title: Association of Physical Function with Clinical and Subclinical Brain Disease: The Framingham Offspring Study

abstract: BACKGROUND ::: Handgrip strength and gait speed are simple measures of physical capability and have been associated with current and future health outcomes. However, studies on their associations with brain structure and function in middle-aged adults are lacking. ::: ::: ::: OBJECTIVE ::: To assess the relationship of fast-paced walking speed and handgrip strength with risk of dementia, Alzheimer's disease (AD), and stroke, as well as the cross-sectional associations with cognitive and brain magnetic resonance imaging (MRI) measures in a middle-aged community sample. ::: ::: ::: METHODS ::: Framingham Offspring (n = 2,176; mean age 62, 54% female) had physical function, brain MRI, and cognitive evaluations between 1999 and 2005 and were followed-up for incident dementia AD and stroke until 11 years later. We related walking speed and handgrip strength to incident dementia, AD, and stroke using Cox models, and to brain and cognitive measures using multivariable linear and logistic regression. Models were adjusted for age, sex, education, and vascular risk factors. ::: ::: ::: RESULTS ::: Slow walking and weak handgrip were associated with more than 2.5-fold increase in risk of AD. Weaker handgrip was associated with an increased risk of incident stroke (HR 1.74, 95% CI: 1.12-2.70/SDU, p = 0.01) in persons ≥65 years. Both measures were associated with lower total brain volume and poorer performance on tests of visual memory, language, executive function, and visuoperceptual function. Slower gait was also related to poorer verbal memory, and weaker handgrip to poorer abstraction. ::: ::: ::: CONCLUSION ::: Tests of walking speed and handgrip strength may serve as clinical markers of brain structure and function and may improve dementia risk prediction.


Knowledge Transfer for Out-of-Knowledge-Base Entities : A Graph Neural Network Approach


title: Probabilistic Selection in AgentSpeak(L)

abstract: Agent programming is mostly a symbolic discipline and, as such, draws little benefits from probabilistic areas as machine learning and graphical models. However, the greatest objective of agent research is the achievement of autonomy in dynamical and complex environments --- a goal that implies embracing uncertainty and therefore the entailed representations, algorithms and techniques. This paper proposes an innovative and conflict free two layer approach to agent programming that uses already established methods and tools from both symbolic and probabilistic artificial intelligence. Moreover, this framework is illustrated by means of a widely used agent programming example, GoldMiners.


title: Chewing Gum in the Preoperative Fasting Period: An Analysis of De-Identified Incidents Reported to Webairs

abstract: The role of preoperative fasting is well established in current anaesthetic practice with different guidelines for clear fluids and food. However, chewing gum may not be categorised as either food or drink by some patients, and may not always be specified in instructions given to patients about preoperative fasting. The aim of this paper was to review anaesthesia incidents involving gum chewing reported to webAIRS to obtain information on the risks, if any, of gum chewing during the preoperative fasting period. There were nine incidents involving chewing gum reported between late 2009 and early 2015. There were no adverse outcomes from the nine incidents other than postponement of surgery in three cases and cancellation in one. In particular, there were no reports of aspiration or airway obstruction. Nevertheless, there were five cases in which the gum was not detected preoperatively and was found in the patient's mouth either intraoperatively or postoperatively. These cases of undetected gum occurred despite patient and staff compliance with their current preoperative checklists. While the risk of increased gastric secretions related to chewing gum preoperatively are not known, the potential for airway obstruction if the gum is not detected and removed preoperatively is very real. We recommend that patients should be specifically advised to avoid gum chewing once fasting from clear fluids is commenced, and that a specific question regarding the presence of chewing gum should be added to all preoperative checklists.


title: Graph Search Techniques for Mobile Robot Path Planning

abstract: In a number of applications, the problem of determining the optimum path occurs. This applications range from finding the fastest path in a network, to determining the safest path for mobile vehicle, wandering on the surface of Mars. In this context, we shall limit our scope to the case of finding paths in Euclidean two-dimensional space. Moreover we shall limit the case study to movements along a surface that can be projected onto a directed graph. To be specific, we shall look at the case of finding the optimum path for a mobile robot moving along a flat surface, the robot’s configurations in the configuration space being the graph’s nodes while the graph’s arcs represent the cost of moving from one configuration to another. Researchers have tried to come with new and better navigation technologies in the last years. With the development of path finding, several new classical routing algorithms have been introduced to generate better routing solution. For example the Dijkstra algorithm is the most famous one, which evaluates the moving cost from one node to any other node and sets the shortest moving cost as the connecting cost of two nodes (Eklund et al., 1996). Around the same period of time, Best-First-Search algorithm is also introduced in the researchers’ community. A little different from the Dijkstra algorithm, Best-First-Search algorithm has a different approach because it estimates the distance from current position to goal position, and it chooses the step that is closer to the goal position (LaValle, 2006). The difficulty was growing with the new path finding situations so the old path finding algorithm had to be improved to meet the new introduced requirements. A new path finding algorithm was introduced and it was named the A* algorithm. The A* algorithm tries to combine the advantages offered by Dijkstra algorithm and Best-FirstSearch algorithm. This paper presents tests performed with various implementations of graph search algorithms (A*, D*, focused D*) as path planners for a mobile robot, focused on strong points and drawbacks of each implementation. 8


title: A Systematic Literature Review of Mobile Cloud Computing

abstract: Mobile cloud computing (MCC) is a relatively new concept that leverages the combination of cloud technology, mobile computing, and wireless networking to enrich the usability experiences of mobile users. Many fields of application such as mobile health, mobile learning, mobile commerce and mobile entertainment are now taking advantage of MCC technologies. Since MCC is new, there is need to advance research in MCC in order to deepen practice. Currently, what exist are mostly descriptive literature reviews in the area of MCC. ::: In this paper, a systematic literature review (SLR), which offers a structured, methodical, and rigorous approach to the understanding of the trend of research in MCC, and the least and most researched issue is presented. The objective of the study is to provide a credible intellectual guide for upcoming researchers in MCC to help them identify areas in MCC research where they can make the most impact. The SLR was limited to peer-reviewed conference papers and journal articles published from 2002 to 2014. ::: The study reveals that privacy, security and trust in MCC are the least researched, whereas issues of architecture, context awareness and data management have been averagely researched, while issues on operations, end users, service and applications have received a lot of attention in the literature.


Managing the cardiovascular risk in cancer therapy: a paradigm shift?


title: How Many Workers to Ask?: Adaptive Exploration for Collecting High Quality Labels

abstract: Crowdsourcing has been part of the IR toolbox as a cheap and fast mechanism to obtain labels for system development and evaluation. Successful deployment of crowdsourcing at scale involves adjusting many variables, a very important one being the number of workers needed per human intelligence task (HIT). We consider the crowdsourcing task of learning the answer to simple multiple-choice HITs, which are representative of many relevance experiments. In order to provide statistically significant results, one often needs to ask multiple workers to answer the same HIT. A stopping rule is an algorithm that, given a HIT, decides for any given set of worker answers to stop and output an answer or iterate and ask one more worker. In contrast to other solutions that try to estimate worker performance and answer at the same time, our approach assumes the historical performance of a worker is known and tries to estimate the HIT difficulty and answer at the same time. The difficulty of the HIT decides how much weight to give to each worker's answer. In this paper we investigate how to devise better stopping rules given workers' performance quality scores. We suggest adaptive exploration as a promising approach for scalable and automatic creation of ground truth. We conduct a data analysis on an industrial crowdsourcing platform, and use the observations from this analysis to design new stopping rules that use the workers' quality scores in a non-trivial manner. We then perform a number of experiments using real-world datasets and simulated data, showing that our algorithm performs better than other approaches.


title: Muscle atrophy is not always sarcopenia

abstract: Sarcopenia is a term originally coined in reference to the decline of muscle mass with aging. However, in recent times this term is being used operationally in many clinical disciplines (e.g., cancer cachexia) to define a level of muscle atrophy independent of aging per se. It is argued here that aging muscle is distinct from several other forms of muscle atrophy, particularly cancer cachexia, and therefore that use of the term sarcopenia in these non-aging contexts is unwarranted and likely to cause confusion in the field. This argument is made based upon the established histopathology of aging muscle showing it to exhibit characteristic features of muscle afflicted by repeating cycles of denervation and reinnervation. It is argued that these and other histological features should be used to distinguish sarcopenia from other clinical conditions associated with muscle atrophy and to objectively justify return of the term sarcopenia to use in the aging field exclusively. Furthermore, the histopathological features of aging muscle should be more rigorously employed to refine our search for the mechanisms of sarcopenia and its successful treatment.


title: Pattern Recognition Techniques for the Identification of Activities of Daily Living using Mobile Device Accelerometer

abstract: This paper focuses on the recognition of Activities of Daily Living (ADL) applying pattern recognition techniques to the data acquired by the accelerometer available in the mobile devices. The recognition of ADL is composed by several stages, including data acquisition, data processing, and artificial intelligence methods. The artificial intelligence methods used are related to pattern recognition, and this study focuses on the use of Artificial Neural Networks (ANN). The data processing includes data cleaning, and the feature extraction techniques to define the inputs for the ANN. Due to the low processing power and memory of the mobile devices, they should be mainly used to acquire the data, applying an ANN previously trained for the identification of the ADL. The main purpose of this paper is to present a new method implemented with ANN for the identification of a defined set of ADL with a reliable accuracy. This paper also presents a comparison of different types of ANN in order to choose the type for the implementation of the final method. Results of this research probes that the best accuracies are achieved with Deep Learning techniques with an accuracy higher than 80%.


title: Leveraging Sentiment Analysis for Topic Detection

abstract: The emergence of new social media such as blogs, message boards, news, and Web content in general has dramatically changed the ecosystems of corporations. Consumers, non-profit organizations, and other forms of communities are extremely vocal about their opinions and perceptions on companies and their brands on the Web. The ability to leverage such "voice of the Web" to gain consumer, brand, and market insights can be truly differentiating and valuable to todaypsilas corporations. In particular, one important form of insights can be derived from sentiment analysis on Web content. Sentiment analysis traditionally emphasizes on classification of Web comments into positive, neutral, and negative categories. This paper goes beyond sentiment classification by focusing on techniques that could detect the topics that are highly correlated with the positive and negative opinions. Such techniques, when coupled with sentiment classification, can help the business analysts to understand both the overall sentiment scope as well as the drivers behind the sentiment. In this paper, we describe our overall sentiment analysis system that consists of such sentiment analysis techniques. We then detail a novel topic detection method using point-wise mutual information and term frequency distribution. We demonstrate the effectiveness of our overall approaches via several case studies on different social media data sets.


title: Predicting Multiple Attributes via Relative Multi-task Learning

abstract: Relative attributes learning aims to learn ranking functions describing the relative strength of attributes. Most of current learning approaches learn ranking functions for each attribute independently without considering possible intrinsic relatedness among the attributes. For a problem involving multiple attributes, it is reasonable to assume that utilizing such relatedness among the attributes would benefit learning, especially when the number of labeled training pairs are very limited. In this paper, we proposed a relative multi-attribute learning framework that integrates relative attributes into a multi-task learning scheme. The formulation allows us to exploit the advantages of the state-of-the-art regularization-based multi-task learning for improved attribute learning. In particular, using joint feature learning as the case studies, we evaluated our framework with both synthetic data and two real datasets. Experimental results suggest that the proposed framework has clear performance gain in ranking accuracy and zero-shot learning accuracy over existing methods of independent relative attributes learning and multi-task learning.


title: Robust moving camera calibration for synthesizing free viewpoint soccer video

abstract: In this paper, a robust moving camera calibration method is proposed in order to synthesize a free viewpoint soccer video with a high degree of accuracy. The main problem in video registration-based moving camera calibration is that the calibration accuracy is very low if the detected feature points are from moving objects. In order to solve this problem, the proposed method tracks the feature points along video frames to construct a trajectory matrix of feature points, and the trajectory matrix is decomposed into a low-rank matrix representing global camera motion and a sparse matrix representing local individual motion. Therefore, according to such decomposition, the individual motions of dynamic feature points that are from moving objects could be suppressed and removed. Experimental results show that the proposed method achieves more accurate calibration result and the visual quality of a synthesized free viewpoint soccer video is also improved by the proposed method.


title: Vascular mapping of the face: B-mode and doppler ultrasonography study

abstract: Background ::: To analyze the face vascularization pattern using B-mode and Doppler ultrasonography, and also propose an arterial vessel mapping.


title: HL7 FHIR: An Agile and RESTful approach to healthcare information exchange

abstract: This research examines the potential for new Health Level 7 (HL7) standard Fast Healthcare Interoperability Resources (FHIR, pronounced “fire”) standard to help achieve healthcare systems interoperability. HL7 messaging standards are widely implemented by the healthcare industry and have been deployed internationally for decades. HL7 Version 2 (“v2”) health information exchange standards are a popular choice of local hospital communities for the exchange of healthcare information, including electronic medical record information. In development for 15 years, HL7 Version 3 (“v3”) was designed to be the successor to Version 2, addressing Version 2's shortcomings. HL7 v3 has been heavily criticized by the industry for being internally inconsistent even in it's own documentation, too complex and expensive to implement in real world systems and has been accused of contributing towards many failed and stalled systems implementations. HL7 is now experimenting with a new approach to the development of standards with FHIR. This research provides a chronicle of the evolution of the HL7 messaging standards, an introduction to HL7 FHIR and a comparative analysis between HL7 FHIR and previous HL7 messaging standards.


title: Name disambiguation in author citations using a K-way spectral clustering method

abstract: An author may have multiple names and multiple authors may share the same name simply due to name abbreviations, identical names, or name misspellings in publications or bibliographies (citations). This can produce name ambiguity which can affect the performance of document retrieval, web search, and database integration, and may cause improper attribution of credit. Proposed here is an unsupervised learning approach using K-way spectral clustering that disambiguates authors in citations. The approach utilizes three types of citation attributes: co-author names, paper titles, and publication venue titles. The approach is illustrated with 16 name datasets with citations collected from the DBLP database bibliography and author home pages and shows that name disambiguation can be achieved using these citation attributes


title: A Fluxgate Current Sensor With a U-Shaped Magnetic Gathering Shell

abstract: This paper presents a current sensor based on fluxgate principle, which consists of a cylinder-shaped amorphous core with a U-shaped magnetic gathering shell. In the designed sensor, the exciting winding and secondary winding are arranged perpendicularly so that the magnetic fields produced are mutual orthogonally, that is, the exciting magnetic field and the feedback magnetic field is decoupled. Magnetic gathering effectiveness and magnetic coefficient of the shell are analyzed by finite element method (FEM) and some concerned parameters affecting the gathering effects are also discussed. Based on theoretical analysis and simulation results, a prototype was designed. The experiments show introducing of a magnetic gathering shell into the current sensor effectively concentrates the wanted signal and reduces the interference of external stray field, thus making the sensor have an excellent sensitivity and precision. The relative error of the measurement of the current sensor with and without magnetic gathering shell were tested and compared. The sensor we proposed with this design has an accuracy of 6‰ and a remarkable resolution of 8pT/√Hz for frequencies greater than 1 Hz.


title: Automatic detection and recognition of signs from natural scenes

abstract: In this paper, we present an approach to automatic detection and recognition of signs from natural scenes, and its application to a sign translation task. The proposed approach embeds multiresolution and multiscale edge detection, adaptive searching, color analysis, and affine rectification in a hierarchical framework for sign detection, with different emphases at each phase to handle the text in different sizes, orientations, color distributions and backgrounds. We use affine rectification to recover deformation of the text regions caused by an inappropriate camera view angle. The procedure can significantly improve text detection rate and optical character recognition (OCR) accuracy. Instead of using binary information for OCR, we extract features from an intensity image directly. We propose a local intensity normalization method to effectively handle lighting variations, followed by a Gabor transform to obtain local features, and finally a linear discriminant analysis (LDA) method for feature selection. We have applied the approach in developing a Chinese sign translation system, which can automatically detect and recognize Chinese signs as input from a camera, and translate the recognized text into English.


title: Cholestasis in the newborn: experience of a level III Neonatal Intensive Care Unit during 19 years

abstract: Introduction: Neonatal cholestasis is a rare and always pathological condition that must be distinguished from physiologic jaundice of the newborn. It is characterized by a conjugated hyperbilirubinemia with accumulation of biliary products due to multiple causes, some of which need prompt treatment. The objectives of this study are to characterize a population of neonates with neonatal cholestasis in a level III Neonatal Intensive Care Unit (NICU) and to identify predictors of mortality. Materials and methods: All patients presenting cholestasis and admitted to “Centro Hospitalar Sao Joao” NICU from January 1996 to December 2014 were included. Results: A total of 83 newborns were included with a prevalence of prematurity of 78.3% and of major malformations of 30.1%. Sixty-seven newborns developed sepsis and 71 needed total parenteral nutrition for a median length of 32 days. A multifactorial etiology for cholestasis was found in 57.8%; extra- and intrahepatic diseases accounted for 19.3% and 22.9% of the cases, respectively. Maximum values for total bilirubin (TB) and direct bilirubin (DB) were significantly higher in newborns who died (TB: median 22.9 versus 13.5 mg/dl, p = 0.005; DB: median 15.0 versus 6.4 mg/dl, p = 0.009). The same was observed for minimum values of albumin and total proteins. Ursodeoxycholic acid (UDCA) was more often used in patients that survived than in those that died (50.9% versus 19.2%) and this difference was statistically significant (p = 0.007). Conclusions: Cholestasis in our NICU has a multifactorial etiology and a prevalence of 1%. TB and total proteins can be used as predictors of mortality in newborns with cholestasis. Higher levels of DB and lower levels of albumin were also associated with worse prognosis with a statistically significant difference between the groups. UDCA is a possible agent in this context and clinicians should be reminded of its utility.


title: Adversarial Attacks on Variational Autoencoders

abstract: Adversarial attacks are malicious inputs that derail machine-learning models. We propose a scheme to attack autoencoders, as well as a quantitative evaluation framework that correlates well with the qualitative assessment of the attacks. We assess --- with statistically validated experiments --- the resistance to attacks of three variational autoencoders (simple, convolutional, and DRAW) in three datasets (MNIST, SVHN, CelebA), showing that both DRAW's recurrence and attention mechanism lead to better resistance. As autoencoders are proposed for compressing data --- a scenario in which their safety is paramount --- we expect more attention will be given to adversarial attacks on them.


title: Weaving together requirements and architectures

abstract: Software development organizations often choose between alternative starting points-requirements or architectures. This invariably results in a waterfall development process that produces artificially frozen requirements documents for use in the next step in the development life cycle. Alternatively, this process creates systems with constrained architectures that restrict users and handicap developers by resisting inevitable and desirable changes in requirements. The spiral life-cycle model addresses many drawbacks of a waterfall model by providing an incremental development process, in which developers repeatedly evaluate changing project risks to manage unstable requirements and funding. An even finer-grain spiral life cycle reflects both the realities and necessities of modern software development. Such a life cycle acknowledges the need to develop software architectures that are stable, yet adaptable, in the presence of changing requirements. The cornerstone of this process is that developers craft a system's requirements and its architecture concurrently, and interleave their development.


title: A low-cost built-in error correction circuit design for STT-MRAM reliability improvement

abstract: Abstract Spin Transfer Torque Magnetic Random Access Memory (STT-MRAM) possesses various merits, such as non-volatility, low power and high speed. It has been considered as a promising non-volatile memory candidate used universally in logic computing, cache and storage applications. However it suffers from serious reliability issues compared with conventional schemes, especially in deep submicron technologies. This paper proposes a low-cost built-in error correction circuit to improve STT-MRAM reliability. Its straightforward “XOR” encoder and one-step majority-voting decoder provide much lower area and higher speed compared with conventional ECCs, and its modular codec structure allows adaptive error correction capability according to the system requirement. Simulation based on a compact STT model and STMicroelectronics 40 nm technology node was carried out to confirm its effectiveness.


title: Doppelganger Mining for Face Representation Learning

abstract: In this paper we present Doppelganger mining - a method to learn better face representations. The main idea of this method is to maintain a list with the most similar identities for each identity in the training set. This list is used to generate better mini-batches by sampling pairs of similar-looking identities ("doppelgangers") together. It is especially useful for methods, based on exemplar-based supervision. Usually hard example mining comes with a price of necessity to use large mini-batches or substantial extra computation and memory cost, particularly for datasets with large numbers of identities. Our method needs only a negligible extra computation and memory. In our experiments on a benchmark dataset with 21,000 persons we show that Doppelganger mining, being inserted in the face representation learning process with joint prototype-based and exemplar-based supervision, significantly improves the discriminative power of learned face representations.


title: Deal maker or deal breaker: human resources issues in successful outsourcing projects

abstract: A large scale study shows the importance of human resources (HR) issues in outsourcing and facility management. Success requires a new role for human resources officers of the selling organization. Human resources officers should be on the marketing and sales team. They should prepare the ground work for the transfer of employees. The role should cover the entire process. They must be able to work ‘inside’ the potential client. They should provide stewardship of the integration process and its ongoing maintenance. Copyright © 1999 John Wiley & Sons, Ltd.


title: PRISM: A Framework for Authoring Interactive Narratives

abstract: The advances in computing technologies enable the computer users to create and share their own stories to the community at large. However, it is still regarded as complicated and laborious to author interactive narratives, where a story adapts as the user interacts with it. In authoring interactive narratives, two main approaches--branching graphs and AI planning--have been significantly used to augment interactivity into conventional linear narratives. Although each approach offers its own possibilities and limitations, few efforts have been made to blend these approaches. This paper describes a framework for authoring interactive narratives that employs an adapted branching narrative structure that also uses planning formalism to enable automated association between nodes. We expect that our work is valuable for non-expert users as well as AI researchers in interactive storytelling who need to create a large quantity of story contents for varied endings for a story.


title: A Dual-Active-Bridge-Based Single-Phase AC to DC Power Electronic Transformer With Advanced Features

abstract: Power electronic transformers (PETs) offer the advantage of size and weight reduction compared to line-frequency transformers by operating at much higher frequencies than line frequency. In this paper, a push–pull-based ac/dc PET has been proposed and analyzed. The PET offers bidirectional power flow between single-phase ac and dc, using the dual-active bridge principle. Such a system may find applications in interfacing plug-in hybrid and electric vehicles to the grid. The proposed PET offers advantages of open-loop unity power factor operation, soft switching of secondary-side converter power switches for all operating points, high power density owing to use of a high-frequency transformer, and high utilization factor (UF), compared to previous work. Analysis has been done for power transfer, UF, and soft switching. Simulation and experimental results have been provided to demonstrate the operation of the PET.


title: Frequency Control in Autonomous Power Systems With High Wind Power Penetration

abstract: This paper presents an investigation on wind turbine (WT) contribution to the frequency control of noninterconnected island systems. The capability of WTs to participate in the primary frequency control and offer primary reserve is discussed. The investigation includes both transient frequency support (inertial response) and permanent frequency response (droop characteristic), as well as the combined application of these concepts. A quantitative analysis is presented for the expected benefits and drawbacks of each method, including the appropriate selection of their parameters. The power system of Rhodes Island has been selected as a study case, which includes different types of conventional generation and the three basic WT types, based on Active-Stall Induction Generator (ASIG), Doubly Fed Induction Generator (DFIG), and Permanent Magnet Synchronous Generator (PMSG).


title: Absorptive capacity and information systems research: review, synthesis, and directions for future research

abstract: Absorptive capacity is a firm's ability to identify, assimilate, transform, and apply valuable external knowledge. It is considered an imperative for business success. Modern information technologies perform a critical role in the development and maintenance of a firm's absorptive capacity. We provide an assessment of absorptive capacity in the information systems literature. IS scholars have used the absorptive capacity construct in diverse and often contradictory ways. Confusion surrounds how absorptive capacity should be conceptualized, its appropriate level of analysis, and how it can be measured. Our aim in reviewing this construct is to reduce such confusion by improving our understanding of absorptive capacity and guiding its effective use in IS research. We trace the evolution of the absorptive capacity construct in the broader organizational literature and pay special attention to its conceptualization, assumptions, and relationship to organizational learning. Following this, we investigate how absorptive capacity has been conceptualized, measured, and used in IS research. We also examine how absorptive capacity fits into distinct IS themes and facilitates understanding of various IS phenomena. Based on our analysis, we provide a framework through which IS researchers can more fully leverage the rich aspects of absorptive capacity when investigating the role of information technology in organizations.


Point Cloud Segmentation and 3 D Path Planning for Tracked Vehicles in Cluttered and Dynamic Environments


title: Pattern-based synonym and antonym extraction

abstract: Many research studies adopt manually selected patterns for semantic relation extraction. However, manually identifying and discovering patterns is time consuming and it is difficult to discover all potential candidates. Instead, we propose an automatic pattern construction approach to extract verb synonyms and antonyms from English newspapers. Instead of relying on a single pattern, we combine results indicated by multiple patterns to maximize the recall.


title: An exploratory assessment of weight loss videos on YouTube™.

abstract: OBJECTIVE ::: To describe the variation in contents between different video sources on YouTube ™ regarding weight loss. ::: ::: ::: STUDY DESIGN ::: The 100 most widely viewed weight loss videos on YouTube™ were selected for manual coding. ::: ::: ::: METHODS ::: Videos were manually coded based on sources and contents, we also identify themes for each of the videos. Univariate logistic regression was used to assess difference in contents between videos from different sources. ::: ::: ::: RESULTS ::: After excluding two videos, a sample of 98 videos remained. Among them, there were 74 consumer-generated videos, 12 news videos, and 12 from commercial television and companies. Collectively, the 98 videos were viewed more than 365 million times. The odds of mentioning ongoing lifestyle that includes long-term changes in daily eating was 90% less in commercial and company videos compared to consumer videos (odds ratio [OR] = 0.10, 95% confidence interval [CI] 0.01-0.85, P = 0.03). The odds of showing a female talking in the video about weight loss was 81% less in news videos compared to consumer videos (OR = 0.19, 95% CI 0.05-0.68, P = 0.01). In addition, commercial and company videos were found to be 18 times as likely to carry a theme on workout as consumer videos (OR = 18.13, 95% CI 3.60-91.22, P < 0.01). ::: ::: ::: CONCLUSION ::: Public health professionals need to be more creative to make their videos popular if they want to reach a wide audience.


title: PolyPUF: Physically Secure Self-Divergence

abstract: Physically unclonable functions (PUFs) provide a device-unique challenge–response mapping and are employed for authentication and encryption purposes. Unpredictability and reliability are the core requirements of PUFs: unpredictability implies that an adversary cannot sufficiently predict future responses from previous observations. Reliability is important as it increases the reproducibility of PUF responses and hence allows validation of expected responses. However, advanced machine-learning algorithms have been shown to be a significant threat to the practical validity of PUFs, as they are able to accurately model PUF behavior. The most effective technique was shown to be the XOR-based combination of multiple PUFs, but as this approach drastically reduces reliability, it does not scale well against software-based machine-learning attacks. In this paper, we analyze threats to PUF security and propose PolyPUF, a scalable and secure architecture to introduce polymorphic PUF behavior. This architecture significantly increases model-building resistivity while maintaining reliability. An extensive experimental evaluation and comparison demonstrate that the PolyPUF architecture can secure various PUF configurations and is the only evaluated approach to withstand highly complex neural network machine-learning attacks. Furthermore, we show that PolyPUF consumes less energy and has less implementation overhead in comparison to lightweight reference architectures.


title: Understanding Latency Variation in Modern DRAM Chips: Experimental Characterization, Analysis, and Optimization

abstract: Long DRAM latency is a critical performance bottleneck in current systems. DRAM access latency is defined by three fundamental operations that take place within the DRAM cell array: (i) activation of a memory row, which opens the row to perform accesses; (ii) precharge, which prepares the cell array for the next memory access; and (iii) restoration of the row, which restores the values of cells in the row that were destroyed due to activation. There is significant latency variation for each of these operations across the cells of a single DRAM chip due to irregularity in the manufacturing process. As a result, some cells are inherently faster to access, while others are inherently slower. Unfortunately, existing systems do not exploit this variation. The goal of this work is to (i) experimentally characterize and understand the latency variation across cells within a DRAM chip for these three fundamental DRAM operations, and (ii) develop new mechanisms that exploit our understanding of the latency variation to reliably improve performance. To this end, we comprehensively characterize 240 DRAM chips from three major vendors, and make several new observations about latency variation within DRAM. We find that (i) there is large latency variation across the cells for each of the three operations; (ii) variation characteristics exhibit significant spatial locality: slower cells are clustered in certain regions of a DRAM chip; and (iii) the three fundamental operations exhibit different reliability characteristics when the latency of each operation is reduced. Based on our observations, we propose Flexible-LatencY DRAM (FLY-DRAM), a mechanism that exploits latency variation across DRAM cells within a DRAM chip to improve system performance. The key idea of FLY-DRAM is to exploit the spatial locality of slower cells within DRAM, and access the faster DRAM regions with reduced latencies for the fundamental operations. Our evaluations show that FLY-DRAM improves the performance of a wide range of applications by 13.3%, 17.6%, and 19.5%, on average, for each of the three different vendors' real DRAM chips, in a simulated 8-core system. We conclude that the experimental characterization and analysis of latency variation within modern DRAM, provided by this work, can lead to new techniques that improve DRAM and system performance.


Economics, Innovations, Technology, and Engineering Education: The Connections


title: Virtual reality to teach anatomy

abstract: Virtual Reality (VR) and Augmented Reality (AR) have been gradually introduced in the curriculum of schools given the benefits they bring to classical education. We present an experiment designed to expose students to a VR session where they can directly inspect 3D models of several human organs by using Virtual Reality systems. Our systems allow the students to see the models directly visualized in 3D and to interact with them as if they were real. The experiment has involved 254 students of a Nursing Degree, enrolled in the Human anatomy and physiology course during 2 years (2 consecutive courses). It includes 10 3D models representing different anatomical structures which have been enhanced with meta-data to help the students understand the structure. In order to evaluate the students' satisfaction facing such a new teaching methodology, the students were asked to fill in a questionnaire with two categories. The first one measured whether or not, the teaching session using VR facilitates the understanding of the structures. The second one measured the student's satisfaction with this VR session. From the results we can see that the items most valuated are the use of the activity as a learning tool, and the satisfaction of the students' expectations. We can therefore conclude that VR session for teaching is a powerful learning tool that helps to understand the anatomical structures.


title: Deprecating the Observer Pattern

abstract: Programming interactive systems by means of the observer pattern is hard and error-prone yet is still the implementation standard in many production environments. We present an approach to gradually deprecate observers in favor of reactive programming abstractions. Several library layers help programmers to smoothly migrate existing code from callbacks to a more declarative programming model. Our central high-level API layer embeds an extensible higher-order data-flow DSL into our host language. This embedding is enabled by a continuation passing style transformation.


title: Abnormality Detection and Localization in Chest X-Rays using Deep Convolutional Neural Networks

abstract: Chest X-Rays (CXRs) are widely used for diagnosing abnormalities in the heart and lung area. Automatically detecting these abnormalities with high accuracy could greatly enhance real world diagnosis processes. Lack of standard publicly available dataset and benchmark studies, however, makes it difficult to compare various detection methods. In order to overcome these difficulties, we have used a publicly available Indiana CXR, JSRT and Shenzhen dataset and studied the performance of known deep convolutional network (DCN) architectures on different abnormalities. We find that the same DCN architecture doesn't perform well across all abnormalities. Shallow features or earlier layers consistently provide higher detection accuracy compared to deep features. We have also found ensemble models to improve classification significantly compared to single model. Combining these insight, we report the highest accuracy on chest X-Ray abnormality detection on these datasets. We find that for cardiomegaly detection, the deep learning method improves the accuracy by a staggering 17 percentage point compared to rule based methods. We applied the techniques to the problem of tuberculosis detection on a different dataset and achieved the highest accuracy. Our localization experiments using these trained classifiers show that for spatially spread out abnormalities like cardiomegaly and pulmonary edema, the network can localize the abnormalities successfully most of the time. One remarkable result of the cardiomegaly localization is that the heart and its surrounding region is most responsible for cardiomegaly detection, in contrast to the rule based models where the ratio of heart and lung area is used as the measure. We believe that through deep learning based classification and localization, we will discover many more interesting features in medical image diagnosis that are not considered traditionally.


title: TALMUD: transfer learning for multiple domains

abstract: Most collaborative Recommender Systems (RS) operate in a single domain (such as movies, books, etc.) and are capable of providing recommendations based on historical usage data which is collected in the specific domain only. Cross-domain recommenders address the sparsity problem by using Machine Learning (ML) techniques to transfer knowledge from a dense domain into a sparse target domain. In this paper we propose a transfer learning technique that extracts knowledge from multiple domains containing rich data (e.g., movies and music) and generates recommendations for a sparse target domain (e.g., games). Our method learns the relatedness between the different source domains and the target domain, without requiring overlapping users between domains. The model integrates the appropriate amount of knowledge from each domain in order to enrich the target domain data. Experiments with several datasets reveal that, using multiple sources and the relatedness between domains improves accuracy of results.


title: Determinants and patterns of soft drink consumption in young adults: a qualitative analysis

abstract: Objective: To explore knowledge, attitudes and behaviours regarding caloric soft drinks in a group of young adults attending university and to identify opportunities for a health promotion intervention aimed at reducing consumption. Design: In-depth, semi-structured focus groups segmented by gender. Setting: Sydney, Australia. Subjects: Undergraduate University of Sydney students aged 18–30 years (n 35). Results: Social and environmental cues, intrinsic qualities of beverages and personal health beliefs were identified as important influences on consumption. Social cues included settings in which alcohol is usually consumed, socialising with friends, and family influences. Environmental cues included purchasing of fast foods, and ready availability, preferential pricing and promotion of caloric beverages. Reinforcing intrinsic qualities of caloric soft drinks included taste, sugar and caffeine content, and their association with treats and rewards. Major gender differences as well as variations in individual readiness for behaviour change were observed. Raising awareness of the sugar content of various beverages and the potential health impacts associated with their consumption was considered important. Conclusions: The findings provide new insights with important implications for policy and practice, and suggest that there is considerable scope for promoting awareness in this group. Carefully designed social marketing campaigns highlighting the health issues and addressing social and environmental cues relating to caloric soft drink consumption are required. There is a need for gender-differentiated intervention programmes which are both informational and appealing to young adults. Further research is warranted, particularly to investigate beverage consumption relating to fast-food meal deals and young adults’ consumption patterns in more depth.


title: Statistical source expansion for question answering

abstract: A source expansion algorithm automatically extends a given text corpus with related content from large external sources such as the Web. The expanded corpus is not intended for human consumption but can be used in question answering (QA) and other information retrieval or extraction tasks to find more relevant information and supporting evidence. We propose an algorithm that extends a corpus of seed documents with web content, using a statistical model to select text passages that are both relevant to the topics of the seeds and complement existing information. In an evaluation on 1,500 hand-labeled web pages, our algorithm ranked text passages by relevance with 81% MAP, compared to 43% when relying on web search engine ranks alone and 75% when using a multi-document summarization algorithm. Applied to QA, the proposed method yields consistent and significant performance gains. We evaluated the impact of source expansion on over 6,000 questions from the Jeopardy! quiz show and TREC evaluations using Watson, a state-of-the-art QA system. Accuracy increased from 66% to 71% on Jeopardy! questions and from 59% to 64% on TREC questions.


2000a, “Combination and Recombination in Genetic Algorithms


title: Trends in Fault Diagnosis for Electrical Machines: A Review of Diagnostic Techniques

abstract: The fault diagnosis of rotating electrical machines has received an intense amount of research interest during the last 30 years. Reducing maintenance costs and preventing unscheduled downtimes, which result in losses of production and financial incomes, are the priorities of electrical drives manufacturers and operators. In fact, both correct diagnosis and early detection of incipient faults lead to fast unscheduled maintenance and short downtime for the process under consideration. They also prevent the harmful and sometimes devastating consequences of faults and failures. This topic has become far more attractive and critical as the population of electric machines has greatly increased in recent years. The total number of operating electrical machines in the world was around 16.1 billion in 2011, with a growth rate of about 50% in the last five years [1].


title: Impact of an individualist vs. collectivist context on the social valorization of internal explanations

abstract: The theory of the norm of internality emphasizes the role of Western individualism in the normativity of internal explanations. The present study examines the link between the social value accorded to targets expressing internal vs. external explanations and individualist vs. collectivist contexts. Sixty-three male and female French management sciences students evaluated two targets (internal vs. external) in a simulated recruitment situation. The job vacancy was partially manipulated to create individualist vs. collectivist contexts. Participants were asked to state whether or not they would recruit the targets and to describe the targets on traits relating to social utility (market value) and social desirability (likeability). As expected, the results showed that the effect of the targets’ internality on recruitment judgments and perceived social utility was stronger in the individualist context than in the collectivist context. However, the analysis also revealed that the participant's gender moderated...


title: Repetitive surge current test of SiC MPS diode with load in bipolar regime

abstract: The reliability of power diodes under surge current is an important factor that has to be taken into account for power electronic applications. In this work, latest generation SiC MPS (Merged Pin Schottky) diodes (650V, 1200V, 1700V) with different current classes from Infineon are exposed to repetitive high surge current stress. Furthermore, the temperature was estimated using different methods including direct measurement, Sentaurus TCAD simulation, Cauer network and an analytical model using temperature dependent mobility. It was found that the diodes could withstand a high number of high-current pulses. However, before reaching final destruction, different ageing phenomenon were observed at the unipolar and bipolar regime of the MPS diode. A detailed investigation on the aging mechanisms including failure analysis was performed.


title: Supervised Learning Based Approach to Aspect Based Sentiment Analysis

abstract: Aspect base sentiment analysis is a very popular concept in the machine learning era which is under the research domain still at the movement. This research mainly consist of the way of exploring the sentiment analysis based on the trained data set to provide the positive, negative and neutral reviews for different products in the marketing world. Most of the existing approaches for opinion mining are based on word level analysis of texts and are able to detect only explicitly expressed opinions. In aspect-based sentiment analysis (ABSA) the aim is to identify the aspects of entities and the sentiment expressed for each aspect. The ultimate goal is to be able to generate summaries listing all the aspects and their overall polarity. For this research mainly natural language and machine learning techniques are used. To train the application for the given data sets SVM (support vector machine) and ME (Maximum Entropy) classification algorithms have been used. Differentiation of the performance of the each algorithm will be analyzed through this research using the proven technologies available in the world like "Re call", "F-Measure" and Accuracy.


title: Feature selection using genetic algorithms for premature ventricular contraction classification

abstract: Cardiac arrhythmia is one of the most important indicators of heart disease. Premature ventricular contractions (PVCs) are a common form of cardiac arrhythmia caused by ectopic heartbeats. The detection of PVCs by means of ECG (electrocardiogram) signals is important for the prediction of possible heart failure. This study focuses on the classification of PVC heartbeats from ECG signals and, in particular, on the performance evaluation of selected features using genetic algorithms (GA) to the classification of PVC arrhythmia. The objective of this study is to apply GA as a feature selection method to select the best feature subset from 200 time series features and to integrate these best features to recognize PVC forms. Neural networks, support vector machines and k-nearest neighbour classification algorithms were used. Findings were expressed in terms of accuracy, sensitivity, and specificity for the MIT-BIH Arrhythmia Database. The results showed that the proposed model achieved higher accuracy rates than those of other works on this topic.


title: Perceptual Organization and Recognition of Indoor Scenes from RGB-D Images

abstract: We address the problems of contour detection, bottom-up grouping and semantic segmentation using RGB-D data. We focus on the challenging setting of cluttered indoor scenes, and evaluate our approach on the recently introduced NYU-Depth V2 (NYUD2) dataset [27]. We propose algorithms for object boundary detection and hierarchical segmentation that generalize the gPb-ucm approach of [2] by making effective use of depth information. We show that our system can label each contour with its type (depth, normal or albedo). We also propose a generic method for long-range amodal completion of surfaces and show its effectiveness in grouping. We then turn to the problem of semantic segmentation and propose a simple approach that classifies super pixels into the 40 dominant object categories in NYUD2. We use both generic and class-specific features to encode the appearance and geometry of objects. We also show how our approach can be used for scene classification, and how this contextual information in turn improves object recognition. In all of these tasks, we report significant improvements over the state-of-the-art.


title: Learning Non-maximum Suppression

abstract: Object detectors have hugely profited from moving towards an end-to-end learning paradigm: proposals, fea tures, and the classifier becoming one neural network improved results two-fold on general object detection. One indispensable component is non-maximum suppression (NMS), a post-processing algorithm responsible for merging all detections that belong to the same object. The de facto standard NMS algorithm is still fully hand-crafted, suspiciously simple, and — being based on greedy clustering with a fixed distance threshold — forces a trade-off between recall and precision. We propose a new network architecture designed to perform NMS, using only boxes and their score. We report experiments for person detection on PETS and for general object categories on the COCO dataset. Our approach shows promise providing improved localization and occlusion handling.


title: Is exploratory search different? A comparison of information search behavior for exploratory and lookup tasks

abstract: Exploratory search is an increasingly important activity yet challenging for users. Although there exists an ample amount of research into understanding exploration, most of the major information retrieval IR systems do not provide tailored and adaptive support for such tasks. One reason is the lack of empirical knowledge on how to distinguish exploratory and lookup search behaviors in IR systems. The goal of this article is to investigate how to separate the 2 types of tasks in an IR system using easily measurable behaviors. In this article, we first review characteristics of exploratory search behavior. We then report on a controlled study of 6 search tasks with 3 exploratory-comparison, knowledge acquisition, planning-and 3 lookup tasks-fact-finding, navigational, question answering. The results are encouraging, showing that IR systems can distinguish the 2 search categories in the course of a search session. The most distinctive indicators that characterize exploratory search behaviors are query length, maximum scroll depth, and task completion time. However, 2 tasks are borderline and exhibit mixed characteristics. We assess the applicability of this finding by reporting on several classification experiments. Our results have valuable implications for designing tailored and adaptive IR systems.


title: An approach to topology synthesis of analog circuits using hierarchical blocks and symbolic analysis

abstract: This paper presents a method of design automation for analog circuits, focusing on topology generation and quick performance evaluation. First we describe mechanisms to generate circuit topologies with hierarchical blocks. Those blocks are specialized by adding terminal information. The connection between blocks is in compliance with a set of synthesis rules, which are extracted from typical schematics in the literature. Symbolic analysis has been used to select an appropriate topology quickly and to help the designer gain a better understanding of a circuit's behavior. Finally, experimental results show the creativity and efficiency of our method.


title: Fusion of color images and LiDAR data for lane classification

abstract: Lane classification is a fundamental problem for autonomous driving and map-aided localization. Many existing algorithms rely on special designed 1D or 2D filters to extract features of lane markings from either color images or LiDAR data. However, these handcrafted features could not be robust under various driving and lighting conditions. In this paper, we propose a novel algorithm to fuse color images and LiDAR data together. Our algorithm consists of two stages. In the first stage, we segment road surfaces and register LiDAR data with the corresponding color images. In the second stage, we train convolutional neural networks (CNNs) to classify image patches into lane markings and non-markings. Comparing with the algorithms based on handcrafted features, our algorithm learns a set of kernels to extract and integrate features from two different modalities. The pixel-level classification rate in our experiments shows that our algorithm is robust to different conditions such as shadows and occlusions.


title: Personalized ranking metric embedding for next new POI recommendation

abstract: The rapidly growing of Location-based Social Networks (LBSNs) provides a vast amount of check-in data, which enables many services, e.g., point-of-interest (POI) recommendation. In this paper, we study the next new POI recommendation problem in which new POIs with respect to users' current location are to be recommended. The challenge lies in the difficulty in precisely learning users' sequential information and personalizing the recommendation model. To this end, we resort to the Metric Embedding method for the recommendation, which avoids drawbacks of the Matrix Factorization technique. We propose a personalized ranking metric embedding method (PRME) to model personalized check-in sequences. We further develop a PRME-G model, which integrates sequential information, individual preference, and geographical influence, to improve the recommendation performance. Experiments on two real-world LBSN datasets demonstrate that our new algorithm outperforms the state-of-the-art next POI recommendation methods.


title: A 3.3-V single-poly CMOS audio ADC delta-sigma modulator with 98-dB peak SINAD and 105-dB peak SFDR

abstract: This paper presents a second-order /spl Delta//spl Sigma/ modulator for audio-band analog-to-digital conversion implemented in a 3.3-V, 0.5-/spl mu/m, single-poly CMOS process using metal-metal capacitors that achieves 98-dB peak signal-to-noise-and-distortion ratio and 105-dB peak spurious-free dynamic range. The design uses a low-complexity, first-order mismatch shaping 33-level digital-to-analog converter and a 33-level flash analog-to-digital converter with digital common-mode rejection and dynamic element matching of comparator offsets. These signal-processing innovations, combined with established circuit techniques, enable state-of-the art performance in CMOS technology optimized for digital circuits.


Fine-grained essay scoring of a complex writing task for native speakers.


title: MEANS: A medical question-answering system combining NLP techniques and semantic Web technologies

abstract: Abstract The Question Answering (QA) task aims to provide precise and quick answers to user questions from a collection of documents or a database. This kind of IR system is sorely needed with the dramatic growth of digital information. In this paper, we address the problem of QA in the medical domain where several specific conditions are met. We propose a semantic approach to QA based on (i) Natural Language Processing techniques, which allow a deep analysis of medical questions and documents and (ii) semantic Web technologies at both representation and interrogation levels. We present our Semantic Question-Answering System, called MEANS and our proposed method for “Answer Search” based on semantic search and query relaxation. We evaluate the overall system performance on real questions and answers extracted from MEDLINE articles. Our experiments show promising results and suggest that a query-relaxation strategy can further improve the overall performance.


title: Learning model-based planning from scratch

abstract: Conventional wisdom holds that model-based planning is a powerful approach to sequential decision-making. It is often very challenging in practice, however, because while a model can be used to evaluate a plan, it does not prescribe how to construct a plan. Here we introduce the "Imagination-based Planner", the first model-based, sequential decision-making agent that can learn to construct, evaluate, and execute plans. Before any action, it can perform a variable number of imagination steps, which involve proposing an imagined action and evaluating it with its model-based imagination. All imagined actions and outcomes are aggregated, iteratively, into a "plan context" which conditions future real and imagined actions. The agent can even decide how to imagine: testing out alternative imagined actions, chaining sequences of actions together, or building a more complex "imagination tree" by navigating flexibly among the previously imagined states using a learned policy. And our agent can learn to plan economically, jointly optimizing for external rewards and computational costs associated with using its imagination. We show that our architecture can learn to solve a challenging continuous control problem, and also learn elaborate planning strategies in a discrete maze-solving task. Our work opens a new direction toward learning the components of a model-based planning system and how to use them.


title: Future Frame Prediction for Anomaly Detection - A New Baseline

abstract: Anomaly detection in videos refers to the identification of events that do not conform to expected behavior. However, almost all existing methods tackle the problem by minimizing the reconstruction errors of training data, which cannot guarantee a larger reconstruction error for an abnormal event. In this paper, we propose to tackle the anomaly detection problem within a video prediction framework. To the best of our knowledge, this is the first work that leverages the difference between a predicted future frame and its ground truth to detect an abnormal event. To predict a future frame with higher quality for normal events, other than the commonly used appearance (spatial) constraints on intensity and gradient, we also introduce a motion (temporal) constraint in video prediction by enforcing the optical flow between predicted frames and ground truth frames to be consistent, and this is the first work that introduces a temporal constraint into the video prediction task. Such spatial and motion constraints facilitate the future frame prediction for normal events, and consequently facilitate to identify those abnormal events that do not conform the expectation. Extensive experiments on both a toy dataset and some publicly available datasets validate the effectiveness of our method in terms of robustness to the uncertainty in normal events and the sensitivity to abnormal events


title: Integrating large-scale functional genomic data to dissect the complexity of yeast regulatory networks

abstract: A key goal of biology is to construct networks that predict complex system behavior. We combine multiple types of molecular data, including genotypic, expression, transcription factor binding site (TFBS), and protein-protein interaction (PPI) data previously generated from a number of yeast experiments, in order to reconstruct causal gene networks. Networks based on different types of data are compared using metrics devised to assess the predictive power of a network. We show that a network reconstructed by integrating genotypic, TFBS and PPI data is the most predictive. This network is used to predict causal regulators responsible for hot spots of gene expression activity in a segregating yeast population. We also show that the network can elucidate the mechanisms by which causal regulators give rise to larger-scale changes in gene expression activity. We then prospectively validate predictions, providing direct experimental evidence that predictive networks can be constructed by integrating multiple, appropriate data types.


title: A characterization of the Rodinia benchmark suite with comparison to contemporary CMP workloads

abstract: The recently released Rodinia benchmark suite enables users to evaluate heterogeneous systems including both accelerators, such as GPUs, and multicore CPUs. As Rodinia sees higher levels of acceptance, it becomes important that researchers understand this new set of benchmarks, especially in how they differ from previous work. In this paper, we present recent extensions to Rodinia and conduct a detailed characterization of the Rodinia benchmarks (including performance results on an NVIDIA GeForce GTX480, the first product released based on the Fermi architecture). We also compare and contrast Rodinia with Parsec to gain insights into the similarities and differences of the two benchmark collections; we apply principal component analysis to analyze the application space coverage of the two suites. Our analysis shows that many of the workloads in Rodinia and Parsec are complementary, capturing different aspects of certain performance metrics.


title: Cross-tenant trust models in cloud computing

abstract: Most cloud services are built with multi-tenancy which enables data and configuration segregation upon shared infrastructures. Each tenant essentially operates in an individual silo without interacting with other tenants. As cloud computing evolves we anticipate there will be increased need for tenants to collaborate across tenant boundaries. This will require cross-tenant trust models supported and enforced by the cloud service provider. Considering the on-demand self-service feature intrinsic to cloud computing, we propose a formal cross-tenant trust model (CTTM) and its role-based extension (RB-CTTM) integrating various types of trust relations into cross-tenant access control models which can be enforced by the multi-tenant authorization as a service (MTAaaS) platform in the cloud.


title: Online SSVEP-based BCI using Riemannian geometry

abstract: Challenges for the next generation of Brain Computer Interfaces (BCI) are to mitigate the common sources of variability (electronic, electrical, biological) and to develop online and adaptive systems following the evolution of the subject׳s brain waves. Studying electroencephalographic (EEG) signals from their associated covariance matrices allows the construction of a representation which is invariant to extrinsic perturbations. As covariance matrices should be estimated, this paper first presents a thorough study of all estimators conducted on real EEG recording. Working in Euclidean space with covariance matrices is known to be error-prone, one might take advantage of algorithmic advances in Riemannian geometry and matrix manifold to implement methods for Symmetric Positive-Definite (SPD) matrices. Nonetheless, existing classification algorithms in Riemannian spaces are designed for offline analysis. We propose a novel algorithm for online and asynchronous processing of brain signals, borrowing principles from semi-unsupervised approaches and following a dynamic stopping scheme to provide a prediction as soon as possible. The assessment is conducted on real EEG recording: this is the first study on Steady-State Visually Evoked Potential (SSVEP) experimentations to exploit online classification based on Riemannian geometry. The proposed online algorithm is evaluated and compared with state-of-the-art SSVEP methods, which are based on Canonical Correlation Analysis (CCA). It is shown to improve both the classification accuracy and the information transfer rate in the online and asynchronous setup.


title: A 76- to 81-GHz Multi-Channel Radar Transceiver

abstract: This paper presents a packaged 76- to 81-GHz transceiver chip implemented in SiGe BiCMOS for both long-range and short-range automotive radars. The chip contains a two-channel transmitter (TX), a six-channel receiver (RX), a local-oscillator (LO) chain, and built-in self-test (BIST) circuitry. Each transmit channel includes multiple variable-gain amplifiers and a two-stage power amplifier. Measured on-die output power per channel is +18 dBm at 25 °C, decreasing to +16 dBm at 125 °C. Each receive channel includes a current-mode mixer, followed by intermediate-frequency buffers. At 25 °C, measured on-die noise figure is 10–11 dB, conversion gain is 14–15 dB, and input 1-dB compression point exceeds +1 dBm. An integrated LO chain drives the transmit and receive chains and includes an 18.5- to 20.6-GHz voltage-controlled oscillator connected to cascaded frequency doublers and a divide-by-four prescaler. At 25 °C, measured phase noise is −100 dBc/Hz at 1-MHz offset from a 77-GHz carrier. Integrated BIST circuits enable the measurement of signal power, RX gain, channel-to-channel phase, and internal temperature. The chip is flip-chip packaged into a ball-grid array and extracted interconnect loss for the package is 1.5 to 2 dB. Total power consumption for the chip is 1.8 W from 3.3 V for a single-TX, six-RX mode.


Understanding and Improving Morphological Learning in the Neural Machine Translation Decoder


title: Following High-level Navigation Instructions on a Simulated Quadcopter with Imitation Learning

abstract: We introduce a method for following high-level navigation instructions by mapping directly from images, instructions and pose estimates to continuous low-level velocity commands for real-time control. The Grounded Semantic Mapping Network (GSMN) is a fully-differentiable neural network architecture that builds an explicit semantic map in the world reference frame by incorporating a pinhole camera projection model within the network. The information stored in the map is learned from experience, while the local-to-world transformation is computed explicitly. We train the model using DAggerFM, a modified variant of DAgger that trades tabular convergence guarantees for improved training speed and memory use. We test GSMN in virtual environments on a realistic quadcopter simulator and show that incorporating an explicit mapping and grounding modules allows GSMN to outperform strong neural baselines and almost reach an expert policy performance. Finally, we analyze the learned map representations and show that using an explicit map leads to an interpretable instruction-following model.


title: Laius: An 8-Bit Fixed-Point CNN Hardware Inference Engine

abstract: Convolutional Neural Network (CNN) is one of the most effective neural network model for many classification tasks, such as voice recognition, computer vision and biological information processing. Unfortunately, Computation of CNN is both memory-intensive and computation-intensive, which brings a huge challenge to the design of the hardware accelerators. A large number of hardware accelerators for CNN inference are designed by the industry and the academia. Most of the engines are based on 32-bit floating point matrix multiplication, where the data precision is over-provisioned for inference job and the hardware cost are too high. In this paper, a 8-bit fixed-point LeNet inference engine (Laius) is designed and implemented on FPGA. In order to reduce the consumption of FPGA resource, we proposed a methodology to find the optimal bit-length for weight and bias in LeNet, which results in using 8-bit fixed point for most of the computation and using 16-bit fixed point for other computation. The PE (Processing Element) design is proposed. Pipelining and PE tiling technique is use to improve the performance of the inference engine. By theoretical analysis, we came to the conclusion that DSP resource in FPGA is the most critical resource, it should be carefully used during the design process. We implement the inference engine on Xilinx 485t FPGA. Experiment result shows that the designed LeNet inference engine can achieve 44.9 Gops throughput with 8-bit fixed-point operation after pipelining. Moreover, with only 1% loss of accuracy, the 8-bit fixed-point engine largely reduce 31.43% in latency, 87.01% in LUT consumption, 66.50% in BRAM consumption, 65.11% in DSP consumption and 47.95% reduction in power compared to a 32-bit fixed-point inference engine with the same structure.


title: Commercially Available Mobile Phone Headache Diary Apps: A Systematic Review

abstract: Background: Headache diaries are often used by headache sufferers to self-monitor headaches. With advances in mobile technology, mobile electronic diary apps are becoming increasingly common. Objective: This review aims to identify and evaluate all commercially available mobile headache diary apps for the two most popular mobile phone platforms, iOS and Android. Methods: The authors developed a priori a set of 7 criteria that define an ideal headache diary app intended to help headache sufferers better understand and manage their headaches, while providing relevant data to health professionals. The app criteria were intended as minimum requirements for an acceptable headache diary app that could be prescribed by health care professionals. Each app was evaluated and scored against each criterion. Results: Of the 38 apps identified, none of the apps met all 7 app criteria. The 3 highest scoring apps, meeting 5 of the app criteria, were iHeadache (developed by Better QOL), ecoHeadache (developed by ecoTouchMedia), and Headache Diary Pro (developed by Froggyware). Only 18% of the apps were created with scientific or clinical headache expertise and none of the apps reported on psychometric properties. Conclusions: Despite the growing market and demand, there is a concerning lack of scientific expertise and evidence base associated with headache diary apps. [JMIR Mhealth Uhealth 2014;2(3):e36]


title: The Design and Operation of the IEEE 802.22.1 Disabling Beacon for the Protection of TV Whitespace Incumbents

abstract: Recent proposed rulemakings from the Federal Communications Commission in the United States offers the hope of unique access to valuable spectrum; so-called television whitespace (TVWS). Use of this spectrum is contingent upon the protection of the incumbent occupants of the proposed allocation. Television signals are among the most powerful terrestrial RF transmissions on Earth. Even so, detection of these signals to the required levels sufficient to protect these services has proven daunting. Supplemental techniques, such as geo-location, mitigate these challenges for fixed TV broadcast; however, other nomadic low power incumbents also occupy the TVWS spectrum. The most common of these are wireless microphones, a subset of which are licensed and entitled to protection. These devices are allowed a maximum conducted power level of 50 mW and 250 mW on the VHF and UHF channels, respectively. Critical to day-to-day television operations, these devices must also be afforded protection from unlicensed transmitters. Wireless microphones often operate at power levels of 25 mW or less, with inefficient antennas placed physically near the body, yielding effective radiated power levels of 5 to 10 mW, often times even less. In addition, the emissions from these devices are often audio-companded FM, making legitimate, licensed operations indistinguishable from narrowband unlicensed transmissions and other discrete carriers. To that end the IEEE 802.22 working group established task group 1 (TG1) to develop a standard for a protective, disabling beacon method capable of insuring detection of legitimate devices.


title: Single image depth estimation by dilated deep residual convolutional neural network and soft-weight-sum inference

abstract: This paper proposes a new residual convolutional neural network (CNN) architecture for single image depth estimation. Compared with existing deep CNN based methods, our method achieves much better results with fewer training examples and model parameters. The advantages of our method come from the usage of dilated convolution, skip connection architecture and soft-weight-sum inference. Experimental evaluation on the NYU Depth V2 dataset shows that our method outperforms other state-of-the-art methods by a margin.


title: Optimizing the Seakeeping Performance of Ship Hull Forms Using Genetic Algorithm

abstract: Hull form optimization from a hydrodynamic performance point of view is an important aspect of ship design. This study presents a computational method to estimate the ship seakeeping in regular head wave. In the optimization process the Genetic Algorithm (GA) is linked to the computational method to obtain an optimum hull form by taking into account the displacement as design constraint. New hull forms are obtained from the well-known S60 hull and the classical Wigley hull taken as initial hulls in the optimization process at two Froude numbers (Fn=0.2 and Fn=0.3). The optimization variables are a combination of ship hull offsets and main dimensions. The objective function of the optimization procedure is the peak values for vertical absolute motion at a point 0.15LBP behind the forward perpendicular, in regular head waves.


title: A visual analytics approach to author name disambiguation

abstract: Academic publication archives often draw from numerous, heterogeneous sources, whose records can follow differing naming conventions. As such, ambiguity issues concerning authorship of scientific papers often arise, such as authors sharing similar names, the use of first names versus initials, or alternate name spellings for the same author. These ambiguities have plagued research on scientific collaboration and influence. Detecting and correcting these errors is important for maintaining the archive, as well as for ensuring correctness and reliability in any desired subsequent analysis. There are existing analytic methods designed to accomplish this with varying degrees of accuracy, but many of them require fine tuning or manual categorization. We have developed a visual analytics system to interactively control and apply several analytic name disambiguation algorithms in a finely controlled manner, and to present the results to the user for verification or correction. We demonstrate the efficacy of our system by using it to find and resolve ambiguities in authorship data collected from Cornell University Library's arXiv.org and the InfoVis 2004 contest dataset with improved accuracy and speed over existing approaches.


title: Exploring the Use of Query Auto Completion: Search Behavior and Query Entry Profiles

abstract: Query auto completion (QAC) is nearly ubiquitous in modern search systems, however, there are few published studies on how searchers use QAC query suggestions. This study describes the use of QAC by 29 searchers working on eight assigned search topics in a lab setting. We found that our subjects had differing propensities to use QAC, with some searchers rarely using it, while others used QACs for about half their queries. This study extends prior work by examining the use of QAC in the context of whole search sessions across multiple topics, and by comparing search behavior and visual attention for queries that used QAC and those that did not. The study concludes with an exploration of differences in QAC usage and query behavior among searchers, which reveals six possible query entry profiles.


Hardware Design for Machine Learning


title: Drone Indoor Self-Localization

abstract: Drones are becoming more and more popular for their possible use in different scenarios, not only outdoor, but even indoor. In this context, indoor self-localization may become crucial and, clearly, GPS is not an option. In this poster we discuss a possible strategy for drone indoor self-localization based on printed markers, drone cameras and software that is generally used for augmented reality purposes. Achieved results from a preliminary experimental campaign demonstrate the effectiveness of the approach with localization errors in the order of 20cm.


title: Computational and Bioinformatics Frameworks for Next-Generation Whole Exome and Genome Sequencing

abstract: It has become increasingly apparent that one of the major hurdles in the genomic age will be the bioinformatics challenges of next-generation sequencing. We provide an overview of a general framework of bioinformatics analysis. For each of the three stages of (1) alignment, (2) variant calling, and (3) filtering and annotation, we describe the analysis required and survey the different software packages that are used. Furthermore, we discuss possible future developments as data sources grow and highlight opportunities for new bioinformatics tools to be developed.


title: Dual-microphone echo canceller for suppressing loud nonlinear echo

abstract: This paper proposes a new dual microphone echo canceller (DMEC) for suppressing loud nonlinear echo common with hands-free talk on small terminals such as cellphones. The proposed DMEC has an adaptive null former whose nulls by the dual microphones focus on cancelling the nonlinear echo which can not be cancelled by ordinary linear echo canceller. To focus on the nonlinear echo, an adaptive filter eliminates the linear echo components which may perturb the adaptive null former from the second microphone signal before the adaptive null former. The following linear echo canceller and echo suppressor eliminate the linear echo and the residual nonlinear echo in the first microphone signal. Nearend speech degradation by the echo suppressor is reduced because the nonlinear echo is already reduced by the adaptive null former. Evaluations with real cellphones demonstrate that the proposed DMEC can cancel out the loud nonlinear echo almost completely and obtain stable nearend speech quality.


title: Auditory Short-term Memory Capacity Correlates with Gray Matter Density in the Left Posterior STS in Cognitively Normal and Dyslexic Adults

abstract: A central feature of auditory STM is its item-limited processing capacity. We investigated whether auditory STM capacity correlated with regional gray and white matter in the structural MRI images from 74 healthy adults, 40 of whom had a prior diagnosis of developmental dyslexia whereas 34 had no history of any cognitive impairment. Using whole-brain statistics, we identified a region in the left posterior STS where gray matter density was positively correlated with forward digit span, backward digit span, and performance on a "spoonerisms" task that required both auditory STM and phoneme manipulation. Across tasks and participant groups, the correlation was highly significant even when variance related to reading and auditory nonword repetition was factored out. Although the dyslexics had poorer phonological skills, the effect of auditory STM capacity in the left STS was the same as in the cognitively normal group. We also illustrate that the anatomical location of this effect is in proximity to a lesion site recently associated with reduced auditory STM capacity in patients with stroke damage. This result, therefore, indicates that gray matter density in the posterior STS predicts auditory STM capacity in the healthy and damaged brain. In conclusion, we suggest that our present findings are consistent with the view that there is an overlap between the mechanisms that support language processing and auditory STM.


Which Reward Should I Choose? Preliminary Evidence for the Middle-Option Bias in Reward-Based Crowdfunding


title: Wideband circularly-polarized 3-bit transmitarray antenna in Ka-band

abstract: This paper describes the design of a 3-bit circularly-polarized transmitarray antenna operating in Ka-band. The array is based on a unit-cell composed of two U-shaped slot loaded patch antennas, which are connected by a metallized via hole and printed on two identical dielectric substrates. The latter are separated by a ground plane and bonded by a CuClad 6700 film. A 400-element transmitarray is simulated and demonstrates a maximum gain of 27.5 dBi, an aperture efficiency of 47.8% at 29 GHz. The 1-dB gain-bandwidth (LHCP) is equal to 14.8% at 29 GHz. The 1-dB axial ratio bandwidth covers the frequency band between 27–32 GHz. A good agreement is obtained between the full-wave simulation results performed with Ansys HFSS and the ones obtained using our hybrid in-house simulation tool. The impact of phase quantization on the performance of a 40×40-element array is also presented. The corresponding radiation patterns (co- and cross-polarization components) are compared to the masks defined by the European International Standardization Institutions (ETSI) in the case of point-to-point applications.


title: gl2vec: learning feature representation using graphlets for directed networks

abstract: Learning network representation has a variety of applications, such as network classification. Most existing work in this area focuses on static undirected networks and does not account for presence of directed edges or temporal changes. Furthermore, most work focuses on node representations that do poorly on tasks like network classification. In this paper, we propose a novel network embedding methodology, gl2vec, for network classification in both static and temporal directed networks. gl2vec constructs vectors for feature representation using static or temporal network graphlet distributions and a null model for comparing them against random graphs. We demonstrate the efficacy and usability of gl2vec over existing state-of-the-art methods on network classification tasks such as network type classification and subgraph identification in several real-world static and temporal directed networks. We argue that gl2vec provides additional network features that are not captured by state-of-the-art methods, which can significantly improve their classification accuracy by up to 10% in real-world applications


title: New insights into the imprinted MEG8-DMR in 14q32 and clinical and molecular description of novel patients with Temple syndrome

abstract: The chromosomal region 14q32 contains several imprinted genes, which are expressed either from the paternal (DLK1 and RTL1) or the maternal (MEG3, RTL1as and MEG8) allele only. Imprinted expression of these genes is regulated by two differentially methylated regions (DMRs), the germline DLK1/MEG3 intergenic (IG)-DMR (MEG3/DLK1:IG-DMR) and the somatic MEG3-DMR (MEG3:TSS-DMR), which are methylated on the paternal and unmethylated on the maternal allele. Disruption of imprinting in the 14q32 region results in two clinically distinct imprinting disorders, Temple syndrome (TS14) and Kagami-Ogata syndrome (KOS14). Another DMR with a yet unknown function is located in intron 2 of MEG8 (MEG8-DMR, MEG8:Int2-DMR). In contrast to the IG-DMR and the MEG3-DMR, this somatic DMR is methylated on the maternal chromosome and unmethylated on the paternal chromosome. We have performed extensive methylation analyses by deep bisulfite sequencing of the IG-DMR, MEG3-DMR and MEG8-DMR in different prenatal tissues including amniotic fluid cells and chorionic villi. In addition, we have studied the methylation pattern of the MEG8-DMR in different postnatal tissues. We show that the MEG8-DMR is hypermethylated in each of 13 non-deletion TS14 patients (seven newly identified and six previously published patients), irrespective of the underlying molecular cause, and is always hypomethylated in the four patients with KOS14, who have different deletions not encompassing the MEG8-DMR itself. The size and the extent of the deletions and the resulting methylation pattern suggest that transcription starting from the MEG3 promoter may be necessary to establish the methylation imprint at the MEG8-DMR.


title: A syntactic path-based hybrid neural network for negation scope detection

abstract: The automatic detection of negation is a crucial task in a wide-range of natural language processing (NLP) applications, including medical data mining, relation extraction, question answering, and sentiment analysis. In this paper, we present a syntactic path-based hybrid neural network architecture, a novel approach to identify the scope of negation in a sentence. Our hybrid architecture has the particularity to capture salient information to determine whether a token is in the scope or not, without relying on any human intervention. This approach combines a bidirectional long short-term memory (Bi-LSTM) network and a convolutional neural network (CNN). The CNN model captures relevant syntactic features between the token and the cue within the shortest syntactic path in both constituency and dependency parse trees. The Bi-LSTM learns the context representation along the sentence in both forward and backward directions. We evaluate our model on the Bioscope corpus, and get 90.82% F-score (78.31% PCS) on the abstract sub-corpus, outperforming features-dependent approaches.


title: Early Warnings of Cyber Threats in Online Discussions

abstract: We introduce a system for automatically generating warnings of imminent or current cyber-threats. Our system leverages the communication of malicious actors on the darkweb, as well as activity of cyber security experts on social media platforms like Twitter. In a time period between September, 2016 and January, 2017, our method generated 661 alerts of which about 84% were relevant to current or imminent cyber-threats. In the paper, we first illustrate the rationale and workflow of our system, then we measure its performance. Our analysis is enriched by two case studies: the first shows how the method could predict DDoS attacks, and how it would have allowed organizations to prepare for the Mirai attacks that caused widespread disruption in October 2016. Second, we discuss the method's timely identification of various instances of data breaches.


title: Saw singulation characterization on high profile multi chip module packages with thick leadframe

abstract: Increasing demand for multi chip module (MCM) power devices in a quad flat nonleaded (QFN) package outline drives the development of the LFCSPs-P (lead frame chip scale package-saw-power). Multi chip module offers host benefit including performance improvements such as shorter interconnect lengths between dies, lower power supply inductance and lower off-chip driver. Besides, it can reduce the manufacturing cost and increase the utility of programmable systems based on V. Biancomano (2002). Compared to the leaded package with similar body sizes and lead counts, QFN offers far superior thermal performance and provides excellent electrical characteristics as stated in D. Comley and P. Smith (2002). Package designers used nominal thickness of standard Cu-Alloy leadframe and standard package height meant for power packages with soft solder die attach and heavy Al wire bonding to enhance heat dissipation capability other than the heat sinks. Map type design is used to enhance productivity by maximizing the device density. Singulation is achieved by sawing through the molded leadframe channels with diamond type blades that separated the arrays rows and columns according to D. Comley and P. Smith (2002). However, the combination of ductile and brittle material in the same sawing process creates real challenges in term of sawing quality, blade life and throughput. Experiments were carried out and its results show that integration of the blade type, parameters, methodology and other external features give the maximum result for the said challenges.


title: Do Semantic Parts Emerge in Convolutional Neural Networks?

abstract: Semantic object parts can be useful for several visual recognition tasks. Lately, these tasks have been addressed using Convolutional Neural Networks (CNN), achieving outstanding results. In this work we study whether CNNs learn semantic parts in their internal representation. We investigate the responses of convolutional filters and try to associate their stimuli with semantic parts. We perform two extensive quantitative analyses. First, we use ground-truth part bounding-boxes from the PASCAL-Part dataset to determine how many of those semantic parts emerge in the CNN. We explore this emergence for different layers, network depths, and supervision levels. Second, we collect human judgements in order to study what fraction of all filters systematically fire on any semantic part, even if not annotated in PASCAL-Part. Moreover, we explore several connections between discriminative power and semantics. We find out which are the most discriminative filters for object recognition, and analyze whether they respond to semantic parts or to other image patches. We also investigate the other direction: we determine which semantic parts are the most discriminative and whether they correspond to those parts emerging in the network. This enables to gain an even deeper understanding of the role of semantic parts in the network.


title: Health App Use Among US Mobile Phone Users: Analysis of Trends by Chronic Disease Status

abstract: BACKGROUND ::: Mobile apps hold promise for serving as a lifestyle intervention in public health to promote wellness and attenuate chronic conditions, yet little is known about how individuals with chronic illness use or perceive mobile apps. ::: ::: ::: OBJECTIVE ::: The objective of this study was to explore behaviors and perceptions about mobile phone-based apps for health among individuals with chronic conditions. ::: ::: ::: METHODS ::: Data were collected from a national cross-sectional survey of 1604 mobile phone users in the United States that assessed mHealth use, beliefs, and preferences. This study examined health app use, reason for download, and perceived efficacy by chronic condition. ::: ::: ::: RESULTS ::: Among participants, having between 1 and 5 apps was reported by 38.9% (314/807) of respondents without a condition and by 6.6% (24/364) of respondents with hypertension. Use of health apps was reported 2 times or more per day by 21.3% (172/807) of respondents without a condition, 2.7% (10/364) with hypertension, 13.1% (26/198) with obesity, 12.3% (20/163) with diabetes, 12.0% (32/267) with depression, and 16.6% (53/319) with high cholesterol. Results of the logistic regression did not indicate a significant difference in health app download between individuals with and without chronic conditions (P>.05). Compared with individuals with poor health, health app download was more likely among those with self-reported very good health (odds ratio [OR] 3.80, 95% CI 2.38-6.09, P<.001) and excellent health (OR 4.77, 95% CI 2.70-8.42, P<.001). Similarly, compared with individuals who report never or rarely engaging in physical activity, health app download was more likely among those who report exercise 1 day per week (OR 2.47, 95% CI 1.6-3.83, P<.001), 2 days per week (OR 4.77, 95% CI 3.27-6.94, P<.001), 3 to 4 days per week (OR 5.00, 95% CI 3.52-7.10, P<.001), and 5 to 7 days per week (OR 4.64, 95% CI 3.11-6.92, P<.001). All logistic regression results controlled for age, sex, and race or ethnicity. ::: ::: ::: CONCLUSIONS ::: Results from this study suggest that individuals with poor self-reported health and low rates of physical activity, arguably those who stand to benefit most from health apps, were least likely to report download and use these health tools.


title: Accelerated Evaluation of Automated Vehicles in Car-Following Maneuvers

abstract: The safety of automated vehicles (AVs) must be assured before their release and deployment. The current approach to evaluation relies primarily on 1) testing AVs on public roads or 2) track testing with scenarios defined in a test matrix. These two methods have completely opposing drawbacks: the former, while offering realistic scenarios, takes too much time to execute and the latter, though it can be completed in a short amount of time, has no clear correlation to safety benefits in the real world. To avoid the aforementioned problems, we propose accelerated evaluation, focusing on the car-following scenario. The stochastic human-controlled vehicle (HV) motions are modeled based on 1.3 million miles of naturalistic driving data collected by the University of Michigan Safety Pilot Model Deployment Program. The statistics of the HV behaviors are then modified to generate more intense interactions between HVs and AVs to accelerate the evaluation procedure. The importance sampling theory was used to ensure that the safety benefits of AVs are accurately assessed under accelerated tests. Crash, injury and conflict rates for a simulated AV are simulated to demonstrate the proposed approach. Results show that test duration is reduced by a factor of 300 to 100 000 compared with the non-accelerated (naturalistic) evaluation. In other words, the proposed techniques have great potential for accelerating the AV evaluation process.


title: Generic Statistical Relational Entity Resolution in Knowledge Graphs

abstract: Entity resolution, the problem of identifying the underlying entity of references found in data, has been researched for many decades in many communities. A common theme in this research has been the importance of incorporating relational features into the resolution process. Relational entity resolution is particularly important in knowledge graphs (KGs), which have a regular structure capturing entities and their interrelationships. We identify three major problems in KG entity resolution: (1) intra-KG reference ambiguity; (2) inter-KG reference ambiguity; and (3) ambiguity when extending KGs with new facts. We implement a framework that generalizes across these three settings and exploits this regular structure of KGs. Our framework has many advantages over custom solutions widely deployed in industry, including collective inference, scalability, and interpretability. We apply our framework to two real-world KG entity resolution problems, ambiguity in NELL and merging data from Freebase and MusicBrainz, demonstrating the importance of relational features.