title: Distributed Hessian-Free Optimization for Deep Neural Network

abstract: Training deep neural network is a high dimensional and a highly non-convex optimization problem. Stochastic gradient descent (SGD) algorithm and it's variations are the current state-of-the-art solvers for this task. However, due to non-covexity nature of the problem, it was observed that SGD slows down near saddle point. Recent empirical work claim that by detecting and escaping saddle point efficiently, it's more likely to improve training performance. With this objective, we revisit Hessian-free optimization method for deep networks. We also develop its distributed variant and demonstrate superior scaling potential to SGD, which allows more efficiently utilizing larger computing resources thus enabling large models and faster time to obtain desired solution. Furthermore, unlike truncated Newton method (Marten's HF) that ignores negative curvature information by using na\"ive conjugate gradient method and Gauss-Newton Hessian approximation information - we propose a novel algorithm to explore negative curvature direction by solving the sub-problem with stabilized bi-conjugate method involving possible indefinite stochastic Hessian information. We show that these techniques accelerate the training process for both the standard MNIST dataset and also the TIMIT speech recognition problem, demonstrating robust performance with upto an order of magnitude larger batch sizes. This increased scaling potential is illustrated with near linear speed-up on upto 16 CPU nodes for a simple 4-layer network.


title: Mime: compact, low power 3D gesture sensing for interaction with head mounted displays

abstract: We present Mime, a compact, low-power 3D sensor for unencumbered free-form, single-handed gestural interaction with head-mounted displays (HMDs). Mime introduces a real-time signal processing framework that combines a novel three-pixel time-of-flight (TOF) module with a standard RGB camera. The TOF module achieves accurate 3D hand localization and tracking, and it thus enables motion-controlled gestures. The joint processing of 3D information with RGB image data enables finer, shape-based gestural interaction. Our Mime hardware prototype achieves fast and precise 3D gestural control. Compared with state-of-the-art 3D sensors like TOF cameras, the Microsoft Kinect and the Leap Motion Controller, Mime offers several key advantages for mobile applications and HMD use cases: very small size, daylight insensitivity, and low power consumption. Mime is built using standard, low-cost optoelectronic components and promises to be an inexpensive technology that can either be a peripheral component or be embedded within the HMD unit. We demonstrate the utility of the Mime sensor for HMD interaction with a variety of application scenarios, including 3D spatial input using close-range gestures, gaming, on-the-move interaction, and operation in cluttered environments and in broad daylight conditions.


title: Inventory Management of a Fast-Fashion Retail Network

abstract: Working in collaboration with Spain-based retailer Zara, we address the problem of distributing, over time, a limited amount of inventory across all the stores in a fast-fashion retail network. Challenges specific to that environment include very short product life cycles, and store policies whereby an article is removed from display whenever one of its key sizes stocks out. To solve this problem, we first formulate and analyze a stochastic model predicting the sales of an article in a single store during a replenishment period as a function of demand forecasts, the inventory of each size initially available, and the store inventory management policy just stated. We then formulate a mixed-integer program embedding a piecewise-linear approximation of the first model applied to every store in the network, allowing us to compute store shipment quantities maximizing overall predicted sales, subject to inventory availability and other constraints. We report the implementation of this optimization model by Zara to support its inventory distribution process, and the ensuing controlled pilot experiment performed to assess the model's impact relative to the prior procedure used to determine weekly shipment quantities. The results of that experiment suggest that the new allocation process increases sales by 3% to 4%, which is equivalent to $275 M in additional revenues for 2007, reduces transshipments, and increases the proportion of time that Zara's products spend on display within their life cycle. Zara is currently using this process for all of its products worldwide.


title: An efficient extrinsic calibration of a multiple laser scanners and cameras' sensor system on a mobile platform

abstract: This work is motivated by a development of a portable and low-cost solution for road mapping in downtown area using a number of laser scanners and video cameras that are mounted on an intelligent vehicle. Sensors on the vehicle platform are considered to be removable, so that extrinsic calibrations are required after each sensors' setting up. Extrinsic calibration might always happen at or near measuremental sites, so that the facilities such as specially marked large environment could not be supposed as a given in the process. In this research, we present a practical method for extrinsic calibration of multiple laser scanners and video cameras that are mounted on a vehicle platform. Referring to a fiducial coordinate system on vehicle platform, a constraint between the data of a laser scanner and of a video camera is established. It is solved in an iterative way to find a best solution from the laser scanner and from the video camera to the fiducial coordinate system. On the other hand, all laser scanners and video cameras are calibrated for each laser scanner and video camera pair that has common in feature points in a sequential way. An experiment is conducted using the data measured on a normal street road. Calibration results are demonstrated by fusing the sensor data into a global coordinate system.


title: Validation and psychometric properties of a short version of Young's Internet Addiction Test

abstract: A key element of research on Internet addiction is a valid and reliable assessment of problems individuals experience in their daily life due to an excessive or pathological use of the Internet. One of the most frequently used questionnaires is Young's Internet Addiction Test (IAT). However, the factorial structure of the IAT is still discussed controversially. In four studies with different samples we (a) addressed the factorial structure of the IAT with exploratory factor analysis and reduced the items to those with sufficient factor loadings and good item characteristics, (b) checked the factorial structure using confirmatory factor analysis, and (c) analyzed convergent, divergent and incremental validities. We revealed a short version of the IAT, which consists of 12 items and a two-factorial solution with good reliability (study 1). The two factors were named ''loss of control/time management'' and ''craving/social problems''. This two-factorial solution was confirmed by the confirmatory factor analysis (study 2) and we have found good indices for convergent, divergent and incremental validity (studies 3 and 4). In conclusion, the short version of the IAT has good psychometric properties and represents the Internet addiction's key elements based on the proposed diagnostic criteria.


title: Efficient and extensible security enforcement using dynamic data flow analysis

abstract: Current taint tracking systems suffer from high overhead and a lack of generality. In this paper, we solve both of these issues with an extensible system that is an order of magnitude more efficient than previous software taint tracking systems and is fully general to dynamic data flow tracking problems. Our system uses a compiler to transform untrusted programs into policy-enforcing programs, and our system can be easily reconfigured to support new analyses and policies without modifying the compiler or runtime system. Our system uses a sound and sophisticated static analysis that can dramatically reduce the amount of data that must be dynamically tracked. For server programs, our system's average overhead is 0.65% for taint tracking, which is comparable to the best hardware-based solutions. For a set of compute-bound benchmarks, our system produces no runtime overhead because our compiler can prove the absence of vulnerabilities, eliminating the need to dynamically track taint. After modifying these benchmarks to contain format string vulnerabilities, our system's overhead is less than 13%, which is over 6X lower than the previous best solutions. We demonstrate the flexibility and power of our system by applying it to file disclosure vulnerabilities, a problem that taint tracking cannot handle. To prevent such vulnerabilities, our system introduces an average runtime overhead of 0.25% for three open source server programs.


title: TextonBoost for Image Understanding: Multi-Class Object Recognition and Segmentation by Jointly Modeling Texture, Layout, and Context

abstract: This paper details a new approach for learning a discriminative model of object classes, incorporating texture, layout, and context information efficiently. The learned model is used for automatic visual understanding and semantic segmentation of photographs. Our discriminative model exploits texture-layout filters, novel features based on textons, which jointly model patterns of texture and their spatial layout. Unary classification and feature selection is achieved using shared boosting to give an efficient classifier which can be applied to a large number of classes. Accurate image segmentation is achieved by incorporating the unary classifier in a conditional random field, which (i) captures the spatial interactions between class labels of neighboring pixels, and (ii) improves the segmentation of specific object instances. Efficient training of the model on large datasets is achieved by exploiting both random feature selection and piecewise training methods. ::: ::: High classification and segmentation accuracy is demonstrated on four varied databases: (i) the MSRC 21-class database containing photographs of real objects viewed under general lighting conditions, poses and viewpoints, (ii) the 7-class Corel subset and (iii) the 7-class Sowerby database used in He et al. (Proceeding of IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 695---702, June 2004), and (iv) a set of video sequences of television shows. The proposed algorithm gives competitive and visually pleasing results for objects that are highly textured (grass, trees, etc.), highly structured (cars, faces, bicycles, airplanes, etc.), and even articulated (body, cow, etc.).


title: Delay-Constrained Hybrid Computation Offloading With Cloud and Fog Computing

abstract: To satisfy the delay constraint, the computation tasks can be offloaded to some computing servers, referred to as offloading destinations. Different to most of existing works which usually consider only a single type of offloading destinations, in this paper, we study the hybrid computation offloading problem considering diverse computation and communication capabilities of two types of offloading destinations, i.e., cloud computing servers and fog computing servers. The aim is to minimize the total energy consumption for both communication and computation while completing the computation tasks within a given delay constraint. It is quite challenging because the delay cannot be easily formulated as an explicit expression but depends on the embedded communication-computation scheduling problem for the computation offloading to different destinations. To solve the computation offloading problem, we first define a new concept named computation energy efficiency and divide the problem into four subproblems according to the computation energy efficiency of different types of computation offloading and the maximum tolerable delay. For each subproblem, we give a closed-form computation offloading solution with the analysis of communication-computation scheduling under the delay constraint. The numerical results show that the proposed hybrid computation offloading solution achieves lower energy consumption than the conventional single-type computation offloading under the delay constraint.


title: Global Haar-Like Features: A New Extension of Classic Haar Features for Efficient Face Detection in Noisy Images

abstract: This paper addresses the problem of detecting human faces in noisy images. We propose a method that includes a denoising preprocessing step, and a new face detection approach based on a novel extension of Haar-like features. Preprocessing of the input images is focused on the removal of different types of noise while preserving the phase data. For the face detection process, we introduce the concept of global and dynamic global Haar-like features, which are complementary to the well known classical Haar-like features. Matching dynamic global Haar-like features is faster than that of the traditional approach. Also, it does not increase the computational burden in the learning process. Experimental results obtained using images from the MIT-CMU dataset are promising in terms of detection rate and the false alarm rate in comparison with other competing algorithms.


title: Understanding consumer perceptions of fermented yoghurt products using conjoint and generalised procrustes analysis

abstract: Two methods, Conjoint Analysis and Generalised Procrustes Analysis, were used to evaluate the acceptance of a novel category of functional foods: a yoghurt with the probiotic Lactobacillus acidophilus and a yoghurt fortified with calcium and vitamins C and E. Three attributes, function claims, method of production, and certification by an authority, were selected. The results indicated that the respondents attached importance mainly to the presence of the certification by an authority. The genetic technology, used as process of production, was seen as a negative feature by the respondents, whereas they showed little interest in function claims.


title: Learning Multi-touch Conversion Attribution with Dual-attention Mechanisms for Online Advertising

abstract: In online advertising, the Internet users may be exposed to a sequence of different ad campaigns, i.e., display ads, search, or referrals from multiple channels, before led up to any final sales conversion and transaction. For both campaigners and publishers, it is fundamentally critical to estimate the contribution from ad campaign touch-points during the customer journey (conversion funnel) and assign the right credit to the right ad exposure accordingly. However, the existing research on the multi-touch attribution problem lacks a principled way of utilizing the users' pre-conversion actions (i.e., clicks), and quite often fails to model the sequential patterns among the touch points from a user's behavior data. To make it worse, the current industry practice is merely employing a set of arbitrary rules as the attribution model, e.g., the popular last-touch model assigns 100% credit to the final touch-point regardless of actual attributions. In this paper, we propose a Dual-attention Recurrent Neural Network (DARNN) for the multi-touch attribution problem. It learns the attribution values through an attention mechanism directly from the conversion estimation objective. To achieve this, we utilize sequence-to-sequence prediction for user clicks, and combine both post-view and post-click attribution patterns together for the final conversion estimation. To quantitatively benchmark attribution models, we also propose a novel yet practical attribution evaluation scheme through the proxy of budget allocation (under the estimated attributions) over ad channels. The experimental results on two real datasets demonstrate the significant performance gains of our attribution model against the state of the art.


title: Exploring Panarchy in Alpine Grasslands: an Application of Adaptive Cycle Concepts to the Conservation of a Cultural Landscape

abstract: This paper explores approaches of applying the panarchy perspective to a case study of natural resource management in the cultural landscape of upland alpine pastures in northern Italy. The close interaction within the cultural landscape between alpine pasture ecology and the management regimes offers a strong fit with the concept of social-ecological systems and provides insights to appropriate and adaptive management of sites of conservation interest. We examine the limited literature available that offers a resilience understanding of such landscapes and address apparent gaps in the application through our interpretation and use of adaptive cycles and panarchy. We draft conceptual models of adaptive cycles considering ecological and socioeconomic information as acting in separate but interacting domains. Notwithstanding the difficulties in defining and measuring quantitative state variables, we found that a panarchy model can offer a powerful metaphor with practical implications for the maintenance of such alpine cultural landscapes. In effect, our panarchy interpretation of interacting adaptive cycles provides new insights into the description of and the future options for land use in our case study area. Some issues are only partly developed. We hypothesized measurable parameters that could be related to system resilience, such as alternative states, shifting thresholds, and regime stability, which are all dependent on adaptive processes; but we found quantification difficult even at a conceptual level. Nevertheless, we found it helpful to use nature conservation evaluation as a useful surrogate for measures of capital in adaptive cycles of vegetation. However, care is needed to distinguish between the descriptive metaphor using selective surrogate measures and real ecological behavior. Additionally we recognize the need to integrate this ecological understanding with cycles in socioeconomic domains and consider that interactions between the loss of both social and ecological capital would be interesting issues to explore further in our case study. We suggest that resilience theory, through its focus on adaptive cycles interacting at different speeds and across varying geographic scales, offers useful insights into resource management and in particular for nature conservation interest sites, by focusing more on dynamics than on an optimal state of species assemblages. This may help to define sites and to achieve the objectives of Natura 2000 through the European Habitats Directive, offering a basis to guide a conservation of processes, in which cultural tradition and local ecological knowledge are valued.


title: Automatic Factual Question Generation from Text

abstract: Texts with potential educational value are becoming available through the Internet (e.g., Wikipedia, news services). However, using these new tests in classrooms introduces many challenges, one of which is that they usually lack practice exercises and assessments. Here, we address part of this challenge by automating the creation of specific type of assessment item. ::: Specifically, we focus automatically generating factual WH questions. Our goal is to create an automated system that can take as input a text and produce as output questions for assessing a reader's knowledge of the information in the text. The questions could then be presented to a teacher, who could select and revise the ones that he or she judges to be useful. ::: After introducing the problem, we describe some of the computational and linguistic challenges presented by factual question generation. We then present an implemented system that leverages existing natural language processing techniques to address some of these challenges. The system uses a combination or manually encoded transformation rules and a statistical question ranker trained on a tailored dataset of labeled system output. ::: We present experiments that evaluate individual components of the system as well as the system as a whole. We found, among other things, that the question ranker roughly doubled the acceptability rate of top-ranked questions. ::: In a user study, we tested whether K-12 teachers could efficiently create factual questions by selecting and revising suggestions from the system. Offering automatic suggestions reduced the time and effort spent by participants, though it also affected the types or questions that were created. ::: This research supports the idea that natural language processing can help teachers efficiently create instructional content. It provides solutions to some of the major challenges in question generation and an analysis and better understanding of those that remain.


title: Towards Machine Learning on the Semantic Web

abstract: In this paper we explore some of the opportunities and challenges for machine learning on the Semantic Web. The Semantic Web provides standardized formats for the representation of both data and ontological background knowledge. Semantic Web standards are used to describe meta data but also have great potential as a general data format for data communication and data integration. Within a broad range of possible applications machine learning will play an increasingly important role: Machine learning solutions have been developed to support the management of ontologies, for the semi-automatic annotation of unstructured data, and to integrate semantic information into web mining. Machine learning will increasingly be employed to analyze distributed data sources described in Semantic Web formats and to support approximate Semantic Web reasoning and querying. In this paper we discuss existing and future applications of machine learning on the Semantic Web with a strong focus on learning algorithms that are suitable for the relational character of the Semantic Web's data structure. We discuss some of the particular aspects of learning that we expect will be of relevance for the Semantic Web such as scalability, missing and contradicting data, and the potential to integrate ontological background knowledge. In addition we review some of the work on the learning of ontologies and on the population of ontologies, mostly in the context of textual data.


title: Stochastic Variational Inference for Bayesian Time Series Models

abstract: Bayesian models provide powerful tools for analyzing complex time series data, but performing inference with large datasets is a challenge. Stochastic variational inference (SVI) provides a new framework for approximating model posteriors with only a small number of passes through the data, enabling such models to be fit at scale. However, its application to time series models has not been studied. ::: ::: In this paper we develop SVI algorithms for several common Bayesian time series models, namely the hidden Markov model (HMM), hidden semi-Markov model (HSMM), and the non-parametric HDP-HMM and HDP-HSMM. In addition, because HSMM inference can be expensive even in the minibatch setting of SVI, we develop fast approximate updates for HSMMs with durations distributions that are negative binomials or mixtures of negative binomials.


title: Accurate and practical calibration of a depth and color camera pair

abstract: We present an algorithm that simultaneously calibrates a color camera, a depth camera, and the relative pose between them. The method is designed to have three key features that no other available algorithm currently has: accurate, practical, applicable to a wide range of sensors. The method requires only a planar surface to be imaged from various poses. The calibration does not use color or depth discontinuities in the depth image which makes it flexible and robust to noise. We perform experiments with particular depth sensor and achieve the same accuracy as the propietary calibration procedure of the manufacturer.


title: A case study of expressively constrainable level design automation tools for a puzzle game

abstract: Some problems in procedural content generation for games involve hard constraints (e.g. that a generated puzzle is necessarily solvable). Common techniques for generator design lack a way to specify crisp (yes/no) constraints on what counts as a valid content artifact and guarantee these constraints are satisfied in the generator's output. In this paper we present two independent implementations of three diverse level design automation tools for the popular online educational game Refraction. All of the systems guarantee key properties of their output. Applying a constraint-focused generator design perspective in depth, we found that even emergent aesthetic style properties were straightforward to directly control. Our results with Refraction provide further concrete evidence for the claim that the expressive power of constraints and the ease with which they can be incorporated into suitably designed generative processes makes them a powerful tool for producing reliably-controllable generators for game content.


title: Plasma Epstein‐Barr virus DNA for pediatric Burkitt lymphoma diagnosis, prognosis and response assessment in Malawi

abstract: Point-of-care tools are needed in sub-Saharan Africa (SSA) to improve pediatric Burkitt lymphoma (BL) diagnosis and treatment. We evaluated plasma Epstein-Barr virus (pEBV) DNA as a pediatric BL biomarker in Malawi. Prospectively enrolled children with BL were compared to classical Hodgkin lymphoma (cHL) and nonlymphoma diagnoses. Pediatric BL patients received standardized chemotherapy and supportive care. pEBV DNA was measured at baseline, mid-treatment, and treatment completion. Of 121 assessed children, pEBV DNA was detected in 76/88 (86%) with BL, 16/17 (94%) with cHL, and 2/16 (12%) with nonlymphoma, with proportions higher in BL versus nonlymphoma (p < 0.001) and similar in BL versus cHL (p = 0.69). If detected, median pEBV DNA was 6.1 log10 copies/mL for BL, 4.8 log10 copies/mL for cHL, and 3.4 log10 copies/mL for nonlymphoma, with higher levels in BL versus cHL (p = 0.029), and a trend toward higher levels in BL versus nonlymphoma (p = 0.062). pEBV DNA declined during treatment in the cohort overall and increased in several children before clinical relapse. Twelve-month overall survival was 40% in the cohort overall, and for children with baseline pEBV detected, survival was worse if baseline pEBV DNA was ≥6 log10 copies/mL versus <6 log10 copies/mL (p = 0.0002), and also if pEBV DNA was persistently detectable at mid-treatment versus undetectable (p = 0.041). Among children with baseline pEBV DNA detected, viremia was the only significant risk factor for death by 12 months in multivariate analyses (adjusted hazard ratio 1.35 per log10 copies/mL, 95% CI 1.04-1.75, p = 0.023). Quantitative pEBV DNA has potential utility for diagnosis, prognosis, and response assessment for pediatric BL in SSA.


title: Automatic Age Estimation Based on Facial Aging Patterns

abstract: While recognition of most facial variations, such as identity, expression, and gender, has been extensively studied, automatic age estimation has rarely been explored. In contrast to other facial variations, aging variation presents several unique characteristics which make age estimation a challenging task. This paper proposes an automatic age estimation method named AGES (AGing pattErn Subspace). The basic idea is to model the aging pattern, which is defined as the sequence of a particular individual's face images sorted in time order, by constructing a representative subspace. The proper aging pattern for a previously unseen face image is determined by the projection in the subspace that can reconstruct the face image with minimum reconstruction error, while the position of the face image in that aging pattern will then indicate its age. In the experiments, AGES and its variants are compared with the limited existing age estimation methods (WAS and AAS) and some well-established classification methods (kNN, BP, C4.5, and SVM). Moreover, a comparison with human perception ability on age is conducted. It is interesting to note that the performance of AGES is not only significantly better than that of all the other algorithms, but also comparable to that of the human observers.


title: Forecasting electric vehicles sales with univariate and multivariate time series models: The case of China

abstract: The market demand for electric vehicles (EVs) has increased in recent years. Suitable models are necessary to understand and forecast EV sales. This study presents a singular spectrum analysis (SSA) as a univariate time-series model and vector autoregressive model (VAR) as a multivariate model. Empirical results suggest that SSA satisfactorily indicates the evolving trend and provides reasonable results. The VAR model, which comprised exogenous parameters related to the market on a monthly basis, can significantly improve the prediction accuracy. The EV sales in China, which are categorized into battery and plug-in EVs, are predicted in both short term (up to December 2017) and long term (up to 2020), as statistical proofs of the growth of the Chinese EV industry.


title: Pixel Level Characterization of Pinned Photodiode and Transfer Gate Physical Parameters in CMOS Image Sensors

abstract: A method to extract the pinned photodiode (PPD) physical parameters inside a CMOS image sensor pixel array is presented. The proposed technique is based on the Tan et al. pinning voltage characteristic. This pixel device characterization can be performed directly at the solid-state circuit output without the need of any external test structure. The presented study analyzes the different injection mechanisms involved in the different regimes of the characteristic. It is demonstrated that in addition to the pinning voltage, this fast measurement can be used to retrieve the PPD capacitance, the pixel equilibrium full well capacity, and both the transfer gate threshold voltage and its channel potential at a given gate voltage. An alternative approach is also proposed to extract an objective pinning voltage value from this measurement.


title: A fast and robust method for web page template detection and removal

abstract: The widespread use of templates on the Web is considered harmful for two main reasons. Not only do they compromise the relevance judgment of many web IR and web mining methods such as clustering and classification, but they also negatively impact the performance and resource usage of tools that process web pages. In this paper we present a new method that efficiently and accurately removes templates found in collections of web pages. Our method works in two steps. First, the costly process of template detection is performed over a small set of sample pages. Then, the derived template is removed from the remaining pages in the collection. This leads to substantial performance gains when compared to previous approaches that combine template detection and removal. We show, through an experimental evaluation, that our approach is effective for identifying terms occurring in templates - obtaining F-measure values around 0.9, and that it also boosts the accuracy of web page clustering and classification methods.


title: Improved DV-Hop localization algorithm for wireless sensor networks

abstract: Wireless sensor networks (WSN) are usually composed of a great number of randomly deployed nodes that communicate among themselves and gather information about the environment. In many applications, it is required to know geographical location of the sensor which detected an event. The existing localization algorithms can be classified into two categories: range-based and range-free. Range-based algorithms measure the actual distances between nodes, while range-free algorithms approximate the distance based on connectivity information. Range-free localization is cost-effective alternative to a more expensive range based approaches since there is no necessity for additional hardware. However, these techniques usually have higher localization error compared to the range-based algorithms. DV-Hop is one of the range-free localization algorithms utilizing hop-distance estimation. In this paper, we propose an improvement of the range-free DV-Hop localization algorithm and present simulations results to evaluate it.


title: Unbiased Metric Learning: On the Utilization of Multiple Datasets and Web Images for Softening Bias

abstract: Many standard computer vision datasets exhibit biases due to a variety of sources including illumination condition, imaging system, and preference of dataset collectors. Biases like these can have downstream effects in the use of vision datasets in the construction of generalizable techniques, especially for the goal of the creation of a classification system capable of generalizing to unseen and novel datasets. In this work we propose Unbiased Metric Learning (UML), a metric learning approach, to achieve this goal. UML operates in the following two steps: (1) By varying hyper parameters, it learns a set of less biased candidate distance metrics on training examples from multiple biased datasets. The key idea is to learn a neighborhood for each example, which consists of not only examples of the same category from the same dataset, but those from other datasets. The learning framework is based on structural SVM. (2) We do model validation on a set of weakly-labeled web images retrieved by issuing class labels as keywords to search engine. The metric with best validation performance is selected. Although the web images sometimes have noisy labels, they often tend to be less biased, which makes them suitable for the validation set in our task. Cross-dataset image classification experiments are carried out. Results show significant performance improvement on four well-known computer vision datasets.


title: Bayesian Hypernetworks

abstract: We propose Bayesian hypernetworks: a framework for approximate Bayesian inference in neural networks. A Bayesian hypernetwork, h, is a neural network which learns to transform a simple noise distribution, p(e) = N(0,I), to a distribution q(t) := q(h(e)) over the parameters t of another neural network (the ``primary network). We train q with variational inference, using an invertible h to enable efficient estimation of the variational lower bound on the posterior p(t | D) via sampling. In contrast to most methods for Bayesian deep learning, Bayesian hypernets can represent a complex multimodal approximate posterior with correlations between parameters, while enabling cheap iid sampling of q(t). In practice, Bayesian hypernets provide a better defense against adversarial examples than dropout, and also exhibit competitive performance on a suite of tasks which evaluate model uncertainty, including regularization, active learning, and anomaly detection.


title: Intelligent Condition Based Monitoring Using Acoustic Signals for Air Compressors

abstract: Intelligent fault diagnosis of machines for early recognition of faults saves industry from heavy losses occurring due to machine breakdowns. This paper proposes a process with a generic data mining model that can be used for developing acoustic signal-based fault diagnosis systems for reciprocating air compressors. The process includes details of data acquisition, sensitive position analysis for deciding suitable sensor locations, signal pre-processing, feature extraction, feature selection, and a classification approach. This process was validated by developing a real time fault diagnosis system on a reciprocating type air compressor having 8 designated states, including one healthy state, and 7 faulty states. The system was able to accurately detect all the faults by analyzing acoustic recordings taken from just a single position. Additionally, thorough analysis has been presented where performance of the system is compared while varying feature selection techniques, the number of selected features, and multiclass decomposition algorithms meant for binary classifiers.


title: The Switch-Mode Flying-Capacitor DC–DC Converters With Improved Natural Balancing

abstract: This paper presents investigations of voltage-sharing stabilization with the use of passive RLC circuit in switch-mode flying capacitor DC-DC converters. Practical and simulation results and also a mathematical analysis of the balancing process in boost and buck-boost converters are presented. Analyzed converters use additional capacitors (flying capacitors), charged to proper value, for decreasing the voltage on switches and increasing the inductor-current frequency. Such advantages are achieved under proper voltage sharing on the flying capacitors. The voltages are stabilized in a natural way by the load current and with the use of external RLC circuit to force the current that flows through the converters' capacitors under unbalance state. This paper focuses on the analysis of the balancing phenomenon with the use of the external RLC circuit in these topologies. The balancing booster improves the balancing process in these converters, making it independent of the load. It can also reduce oscillations that arise in the converters in transient states.


title: Indole-3-butyric acid biosynthesis in maize is enhanced by cyclohexanedione herbicides

abstract: It has recently been shown that indole-3-butyric acid (IBA) is formed in maize, via acetylation of indoleacetic acid, by a microsomal membrane fraction, ATP and acetyl-CoA. A similarity of this reaction to the carboxylation of acetyl-CoA is proposed and therefore the effects of herbicides specific for the inhibition of acetyl-CoA carboxylase were tested on IBA synthesis. Aryloxyphenoxypropionates and cyclohexanediones inhibited the growth of the maize seedlings as predicted, whereas IBA biosynthesis was promoted after the application of cyclohexanediones. Herbicides with different targets, however, did not increase IBA synthesis. IBA synthetase was not influenced in sethoxydim-tolerant maize seedlings as compared to susceptible plants. It was shown that IBA synthetase from dicotyledonous plants was not affected by sethoxydim application, whereas IBA synthesis in wheat was modulated in the same way as in maize.


title: Performance evaluation of a high speed segmented rotor axial flux switched reluctance traction motor

abstract: This paper deals with the performance evaluation of a high speed axial flux switched reluctance motor for automotive traction applications. The motor makes use of a novel axial flux segmented rotor topology. The main aim of the design is to push the continuous power density as high as possible. The high speed nature of the design and the resulting high electrical frequency, as well as the machine being operated in deep saturation, leads to significant losses. These losses manifest in significant thermal challenges. It is very important that the performance of the motor is adequately predicted in order to optimise the design. Simulated results as well as preliminary performance results are shown.


title: Bipartite Graph Based Entity Ranking for Related Entity Finding

abstract: Related entity finding (REF) is a promising application, which aims to return a list of related entities given a query that describes the source entity, the specific type of target entities, and the relation between the source entity and target entities. One typical entity ranking strategy is to rank the candidate entities based on the co-occurrence between the candidate entities and the given query. However, such a strategy is inadequate to rank entities properly especially for those related but unpopular entities. In this paper, we propose a bipartite graph based entity ranking method, which leverage the Co-List relationship between candidate entities (i.e., entities co-occurring in the same structured/unstructured lists) to help improve the entity ranking. Specifically, we first estimate the initial relevance scores for the candidate entities based on a generative probabilistic model. We then construct a bipartite graph based on Co-List relation between candidate entities, and apply an iterative refinement process analogous to heat diffusion on the graph to propagate the relevance scores over entities. Finally the candidate entities are ranked according to their refined scores. We further develop an optimization framework for the iterative refinement process in our ranking method. Experimental results on the data collection from the TREC 2010 Entity Track show the effectiveness of our proposed method.


title: The Law: The Boon and Bane of IT-enabled Peer-to-Peer Sharing and Collaborative Consumption Services

abstract: IT-enabled peer-to-peer (P2P) sharing and collaborative consumption services (SCCS) allow private persons to provide access for others to their cars, accommodation and other physical assets. Together they constitute the so called Share Economy. These services often operate in a legal gray area. The relation- ship between the law and SCCS is bidirectional. On the one hand, the develop- ment of new SCCS offerings has to comply with a broad body of existing regu- lations. On the other hand, new P2P SCCS businesses often discover legislative loopholes, thereby forcing the law to react. This article conceptualizes the com- plex relationship between the law and IT-enabled P2P SCCS by means of a framework. The applicability of the framework is demonstrated by analyzing a specific SCCS in the electric vehicle charging infrastructure domain. The framework should constitute an effective tool in the design of new and legal P2P SCCS offerings.


title: Drone Detection Based on an Audio-Assisted Camera Array

abstract: In recent years, small, inexpensive UAV - also known as drone - has made great progress, which brings privacy and security issues. Drone detection is one of the important methods to solve these problems. The main challenges of drone detection are: (1) A drone is very confusing with other flying objects such as a bird, (2) the low-flying UAV occlusion happens very frequent, and (3) the existing surveillance coverage is limited. Camera array can be used for large-scale airspace observation. In this paper, we propose a modular camera array system with audio assistance, which consists of dozens of high-definition cameras and multiple microphones, to monitor drones. It can be mounted on a carrier after reducing the size. The system captures the image and audio information of the surroundings in various directions. The system can integrate the information of multiple sensors, and identify the characteristics of the drone to achieve higher efficiency of drone monitoring.


title: Mixed reproductive strategy and mate guarding in a semi-colonial passerine, the swallow Hirundo rustica

abstract: Both male and female swallows Hirundo rustica have a mixed reproductive strategy (parental care for offspring and extra-pair couplations). Mate guarding protects females from male harassment and male swallows from being cuckolded. Females hide their fertile period by copulating successfully with their mates for an extended period during first clutches. Males guard in the pre-fertile period, when many unpaired males are present. Early breeding swallows guard more than late breeders since more sexual chases of females by non-mate males take place in the early period. Solitarily breeding females experience few chases by strange males; copulation frequency, length of copulation period and mate guarding is adjusted to a lower level than among colonial birds. Male guarding activity is more intense in the fertile than in the pre-fertile period. Guarding reduces success of extra-pair copulation attempts.


title: Automatic Music Genres Classification using Machine Learning

abstract: Classification of music genre has been an inspiring job in the area of music information retrieval (MIR). Classification of genre can be valuable to explain some actual interesting problems such as creating song references, finding related songs, finding societies who will like that specific song. The purpose of our research is to find best machine learning algorithm that predict the genre of songs using k-nearest neighbor (k-NN) and Support Vector Machine (SVM). This paper also presents comparative analysis between k-nearest neighbor (k-NN) and Support Vector Machine (SVM) with dimensionality return and then without dimensionality reduction via principal component analysis (PCA). The Mel Frequency Cepstral Coefficients (MFCC) is used to extract information for the data set. In addition, the MFCC features are used for individual tracks. From results we found that without the dimensionality reduction both k-nearest neighbor and Support Vector Machine (SVM) gave more accurate results compare to the results with dimensionality reduction. Overall the Support Vector Machine (SVM) is much more effective classifier for classification of music genre. It gave an overall accuracy of 77%.


title: Monkey: Optimal Navigable Key-Value Store

abstract: In this paper, we show that key-value stores backed by an LSM-tree exhibit an intrinsic trade-off between lookup cost, update cost, and main memory footprint, yet all existing designs expose a suboptimal and difficult to tune trade-off among these metrics. We pinpoint the problem to the fact that all modern key-value stores suboptimally co-tune the merge policy, the buffer size, and the Bloom filters' false positive rates in each level. We present Monkey, an LSM-based key-value store that strikes the optimal balance between the costs of updates and lookups with any given main memory budget. The insight is that worst-case lookup cost is proportional to the sum of the false positive rates of the Bloom filters across all levels of the LSM-tree. Contrary to state-of-the-art key-value stores that assign a fixed number of bits-per-element to all Bloom filters, Monkey allocates memory to filters across different levels so as to minimize this sum. We show analytically that Monkey reduces the asymptotic complexity of the worst-case lookup I/O cost, and we verify empirically using an implementation on top of LevelDB that Monkey reduces lookup latency by an increasing margin as the data volume grows (50%-80% for the data sizes we experimented with). Furthermore, we map the LSM-tree design space onto a closed-form model that enables co-tuning the merge policy, the buffer size and the filters' false positive rates to trade among lookup cost, update cost and/or main memory, depending on the workload (proportion of lookups and updates), the dataset (number and size of entries), and the underlying hardware (main memory available, disk vs. flash). We show how to use this model to answer what-if design questions about how changes in environmental parameters impact performance and how to adapt the various LSM-tree design elements accordingly.


title: Using Task Descriptions in Lifelong Machine Learning for Improved Performance and Zero-Shot Transfer

abstract: Knowledge transfer between tasks can improve the performance of learned models, but requires an accurate estimate of the inter-task relationships to identify the relevant knowledge to transfer. These inter-task relationships are typically estimated based on training data for each task, which is inefficient in lifelong learning settings where the goal is to learn each consecutive task rapidly from as little data as possible. To reduce this burden, we develop a lifelong learning method based on coupled dictionary learning that utilizes high-level task descriptions to model the inter-task relationships. We show that using task descriptors improves the performance of the learned task policies, providing both theoretical justification for the benefit and empirical demonstration of the improvement across a variety of learning problems. Given only the descriptor for a new task, the lifelong learner is also able to accurately predict a model for the new task through zero-shot learning using the coupled dictionary, eliminating the need to gather training data before addressing the task.


title: Nursing students’ learning dynamics and influencing factors in clinical contexts

abstract: Abstract Clinical placements are essential for students to develop clinical skills to qualify as nurses. However, various difficulties encountered by nursing students during their clinical education detract from developing clinical competencies. This constructivist grounded theory study aims to explore nursing students' experiences in clinical nursing education, and to identify the factors that influence the clinical education students receive. Twenty-one individual and six group semi-structured interviews were conducted with sixteen fourth year nursing students and four registered nurses. This research identified six factors that influence nursing students' clinical education: interpersonal, socio-cultural, instructional, environmental, emotional and physical factors. The research has developed a dynamic model of learning in clinical contexts, which offers opportunities to understand how students' learning is influenced multifactorially during clinical placements. The understanding and application of the model can improve nursing instructional design, and subsequently, nursing students' learning in clinical contexts.


title: Nepalese Esthetic Dental (NED) Proportion in Nepalese Population

abstract: Background Lots of studies on maxillary anterior teeth proportions have been studied in different populations in various countries, but no studies have been conducted in Nepal on the esthetic maxillary anterior teeth proportions. Objective The study was done to investigate the maxillary anterior teeth proportions in a Nepalese population. Teeth proportions in the total population were compared with golden proportion (GP) and golden standard (GS). Method A total of 150 Nepalese subjects were divided in three facial types; broad, average and narrow group. Maxillary anterior teeth were measured from dental casts using digital calipers. The perceived width ratios of lateral to central incisor (LI/CI) and canine to lateral incisor (CN/LI), ratios of mean central incisor length to 2 central incisor widths (CIL/2CIW), and actual width to length ratios (WLRs) (%) were calculated in each facial type and compared. Mean LI/CI, CN/LI and CIL/2CIW in total population were calculated and compared with GP (0.618), and mean WLRs in the total population were compared with GS (80%). All teeth proportions were compared among three facial types. One-sample t-test and one-way ANOVA were performed to analyze the data (α = 0.05). Result The LI/CI, CN/LI, CIL/2CIW and WLRs in three facial types showed no significant difference. The LI/CI, CN/LI and CIL/2CIW in the total population were 66%, 70% and 55% respectively, and were significantly different from GP. The WLRs for CI, LI and CN in the total population were 90%, 86% and 89% respectively and significantly differed from GS. These values were considered to constitute the Nepalese Esthetic Dental (NED) proportion. Conclusion No significant difference of maxillary anterior teeth proportions were observed among three facial types. Teeth proportions in the total population significantly differed from GP and GS. We propose the NED proportion as a guideline for dental treatment in the maxillary anterior region in Nepalese populations.


title: Xyloside inhibits synthesis of the class II-associated chondroitin sulfate proteoglycan and antigen presentation events.

abstract: An alternative form of the human invariant chain exists as a chondroitin sulfate proteoglycan (CSPG) with invariant chain as the core protein. The selective inhibitor of proteoglycan synthesis, p-nitrophenyl beta-D-xyloside was used to study the role of this CSPG in class II biology. At xyloside concentrations of 2.5 and 5.0 mM, CSPG synthesis was completely inhibited with marginal inhibition of protein synthesis. The inhibitory effect on CSPG synthesis was completely reversible. The number of class II molecules on the cell surface was not affected by xyloside, but biosynthesis and appearance of newly synthesized class II molecules at the cell surface were both decreased by xyloside. Recognition of influenza virus-infected cells by class II-restricted, virus-specific cytotoxic T lymphocytes was not diminished by the presence of xyloside in the effector phase of the cytotoxicity assay. However, sensitization of target cells was markedly inhibited when target cells were exposed to virus in the presence of xyloside. These results are consistent with the hypothesis that the CSPG form of invariant chain has a role in antigen processing.


title: Fatiguing STDP: Learning from spike-timing codes in the presence of rate codes

abstract: Spiking neural networks (SNNs) could play a key role in unsupervised machine learning applications, by virtue of strengths related to learning from the fine temporal structure of event-based signals. However, some spike-timing-related strengths of SNNs are hindered by the sensitivity of spike-timing-dependent plasticity (STDP) rules to input spike rates, as fine temporal correlations may be obstructed by coarser correlations between firing rates. In this article, we propose a spike-timing-dependent learning rule that allows a neuron to learn from the temporally-coded information despite the presence of rate codes. Our long-term plasticity rule makes use of short-term synaptic fatigue dynamics. We show analytically that, in contrast to conventional STDP rules, our fatiguing STDP (FSTDP) helps learn the temporal code, and we derive the necessary conditions to optimize the learning process. We showcase the effectiveness of FSTDP in learning spike-timing correlations among processes of different rates in synthetic data. Finally, we use FSTDP to detect correlations in real-world weather data from the United States in an experimental realization of the algorithm that uses a neuro-morphic hardware platform comprising phase-change memristive devices. Taken together, our analyses and demonstrations suggest that FSTDP paves the way for the exploitation of the spike-based strengths of SNNs in real-world applications.


TRIPLET MATCHNET FOR MUSIC METRIC LEARNING


title: Confidence Inference for Focused Learning in Stereo Matching

abstract: In this paper, we present confidence inference approachin an unsupervised way in stereo matching. Deep Neu-ral Networks (DNNs) have recently been achieving state-of-the-art performance. However, it is often hard to tellwhether the trained model was making sensible predictionsor just guessing at random. To address this problem, westart from a probabilistic interpretation of theL1loss usedin stereo matching, which inherently assumes an indepen-dent and identical (aka i.i.d.) Laplacian distribution. Weshow that with the newly introduced dense confidence map,the identical assumption is relaxed. Intuitively, the vari-ance in the Laplacian distribution is large for low confidentpixels while small for high-confidence pixels. In practice,the network learns toattenuatelow-confidence pixels (e.g.,noisy input, occlusions, featureless regions) andfocusonhigh-confidence pixels. Moreover, it can be observed fromexperiments that the focused learning is very helpful in find-ing a better convergence state of the trained model, reduc-ing over-fitting on a given dataset.


title: A methodology for modeling the behavior of electricity prosumers within the smart grid

abstract: Currently, there is a deep discussion going on among the power system society about the architecture of the future system. In practice, the smart grid concept is expected to be applied in various different forms and there will be a need for significant investments in both existing and new infrastructures. In this drastically evolving environment, policy makers and electric utilities are finding themselves in challenging positions to plan and prioritize methodologies and initiatives for future developments. Among others, decentralized power generation is gaining significance in liberalized electricity markets, and small size electricity consumers become also producers (prosumers). The scope of this paper is to map those factors (and their interactions) that influence the load profile, and provide a methodology for modeling the behavior of electricity prosumers. Simulations provide a time and cost effective way to vision the future power system and promote the most efficient solutions. The importance of including the behavior of a large amount of small size prosumers in power system simulations will be outlined, and this concept will be illustrated through an example of modeling car drivers' behavior in order to assess the grid impact of electric vehicles charging in Dutch residential areas.


title: Adynamic bone disease: from bone to vessels in chronic kidney disease.

abstract: Adynamic bone disease (ABD) is a well-recognized clinical entity in the complex chronic kidney disease (CKD)-mineral and bone disorder. Although the combination of low intact parathyroid hormone (PTH) and low bone alkaline phosphatase levels may be suggestive of ABD, the gold standard for precise diagnosis is histomorphometric analysis of tetracycline double-labeled bone biopsies. ABD essentially is characterized by low bone turnover, low bone volume, normal mineralization, and markedly decreased cellularity with minimal or no fibrosis. ABD is increasing in prevalence relative to other forms of renal osteodystrophy, and is becoming the most frequent type of bone lesion in some series. ABD develops in situations with reduced osteoanabolic stimulation caused by oversuppression of PTH, multifactorial skeletal resistance to PTH actions in uremia, and/or dysregulation of Wnt signaling. All may contribute not only to bone disease but also to the early vascular calcification processes observed in CKD. Various risk factors have been linked to ABD, including calcium loading, ageing, diabetes, hypogonadism, parathyroidectomy, peritoneal dialysis, and antiresorptive therapies, among others. The relationship between low PTH level, ABD, increased risk fracture, and vascular calcifications may at least partially explain the association of ABD with increased mortality rates. To achieve optimal bone and cardiovascular health, attention should be focused not only on classic control of secondary hyperparathyroidism but also on prevention of ABD, especially in the steadily growing proportions of diabetic, white, and elderly patients. Overcoming the insufficient osteoanabolic stimulation in ABD is the ultimate treatment goal.


title: Analog-to-Information Conversion via Random Demodulation

abstract: Many problems in radar and communication signal processing involve radio frequency (RF) signals of very high bandwidth. This presents a serious challenge to systems that might attempt to use a high-rate analog-to-digital converter (ADC) to sample these signals, as prescribed by the Shannon/Nyquist sampling theorem. In these situations, however, the information level of the signal is often far lower than the actual bandwidth, which prompts the question of whether more efficient schemes can be developed for measuring such signals. In this paper we propose a system that uses modulation, filtering, and sampling to produce a low-rate set of digital measurements. Our "analog-to-information converter" (AIC) is inspired by the recent theory of Compressive Sensing (CS), which states that a discrete signal having a sparse representation in some dictionary can be recovered from a small number of linear projections of that signal. We generalize the CS theory to continuous-time sparse signals, explain our proposed AIC system in the CS context, and discuss practical issues regarding implementation.


title: High performance computing for electric power systems: Applications and trends

abstract: The last significant works that made an effort to review and summarize the relationship between High Performance Computing (HPC) and Power Systems Analysis were published in the mid 1990's. Since that time significant changes have occurred in both fields. HPC has seen the maturation of cluster computing, the advent of multicore, the creation of Grid Computing, the rise of the GPU for general purpose computing and the gradual ending of Moore's law. Power systems have also changed and matured at a rapid pace through deregulation, the integration of renewable energy, the integration of distributed energy production and storage, concerns regarding climate change, and recent discussions concerning the smart grid. Considering the vast amount of change that has occurred in both industries, this work reviews ways in which HPC has been used in the field of power systems over the last 15 years while also identifying some emerging trends and suggesting possible future applications.


title: Twenty-five pitfalls in the analysis of diffusion MRI data.

abstract: Obtaining reliable data and drawing meaningful and robust inferences from diffusion MRI can be challenging and is subject to many pitfalls. The process of quantifying diffusion indices and eventually comparing them between groups of subjects and/or correlating them with other parameters starts at the acquisition of the raw data, followed by a long pipeline of image processing steps. Each one of these steps is susceptible to sources of bias, which may not only limit the accuracy and precision, but can lead to substantial errors. This article provides a detailed review of the steps along the analysis pipeline and their associated pitfalls. These are grouped into 1 pre-processing of data; 2 estimation of the tensor; 3 derivation of voxelwise quantitative parameters; 4 strategies for extracting quantitative parameters; and finally 5 intra-subject and inter-subject comparison, including region of interest, histogram, tract-specific and voxel-based analyses. The article covers important aspects of diffusion MRI analysis, such as motion correction, susceptibility and eddy current distortion correction, model fitting, region of interest placement, histogram and voxel-based analysis. We have assembled 25 pitfalls (several previously unreported) into a single article, which should serve as a useful reference for those embarking on new diffusion MRI-based studies, and as a check for those who may already be running studies but may have overlooked some important confounds. While some of these problems are well known to diffusion experts, they might not be to other researchers wishing to undertake a clinical study based on diffusion MRI.


title: Automatic Pain Assessment with Facial Activity Descriptors

abstract: Pain is a primary symptom in medicine, and accurate assessment is needed for proper treatment. However, today’s pain assessment methods are not sufficiently valid and reliable in many cases. Automatic recognition systems may contribute to overcome this problem by facilitating objective and continuous assessment. In this article we propose a novel feature set for describing facial actions and their dynamics, which we call facial activity descriptors. We apply them to detect pain and estimate the pain intensity. The proposed method outperforms previous state-of-the-art approaches in sequence-level pain classification on both, the BioVid Heat Pain and the UNBC-McMaster Shoulder Pain Expression database. We further discuss major challenges of pain recognition research, benefits of temporal integration, and shortcomings of widely used frame-based pain intensity ground truth.


title: Camera-IMU-based localization: Observability analysis and consistency improvement

abstract: This work investigates the relationship between system observability properties and estimator inconsistency for a Vision-aided Inertial Navigation System (VINS). In particular, first we introduce a new methodology for determining the unobservable directions of nonlinear systems by factorizing the observability matrix according to the observable and unobservable modes. Subsequently, we apply this method to the VINS nonlinear model and determine its unobservable directions analytically. We leverage our analysis to improve the accuracy and consistency of linearized estimators applied to VINS. Our key findings are evaluated through extensive simulations and experimental validation on real-world data, demonstrating the superior accuracy and consistency of the proposed VINS framework compared to standard approaches.


title: Listen to Others, Listen to Yourself: Combining Feedback Review and Reflection to Improve Iterative Design

abstract: Feedback from diverse audiences can contain ambiguity and contradictions, making it difficult to interpret and act on. To promote deeper interpretation of feedback, we tested the effects of combining a reflection activity and reviewing external feedback for an iterative design task. Designers (N=90) created a design and revised it after a) performing a reflection activity before reviewing feedback, b) performing the reflection after reviewing feedback, c) performing the reflection only, or d) reviewing the feedback only. We measured design quality, depth of revision, perceived effort, and confidence; and categorized the content produced from the reflections. We found that performing reflection after feedback review led to the largest increase in perceived quality for the revised designs, and performing reflection and feedback review regardless of the order resulted in the most extensive revision. Our results also showed that performing the reflection alone yielded outcomes that were similar to when only reviewing feedback, and either activity led to better outcomes than the control condition (no feedback or reflection). Designers stated that the reflection helped them recall their goals, question their choices, and prioritize revisions. We argue that designers should perform a lightweight, explicit reflection to enhance their iterative process, and discuss implications for feedback platforms.


title: Apologising 'Safely' in Mediation

abstract: Apologies are often given during mediation. Expressions of regret, remorse, sorrow or sympathy may go some way to resolving the conflict between parties in mediation. Parties and mediators will benefit from understanding how the law and lawyers view apologies. What are the legal implications of making an apology? Why are parties sometimes advised by their lawyers not to apologise? What benefits are there from a legal perspective for a party to make an apology in mediation? Is it possible to apologise “safely”? This article explores the answers to these questions and the meaning of “apology” in the legal context.


Virtual Reality -- A Survival Guide for the Social Scientist


title: Automation of IC Layout with Analog Constraints

abstract: A methodology for the automatic synthesis of full-custom IC layout with analog constraints is presented. The methodology guarantees that all performance constraints are met when feasible, or otherwise, infeasibility is detected as soon as possible, thus providing a robust and efficient design environment. In the proposed approach, performance specifications are translated into lower-level bounds on parasitics or geometric parameters, using sensitivity analysis. Bounds can be used by a set of specialized layout tools performing stack generation, placement, routing, and compaction. For each tool, a detailed description is provided of its functionality, of the way constraints are mapped and enforced, and of its impact on the design flow. Examples drawn from industrial applications are reported to illustrate the effectiveness of the approach.


Giant MagnetoResistive ( GMR ) Sensors for Non-Destructive Detection of Magnetic Flux Leakage from Sub-Surface Defects in Steels


title: Purely Functional Data Structures

abstract: When a C programmer needs an efficient data structure for a particular problem, he or she can often simply look one up in any of a number of good textbooks or handbooks. Unfortunately, programmers in functional languages such as Standard ML or Haskell do not have this luxury. Although some data structures designed for imperative languages such as C can be quite easily adapted to a functional setting, most cannot, usually because they depend in crucial ways on assignments, which are disallowed, or at least discouraged, in functional languages. To address this imbalance, we describe several techniques for designing functional data structures, and numerous original data structures based on these techniques, including multiple variations of lists, queues, double-ended queues, and heaps, many supporting more exotic features such as random access or efficient catenation. ::: In addition, we expose the fundamental role of lazy evaluation in amortized functional data structures. Traditional methods of amortization break down when old versions of a data structure, not just the most recent, are available for further processing. This property is known as persistence, and is taken for granted in functional languages. On the surface, persistence and amortization appear to be incompatible, but we show how lazy evaluation can be used to resolve this conflict, yielding amortized data structures that are efficient even when used persistently. Turning this relationship between lazy evaluation and amortization around, the notion of amortization also provides the first practical techniques for analyzing the time requirements of non-trivial lazy programs. ::: Finally, our data structures offer numerous hints to programming language designers, illustrating the utility of combining strict and lazy evaluation in a single language, and providing non-trivial examples using polymorphic recursion and higher-order, recursive modules.


title: Information security: The moving target

abstract: Information security has evolved from addressing minor and harmless security breaches to managing those with a huge impact on organisations' economic growth. This paper investigates the evolution of information security; where it came from, where it is today and the direction in which it is moving. It is argued that information security is not about looking at the past in anger of an attack once faced; neither is it about looking at the present in fear of being attacked; nor about looking at the future with uncertainty about what might befall us. The message is that organisations and individuals must be alert at all times. Research conducted for this paper explored literature on past security issues to set the scene. This is followed by the assessment and analysis of information security publications in conjunction with surveys conducted in industry. Results obtained are compared and analysed, enabling the development of a comprehensive view regarding the current status of the information security landscape. Furthermore, this paper also highlights critical information security issues that are being overlooked or not being addressed by research efforts currently undertaken. New research efforts are required that minimise the gap between regulatory issues and technical implementations.


title: Concentric Network Symmetry

abstract: We developed a new node-center symmetry measurement for complex networks.The symmetry values were compared among six network models as well as four real-world networks.By using principal component analysis we obtained distinctive signatures of symmetry for each considered network. Quantification of symmetries in complex networks is typically done globally in terms of automorphisms. Extending previous methods to locally assess the symmetry of nodes is not straightforward. Here we present a new framework to quantify the symmetries around nodes, which we call connectivity patterns. We develop two topological transformations that allow a concise characterization of the different types of symmetry appearing on networks and apply these concepts to six network models, namely the Erd?s-Renyi, Barabasi-Albert, random geometric graph, Waxman, Voronoi and rewired Voronoi. Real-world networks, namely the scientific areas of Wikipedia, the world-wide airport network and the street networks of Oldenburg and San Joaquin, are also analyzed in terms of the proposed symmetry measurements. Several interesting results emerge from this analysis, including the high symmetry exhibited by the Erd?s-Renyi model. Additionally, we found that the proposed measurements present low correlation with other traditional metrics, such as node degree and betweenness centrality. Principal component analysis is used to combine all the results, revealing that the concepts presented here have substantial potential to also characterize networks at a global scale. We also provide a real-world application to the financial market network.


Faltering from ethnography to design


title: Scalable Semantic Querying of Text

abstract: We present the Koko system that takes declarative information extraction to a new level by incorporating advances in natural language processing techniques in its extraction language. Koko is novel in that its extraction language simultaneously supports conditions on the surface of the text and on the structure of the dependency parse tree of sentences, thereby allowing for more refined extractions. Koko also supports conditions that are forgiving to linguistic variation of expressing concepts and allows to aggregate evidence from the entire document in order to filter extractions. ::: ::: To scale up, Koko exploits a multi-indexing scheme and heuristics for efficient extractions. We extensively evaluate Koko over publicly available text corpora. We show that Koko indices take up the smallest amount of space, are notably faster and more effective than a number of prior indexing schemes. Finally, we demonstrate Koko's scalability on a corpus of 5 million Wikipedia articles.


title: Learning to Listen to Your Ego-(motion): Metric Motion Estimation from Auditory Signals

abstract: This paper is about robot ego-motion estimation relying solely on acoustic sensing. By equipping a robot with microphones, we investigate the possibility of employing the noise generated by the motors and actuators of the vehicle to estimate its motion. Audio-based odometry is not affected by the scene’s appearance, lighting conditions, and structure. This makes sound a compelling auxiliary source of information for ego-motion modelling in environments where more traditional methods, such as those based on visual or laser odometry, are particularly challenged. By leveraging multi-task learning and deep architectures, we provide a regression framework able to estimate the linear and the angular velocity at which the robot has been travelling. Our experimental evaluation conducted on approximately two hours of data collected with an unmanned outdoor field robot demonstrated an absolute error lower than 0.07 m/s and 0.02 rad/s for the linear and angular velocity, respectively. When compared to a baseline approach, making use of single-task learning scheme, our system shows an improvement of up to 26% in the ego-motion estimation.


title: Efficient online and batch learning using forward backward splitting

abstract: We describe, analyze, and experiment with a framework for empirical loss minimization with regularization. Our algorithmic framework alternates between two phases. On each iteration we first perform an unconstrained gradient descent step. We then cast and solve an instantaneous optimization problem that trades off minimization of a regularization term while keeping close proximity to the result of the first phase. This view yields a simple yet effective algorithm that can be used for batch penalized risk minimization and online learning. Furthermore, the two phase approach enables sparse solutions when used in conjunction with regularization functions that promote sparsity, such as l1. We derive concrete and very simple algorithms for minimization of loss functions with l1, l2, l22, and l∞ regularization. We also show how to construct efficient algorithms for mixed-norm l1/lq regularization. We further extend the algorithms and give efficient implementations for very high-dimensional data with sparsity. We demonstrate the potential of the proposed framework in a series of experiments with synthetic and natural data sets.


title: Perception driven texture generation

abstract: This paper investigates a novel task of generating texture images from perceptual descriptions. Previous work on texture generation focused on either synthesis from examples or generation from procedural models. Generating textures from perceptual attributes have not been well studied yet. Meanwhile, perceptual attributes, such as directionality, regularity and roughness are important factors for human observers to describe a texture. In this paper, we propose a joint deep network model that combines adversarial training and perceptual feature regression for texture generation, while only random noise and user-defined perceptual attributes are required as input. In this model, a preliminary trained convolutional neural network is essentially integrated with the adversarial framework, which can drive the generated textures to possess given perceptual attributes. An important aspect of the proposed model is that, if we change one of the input perceptual features, the corresponding appearance of the generated textures will also be changed. We design several experiments to validate the effectiveness of the proposed method. The results show that the proposed method can produce high quality texture images with desired perceptual properties.


abstract: PubChem (https://pubchem.ncbi.nlm.nih.gov) is a public repository for information on chemical substances and their biological activities, launched in 2004 as a component of the Molecular Libraries Roadmap Initiatives of the US National Institutes of Health (NIH). For the past 11 years, PubChem has grown to a sizable system, serving as a chemical information resource for the scientific research community. PubChem consists of three inter-linked databases, Substance, Compound and BioAssay. The Substance database contains chemical information deposited by individual data contributors to PubChem, and the Compound database stores unique chemical structures extracted from the Substance database. Biological activity data of chemical substances tested in assay experiments are contained in the BioAssay database. This paper provides an overview of the PubChem Substance and Compound databases, including data sources and contents, data organization, data submission using PubChem Upload, chemical structure standardization, web-based interfaces for textual and non-textual searches, and programmatic access. It also gives a brief description of PubChem3D, a resource derived from theoretical three-dimensional structures of compounds in PubChem, as well as PubChemRDF, Resource Description Framework (RDF)-formatted PubChem data for data sharing, analysis and integration with information contained in other databases.


title: An Efficient Approach for Assessing Hyperparameter Importance

abstract: The performance of many machine learning methods depends critically on hyperparameter settings. Sophisticated Bayesian optimization methods have recently achieved considerable successes in optimizing these hyperparameters, in several cases surpassing the performance of human experts. However, blind reliance on such methods can leave end users without insight into the relative importance of different hyperparameters and their interactions. This paper describes efficient methods that can be used to gain such insight, leveraging random forest models fit on the data already gathered by Bayesian optimization. We first introduce a novel, linear-time algorithm for computing marginals of random forest predictions and then show how to leverage these predictions within a functional ANOVA framework, to quantify the importance of both single hyperparameters and of interactions between hyperparameters. We conducted experiments with prominent machine learning frameworks and state-of-the-art solvers for combinatorial problems. We show that our methods provide insight into the relationship between hyperparameter settings and performance, and demonstrate that--even in very highdimensional cases--most performance variation is attributable to just a few hyperparameters.


title: Investigating reproducibility and tracking provenance – A genomic workflow case study

abstract: BackgroundComputational bioinformatics workflows are extensively used to analyse genomics data, with different approaches available to support implementation and execution of these workflows. Reproducibility is one of the core principles for any scientific workflow and remains a challenge, which is not fully addressed. This is due to incomplete understanding of reproducibility requirements and assumptions of workflow definition approaches. Provenance information should be tracked and used to capture all these requirements supporting reusability of existing workflows.ResultsWe have implemented a complex but widely deployed bioinformatics workflow using three representative approaches to workflow definition and execution. Through implementation, we identified assumptions implicit in these approaches that ultimately produce insufficient documentation of workflow requirements resulting in failed execution of the workflow. This study proposes a set of recommendations that aims to mitigate these assumptions and guides the scientific community to accomplish reproducible science, hence addressing reproducibility crisis.ConclusionsReproducing, adapting or even repeating a bioinformatics workflow in any environment requires substantial technical knowledge of the workflow execution environment, resolving analysis assumptions and rigorous compliance with reproducibility requirements. Towards these goals, we propose conclusive recommendations that along with an explicit declaration of workflow specification would result in enhanced reproducibility of computational genomic analyses.


title: Resource discovery in Internet of Things: Current trends and future standardization aspects

abstract: To realize the vision of Internet of Things, there must be mechanisms to discover resources and their capabilities. Thus resource discovery becomes a fundamental requirement of any IoT platform. This paper provides a comprehensive categorization of the current technology landscape for discovery while pointing out their advantages and limitations. Then, a novel search engine based resource discovery framework is proposed. The framework comprises of a proxy layer which includes drivers for various communication technologies and protocols. There is a central registry to store the configurations of the resources and index them based on the configuration parameters. A “lifetime” attribute is introduced which denotes the period through which a resource is discoverable. The search engine ranks the resulting resources of a discovery request and returns an URI to directly access each resource. The functionalities of the proposed framework are exposed using RESTful web services. The framework allows discovery of both smart and legacy devices. We have performed gap analysis of current IoT standards for discovery mechanisms and provide suggestions to improve them to maintain interoperability. The outcomes are communicated to Standard Development Organizations like W3C (in Web of Things Interest Group) and oneM2M.


title: The impact of seizures on pregnancy and delivery

abstract: Abstract Purpose and methods The treatment of women with epilepsy during pregnancy is known to increase the risk of teratogenic effects. Whether seizures during pregnancy have a deleterious effect on the developing child is difficult to determine, but recent animal studies, case studies, cohort studies and population studies have provided useful insights. Results and conclusion Seizures before pregnancy are a predictor for seizures during pregnancy, and catamenial epilepsy may also predict the course of seizures during pregnancy. A first epileptic seizure may also have implications for the pregnancy, depending on the seizure aetiology. Seizures affecting maternal awareness and responsiveness may have cardiac effects on the foetus and may impact on the weight of the newborn. Status epilepticus in pregnancy is rare, but isolated cases of perinatal death and malformations after status epilepticus have been reported in women on antiepileptic drugs. Seizures during delivery occur in about 2% of pregnancies of women with epilepsy, and case studies indicate that the foetal heart may be affected. However, a diagnosis of epilepsy is not an indication per se for caesarean delivery. A well-planned pregnancy can reduce the likelihood of seizures occurring.


title: Regression testing of web applications using Record/Replay tools

abstract: Software engineers often use record/replay tools to enable the automated testing of web applications. Tests created in this man- ner can then be used to regression test new versions of the web applications as they evolve. Web application tests recorded by record/replay tools, however, can be quite brittle; they can easily break as applications change. For this reason, researchers have be- gun to seek approaches for automatically repairing record/replay tests. This research investigates different aspects in relation to test- ing web applications using record/replay tools. The areas that we are interested in include taxonomizing the causes behind breakages and developing automated techniques to repair breakages, creating prevention techniques to stop the occurrence of breakages and de- veloping automated frameworks for root cause analysis. Finally, we intend to evaluate all of these activities via controlled studies involving software engineers and real web application tests.


title: TID and SEE Characterization of Rad-Hardened 1.2GHz PLL IP from New ST CMOS 65nm Space Technology

abstract: We present Single Event Effects characterization and Total Ionizing Dose behavior up to 300 krad(Si) on Rad-Hardened 1.2GHz PLL IP and cold-spare I/O from new ST CMOS 65nm space technology.


title: Real-Time Video Analytics: The Killer App for Edge Computing

abstract: Video analytics will drive a wide range of applications with great potential to impact society. A geographically distributed architecture of public clouds and edges that extend down to the cameras is the only feasible approach to meeting the strict real-time requirements of large-scale live video analytics.


title: Visual tracking via adaptive structural local sparse appearance model

abstract: Sparse representation has been applied to visual tracking by finding the best candidate with minimal reconstruction error using target templates. However most sparse representation based trackers only consider the holistic representation and do not make full use of the sparse coefficients to discriminate between the target and the background, and hence may fail with more possibility when there is similar object or occlusion in the scene. In this paper we develop a simple yet robust tracking method based on the structural local sparse appearance model. This representation exploits both partial information and spatial information of the target based on a novel alignment-pooling method. The similarity obtained by pooling across the local patches helps not only locate the target more accurately but also handle occlusion. In addition, we employ a template update strategy which combines incremental subspace learning and sparse representation. This strategy adapts the template to the appearance change of the target with less possibility of drifting and reduces the influence of the occluded target template as well. Both qualitative and quantitative evaluations on challenging benchmark image sequences demonstrate that the proposed tracking algorithm performs favorably against several state-of-the-art methods.


title: An improved motion detection algorithm using ViBe

abstract: Detection of moving objects in video sequences is a vital task in many computer vision applications. Many different algorithms have been proposed to detect moving objects in successive frames. In this paper we present a novel algorithm for moving object detection from a video. The improved approach of ViBe follows a new background model employing the frame differencing information which is being used in detection of the foreground objects zone. The new algorithm which is the combination of temporal differencing and background subtraction, can save the processing time and reduce the complexity of ViBe. Simulation results confirm the superiority of proposed algorithm to the original ViBe motion detection algorithm. It can detect moving objects more accurately and efficiently and its output optimization ability is stronger than original ViBe.


title: Business Process Modeling Language for Performance Evaluation

abstract: Evaluation of business processes is important for analysis and improvement of an organization. Different methods are used to evaluate the performance like statistics or visualization. However, these methods meet demands mainly on the top organizational level. There is insufficient support to evaluate processes at the process managerial level leading to a limited visibility of deficiencies in business processes at process level. In this paper, we address this challenge and focus on the relation between evaluation of business processes and their representation at the process managerial level. In our research, we follow the design science methodology in order to provide business process models for performance analysis. We also provide constructs and patterns of our proposed modeling language for analysis and improvement of business processes. The analytical business process modeling language is further explained with the help of a case study and demonstrated by extending an existing modeling language.


title: On-demand power management for ad hoc networks

abstract: Abstract Battery power is an important resource in ad hoc networks. It has been observed that in ad hoc networks, energy consumption does not reflect the communication activities in the network. Many existing energy conservation protocols based on electing a routing backbone for global connectivity are oblivious to traffic characteristics. In this paper, we propose an extensible on-demand power management framework for ad hoc networks that adapts to traffic load. Nodes maintain soft-state timers that determine power management transitions. By monitoring routing control messages and data transmission, these timers are set and refreshed on-demand. Nodes that are not involved in data delivery may go to sleep as supported by the MAC protocol. This soft state is aggregated across multiple flows and its maintenance requires no additional out-of-band messages. We implement a prototype of our framework in the ns-2 simulator that uses the IEEE 802.11 MAC protocol. Simulation studies using our scheme with the Dynamic Source Routing protocol show a reduction in energy consumption near 50% when compared to a network without power management under both long-lived CBR traffic and on–off traffic loads, with comparable throughput and latency. Preliminary results also show that it outperforms existing routing backbone election approaches.


title: A Literature Analysis About Social Information Contribution and Consumption on Social Networking Sites

abstract: Social networking sites (SNSs) have emerged as a center for daily social interactions. Every day, millions of users contribute information about themselves, and consume information about others on SNSs. In recent years, we have witnessed a growing number of studies on the issue of social information contribution and consumption behaviors on SNSs. This paper aims to provide a systematic literature review on this topic across different disciplines to understand the current research state and shed light on controversial findings of SNS usage regarding users’ well-being. We identified 126 relevant articles published between 2008 and 2014, and provide an overview of their antecedents and associated outcomes. Our analysis reveals that a majority of existing work focused primarily on social information contribution, its antecedents and favorable outcomes. Only few studies have dealt with contribution behavior and the dark sides of SNS use. Nevertheless, we could identify different characteristics of social information determining the favorability of contribution behavior. Further, we categorized the scarce papers of consumption behavior regarding the social information characteristics and identified different underlying processes: social comparison, monitoring and browsing. These findings contribute to the Information Systems (IS) discipline by consolidating previous knowledge about SNS usage patterns and individual well-being.


title: Quantum Theory and Beyond: Is Entanglement Special?

abstract: Quantum theory makes the most accurate empirical predictions and yet it lacks simple, comprehensible physical principles from which the theory can be uniquely derived. A broad class of probabilistic theories exist which all share some features with quantum theory, such as probabilistic predictions for individual outcomes (indeterminism), the impossibility of information transfer faster than speed of light (no-signaling) or the impossibility of copying of unknown states (no-cloning). A vast majority of attempts to find physical principles behind quantum theory either fall short of deriving the theory uniquely from the principles or are based on abstract mathematical assumptions that require themselves a more conclusive physical motivation. Here, we show that classical probability theory and quantum theory can be reconstructed from three reasonable axioms: (1) (Information capacity) All systems with information carrying capacity of one bit are equivalent. (2) (Locality) The state of a composite system is completely determined by measurements on its subsystems. (3) (Reversibility) Between any two pure states there exists a reversible transformation. If one requires the transformation from the last axiom to be continuous, one separates quantum theory from the classical probabilistic one. A remarkable result following from our reconstruction is that no probability theory other than quantum theory can exhibit entanglement without contradicting one or more axioms.


title: SE3-nets: Learning rigid body motion using deep neural networks

abstract: We introduce SE3-Nets, which are deep networks designed to model rigid body motion from raw point cloud data. Based only on pairs of depth images along with an action vector and point wise data associations, SE3-Nets learn to segment effected object parts and predict their motion resulting from the applied force. Rather than learning point wise flow vectors, SE3-Nets predict SE3 transformations for different parts of the scene. Using simulated depth data of a table top scene and a robot manipulator, we show that the structure underlying SE3-Nets enables them to generate a far more consistent prediction of object motion than traditional flow based networks.


title: SUTIME: A Library for Recognizing and Normalizing Time Expressions

abstract: We describe SUTIME, a temporal tagger for recognizing and normalizing temporal expressions in English text. SUTIME is available as part of the Stanford CoreNLP pipeline and can be used to annotate documents with temporal information. It is a deterministic rule-based system designed for extensibility. Testing on the TempEval-2 evaluation corpus shows that this system outperforms state-of-the-art techniques.


title: Application of Multifractal Analysis to Segmentation of Water Bodies in Optical and Synthetic Aperture Radar Satellite Images

abstract: A method for segmenting water bodies in optical and synthetic aperture radar (SAR) satellite images is proposed. It makes use of the textural features of the different regions in the image for segmentation. The method consists in a multiscale analysis of the images, which allows us to study the images regularity both, locally and globally. As results of the analysis, coarse multifractal spectra of studied images and a group of images that associates each position (pixel) with its corresponding value of local regularity (or singularity) spectrum are obtained. Thresholds are then applied to the multifractal spectra of the images for the classification. These thresholds are selected after studying the characteristics of the spectra under the assumption that water bodies have larger local regularity than other soil types. Classifications obtained by the multifractal method are compared quantitatively with those obtained by neural networks trained to classify the pixels of the images in covered against uncovered by water. In optical images, the classifications are also compared with those derived using the so-called Normalized Differential Water Index (NDWI).


title: GRASP with Path Relinking for the Two-Echelon Vehicle Routing Problem

abstract: We propose a meta-heuristic based on greedy randomized adaptive search procedure (GRASP) combined with path relinking to address the two-echelon vehicle routing problem (2E-VRP), an extension of the capacitated vehicle routing problem (CVRP) in which the delivery from a single depot to customers is achieved by routing and consolidating the freight through intermediate depots called satellites. The problem is treated by separating the depot-to-satellite transfer and the satellite-to-customer delivery, and iteratively solving the two resulting routing subproblems, while adjusting the satellite workloads that link them. The meta-heuristic scheme consists of applying a GRASP and a local search (LS) procedures in sequence. Then, the resulting solution is linked to an elite solution by means of a path relinking procedure. To escape from infeasible solutions, which are quite common in this kind of problem, a feasibility search (FS) procedure is applied within path relinking. Extensive computational results on instances with up to 50 customers and 5 satellites show that the meta-heuristic is able to improve literature results, both in efficiency and accuracy.