title: The Qualitas Corpus: A Curated Collection of Java Code for Empirical Studies

abstract: In order to increase our ability to use measurement to support software development practise we need to do more analysis of code. However, empirical studies of code are expensive and their results are difficult to compare. We describe the Qualitas Corpus, a large curated collection of open source Java systems. The corpus reduces the cost of performing large empirical studies of code and supports comparison of measurements of the same artifacts. We discuss its design, organisation, and issues associated with its development.


title: Unstructured Human Activity Detection from RGBD Images

abstract: Being able to detect and recognize human activities is essential for several applications, including personal assistive robotics. In this paper, we perform detection and recognition of unstructured human activity in unstructured environments. We use a RGBD sensor (Microsoft Kinect) as the input sensor, and compute a set of features based on human pose and motion, as well as based on image and point-cloud information. Our algorithm is based on a hierarchical maximum entropy Markov model (MEMM), which considers a person's activity as composed of a set of sub-activities. We infer the two-layered graph structure using a dynamic programming approach. We test our algorithm on detecting and recognizing twelve different activities performed by four people in different environments, such as a kitchen, a living room, an office, etc., and achieve good performance even when the person was not seen before in the training set.1


title: Selection Test Anxiety: Exploring Tension and Fear of Failure Across the Sexes in Simulated Selection Scenarios

abstract: Standardized tests are commonly used to select individuals in both pre-employment and educational settings. Nevertheless, research has yet to sufficiently explore the relationship between test anxiety (TA) and test performance in selection contexts. The goal of this study was to assess the dimensionality of TA, the relations between TA and test performance, and the impact of gender on TA within the realm of personnel selection. Test anxiety was found to be bi-dimensional and negatively related to performance on a cognitively based selection instrument. Gender moderated the relation between TA and test performance, with stronger associations for males. This finding is consistent with the sex-linked anxiety coping theory that is advanced here. Implications of this study and avenues for future research are discussed.


title: A literature review on the vehicle routing problem with multiple depots

abstract: In this paper, we present a state-of-the-art survey on the vehicle routing problem with multiple depots (MDVRP). Our review considered papers published between 1988 and 2014, in which several variants of the model are studied: time windows, split delivery, heterogeneous fleet, periodic deliveries, and pickup and delivery. The review also classifies the approaches according to the single or multiple objectives that are optimized. Some lines for further research are presented as well.


title: A Pitch Salience Function Derived from Harmonic Frequency Deviations for Polyphonic Music Analysis

abstract: In this paper, a novel approach for the computation of a pitch salience function is presented. The aim of a pitch (considered here as synonym for fundamental frequency) salience function is to es- timate the relevance of the most salient musical pitches that are present in a certain audio excerpt. Such a function is used in nu- merous Music Information Retrieval (MIR) tasks such as pitch, multiple-pitch estimation, melody extraction and audio features computation (such as chroma or Pitch Class Profiles). In order to compute the salience of a pitch candidate f , the classical approach uses the weighted sum of the energy of the short time spectrum at its integer multiples frequencies hf. In the present work, we pro- pose a different approach which does not rely on energy but only on frequency location. For this, we first estimate the peaks of the short time spectrum. From the frequency location of these peaks, we evaluate the likelihood that each peak is an harmonic of a given fundamental frequency. The specificity of our method is to use as likelihood the deviation of the harmonic frequency locations from the pitch locations of the equal tempered scale. This is used to cre- ate a theoretical sequence of deviations which is then compared to an observed one. The proposed method is then evaluated for a task of multiple-pitch estimation using the MAPS test-set.


Fake Co-visitation Injection Attacks to Recommender Systems.


title: A Note on the Convergence of SOR for the PageRank Problem

abstract: A curious phenomenon when it comes to solving the linear system formulation of the PageRank problem is that while the convergence rate of Gauss-Seidel shows an improvement over Jacobi by a factor of approximately two, successive overrelaxation (SOR) does not seem to offer a meaningful improvement over Gauss-Seidel. This has been observed experimentally and noted in the literature, but to the best of our knowledge there has been no analytical explanation for this thus far. This convergence behavior is surprising because there are classes of matrices for which Gauss-Seidel is faster than Jacobi by a similar factor of two, and SOR accelerates convergence by an order of magnitude compared to Gauss-Seidel. In this short paper we prove analytically that the PageRank model has the unique feature that there exist PageRank linear systems for which SOR does not converge outside a very narrow interval depending on the damping factor, and that in such situations Gauss-Seidel may be the best choice among the relaxation parameters. Conversely, we show that within that narrow interval, there exists no PageRank problem for which SOR does not converge. Our result may give an analytical justification for the popularity of Gauss-Seidel as a solver for the linear system formulation of PageRank.


title: Selectivity for polar, hyperbolic, and Cartesian gratings in macaque visual cortex.

abstract: The neural basis of pattern recognition is a central problem in visual neuroscience. Responses of single cells were recorded in area V4 of macaque monkey to three classes of periodic stimuli that are based on spatial derivative operators: polar (concentric and radial), hyperbolic, and conventional sinusoidal (Cartesian) gratings. Of 118 cells tested, 16 percent responded significantly more to polar or hyperbolic (non-Cartesian) gratings than to Cartesian gratings and only 8 percent showed a significant preference for Cartesian gratings. Among cells selective for non-Cartesian gratings, those that preferred concentric gratings were most common. Cells selective for non-Cartesian gratings may constitute an important intermediate stage in pattern recognition and the representation of surface shape.


title: Interaction between a nanovirus-like component and the Tobacco curly shoot virus/satellite complex.

abstract: The biological role of DNA1, a nanovirus-like component shown to be associated with the begomovirus/satellite complex, has not yet been identified. Here, we demonstrated that DNA1 of Tobacco curly shoot virus isolate Y35 (TbCSV-Y35) attenuated leaf-curling symptoms induced by TbCSV-Y35 or TbCSV-Y35 plus Y35 DNAbeta in the early stage of symptom development and induced leaf cluster at a later stage of symptom development in Nicotiana benthamiana plants. The leaf disc assay demonstrated that TbCSV-Y35 DNA1 replicated autonomously. Southern blot analysis revealed that TbCSV-Y35 DNA1 reduced viral DNA accumulation. Viral DNA accumulation was not reduced when plants were co-inoculated with TbCSV-Y35 DNAbeta, but the TbCSV-Y35 DNAbeta level was dramatically reduced in the presence of TbCSV-Y35 DNA1. To determine whether the interaction between TbCSV/satellite complex and DNA1 had isolate specificity, DNA1 of TbCSV isolate Y132 was cloned and sequenced. It was found to have 75% nucleotide sequence identity with TbCSV-Y35 DNA1. Infectivity tests showed that TbCSV-Y132 DNA1 had no effect on the symptoms induced by TbCSV-Y35 or TbCSV-Y35 and Y35 DNAbeta in N. benthamiana plants, although Y132 DNA1 could replicate in these plants.


title: i-LAND: an interactive landscape for creativity and innovation

abstract: We describe the i-LAND environment which constitutes an exampleof our vision of the workspaces of the future, in this casesupporting cooperative work of dynamic teams with changing needs.i-LAND requires and provides new forms of human-computerinteraction and new forms of computer-supported cooperative work.Its design is based on an integration of information andarchitectural spaces, implications of new work practices and anempirical requirements study informing our design. i-LAND consistsof several roomware components, i.e. computer-aug- mented objectsintegrating room elements with information technology. We presentthe current realization of i-LAND in terms of an interactiveelectronic wall, an interactive table, two computer-enhancedchairs, and two bridges for the Passage-mechanism. This iscomplemented by the description of the creativity supportapplication and the technological infrastructure. The paper isaccompanied by a video figure in the CHI99 video program.


title: Use of Application Characteristics and Limited Preemption for Run-To-Completion Parallel Processor Scheduling Policies

abstract: The performance potential of run-to-completion (RTC) parallel processor scheduling policies is investigated by examining whether (1) application execution rate characteristics such as average parallelism (avg) and processor working set (PWS) and/or (2) limited preemption can be used to improve the performance of these policies. We address the first question by comparing policies (previous as well as new) that differ only in whether or not they use execution rate characteristics and by examining a wider range of the workload parameter space than previous studies. We address the second question by comparing a simple two-level queueing policy with RTC scheduling in the second level queue against RTC policies that don't allow any preemption and against dynamic equiallocation(EQ). Using simulation to estimate mean response times we find that for promising RTC policies such as adaptive static partitioning (ASP) and shortest demand first (SDF), a maximum allocation constraint that is for all practical purposes independent of avg and pws provides greater and more consistent improvement in policy performance than using avg or pws . Also, under the assumption that job demand information is unavailable to the scheduler we show that the ASP-max policy outperforms all previous high performance RTC policies for workloads with coefficient of variation in processing requirement greater than one. Furthermore, a two-level queue that allows at most one preemption per job outperforms ASP-max but is not competitive with EQ.


title: Drug Synergy Screen and Network Modeling in Dedifferentiated Liposarcoma Identifies CDK4 and IGF1R as Synergistic Drug Targets

abstract: Dedifferentiated liposarcoma (DDLS) is a rare but aggressive cancer with high recurrence and low response rates to targeted therapies. Increasing treatment efficacy may require combinations of targeted agents that counteract the effects of multiple abnormalities. To identify a possible multicomponent therapy, we performed a combinatorial drug screen in a DDLS-derived cell line and identified cyclin-dependent kinase 4 (CDK4) and insulin-like growth factor 1 receptor (IGF1R) as synergistic drug targets. We measured the phosphorylation of multiple proteins and cell viability in response to systematic drug combinations and derived computational models of the signaling network. These models predict that the observed synergy in reducing cell viability with CDK4 and IGF1R inhibitors depends on the activity of the AKT pathway. Experiments confirmed that combined inhibition of CDK4 and IGF1R cooperatively suppresses the activation of proteins within the AKT pathway. Consistent with these findings, synergistic reductions in cell viability were also found when combining CDK4 inhibition with inhibition of either AKT or epidermal growth factor receptor (EGFR), another receptor similar to IGF1R that activates AKT. Thus, network models derived from context-specific proteomic measurements of systematically perturbed cancer cells may reveal cancer-specific signaling mechanisms and aid in the design of effective combination therapies.


title: Combinatorial Auctions with Structured Item Graphs

abstract: Combinatorial auctions (CAs) are important mechanisms for allocating interrelated items. Unfortunately, winner determination is NP-complete unless there is special structure. We study the setting where there is a graph (with some desired property), with the items as vertices, and every bid bids on a connected set of items. Two computational problems arise: 1) clearing the auction when given the item graph, and 2) constructing an item graph (if one exists) with the desired property. 1 was previously solved for the case of a tree or a cycle, and 2 for the case of a line graph or a cycle. ::: ::: We generalize the first result by showing that given an item graph with bounded treewidth, the clearing problem can be solved in polynomial time (and every CA instance has some treewidth; the complexity is exponential in only that parameter). We then give an algorithm for constructing an item tree (treewidth 1) if such a tree exists, thus closing a recognized open problem. We show why this algorithm does not work for treewidth greater than 1, but leave open whether item graphs of (say) treewidth 2 can be constructed in polynomial time. We show that finding the item graph with the fewest edges is NP-complete (even when a graph of treewidth 2 exists). Finally, we study how the results change if a bid is allowed to have more than one connected component. Even for line graphs, we show that clearing is hard even with 2 components, and constructing the line graph is hard even with 5.


title: An Automatic System for Classification of Nuclear Sclerosis from Slit-Lamp Photographs 1

abstract: A robust and automatic system has been developed to detect the visual axis and extract important feature landmarks from slit-lamp photographs, and objectively grade the severity of nuclear sclerosis based on the intensities of those landmarks. Using linear regression, we first select the features that play important roles in classification, and then fit a linear grading function. We evaluated the grading function using human grades as error bounds for “ground truth” grades, and compared the machine grades with the human grades. As expected, the automatic system significantly speeds up the process of grading, and grades computed are consistent and reproducible. Machine grading time for one image is less than 2 seconds on a Pentium III 996MHz machine while human grading takes about 2 minutes. Statistical results show that the predicted grades by the system are very reliable. For the testing set of 141 images, with correct grading defined by a tolerance of one grade level difference from the human grade, the automated system has a grading accuracy of 95.8% based on the AREDS grading scale.


title: Effects of ximelagatran, the oral form of melagatran, in the treatment of caval vein thrombosis in conscious rats.

abstract: The antithrombotic effects of direct (ximelagatran and hirudin) and indirect (dalteparin) anticoagulants were compared using a deep venous thrombosis (DVT) treatment model in conscious rats. Thrombus formation was induced in the inferior caval vein by total stasis plus topically applied ferric chloride. After 1-h thrombus maturation, one group of 10 rats were sacrificed and the mean thrombus weight in this group was 27.3 +/- 2.7 mg. This thrombus weight was handled as a reference to which all other results were compared. In all other groups, the total occlusion was removed after 1 h but a partial stasis was retained, permitting some blood flow around the thrombus. Groups of animals received subcutaneous (s.c.) dalteparin (200 IU/kg), s.c. hirudin (0.75 micromol/kg), one of four oral doses of ximelagatran (2.5, 5, 10 or 20 micromol/kg) or s.c. saline (control). After the 3-h treatment, mean thrombus weight in the saline group (26.5 +/- 3.3 mg) did not differ significantly from that of the reference group (27.3 +/- 2.7 mg, see above). Ximelagatran decreased thrombus weight in a dose-dependent manner, with an estimated ID(50) of 15 micromol/kg. Mean thrombus weight with the highest ximelagatran dose (11.1 +/- 1.3 mg) was similar to that with hirudin (13.0 +/- 1.5 mg). The effect of dalteparin on thrombus regression was much less pronounced (20.2 +/- 1.2 mg), compared with ximelagatran and hirudin, even though it was administered at a dose that yielded a similar activated partial thromboplastin time (APTT) prolongation. In conclusion, the results from this DVT treatment model showed that direct thrombin inhibitors ximelagatran and hirudin exhibited superior antithrombotic properties to low molecular weight heparin (LMWH).


title: Modifiable factors that alter the size of the hippocampus with ageing

abstract: The hippocampus is a dynamic structure that can change in size throughout life. Hippocampal atrophy, which is associated with cognitive decline and late-life dementia, can be exacerbated by a number of medical conditions. In this Review, Fotuhi and colleagues examine the modifiable factors that can affect hippocampal size, and discuss lifestyle and therapeutic interventions that might prevent or even reverse hippocampal atrophy.


The importance of experience replay database composition in deep reinforcement learning


title: HYDRA: large-scale social identity linkage via heterogeneous behavior modeling

abstract: We study the problem of large-scale social identity linkage across different social media platforms, which is of critical importance to business intelligence by gaining from social data a deeper understanding and more accurate profiling of users. This paper proposes HYDRA, a solution framework which consists of three key steps: (I) modeling heterogeneous behavior by long-term behavior distribution analysis and multi-resolution temporal information matching; (II) constructing structural consistency graph to measure the high-order structure consistency on users' core social structures across different platforms; and (III) learning the mapping function by multi-objective optimization composed of both the supervised learning on pair-wise ID linkage information and the cross-platform structure consistency maximization. Extensive experiments on 10 million users across seven popular social network platforms demonstrate that HYDRA correctly identifies real user linkage across different platforms, and outperforms existing state-of-the-art algorithms by at least 20% under different settings, and 4 times better in most settings.


title: Wildlife and habitats in managed landscapes

abstract: A headphone device comprising a pair of headphones for receiving output signals from various acoustic apparatus, wherein a direct sound signal or an output signal of an acoustic apparatus is applied to a reverberation supply circuit to produce an indirect sound signal, said indirect sound signal being mixed with said direct sound signal with a ratio of more than 1 : 10 to control the feeling of distance to a sound image existing outside the head, and further the direct sound signal is added to another channel through an attenuator to control the feeling of the direction of the sound image, whereby a natural listening condition similar to the case of loudspeaker listening is obtained.


title: TSSD: Temporal Single-Shot Object Detection Based on Attention-Aware LSTM

abstract: Temporal object detection has attracted significant attention, but most popular detection methods can not leverage the rich temporal information in video or robotic vision. Although many different algorithms have been developed for video detection task, real-time online approaches are frequently deficient. In this paper, based on attention mechanism and convolutional long short-term memory (ConvLSTM), we propose a temporal single-shot detector (TSSD) for robotic vision. Distinct from previous methods, we take aim at temporally integrating pyramidal feature hierarchy using ConvLSTM, and design a novel structure including a high-level temporal unit as well as a low-level one (HL-TU) for multi-scale feature maps. Moreover, we develop a creative temporal analysis unit, namely, attention-aware ConvLSTM (AC-LSTM), in which a temporal attention module is specially tailored for background suppression and scale suppression while ConvLSTM temporally integrates attention-aware features. An association loss is designed for temporal coherence. Finally, our method is evaluated on ImageNet VID dataset. Extensive comparisons on the detection capability confirm or validate the superiority of the proposed approach. Consequently, the developed TSSD is fairly faster and achieves an overall competitive performance in terms of mean average precision. As a temporal, real-time, and online detector, TSSD is applicable to robot's intelligent perception.


title: Creating Environmental Awareness with Upcycling Making Activities: A Study of Children in Germany and Palestine

abstract: This study explores the development of environmental awareness and computational literacy among children by means of upcycling making activities in Germany and Palestine. Our research supports the idea that the combination of handcraft and digital work, documentation and social interaction in the upcycling can foster the development of a maker identity in children. Findings indicate that upcycling can sensitize children for environmental issues of global relevance, foster their environmental learning and encourage them in the exploration of creative answers.


title: Autonomous multilateral debridement with the Raven surgical robot

abstract: Autonomous robot execution of surgical sub-tasks has the potential to reduce surgeon fatigue and facilitate supervised tele-surgery. This paper considers the sub-task of surgical debridement: removing dead or damaged tissue fragments to allow the remaining healthy tissue to heal. We present an autonomous multilateral surgical debridement system using the Raven, an open-architecture surgical robot with two cabledriven 7 DOF arms. Our system combines stereo vision for 3D perception with trajopt, an optimization-based motion planner, and model predictive control (MPC). Laboratory experiments involving sensing, grasping, and removal of 120 fragments suggest that an autonomous surgical robot can achieve robustness comparable to human performance. Our robot system demonstrated the advantage of multilateral systems, as the autonomous execution was 1.5× faster with two arms than with one; however, it was two to three times slower than a human. Execution speed could be improved with better state estimation that would allow more travel between MPC steps and fewer MPC replanning cycles. The three primary contributions of this paper are: (1) introducing debridement as a sub-task of interest for surgical robotics, (2) demonstrating the first reliable autonomous robot performance of a surgical sub-task using the Raven, and (3) reporting experiments that highlight the importance of accurate state estimation for future research. Further information including code, photos, and video is available at: http://rll.berkeley.edu/raven.


title: Tangential excision of pigmented nail matrix lesions responsible for longitudinal melanonychia: evaluation of the technique on a series of 30 patients.

abstract: OBJECTIVES ::: We sought to assess the shave biopsy technique, which is a new surgical procedure for complete removal of longitudinal melanonychia. We evaluated the quality of the specimen submitted for pathological examination, assessed the postoperative outcome, and ascertained its indication between the other types of matrix biopsies. ::: ::: ::: DESIGN ::: This was a retrospective study performed at the dermatologic departments of the Universities of Liège and Brussels, Belgium, of 30 patients with longitudinal or total melanonychia. ::: ::: ::: RESULTS ::: Pathological diagnosis was made in all cases; 23 patients were followed up during a period of 6 to 40 months. Seventeen patients had no postoperative nail plate dystrophy (74%) but 16 patients had recurrence of pigmentation (70%). ::: ::: ::: LIMITATIONS ::: This was a retrospective study. ::: ::: ::: CONCLUSIONS ::: Shave biopsy is an effective technique for dealing with nail matrix lesions that cause longitudinal melanonychia over 4 mm wide. Recurrence of pigmentation is the main drawback of the procedure.


title: Bacteriocins — a viable alternative to antibiotics?

abstract: Solutions are urgently required for the growing number of infections caused by antibiotic-resistant bacteria. Bacteriocins, which are antimicrobial peptides produced by certain bacteria, might warrant serious consideration as alternatives to traditional antibiotics. These molecules exhibit significant potency against other bacteria (including antibiotic-resistant strains), are stable and can have narrow or broad activity spectra. Bacteriocins can even be produced in situ in the gut by probiotic bacteria to combat intestinal infections. Although the application of specific bacteriocins might be curtailed by the development of resistance, an understanding of the mechanisms by which such resistance could emerge will enable researchers to develop strategies to minimize this potential problem.


title: Confusions Among Visually Perceived Consonants

abstract: Eighteen college students with normal hearing responded to the visual perception of initial and final consonants in an English-like phonetic environment in a test of the homopheny of consonant soun...


title: Use of Augmented Reality in Terms of Creativity in School Learning

abstract: Education provides a plethora of tools that can be used (alone or combined) for achieving better results. One of the most recent technological advances that can be used as an educational tool is Augmented Reality, a technology that can combine virtual and physical objects in order to enhance the real world. However, little is known about this technology and its possible applications in primary and secondary education. This paper consists a literature review focused on AR and its current and future incorporation in modern education via various context aware technologies (e.g. tablets, smartphones) which can provide opportunities for more interactive and joyful educational experiences. Also, it is described the possibility of implementing AR in Open Course Project situations, such as the one which is available at the Eastern Macedonia and Thrace Institute of Technology. Its purpose is to inform “creators” and stimulate “users” so that the benefits of this promising technology may be diffused throughout the educational process.


title: Microstructure transition of hydrophilic modified ibuprofen and Pluronic copolymer F127 complexes

abstract: The effect of the aggregation state of Pluronic copolymer (PEO100–PPO65–PEO100, F127) and the concentration of hydrophilic modified ibuprofen (Ibuprofen–PEG800, IP800) on the interaction between F127 and IP800 was systematically investigated by nuclear magnetic resonance, dynamic light scatter (DLS), surface tension, and freeze-fractured transmission electron microscopy. In the solution of F127 unimers (5 °C), F127 unimers tended to wrap around IP800 micelles, and the binding model of F127 unimers to IP800 micelles transferred from wrapping around to partly threading through with increasing IP800 concentration. The latter binding model was straightly confirmed by nuclear Overhauser enhancement spectroscopy. As the aggregation state of F127 is in the beginning of the micellization (20 °C), the addition of IP800 significantly promoted the micellization of F127 to form the F127/IP800 complex with F127 micelles as the skeleton called the F127–micelle complex. The sudden decrease of the size obtained from DLS stemmed from the disruption of the F127–micelle complex and accompanying rehydration of PPO which is weaker compared with refs. The amount of IP800 to disintegrate the F127–micelle complex increased in the F127–micelle-dominated solution (40 °C) compared to that at 20 °C.


title: Investigation of meperidine and its metabolites in urine of an addict by gas chromatography-flame ionization detection and gas chromatography-mass spectrometry.

abstract: A gas chromatographic method with flame ionization detection (FID) for the quantitative analysis of meperidine and mass spectrometry (MS) for the qualitative analyses of meperidine and its metabolites in urine was established. Meperidine, normeperidine and acetyl normeperidine in urine were extracted with ether. Free and conjugated meperidinic and normeperidinic acids in urine, which are hydrophillic, were hydrolyzed by acid, esterified with methanol and derivatized with acetic anhydride. Meperidine and its four metabolites were identified by GC-MS. Meperidine was measured by GC-FID during 72 h after intramuscular administration of meperidine to an addict.


title: Robust Pedestrian Detection by Combining Visible and Thermal Infrared Cameras

abstract: With the development of intelligent surveillance systems, the need for accurate detection of pedestrians by cameras has increased. However, most of the previous studies use a single camera system, either a visible light or thermal camera, and their performances are affected by various factors such as shadow, illumination change, occlusion, and higher background temperatures. To overcome these problems, we propose a new method of detecting pedestrians using a dual camera system that combines visible light and thermal cameras, which are robust in various outdoor environments such as mornings, afternoons, night and rainy days. Our research is novel, compared to previous works, in the following four ways: First, we implement the dual camera system where the axes of visible light and thermal cameras are parallel in the horizontal direction. We obtain a geometric transform matrix that represents the relationship between these two camera axes. Second, two background images for visible light and thermal cameras are adaptively updated based on the pixel difference between an input thermal and pre-stored thermal background images. Third, by background subtraction of thermal image considering the temperature characteristics of background and size filtering with morphological operation, the candidates from whole image (CWI) in the thermal image is obtained. The positions of CWI (obtained by background subtraction and the procedures of shadow removal, morphological operation, size filtering, and filtering of the ratio of height to width) in the visible light image are projected on those in the thermal image by using the geometric transform matrix, and the searching regions for pedestrians are defined in the thermal image. Fourth, within these searching regions, the candidates from the searching image region (CSI) of pedestrians in the thermal image are detected. The final areas of pedestrians are located by combining the detected positions of the CWI and CSI of the thermal image based on OR operation. Experimental results showed that the average precision and recall of detecting pedestrians are 98.13% and 88.98%, respectively.


title: Impact of plant derivatives on the growth of foodborne pathogens and the functionality of probiotics

abstract: Numerous studies have been published on the antimicrobial and antioxidant properties of various plant components. However, there is relatively little information on the impact of such components on the enhancement of probiotics and production of antimicrobial compounds from these probiotics. Hence, this paper focuses on the influence of plant-derived components against pathogens, enhancement of cell viability and functionality of probiotics, and potential applications of such components in food safety and human health.


title: Towards Better Fault Localization: A Crosstab-Based Statistical Approach

abstract: It is becoming prohibitively expensive and time consuming, as well as tedious and error-prone, to perform debugging manually. Among the debugging activities, fault localization has been one of the most expensive, and therefore, a large number of fault-localization techniques have been proposed over the recent years. This paper presents a crosstab-based statistical technique that makes use of the coverage information of each executable statement and the execution result (success or failure) with respect to each test case to localize faults in an effective and efficient manner. A crosstab is constructed for each executable statement, and a statistic is computed to determine the suspiciousness of the corresponding statement. Statements with a higher suspiciousness are more likely to contain bugs and should be examined before those with a lower suspiciousness. Case studies are performed on both small- (the Siemens and Unix suites) and large-sized programs (space, grep, gzip, and make), and results suggest that the crosstab-based technique (CBT) is more effective (in terms of a smaller percentage of executable statements that have to be examined until the first statement containing the fault is reached) than other techniques, such as Tarantula. Further studies using the Siemens suite reveal that the proposed technique is also more effective at locating faults than other statistically oriented techniques, such as SOBER and Liblit05. Additional experiments evaluate the CBT from other perspectives, such as its efficiency in terms of time taken, its applicability to object-oriented languages (on a very large Java program: Ant), and its sensitivity to test suite size, and demonstrate its superior performance.


title: Back-Pressure Traffic Signal Control With Fixed and Adaptive Routing for Urban Vehicular Networks

abstract: City-wide control and coordination of traffic flow can improve efficiency, fuel consumption, and safety. We consider the problem of controlling traffic lights under fixed and adaptive routing of vehicles in urban road networks. Multicommodity back-pressure algorithms, originally developed for routing and scheduling in communication networks, are applied to road networks to control traffic lights and adaptively reroute vehicles. The performance of the algorithms is analyzed using a microscopic traffic simulator. The results demonstrate that the proposed multicommodity and adaptive routing algorithms provide significant improvement over a fixed schedule controller and a single-commodity back-pressure controller in terms of various performance metrics, including queue length, trips completed, travel times, and fair traffic distribution.


title: Emerging trends in augmented reality games

abstract: Augmented Reality (AR) can extend digital world to real world. Augmented Reality not just developed; it had been around there for many years. Actual term of AR came around in 1990's. AR experienced by using headset or mobiles. AR has many applications in military, medical field, entertainment (games) and education. In this paper different AR games are analyzed that are categorize as entertainment and educational games, some tools to develop augmented reality games are also discussed. Some parameters are also used and describe in this paper to analyze survey papers and to analyze tools for making augmented reality games. As most people think that games are just for fun but AR games usage for educational purpose increases; for making lectures attractive, grasping attention of students, teaching history and for higher retention. Augmented reality in mobile games enhances learning methodologies and also provides paramount entertainment.


title: LAVEA: Latency-Aware Video Analytics on Edge Computing Platform

abstract: We present LAVEA, a system built for edge computing, which offloads computation tasks between clients and edge nodes, collaborates nearby edge nodes, to provide low-latency video analytics at places closer to the users. We have utilized an edge-first design to minimize the response time, and compared various task placement schemes tailed for inter-edge collaboration. Our results reveal that the client-edge configuration has task speedup against local or client-cloud configurations.


title: Towards the smart sensors based human emotion recognition

abstract: A smart sensing system, which would help in detecting human emotions based on information from physiological parameters obtained from sensors, has been designed and developed. Sensors continuously monitor the heart rate, skin conductance and skin temperature. The amplified and filtered signals from the sensors are then processed by a microcontroller and transmitted wirelessly using ZigBee technology. The received signals from the system are displayed and stored on the computer where they are analysed visually for obvious patterns. An algorithm is being developed for automatic recognition of emotions using various clustering techniques. The partial developed system has shown good results in monitoring the physiological parameters.


title: CNN-based object detection solutions for embedded heterogeneous multicore SoCs

abstract: This paper surveys how to use Convolutional Neural Networks (CNN) to hypothesize object location and categorization from images or videos in mobile heterogeneous SoCs. Recently a variety of CNN-based object detection frameworks have demonstrated both increasing accuracy and speed. Though they are making fast progress in high quality image recognition, state-of-the-art CNN-based detection frameworks seldom discuss their hardware-depended aspects and the cost-effectiveness of real-time image analysis in off-the-shelf low-power devices. As the focus of deep learning and convolutional neural nets is shifting to the embedded or mobile applications with limited power and computational resources, scaling down object detection framework and CNNs is becoming a new and important direction. In this work we conduct a comprehensive comparative study of state-of-the-art real-time object detection frameworks about their performance, cost-effectiveness/energy-efficiency (in the metric of mAP/Wh) in off-the-shelf mobile GPU devices. Based on the analysis results and observation in investigation, we propose to adjust the design parameters of such frameworks and employ a design space exploration procedure to maximize the energy-efficiency (mAP/Wh) of real-time object detection solution in mobile GPUs. As shown in the benchmarking result, we successfully boost the energy-efficiency of multiple popular CNN-based detection solutions by maximizing the utility of computation resources of SoC and trading-off between prediction accuracy and energy cost. In the second Low-Power Image Recognition Challenge (LPIRC), our system achieved the best result measured in mAP/Energy on the embedded Jetson TX1 CPU+GPU SoC.


title: Relation Extraction: Perspective from Convolutional Neural Networks

abstract: Up to now, relation extraction systems have made extensive use of features generated by linguistic analysis modules. Errors in these features lead to errors of relation detection and classification. In this work, we depart from these traditional approaches with complicated feature engineering by introducing a convolutional neural network for relation extraction that automatically learns features from sentences and minimizes the dependence on external toolkits and resources. Our model takes advantages of multiple window sizes for filters and pre-trained word embeddings as an initializer on a non-static architecture to improve the performance. We emphasize the relation extraction problem with an unbalanced corpus. The experimental results show that our system significantly outperforms not only the best baseline systems for relation extraction but also the state-of-the-art systems for relation classification.


title: Brain uptake and safety of Flutemetamol F 18 injection in Japanese subjects with probable Alzheimer’s disease, subjects with amnestic mild cognitive impairment and healthy volunteers

abstract: ObjectiveThis Phase 2 study assessed the performance of positron emission tomography (PET) brain images made with Flutemetamol F 18 Injection in detecting β-amyloid neuritic plaques in Japanese subjects.MethodsSeventy subjects (25 with probable Alzheimer’s disease (pAD), 20 with amnestic mild cognitive impairment (aMCI), and 25 cognitively normal healthy volunteers[HVs]) underwent PET brain imaging after intravenous Flutemetamol F 18 Injection (185 MBq). Images were interpreted as normal or abnormal for neuritic plaque density by each of five non-Japanese and five Japanese readers who were blinded to clinical data. The primary efficacy analysis (based on HV and pAD data) was the agreement of the non-Japanese readers’ image interpretations with the clinical diagnosis, resulting in estimates of positive percent agreement (PPA; based on AD subjects; similar to sensitivity) and negative percent agreement (NPA; based on HVs; similar to specificity). Secondary analyses included PPA and NPA for the Japanese readers; inter-reader agreement (IRA); intra-reader reproducibility (IRR); quantitative image interpretations (standardized uptake value ratios [SUVRs]) by diagnostic subgroup; test–retest variability in five pAD subjects; and safety.ResultsPPA was 92% for all non-Japanese readers and ranged from 88 to 92% for the Japanese readers. NPA ranged from 96 to 100% for both the non-Japanese readers and the Japanese readers. The majority image interpretations (the interpretations made independently by ≥3 of 5 readers) resulted in PPA values of 92 and 92% and NPA values of 100 and 96% for the non-Japanese and Japanese readers, respectively. IRA and IRR were strong. Composite SUVR values (mean of multiple regional values) allowed clear differentiation between pAD subjects and HVs. Test–retest variability ranged from 1.14 to 2.27%, and test–retest agreement of the blinded visual interpretations was 100% for all readers. Flutemetamol F 18 Injection was generally well tolerated.ConclusionsThe detection of brain neuritic plaques in Japanese subjects using [18F]Flutemetamol PET images gave results highly consistent with clinical diagnosis, with non-Japanese and Japanese readers giving similar results. Inter-reader agreement and intra-reader reproducibility were high for both sets of readers. Visual delineation of abnormal and normal scans was corroborated by quantitative assessment, with low test–retest variability.Trial registrationClinicaltrials.gov registration number NCT02813070.


title: Condensed List Relevance Models

abstract: Pseudo-relevance feedback has traditionally been implemented as an expensive re-retrieval of documents from the target corpus. In this work, we demonstrate that, for high precision metrics, re-ranking the original feedback set provides nearly identical performance to re-retrieval with significantly lower latency.


Mining for Bidding Strategies on eBay


title: Brain impalement by an angle metal bar

abstract: Abstract The author reports the case of a 37-year-old right-handed man who was impaled in the head by an angle metal bar at a construction work site. Impalement injuries of the brain are rare, and their management is complex. The surgical treatment of the injury and the medical management of complications are described in detail. The patient made a good recovery although he has functional deficits related to the injury to his frontal lobes.


title: Fine-grained and accurate source code differencing

abstract: At the heart of software evolution is a sequence of edit actions, called an edit script, made to a source code file. Since software systems are stored version by version, the edit script has to be computed from these versions, which is known as a complex task. Existing approaches usually compute edit scripts at the text granularity with only add line and delete line actions. However, inferring syntactic changes from such an edit script is hard. Since moving code is a frequent action performed when editing code, it should also be taken into account. In this paper, we tackle these issues by introducing an algorithm computing edit scripts at the abstract syntax tree granularity including move actions. Our objective is to compute edit scripts that are short and close to the original developer intent. Our algorithm is implemented in a freely-available and extensible tool that has been intensively validated.


Broken Fingers: On the Usage of the Fingerprint API in Android


title: Influence Maximization in Trajectory Databases

abstract: We study a novel problem of influence maximization in trajectory databases that is very useful in precise locationaware advertising. It finds k best trajectories to be attached with a given advertisement and maximizes the expected influence among a large group of audience. We show that the problem is NP-hard and propose both exact and approximate solutions to find the best set of trajectories. We also extend our problem to support the scenario when there are a group of advertisements. We validate our approach via extensive experiments with real datasets.


title: Private genome analysis through homomorphic encryption

abstract: The rapid development of genome sequencing technology allows researchers to access large genome datasets. However, outsourcing the data processing o the cloud poses high risks for personal privacy. The aim of this paper is to give a practical solution for this problem using homomorphic encryption. In our approach, all the computations can be performed in an untrusted cloud without requiring the decryption key or any interaction with the data owner, which preserves the privacy of genome data. We present evaluation algorithms for secure computation of the minor allele frequencies and χ2 statistic in a genome-wide association studies setting. We also describe how to privately compute the Hamming distance and approximate Edit distance between encrypted DNA sequences. Finally, we compare performance details of using two practical homomorphic encryption schemes - the BGV scheme by Gentry, Halevi and Smart and the YASHE scheme by Bos, Lauter, Loftus and Naehrig. The approach with the YASHE scheme analyzes data from 400 people within about 2 seconds and picks a variant associated with disease from 311 spots. For another task, using the BGV scheme, it took about 65 seconds to securely compute the approximate Edit distance for DNA sequences of size 5K and figure out the differences between them. The performance numbers for BGV are better than YASHE when homomorphically evaluating deep circuits (like the Hamming distance algorithm or approximate Edit distance algorithm). On the other hand, it is more efficient to use the YASHE scheme for a low-degree computation, such as minor allele frequencies or χ2 test statistic in a case-control study.


title: SmaCH: A Framework for Smart Cultural Heritage Spaces

abstract: Cultural Heritage represents a world wide resource of inestimable value, attracting millions of visitors every year to monuments, museums and art exhibitions. A fundamental aspect of this resource is represented by its fruition and promotion. Indeed, to achieve a fruition of a cultural space that is sustainable, it is necessary to realize smart solutions for visitors' interaction to enrich their visiting experience. In this paper we present a service-oriented framework aimed to transform indoor Cultural Heritage sites in smart environments, which enforces a set of multimedia and communication services to support the changing of these spaces in an indispensable dynamic instrument for knowledge, fruition and growth for all the people. Following the Internet of Things paradigm, the proposed framework relies on the integration of a Wireless Sensor Network (WSN) with Wi-Fi and Bluetooth technologies to identify, locate and support visitors equipped with their own mobile devices.


title: A Meander-Line Circular Polarizer Optimized for Oblique Incidence

abstract: This paper presents the design of a meander-line circular polarizer optimized for oblique incidence and for a direction off from the principal planes ( $\phi=\text{0}^\circ$ , $\text{90}^\circ$ , $\text{180}^\circ$ , or $\text{270}^\circ$ ). This polarizing screen converts linearly polarized (LP) waves into circularly polarized (CP) waves and vice versa. To illustrate the design process, a three-layer meander-line circular polarizer is designed for an oblique incidence angle of $(\theta=\text{40}^\circ, \phi=\text{45}^\circ)$ and its performance is examined in detail over the 18–20 GHz band. Measurements and simulations are in good agreement. Moreover, the sensitivity of the results to frequency and incidence angle variations is studied. The results show that the bandwidth of the meander-line circular polarizer is maximum when it is designed for normal incidence or for an oblique incidence in the two principal planes because coupling between orthogonal modes is weaker.


title: Deep Autoencoding Models for Unsupervised Anomaly Segmentation in Brain MR Images

abstract: Reliably modeling normality and differentiating abnormal appearances from normal cases is a very appealing approach for detecting pathologies in medical images. A plethora of such unsupervised anomaly detection approaches has been made in the medical domain, based on statistical methods, content-based retrieval, clustering and recently also deep learning. Previous approaches towards deep unsupervised anomaly detection model patches of normal anatomy with variants of Autoencoders or GANs, and detect anomalies either as outliers in the learned feature space or from large reconstruction errors. In contrast to these patch-based approaches, we show that deep spatial autoencoding models can be efficiently used to capture normal anatomical variability of entire 2D brain MR images. A variety of experiments on real MR data containing MS lesions corroborates our hypothesis that we can detect and even delineate anomalies in brain MR images by simply comparing input images to their reconstruction. Results show that constraints on the latent space and adversarial training can further improve the segmentation performance over standard deep representation learning.


title: Large-Scale Discovery of Spatially Related Images

abstract: We propose a randomized data mining method that finds clusters of spatially overlapping images. The core of the method relies on the min-Hash algorithm for fast detection of pairs of images with spatial overlap, the so-called cluster seeds. The seeds are then used as visual queries to obtain clusters which are formed as transitive closures of sets of partially overlapping images that include the seed. We show that the probability of finding a seed for an image cluster rapidly increases with the size of the cluster. The properties and performance of the algorithm are demonstrated on data sets with 104, 105, and 5 × 106 images. The speed of the method depends on the size of the database and the number of clusters. The first stage of seed generation is close to linear for databases sizes up to approximately 234 ? 1010 images. On a single 2.4 GHz PC, the clustering process took only 24 minutes for a standard database of more than 100,000 images, i.e., only 0.014 seconds per image.


title: Effects of acute psychosocial stress on working memory related brain activity in men

abstract: r Abstract: Acute psychosocial stress in humans triggers the release of glucocorticoids (GCs) and influ- ences performance in declarative and working memory (WM) tasks. These memory systems rely on the hippocampus and prefrontal cortex (PFC), where GC-binding receptors are present. Previous stud- ies revealed contradictory results regarding effects of acute stress on WM-related brain activity. We combined functional magnetic resonance imaging with a standardized psychosocial stress protocol to investigate the effects of acute mental stress on brain activity during encoding, maintenance, and re- trieval of WM. Participants (41 healthy young men) underwent either a stress or a control procedure before performing a WM task. Stress increased salivary cortisol levels and tended to increase WM ac- curacy. Neurally, stress-induced increases in cortical activity were evident in PFC and posterior parie- tal cortex (PPC) during WM maintenance. Furthermore, hippocampal activity was modulated by stress during encoding and retrieval with increases in the right anterior hippocampus during WM encoding and decreases in the left posterior hippocampus during retrieval. Our study demonstrates that stress increases activity in PFC and PPC specifically during maintenance of items in WM, whereas effects on hippocampal activity are restricted to encoding and retrieval. The finding that psychosocial stress can increase and decrease activity in two different hippocampal areas may be relevant for understanding the often-reported phase-dependent opposing behavioral effects of stress on long-term memory. Hum Brain Mapp 00:000-000, 2010. V C 2010 Wiley-Liss, Inc.


title: EvaluationNet: Can Human Skill be Evaluated by Deep Networks?

abstract: With the recent substantial growth of media such as YouTube, a considerable number of instructional videos covering a wide variety of tasks are available online. Therefore, online instructional videos have become a rich resource for humans to learn everyday skills. In order to improve the effectiveness of the learning with instructional video, observation and evaluation of the activity are required. However, it is difficult to observe and evaluate every activity steps by expert. In this study, a novel deep learning framework which targets human activity evaluation for learning from instructional video has been proposed. In order to deal with the inherent variability of activities, we propose to model activity as a structured process. First, action units are encoded from dense trajectories with LSTM network. The variable-length action unit features are then evaluated by a Siamese LSTM network. By the comparative experiments on public dataset, the effectiveness of the proposed method has been demonstrated.


title: A Bayesian Framework for the Analysis of Microarray Expression Data: Regularized t-Test and Statistical Inferences of Gene Changes

abstract: Motivation: DNA microarrays are now capable of providing genome-wide patterns of gene expression across many different conditions. The first level of analysis of these patterns requires determining whether observed differences in expression are significant or not. Current methods are unsatisfactory due to the lack of a systematic framework that can accommodate noise, variability, and low replication often typical of microarray data. Results: We develop a Bayesian probabilistic framework for microarray data analysis. At the simplest level, we model log-expression values by independent normal distributions, parameterized by corresponding means and variances with hierarchical prior distributions. We derive point estimates for both parameters and hyperparameters, and regularized expressions for the variance of each gene by combining the empirical variance with a local background variance associated with neighboring genes. An additional hyperparameter, inversely related to the number of empirical observations, determines the strength of the background variance. Simulations show that these point estimates, combined with a t-test, provide a systematic inference approach that compares favorably with simple t-test or fold methods, and partly compensate for the lack of replication.


title: Maximizing Social Influence in Nearly Optimal Time

abstract: Diffusion is a fundamental graph process, underpinning such phenomena as epidemic disease contagion and the spread of innovation by word-of-mouth. We address the algorithmic problem of finding a set of k initial seed nodes in a network so that the expected size of the resulting cascade is maximized, under the standard independent cascade model of network diffusion. Runtime is a primary consideration for this problem due to the massive size of the relevant input networks. We provide a fast algorithm for the influence maximization problem, obtaining the near-optimal approximation factor of (1 - 1/e - epsilon), for any epsilon>0, in time O((m+n)k log(n) / epsilon^2). Our algorithm is runtime-optimal (up to a logarithmic factor) and substantially improves upon the previously best-known algorithms which run in time Omega(mnk POLY(1/epsilon)). Furthermore, our algorithm can be modified to allow early termination: if it is terminated after O(beta(m+n)k log(n)) steps for some beta<1 (which can depend on n), then it returns a solution with approximation factor O(beta). Finally, we show that this runtime is optimal (up to logarithmic factors) for any beta and fixed seed size k.


title: Customer lifetime network value: customer valuation in the context of network effects

abstract: Nowadays customers are increasingly connected and extensively interact with each other using technology-enabled media like online social networks. Hence, customers are frequently exposed to social influence when making purchase decisions. However, established approaches for customer valuation mostly neglect network effects based on social influence. This leads to a misallocation of resources. Following a design-oriented approach, this paper develops a model for customer valuation referred to as the customer lifetime network value (CLNV) incorporating an integrated network perspective. By considering the customers’ net contribution to the network, the CLNV reallocates values between customers based on social influence. Inspired by common prestige- and eigenvector-related centrality measures it incorporates social influence among all degrees of separation acknowledging its viral spread. Using a real-world dataset, we demonstrate the practicable applicability of the CLNV to determine individual customers’ value.


title: Digital Control Strategy for Four Quadrant Operation of Three Phase BLDC Motor With Load Variations

abstract: Brushless DC (BLDC) motor drives are becoming more popular in industrial, traction applications. This makes the control of BLDC motor in all the four quadrants very vital. This paper deals with the digital control of three phase BLDC motor. The motor is controlled in all the four quadrants without any loss of power; in fact energy is conserved during the regenerative period. The digital controller dsPIC30F4011, which is very advantageous over other controllers, as it combines the calculation capability of Digital Signal Processor and controlling capability of PIC microcontroller, to achieve precise control.


title: Smartphone Fingerprinting Combining Features of On-Board Sensors

abstract: Many everyday activities involve the exchange of confidential information through the use of a smartphone in mobility, i.e., sending on e-mail, checking bank account, buying on-line, accessing cloud platforms, and health monitoring. This demonstrates how security issues related to these operations are a major challenge in our society and in particular in the cyber-security domain. This paper focuses on the use of the smartphone intrinsic and physical characteristics as a mean to build a smartphone fingerprint to enable devices identification. The basic idea proposed in this paper is to investigate how to generate a specific fingerprint that allows to distinctively and reliably characterize each smartphone. In particular, the accelerometer, the gyroscope, the magnetometer, and the audio system (microphone-speaker) are taken into account to build up a composite fingerprint based on a set of their distinctive features. Many experiments have been carried out by analyzing different classification methods, diverse features combination configurations, and operative scenarios. Satisfactory results have been obtained showing that the combination of such sensors improves smartphone distinctiveness.


title: Security in the Internet of Things (IoT)

abstract: Internet of Things (IoT) are affecting our daily lives significantly. They are used in the homes, hospitals; installed outside to control and report the changes in the environment. IoT is a worldwide system of physical and, virtual “things” associated with the web. The Internet of Things everywhere will encourage billions of devices, individuals and administrations to interconnect to trade data and helpful information. Each object has special ID which is used for recognizable proof. In future, practically every electronic device will be a smart device which can register and speak with handheld and other framework equipment. As most of the IoT devices are battery operated and requires less power consumption, the safety and protection is a real issue in IoT. Identification, Authentication and device diversity are the real security and protection concern in IoT. This paper discusses critical issues related to safety and privacy of IoT.


The minimal sum of altitudes decomposition for coverage algorithms


title: Incremental Joint Extraction of Entity Mentions and Relations

abstract: We present an incremental joint framework to simultaneously extract entity mentions and relations using structured perceptron with efficient beam-search. A segment-based decoder based on the idea of semi-Markov chain is adopted to the new framework as opposed to traditional token-based tagging. In addition, by virtue of the inexact search, we developed a number of new and effective global features as soft constraints to capture the interdependency among entity mentions and relations. Experiments on Automatic Content Extraction (ACE) 1 corpora demonstrate that our joint model significantly outperforms a strong pipelined baseline, which attains better performance than the best-reported end-to-end system.


title: doi: 10.3758/MC.36.2.438 The effects of tests on learning and forgetting

abstract: In three experiments, we investigated whether memory tests enhance learning and reduce forgetting more than additional study opportunities do. Subjects learned obscure facts (Experiments 1 and 2) or Swahili-English word pairs (Experiment 3) by either completing a test with feedback (test/study) or receiving an additional study opportunity (study). Recall was tested after 5 min or 1, 2, 7, 14, or 42 days. We explored forgetting by means of an ANOVA and also by fitting a power function to the data. In all three experiments, testing enhanced overall recall more than restudying did. According to the power function, in two out of three experiments, testing also reduced forgetting more than restudying did, although this was not always the case according to the ANOVA. We discuss the implications of these results both for approaches to measuring forgetting and for the use of tests in promoting long-term retention. The stimuli used in these experiments may be found at www.psychonomic.org/archive.


title: Sequential 3D U-Nets for Biologically-Informed Brain Tumor Segmentation

abstract: Deep learning has quickly become the weapon of choice for brain lesion segmentation. However, few existing algorithms pre-configure any biological context of their chosen segmentation tissues, and instead rely on the neural network's optimizer to develop such associations de novo. We present a novel method for applying deep neural networks to the problem of glioma tissue segmentation that takes into account the structured nature of gliomas - edematous tissue surrounding mutually-exclusive regions of enhancing and non-enhancing tumor. We trained multiple deep neural networks with a 3D U-Net architecture in a tree structure to create segmentations for edema, non-enhancing tumor, and enhancing tumor regions. Specifically, training was configured such that the whole tumor region including edema was predicted first, and its output segmentation was fed as input into separate models to predict enhancing and non-enhancing tumor. Our method was trained and evaluated on the publicly available BraTS dataset, achieving Dice scores of 0.882, 0.732, and 0.730 for whole tumor, enhancing tumor and tumor core respectively.


title: Trajectory optimization for constrained UAVs: A Virtual Target Vehicle approach

abstract: In this paper we propose a novel approach for trajectory optimization for constrained Unmanned Aerial Vehicles (UAVs). With regard to the classical trajectory optimization problem, we take a Virtual Target Vehicle (VTV) perspective by introducing a virtual target that plays the role of an additional control input. Based on a nonlinear projection operator optimal control technique and extending the concepts of the maneuver regulation framework, we propose a trajectory optimization based strategy to compute, for any given desired path with a specified desired speed profile, the (local) optimal feasible trajectory that best approximates the desired one. The optimization procedure takes explicitly into account the extra flexibility of the VTV by changing (during the transient period) the velocity of the virtual target with the benefit of improving the convergence of the solver to obtain the optimal feasible path, and also avoid the singularities that occur in some maneuver regulation techniques described in the literature. We provide numerical computations for three testing scenarios that illustrates the effectiveness of the proposed strategy.


title: 2-D antenna array geometries for MIMO radar imaging by Digital Beamforming

abstract: In general, two-dimensional radar imaging applications, which require angular information in azimuth and elevation, use full planar antenna arrays. If orthogonally arranged transmit and receive arrays are used instead, the angular resolution equals that of a radar system using only a single antenna on the transmit and a full antenna array on the receiver side or vice-versa. This paper evaluates the influence of different antenna array configurations on the angular resolution capability by means of a 24GHz Digital Beamforming radar system. Besides the so-called inverted T-shaped antenna arrangement, various array geometries are discussed. Compared to the full planar antenna array the introduced geometries provide better angular resolution, while allowing for a highly reduced number of antennas, saving space and cost. Each presented antenna array is realized in hardware and the angular resolution capability is evaluated by means of a 24 GHz Digital Beamforming radar system.


title: SUIT: a supervised user-item based topic model for sentiment analysis

abstract: Probabilistic topic models have been widely used for sentiment analysis. However, most of existing topic methods only model the sentiment text, but do not consider the user, who expresses the sentiment, and the item, which the sentiment is expressed on. Since different users may use different sentiment expressions for different items, we argue that it is better to incorporate the user and item information into the topic model for sentiment analysis. In this paper, we propose a new Supervised User-Item based Topic model, called SUIT model, for sentiment analysis. It can simultaneously utilize the textual topic and latent user-item factors. Our proposed method uses the tensor outer product of text topic proportion vector, user latent factor and item latent factor to model the sentiment label generalization. Extensive experiments are conducted on two datasets: review dataset and microblog dataset. The results demonstrate the advantages of our model. It shows significant improvement compared with supervised topic models and collaborative filtering methods.


title: Optimal sliding-mode guidance of a homing-missile

abstract: An optimal guidance law based on missile-target LOS angular rate is presented for intercepting a nonmaneuvering target. It is then integrated with sliding-mode control theory to derive an optimal sliding-mode guidance law for intercepting maneuvering target. The new guidance method's robustness against target manoeuvres and good miss distance performance is proved by the second method of Lyapunov and simulation results. The presented guidance law is simple to implement in practical applications.


title: Gamification for productive interaction: Reading and working with the gamification debate in education

abstract: We examine the gamification debate of recent years and we propose an alternative, heuristic definition for gamification in learning situations. After considering several critiques of the gamification concept, we privilege in our definition `interaction' over `motivation', `simple gameplay' over `game mechanics', and we highlight the diverse and changing behaviors of user/players. We re-define gamification in learning contexts as `simple gameplay to support productive interaction for expected types of learners and instructors'. We argue that this definition offers a lowest common denominator to inform gamification in education.


title: Split Compilation: an Application to Just-in-Time Vectorization

abstract: In a world of ubiquitous, heterogeneous parallelism, achieving portable performance is a challenge. It requires finely tuned coordination, from the programming language to the hardware, through the compiler and multiple layers of the run-time system. This document presents our work in split compilation and parallelization. Split compilation relies on automatically generated semantical annotations to enrich the intermediate format, decoupling costly offline analyzes from lighter, online or just-in-time program transformations. Our work focuses on automatic vectorization, a key optimization playing an increasing role in modern, power-efficient architectures. Our research platform uses GCC’s support for the Common Language Infrastructure (CLI ECMA-335 [8] and ISO 23271:2006 [10]); this choice is motivated by the unique combination of optimizations and portability of GCC, through a semantically rich and performance-friendly intermediate format. Implementation is still in progress.


title: INTEGRATING ARTIFICIAL INTELLIGENCE INTO ORGANIZATIONAL INTELLIGENCE

abstract: Organizational intelligence is the capability of an organization to create knowledge and to use it in order to strategically adapt to its environment. Intelligence of an organization is more than the aggregated intelligence of its members – it is an emergent property of the complex interactions of its subsystems and the way they are aggregated. Processes analyse related to organizational intelligence can be achieved by means of agent-based simulations. Distributed artificial intelligence addresses the study and design of systems composed of several interacting entities, which are distributed from the logical and often spatial point of view, considered in a certain sense autonomous and intelligent. An intelligent agent is a hardware or (especially) software system that is autonomous and situated in its environment and enjoys the following properties: autonomy, reactivity, pro-activeness and social ability. Organizational intelligence can be improved by extracting aggregated information about past experience which can be analysed and used in current situations. This helps organizations to understand past tendencies respectively outcomes and to anticipate future trends by applying previous patterns from organizational data.


title: Simulation of a permanent magnet synchronous motor using Matlab-Simulink

abstract: In the recent past, the use of permanent magnet synchronous motors (PMSMs) has increased considerably owing to their inherent advantages. The high performance speed and/or position control of a PMS motor requires an accurate knowledge of rotor shaft position and angular speed in order to synchronize the phase excitation pulses to the rotor position. The vector control method is used for dynamic speed control. In this paper mathematical model of PMSM was developed using PARK'S transformation. The conventional vector control of drive is implemented using SVPWM. In order to test and validate the control scheme, the model is developed in Matlab - Simulink and is tested for various operating conditions. MATLAB version RIO is used.


title: Tomato (Solanum lycopersicum L.) SlIPT3 and SlIPT4 isopentenyltransferases mediate salt stress response in tomato

abstract: BACKGROUND ::: Cytokinins (CKs) are involved in response to various environmental cues, including salinity. It has been previously reported that enhancing CK contents improved salt stress tolerance in tomato. However, the underlying mechanisms of CK metabolism and signaling under salt stress conditions remain to be deciphered. ::: ::: ::: RESULTS ::: Two tomato isopentenyltransferases, SlIPT3 and SlIPT4, were characterized in tomato and Arabidopsis. Both proteins displayed isopentenyltransferase (IPT) activity in vitro, while their encoding genes exhibited different spatio-temporal expression patterns during tomato plant development. SlIPT3 and SlIPT4 were affected by the endogenous CK status, tightly connected with CKs feedback regulation, as revealed by hormonal treatements. In response to salt stress, SlIPT3 and SlIPT4 were strongly repressed in tomato roots, and differently affected in young and old leaves. SlIPT3 overexpression in tomato resulted in high accumulation of different CK metabolites, following modifications of CK biosynthesis-, signaling- and degradation-gene expression. In addition, 35S::SlIPT3 tomato plants displayed improved tolerance to salinity consecutive to photosynthetic pigments and K(+)/Na(+) ratio retention. Involvement of SlIPT3 and SlIPT4 in salt stress response was also observed in Arabidopsis ipt3 knock-out complemented plants, through maintenance of CK homeostasis. ::: ::: ::: CONCLUSIONS ::: SlIPT3 and SlIPT4 are functional IPTs encoded by differently expressed genes, distinctively taking part in the salinity response. The substantial participation of SlIPT3 in CK metabolism during salt stress has been determined in 35S::SlIPT3 tomato transformants, where enhancement of CKs accumulation significantly improved plant tolerance to salinity, underlining the importance of this phytohormone in stress response.


title: Effects of doxycycline on heartworm embryogenesis, transmission, circulating microfilaria, and adult worms in microfilaremic dogs.

abstract: Abstract Tetracycline treatment of animals or humans infected with filariae that harbor Wolbachia endosymbionts blocks further embryogenesis, and existing microfilariae gradually die. This treatment also kills developing larvae and has a slow-kill effect on adult filariae, all presumably due to elimination of the Wolbachia . Also, Dirofilaria immitis microfilariae in blood collected from dogs up to 25 days after the last dose of doxycycline developed to infective L 3 that were normal in appearance and motility in mosquitoes but did not continue to develop or migrate normally after subcutaneous (SC) injection into dogs. The present study was designed to determine whether heartworm microfilariae collected at later times after treatment would regain the ability to continue normal development in a dog. The study also was expected to yield valuable data on the effects of treatment on microfilariae and antigen levels and adult worms. The study was conducted in 16 dogs as two separate replicates at different times. A total of five dogs (two in Replicate A and three in Replicate B) infected either by SC injection of L 3 or intravenous transplantation of adult heartworms were given doxycycline orally at 10 mg/kg twice daily for 30 days, with three untreated controls. Microfilarial counts in the five treated dogs gradually declined during the 12–13 months after treatment initiation. Two dogs were amicrofilaremic before necropsy and three had 13 or fewer microfilariae/ml. Only one treated dog was negative for heartworm antigen before necropsy. Overall, treated dogs generally had fewer live adult heartworms than controls, and most of their live worms were moribund. All three control dogs remained positive for microfilariae and antigen and had many live worms. L 3 from mosquitoes fed on blood collected 73–77 or 161–164 days after initiation of doxycycline treatments were injected SC into five dogs. None of the dogs injected with L 3 from mosquitoes fed on blood from doxycycline-treated dogs were ever positive for microfilariae or antigen, and none had worms at necropsy; three control dogs were positive for microfilariae and antigen and had many live worms. These data indicate that doxycycline treatment of microfilaremic dogs gradually reduces numbers of microfilariae and blocks further transmission of heartworms. This latter effect should be highly effective in reducing the rate of selection of heartworms with genes that confer resistance to macrocyclic lactone preventives and microfilaricides. The data also suggest that doxycycline has a slow-kill effect on adult heartworms.


title: Reducing Wait Times through Operations Research: Optimizing the Use of Surge Capacity*

abstract: Widespread public demand for improved access, political pressure for shorter wait times, a stretched workforce, an aging population and overutilized equipment and facilities challenge healthcare leaders to adopt new management approaches. This paper highlights the significant benefits that can be achieved by applying operations research (OR) methods to healthcare management. It shows how queuing theory provides managers with insights into the causes for excessive wait times and the relationship between wait times and capacity. It provides a case study of the use of several OR methods, including Markov decision processes, linear programming and simulation, to optimize the scheduling of patients with multiple priorities. The study shows that by applying this approach, wait time targets can be attained with the judicious use of surge capacity in the form of overtime. It concludes with some policy insights.


Generalized fast on-the-fly composition algorithm for WFST-based speech recognition.


title: Noise as a Resource for Computation and Learning in Networks of Spiking Neurons

abstract: We are used to viewing noise as a nuisance in computing systems. This is a pity, since noise will be abundantly available in energy-efficient future nanoscale devices and circuits. I propose here to learn from the way the brain deals with noise, and apparently even benefits from it. Recent theoretical results have provided insight into how this can be achieved: how noise enables networks of spiking neurons to carry out probabilistic inference through sampling and also enables creative problem solving. In addition, noise supports the self-organization of networks of spiking neurons, and learning from rewards. I will sketch here the main ideas and some consequences of these results. I will also describe why these results are paving the way for a qualitative jump in the computational capability and learning performance of neuromorphic networks of spiking neurons with noise, and for other future computing systems that are able to treat noise as a resource.


title: HMM-based Error Recovery of Dance Step Selection for Dance Partner Robot

abstract: A dance partner robot has been developed as an example of platforms for realizing the effective human-robot coordination with physical interactions. This robot could dance together with a human by estimating the next step intended by the human. If the robot would mistake the step estimation, the human-robot coordination could not be continued. In this paper, an error recovery method for step selections, which changes robot's behavior according to human's behavior, is designed using hidden Markov models. Experimental results illustrate the validity of the proposed method.


title: Reconstruction of the Nasal Columella

abstract: Objective To report techniques successful for nasal columella reconstruction. Methods Retrospective medical chart review of patients undergoing columella reconstruction by 2 of us (D.A.S. and P.A.H.) from January 1, 1982, to December 31, 2000. Photographs before tumor resection or trauma, after resection or trauma, and after reconstruction were examined by facial plastic surgeons masked to the cases and were rated on a 10-cm visual analogue scale. Result Sixteen patients were identified, most of whom had columellar defects repaired with forehead flaps, nasolabial flaps, or nasofacial sulcus flaps. The mean improvement on the 10-cm visual analogue scale was 2.0 from before tumor resection or trauma to after reconstruction, and 5.0 from tumor resection or trauma to after reconstruction. Conclusion Skin grafts, composite grafts, and several flaps, including nasolabial, nasofacial sulcus, and forehead flaps, are useful in repairing defects of the nasal columella.


title: Environmental and economic benefits of variable rate nitrogen fertilization in a nitrate vulnerable zone.

abstract: Agronomic input and management practices have traditionally been applied uniformly on agricultural fields despite the presence of spatial variability of soil properties and landscape position. When spatial variability is ignored, uniform agronomic management can be both economically and environmentally inefficient. The objectives of this study were to: i) identify optimal N fertilizer rates using an integrated spatio-temporal analysis of yield and site-specific N rate response; ii) test the sensitivity of site specific N management to nitrate leaching in response to different N rates; and iii) demonstrate the environmental benefits of variable rate N fertilizer in a Nitrate Vulnerable Zone. This study was carried out on a 13.6 ha field near the Venice Lagoon, northeast Italy over four years (2005-2008). We utilized a validated crop simulation model to evaluate crop response to different N rates at specific zones in the field based on localized soil and landscape properties under rainfed conditions. The simulated rates were: 50 kg N ha(-1) applied at sowing for the entire study area and increasing fractions, ranging from 150 to 350 kg N ha(-1) applied at V6 stage. Based on the analysis of yield maps from previous harvests and soil electrical resistivity data, three management zones were defined. Two N rates were applied in each of these zones, one suggested by our simulation analysis and the other with uniform N fertilization as normally applied by the producer. N leaching was lower and net revenue was higher in the zones where variable rates of N were applied when compared to uniform N fertilization. This demonstrates the efficacy of using crop models to determine variable rates of N fertilization within a field and the application of variable rate N fertilizer to achieve higher profit and reduce nitrate leaching.


title: Assemblable three-fingered nine-degree of freedom hand for laparoscopic surgery

abstract: This paper proposes an assemblable hand that can be inserted through trocars for robotic hand assisted laparoscopic surgery (HALS). When it is difficult to perform surgery using only slender laparoscopic surgery instruments, surgeons often apply HALS, which makes an incision about 7-8 cm through which their hand is inserted. This is invasive compared with complete laparoscopic surgery. We proposed robotic HALS to replace a human hand with a robotic hand. We previously developed a three-fingered five-degree of freedom assemblable hand. It is challenging to an assemblable hand with more degrees of freedom (DOF) that can be assembled with a simple assembly procedure. This paper presents an assemblable hand with three fingers and nine degrees of freedom-the 3f9d-hand. Its power transmission mechanisms and assembly procedure are completely different from those of our previous 3f5d-hand. The new hand consists of center, right, and left finger units. The center finger unit connects the operational part at its end and the right and left finger units connect to the operational part outside the abdominal cavity. This facilitates assembly and improves safety, which is a significant improvement compared with the previous hand. Although the hand has no wrist joint, its three finger joints play the role of a wrist joint. A preliminary experiment with a plastic model verified that the proposed assembly procedure was feasible and the hand was easily asembled and disassembled.


title: Opinion mining from online hotel reviews A text summarization approach

abstract: Text summarization technique can extract essential information from online reviews.Our method can identify top-k most informative sentences from online hotel reviews.We jointly considered author, review time, usefulness, and opinion factors.Online hotel reviews were collected from TripAdvisor in experimental evaluation.The results show that our approach provides more comprehensive hotel information. Online travel forums and social networks have become the most popular platform for sharing travel information, with enormous numbers of reviews posted daily. Automatically generated hotel summaries could aid travelers in selecting hotels. This study proposes a novel multi-text summarization technique for identifying the top-k most informative sentences of hotel reviews. Previous studies on review summarization have primarily examined content analysis, which disregards critical factors like author credibility and conflicting opinions. We considered such factors and developed a new sentence importance metric. Both the content and sentiment similarities were used to determine the similarity of two sentences. To identify the top-k sentences, the k-medoids clustering algorithm was used to partition sentences into k groups. The medoids from these groups were then selected as the final summarization results. To evaluate the performance of the proposed method, we collected two sets of reviews for the two hotels posted on TripAdvisor.com. A total of 20 subjects were invited to review the text summarization results from the proposed approach and two conventional approaches for the two hotels. The results indicate that the proposed approach outperforms the other two, and most of the subjects believed that the proposed approach can provide more comprehensive hotel information.


title: GAN Q-learning

abstract: Distributional reinforcement learning (distributional RL) has seen empirical success in complex Markov Decision Processes (MDPs) in the setting of nonlinear function approximation. However there are many different ways in which one can leverage the distributional approach to reinforcement learning. In this paper, we propose GAN Q-learning, a novel distributional RL method based on generative adversarial networks (GANs) and analyze its performance in simple tabular environments, as well as OpenAI Gym. We empirically show that our algorithm leverages the flexibility and blackbox approach of deep learning models while providing a viable alternative to traditional methods.


title: Analyzing high-dimensional time-series data using kernel transfer operator eigenfunctions

abstract: Kernel transfer operators, which can be regarded as approximations of transfer operators such as the Perron-Frobenius or Koopman operator in reproducing kernel Hilbert spaces, are defined in terms of covariance and cross-covariance operators and have been shown to be closely related to the conditional mean embedding framework developed by the machine learning community. The goal of this paper is to show how the dominant eigenfunctions of these operators in combination with gradient-based optimization techniques can be used to detect long-lived coherent patterns in high-dimensional time-series data. The results will be illustrated using video data and a fluid flow example.


title: Putting hands to rest: efficient deep CNN-RNN architecture for chemical named entity recognition with no hand-crafted rules

abstract: Chemical named entity recognition (NER) is an active field of research in biomedical natural language processing. To facilitate the development of new and superior chemical NER systems, BioCreative released the CHEMDNER corpus, an extensive dataset of diverse manually annotated chemical entities. Most of the systems trained on the corpus rely on complicated hand-crafted rules or curated databases for data preprocessing, feature extraction and output post-processing, though modern machine learning algorithms, such as deep neural networks, can automatically design the rules with little to none human intervention. Here we explored this approach by experimenting with various deep learning architectures for targeted tokenisation and named entity recognition. Our final model, based on a combination of convolutional and stateful recurrent neural networks with attention-like loops and hybrid word- and character-level embeddings, reaches near human-level performance on the testing dataset with no manually asserted rules. To make our model easily accessible for standalone use and integration in third-party software, we've developed a Python package with a minimalistic user interface.


title: Deep convolutional neural networks for pedestrian detection

abstract: Pedestrian detection is a popular research topic due to its paramount importance for a number of applications, especially in the fields of automotive, surveillance and robotics. Despite the significant improvements, pedestrian detection is still an open challenge that calls for more and more accurate algorithms. In the last few years, deep learning and in particular Convolutional Neural Networks emerged as the state of the art in terms of accuracy for a number of computer vision tasks such as image classification, object detection and segmentation, often outperforming the previous gold standards by a large margin. In this paper, we propose a pedestrian detection system based on deep learning, adapting a general-purpose convolutional network to the task at hand. By thoroughly analyzing and optimizing each step of the detection pipeline we propose an architecture that outperforms traditional methods, achieving a task accuracy close to that of state-of-the-art approaches, while requiring a low computational time. Finally, we tested the system on an NVIDIA Jetson TK1, a 192-core platform that is envisioned to be a forerunner computational brain of future self-driving cars. HighlightsA deep convolutional neural network for pedestrian detection is proposed.The proposed method improves the accuracy performance with respect to handcrafted features.Our method detects pedestrians with a good accuracy and has a reasonable computational complexity.


title: Multi-Dimensional MEMS/Micro Sensor for Force and Moment Sensing: A Review

abstract: The importance of force sensing technologies was recognized in the 1970s. Since then, multidimensional microelectromechanical systems (MEMS)/micro force/moment (F/M) sensors have permeated a wide variety of products. A multidimensional MEMS/micro-F/M sensor can measure the tangential force terms along x-, y-, and z-axis (F ::: x ::: , F ::: y ::: and F ::: z ::: ) as well as the moments terms about x-, y-, and z-axis (M ::: x ::: , M ::: y ::: and M ::: z ::: ) simultaneously with micro-Newton and nano-Newtonmeter resolution. This paper presents an overview of MEMS/micro-F/M sensors. This field is critical to many biomedical applications, materials science, industrial automation, dimension measurements in microcomponents, and nanomanufacturing applications, and has attracted great activity in the past 15-25 years. The evaluation of different F/M sensing principles, recent advances in various designs, and their significance and limitations are analyzed through specific examples. Furthermore, current challenges and new area for future applications of micro-F/M sensing technology have been identified.


title: Justifying an ERP Investment: Critical Success Factors for Transformation Investments

abstract: Enterprise Resource Planning (ERP) projects often lead to disappointing outcomes, even downright failures, which is not in keeping with the vast investments they require. It is argued in this paper that a lack of managerial focus on the Critical Success Factors (CSFs) for ERP implementation at the outset of their project (however well captured they have been in the literature to date) is the root cause of organisations not achieving the expected outcomes from their Transformation investment. This paper examines the ERP project implementations of four organisations where data and observations were collected over a two year period. From this rich body of empirical evidence, we propose to contribute to our collective understanding of the CSFs for ERP implementation that play a key role in ensuring that investments in ERP packages (Transformation investments) have a greater likelihood of producing desired project outcomes. This research paper also contributes to increasing the perceived usefulness of CSFs to managers and practitioners and illustrates the fact that organisations should undertake self-assessment exercises to improve their preparations for ERP project implementations.


title: Context-aware ranking in web search

abstract: The context of a search query often provides a search engine meaningful hints for answering the current query better. Previous studies on context-aware search were either focused on the development of context models or limited to a relatively small scale investigation under a controlled laboratory setting. Particularly, about context-aware ranking for Web search, the following two critical problems are largely remained unsolved. First, how can we take advantage of different types of contexts in ranking? Second, how can we integrate context information into a ranking model? In this paper, we tackle the above two essential problems analytically and empirically. We develop different ranking principles for different types of contexts. Moreover, we adopt a learning-to-rank approach and integrate the ranking principles into a state-of-the-art ranking model by encoding the context information as features of the model. We empirically test our approach using a large search log data set obtained from a major commercial search engine. Our evaluation uses both human judgments and implicit user click data. The experimental results clearly show that our context-aware ranking approach improves the ranking of a commercial search engine which ignores context information. Furthermore, our method outperforms a baseline method which considers context information in ranking.


title: Nanostructured lipid carriers: Promising drug delivery systems for future clinics.

abstract: Abstract During the past decade, the number of studies describing nanostructured lipid carriers (NLCs)-based formulations has been dramatically increased. The raise in NLC exploitation is essentially due to defeated barriers within the technological process of lipid-based nanoparticles' formulation and increased knowledge of the underlying mechanisms of transport of NLCs via different routes of administration. This review article aims to give an overview on the current state of the art of NLC as controlled drug delivery systems for future clinics through novel NLC applications providing examples of successfull outcomes. The reported data clearly illustrate the promise of these nanoparticles for novel treatments in the near future. From the Clinical Editor The understanding of the nanostructured lipid carriers (NLC)-based formulations has improved with continuing research recently. The result has seen an increase in the use of these in the clinical setting. In this comprehensive review, the authors discussed the current state and major challenges in the use of nanostructured lipid carriers as controlled drug delivery systems.


A METHOD FOR ANALYZING BROADCAST BEAMFORMING OF MASSIVE MIMO ANTENNA ARRAY


title: Robust Kernel Low-Rank Representation

abstract: Recently, low-rank representation (LRR) has shown promising performance in many real-world applications such as face clustering. However, LRR may not achieve satisfactory results when dealing with the data from nonlinear subspaces, since it is originally designed to handle the data from linear subspaces in the input space. Meanwhile, the kernel-based methods deal with the nonlinear data by mapping it from the original input space to a new feature space through a kernel-induced mapping. To effectively cope with the nonlinear data, we first propose the kernelized version of LRR in the clean data case. We also present a closed-form solution for the resultant optimization problem. Moreover, to handle corrupted data, we propose the robust kernel LRR (RKLRR) approach, and develop an efficient optimization algorithm to solve it based on the alternating direction method. In particular, we show that both the subproblems in our optimization algorithm can be efficiently and exactly solved, and it is guaranteed to obtain a globally optimal solution. Besides, our proposed algorithm can also solve the original LRR problem, which is a special case of our RKLRR when using the linear kernel. In addition, based on our new optimization technique, the kernelization of some variants of LRR can be similarly achieved. Comprehensive experiments on synthetic data sets and real-world data sets clearly demonstrate the efficiency of our algorithm, as well as the effectiveness of RKLRR and the kernelization of two variants of LRR.


title: BlastNeuron for Automated Comparison, Retrieval and Clustering of 3D Neuron Morphologies

abstract: Characterizing the identity and types of neurons in the brain, as well as their associated function, requires a means of quantifying and comparing 3D neuron morphology. Presently, neuron comparison methods are based on statistics from neuronal morphology such as size and number of branches, which are not fully suitable for detecting local similarities and differences in the detailed structure. We developed BlastNeuron to compare neurons in terms of their global appearance, detailed arborization patterns, and topological similarity. BlastNeuron first compares and clusters 3D neuron reconstructions based on global morphology features and moment invariants, independent of their orientations, sizes, level of reconstruction and other variations. Subsequently, BlastNeuron performs local alignment between any pair of retrieved neurons via a tree-topology driven dynamic programming method. A 3D correspondence map can thus be generated at the resolution of single reconstruction nodes. We applied BlastNeuron to three datasets: (1) 10,000+ neuron reconstructions from a public morphology database, (2) 681 newly and manually reconstructed neurons, and (3) neurons reconstructions produced using several independent reconstruction methods. Our approach was able to accurately and efficiently retrieve morphologically and functionally similar neuron structures from large morphology database, identify the local common structures, and find clusters of neurons that share similarities in both morphology and molecular profiles.