title: ABC: A Big CAD Model Dataset for Geometric Deep Learning

abstract: We introduce ABC-Dataset, a collection of one million Computer-Aided Design (CAD) models for research of geometric deep learning methods and applications. Each model is a collection of explicitly parametrized curves and surfaces, providing ground truth for differential quantities, patch segmentation, geometric feature detection, and shape reconstruction. Sampling the parametric descriptions of surfaces and curves allows generating data in different formats and resolutions, enabling fair comparisons for a wide range of geometric learning algorithms. As a use case for our dataset, we perform a large-scale benchmark for estimation of surface normals, comparing existing data driven methods and evaluating their performance against both the ground truth and traditional normal estimation methods.


title: Locomotion Enhances Neural Encoding of Visual Stimuli in Mouse V1

abstract: Neurons in mouse primary visual cortex (V1) are selective for particular properties of visual stimuli. Locomotion causes a change in cortical state that leaves their selectivity unchanged but strengthens their responses. Both locomotion and the change in cortical state are thought to be initiated by projections from the mesencephalic locomotor region, the latter through a disinhibitory circuit in V1. By recording simultaneously from a large number of single neurons in alert mice viewing moving gratings, we investigated the relationship between locomotion and the information contained within the neural population. We found that locomotion improved encoding of visual stimuli in V1 by two mechanisms. First, locomotion-induced increases in firing rates enhanced the mutual information between visual stimuli and single neuron responses over a fixed window of time. Second, stimulus discriminability was improved, even for fixed population firing rates, because of a decrease in noise correlations across the population. These two mechanisms contributed differently to improvements in discriminability across cortical layers, with changes in firing rates most important in the upper layers and changes in noise correlations most important in layer V. Together, these changes resulted in a threefold to fivefold reduction in the time needed to precisely encode grating direction and orientation. These results support the hypothesis that cortical state shifts during locomotion to accommodate an increased load on the visual system when mice are moving. SIGNIFICANCE STATEMENT This paper contains three novel findings about the representation of information in neurons within the primary visual cortex of the mouse. First, we show that locomotion reduces by at least a factor of 3 the time needed for information to accumulate in the visual cortex that allows the distinction of different visual stimuli. Second, we show that the effect of locomotion is to increase information in cells of all layers of the visual cortex. Third, we show that the means by which information is enhanced by locomotion differs between the upper layers, where the major effect is the increasing of firing rates, and in layer V, where the major effect is the reduction in noise correlations.


title: Unsupervised Classification: Similarity Measures, Classical and Metaheuristic Approaches, and Applications

abstract: Clustering is an important unsupervised classification technique where data points are grouped such that points that are similar in some sense belong to the same cluster. Cluster analysis is a complex problem as a variety of similarity and dissimilarity measures exist in the literature. This is the first book focused on clustering with a particular emphasis on symmetry-based measures of similarity and metaheuristic approaches. The aim is to find a suitable grouping of the input data set so that some criteria are optimized, and using this the authors frame the clustering problem as an optimization one where the objectives to be optimized may represent different characteristics such as compactness, symmetrical compactness, separation between clusters, or connectivity within a cluster. They explain the techniques in detail and outline many detailed applications in data mining, remote sensing and brain imaging, gene expression data analysis, and face detection. The book will be useful to graduate students and researchers in computer science, electrical engineering, system science, and information technology, both as a text and as a reference book. It will also be useful to researchers and practitioners in industry working on pattern recognition, data mining, soft computing, metaheuristics, bioinformatics, remote sensing, and brain imaging.


title: A fully on-chip LDO regulator with a novel PSRR boosting circuit

abstract: A novel PSRR boosting circuit is presented to improve mid-frequency power supply rejection ratio (PSRR) of fully on-chip low dropout regulator (LDO). With this method, the zeros of PSRR are rearranged in a nested Miller compensation (NMC) based fully on-chip LDO. The mid-frequency PSRR is boosted when the dominant zero of PSRR is moved to higher frequency by the PSRR boosting circuit. Fabricated with GSMC 0.18μm CMOS process, the LDO features a −63dB PSRR at 100 kHz and about −40dB at 1MHz.


title: A Microgrid Energy Management System Based on the Rolling Horizon Strategy

abstract: A novel energy management system (EMS) based on a rolling horizon (RH) strategy for a renewable-based microgrid is proposed. For each decision step, a mixed integer optimization problem based on forecasting models is solved. The EMS provides online set points for each generation unit and signals for consumers based on a demand-side management (DSM) mechanism. The proposed EMS is implemented for a microgrid composed of photovoltaic panels, two wind turbines, a diesel generator and an energy storage system. A coherent forecast information scheme and an economic comparison framework between the RH and the standard unit commitment (UC) are proposed. Solar and wind energy forecasting are based on phenomenological models with updated data. A neural network for two-day-ahead electric consumption forecasting is also designed. The system is tested using real data sets from an existent microgrid in Chile (ESUSCON). The results based on different operation conditions show the economic sense of the proposal. A full practical implementation of the system for ESUSCON is envisioned.


title: Hyperparameter Importance Across Datasets

abstract: With the advent of automated machine learning, automated hyperparameter optimization methods are by now routinely used in data mining. However, this progress is not yet matched by equal progress on automatic analyses that yield information beyond performance-optimizing hyperparameter settings. In this work, we aim to answer the following two questions: Given an algorithm, what are generally its most important hyperparameters, and what are typically good values for these? We present methodology and a framework to answer these questions based on meta-learning across many datasets. We apply this methodology using the experimental meta-data available on OpenML to determine the most important hyperparameters of support vector machines, random forests and Adaboost, and to infer priors for all their hyperparameters. The results, obtained fully automatically, provide a quantitative basis to focus efforts in both manual algorithm design and in automated hyperparameter optimization. The conducted experiments confirm that the hyperparameters selected by the proposed method are indeed the most important ones and that the obtained priors also lead to statistically significant improvements in hyperparameter optimization.


title: Towards a domain-specific framework for predictive analytics in manufacturing

abstract: Data analytics is proving to be very useful for achieving productivity gains in manufacturing. Predictive analytics (using advanced machine learning) is particularly valuable in manufacturing, as it leads to production improvement with respect to the cost, quantity, quality and sustainability of manufactured products by anticipating changes to the manufacturing system states. Many small and medium manufacturers do not have the infrastructure, technical capability or financial means to take advantage of predictive analytics. A domain-specific language and framework for performing predictive analytics for manufacturing and production frameworks can counter this deficiency. In this paper, we survey some of the applications of predictive analytics in manufacturing and we discuss the challenges that need to be addressed. Then, we propose a core set of abstractions and a domain-specific framework for applying predictive analytics on manufacturing applications. Such a framework will allow manufacturers to take advantage of predictive analytics to improve their production.


title: Conformal orbit sparing radiation therapy: a treatment option for advanced skin cancer of the parotid and ear region

abstract: Introduction ::: ::: New surgical methods have enabled resection of previously in-operable tumours in the region of the parotid gland and ear. This has translated to deeper target volumes being treated with adjuvant radiotherapy. Due to the limitations of existing conformal techniques, alternative planning approaches are required to cover the target volume with appropriate sparing of adjacent critical structures. Although intensity modulated radiation therapy (IMRT) may be able to achieve these goals compared with the existing conformal method, a new orbital sparing radiation therapy (OSRaT) technique was evaluated as an alternative conformal planning process. The study objective was to evaluate the dosimetry of three planning methods: pre-existing conformal, IMRT and OSRaT techniques. ::: ::: ::: ::: Methods ::: ::: Ten patients were planned retrospectively using the existing three-dimensional conformal radiotherapy (3DCRT), IMRT and OSRaT techniques. Dosimetry was analysed using the homogeneity index (HI), conformity index (CI), the volume of planning target volumes (PTV) under and over treated by the 95% isodose and dose to critical structures. ::: ::: ::: ::: Results ::: ::: OSRaT achieved superior 95% coverage of the high-dose PTV while delivering HI similar to IMRT for intermediate and high-dose PTVs. The CI for the high-dose PTV was comparable between the three techniques, however IMRT was statistically better for the low- and intermediate dose PTVs. All three techniques showed adequate orbital sparing, however OSRaT and IMRT achieved this with less under dosing of the PTVs. ::: ::: ::: ::: Conclusion ::: ::: For the treatment of patients with advanced skin cancer of the parotid and ear, both IMRT and the OSRaT techniques are viable options.


title: An Introduction to Distributed Smart Cameras Smart camera systems are designed to produce data, and to recognize and report on objects and activities of interest, rather than to capture images.

abstract: Distributed smart cameras (DSCs) are real-time distributed embedded systems that perform computer vision using multiple cameras. This new approach has emerged thanks to a confluence of simultaneous advances in four key disciplines: computer vision, image sensors, embedded com- puting, and sensor networks. Processing images in a network of distributed smart cameras introduces several complications. However, we believe that the problems DSCs solve are much more important than the challenges of designing and building a distributed video system. We argue that distributed smart cameras represent key components for future embedded computer vision systems and that smart cameras will become an enabling technology for many new applications. We summarize smart camera technology and applications, discuss current trends, and identify important research challenges.


title: Design and process technology co-optimization with SADP BEOL in sub-10nm SRAM bitcell

abstract: Due to the resolution limit of the lithography tools, multiple patterning technologies are being introduced to the back-end of the line (BEOL). For example, LELELE (or LE3, triple litho-etch) or SADP (self-aligned double patterning) [1] are already implemented in 10nm node technology based on a polygon's geometry, its orientation and pitch requirement. However, metal architecture, arrangement of signal and power lines in a metal layer or across metal layers, cause a significant impact on operating performance as well as determine the metal's orientation, which is also an important element for lithographic performance. Therefore, all the factors should be addressed together to arrive at an optimal chip performance.


title: Open-Ended Content-Style Recombination Via Leakage Filtering

abstract: We consider visual domains in which a class label specifies the content of an image, and class-irrelevant properties that differentiate instances constitute the style. We present a domain-independent method that permits the open-ended recombination of style of one image with the content of another. Open ended simply means that the method generalizes to style and content not present in the training data. The method starts by constructing a content embedding using an existing deep metric-learning technique. This trained content encoder is incorporated into a variational autoencoder (VAE), paired with a to-be-trained style encoder. The VAE reconstruction loss alone is inadequate to ensure a decomposition of the latent representation into style and content. Our method thus includes an auxiliary loss, leakage filtering, which ensures that no style information remaining in the content representation is used for reconstruction and vice versa. We synthesize novel images by decoding the style representation obtained from one image with the content representation from another. Using this method for data-set augmentation, we obtain state-of-the-art performance on few-shot learning tasks.


title: Accelerated Gradient Descent Escapes Saddle Points Faster than Gradient Descent

abstract: Nesterov's accelerated gradient descent (AGD), an instance of the general family of "momentum methods", provably achieves faster convergence rate than gradient descent (GD) in the convex setting. However, whether these methods are superior to GD in the nonconvex setting remains open. This paper studies a simple variant of AGD, and shows that it escapes saddle points and finds a second-order stationary point in $\tilde{O}(1/\epsilon^{7/4})$ iterations, faster than the $\tilde{O}(1/\epsilon^{2})$ iterations required by GD. To the best of our knowledge, this is the first Hessian-free algorithm to find a second-order stationary point faster than GD, and also the first single-loop algorithm with a faster rate than GD even in the setting of finding a first-order stationary point. Our analysis is based on two key ideas: (1) the use of a simple Hamiltonian function, inspired by a continuous-time perspective, which AGD monotonically decreases per step even for nonconvex functions, and (2) a novel framework called improve or localize, which is useful for tracking the long-term behavior of gradient-based optimization algorithms. We believe that these techniques may deepen our understanding of both acceleration algorithms and nonconvex optimization.


title: An Easy-to-Use Airborne LiDAR Data Filtering Method Based on Cloth Simulation

abstract: Separating point clouds into ground and non-ground measurements is an essential step to generate digital terrain models (DTMs) from airborne LiDAR (light detection and ranging) data. However, most filtering algorithms need to carefully set up a number of complicated parameters to achieve high accuracy. In this paper, we present a new filtering method which only needs a few easy-to-set integer and Boolean parameters. Within the proposed approach, a LiDAR point cloud is inverted, and then a rigid cloth is used to cover the inverted surface. By analyzing the interactions between the cloth nodes and the corresponding LiDAR points, the locations of the cloth nodes can be determined to generate an approximation of the ground surface. Finally, the ground points can be extracted from the LiDAR point cloud by comparing the original LiDAR points and the generated surface. Benchmark datasets provided by ISPRS (International Society for Photogrammetry and Remote Sensing) working Group III/3 are used to validate the proposed filtering method, and the experimental results yield an average total error of 4.58%, which is comparable with most of the state-of-the-art filtering algorithms. The proposed easy-to-use filtering method may help the users without much experience to use LiDAR data and related technology in their own applications more easily.


title: Diffeomorphic Surface Registration with Atrophy Constraints

abstract: Diffeomorphic registration using optimal control on the diffeomorphism group and on shape spaces has become widely used since the development of the large deformation diffeomorphic metric mapping (LDDMM) algorithm. More recently, a series of algorithms involving sub-Riemannian constraints have been introduced in which the velocity fields that control the shapes in the LDDMM framework are constrained in accordance with a specific deformation model. Here, we extend this setting by considering, for the first time, inequality constraints in order to estimate surface deformations that only allow for atrophy, introducing for this purpose an algorithm that uses the augmented Lagrangian method. We prove the existence of solutions of the associated optimal control problem and the consistency of our approximation scheme. These developments are illustrated by numerical experiments on simulated and real data.


title: Sequential Attention: A Context-Aware Alignment Function for Machine Reading

abstract: In this paper we propose a neural network model with a novel Sequential Attention layer that extends soft attention by assigning weights to words in an input sequence in a way that takes into account not just how well that word matches a query, but how well surrounding words match. We evaluate this approach on the task of reading comprehension (on the Who did What and CNN datasets) and show that it dramatically improves a strong baseline--the Stanford Reader--and is competitive with the state of the art.


title: Hydrogen recombination kinetics and nuclear thermal rocket performance prediction

abstract: The rate constants for the hydrogen three-body collisional recombination reaction with atomic and molecular hydrogen acting as third bodies have been determined by numerous investigators during the past 30 yr, but these rates exhibit significant scatter. The discrepancies in the rate constants determined by different investigators are as great as two orders of magnitude in the temperature range of interest for nuclear thermal rocket (NTR) operation, namely, 2000-3300 K. The impact of this scatter on our ability to predict the specific impulse (I(sub sp)) delivered by a 30-klbf NTR has been determined for chamber pressures and temperatures from, respectively, 20-1000 psia and 2700-3300 K. The variation in I(sub sp) produced by using the different rate constants is as great as 10%, or 100 s. This variation also obscures the influence of chamber pressure on I(sub sp); using fast kinetics, low pressures yield significantly improved performance, while using slow or nominal kinetics, the pressure dependence of I(sub sp) is negligible. Because the flow composition freezes at very small area ratios, optimization of the nozzle contour in the near-throat region maximizes recombination. Vibrational relaxation is found to produce negligible losses in I(sub sp). 36 refs.


title: Personalized Risk Scoring for Critical Care Prognosis Using Mixtures of Gaussian Processes

abstract: OBJECTIVE ::: In this paper, we develop a personalized real-time risk scoring algorithm that provides timely and granular assessments for the clinical acuity of ward patients based on their (temporal) lab tests and vital signs; the proposed risk scoring system ensures timely intensive care unit admissions for clinically deteriorating patients. ::: ::: ::: METHODS ::: The risk scoring system is based on the idea of sequential hypothesis testing under an uncertain time horizon. The system learns a set of latent patient subtypes from the offline electronic health record data, and trains a mixture of Gaussian Process experts, where each expert models the physiological data streams associated with a specific patient subtype. Transfer learning techniques are used to learn the relationship between a patient's latent subtype and her static admission information (e.g., age, gender, transfer status, ICD-9 codes, etc). ::: ::: ::: RESULTS ::: Experiments conducted on data from a heterogeneous cohort of 6321 patients admitted to Ronald Reagan UCLA medical center show that our score significantly outperforms the currently deployed risk scores, such as the Rothman index, MEWS, APACHE, and SOFA scores, in terms of timeliness, true positive rate, and positive predictive value. ::: ::: ::: CONCLUSION ::: Our results reflect the importance of adopting the concepts of personalized medicine in critical care settings; significant accuracy and timeliness gains can be achieved by accounting for the patients' heterogeneity. ::: ::: ::: SIGNIFICANCE ::: The proposed risk scoring methodology can confer huge clinical and social benefits on a massive number of critically ill inpatients who exhibit adverse outcomes including, but not limited to, cardiac arrests, respiratory arrests, and septic shocks.


title: Understanding customer relationship management (CRM): People, process and technology

abstract: Customer relationship management (CRM) is a combination of people, processes and technology that seeks to understand a company's customers. It is an integrated approach to managing relationships by focusing on customer retention and relationship development. CRM has evolved from advances in information technology and organizational changes in customer‐centric processes. Companies that successfully implement CRM will reap the rewards in customer loyalty and long run profitability. However, successful implementation is elusive to many companies, mostly because they do not understand that CRM requires company‐wide, cross‐functional, customer‐focused business process re‐engineering. Although a large portion of CRM is technology, viewing CRM as a technology‐only solution is likely to fail. Managing a successful CRM implementation requires an integrated and balanced approach to technology, process, and people.


SpamBayes: Effective open-source, Bayesian based, email classification system


title: Content-Based Scheduling of Virtual Machines (VMs) in the Cloud

abstract: Organizations of all sizes are shifting their IT infrastructures to the cloud because of its cost efficiency and convenience. Because of the on-demand nature of the Infrastructure as a Service (IaaS) clouds, hundreds of thousands of virtual machines (VMs) may be deployed and terminated in a single large cloud data center each day. In this paper, we propose a content-based scheduling algorithm for the placement of VMs in data centers. We take advantage of the fact that it is possible to find identical disk blocks in different VM disk images with similar operating systems by scheduling VMs with high content similarity on the same hosts. That allows us to reduce the amount of data transferred when deploying a VM on a destination host. In this paper, we first present our study of content similarity between different VMs, based on a large set of VMs with different operating systems that represent the majority of popular operating systems in use today. Our analysis shows that content similarity between VMs with the same operating system and close version numbers (e.g., Ubuntu 12.04 vs. Ubuntu 11.10) can be as high as 60%. We also show that there is close to zero content similarity between VMs with different operating systems. Second, based on the above results, we designed a content-based scheduling algorithm that lowers the network traffic associated with transfer of VM disk images inside data centers. Our experimental results show that the amount of data transfer associated with deployment of VMs and transfer of virtual disk images can be lowered by more than 70%, resulting in significant savings in data center network utilization and congestion.


title: Linking obesity and activity level with children's television and video game use

abstract: Abstract This study examined the links between childhood obesity, activity participation and television and video game use in a nationally representative sample of children ( N =2831) ages 1–12 using age-normed body mass index (BMI) ratings. Results indicated that while television use was not related to children's weight status, video game use was. Children with higher weight status played moderate amounts of electronic games, while children with lower weight status played either very little or a lot of electronic games. Interaction analyses revealed that this curvilinear relationship applied to children under age 8 and that girls, but not boys, with higher weight status played more video games. Children ages 9–12 with lower weight status used the computer (non-game) for moderate amounts of time, while those with higher weight status used the computer either very little or a lot. This was also true for the relationship between print use and weight status for children of all ages. Results also indicated that children with higher weight status spent more time in sedentary activities than those with lower weight status.


Detecting Camouflaged Content Polluters.


title: Ulerythema ophryogenes: updates and insights.

abstract: Ulerythema ophryogenes is a rare cutaneous atrophic disorder that occasionally is associated with Noonan syndrome, de Lange syndrome, Rubinstein-Taybi syndrome, and cardiofaciocutaneous (CFC) syndrome. Often presenting in pediatric patients, the pathogenesis of ulerythema ophryogenes remains unclear, though several genetic causes have been suggested. Treatment recommendations remain anecdotal, but clearance has been noted as the patient ages. Although topical agents have been the mainstay of therapy, recent advancement in laser intervention for treatment of ulerythema ophryogenes is promising.


title: PULSE WIDTH MODULATED IGBT AC CHOPPER

abstract: A new circuit is presented for three-phase voltage controller (AC chopper) constructed from IGBTs. Traditionally such an equipment contains thyristors as switching elements. All drawbacks of this solution come from its only possible control method: firing control. The new circuit allows Pulse Width Modulation (PWM) control, providing much better properties. A new and advantageous control method is presented, including the protections also. Many application fields can be found, all require AC voltage control. The examined ones are: Energy-saving control of induction motor drive; Compensation of an unbalanced supply; Active filtering of the upper harmonics; Excitation system of brushless motor.


title: An Eulerian PDE approach for computing tissue thickness

abstract: We outline an Eulerian framework for computing the thickness of tissues between two simply connected boundaries that does not require landmark points or parameterizations of either boundary. Thickness is defined as the length of correspondence trajectories, which run from one tissue boundary to the other, and which follow a smooth vector field constructed in the region between the boundaries. A pair of partial differential equations (PDEs) that are guided by this vector field are then solved over this region, and the sum of their solutions yields the thickness of the tissue region. Unlike other approaches, this approach does not require explicit construction of any correspondence trajectories. An efficient, stable, and computationally fast solution to these PDEs is found by careful selection of finite differences according to an upwinding condition. The behavior and performance of our method is demonstrated on two simulations and two magnetic resonance imaging data sets in two and three dimensions. These experiments reveal very good performance and show strong potential for application in tissue thickness visualization and quantification.


title: ATOMIC: An Atlas of Machine Commonsense for If-Then Reasoning

abstract: We present ATOMIC, an atlas of everyday commonsense reasoning, organized through 877k textual descriptions of inferential knowledge. Compared to existing resources that center around taxonomic knowledge, ATOMIC focuses on inferential knowledge organized as typed if-then relations with variables (e.g., “if X pays Y a compliment, then Y will likely return the compliment”). We propose nine if-then relation types to distinguish causes vs. effects, agents vs. themes, voluntary vs. involuntary events, and actions vs. mental states. By generatively training on the rich inferential knowledge described in ATOMIC, we show that neural models can acquire simple commonsense capabilities and reason about previously unseen events. Experimental results demonstrate that multitask models that incorporate the hierarchical structure of if-then relation types lead to more accurate inference compared to models trained in isolation, as measured by both automatic and human evaluation.


title: A 14.4nW 122KHz dual-phase current-mode relaxation oscillator for near-zero-power sensors

abstract: This paper presents a novel ultra-low-power dual-phase current-mode relaxation oscillator, which produces a 122 kHz digital clock and has total power consumption of 14.4 nW at 0.6 V. Its frequency dependence is 327 ppm/°C over a temperature range of −20° C to 100° C, and its supply voltage coefficient is ±3.0%/V from 0.6 V to 1.8 V. The proposed oscillator is fabricated in 0.18 μm CMOS technology and occupies 0.03 mm2. At room temperature it achieves a figure of merit of 120 pW/kHz, making it one of the most efficient relaxation oscillators reported to date.


title: Data mining approaches for network intrusion detection: from dimensionality reduction to misuse and anomaly detection

abstract: This paper describes the use of data mining techniques to solve three important issues in network intrusion detection problems. The first goal is finding the best dimensionality reduction algorithm which reduces the computational cost while still maintains the accuracy. We implement both feature extraction (Principal Component Analysis and Independent Component Analysis) and feature selection (Genetic Algorithm and Particle Swarm Optimization) techniques for dimensionality reduction. The second goal is finding the best algorithm for misuse detection system to detect known intrusion. We implement four basic machine learning algorithms (Naive Bayes, Decision Tree, Nearest Neighbour and Rule Induction) and then apply ensemble algorithms such as bagging, boosting and stacking to improve the performance of these four basic algorithms. The third goal is finding the best clustering algorithms to detect network anomalies which contains unknown intrusion. We analyze and compare the performance of four unsupervised clustering algorithms (k-Means, k-Medoids, EM clustering and distance-based outlier detection) in terms of accuracy and false positives. Our experiment shows that the Nearest Neighbour (NN) classifier when implemented with Particle Swarm Optimization (PSO) as an attribute selection algorithm, achieved the best performance, which is 99.71% accuracy and 0.27% false positive. The misuse detection technique achieves a very good performance with more than 99% accuracy when detecting known intrusion but it fails to accurately detect data set with a large number of unknown intrusions where the highest accuracy is only 63.97%. In contrast, the anomaly detection approach shows promising results where the distance-based outlier detection method outperforms the other three clustering algorithms with the accuracy of 80.15%, followed by EM clustering (78.06%), k-Medoids (76.71%), improved k-Means (65.40%) and k-Means (57.81%).


title: 10 kV/120 A SiC DMOSFET half H-bridge power modules for 1 MVA solid state power substation

abstract: In this paper, the extension of SiC power technology to higher voltage 10 kV/10 A SiC DMOSFETs and SiC JBS diodes is discussed. A new 10 kV/120 A SiC power module using these 10 kV SiC devices is also described which enables a compact 13.8 kV to 465/√3 solid state power substation (SSPS) rated at 1 MVA.


title: Photo Realistic Image Completion via Dense Correspondence

abstract: In this paper, we propose an image completion algorithm based on dense correspondence between the input image and an exemplar image retrieved from the Internet. Contrary to traditional methods which register two images according to sparse correspondence, in this paper, we propose a hierarchical PatchMatch method that progressively estimates a dense correspondence, which is able to capture small deformations between images. The estimated dense correspondence has usually large occlusion areas that correspond to the regions to be completed. A nearest neighbor field (NNF) interpolation algorithm interpolates a smooth and accurate NNF over the occluded region. Given the calculated NNF, the correct image content from the exemplar image is transferred to the input image. Finally, as there could be a color difference between the completed content and the input image, a color correction algorithm is applied to remove the visual artifacts. Numerical results show that our proposed image completion method can achieve photo realistic image completion results.


Efficient Deep Learning Inference on Edge Devices


title: Oral solid dosage forms containing a low dose of estradiol

abstract: The present invention relates to oral solid dosage forms containing a very low dose of estradiol. The dosage forms are formulated in a manner so as to avoid degradation of the estradiol and to minimise the content of polyvinylpyrrolidone, while still achieving similar fast dissolution of the estradiol. The dosage forms are useful in preventing or treating a physical condition in a woman caused by insufficient endogenous levels of estradiol.


title: Making virtual walking real: Perceptual evaluation of a new treadmill control algorithm

abstract: For us humans, walking is our most natural way of moving through the world. One of the major challenges in present research on navigation in virtual reality is to enable users to physically walk through virtual environments. Although treadmills, in principle, allow users to walk for extended periods of time through large virtual environments, existing setups largely fail to produce a truly immersive sense of navigation. Partially, this is because of inadequate control of treadmill speed as a function of walking behavior. Here, we present a new control algorithm that allows users to walk naturally on a treadmill, including starting to walk from standstill, stopping, and varying walking speed. The treadmill speed control consists of a feedback loop based on the measured user position relative to a given reference position, plus a feed-forward term based on online estimation of the user's walking velocity. The purpose of this design is to make the treadmill compensate fully for any persistent walker motion, while keeping the accelerations exerted on the user as low as possible. We evaluated the performance of the algorithm by conducting a behavioral experiment in which we varied its most important parameters. Participants walked at normal walking speed and then, on an auditory cue, abruptly stopped. After being brought back to the center of the treadmill by the control algorithm, they rated how smoothly the treadmill had changed its velocity in response to the change in walking speed. Ratings, in general, were quite high, indicating good control performance. Moreover, ratings clearly depended on the control algorithm parameters that were varied. Ratings were especially affected by the way the treadmill reversed its direction of motion. In conclusion, controlling treadmill speed in such a way that changes in treadmill speed are unobtrusive and do not disturb VR immersiveness is feasible on a normal treadmill with a straightforward control algorithm.


title: Evolutionary fuzzy rule extraction for subgroup discovery in a psychiatric emergency department

abstract: This paper describes the application of evolutionary fuzzy systems for subgroup discovery to a medical problem, the study on the type of patients who tend to visit the psychiatric emergency department in a given period of time of the day. In this problem, the objective is to characterise subgroups of patients according to their time of arrival at the emergency department. To solve this problem, several subgroup discovery algorithms have been applied to determine which of them obtains better results. The multiobjective evolutionary algorithm MESDIF for the extraction of fuzzy rules obtains better results and so it has been used to extract interesting information regarding the rate of admission to the psychiatric emergency department.


title: Multiple Query Optimization on the D-Wave 2X Adiabatic Quantum Computer

abstract: The D-Wave adiabatic quantum annealer solves hard combinatorial optimization problems leveraging quantum physics. The newest version features over 1000 qubits and was released in August 2015. We were given access to such a machine, currently hosted at NASA Ames Research Center in California, to explore the potential for hard optimization problems that arise in the context of databases. ::: ::: In this paper, we tackle the problem of multiple query optimization (MQO). We show how an MQO problem instance can be transformed into a mathematical formula that complies with the restrictive input format accepted by the quantum annealer. This formula is translated into weights on and between qubits such that the configuration minimizing the input formula can be found via a process called adiabatic quantum annealing. We analyze the asymptotic growth rate of the number of required qubits in the MQO problem dimensions as the number of qubits is currently the main factor restricting applicability. We experimentally compare the performance of the quantum annealer against other MQO algorithms executed on a traditional computer. While the problem sizes that can be treated are currently limited, we already find a class of problem instances where the quantum annealer is three orders of magnitude faster than other approaches.


title: ERP implementation factors

abstract: Past research has identified factors that are important to the successful implementation of enterprise resource planning (ERP) systems. However, the identification of these factors has often been based on the perceptions of senior members within organizations that are implementing these systems. In this study, the perceptions of managers and end‐users on selected implementation factors are compared. Understanding if differences exist in the perceptions of different groups within an organization and the nature of these differences can help implementers develop appropriate intervention mechanisms such as training and communication that can lead to successful ERP implementation.


title: Generating Photo-Realistic Training Data to Improve Face Recognition Accuracy

abstract: In this paper we investigate the feasibility of using synthetic data to augment face datasets. In particular, we propose a novel generative adversarial network (GAN) that can disentangle identity-related attributes from non-identity-related attributes. This is done by training an embedding network that maps discrete identity labels to an identity latent space that follows a simple prior distribution, and training a GAN conditioned on samples from that distribution. Our proposed GAN allows us to augment face datasets by generating both synthetic images of subjects in the training set and synthetic images of new subjects not in the training set. By using recent advances in GAN training, we show that the synthetic images generated by our model are photo-realistic, and that training with augmented datasets can indeed increase the accuracy of face recognition models as compared with models trained with real images alone.


title: Assessment of Voice-Coil Peak Displacement Xmax

abstract: The voice coil peak displacement Xmax is an important driver parameter for assessing the maximal acoustic output at low frequencies. The method defined in standard AES 2-1984 is based on a harmonic distortion measurement, which does not give a definite and meaningful value of Xmax. After a critical review of this performance -based technique, an amendment of this method is suggested by measuring both harmonic and modulation distortion in the near field sound pressure using a two tone excitation signal. Alternatively, a parameter-based method is developed giving detailed information about the cause of the distortion, limiting and defects. The relationship between performance -based and parameter -based methods is discussed,and both techniques are tested with real drivers.


title: Remembering the Details: Effects of Emotion:

abstract: Though emotion conveys memory benefits, it does not enhance memory equally for all aspects of an experience, nor for all types of emotional events. In this review, I outline the behavioral evidence for arousal's focal enhancements of memory and describe the neural processes that may support those focal enhancements. I also present behavioral evidence to suggest that these focal enhancements occur more often for negative experiences than for positive ones. This result appears to arise because of valence-dependent effects on the neural processes recruited during episodic encoding and retrieval, with negative affect associated with increased engagement of sensory processes, and positive affect leading to enhanced recruitment of conceptual processes.


title: Recent approaches to global optimization problems through Particle Swarm Optimization

abstract: This paper presents an overview of our most recent results concerning the Particle Swarm Optimization (PSO) method. Techniques for the alleviation of local minima, and for detecting multiple minimizers are described. Moreover, results on the ability of the PSO in tackling Multiobjective, Minimax, Integer Programming and e1 errors-in-variables problems, as well as problems in noisy and continuously changing environments, are reported. Finally, a Composite PSO, in which the heuristic parameters of PSO are controlled by a Differential Evolution algorithm during the optimization, is described, and results for many well-known and widely used test functions are given.


title: A 78.8–92.8 GHz 4-bit 0–360° active phase shifter in 28nm FDSOI CMOS with 2.3 dB average peak gain

abstract: A 78.8-92.8 GHz 4-bit (16 phases) digitally controlled vector modulator active phase shifter has been designed and implemented in 28nm FDSOI CMOS by STMicroelectronics. The phase shifter exploits a novel IQ generator based on a cascode amplifier incorporating a lumped element coupled line quadrature coupler to generate the in-phase (I) and quadrature (Q) signals. The phase shifter consumes 18 mA from a 1.2 V supply. The measured performances are: average gain of 2.3 dB at 87.4 GHz (between 3.2 and -1.6 dB for the 16 phase states) and -3 dB bandwidth (B3dB) from 78.8 to 92.8 GHz; RMS gain error equal to 1.68 dB at 87.4 GHz and lower than 2 dB in the B3dB; RMS phase error equal to 9.4o at 87.4 GHz and lower than 11.9o in the B3dB; S11 lower than -10.5 dB in the B3dB; average input referred 1 dB compression point of -7 dBm (between -8 and -5 dBm for the 16 phase states); average noise figure equal to 10.8 dB at 87 GHz (between -9 and -12 dB for the 16 phase states).


title: Improving the postpartum care of women with a recent history of preeclampsia: a focus group study

abstract: ABSTRACTObjective: Women with prior preeclampsia are at increased risk of cardiovascular disease (CVD). This study investigated barriers and facilitators toward learning about this link and engaging in lifestyle modifications to reduce this risk. Methods: Four focus groups were held with 14 women within 6 months of a preeclamptic pregnancy. Results: Participants were unaware of the link between preeclampsia and CVD, suggested improvements to provider–patient communication, and discussed the benefits of social support and online tracking (weight, blood pressure) in making lifestyle modifications. Conclusions: Solutions offered may improve efforts to modify lifestyle and communication between providers and women about this link.


Working memory capacity and resistance to interference q


title: Dual Rectified Linear Units (DReLUs): A replacement for tanh activation functions in Quasi-Recurrent Neural Networks

abstract: Abstract In this paper, we introduce a novel type of Rectified Linear Unit (ReLU), called a Dual Rectified Linear Unit (DReLU). A DReLU, which comes with an unbounded positive and negative image, can be used as a drop-in replacement for a tanh activation function in the recurrent step of Quasi-Recurrent Neural Networks (QRNNs) [1]. Similar to ReLUs, DReLUs are less prone to the vanishing gradient problem, they are noise robust, and they induce sparse activations. We independently reproduce the QRNN experiments of Bradbury et al. [1] and compare our DReLU-based QRNNs with the original tanh-based QRNNs and Long Short-Term Memory networks (LSTMs) on sentiment classification and word-level language modeling. Additionally, we evaluate on character-level language modeling, showing that we are able to stack up to eight QRNN layers with DReLUs, thus making it possible to improve the current state-of-the-art in character-level language modeling over shallow architectures based on LSTMs.


title: Ensemble classification based on supervised clustering for credit scoring

abstract: Supervised clustering is employed to generate a number of clusters.Clusters from different classes are pairwise combined to get a number of training subsets.The base classifiers constructed in the training subsets are diverse and locally accurate.Weighted voting mechanism is used to ensemble the base classifiers.The proposed approach improves the accuracy of credit scoring. Credit scoring aims to assess the risk associated with lending to individual consumers. Recently, ensemble classification methodology has become popular in this field. However, most researches utilize random sampling to generate training subsets for constructing the base classifiers. Therefore, their diversity is not guaranteed, which may lead to a degradation of overall classification performance. In this paper, we propose an ensemble classification approach based on supervised clustering for credit scoring. In the proposed approach, supervised clustering is employed to partition the data samples of each class into a number of clusters. Clusters from different classes are then pairwise combined to form a number of training subsets. In each training subset, a specific base classifier is constructed. For a sample whose class label needs to be predicted, the outputs of these base classifiers are combined by weighted voting. The weight associated with a base classifier is determined by its classification performance in the neighborhood of the sample. In the experimental study, two benchmark credit data sets are adopted for performance evaluation, and an industrial case study is conducted. The results show that compared to other ensemble classification methods, the proposed approach is able to generate base classifiers with higher diversity and local accuracy, and improve the accuracy of credit scoring.


title: OpenNetVM: A Platform for High Performance Network Service Chains

abstract: Network middleboxes are growing in number and diversity. Middleboxes have been deployed widely to complement the basic end-to-end functionality provided by the Internet Protocol suite that depends only on the minimal functionality of a best-effort network layer. The move from purpose-built hardware middleboxes to software appliances running in virtual machines provides much needed deployment flexibility, but significant challenges remain. Just as Software Defined Networking (SDN) research and product development was greatly accelerated with the release of several open source SDN platforms, we believe that Network Function Virtualization (NFV) research can see similar growth with the development of a flexible platform that enables high performance NFV prototypes. Towards this end we have been building OpenNetVM, an efficient packet processing framework that greatly simplifies the development of network functions, as well as research into their management and optimization. OpenNetVM runs network functions in lightweight Docker containers, enabling fast startup and reducing memory overheads. The OpenNetVM platform manager provides load balancing, flexible flow management, and service name abstractions. OpenNetVM efficiently routes packets through dynamically created service chains, achieving throughputs of 10 Gbps even when traversing a chain of 6 NFs. In this paper we describe our architecture and evaluate its performance compared to existing NFV platforms.


title: SpaSM: A MATLAB Toolbox for Sparse Statistical Modeling

abstract: Applications in biotechnology such as gene expression analysis and image processing have led to a tremendous development of statistical methods with emphasis on reliable solutions to severely underdetermined systems. Furthermore, interpretations of such solutions are of importance, meaning that the surplus of inputs has been reduced to a concise model. At the core of this development are methods which augment the standard linear models for regression, classification and decomposition such that sparse solutions are obtained. This toolbox aims at making public available carefully implemented and well-tested variants of the most popular of such methods for the MATLAB programming environment. These methods consist of easy-to-read yet efficient implementations of various coefficient-path following algorithms and implementations of sparse principal component analysis and sparse discriminant analysis which are not available in MATLAB. The toolbox builds on code made public in 2005 and which has since been used in several studies.


title: Experimental study of OFDM implementation utilizing GNU Radio and USRP - SDR

abstract: A Software Defined Radio (SDR) is a radio which can tune to any frequency band, transmit and receive different modulations and different physical parameters across a large frequency spectrum by using a programmable hardware and powerful software. An SDR performs significant amounts of signal processing in a general purpose computer, or a reconfigurable piece of digital electronics or the combination of both. In this paper, we seek to explore the viability of using GNU Radio; an open source SDR implementation and the Universal Software Radio Peripheral (USRP); an SDR hardware platform, to transmit and receive the OFDM radio signal with QPSK and BPSK modulation. Quality of Service (QoS) in terms of Packet Received Ratio (PRR) on the data transmitted will then be investigated and analyzed.


title: Fast connected-component labeling

abstract: Labeling of connected components in a binary image is one of the most fundamental operations in pattern recognition: labeling is required whenever a computer needs to recognize objects (connected components) in a binary image. This paper presents a fast two-scan algorithm for labeling of connected components in binary images. We propose an efficient procedure for assigning provisional labels to object pixels and checking label equivalence. Our algorithm is very simple in principle, easy to implement, and suitable for hardware and parallel implementation. We show the correctness of our algorithm, analyze its complexity, and compare it with other labeling algorithms. Experimental results demonstrated that our algorithm is superior to conventional labeling algorithms.


title: Framing the evidence for health smart homes and home-based consumer health technologies as a public health intervention for independent aging: A systematic review

abstract: Abstract Introduction There is a critical need for public health interventions to support the independence of older adults as the world's population ages. Health smart homes (HSH) and home-based consumer health (HCH) technologies may play a role in these interventions. Methods We conducted a systematic review of HSH and HCH literature from indexed repositories for health care and technology disciplines (e.g., MEDLINE, CINAHL, and IEEE Xplore) and classified included studies according to an evidence-based public health (EBPH) typology. Results One thousand, six hundred and thirty-nine candidate articles were identified. Thirty-one studies from the years 1998–2011 were included. Twenty-one included studies were classified as emerging , 10 as promising and 3 as effective (first tier) . Conclusion The majority of included studies were published in the period beginning in the year 2005. All 3 effective (first tier) studies and 9 of 10 of promising studies were published during this period. Almost all studies included an activity sensing component and most of them used passive infrared motion sensors. The three effective (first tier) studies all used a multicomponent technology approach that included activity sensing, reminders and other technologies tailored to individual preferences. Future research should explore the use of technology for self-management of health by older adults; social support; and self-reported health measures incorporated into personal health records, electronic medical records, and community health registries.


title: SEER: Auto-Generating Information Extraction Rules from User-Specified Examples

abstract: Time-consuming and complicated best describe the current state of the Information Extraction (IE) field. Machine learning approaches to IE require large collections of labeled datasets that are difficult to create and use obscure mathematical models, occasionally returning unwanted results that are unexplainable. Rule-based approaches, while resulting in easy-to-understand IE rules, are still time-consuming and labor-intensive. SEER combines the best of these two approaches: a learning model for IE rules based on a small number of user-specified examples. In this paper, we explain the design behind SEER and present a user study comparing our system against a commercially available tool in which users create IE rules manually. Our results show that SEER helps users complete text extraction tasks more quickly, as well as more accurately.


title: Modular Architecture for StarCraft II with Deep Reinforcement Learning

abstract: We present a novel modular architecture for StarCraft II AI. The architecture splits responsibilities between multiple modules that each control one aspect of the game, such as build-order selection or tactics. A centralized scheduler reviews macros suggested by all modules and decides their order of execution. An updater keeps track of environment changes and instantiates macros into series of executable actions. Modules in this framework can be optimized independently or jointly via human design, planning, or reinforcement learning. We apply deep reinforcement learning techniques to training two out of six modules of a modular agent with self-play, achieving 94% or 87% win rates against the "Harder" (level 5) built-in Blizzard bot in Zerg vs. Zerg matches, with or without fog-of-war.


title: MEBO OINTMENT IN THE TREATMENT OF BURN WOUNDS: A MULTICENTER STUDY

abstract: One hundred and thirty-eight (138) patients, with five hundred and sixty-six (566) different burn sites with different degrees, severity and locations, were enrolled to a multicenter clinical trial at 25 study centers. The goal of this trial was to evaluate the efficacy of MEBO (Moist Exposed Burn Ointment) in the treatment of burn wound. Various parameters were studied including: (i) healing time, (ii) need for pain killers, (iii) consumption of i. v. Fluids, (iv) use of systemic antibiotics and (v) aesthetic appearance. There was remarkable decrease in the healing time and most patients either did not need any pain killer or were relieved with only mild analgesics, except in few cases of deep second degree and third degree burns. A decrease in the volume of i. v. fluids needed was also reported. Only 19% of cases developed clinical infection, especially in those who were not treated with MEBO immediately post burn. Even though systemic antibiotic treatment was prophylactically implemented in most cases as a routine measure, 32 cases did not receive any antibiotic prophylaxis with MEBO and yet did not develop any infection. The aesthetic appearance reported was very acceptable. This treatment modality was not associated with any side effects, and patient discomfort during application and change was negligible. Thus, MEBO treatment was associated with excellent and relatively faster healing, low incidence of wound infection, and excellent patient compliance.


title: Multiple kernel tracking with SSD

abstract: Kernel-based objective functions optimized using the mean shift algorithm have been demonstrated as an effective means of tracking in video sequences. The resulting algorithms combine the robustness and invariance properties afforded by traditional density-based measures of image similarity, while connecting these techniques to continuous optimization algorithms. This paper demonstrates a connection between kernel-based algorithms and more traditional template tracking methods. here is a well known equivalence between the kernel-based objective function and an SSD-like measure on kernel-modulated histograms. It is shown that under suitable conditions, the SSD-like measure can be optimized using Newton-style iterations. This method of optimization is more efficient (requires fewer steps to converge) than mean shift and makes fewer assumptions on the form of the underlying kernel structure. In addition, the methods naturally extend to objective functions optimizing more elaborate parametric motion models based on multiple spatially distributed kernels. We demonstrate multi-kernel methods on a variety of examples ranging from tracking of unstructured objects in image sequences to stereo tracking of structured objects to compute full 3D spatial location.


title: Fingerprint liveness detection using local texture features

abstract: The problem of fingerprint liveness detection has received an increasing attention in the last decade, as attested by the organisation of three editions of an international competition, named LivDet, dedicated to this challenge. LivDet editions and other works in the literature showed that the performance of current fingerprint liveness detection algorithms is not good enough to allow empowering a fingerprint verification system with a module aimed to distinguish alive from fake fingerprint images. However, recent developments have shown that texture-based features can provide promising solutions to this problem. In this study, a novel fingerprint liveness descriptor named binarised statistical image features (BSIFs) is adopted. Similarly to local binary pattern and local phase quantisation-based representations, BSIF encodes the local fingerprint texture into a feature vector by using a set of filters that, unlike other methods, are learnt from natural images. Extensive experiments with over 40,000 live and fake fingerprint images show that the authors’ proposed method outperforms most of the state-of-the-art algorithms, allowing a step ahead to the real integration of fingerprint liveness detectors into verification systems.


title: Semantic advertising for web 3.0

abstract: Advertising on the World Wide Web is based around automatically matching web pages with appropriate advertisements, in the form of banner ads, interactive adverts, or text links. Traditionally this has been done by manual classification of pages, or more recently using information retrieval techniques to find the most important keywords from the page, and match these to keywords being used by adverts. In this paper, we propose a new model for online advertising, based around lightweight embedded semantics. This will improve the relevancy of adverts on the World Wide Web and help to kick-start the use of RDFa as a mechanism for adding lightweight semantic attributes to the Web. Furthermore, we propose a system architecture for the proposed new model, based on our scalable ontology reasoning infrastructure TrOWL.


title: Tracking the "Odd": Meter Inference in a Culturally Diverse Music Corpus

abstract: In this paper, we approach the tasks of beat tracking, downbeat recognition and rhythmic style classification in nonWestern music. Our approach is based on a Bayesian model, which infers tempo, dow ...


title: Elections in a Distributed Computing System

abstract: After a failure occurs in a distributed computing system, it is often necessary to reorganize the active nodes so that they can continue to perform a useful task. The first step in such a reorganization or reconfiguration is to elect a coordinator node to manage the operation. This paper discusses such elections and reorganizations. Two types of reasonable failure environments are studied. For each environment assertions which define the meaning of an election are presented. An election algorithm which satisfies the assertions is presented for each environment.


Context-based Quasi-Synonym Extraction


A Block Processing Approach to Fingerprint Ridge-Orientation Estimation


title: A Deep Context Grammatical Model For Authorship Attribution

abstract: We define a variable-order Markov model, representing a Probabilistic Context Free Grammar, built from the sentence-level, delexicalized ::: parse of source texts generated by a standard lexicalized parser, which we apply to the authorship attribution task. First, we ::: motivate this model in the context of previous research on syntactic features in the area, outlining some of the general strengths and ::: limitations of the overall approach. Next we describe the procedure for building syntactic models for each author based on training ::: cases. We then outline the attribution process – assigning authorship to the model which yields the highest probability for the given ::: test case. We demonstrate the efficacy for authorship attribution over different Markov orders and compare it against syntactic features ::: trained by a linear kernel SVM. We find that the model performs somewhat less successfully than the SVM over similar features. In the ::: conclusion, we outline how we plan to employ the model for syntactic evaluation of literary texts.


title: A flexible tonoarteriography-based body sensor network for cuffless measurement of arterial blood pressure

abstract: Recent advances in unobtrusive sensing technology, especially those in flexible, stretchable, and printable sensing, have given rise to various novel signal acquisition modalities, such as stretchable epidermal electrocardiography (ECG), organic photoplethysmography (PPG), and flexible tonoarteriography (TAG) which is the cuffless and continuous recording of arterial blood pressure (BP). With the fast development of wearable computing and wireless communication technologies, all these modalities can be integrated into a body sensor network (BSN) for remote physiological multi-parameter monitoring. In this paper, we propose a TAG-based BSN for unobtrusive BP measurement with possible automatic cuffless BP calibration, and our efforts focus on the effect of posture change on the various pulse transit time (PTT) calculated from different BSN nodes consisting of TAG, ECG, and PPG sensors. Specifically, correlations of different PTTs with reference continuous BP at different postures are examined. The results of this study demonstrate that the PTT from ECG and TAG sensors has higher correlation with the reference BP as compared to that from ECG and PPG sensors, which suggests that flexible TAG sensor may potentially be utilized not only for cuffless calibration, but also as an alternative node in the BSN for continuous, cuffless BP measurement with better accuracy.


title: Fast and accurate image upscaling with super-resolution forests

abstract: The aim of single image super-resolution is to reconstruct a high-resolution image from a single low-resolution input. Although the task is ill-posed it can be seen as finding a non-linear mapping from a low to high-dimensional space. Recent methods that rely on both neighborhood embedding and sparse-coding have led to tremendous quality improvements. Yet, many of the previous approaches are hard to apply in practice because they are either too slow or demand tedious parameter tweaks. In this paper, we propose to directly map from low to high-resolution patches using random forests. We show the close relation of previous work on single image super-resolution to locally linear regression and demonstrate how random forests nicely fit into this framework. During training the trees, we optimize a novel and effective regularized objective that not only operates on the output space but also on the input space, which especially suits the regression task. During inference, our method comprises the same well-known computational efficiency that has made random forests popular for many computer vision problems. In the experimental part, we demonstrate on standard benchmarks for single image super-resolution that our approach yields highly accurate state-of-the-art results, while being fast in both training and evaluation.


title: Design and Implementation of a Fast Dynamic Packet Filter

abstract: This paper presents Swift, a packet filter for high-performance packet capture on commercial off-the-shelf hardware. The key features of the Swift include: 1) extremely lowfilter update latency for dynamic packet filtering, and 2) gigabits-per-second high-speed packet processing. Based on complex instruction set computer (CISC) instruction set architecture (ISA), Swift achieves the former with an instruction set design that avoids the need for compilation and security checking, and the latter by mainly utilizing single instruction, multiple data (SIMD). We implement Swift in the Linux 2.6 kernel for both i386 and ×86-64 architectures and extensively evaluate its dynamic and static filtering performance on multiple machines with different hardware setups. We compare Swift to BPF (the BSD packet filter)--the de facto standard for packet filtering in modern operating systems--and hand-coded optimized C filters that are used for demonstrating possible performance gains. For dynamic filtering tasks, Swift is at least three orders of magnitude faster than BPF in terms of filter update latency. For static filtering tasks, Swift outperforms BPF up to three times in terms of packet processing speed and achieves much closer performance to the optimized C filters. We also show that Swift can harness the processing power of hardware SIMD instructions by virtue of its SIMD-capable instruction set.


title: Autonomous Grocery Delivery Service in Urban Areas

abstract: In big cities around the world air pollution is reaching harmful levels. One of the reasons for air contamination is the automotive emissions. Soft appeals by the authorities to the citizens did not change much of the situation. Many people use the car in everyday activities, e.g. going to work or every day food shopping in supermarkets. Recent studies showed that a replacement of the current vehicles through autonomous vehicles would help in reducing these emissions. A radical replacement is not possible. The actual infrastructure needs to be extended by technologies that facilitate autonomous driving. Therefor integration of knowhow from other domains like internet of things, big data analysis or transportation planning has to be considered. This paper presents a smart service where, based on the information provided by principals of industry 4.0 and internet of things, the planning of an autonomous grocery delivery fleet occurs. This service is implemented then at a smaller scale, as an autonomous ordering and delivery system for coffees, and realised at the Heilbronn University. Furthermore, the paper presents the benefits and the limitations of this kind of system.


title: BiDirectional optical communication with AquaOptical II

abstract: This paper describes AquaOptical II, a bidirectional, high data-rate, long-range, underwater optical communication system. The system uses the software radio principle. Each AquaOptical II modem can be programmed to transmit user defined waveforms and record the received waveforms for detailed analysis. This allows for the use of many different modulation schemes. We describe the hardware and software architecture we developed for these goals. We demonstrate bidirectional communication between two AquaOptical II modems in a pool experiment. During the experiment AquaOptical II achieved a signal to noise ration of 5.1 over a transmission distance of 50 m at pulse widths of 1 µsec, 500 ns, and 250 ns. When using discrete pulse interval modulation (DPIM) this corresponds to a bit-rate of 0.57 Mbit/s, 1.14 Mbit/s, and 2.28 Mbit/s.


title: The Application of a Convolution Neural Network on Face and License Plate Detection

abstract: In this paper, two detectors, one for face and the other for license plates, are proposed, both based on a modified convolutional neural network(CNN) verifier. In our proposed verifier, a single feature map and a fully connected MLP were trained by examples to classify the possible candidates. Pyramid-based localization techniques were applied to fuse the candidates and to identify the regions of faces or license plates. In addition, geometrical rules filtered out false alarms in license plate detection. Some experimental results are given to show the effectiveness of the approach. Keywords: Face detection, license plate detection, convolution neural network, feature map.


title: A Modular Grid-Connected Photovoltaic Generation System Based on DC Bus

abstract: In order to enhance the efficiency and reliability for distributed microgeneration, a modular grid-connected photovoltaic (PV) generation system is proposed. It consists of modular dc-dc converters and modular dc-ac inverters. The outputs of dc-dc converter and the inputs of dc-ac inverter share a dc bus. AC current coupling between the parallel-operated inverters, which is the key issue in this generation system, has been investigated. A current-decoupling method is proposed and implemented by regulating the currents of split-filter inductors, respectively. An optimal control strategy for the efficiency enhancement of PV generation system is proposed by utilizing the dispersion of control parameters. During power generation, only one modular dc-ac inverter is operating with nonfull load, and the other modular dc-ac inverters are operating with full load or at standby mode. A prototype of this modularized grid-connected PV generation system is implemented. The steady state and dynamic experimental results show that the fundamental components of two split-filter inductor currents in one inverter module are equivalent, which are decoupled completely, and only one inverter module operates with nonfull load among the parallel-operated inverter modules. The feasibility of the proposed system and the effectiveness of the control strategies have been verified by experimental results.


title: Automated Verification for Secure Messaging Protocols and Their Implementations: A Symbolic and Computational Approach

abstract: Many popular web applications incorporate end-to-end secure messaging protocols, which seek to ensure that messages sent between users are kept confidential and authenticated, even if the web application's servers are broken into or otherwise compelled into releasing all their data. Protocols that promise such strong security guarantees should be held up to rigorous analysis, since protocol flaws and implementations bugs can easily lead to real-world attacks. We propose a novel methodology that allows protocol designers, implementers, and security analysts to collaboratively verify a protocol using automated tools. The protocol is implemented in ProScript, a new domain-specific language that is designed for writing cryptographic protocol code that can both be executed within JavaScript programs and automatically translated to a readable model in the applied pi calculus. This model can then be analyzed symbolically using ProVerif to find attacks in a variety of threat models. The model can also be used as the basis of a computational proof using CryptoVerif, which reduces the security of the protocol to standard cryptographic assumptions. If ProVerif finds an attack, or if the CryptoVerif proof reveals a weakness, the protocol designer modifies the ProScript protocol code and regenerates the model to enable a new analysis. We demonstrate our methodology by implementing and analyzing a variant of the popular Signal Protocol with only minor differences. We use ProVerif and CryptoVerif to find new and previously-known weaknesses in the protocol and suggest practical countermeasures. Our ProScript protocol code is incorporated within the current release of Cryptocat, a desktop secure messenger application written in JavaScript. Our results indicate that, with disciplined programming and some verification expertise, the systematic analysis of complex cryptographic web applications is now becoming practical.


title: Revisiting Embedding Features for Simple Semi-supervised Learning

abstract: Recent work has shown success in using continuous word embeddings learned from unlabeled data as features to improve supervised NLP systems, which is regarded as a simple semi-supervised learning mechanism. However, fundamental problems on effectively incorporating the word embedding features within the framework of linear models remain. In this study, we investigate and analyze three different approaches, including a new proposed distributional prototype approach, for utilizing the embedding features. The presented approaches can be integrated into most of the classical linear models in NLP. Experiments on the task of named entity recognition show that each of the proposed approaches can better utilize the word embedding features, among which the distributional prototype approach performs the best. Moreover, the combination of the approaches provides additive improvements, outperforming the dense and continuous embedding features by nearly 2 points of F1 score.


title: A personalized recommendation system on scholarly publications

abstract: Researchers, as well as ordinary users who seek information in diverse academic fields, turn to the web to search for publications of interest. Even though scholarly publication recommenders have been developed to facilitate the task of discovering literature pertinent to their users, they (i) are not personalized enough to meet users' expectations, since they provide the same suggestions to users sharing similar profiles/preferences, (ii) generate recommendations pertaining to each user's general interests as opposed to the specific need of the user, and (iii) fail to take full advantages of valuable user-generated data at social websites that can enhance their performance. To address these problems, we propose PubRec, a recommender that suggests closely-related references to a particular publication P tailored to a specific user U, which minimizes the time and efforts imposed on U in browsing through general recommended publications. Empirical studies conducted using data extracted from CiteULike (i) verify the efficiency of the recommendation and ranking strategies adopted by PubRec and (ii) show that PubRec significantly outperforms other baseline recommenders.


title: A Survey of Facial Modeling and Animation Techniques

abstract: Realistic facial animation is achieved through geometric and image manipulations. Geometric deformations usually account for the shape and deformations unique to the physiology and expressions of a person. Image manipulations model the reflectance properties of the facial skin and hair to achieve smallscale detail that is difficult to model by geometric manipulation alone. Modeling and animation methods often exhibit elements of each realm. This paper summarizes the theoretical approaches used in published work and describes their strengths, weaknesses, and relative performance. Taxonomy groups the methods into classes that highlight their similarities and differences.


title: A Path-Connected-Cluster Wireless Sensor Network and Its Formation, Addressing, and Routing Protocols

abstract: Although wireless sensor networks (WSNs) have been extensively researched, their deployment is still a main concern. We observe that many monitoring applications for WSNs have adopted a path-connected-cluster (PCC) topology, where regions to be monitored are deployed with clusters of sensor nodes. Since these clusters might be physically separated, paths of sensor nodes are used to connect them together. We call such networks PCC-WSNs. PCC-WSNs may be widely applied in real situations, such as bridge-connected islands, street-connected buildings, and pipe-connected ponds. In this paper, we show that the address assignment scheme defined by ZigBee will perform poorly in terms of address utilization. We then propose a systematical solution, which includes network formation, automatic address assignment, and light-weight routing. Simulation results verify the effectiveness of the proposed solution.


title: Distributed Formation Control of Multi-Agent Systems Using Complex Laplacian

abstract: The paper concentrates on the fundamental coordination problem that requires a network of agents to achieve a specific but arbitrary formation shape. A new technique based on complex Laplacian is introduced to address the problems of which formation shapes specified by inter-agent relative positions can be formed and how they can be achieved with distributed control ensuring global stability. Concerning the first question, we show that all similar formations subject to only shape constraints are those that lie in the null space of a complex Laplacian satisfying certain rank condition and that a formation shape can be realized almost surely if and only if the graph modeling the inter-agent specification of the formation shape is 2-rooted. Concerning the second question, a distributed and linear control law is developed based on the complex Laplacian specifying the target formation shape, and provable existence conditions of stabilizing gains to assign the eigenvalues of the closed-loop system at desired locations are given. Moreover, we show how the formation shape control law is extended to achieve a rigid formation if a subset of knowledgable agents knowing the desired formation size scales the formation while the rest agents do not need to re-design and change their control laws.


title: Expected energy-based restricted Boltzmann machine for classification

abstract: In classification tasks, restricted Boltzmann machines (RBMs) have predominantly been used in the first stage, either as feature extractors or to provide initialization of neural networks. In this study, we propose a discriminative learning approach to provide a self-contained RBM method for classification, inspired by free-energy based function approximation (FE-RBM), originally proposed for reinforcement learning. For classification, the FE-RBM method computes the output for an input vector and a class vector by the negative free energy of an RBM. Learning is achieved by stochastic gradient-descent using a mean-squared error training objective. In an earlier study, we demonstrated that the performance and the robustness of FE-RBM function approximation can be improved by scaling the free energy by a constant that is related to the size of network. In this study, we propose that the learning performance of RBM function approximation can be further improved by computing the output by the negative expected energy (EE-RBM), instead of the negative free energy. To create a deep learning architecture, we stack several RBMs on top of each other. We also connect the class nodes to all hidden layers to try to improve the performance even further. We validate the classification performance of EE-RBM using the MNIST data set and the NORB data set, achieving competitive performance compared with other classifiers such as standard neural networks, deep belief networks, classification RBMs, and support vector machines. The purpose of using the NORB data set is to demonstrate that EE-RBM with binary input nodes can achieve high performance in the continuous input domain.


title: Hyper-parameter selection in non-quadratic regularization-based radar image formation

abstract: We consider the problem of automatic parameter selection in regularization-based radar image formation techniques. It ::: has previously been shown that non-quadratic regularization produces feature-enhanced radar images; can yield ::: superresolution; is robust to uncertain or limited data; and can generate enhanced images in non-conventional data ::: collection scenarios such as sparse aperture imaging. However, this regularized imaging framework involves some ::: hyper-parameters, whose choice is crucial because that directly affects the characteristics of the reconstruction. Hence ::: there is interest in developing methods for automatic parameter choice. We investigate Stein's unbiased risk estimator ::: (SURE) and generalized cross-validation (GCV) for automatic selection of hyper-parameters in regularized radar ::: imaging. We present experimental results based on the Air Force Research Laboratory (AFRL) "Backhoe Data Dome," ::: to demonstrate and discuss the effectiveness of these methods.


title: No Dead-time Modulation Algorithm for an Off-grid Inverter Based on H6 Topology

abstract: In this study, a no-dead-time modulation method based on the H6 topology is proposed. The main objective is to reduce the total harmonic distortion (THD) of the inverter output voltage and improve the output-voltage waveform quality. On the basis of the working state of the main circuit, the proposed method selects an appropriate switch modulation, thereby realising the objective of not inserting a dead time, most of the time. Simulations and experiments demonstrate that the modulation method suppresses the THD better than the conventional modulation algorithm, at all power factors and power levels. Furthermore, with the increase in power, the output current increases, but the current commutation interval decreases and the output-voltage suppression effect THD is better. It can also be concluded that simultaneously there is a higher DC-link voltage utilisation efficiency in the no-dead-time modulation method than in the conventional modulation method.


title: Article Building Reconstruction Using DSM and Orthorectified Images

abstract: High resolution Digital Surface Models (DSMs) produced from airborne laser-scanning or stereo satellite images provide a very useful source of information for automated 3D building reconstruction. In this paper an investigation is reported about extraction of 3D building models from high resolution DSMs and orthorectified images produced from Worldview-2 stereo satellite imagery. The focus is on the generation of 3D models of parametric building roofs, which is the basis for creating Level Of Detail 2 (LOD2) according to the CityGML standard. In particular the building blocks containing several connected buildings with tilted roofs are investigated and the potentials and limitations of the modeling approach are discussed. The edge information extracted from orthorectified image has been employed as additional source of information in 3D reconstruction algorithm. A model driven approach based on the analysis of the 3D points of DSMs in a 2D projection plane is proposed. Accordingly, a building block is divided into smaller parts according to the direction and number of existing ridge lines for parametric building reconstruction. The 3D model is derived for each building part, and finally, a complete parametric model is formed by merging the 3D models of the individual building parts and adjusting the nodes after the merging step. For the remaining building parts that do not contain ridge lines, a prismatic model using polygon approximation of the corresponding boundary pixels is derived and merged to the parametric models to shape the final model of the building. A qualitative and quantitative assessment of the proposed method for the automatic reconstruction of buildings with parametric roofs is then provided by comparing the final model with the existing surface model as well as some field measurements.


title: SIEVE: Scalable user grouping for large MU-MIMO systems

abstract: Multi-user multiple input and multiple output (MU-MIMO) is one predominate approach to improve the wireless capacity. However, since the aggregate capacity of MU-MIMO heavily depends on the channel correlations among the mobile users in a beamforming group, unwisely selecting beamforming groups may result in reduced overall capacity, instead of increasing it. How to select users into a beamforming group becomes the bottleneck of realizing the MU-MIMO gain. The fundamental challenge for user selection is the large searching space, and hence there exists a tradeoff between search complexity and achievable capacity. Previous works have proposed several low complexity heuristic algorithms, but they suffer a significant capacity loss. In this paper, we present a novel MU-MIMO MAC, called SIEVE. The core of SIEVE design is its scalable multi-user selection module that provides a knob to control the aggressiveness in searching the best beamforming group. SIEVE maintains a central database to track the channel and the coherence time for each mobile user, and largely avoids unnecessary computing with a progressive update strategy. Our evaluation, via both small-scale testbed experiments and large-scale trace-driven simulations, shows that SIEVE can achieve around 90% of the capacity compared to exhaustive search.


title: Easy Victories and Uphill Battles in Coreference Resolution

abstract: Classical coreference systems encode various syntactic, discourse, and semantic phenomena explicitly, using heterogenous features computed from hand-crafted heuristics. In contrast, we present a state-of-the-art coreference system that captures such phenomena implicitly, with a small number of homogeneous feature templates examining shallow properties of mentions. Surprisingly, our features are actually more effective than the corresponding hand-engineered ones at modeling these key linguistic phenomena, allowing us to win “easy victories” without crafted heuristics. These features are successful on syntax and discourse; however, they do not model semantic compatibility well, nor do we see gains from experiments with shallow semantic features from the literature, suggesting that this approach to semantics is an “uphill battle.” Nonetheless, our final system 1 outperforms the Stanford system (Lee et al. (2011), the winner of the CoNLL 2011 shared task) by 3.5% absolute on the CoNLL metric and outperforms the IMS system (Bj¨ orkelund and Farkas (2012), the best publicly available English coreference system) by 1.9% absolute.


title: Quetiapine-associated dysphagia

abstract: We report a case of quetiapine-induced dysphagia in a geriatric patient which improved with discontinuation of the antipsychotic. The patient had developed dysphagia while being treated with antipsychotics for bipolar disorder. The patient's dysphagia showed significant improvement when she was taken off quetiapine. We review the available literature on antipsychotic-related dysphagia and suggest that clinicans need to be aware of the potential for this syndrome even with lower potency antipsychotics.


title: 'Artiphysiology' reveals V4-like shape tuning in a deep network trained for image classification

abstract: Deep networks provide a potentially rich interconnection between neuroscientific and artificial approaches to understanding visual intelligence, but the relationship between artificial and neural representations of complex visual form has not been elucidated at the level of single-unit selectivity. Taking the approach of an electrophysiologist to characterizing single CNN units, we found many units exhibit translation-invariant boundary curvature selectivity approaching that of exemplar neurons in the primate mid-level visual area V4. For some V4-like units, particularly in middle layers, the natural images that drove them best were qualitatively consistent with selectivity for object boundaries. Our results identify a novel image-computable model for V4 boundary curvature selectivity and suggest that such a representation may begin to emerge within an artificial network trained for image categorization, even though boundary information was not provided during training. This raises the possibility that single-unit selectivity in CNNs will become a guide for understanding sensory cortex.


title: Crossing Theory and Hierarchy Mapping

abstract: Techniques are introduced which are applicable to machine construction of digraph maps. These techniques are oriented toward reduction of the number of crossings in a map as a means of improving the readability of hierarchical structures. Permuting, psi-factoring, rotating, twirling, and absorbing are among the techniques considered. Examples are given to illustrate these techniques. When these and related procedures are fully developed for use with machine-interactive processes, they will facilitate group modeling efforts.


title: Decoding billions of integers per second through vectorization

abstract: In many important applications -- such as search engines and relational database systems -- data is stored in the form of arrays of integers. Encoding and, most importantly, decoding of these arrays consumes considerable CPU time. Therefore, substantial effort has been made to reduce costs associated with compression and decompression. In particular, researchers have exploited the superscalar nature of modern processors and SIMD instructions. Nevertheless, we introduce a novel vectorized scheme called SIMD-BP128 that improves over previously proposed vectorized approaches. It is nearly twice as fast as the previously fastest schemes on desktop processors (varint-G8IU and PFOR). At the same time, SIMD-BP128 saves up to 2 bits per integer. For even better compression, we propose another new vectorized scheme (SIMD-FastPFOR) that has a compression ratio within 10% of a state-of-the-art scheme (Simple-8b) while being two times faster during decoding.


title: Obfuscation: The Hidden Malware

abstract: A cyberwar exists between malware writers and antimalware researchers. At this war's heart rages a weapons race that originated in the 80s with the first computer virus. Obfuscation is one of the latest strategies to camouflage the telltale signs of malware, undermine antimalware software, and thwart malware analysis. Malware writers use packers, polymorphic techniques, and metamorphic techniques to evade intrusion detection systems. The need exists for new antimalware approaches that focus on what malware is doing rather than how it's doing it.


title: Dense Reconstruction Using 3D Object Shape Priors

abstract: We propose a formulation of monocular SLAM which combines live dense reconstruction with shape priors-based 3D tracking and reconstruction. Current live dense SLAM approaches are limited to the reconstruction of visible surfaces. Moreover, most of them are based on the minimisation of a photo-consistency error, which usually makes them sensitive to specularities. In the 3D pose recovery literature, problems caused by imperfect and ambiguous image information have been dealt with by using prior shape knowledge. At the same time, the success of depth sensors has shown that combining joint image and depth information drastically increases the robustness of the classical monocular 3D tracking and 3D reconstruction approaches. In this work we link dense SLAM to 3D object pose and shape recovery. More specifically, we automatically augment our SLAM system with object specific identity, together with 6D pose and additional shape degrees of freedom for the object(s) of known class in the scene, combining image data and depth information for the pose and shape recovery. This leads to a system that allows for full scaled 3D reconstruction with the known object(s) segmented from the scene. The segmentation enhances the clarity, accuracy and completeness of the maps built by the dense SLAM system, while the dense 3D data aids the segmentation process, yielding faster and more reliable convergence than when using 2D image data alone.


title: Getting More Out of Biomedical Documents with GATE's Full Lifecycle Open Source Text Analytics

abstract: This software article describes the GATE family of open source text analysis tools and processes. GATE is one of the most widely used systems of its type with yearly download rates of tens of thousands and many active users in both academic and industrial contexts. In this paper we report three examples of GATE-based systems operating in the life sciences and in medicine. First, in genome-wide association studies which have contributed to discovery of a head and neck cancer mutation association. Second, medical records analysis which has significantly increased the statistical power of treatment/outcome models in the UK's largest psychiatric patient cohort. Third, richer constructs in drug-related searching. We also explore the ways in which the GATE family supports the various stages of the lifecycle present in our examples. We conclude that the deployment of text mining for document abstraction or rich search and navigation is best thought of as a process, and that with the right computational tools and data collection strategies this process can be made defined and repeatable. The GATE research programme is now 20 years old and has grown from its roots as a specialist development tool for text processing to become a rather comprehensive ecosystem, bringing together software developers, language engineers and research staff from diverse fields. GATE now has a strong claim to cover a uniquely wide range of the lifecycle of text analysis systems. It forms a focal point for the integration and reuse of advances that have been made by many people (the majority outside of the authors' own group) who work in text processing for biomedicine and other areas. GATE is available online under GNU open source licences and runs on all major operating systems. Support is available from an active user and developer community and also on a commercial basis.


title: Historical Y. pestis Genomes Reveal the European Black Death as the Source of Ancient and Modern Plague Pandemics

abstract: Summary Ancient DNA analysis has revealed an involvement of the bacterial pathogen Yersinia pestis in several historical pandemics, including the second plague pandemic (Europe, mid-14 th century Black Death until the mid-18 th century AD). Here we present reconstructed Y. pestis genomes from plague victims of the Black Death and two subsequent historical outbreaks spanning Europe and its vicinity, namely Barcelona, Spain (1300–1420 cal AD), Bolgar City, Russia (1362–1400 AD), and Ellwangen, Germany (1485–1627 cal AD). Our results provide support for (1) a single entry of Y. pestis in Europe during the Black Death, (2) a wave of plague that traveled toward Asia to later become the source population for contemporary worldwide epidemics, and (3) the presence of an historical European plague focus involved in post-Black Death outbreaks that is now likely extinct.


title: How congestion shapes cities: from mobility patterns to scaling

abstract: The recent availability of data for cities has allowed scientists to exhibit scalings which present themselves in the form of a power-law dependence on population of various socio-economical and structural indicators. We propose here a stochastic theory of urban growth which accounts for some of the observed scalings and we confirm these predictions on US and OECD empirical data. In particular, we show that the dependence on population size of the total number of miles driven daily, the total length of the road network, the total traffic delay, the total consumption of gasoline, the quantity of CO2 emitted and the relation between area and population of cities, are all governed by a single parameter which characterizes the sensitivity to congestion. Our results suggest that diseconomies associated with congestion scale superlinearly with population size, implying that -despite polycentrism- cities whose transportation infrastructure rely heavily on traffic sensitive modes are unsustainable.


title: ANTECEDENTS AND DISTINCTIONS BETWEEN ONLINE TRUST AND DISTRUST: PREDICTING HIGH- AND LOW-RISK INTERNET BEHAVIORS

abstract: While previous studies have investigated the determinants and consequences of online trust, online distrust has seldom been studied. Assuming that the positive antecedents of online trust are necessarily negative antecedents of online distrust or that positive consequences of online trust are necessarily negatively affected by online distrust is inappropriate. This study examines the different antecedents of online trust and distrust in relation to consumer and website characteristics. Moreover, this study further examines whether online trust and distrust asymmetrically affect behaviors with different risk levels. A model is developed and tested using a survey of 1,153 online consumers. LISREL was employed to test the proposed model. Overall, different consumer and website characteristics influence online trust and distrust, and online trust engenders different behavioral outcomes to online distrust. The authors also discuss the theoretical and managerial implications of the study findings.


title: Relocation of the Aged: A Review and Theoretical Analysis

abstract: The literature on the relocation of the aged is examined and the findings are presented within a framework of three types of moves--institution to institution, home to institution, and home to home--with each type having a voluntary and involuntary component. A theoretical model is proposed to explain the often contradictory results reported in this literature. In brief, it is argued that an individuals' response to relocation is largely determined by (a) the perceived predictability and controllability of the events surrounding a move and (b) differences in controllability between pre- and post-relocation environments. The validity of this model is tested by examining existing research and suggestions for future research are made.