achieved by various variants of NIRMAL, which indicates that NIRMAL can effectively identify software related tweets from a huge corpus of tweets.


title: Design and Analysis of a Self-Oscillating Class D Audio Amplifier Employing a Hysteretic Comparator

abstract: A third-order self-oscillating class D audio amplifier that utilizes a hysteretic comparator is presented. The design is analyzed and its THD is theoretically determined by employing an equivalent model, that relates the approach to natural sampling pulse-width modulation. The architecture eliminates the requirement for a high-quality carrier generator. A low-cost hysteresis compensation technique is utilized to enhance distortion performance at high output power levels. An implementation is presented in a 0.7 μm CMOS process. It achieves a dynamic range (DR) of 116.5 dB, and a THD+N of 0.0012%, while delivering a power of 125 mW into an 8 Ω load at 1 kHz. The THD+N is under 0.006% up to 90% of the maximum output power. The amplifier can deliver 1.45 W into the load with a THD of 5% with a 5 V power supply. The efficiency is greater than 84% for output power larger than 1 W . The area of the amplifier is 6 mm2 .


title: Super-Resolution with Deep Adaptive Image Resampling

abstract: Deep learning based methods have recently pushed the state-of-the-art on the problem of Single Image Super-Resolution (SISR). In this work, we revisit the more traditional interpolation-based methods, that were popular before, now with the help of deep learning. In particular, we propose to use a Convolutional Neural Network (CNN) to estimate spatially variant interpolation kernels and apply the estimated kernels adaptively to each position in the image. The whole model is trained in an end-to-end manner. We explore two ways to improve the results for the case of large upscaling factors, and propose a recursive extension of our basic model. This achieves results that are on par with state-of-the-art methods. We visualize the estimated adaptive interpolation kernels to gain more insight on the effectiveness of the proposed method. We also extend the method to the task of joint image filtering and again achieve state-of-the-art performance.


title: Integrated stereo delta-sigma class D amplifier

abstract: A 2/spl times/40W (into 4/spl Omega/ with a 20V supply) integrated stereo /spl Delta//spl Sigma/ class D amplifier with 100dB SNR is realized in a 0.6 /spl mu/m CMOS process with DMOS transistors and buried Zener diodes. Feedback from power stage outputs gives 0.001% THD and 65dB PSRR. The modulator clock rate is 6MHz, but dynamically adjusted quantizer hysteresis reduces the output data rate to 450kHz, helping achieve 88% efficiency.


title: Learning Accurate, Compact, And Interpretable Tree Annotation

abstract: We present an automatic approach to tree annotation in which basic nonterminal symbols are alternately split and merged to maximize the likelihood of a training treebank. Starting with a simple X-bar grammar, we learn a new grammar whose nonterminals are subsymbols of the original nonterminals. In contrast with previous work, we are able to split various terminals to different degrees, as appropriate to the actual complexity in the data. Our grammars automatically learn the kinds of linguistic distinctions exhibited in previous work on manual tree annotation. On the other hand, our grammars are much more compact and substantially more accurate than previous work on automatic annotation. Despite its simplicity, our best grammar achieves an F1 of 90.2% on the Penn Treebank, higher than fully lexicalized systems.


title: On finding bicliques in bipartite graphs: a novel algorithm and its application to the integration of diverse biological data types

abstract: BackgroundIntegrating and analyzing heterogeneous genome-scale data is a huge algorithmic challenge for modern systems biology. Bipartite graphs can be useful for representing relationships across pairs of disparate data types, with the interpretation of these relationships accomplished through an enumeration of maximal bicliques. Most previously-known techniques are generally ill-suited to this foundational task, because they are relatively inefficient and without effective scaling. In this paper, a powerful new algorithm is described that produces all maximal bicliques in a bipartite graph. Unlike most previous approaches, the new method neither places undue restrictions on its input nor inflates the problem size. Efficiency is achieved through an innovative exploitation of bipartite graph structure, and through computational reductions that rapidly eliminate non-maximal candidates from the search space. An iterative selection of vertices for consideration based on non-decreasing common neighborhood sizes boosts efficiency and leads to more balanced recursion trees.ResultsThe new technique is implemented and compared to previously published approaches from graph theory and data mining. Formal time and space bounds are derived. Experiments are performed on both random graphs and graphs constructed from functional genomics data. It is shown that the new method substantially outperforms the best previous alternatives.ConclusionsThe new method is streamlined, efficient, and particularly well-suited to the study of huge and diverse biological data. A robust implementation has been incorporated into GeneWeaver, an online tool for integrating and analyzing functional genomics experiments, available at http://geneweaver.org. The enormous increase in scalability it provides empowers users to study complex and previously unassailable gene-set associations between genes and their biological functions in a hierarchical fashion and on a genome-wide scale. This practical computational resource is adaptable to almost any applications environment in which bipartite graphs can be used to model relationships between pairs of heterogeneous entities.


title: Induced Pluripotent Stem Cells for Regenerative Medicine

abstract: With the discovery of induced pluripotent stem (iPS) cells, it is now possible to convert differentiated somatic cells into multipotent stem cells that have the capacity to generate all cell types of adult tissues. Thus, there is a wide variety of applications for this technology, including regenerative medicine, in vitro disease modeling, and drug screening/discovery. Although biological and biochemical techniques have been well established for cell reprogramming, bioengineering technologies offer novel tools for the reprogramming, expansion, isolation, and differentiation of iPS cells. In this article, we review these bioengineering approaches for the derivation and manipulation of iPS cells and focus on their relevance to regenerative medicine.


title: Sigsoftmax: Reanalysis of the Softmax Bottleneck

abstract: Softmax is an output activation function for modeling categorical probability distributions in many applications of deep learning. However, a recent study revealed that softmax can be a bottleneck of representational capacity of neural networks in language modeling (the softmax bottleneck). In this paper, we propose an output activation function for breaking the softmax bottleneck without additional parameters. We re-analyze the softmax bottleneck from the perspective of the output set of log-softmax and identify the cause of the softmax bottleneck. On the basis of this analysis, we propose sigsoftmax, which is composed of a multiplication of an exponential function and sigmoid function. Sigsoftmax can break the softmax bottleneck. The experiments on language modeling demonstrate that sigsoftmax and mixture of sigsoftmax outperform softmax and mixture of softmax, respectively.


title: Enhancing Road Maps by Parsing Aerial Images Around the World

abstract: In recent years, contextual models that exploit maps have been shown to be very effective for many recognition and localization tasks. In this paper we propose to exploit aerial images in order to enhance freely available world maps. Towards this goal, we make use of OpenStreetMap and formulate the problem as the one of inference in a Markov random field parameterized in terms of the location of the road-segment centerlines as well as their width. This parameterization enables very efficient inference and returns only topologically correct roads. In particular, we can segment all OSM roads in the whole world in a single day using a small cluster of 10 computers. Importantly, our approach generalizes very well, it can be trained using only 1.5 km2 aerial imagery and produce very accurate results in any location across the globe. We demonstrate the effectiveness of our approach outperforming the state-of-the-art in two new benchmarks that we collect. We then show how our enhanced maps are beneficial for semantic segmentation of ground images.


Autonomous driving in urban environments: Boss and the urban challenge


title: Perceptual Adversarial Networks for Image-to-Image Transformation

abstract: In this paper, we propose a principled Perceptual Adversarial Networks (PAN) for image-to-image transformation tasks. Unlike existing application-specific algorithms, PAN provides a generic framework of learning mapping relationship between paired images (Fig. 1), such as mapping a rainy image to its de-rained counterpart, object edges to its photo, semantic labels to a scenes image, etc. The proposed PAN consists of two feed-forward convolutional neural networks (CNNs), the image transformation network T and the discriminative network D. Through combining the generative adversarial loss and the proposed perceptual adversarial loss, these two networks can be trained alternately to solve image-to-image transformation tasks. Among them, the hidden layers and output of the discriminative network D are upgraded to continually and automatically discover the discrepancy between the transformed image and the corresponding ground-truth. Simultaneously, the image transformation network T is trained to minimize the discrepancy explored by the discriminative network D. Through the adversarial training process, the image transformation network T will continually narrow the gap between transformed images and ground-truth images. Experiments evaluated on several image-to-image transformation tasks (e.g., image de-raining, image inpainting, etc.) show that the proposed PAN outperforms many related state-of-the-art methods.


title: Racial/Ethnic Socialization Mediates Perceived Racism and the Racial Identity of African American Adolescents

abstract: Racism and racial/ethnic socialization are proposed as interactive risk and resilience factors that promote the development of multiple dimensions of racial identity among African American youth are the focus of this study. One-hundred and eight African American students responded to questions about their racial identity and socialization. Controlling for demographic characteristics of ecological support (from family, friends, and neighbors), neighborhood racial composition, and gender, findings revealed that racism exposure significantly explained the variance of several racial identity components and ideologies but that racial/ethnic socialization mediated that influence. We discuss the implications for future study of racism experience on the racial identity development of African American youth.


title: Principles & practice of marketing

abstract: PART 1 - Concepts and Contexts Chapter 1 - Marketing: Managing the Exchange Process Chapter 2 - The Marketing Environment PART 2 - Markets and People Chapter 3 - Consumer Behaviour Chapter 4 - Business-to-Business Marketing Chapter 5 - Marketing Research and Information Systems Chapter 6 - Segmentation, Targeting and Positioning Chapter 7 - Integrated Marketing Communications Chapter 8 - International Marketing PART 3 - Strategy and Stakeholders Chapter 9 - Marketing Strategy, Planning and Creating Competitive Advantage Chapter 10 - Marketing Ethics and Corporate Social Responsibility Chapter 11 - Building Customer Relationships Chapter 12 - Product Portfolio and Strategic Branding PART 4 - Marketing in Practice Chapter 13 - New Product Innovation and Development Chapter 14 - Pricing and Strategic Decision Making Chapter 15 - Advertising across Different Media Chapter 16 - Public Relations and Sponsorship Chapter 17 - Promotion and Sales PART 5 - Marketing in Motion Chapter 18 - Digital Marketing and Social Media hapter 19 - Managing Distribution and Supply Chains hapter 20 - Retail and Wholesale Marketing Chapter 21 - Services Marketing


title: Enhancing the performance of output-capacitorless LDO regulators by pass-transistor segmentation

abstract: This paper deals with a circuit proposal along with theoretical analysis to provide a solution for enhancing the stability and transient performance of external capacitorless low-dropout regulators (CL-LDOs) by segmenting the pass transistor to smaller sizes. The stability and transient analysis is carried out on the CL-LDO with two different size-segmented pass transistors in comparison with the conventional CL-LDO with single large size pass device. The analysis shows that the pass transistor segmentation leads to better stability, i.e., greater phase margin especially at no-load and light-load conditions, wider bandwidth, and improved transient behavior, i.e., lower settling time and output voltage deviations due to the load transients. The aforementioned topologies are modeled and validated in HSPICE using a 0.35 μm CMOS process, and the results are in conformity with the analytical statements.


title: Watching MOOCs together: investigating co-located MOOC study groups

abstract: Research suggests that massive open online course (MOOC) students prefer to study in groups, and that social facilitation within the study groups may render the learning of difficult concepts a pleasing experience. We report on a longitudinal study that investigates how co-located study groups watch and study MOOC videos together. The study was conducted with on-campus flipped classroom students. Our subjects reported an overall high satisfaction with the study group style. The research reveals that students like to stay synchronized in the group while watching MOOC videos. However, they have to find a balance between synchronization, video interaction, and the amount of conversation. Watching MOOCs on a shared display addresses the need of synchronicity, and the distribution of control can increase the video interactivities.


Problems of astrophysical turbulent convection:


title: An empirical study of speed and communication in globally distributed software development

abstract: Global software development is rapidly becoming the norm for technology companies. Previous qualitative research suggests that distributed development may increase development cycle time for individual work items (modification requests). We use both data from the source code change management system and survey data to model the extent of delay in a distributed software development organization and explore several possible mechanisms for this delay. One key finding is that distributed work items appear to take about two and one-half times as long to complete as similar items where all the work is colocated. The data strongly suggest a mechanism for the delay, i.e., that distributed work items involve more people than comparable same-site work items, and the number of people involved is strongly related to the calendar time to complete a work item. We replicate the analysis of change data in a different organization with a different product and different sites and confirm our main findings. We also report survey results showing differences between same-site and distributed social networks, testing several hypotheses about characteristics of distributed social networks that may be related to delay. We discuss implications of our findings for practices and collaboration technology that have the potential for dramatically speeding distributed software development.


title: Immersion and realism in video games - The confused moniker of video game engrossment

abstract: Video games have dramatically increased in realism over the past several years. With an increase in both graphical fidelity and gameplay realism, this increase has been used to further immersion in video games on many levels. Video games have been subject to the term “immersion” for many years now, used in both the context of entertainment and marketing. This paper explores the effects of realism on immersion in video games, as well as considering what immersion means to a video game and gameplay in general.


title: An analysis of crime hot spots using GPS tracking data of children and agent-based simulation modeling

abstract: Although the problem of a lack of an adequate denominator of crime rates has been widely recognized, relatively few solutions have been found. This paper proposes a sophisticated method of analyzing crime hot spots using global positioning system (GPS) tracking data and agent-based simulation modeling. The goal of the proposed analysis technique is to estimate a population at risk across time and space at the micro-level, such as streets and locations (e.g. parks and playgrounds). In order to create valid estimates of a population at risk and to illustrate how the estimated population at risk can be used in crime analysis, this paper presents a series of three sub-studies. First, GPS tracking data were obtained for two weeks for 80 children in the second and fifth grades from one school located in a suburb of Tokyo, Japan. GIS software was used to analyze the GPS data in order to identify streets and places that the children frequented. Second, agent-based simulation modeling was developed to extend the p...


title: Pairing an arbitrary regressor with an artificial neural network estimating aleatoric uncertainty

abstract: Abstract We suggest a general approach to quantification of different forms of aleatoric uncertainty in regression tasks performed by artificial neural networks. It is based on the simultaneous training of two neural networks with a joint loss function and a specific hyperparameter λ > 0 that allows for automatically detecting noisy and clean regions in the input space and controlling their relative contribution to the loss and its gradients. After the model has been trained, one of the networks performs predictions and the other quantifies the uncertainty of these predictions by estimating the locally averaged loss of the first one. Unlike in many classical uncertainty quantification methods, we do not assume any a priori knowledge of the ground truth probability distribution, neither do we, in general, maximize the likelihood of a chosen parametric family of distributions. We analyze the learning process and the influence of clean and noisy regions of the input space on the loss surface, depending on λ. In particular, we show that small values of λ increase the relative contribution of clean regions to the loss and its gradients. This explains why choosing small λ allows for better predictions compared with neural networks without uncertainty counterparts and those based on classical likelihood maximization. Finally, we demonstrate that one can naturally form ensembles of pairs of our networks and thus capture both aleatoric and epistemic uncertainty and avoid overfitting.


title: Machine learning-assisted virtual patching of web applications

abstract: Web applications are permanently being exposed to attacks that exploit their vulnerabilities. In this work we investigate the application of machine learning techniques to leverage Web Application Firewall (WAF), a technology that is used to detect and prevent attacks. We propose a combined approach of machine learning models, based on one-class classification and n-gram analysis, to enhance the detection and accuracy capabilities of MODSECURITY, an open source and widely used WAF. The results are promising and outperform MODSECURITY when configured with the OWASP Core Rule Set, the baseline configuration setting of a widely deployed, rule-based WAF technology. The proposed solution, combining both approaches, allow us to deploy a WAF when no training data for the application is available (using one-class classification), and an improved one using n-grams when training data is available.


title: Android vs Windows Mobile vs Java ME: a comparative study of mobile development environments

abstract: In this paper we look at three mobile development environments: Windows Mobile, Java ME and Android. Through platform comparison, the different environments are examined closely and strengths and weaknesses are brought to life. In all three environments example applications are written to compare the environments in action on respective devices. Environment specific deployment files are created to illustrate amount of boilerplate code and overhead during deployment. Different key areas such as implementation aspects, performance aspects and quality assurance are compared to give an in depth overview of the status of the different platforms. Our results show that although the three environments are similar in some aspects they still represent three distinctive fields each with their respective characteristics. Through our code examples and platform comparison we come to the conclusion that the Windows Mobile and Android platform provides a better development environment, whereas Java ME still struggles with poor emulator support and incompatible implementations. The main features they have in common are also seen in trends directly visible in the programming languages such as unit testing, language features and end user distribution strategies. Big differences are highlighted when inspecting community environments, hardware abilities and platform maturity. This will have large influence on the choice of development platform for creating novel assistive environment applications.


title: Towards reducing the attack surface of software backdoors

abstract: Backdoors in software systems probably exist since the very first access control mechanisms were implemented and they are a well-known security problem. Despite a wave of public discoveries of such backdoors over the last few years, this threat has only rarely been tackled so far. In this paper, we present an approach to reduce the attack surface for this kind of attacks and we strive for an automated identification and elimination of backdoors in binary applications. We limit our focus on the examination of server applications within a client-server model. At the core, we apply variations of the delta debugging technique and introduce several novel heuristics for the identification of those regions in binary application that backdoors are typically installed in (i.e., authentication and command processing functions). We demonstrate the practical feasibility of our approach on several real-world backdoors found in modified versions of the popular software tools ProFTPD and OpenSSH. Furthermore, we evaluate our implementation not only on common instruction set architectures such as x86-64, but also on commercial off-the-shelf embedded devices powered by a MIPS32 processor.


title: Higher body mass index is associated with plantar fasciopathy/'plantar fasciitis': systematic review and meta-analysis of various clinical and imaging risk factors.

abstract: QUESTION ::: What (risk) factors are associated with plantar fasciopathy (PF)? ::: ::: ::: DESIGN ::: Systematic review with meta-analyses. ::: ::: ::: PARTICIPANTS ::: Patients with PF. ::: ::: ::: FACTORS ::: All factors described in prospective, case-control or cross-sectional observational studies. ::: ::: ::: RESULTS ::: 51 included studies (1 prospective, 46 case-control and 4 cross-sectional studies) evaluated a total of 104 variables. Pooling was possible for 12 variables. Higher body mass index (BMI) (BMI>27, OR 3.7 (95% CI 2.93 to 5.62)) in patients with PF was the only significant clinical association, and its effect was the strongest in the non-athletic subgroup. In people with PF compared to controls, pooled imaging data demonstrated a significantly thicker, hypoechogenic plantar fascia with increased vascular signal and perifascial fluid collection. In addition, people with PF were more likely to have a thicker loaded and unloaded heel fat pat, and bone findings, including a subcalcaneal spur and increased Tc-99 uptake. No significant difference was found in the extension of the first metatarsophalangeal joint. ::: ::: ::: CONCLUSIONS ::: We found a consistent clinical association between higher BMI and plantar fasciopathy. This association may differ between athletic and non-athletic subgroups. While consistent evidence supports a range of bone and soft tissue abnormalities, there is lack of evidence for the dogma of clinical and mechanical measures of foot and ankle function. Clinicians can use this information in shared decision-making.


title: Volleyball Premier League Algorithm

abstract: Abstract This article proposes a novel metaheuristic algorithm called Volleyball Premier League (VPL) inspired by the competition and interaction among volleyball teams during a season. It also mimics the coaching process during a volleyball match. To solve global optimization problems using the volleyball metaphor, there are terms such as substitution, coaching, and learning, which are captured in the VPL algorithm. The proposed algorithm is benchmarked on 23 well-known test functions, which are categorized into three groups, namely unimodal, multimodal and fixed-dimension multimodal functions. The solutions obtained using the VPL have been compared with other metaheuristic algorithms including Particle Swarm Optimization (PSO), Differential Evolution (DE), Genetic Algorithm (GA), Artificial Bee Colony (ABC), Firefly Algorithm (FA), Harmony Search (HS), Sin Cosine Algorithm (SCA), Soccer League Competition (SLC), and League Championship Algorithm (LCA). In addition, VPL has been used to solve three classical engineering design optimization problems. Results show that VPL algorithm possesses a strong capability to produce superior performance over the other well-known metaheuristic algorithms. The results of the experiments also show that the VPL is effectively applicable to solve problems with complex search space.


A Hybrid Model of Wavelet and Neural Network for Short Term Load Forecasting


title: Priority Mechanism of Bitcoin and Its Effect on Transaction-Confirmation Process.

abstract: In Bitcoin system, transactions are prioritized according to attributes such as the remittance amount and transaction fees, and transactions with low priority are likely to wait for confirmation. Because the demand of micro payment in Bitcoin is expected to increase due to low remittance cost, it is important to quantitatively investigate how the priority mechanism of Bitcoin affects the transaction-confirmation time. In this paper, we analyze the transaction-confirmation time by queueing theory. We model the transaction priority mechanism of Bitcoin as a priority queueing system with batch service, deriving the mean transaction-confirmation time. Numerical examples show how the demand of transactions of low remittance amount affects the transaction-confirmation time. We also consider the effect of the maximum block size on the transaction-confirmation time.


title: Charge-Trapping Phenomena in HfO2-Based FeFET-Type Nonvolatile Memories

abstract: Ferroelectric field effect transistors (FeFETs) based on ferroelectric hafnium oxide (HfO2) thin films show high potential for future embedded nonvolatile memory applications. However, HfO2 films besides their recently discovered ferroelectric behavior are also prone to undesired charge trapping effects. Therefore, the scope of this paper is to verify the possibility of the charge trapping during standard operation of the HfO2-based FeFET memories. The kinetics of the charge trapping and its interplay with the ferroelectric polarization switching are analyzed in detail using the single-pulse $I_{D}$ – $V_{G}$ technique. Furthermore, the impact of the charge trapping on the important memory characteristics such as retention and endurance is investigated.


title: SERENDIPITY AND INFORMATION SEEKING : AN EMPIRICAL STUDY

abstract: “Serendipity” has both a classical origin in literature and a more modern manifestation where it is found in the descriptions of the problem solving and knowledge acquisition of humanities and science scholars. Studies of information retrieval and information seeking have also discussed the utility of the notion of serendipity. Some have implied that it may be stimulated, or that certain people may “encounter” serendipitous information more than others. All to some extent accept the classical definition of serendipity as a “fortuitous” accident. The analysis presented here is part of a larger study concerning the information‐seeking behaviour of interdisciplinary scholars. This paper considers the nature of serendipity in information‐seeking contexts, and reinterprets the notion of serendipity as a phenomenon arising from both conditions and strategies – as both a purposive and a non‐purposive component of information seeking and related knowledge acquisition.


title: Joint Embedding Learning and Sparse Regression: A Framework for Unsupervised Feature Selection

abstract: Feature selection has aroused considerable research interests during the last few decades. Traditional learning-based feature selection methods separate embedding learning and feature ranking. In this paper, we propose a novel unsupervised feature selection framework, termed as the joint embedding learning and sparse regression (JELSR), in which the embedding learning and sparse regression are jointly performed. Specifically, the proposed JELSR joins embedding learning with sparse regression to perform feature selection. To show the effectiveness of the proposed framework, we also provide a method using the weight via local linear approximation and adding the $\ell_{2,1}$ -norm regularization, and design an effective algorithm to solve the corresponding optimization problem. Furthermore, we also conduct some insightful discussion on the proposed feature selection approach, including the convergence analysis, computational complexity, and parameter determination. In all, the proposed framework not only provides a new perspective to view traditional methods but also evokes some other deep researches for feature selection. Compared with traditional unsupervised feature selection methods, our approach could integrate the merits of embedding learning and sparse regression. Promising experimental results on different kinds of data sets, including image, voice data and biological data, have validated the effectiveness of our proposed algorithm.


title: RoboCup@Home: Demonstrating Everyday Manipulation Skills in RoboCup@Home

abstract: As benchmarking robotics research is inherently difficult, robot competitions are increasingly popular. They bring together researchers, students, and enthusiasts who are in the pursuit of a technological challenge. Prominent examples for such competitions include the Defense Advanced Research Projects Agency's (DARPA's) Grand and Urban Challenges [1], the International Aerial Robotics Competition (IARC) [2], the European Land-Robot Trial (ELROB) [3], and, last but not least, RoboCup [4], [5].


title: A 6.05-Mb/mm2 16-nm FinFET double pumping 1W1R 2-port SRAM with 313 ps read access time

abstract: High-density and low-leakage 1W1R 2-port (2P) SRAM is realized by 6T 1-port SRAM bitcell with double pumping internal clock in 16 nm FinFET technology. Proposed clock generator with address latch circuit enables robust timing design without sever setup/hold margin. We designed a 256 kb 1W1R 2P SRAM macro which achieves the highest density of 6.05 Mb/mm2. Measured data shows that a 313 ps of read-access-time is observed at 0.8 V. Standby leakage power in resume standby (RS) mode is reduced by 79% compared to the conventional dual-port SRAM without RS.


title: A Double Joint Bayesian Approach for J-Vector Based Text-dependent Speaker Verification

abstract: J-vector has been proved to be very effective in text-dependent speaker verification with short-duration speech. However, the current state-of-the-art back-end classifiers, e.g. joint Bayesian model, cannot make full use of such deep features. In this paper, we generalize the standard joint Bayesian approach to model the multi-faceted information in the j-vector explicitly and jointly. In our generalization, the j-vector was modeled as a result derived by a generative Double Joint Bayesian (DoJoBa) model, which contains several kinds of latent variables. With DoJoBa, we are able to explicitly build a model that can combine multiple heterogeneous information from the j-vectors. In verification step, we calculated the likelihood to describe whether the two j-vectors having consistent labels or not. On the public RSR2015 data corpus, the experimental results showed that our approach can achieve 0.02\% EER and 0.02\% EER for impostor wrong and impostor correct cases respectively.


title: Exploring the role of the reader in the activity of blogging

abstract: Within the last decade, blogs have become an important element of popular culture, mass media, and the daily lives of countless Internet users. Despite the medium's interactive nature, most research on blogs focuses on either the blog itself or the blogger, rarely if at all focusing on the reader's impact. In order to gain a better understanding of the social practice of blogging, we must take into account the role, contributions, and significance of the reader. This paper presents the findings of a qualitative study of blog readers, including common blog reading practices, some of the dimensions along which reading practices vary, relationships between identity presentation and perception, the interpretation of temporality, and the ways in which readers feel that they are a part of the blogs they read. It also describes similarities to, and discrepancies with, previous work, and suggests a number of directions and implications for future work on blogging.


title: Beamforming with small diameter microphone array

abstract: This article deals with beamforming in conjunction with 34mm diameter 7-element circular microphone array using MEMS microphones. The main possibilities provided by beamforming are reviewed with emphasis on speech sensing. Problems that are associated with the array's small physical size, requirements on microphones themselves and data acquisition are discussed. A general delay-and-sum and a chosen filter-and-sum beamformer are described. Performance of beamformers combined with purpose built microphone array is evaluated by means of measurements.


title: Deep Multiscale Model Learning

abstract: Abstract The objective of this paper is to design novel multi-layer neural networks for multiscale simulations of flows taking into account the observed fine data and physical modeling concepts. Our approaches use deep learning techniques combined with local multiscale model reduction methodologies to predict flow dynamics. Using reduced-order model concepts is important for constructing robust deep learning architectures since the reduced-order models provide fewer degrees of freedom. We consider flow dynamics in porous media as multi-layer networks in this work. More precisely, the solution (e.g., pressures and saturation) at the time instant n + 1 depends on the solution at the time instant n and input parameters, such as permeability fields, forcing terms, and initial conditions. One can regard the solution as a multi-layer network, where each layer, in general, is a nonlinear forward map and the number of layers relates to the internal time steps. We will rely on rigorous model reduction concepts to define unknowns and connections between layers. It is critical to use reduced-order models for this purpose, which will identify the regions of influence and the appropriate number of variables. Furthermore, due to the lack of available observed fine data, the reduced-order model can provide us sufficient inexpensive data as needed. The designed deep neural network will be trained using both coarse simulation data which is obtained from the reduced-order model and observed fine data. We will present the main ingredients of our approach and numerical examples. Numerical results show that using deep learning with data generated from multiscale models as well as available observed fine data, we can obtain an improved forward map which can better approximate the fine scale model.


title: A case for a coordinated internet video control plane

abstract: Video traffic already represents a significant fraction of today's traffic and is projected to exceed 90% in the next five years. In parallel, user expectations for a high quality viewing experience (e.g., low startup delays, low buffering, and high bitrates) are continuously increasing. Unlike traditional workloads that either require low latency (e.g., short web transfers) or high average throughput (e.g., large file transfers), a high quality video viewing experience requires sustained performance over extended periods of time (e.g., tens of minutes). This imposes fundamentally different demands on content delivery infrastructures than those envisioned for traditional traffic patterns. Our large-scale measurements over 200 million video sessions show that today's delivery infrastructure fails to meet these requirements: more than 20% of sessions have a rebuffering ratio ≥ 10% and more than 14% of sessions have a video startup delay ≥ 10s. Using measurement-driven insights, we make a case for a video control plane that can use a global view of client and network conditions to dynamically optimize the video delivery in order to provide a high quality viewing experience despite an unreliable delivery infrastructure. Our analysis shows that such a control plane can potentially improve the rebuffering ratio by up to 2× in the average case and by more than one order of magnitude under stress.


title: Gradient-based adaptive interpolation in super-resolution image restoration

abstract: This paper presents a super-resolution method based on gradient-based adaptive interpolation. In this method, in addition to considering the distance between the interpolated pixel and the neighboring valid pixel, the interpolation coefficients take the local gradient of the original image into account. The smaller the local gradient of a pixel is, the more influence it should have on the interpolated pixel. And the interpolated high resolution image is finally deblurred by the application of Wiener filter. Experimental results show that our proposed method not only substantially improves the subjective and objective quality of restored images, especially enhances edges, but also is robust to the registration error and has low computational complexity.


title: Social Media and Firm Equity Value

abstract: Companies have increasingly advocated social media technologies to transform businesses and improve organizational performance. This study scrutinizes the predictive relationships between social media and firm equity value, the relative effects of social media metrics compared with conventional online behavioral metrics, and the dynamics of these relationships. The results derived from vector autoregressive models suggest that social media-based metrics Web blogs and consumer ratings are significant leading indicators of firm equity value. Interestingly, conventional online behavioral metrics Google searches and Web traffic are found to have a significant yet substantially weaker predictive relationship with firm equity value than social media metrics. We also find that social media has a faster predictive value, i.e., shorter “wear-in” time, than conventional online media. These findings are robust to a consistent set of volume-based measures total blog posts, rating volume, total page views, and search intensity. Collectively, this study proffers new insights for senior executives with respect to firm equity valuations and the transformative power of social media.


title: Passage retrieval revisited

abstract: Ranking based on passages addresses some of the shortcomings ofwhole-document ranking. It provides convenient units of text toreturn to the user, avoids the difficulties of comparing documentsof different length, and enables identification of short blocks ofrelevant material amongst otherwise irrelevant text. In this paperwe explore the potential of passage retrieval, based on anexperimental evaluation of the ability of passages to identifyrelevant documents. We compare our scheme of arbitrary passageretrieval to several other document retrieval and passage retrievalmethods; we show experimentally that, compared to these methods,ranking via fixed-length passages is robust and effective. Ourexperiments also show that, compared to whole-document ranking,ranking via fixed-length arbitrary passages significantly improvesretrieval effectiveness, by 8% for TREC disks 2 and 4 and by18%-37% for the Federal Register collection.


title: kLog: A Language for Logical and Relational Learning with Kernels

abstract: We introduce kLog, a novel language for kernelbased learning on expressive logical and relational representations. kLog allows users to specify logical and relational learning problems declaratively. It builds on simple but powerful concepts: learning from interpretations, entity/relationship data modeling, and logic programming. Access by the kernel to the rich representation is mediated by a technique we call graphicalization: the relational representation is first transformed into a graph — in particular, a grounded entity/relationship diagram. Subsequently, a choice of graph kernel defines the feature space. The kLog framework can be applied to tackle the same range of tasks that has made statistical relational learning so popular, including classification, regression, multitask learning, and collective classification. An empirical evaluation shows that kLog can be either more accurate, or much faster at the same level of accuracy, than Tilde and Alchemy. kLog is GPLv3 licensed and is available at http://klog.dinfo.unifi.it along with tutorials.


title: Large-scale analysis of frequency modulation in birdsong databases

abstract: Birdsong often contains large amounts of rapid frequency modulation (FM). It is believed that the use or otherwise of FM is adaptive to the acoustic environment, and also that there are specific social uses of FM such as trills in aggressive territorial encounters. Yet temporal fine detail of FM is often absent or obscured in standard audio signal analysis methods such as Fourier analysis or linear prediction. Hence it is important to consider high resolution signal processing techniques for analysis of FM in bird vocalisations. If such methods can be applied at big data scales, this offers a further advantage as large datasets become available. We introduce methods from the signal processing literature which can go beyond spectrogram representations to analyse the fine modulations present in a signal at very short timescales. Focusing primarily on the genus Phylloscopus, we investigate which of a set of four analysis methods most strongly captures the species signal encoded in birdsong. In order to find tools useful in practical analysis of large databases, we also study the computational time taken by the methods, and their robustness to additive noise and MP3 compression. We find three methods which can robustly represent species-correlated FM attributes, and that the simplest method tested also appears to perform the best. We find that features representing the extremes of FM encode species identity supplementary to that captured in frequency features, whereas bandwidth features do not encode additional information. Large-scale FM analysis can efficiently extract information useful for bioacoustic studies, in addition to measures more commonly used to characterise vocalisations.


title: Hierarchical Peer-To-Peer Systems

abstract: Structured peer-to-peer (P2P) lookup services organize peers into a flat overlay network and offer distributed hash table (DHT) functionality. Data is associated with keys and each peer is responsible for a subset of the keys. In hierarchical DHTs, peers are organized into groups, and each group has its autonomous intra-group overlay network and lookup service. Groups are organized in a top-level overlay network. To find a peer that is responsible for a key, the top-level overlay first determines the group responsible for the key; the responsible group then uses its intra-group overlay to determine the specific peer that is responsible for the key. We provide a general framework and a scalable hierarchical overlay management. We study a two-tier hierarchy using Chord for the top level. Our analysis shows that by using the most reliable peers in the top level, the hierarchical design significantly reduces the expected number of hops.


title: LabView Implementation of Ball and Beam System Using Cascade PD Control

abstract: The Ball and Beam system is a popular technique for the study of control systems. The system has highly non-linear characteristics and is an excellent tool to represent an unstable system. The control of such a system presents a challenging task. The ball and beam mirrors the real time unstable complex systems such as flight control, on a small laboratory level and provides for developing control algorithms which can be implemented at a higher scale. The objective of this paper is to design and implement cascade PD control of the ball and beam system in LabVIEW using data acquisition board and DAQmx and use the designed control circuit to verify results in real time.


title: SfSNet: Learning Shape, Reflectance and Illuminance of Faces 'in the Wild'

abstract: We present SfSNet, an end-to-end learning framework for producing an accurate decomposition of an unconstrained image of a human face into shape, reflectance and illuminance. Our network is designed to reflect a physical lambertian rendering model. SfSNet learns from a mixture of labeled synthetic and unlabeled real world images. This allows the network to capture low frequency variations from synthetic images and high frequency details from real images through the photometric reconstruction loss. SfSNet consists of a new decomposition architecture with residual blocks that learns a complete separation of albedo and normal. This is used along with the original image to predict lighting. SfSNet produces significantly better quantitative and qualitative results than state-of-the-art methods for inverse rendering and independent normal and illumination estimation.


title: A Multi-scale Approach to 3 D Scattered Data Interpolation with Compactly Supported Basis Functions

abstract: We propose a hierarchical approach to 3D scattered data interpolation with compactly supported basis functions. Our numerical experiments suggest that the approach integrates the best aspects of scattered data fitting with locally and globally supported basis functions. Employing locally supported functions leads to an efficient computational procedure, while a coarse-to-fine hierarchy makes our method insensitive to the density of scattered data and allows us to restore large parts of missed data. Given a point cloud distributed along a surface, we first use spatial down sampling to construct a coarse-to-fine hierarchy of point sets. Then we interpolate the sets starting from the coarsest level. We interpolate a point set of the hierarchy, as an offsetting of the interpolating function computed at the previous level. An original point set and its coarse-to-fine hierarchy of interpolated sets is presented. According to our numerical experiments, the method is essentially faster than the state-of-the-art scattered data approximation with globally supported RBFs (Carr et al., 2001) and much simpler to implement.