title: A music recommendation system based on music data grouping and user interests

abstract: With the growth of the World Wide Web, a large amount of music data is available on the Internet. In addition to searching expected music objects for users, it becomes necessary to develop a recommendation service. In this paper, we design the Music Recommendation System (MRS) to provide a personalized service of music recommendation. The music objects of MIDI format are first analyzed. For each polyphonic music object, the representative track is first determined, and then six features are extracted from this track. According to the features, the music objects are properly grouped. For users, the access histories are analyzed to derive user interests. The content-based, collaborative and statistics-based recommendation methods are proposed, which are based on the favorite degrees of the users to the music groups. A series of experiments are carried out to show that our approach is feasible.


title: High Accuracy Optical Flow Estimation Based on a Theory for Warping

abstract: We study an energy functional for computing optical flow that combines three assumptions: a brightness constancy assumption, a gradient constancy assumption, and a discontinuity-preserving spatio-temporal smoothness constraint. In order to allow for large displacements, linearisations in the two data terms are strictly avoided. We present a consistent numerical scheme based on two nested fixed point iterations. By proving that this scheme implements a coarse-to-fine warping strategy, we give a theoretical foundation for warping which has been used on a mainly experimental basis so far. Our evaluation demonstrates that the novel method gives significantly smaller angular errors than previous techniques for optical flow estimation. We show that it is fairly insensitive to parameter variations, and we demonstrate its excellent robustness under noise.


title: Review of nonlinear mixed-integer and disjunctive programming techniques

abstract: This paper has as a major objective to present a unified overview and derivation of mixed-integer nonlinear programming (MINLP) techniques, Branch and Bound, Outer-Approximation, Generalized Benders and Extended Cutting Plane methods, as applied to nonlinear discrete optimization problems that are expressed in algebraic form. The solution of MINLP problems with convex functions is presented first, followed by a brief discussion on extensions for the nonconvex case. The solution of logic based representations, known as generalized disjunctive programs, is also described. Theoretical properties are presented, and numerical comparisons on a small process network problem.


title: On the Convergence of A Class of Adam-Type Algorithms for Non-Convex Optimization

abstract: This paper studies a class of adaptive gradient based momentum algorithms that update the search directions and learning rates simultaneously using past gradients. This class, which we refer to as the "Adam-type", includes the popular algorithms such as the Adam, AMSGrad and AdaGrad. Despite their popularity in training deep neural networks, the convergence of these algorithms for solving nonconvex problems remains an open question. This paper provides a set of mild sufficient conditions that guarantee the convergence for the Adam-type methods. We prove that under our derived conditions, these methods can achieve the convergence rate of order $O(\log{T}/\sqrt{T})$ for nonconvex stochastic optimization. We show the conditions are essential in the sense that violating them may make the algorithm diverge. Moreover, we propose and analyze a class of (deterministic) incremental adaptive gradient algorithms, which has the same $O(\log{T}/\sqrt{T})$ convergence rate. Our study could also be extended to a broader class of adaptive gradient methods in machine learning and optimization.


title: IEEE 1149.6: a boundary-scan standard for advanced digital networks

abstract: AC-coupled high-speed differential signals have been a hole in the IEEE 1149.1 boundary-scan standard since its inception. In May 2001, a group formed to address this problem, resulting in the IEEE 1149.6 standard. Several members of the IEEE 1149.6 working group describe how the standard works and how it can test Gigabit Ethernet, Fibre Channel and more.


title: Learning a Bias Correction for Lidar-Only Motion Estimation

abstract: This paper presents a novel technique to correct for bias in a classical estimator using a learning approach. We apply a learned bias correction to a lidar-only motion estimation pipeline. Our technique trains a Gaussian process (GP) regression model using data with ground truth. The inputs to the model are high-level features derived from the geometry of the point-clouds, and the outputs are the predicted biases between poses computed by the estimator and the ground truth. The predicted biases are applied as a correction to the poses computed by the estimator. ::: Our technique is evaluated on over 50km of lidar data, which includes the KITTI odometry benchmark and lidar datasets collected around the University of Toronto campus. After applying the learned bias correction, we obtained significant improvements to lidar odometry in all datasets tested. We achieved around 10% reduction in errors on all datasets from an already accurate lidar odometry algorithm, at the expense of only less than 1% increase in computational cost at run-time.


title: Cross-Domain Collaborative Filtering with Factorization Machines

abstract: Factorization machines offer an advantage over other existing collaborative filtering approaches to recommendation. They make it possible to work with any auxiliary information that can be encoded as a real-valued feature vector as a supplement to the information in the user-item matrix. We build on the assumption that different patterns characterize the way that users interact with i.e., rate or download items of a certain type e.g., movies or books. We view interactions with a specific type of item as constituting a particular domain and allow interaction information from an auxiliary domain to inform recommendation in a target domain. Our proposed approach is tested on a data set from Amazon and compared with a state-of-the-art approach that has been proposed for Cross-Domain Collaborative Filtering. Experimental results demonstrate that our approach, which has a lower computational complexity, is able to achieve performance improvements.


Comparison of Power Consumption of WiFi Inbuilt Internet of Things Device with Bluetooth Low Energy


title: Lease/Release: Architectural Support for Scaling Contended Data Structures

abstract: High memory contention is generally agreed to be a worst-case scenario for concurrent data structures. There has been a significant amount of research effort spent investigating designs which minimize contention, and several programming techniques have been proposed to mitigate its effects. However, there are currently few architectural mechanisms to allow scaling contended data structures at high thread counts. In this paper, we investigate hardware support for scalable contended data structures. We propose Lease/Release, a simple addition to standard directory-based MSI cache coherence protocols, allowing participants to lease memory, at the granularity of cache lines, by delaying coherence messages for a short, bounded period of time. Our analysis shows that Lease/Release can significantly reduce the overheads of contention for both non-blocking (lock-free) and lock-based data structure implementations, while ensuring that no deadlocks are introduced. We validate Lease/Release empirically on the Graphite multiprocessor simulator, on a range of data structures, including queue, stack, and priority queue implementations, as well as on transactional applications. Results show that Lease/Release consistently improves both throughput and energy usage, by up to 5x, both for lock-free and lock-based data structure designs.


title: Using restricted transactional memory to build a scalable in-memory database

abstract: The recent availability of Intel Haswell processors marks the transition of hardware transactional memory from research toys to mainstream reality. DBX is an in-memory database that uses Intel's restricted transactional memory (RTM) to achieve high performance and good scalability across multi-core machines. The main limitation (and also key to practicality) of RTM is its constrained working set size: an RTM region that reads or writes too much data will always be aborted. The design of DBX addresses this challenge in several ways. First, DBX builds a database transaction layer on top of an underlying shared-memory store. The two layers use separate RTM regions to synchronize shared memory access. Second, DBX uses optimistic concurrency control to separate transaction execution from its commit. Only the commit stage uses RTM for synchronization. As a result, the working set of the RTMs used scales with the meta-data of reads and writes in a database transaction as opposed to the amount of data read/written. Our evaluation using TPC-C workload mix shows that DBX achieves 506,817 transactions per second on a 4-core machine.


title: BundleNet: Learning with Noisy Label via Sample Correlations

abstract: Sequential patterns are important, because they can be exploited to improve the prediction accuracy of our classifiers. Sequential data, such as time series/video frames, and event data are becoming more and more ubiquitous in a wide spectrum of application scenarios especially in the background of large data and deep learning. However, large data sets used in training modern machine-learning models, such as deep neural networks, are often affected by label noise. Existing noisy learning approaches mainly focus on building an additional network to clean the noise or find a robust loss function. Few works tackle this problem by exploiting sample correlations. In this paper, we propose BundleNet, a framework of sequential structure (named bundle-module, see Fig. 1) for deep neural networks to handle the label noise. The bundle module naturally takes into account sample correlations by constructing bundles of samples class-by-class, and treats them as independent inputs. Moreover, we prove that the bundle-module performs a form of regularization, which is similar to dropout as regularization during training. The regularization effect endows the BundleNet with strong robustness to the label noise. Extensive experiments on public data sets prove that the proposed approach is effective and promising.


title: Fine-Grained Land Use Classification at the City Scale Using Ground-Level Images

abstract: Multimedia researchers have exploited large collections of community-contributed geo-referenced images to better understand a particular image, such as its subject matter or where it was taken, as well as to better understand a geographic location, such as the most visited tourist spots in a city or what the local cuisine is like. The goal of this paper is to better understand location. In particular, we use geo-referenced image collections to better understand what occurs in different parts of a city at fine spatial and activity class scales. This problem is known as land use mapping in the geographical sciences. We propose a novel framework to perform fine-grained land use mapping at the city scale using ground-level images. Mapping land use is considerably more difficult than mapping land cover and is generally not possible using overhead imagery as it requires close-up views and seeing inside buildings. We postulate that the growing collections of geo-referenced, ground-level images suggest an alternate approach to this geographic knowledge discovery problem. We develop a general framework that uses Flickr images to map 45 different land-use classes for the city of San Francisco, CA, USA. Individual images are classified using a novel convolutional neural network containing two streams: one for recognizing objects and another for recognizing scenes. This network is trained in an end-to-end manner directly on the labeled training images. We propose several novel strategies to overcome the noisiness of our user-generated data including search-based training set augmentation and online adaptive training. We derive a ground truth map of San Francisco in order to evaluate our method. We demonstrate the effectiveness of our approach through geovisualization and quantitative analysis. Our framework achieves over 29% recall at the individual land parcel level that represents a strong baseline for the challenging 45-way land use classification problem, especially given the noisiness of the image data.


title: Development of flexible dry ECG electrodes based on MWCNT/PDMS composite

abstract: Flexible dry electrode based on multi-walled carbon nanotube (MWCNT)/polydimethylsiloxane (PDMS) composite, for monitoring electrocardiogram (ECG) signals, has been developed. The dry ECG electrode was fabricated by screen printing silver (Ag) ink on flexible polyethylene terephthalate (PET) substrate, followed by bar coating of MWCNT/PDMS composite. The performance of the fabricated device was investigated by measuring ECG signals using printed dry electrodes with radii varying from 8 mm to 16 mm. The results were compared with a traditional wet Ag/AgCl ECG electrode (T716). It was observed that the dry ECG electrode, with the largest area, demonstrated better performance, in terms of signal intensity and correlation, when compared to the traditional wet ECG electrode. The response of the dry ECG electrodes are analyzed and presented in this paper.


title: Knowledge management as foundation of smart university

abstract: Functioning in an era of knowledge is forcing organizations to manage this valuable resource in exact way. Very frequently activities of organizations are dependent on the application of knowledge, sometimes even means “to be or not to be” for enterprise. Nowadays, to fulfill business goals of institutions it becomes fundamental for them to use intelligent systems supporting comprehensive management of the organization. Such support allows to increase efficiency and better effectiveness of the running businesses. As we are living in age of international integration, where world economy is tending to reach type of knowledge-based economy (KBE), universities are forced also to change way of their functioning. It is important for modern universities to be not only education centers but mainly the successfully prospering organizations-based-on-knowledge. Such approach is going to provide higher competitiveness of particular institution and will make its functioning more useful for economy of the region. Implementing a comprehensive and intelligent IT solution within a university and providing educational services, which are personalized to the needs of the market, will allow universities to reach a type of institution called “smart”. The aim of the paper is explanation why university centers should evolve in a type of institution based on knowledge. The paper is managed as follows. After short introduction concerning research context the discussed concepts of Knowledge Management and Smart Universities are presented. In the main section real examples of Knowledge Management Systems implementation and examples of Smart Universities are investigated in order to identify and describe roles of Knowledge Management Systems in this area. It allows for formulation conclusions on intersection of two in


title: Automatic feature selection for anomaly detection

abstract: A frequent problem in anomaly detection is to decide among different feature sets to be used. For example, various features are known in network intrusion detection based on packet headers, content byte streams or application level protocol parsing. A method for automatic feature selection in anomaly detection is proposed which determines optimal mixture coefficients for various sets of features. The method generalizes the support vector data description (SVDD) and can be expressed as a semi-infinite linear program that can be solved with standard techniques. The case of a single feature set can be handled as a particular case of the proposed method. The experimental evaluation of the new method on unsanitized HTTP data demonstrates that detectors using automatically selected features attain competitive performance, while sparing practitioners from a priori decisions on feature sets to be used.


title: Automatic Concept Discovery from Parallel Text and Visual Corpora

abstract: Humans connect language and vision to perceive the world. How to build a similar connection for computers? One possible way is via visual concepts, which are text terms that relate to visually discriminative entities. We propose an automatic visual concept discovery algorithm using parallel text and visual corpora, it filters text terms based on the visual discriminative power of the associated images, and groups them into concepts using visual and semantic similarities. We illustrate the applications of the discovered concepts using bidirectional image and sentence retrieval task and image tagging task, and show that the discovered concepts not only outperform several large sets of manually selected concepts significantly, but also achieves the state-of-the-art performance in the retrieval task.


title: Keyword Search on Spatial Databases

abstract: Many applications require finding objects closest to a specified location that contains a set of keywords. For example, online yellow pages allow users to specify an address and a set of keywords. In return, the user obtains a list of businesses whose description contains these keywords, ordered by their distance from the specified address. The problems of nearest neighbor search on spatial data and keyword search on text data have been extensively studied separately. However, to the best of our knowledge there is no efficient method to answer spatial keyword queries, that is, queries that specify both a location and a set of keywords. In this work, we present an efficient method to answer top-k spatial keyword queries. To do so, we introduce an indexing structure called IR2-Tree (Information Retrieval R-Tree) which combines an R-Tree with superimposed text signatures. We present algorithms that construct and maintain an IR2-Tree, and use it to answer top-k spatial keyword queries. Our algorithms are experimentally compared to current methods and are shown to have superior performance and excellent scalability.


title: A three-level flying-capacitor rectifier based supercapacitor direct integration scheme for distributed generation systems

abstract: Supercapacitors are increasingly used as short term energy storage elements in distributed generation systems. The traditional approach in integrating them to the main system is the use of interfacing dc-dc converters which introduce additional costs and power losses. This paper therefore, presents a novel direct integration scheme for supercapacitors and thereby eliminates associated costs and power losses of interfacing converters. The idea is simply to replace ordinary capacitors of three-level flying-capacitor rectifiers with supercapacitors and operate them under variable voltage conditions. An analysis on the reduction of power losses by the proposed system is presented. Furthermore, supercapacitor sizing and implementation issues such as effects of the variable voltage operation and resistive behavior of supercapacitors at high frequencies are also discussed. Simulation results are presented to verify the efficacy of the proposed system in suppressing short term power fluctuations in wind generation system.


title: New pre-estimation algorithm for FMCW radar systems using the Matrix Pencil Method

abstract: This paper shows a technique to enhance the resolution of a frequency modulated continuous wave (FMCW) radar system. The range resolution of an FMCW radar system is limited by the bandwidth of the transmitted signal. By using high resolution methods such as the Matrix Pencil Method (MPM) it is possible to enhance the resolution. In this paper a new method to obtain a better resolution for FMCW radar systems is used. This new method is based on the MPM and is enhanced to require less computing power. To evaluate this new technique, simulations and measurements are used. The result shows that this new method is able to improve the performance of FMCW radar systems.


title: A 20 Gb/s 0.4 pJ/b Energy-Efficient Transmitter Driver Utilizing Constant- ${\rm G}_{\rm m}$ Bias

abstract: This paper describes a transmitter driver based on a CMOS inverter with a resistive feedback. By employing the proposed driver topology, the pre-driver can be greatly simplified, resulting in a remarkable reduction of the overall driver power consumption. It also offers another advantage that the implementation of equalization is straightforward, compared with a conventional voltage-mode driver. Furthermore, the output impedance remains relatively constant while the data is being transmitted, resulting in good signal integrity. For evaluation of the driver performance, a fully functional 20 Gb/s transmitter is implemented, including a PRBS generator, a serializer, and a half-rate clock generator. In order to enhance the overall speed of the digital circuits for 20 Gb/s data transmission, the resistive feedback is applied to the time-critical inverters, which enables shorter rise/fall times. The prototype chip is fabricated in a 65 nm CMOS technology. The implemented driver circuit operates up to the data rate of 20 Gb/s, exhibiting an energy efficiency of 0.4 pJ/b for the output swing of 250 ${\rm mV}_{\mathrm {pp,diff}}$ .


title: A Common Information Model Oriented Graph Database Framework for Power Systems

abstract: Common Information Model (CIM) is widely adopted by many utilities since it offers interoperability through standard information models. Storing, processing, retrieving, and providing concurrent access of the large power network models to the various power system applications in CIM framework are the current challenges faced by utility operators. As the power network models resemble largely connected-data sets, the design of CIM oriented database has to support high-speed data retrieval of the connected-data and efficient storage for processing. The graph database is gaining wide acceptance for storing and processing of largely connected-data for various applications. This paper presents a design of CIM oriented graph database (CIMGDB) for storing and processing the largely connected-data of power system applications. Three significant advantages of the CIMGDB are efficient data retrieval and storage, agility to adapt dynamic changes in CIM profile, and greater flexibility of modeling CIM unified modeling language (UML) in GDB. The CIMGDB does not need a predefined database schema. Therefore, the CIM semantics needs to be added to the artifacts of GDB for every instance of CIM objects storage. A CIM based object-graph mapping methodology is proposed to automate the process. An integration of CIMGDB and power system applications is discussed by an implementation architecture. The data-intensive network topology processing (NTP) is implemented, and demonstrated for six IEEE test networks and one practical 400 kV Maharashtra network. Results such as computation time of executing network topology processing evaluate the performance of the CIMGDB.


title: A fast image dehazing algorithm based on negative correction

abstract: Dehazing is an important but difficult issue for image processing. Recently, many dehazing algorithms have been proposed based on the dark channel prior. However, these algorithms fail to achieve a good tradeoff between the dehazing performance and the computational complexity. Moreover, the perceptual quality of these algorithms can be further improved, especially for sky areas. Therefore, this paper firstly introduces the concept of negative correction inspired by the practical application of photographic developing and a fast image dehazing algorithm is accordingly proposed. Based on the observation of the photographic developing, we find that the contrast of images can be enlarged and their saturation can also be increased when their negative images (or reverse image) are rectified. Thus, instead of estimating the transmission map, the correction factor of negative is estimated and it is used to rectify the corresponding haze images. In order to suppress halos, a modified maximum-filter is proposed to limit the larger value of correction factor of local region. The experimental results demonstrate that the proposed algorithm can effectively remove hazes and maintain the naturalness of images. Moreover, the proposed algorithm can significantly reduce the computational complexity by 56.14% on average when compared with the state-of-the-art. We first introduce the concept of the negative correction of photographic developing into dehazing algorithm.Instead of estimating the transmission map, the correction factor of negative images is estimated and it is used to rectify the corresponding haze images.In order to suppress halos, a modified maximum-filter is proposed to limit the maximum value of modified correction factor of local region.The proposed algorithm can effectively remove hazes. It can not only maintain the naturalness of images, but also enhance the details of images. Moreover, it can significantly reduce the computational complexity.


title: The antidepressant fluoxetine restores plasticity in the adult visual cortex.

abstract: We investigated whether fluoxetine, a widely prescribed medication for treatment of depression, restores neuronal plasticity in the adult visual system of the rat. We found that chronic administration of fluoxetine reinstates ocular dominance plasticity in adulthood and promotes the recovery of visual functions in adult amblyopic animals, as tested electrophysiologically and behaviorally. These effects were accompanied by reduced intracortical inhibition and increased expression of brain-derived neurotrophic factor in the visual cortex. Cortical administration of diazepam prevented the effects induced by fluoxetine, indicating that the reduction of intracortical inhibition promotes visual cortical plasticity in the adult. Our results suggest a potential clinical application for fluoxetine in amblyopia as well as new mechanisms for the therapeutic effects of antidepressants and for the pathophysiology of mood disorders.


title: LSTM-Based Hierarchical Denoising Network for Android Malware Detection

abstract: Mobile security is an important issue on Android platform. Most malware detection methods based on machine learning models heavily rely on expert knowledge for manual feature engineering, which are still difficult to fully describe malwares. In this paper, we present LSTM-based hierarchical denoise network (HDN), a novel static Android malware detection method which uses LSTM to directly learn from the raw opcode sequences extracted from decompiled Android files. However, most opcode sequences are too long for LSTM to train due to the gradient vanishing problem. Hence, HDN uses a hierarchical structure, whose first-level LSTM parallelly computes on opcode subsequences (we called them method blocks) to learn the dense representations; then the second-level LSTM can learn and detect malware through method block sequences. Considering that malicious behavior only appears in partial sequence segments, HDN uses method block denoise module (MBDM) for data denoising by adaptive gradient scaling strategy based on loss cache. We evaluate and compare HDN with the latest mainstream researches on three datasets. The results show that HDN outperforms these Android malware detection methods,and it is able to capture longer sequence features and has better detection efficiency than -gram-based malware detection which is similar to our method.


title: Development of a Methodology for Improving Photovoltaic Inverter Reliability

abstract: In evaluating the energy-generation potential of a photovoltaic (PV) energy system, the system is usually assumed to work without interruptions over its entire life. PV energy systems are fairly reliable, but as any complex system, they may fail. In PV systems, the inverter is responsible for the majority of failures, and most inverter failures are blamed on the aluminum electrolytic capacitors typically used in the dc bus. This paper investigates the effects of common failure modes on the reliability of PV inverters and suggests a model framework for decomposing the inverter into subsystems for more detailed study. The challenges of statistical analysis based on small data sets are discussed, and simulations are performed to illustrate the proposed model using a simple decomposition into subsystems of the inverter used in the 342-kW PV system at the Georgia Tech Aquatic Center.


title: Distributional Semantics in Use

abstract: In this position paper we argue that an adequate semantic model must account for language in use, taking into account how discourse context affects the meaning of words and larger linguistic units. Distributional semantic models are very attractive models of meaning mainly because they capture conceptual aspects and are automatically induced from natural language data. However, they need to be extended in order to account for language use in a discourse or dialogue context. We discuss phenomena that the new generation of distributional semantic models should capture, and propose concrete tasks on which they could be tested.


title: Review of 20 Years of Research on Microwave and Millimeter-wave Lenses at "Instituto de Telecomunicaço˜es"

abstract: Starting from a challenge in the early 1990s to develop a highly shaped beam dielectric lens antenna for a pilot 150 Mb/s cellular mobile broadband system operating in the 60-GHz band, several new developments have been accomplished over more than 20 years at Instituto de Telecomunicacoes [1] in the areas of millimeter-wave shaped dielectric lens antennas and planar metamaterial lenses. We review here a few representative examples with numerical and experimental results, covering applications in mobile broadband communications, radiometry, satellite communications, multigigabit short-range communications, and sublambda near-field target detection.


title: Deep Hyperspherical Learning

abstract: Convolution as inner product has been the founding basis of convolutional neural networks (CNNs) and the key to end-to-end visual representation learning. Benefiting from deeper architectures, recent CNNs have demonstrated increasingly strong representation abilities. Despite such improvement, the increased depth and larger parameter space have also led to challenges in properly training a network. In light of such challenges, we propose hyperspherical convolution (SphereConv), a novel learning framework that gives angular representations on hyperspheres. We introduce SphereNet, deep hyperspherical convolution networks that are distinct from conventional inner product based convolutional networks. In particular, SphereNet adopts SphereConv as its basic convolution operator and is supervised by generalized angular softmax loss - a natural loss formulation under SphereConv. We show that SphereNet can effectively encode discriminative representation and alleviate training difficulty, leading to easier optimization, faster convergence and comparable (even better) classification accuracy over convolutional counterparts. We also provide some theoretical insights for the advantages of learning on hyperspheres. In addition, we introduce the learnable SphereConv, i.e., a natural improvement over prefixed SphereConv, and SphereNorm, i.e., hyperspherical learning as a normalization method. Experiments have verified our conclusions.


title: FACE DETECTION BY HAAR CASCADE CLASSIFIER WITH SIMPLE AND COMPLEX BACKGROUNDS IMAGES USING OPENCV IMPLEMENTATION

abstract: This paper considers the problem of face detection in first attempt using haar cascade classifier from images containing simple and complex backgrounds. It is one of the best detector in terms of reliability and speed. Experiments were carried out on standard database i.e. Indian face database (IFD) and Caltech database. All images are frontal face images because side face views are harder to detect with this technique. Opencv 2.4.2 is used to implement the haar cascade classifier. We achieved 100% face detection rate on Indian database containing simple background and 93.24% detection rate on Caltech database containing complex background. Haar cascade classifier provides high accuracy even the images are highly affected by the illumination. The haar cascade classifier has shown superior performance with simple background images.


title: MoveIt!: Autonomous Underwater Free-Floating Manipulation

abstract: Today, autonomous underwater vehicles (AUVs) are mostly used for survey missions, but many existing applications require manipulation capabilities, such as the maintenance of permanent observatories, submerged oil wells, cabled sensor networks, and pipes; the deployment and recovery of benthic stations; or the search and recovery of black boxes. Currently, these tasks require the use of work-class remotely operated vehicles (ROVs) deployed from vessels equipped with dynamic positioning, leaving such solutions expensive to adopt. To face these challenges during the last 25 years, scientists have researched the idea of increasing the autonomy of underwater intervention systems.


title: Communication Avoiding Algorithms: Analysis and Code Generation for Parallel Systems

abstract: Data movement is a critical bottleneck for futuregenerations of parallel systems. The class of .5Dcommunication-avoiding algorithms were developed to addressthis bottleneck. These algorithms reduce communication andprovide strong scaling in both time and energy. As a firststep towards automating the development of communication-avoidinglibraries, we developed the Maunam compiler. Maunam generates efficient parallel code from a high-level, global view sketch of .5D algorithms that are expressed usingsymbolic data sizes and numbers of processors. It supportsthe expression of data movement and communication throughhigh-level global operations such as TILT and CSHIFT as wellas through element-wise copy operations. With the latter, wraparoundcommunication patterns can also be achieved usingsubscripts based on modulo operations. Maunam employs polyhedral analysis to reason about communicationand computation present in a .5D algorithm. After partitioning data and computation, it inserts point-topointand collective communication as needed. Maunam alsoanalyzes data dependence patterns and data layouts to identifyreductions over processor subsets. Maunam-generated Fortran+MPI code for 2.5D matrix multiplicationrunning on 4096 cores of a Cray XC30 supercomputerachieves 59 TFlops/s (76% of the machine peak). Ourgenerated parallel code achieves 91% of the performance of ahand-coded version.


From Business Case to Value Case-Assessing the Organizational Value of IT Investments


title: Learning to tell brake lights with convolutional features

abstract: In this paper, we present a learning-based brake light classification algorithm for intelligent driver-assistance systems. State-of-the-art approaches apply different image processing techniques with hand-crafted features to determine whether brake lights are on or off. In contrast, we learn a brake light classifier based on discriminative color descriptors and convolutional features fine-tuned for traffic scenes. We show how brake light regions can be segmented and classified in one framework. Numerous experimental results show that the proposed algorithm performs well against state-of-the-art alternatives in real-world scenes.


title: A fractional-N sub-sampling PLL using a pipelined phase-interpolator with a FoM of −246dB

abstract: This paper presents a fractional-N PLL working in sub-sampling mode using a pipelined phase-interpolator. The proposed pipelined phase-interpolator can achieve high phase linearity with very low power consumption. The fractional-N sub-sampling PLL is implemented in a standard 65nm CMOS technology. The PLL works at a frequency ranging from 4.3GHz to 4.9GHz while consuming 3.3mW. The measured in-band phase noise in fractional-N mode is -114dBc/Hz at 400kHz offset from the carrier, while working with a bandwidth of approximately 2MHz. The combination of high-precision low-power phase-interpolation technique and the sub-sampling technique realizes a high-performance fractional-N frequency synthesizer with the highest reported FoM.


title: Infrared passenger flow collection system based on RBF neural net

abstract: As the current level of the people-counting, on the consideration of the cost of the acquisition equipment and the acquisition accuracy, the customer-counting system is constructed based on RBF neural net using technology of infrared photoelectric sensor. For better differential count, extant data segmentation and the method of feature extraction is improved based on the feature of passenger-counting data continuous space-time sequence. Compared with traditional customer-counting using sensor, the accuracy of real-time customer-counting is improved in this method and the situation of customers who entry at the same time can be identified with lower error rate. It is helpful for both principle research and actual application.


title: First Postembryo Transfer Beta-hCG Level and Pregnancy Outcome in an Assisted Reproductive Technology Program

abstract: Aim: To evaluate the prognostic value of first postembryotransfer beta human chorionic gonadotropin (hCG) levels inpregnancy outcome in an assisted reproductive technology(ART) program.Subjects: Seventy-one women with an initial beta-hCG valueof greater than 5 mIU/ml postembryo transfer in the ARTprogram were taken in to the study. The beta-hCG test wasdone 14 days after embryo transfer. The period of study wasfrom January 2008 to August 2010.Observations: A significant correlation was found in beta-hCGvalues between viable and nonviable pregnancies. In womenwho had a day 2 embryo transfer the mean beta-hCG valuewas 608 ± 580 mIU/ml, in comparison to women who had aday 5 transfer 1,527 ± 2,024 mIU/ml, and this was statisticallysignificant.Women who had a single embryo transfer had a mean betahCGlevel of 168 mIU/ml, two embryos 464 mIU/ml and threeembryos 612 mIU /ml.Mean beta-hCG value was highest in women who developedgestational diabetes [2,074 mIU/ml] women with pregnancyinducedhypertension (PIH) had a mean beta-hCG value of674 mIU/ml, and with antepartum hemorrhage the value waslower 220 mIU/ml.Conclusion: To summarize, beta-hCG level is an useful markerfor prognosticating early pregnancy well being, for predictingmultiple pregnancies. When interpreting the first beta-hCG leveluniformly after 2 weeks of embryo transfer, day of transfer ofembryos should be taken into account. The number of embryostransferred does not alter the beta-hCG level significantly. BetahCGlevel implications in pregnancy complications, likegestational diabetes (GDM), PIH, APH, require further researchand would be a useful tool for early screening and surveillanceof pregnancy.


title: Learning spatiotemporal graphs of human activities

abstract: Complex human activities occurring in videos can be defined in terms of temporal configurations of primitive actions. Prior work typically hand-picks the primitives, their total number, and temporal relations (e.g., allow only followed-by), and then only estimates their relative significance for activity recognition. We advance prior work by learning what activity parts and their spatiotemporal relations should be captured to represent the activity, and how relevant they are for enabling efficient inference in realistic videos. We represent videos by spatiotemporal graphs, where nodes correspond to multiscale video segments, and edges capture their hierarchical, temporal, and spatial relationships. Access to video segments is provided by our new, multiscale segmenter. Given a set of training spatiotemporal graphs, we learn their archetype graph, and pdf's associated with model nodes and edges. The model adaptively learns from data relevant video segments and their relations, addressing the “what” and “how.” Inference and learning are formulated within the same framework - that of a robust, least-squares optimization - which is invariant to arbitrary permutations of nodes in spatiotemporal graphs. The model is used for parsing new videos in terms of detecting and localizing relevant activity parts. We out-perform the state of the art on benchmark Olympic and UT human-interaction datasets, under a favorable complexity-vs.-accuracy trade-off.


RELATIONSHIP BETWEEN ORIGAMI AND INTERIOR DESIGN RELATED SPACES MANIPULATION


title: Minimum implant area-aware gate sizing and placement

abstract: With reduction of minimum feature size, the minimum implant area (MinIA) constraint is emerging as a new challenge for the physical implementation flow in sub-22nm technology. In particular, the MinIA constraint induces a new problem formulation wherein gate sizing and V_t-swapping must now be linked closely with detailed placement changes. To solve this new problem, we propose heuristic methods that fix MinIA violations and reduce power with gate sizing while minimizing placement perturbation to avoid creating extra timing violations. Compared to recent versions of commercial P&R tools, our methodologies achieve significant reductions (up to 100%) in the number of MinIA violations under timing/power constraints.


title: Connecting users across social media sites: a behavioral-modeling approach

abstract: People use various social media for different purposes. The information on an individual site is often incomplete. When sources of complementary information are integrated, a better profile of a user can be built to improve online services such as verifying online information. To integrate these sources of information, it is necessary to identify individuals across social media sites. This paper aims to address the cross-media user identification problem. We introduce a methodology (MOBIUS) for finding a mapping among identities of individuals across social media sites. It consists of three key components: the first component identifies users' unique behavioral patterns that lead to information redundancies across sites; the second component constructs features that exploit information redundancies due to these behavioral patterns; and the third component employs machine learning for effective user identification. We formally define the cross-media user identification problem and show that MOBIUS is effective in identifying users across social media sites. This study paves the way for analysis and mining across social media sites, and facilitates the creation of novel online services across sites.


title: Parallel and Integrative Processing Through the Basal Ganglia Reward Circuit: Lessons from Addiction

abstract: chhe basal ganglia (BG) are traditionally considered to pr nformation in parallel and segregated functional streams isting of reward processing, cognition, and motor control oreover, microcircuits within the ventral striatum are assoc ith different aspects of reward processing (1). However, a ke omponent for developing appropriate behavioral-guiding ru s the ability not only to evaluate different aspects of orrectly, including value versus risk and predictability, b nhibit maladaptive choices on the basis of previous exper his requires integration between different aspects of re rocessing as well as interaction with cognitive control re ndeed, an emerging literature demonstrates the complexit he network in which there is a dual organizational s ermitting both parallel and integrative network processing. as particular relevance for chronic drug use, in which init ctivates the ventral striatum but, over time, the dorsal odorsal BG system becomes involved (2). The Sharf et al. pages 175–183), Pissios et al., (pages 184 –191), and Makris et al pages 192–202) articles in this issue nicely illustrate this oncept in which changes occur following drug administr ithin specific parts of the reward system and across rewa ognitive systems. At the center of the reward circuit are the orbital pre ortex (OFC), the anterior cingulate cortex (ACC), the v triatum (VS), and the ventral tegmental dopamine neu VTA). Within the reward system different prefrontal co reas and corresponding striatal regions are involved in va spects of reward identification, evaluation, and incentive-b earning. The pathophysiology of addiction and other imp ontrol-related diseases centers on these structures and onnectivities. The ventral anterior cingulate cortex and edial orbital cortex, collectively referred to as the v edial prefrontal cortex (vmPFC), are connected to the halamus and brainstem and are associated with mood isceral functions. In contrast, the OFC receives multim ensory input and is associated with various aspects of r alue, error, and risk prediction (1,3). These prefrontal areas project topographically to the stri nd terminate in the rostral and ventral striatum (including f the caudate nucleus [n.] and putamen) (4). In general, th entral striatum is similar to the dorsal striatum in tha eceive inputs from cortex, thalamus, and brainstem. How here are two important and unique projections to the v triatum. First, the shell has unique connections with the halamus. Second, the ventral striatum as a whole receiv dditional subcortical input from the amygdala. These two i lace the ventral striatum in a unique position for modu motion and motivation. The striatum, in turn, projects


title: A new algorithm for money laundering detection based on structural similarity

abstract: Money Laundering (ML) is the process of cleaning “dirty” money, thereby making the source of funds no longer identifiable. Detecting money laundering activities is a challenging task due to huge volumes of financial transactions being made in a global market on a daily basis. This paper proposes a novel approach for detecting money laundering transactions among large volumes of financial data in an efficient and accurate manner. We propose a framework that applies case reduction methods to progressively reduce the input data set to a significantly smaller size. The framework then scans the reduced data to find pairs of transactions with common attributes and behaviours that are potentially involved in ML activities. It then applies a clustering method to detect potential ML groups. We present preliminary experimental results that demonstrate the effectiveness of the proposed framework.


title: An ultra-low-power human body motion sensor using static electric field sensing

abstract: Wearable sensor systems have been used in the ubiquitous computing community and elsewhere for applications such as activity and gesture recognition, health and wellness monitoring, and elder care. Although the power consumption of accelerometers has already been highly optimized, this work introduces a novel sensing approach which lowers the power requirement for motion sensing by orders of magnitude. We present an ultra-low-power method for passively sensing body motion using static electric fields by measuring the voltage at any single location on the body. We present the feasibility of using this sensing approach to infer the amount and type of body motion anywhere on the body and demonstrate an ultra-low-power motion detector used to wake up more power-hungry sensors. The sensing hardware consumes only 3.3 μW, and wake-up detection is done using an additional 3.3 μW (6.6 μW total).


title: Differentiating lipedema and Dercum's disease

abstract: BACKGROUND ::: People with lipedema or Dercum's disease (DD) can have a similar distribution of excess painful nodular subcutaneous adipose tissue (SAT), making them difficult to differentiate. ::: ::: ::: METHODS ::: Case series of 94 patients with DD, 160 with lipedema and 18 with both diagnoses (Lip+DD) from a single clinic in an academic medical center to improve identification and differentiation of these disorders by comparison of clinical findings, prevalence of type 2 diabetes (DM2), hypermobility by the Beighton score and assessment of a marker of inflammation, Total complement activity (CH50). ::: ::: ::: RESULTS ::: Differences between groups were by Student's t-test with α of 0.05. The Lipedema Group had significantly greater weight, body mass index (BMI), gynoid distributed nodular SAT and fibrotic and heavy tissue than the DD Group. Hypermobility was significantly higher in the Lipedema (58±0.5%) than DD Group (23±0.4%; P<0.0001). DM2 was significantly greater in the DD (16±0.2%; P=0.0007) than the Lipedema Group (6±0.2%). Average pain by an analog scale was significantly higher in the DD (6±2.5%) than the Lipedema Group (4±2.1%; P<0.0001). Fatigue and swelling were common in both groups. Easy bruising was more common in the Lipedema Group, whereas abdominal pain, shortness of breath, fibromyalgia, migraines and lipomas were more prevalent in the DD Group. The percentage of patients with elevated CH50 was significantly positive in both groups. ::: ::: ::: CONCLUSIONS ::: The significantly lower prevalence of DM2 in people with lipedema compared with DD may be due to the greater amount of gynoid fat known to be protective against metabolic disorders. The high percentage of hypermobility in lipedema patients indicates that it may be a comorbid condition. The location of fat, high average daily pain, presence of lipomas and comorbid painful disorders in DD patients may help differentiate from lipedema.


title: Spotlight on origami robots

abstract: Origami robots promise a future with increased customizability and adaptability in autonomous machines.


title: The History Heuristic and Alpha-Beta Search Enhancements in Practice

abstract: Many enhancements to the alpha-beta algorithm have been proposed to help reduce the size of minimax trees. A recent enhancement, the history heuristic, which improves the order in which branches are considered at interior nodes is described. A comprehensive set of experiments is reported which tries all combinations of enhancements to determine which one yields the best performance. In contrast, previous work on assessing their performance has concentrated on the benefits of individual enhancements or a few combinations. The aim is to find the combination that provides the greatest reduction in tree size. Results indicate that the history heuristic combined with transposition tables significantly outperforms other alpha-beta enhancements in application-generated game trees. For trees up to depth 8, this combination accounts for 99% of the possible reductions in tree size, with the other enhancements yielding insignificant gains. >


title: CXCL6 promotes human hepatocyte proliferation through the CXCR1–NFκB pathway and inhibits collagen I secretion by hepatic stellate cells

abstract: Hepatocyte proliferation and collagen I (COLI) secretion are important processes during liver regeneration. This study aimed to investigate the role of CXCL6 in hepatocyte proliferation and COLI secretion. Serum CXCL6 levels in patients with chronic hepatitis B (CHB) were examined and the effects of CXCL6 on the proliferation of L02 hepatocytes and the secretion of COLI from LX2 human hepatic stellate cells were evaluated. We found that serum CXCL6 levels increased gradually with disease progression of CHB, and there was positive correlation between serum CXCL6 level and alanine transaminase (ALT) and aspartate transaminase (AST). In vitro, CXCL6 promoted L02 proliferation but this was blocked upon CXCR1 knockdown. The level of phospho-IκBα was upregulated by CXCL6 but downregulated by CXCR1 siRNA in L02 cells. CXCL6 inhibited the secretion of COLI by LX2 cells, dependent on CXCR1 and CXCR2. Taken together, these data suggest that increased expression of CXCL6 during CHB could promote hepatocyte prolifera...


title: Time series classification with ensembles of elastic distance measures

abstract: Several alternative distance measures for comparing time series have recently been proposed and evaluated on time series classification (TSC) problems. These include variants of dynamic time warping (DTW), such as weighted and derivative DTW, and edit distance-based measures, including longest common subsequence, edit distance with real penalty, time warp with edit, and move---split---merge. These measures have the common characteristic that they operate in the time domain and compensate for potential localised misalignment through some elastic adjustment. Our aim is to experimentally test two hypotheses related to these distance measures. Firstly, we test whether there is any significant difference in accuracy for TSC problems between nearest neighbour classifiers using these distance measures. Secondly, we test whether combining these elastic distance measures through simple ensemble schemes gives significantly better accuracy. We test these hypotheses by carrying out one of the largest experimental studies ever conducted into time series classification. Our first key finding is that there is no significant difference between the elastic distance measures in terms of classification accuracy on our data sets. Our second finding, and the major contribution of this work, is to define an ensemble classifier that significantly outperforms the individual classifiers. We also demonstrate that the ensemble is more accurate than approaches not based in the time domain. Nearly all TSC papers in the data mining literature cite DTW (with warping window set through cross validation) as the benchmark for comparison. We believe that our ensemble is the first ever classifier to significantly outperform DTW and as such raises the bar for future work in this area.


title: Care of women with female genital mutilation/cutting.

abstract: In multi-ethnic European society medical professionals are faced with an increasing number of women originating from countries where female genital mutilations/cuttings (FGM/C) are practised. Recent studies however emphasise the lack of knowledge on this subject. This review article aims to present FGM/C as a complex socio-healthcare and multidisciplinary issue outlining the definition classification epidemiology and anthropologico-legal aspects of FGM/C. It explains the approach to be adopted to FGM/C women focusing on defibulation clitoral restoration/repair and re-infibulation. Finally it reports on the discussions surrounding pricking/nicking and the proposals for alternative rituals in recent years.


title: Recommending Personalized News in Short User Sessions

abstract: News organizations employ personalized recommenders to target news articles to specific readers and thus foster engagement. Existing approaches rely on extensive user profiles. However frequently possible, readers rarely authenticate themselves on news publishers' websites. This paper proposes an approach for such cases. It provides a basic degree of personalization while complying with the key characteristics of news recommendation including news popularity, recency, and the dynamics of reading behavior. We extend existing research on the dynamics of news reading behavior by focusing both on the progress of reading interests over time and their relations. Reading interests are considered in three levels: short-, medium-, and long-term. Combinations of these are evaluated in terms of added value to the recommendation's performance and ensured news variety. Experiments with 17-month worth of logs from a German news publisher show that most frequent relations between news reading interests are constant in time but their probabilities change. Recommendations based on combined short-term and long-term interests result in increased accuracy while recommendations based on combined short-term and medium-term interests yield higher news variety.


title: Statistical exponential families: A digest with flash cards

abstract: This document describes concisely the ubiquitous class of exponential family distributions met in statistics. The first part recalls definitions and summarizes main properties and duality with Bregman divergences (all proofs are skipped). The second part lists decompositions and related formula of common exponential family distributions. We recall the Fisher-Rao-Riemannian geometries and the dual affine connection information geometries of statistical manifolds. It is intended to maintain and update this document and catalog by adding new distribution items.


title: Clonidine is effective for the treatment of primary idiopathic hyperhidrosis and hot flushes: a case report

abstract: BACKGROUND ::: While primary hyperhidrosis can be seen in men, accompanying hot flushes is rarely seen in men. Primary hyperhidrosis is thought to be related to overactivity of the sympathetic nervous system while hot flushes are believed to be related to altered peripheral vascular reactivity and a narrowed thermoregulatory zone. ::: ::: ::: CASE PRESENTATION ::: I report the case of a 29-year-old man of Arab origin who presented to a dermatology clinic with a complaint of generalized sweating, with heavier involvement of his inguinal region, axilla, and lower back. His complaint was associated with a transient hot sensation and erythema over the affected areas. He did not respond to topical antiperspirants containing aluminum chloride, topical aluminum chloride, or to botulinum toxin A injected in both inguinal areas. He was then referred to an endocrinology clinic to rule out secondary causes of hyperhidrosis and hot flushes; a primary diagnosis was confirmed. He did not respond to oral glycopyrrolate and additionally was complaining of its anticholinergic side effects. The glycopyrrolate was then replaced with oral clonidine 0.15 mg twice a day. Clonidine was well tolerated without remarkable side effects and he quickly started to feel marked improvement which was maintained for 2 years. ::: ::: ::: CONCLUSIONS ::: I report an atypical presentation of primary hyperhidrosis and hot flushes that was effectively controlled by clonidine without remarkable side effects. Further research on a large number of patients may be required before recommending clonidine in similar conditions.


title: Serious games for health: personalized exergames

abstract: In this paper, we describe a set of personalized exergames which combine methods and concepts of serious games, adaptation and personalization, authoring and sensor technologies. Compared to existing systems, the set of games does not only keep track of the user's vital state, but also directly integrates vital parameters into the gameplay and supports the training and motivation for sustainable physical activity in a playful manner.


title: Dual-Energy CT: New Horizon in Medical Imaging

abstract: Dual-energy CT has remained underutilized over the past decade probably due to a cumbersome workflow issue and current technical limitations. Clinical radiologists should be made aware of the potential clinical benefits of dual-energy CT over single-energy CT. To accomplish this aim, the basic principle, current acquisition methods with advantages and disadvantages, and various material-specific imaging methods as clinical applications of dual-energy CT should be addressed in detail. Current dual-energy CT acquisition methods include dual tubes with or without beam filtration, rapid voltage switching, dual-layer detector, split filter technique, and sequential scanning. Dual-energy material-specific imaging methods include virtual monoenergetic or monochromatic imaging, effective atomic number map, virtual non-contrast or unenhanced imaging, virtual non-calcium imaging, iodine map, inhaled xenon map, uric acid imaging, automatic bone removal, and lung vessels analysis. In this review, we focus on dual-energy CT imaging including related issues of radiation exposure to patients, scanning and post-processing options, and potential clinical benefits mainly to improve the understanding of clinical radiologists and thus, expand the clinical use of dual-energy CT; in addition, we briefly describe the current technical limitations of dual-energy CT and the current developments of photon-counting detector.


title: ADVANCED ANIMATION AND RENDERING TECHNIQUES Theory and Practice

abstract: Part I Basics 1. Rendering polygonal objects Part II Theoretical Foundations 2. The theory and practice of light/object interaction 3. The theory and practice of parametric representation techniques 4. The theory and practice of anti-aliasing techniques Part III Advanced Rendering Techniques: Approaches, Applications and Algorithms 5. Shadow generation techniques 6. Mapping techniques: texture and environment mapping 7. Procedural texture mapping and modelling 8. Ray tracing I: basic recursive ray tracing 9. Ray tracing II: practical ray tracing 10. Ray tracing III: advanced ray tracing models 11. Radiosity methods 12. Global illumination models 13. Volume rendering techniques 14. Advanced rendering interfaces: shading languages and RenderMan Part IV Advanced Animation 15. Overview and low-level motion specification 16. Animating articulated structures 17. Soft object animation 18. Procedural animation Reference Index


title: Fast Mass Transport Through Sub-2-Nanometer Carbon Nanotubes

abstract: We report gas and water flow measurements through microfabricated membranes in which aligned carbon nanotubes with diameters of less than 2 nanometers serve as pores. The measured gas flow exceeds predictions of the Knudsen diffusion model by more than an order of magnitude. The measured water flow exceeds values calculated from continuum hydrodynamics models by more than three orders of magnitude and is comparable to flow rates extrapolated from molecular dynamics simulations. The gas and water permeabilities of these nanotube-based membranes are several orders of magnitude higher than those of commercial polycarbonate membranes, despite having pore sizes an order of magnitude smaller. These membranes enable fundamental studies of mass transport in confined environments, as well as more energy-efficient nanoscale filtration.


title: Design and Modeling of Microstrip Line to Substrate Integrated Waveguide Transitions

abstract: Microstrip lines (MSL) are widely used in microwave systems because of its low cost, light weight, and easy integration with other components. Substrate integrated waveguides (SIW), which inherit the advantages from traditional rectangular waveguides without their bulky configuration, aroused recently in low loss and high power planar applications. This chapter proposed the design and modeling of transitions between these two common structures. Research motives will be described firstly in the next subsection, followed by a literature survey on the proposed MSL to SIW transition structures. Outlines of the following sections in this chapter will also be given in the end of this section.


title: Evaluating the Usability of Automatically Generated Captions for People who are Deaf or Hard of Hearing

abstract: The accuracy of Automated Speech Recognition (ASR) technology has improved, but it is still imperfect in many settings. Researchers who evaluate ASR performance often focus on improving the Word Error Rate (WER) metric, but WER has been found to have little correlation with human-subject performance on many applications. We propose a new captioning-focused evaluation metric that better predicts the impact of ASR recognition errors on the usability of automatically generated captions for people who are Deaf or Hard of Hearing (DHH). Through a user study with 30 DHH users, we compared our new metric with the traditional WER metric on a caption usability evaluation task. In a side-by-side comparison of pairs of ASR text output (with identical WER), the texts preferred by our new metric were preferred by DHH participants. Further, our metric had significantly higher correlation with DHH participants' subjective scores on the usability of a caption, as compared to the correlation between WER metric and participant subjective scores. This new metric could be used to select ASR systems for captioning applications, and it may be a better metric for ASR researchers to consider when optimizing ASR systems.


title: Learning Visual Clothing Style with Heterogeneous Dyadic Co-Occurrences

abstract: With the rapid proliferation of smart mobile devices, users now take millions of photos every day. These include large numbers of clothing and accessory images. We would like to answer questions like 'What outfit goes well with this pair of shoes?' To answer these types of questions, one has to go beyond learning visual similarity and learn a visual notion of compatibility across categories. In this paper, we propose a novel learning framework to help answer these types of questions. The main idea of this framework is to learn a feature transformation from images of items into a latent space that expresses compatibility. For the feature transformation, we use a Siamese Convolutional Neural Network (CNN) architecture, where training examples are pairs of items that are either compatible or incompatible. We model compatibility based on co-occurrence in large-scale user behavior data, in particular co-purchase data from Amazon.com. To learn cross-category fit, we introduce a strategic method to sample training data, where pairs of items are heterogeneous dyads, i.e., the two elements of a pair belong to different high-level categories. While this approach is applicable to a wide variety of settings, we focus on the representative problem of learning compatible clothing style. Our results indicate that the proposed framework is capable of learning semantic information about visual style and is able to generate outfits of clothes, with items from different categories, that go well together.


title: Hybrid Page Layout Analysis via Tab-Stop Detection

abstract: A new hybrid page layout analysis algorithm is proposed, which uses bottom-up methods to form an initial data-type hypothesis and locate the tab-stops that were used when the page was formatted. The detected tab-stops, are used to deduce the column layout of the page. The column layout is then applied in a top-down manner to impose structure and reading-order on the detected regions. The complete C++ source code implementation is available as part of the Tesseract open source OCR engine at http://code.google.com/p/tesseract-ocr.


title: Human resources management and firm performance: The differential role of managerial affective and continuance commitment.

abstract: In this study, the authors developed a dual-concern (i.e., maintenance and performance) model of human resources (HR) management. The authors identified commonly examined HR practices that apply to the middle manager level and classified them into the maintenance- and performance-oriented HR subsystems. The authors found support for the 2-factor model on the basis of responses from 2,148 managers from 463 firms operating in China. Regression results indicate that the performance-oriented HR subsystems had a positive relationship with firm performance and that the relationship was mediated by middle managers' affective commitment to the firm. The maintenance-oriented HR subsystems had a positive relationship with middle managers' continuance commitment but not with their affective commitment and firm performance. This study contributes to the understanding of how HR practices relate to firm performance and offers an improved test of the argument that valuable and firm-specific HR provide a source of competitive advantage.


title: Application of K-Means method to pattern recognition in on-line cable partial discharge monitoring

abstract: On-line Partial Discharge (PD) monitoring is being increasingly adopted in an effort to improve asset management of the vast network of MV and HV power cables. This paper presents a novel method for autonomous recognition of PD patterns recorded under conditions in which a phase-reference voltage waveform from the HV conductors is not available, as is often the case in on-line PD based insulation condition monitoring. The paper begins with an analysis of two significant challenges for automatic PD pattern recognition. A methodology is then proposed for applying the K-Means method to the task of recognizing PD patterns without phase reference information. Results are presented to show that the proposed methodology is capable of recognising patterns of PD activity in on-line monitoring applications for both single-phase and three-phase cables and is also effective technique for rejecting interference signals.


title: OLAP over probabilistic data cubes I: Aggregating, materializing, and querying

abstract: On-Line Analytical Processing (OLAP) enables powerful analytics by quickly computing aggregate values of numerical measures over multiple hierarchical dimensions for massive datasets. However, many types of source data, e.g., from GPS, sensors, and other measurement devices, are intrinsically inaccurate (imprecise and/or uncertain) and thus OLAP cannot be readily applied. In this paper, we address the resulting data veracity problem in OLAP by proposing the concept of probabilistic data cubes. Such a cube is comprised of a set of probabilistic cuboids which summarize the aggregated values in the form of probability mass functions (pmfs in short) and thus offer insights into the underlying data quality and enable confidence-aware query evaluation and analysis. However, the probabilistic nature of data poses computational challenges as even simple operations are #P-hard under the possible world semantics. Even worse, it is hard to share computations among different cuboids, as aggregation functions that are distributive for traditional data cubes, e.g., SUM and COUNT, become holistic in probabilistic settings. In this paper, we propose a complete set of techniques for probabilistic data cubes, from cuboid aggregation, over cube materialization, to query evaluation. For aggregation, we focus on how to maximize the sharing of computation among cells and cuboids. We present two aggregation methods: convolution and sketch-based. The two methods scale down the time complexities of building a probabilistic cuboid to polynomial and linear, respectively. Each of the two supports both full and partial data cube materialization. Then, we devise a cost model which guides the aggregation methods to be deployed and combined during the cube materialization. We further provide algorithms for probabilistic slicing and dicing queries on the data cube. Extensive experiments over real and synthetic datasets are conducted to show that the techniques are effective and scalable.


title: Understanding User's Behaviors in Coping with Security Threat of Mobile Devices Loss and Theft

abstract: As mobile devices widely used for personal and business purpose, mobile devices loss and theft have become one big security threat. In this paper, we analyze the potential risks of mobile devices loss and theft and summarize the countermeasures that can be used to cope with such risks. Based on protection motivation theory, we propose a research framework to investigate the factors that affect users' behavior to cope with the risk of mobile devices loss and theft.


title: Interplay between Metabolism and Epigenetics: A Nuclear Adaptation to Environmental Changes.

abstract: The physiological identity of every cell is maintained by highly specific transcriptional networks that establish a coherent molecular program that is in tune with nutritional conditions. The regulation of cell-specific transcriptional networks is accomplished by an epigenetic program via chromatin-modifying enzymes, whose activity is directly dependent on metabolites such as acetyl-coenzyme A, S-adenosylmethionine, and NAD+, among others. Therefore, these nuclear activities are directly influenced by the nutritional status of the cell. In addition to nutritional availability, this highly collaborative program between epigenetic dynamics and metabolism is further interconnected with other environmental cues provided by the day-night cycles imposed by circadian rhythms. Herein, we review molecular pathways and their metabolites associated with epigenetic adaptations modulated by histone- and DNA-modifying enzymes and their responsiveness to the environment in the context of health and disease.


title: 11.4 A 512Gb 3b/cell 64-stacked WL 3D V-NAND flash memory

abstract: The advent of emerging technologies such as cloud computing, big data, the internet of things and mobile computing is producing a tremendous amount of data. In the era of big data, storage devices with versatile characteristics are required for ultra-fast processing, higher capacity storage, lower cost, and lower power operation. SSDs employing 3D NAND are a promising to meet these requirements. Since the introduction of 3D NAND technology to marketplace in 2014 [1], the memory array size has nearly doubled every year [2,3]. To continue scaling 3D NAND array density, it is essential to scale down vertically to minimize total mold height. However, vertical scaling results in critical problems such as increasing WL capacitance and non-uniformity of stacked WLs due to variation in the channel hole diameter. To tackle these issues, this work proposes schemes for programming speed improvement and power reduction, and on-chip processing algorithms for error correction.


title: A Dataset for Research on Short-Text Conversations

abstract: Natural language conversation is widely regarded as a highly difficult problem, which is usually attacked with either rule-based or learning-based models. In this paper we propose a retrieval-based automatic response model for short-text conversation, to exploit the vast amount of short conversation instances available on social media. For this purpose we introduce a dataset of short-text conversation based on the real-world instances from Sina Weibo (a popular Chinese microblog service), which will be soon released to public. This dataset provides rich collection of instances for the research on finding natural and relevant short responses to a given short text, and useful for both training and testing of conversation models. This dataset consists of both naturally formed conversations, manually labeled data, and a large repository of candidate responses. Our preliminary experiments demonstrate that the simple retrieval-based conversation model performs reasonably well when combined with the rich instances in our dataset.


title: Human and Rodent Homologies in Action Control: Corticostriatal Determinants of Goal-Directed and Habitual Action

abstract: Recent behavioral studies in both humans and rodents have found evidence that performance in decision-making tasks depends on two different learning processes; one encoding the relationship between actions and their consequences and a second involving the formation of stimulus–response associations. These learning processes are thought to govern goal-directed and habitual actions, respectively, and have been found to depend on homologous corticostriatal networks in these species. Thus, recent research using comparable behavioral tasks in both humans and rats has implicated homologous regions of cortex (medial prefrontal cortex/medial orbital cortex in humans and prelimbic cortex in rats) and of dorsal striatum (anterior caudate in humans and dorsomedial striatum in rats) in goal-directed action and in the control of habitual actions (posterior lateral putamen in humans and dorsolateral striatum in rats). These learning processes have been argued to be antagonistic or competing because their control over performance appears to be all or none. Nevertheless, evidence has started to accumulate suggesting that they may at times compete and at others cooperate in the selection and subsequent evaluation of actions necessary for normal choice performance. It appears likely that cooperation or competition between these sources of action control depends not only on local interactions in dorsal striatum but also on the cortico-basal ganglia network within which the striatum is embedded and that mediates the integration of learning with basic motivational and emotional processes. The neural basis of the integration of learning and motivation in choice and decision-making is still controversial and we review some recent hypotheses relating to this issue.


title: The Relationships Between Mass Media, Public Opinion, and Foreign Policy: Toward a Theoretical Synthesis

abstract: Democracy requires that citizens' opinions play some role in shaping policy outcomes, including in foreign policy. Yet, although the literature on public opinion and foreign policy has made great progress in recent decades, scholars have reached no consensus concerning what the public thinks, or thinks about, with respect to foreign policy; how it comes to hold those opinions; or whether those opinions influence (or even should influence) foreign policy. In this article, we first review the extensive gains in scholarly knowledge in the area of public opinion and foreign policy over the past several decades, emphasizing relatively recent work. We then suggest a framework, based on the concept of market equilibrium, aimed at synthesizing the disparate research programs that constitute the literature on public opinion and foreign policy. To do so, in addition to considering the relationship between leaders and the public, we incorporate a third strategic actor, the mass media, which we believe plays a critic...


title: Carbon-Aware Energy Capacity Planning for Datacenters

abstract: Datacenters are facing increasing pressure to cap their carbon footprints at low cost. Recent work has shown the significant environmental benefits of using renewable energy for datacenters by supply-following techniques (workload scheduling, geographical load balancing, etc.) However, all such prior work has only considered on-site renewable generation when numerous other options also exist, which may be superior to on-site renewables for many datacenters. Alternative ways for datacenters to incorporate renewable energy into their overall energy portfolio include: construction of or investment into off-site renewable farms at locations with more abundant renewable energy potential, indirect purchase of renewable energy through buying renewable energy certificates (RECs), purchase of renewable energy products such as power purchase agreements (PPAs) or through third-party renewable providers. We propose a general, optimization-based framework to minimize datacenter costs in the presence of different carbon footprint reduction goals, renewable energy characteristics, policies, utility tariff, and energy storage devices (ESDs). We expect that our work can help datacenter operators make informed decisions about sustainable, renewable-energy-powered IT system design.


title: Towards a Uniform Template-based Architecture for Accelerating 2D and 3D CNNs on FPGA

abstract: Three-dimensional convolutional neural networks (3D CNNs) are used efficiently in many computer vision applications. Most previous work in this area has concentrated only on designing and optimizing accelerators for 2D CNN, with few attempts made to accelerate 3D CNN on FPGA. We find accelerating 3D CNNs on FPGA to be challenge due to their high computational complexity and storage demands. More importantly, although the computation patterns of 2D and 3D CNNs are analogous, the conventional approaches adopted for accelerating 2D CNNs may be unfit for 3D CNN acceleration. In this paper, in order to accelerate 2D and 3D CNNs using a uniform framework, we propose a uniform template-based architecture that uses templates based on the Winograd algorithm to ensure fast development of 2D and 3D CNN accelerators. Furthermore, we also develop a uniform analytical model to facilitate efficient design space explorations of 2D and 3D CNN accelerators based on our architecture. Finally, we demonstrate the effectiveness of the template-based architecture by implementing accelerators for real-life 2D and 3D CNNs (VGG16 and C3D) on multiple FPGA platforms. On S2C VUS440, we achieve up to 1.13 TOPS and 1.11 TOPS under low resource utilization for VGG16 and C3D, respectively. End-to-end comparisons with CPU and GPU solutions demonstrate that our implementation of C3D achieves gains of up to 13x and 60x in performance and energy relative to a CPU solution, and a 6.4x energy efficiency gain over a GPU solution.


title: Intensity of Facebook Use Is Associated With Lower Self-Concept Clarity: Cross-Sectional and Longitudinal Evidence

abstract: Abstract. Social networking sites such as Facebook provide individuals with opportunities to express and gather information relevant to their self-concept. Previous theoretical work yielded contrasting assumptions about a potential link between individuals’ Internet use and their self-concept clarity, that is, individuals’ perception of a clear and internally consistent self-concept content. Focusing on social networking sites, our aim was to provide cross-sectional as well as longitudinal evidence regarding the relationship between individuals’ feelings of connectedness to Facebook (Facebook intensity) and self-concept clarity. Two cross-sectional studies (N1 = 244; N2 = 166) and one longitudinal study (N3 = 101) are presented. Independent samples of adolescents, adults, and students from Austria participated. The statistical procedures included hierarchical regression analyses (Studies 1 and 2) and a cross-lagged panel analysis (Study 3). The studies provided consistent evidence of a negative relationsh...


CYBER-ATTACKS ON THE DATA COMMUNICATION OF DRONES MONITORING CRITICAL INFRASTRUCTURE


title: Spectrally-normalized margin bounds for neural networks

abstract: This paper presents a margin-based multiclass generalization bound for neural networks which scales with their margin-normalized"spectral complexity": their Lipschitz constant, meaning the product of the spectral norms of the weight matrices, times a certain correction factor. This bound is empirically investigated for a standard AlexNet network on the mnist and cifar10 datasets, with both original and random labels, where it tightly correlates with the observed excess risks.


title: Traffic sign recognition — How far are we from the solution?

abstract: Traffic sign recognition has been a recurring application domain for visual objects detection. The public datasets have only recently reached large enough size and variety to enable proper empirical studies. We revisit the topic by showing how modern methods perform on two large detection and classification datasets (thousand of images, tens of categories) captured in Belgium and Germany. We show that, without any application specific modification, existing methods for pedestrian detection, and for digit and face classification; can reach performances in the range of 95% ~ 99% of the perfect solution. We show detailed experiments and discuss the trade-off of different options. Our top performing methods use modern variants of HOG features for detection, and sparse representations for classification.


title: The complex dielectric constant of pure and sea water from microwave satellite observations

abstract: We provide a new fit for the microwave complex dielectric constant of water in the salinity range between 0-40 ppt using two Debye relaxation wavelengths. For pure water, the fit is based on laboratory measurements in the temperature range between -20/spl deg/C and +40/spl deg/C including supercooled water and for frequencies up to 500 GHz. For sea water, our fit is valid for temperatures between -2/spl deg/C and +29/spl deg/C and for frequencies up to at least 90 GHz. At low frequencies, our new model is a modified version of the Klein-Swift model. We compare the results of the new fit with various other models and provide a validation using an extensive analysis of brightness temperatures from the Special Sensor Microwave Imager.


title: Distributed Deep Q-Learning

abstract: We propose a distributed deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning. The model is based on the deep Q-network, a convolutional neural network trained with a variant of Q-learning. Its input is raw pixels and its output is a value function estimating future rewards from taking an action given a system state. To distribute the deep Q-network training, we adapt the DistBelief software framework to the context of efficiently training reinforcement learning agents. As a result, the method is completely asynchronous and scales well with the number of machines. We demonstrate that the deep Q-network agent, receiving only the pixels and the game score as inputs, was able to achieve reasonable success on a simple game with minimal parameter tuning.


title: Evolution ofSh24 Neural Controllers for Autonomous Vision-Based Robots Dario Floreanoand Claudn Mattiussi

abstract: We describe a set of preliminary experiments to evolve spiking neural controllers for a vision-based mobile robot. All the evolutionary experiments are carried out on physical robots without human intervention. After discussing how to implement and interface these neurons with a physical robot, we show that evolution finds relatively quickly functional spiking controllers capable of navigating in irregularly textured environments without hitting obstacles using a very simple genetic encoding and fitness function. Neuroethological analysis of the network activity let us understand the functioning of evolved controllers and tell the relative importance of single neurons independently of their observed firing rate. Finally, a number of systematic lesion experiments indicate that evolved spiking controllers are very robust to synaptic strength decay that typically occurs in hardware implementations of spiking circuits.


title: Real-time object recognition and orientation estimation using an event-based camera and CNN

abstract: Real-time visual identification and tracking of objects is a computationally intensive task, particularly in cluttered environments which contain many visual distracters. In this paper we describe a real-time bio-inspired system for object tracking and identification which combines an event-based vision sensor with a convolutional neural network running on FPGA for recognition. The event-based vision sensor detects only changes in the scene, naturally responding to moving objects and ignoring static distracters in the background. We present operation of the system for two tasks. The first is proof of concept for a remote monitoring application in which the system tracks and distinguishes between cars, bikes, and pedestrians on a road. The second task targets application to grasp planning for an upper limb prosthesis and involves detecting and identifying household objects, as well as determining their orientation relative to the camera. The second task is used to quantify performance of the system, which can discriminate between 8 different objects in 2.25 ms with accuracy of 99.10% and is able to determine object orientation with -4.5° accuracy in an additional 2.28 ms with accuracy of 97.76%.


title: ReconNet: Non-Iterative Reconstruction of Images from Compressively Sensed Random Measurements

abstract: The goal of this paper is to present a non-iterative and more importantly an extremely fast algorithm to reconstruct images from compressively sensed (CS) random measurements. To this end, we propose a novel convolutional neural network (CNN) architecture which takes in CS measurements of an image as input and outputs an intermediate reconstruction. We call this network, ReconNet. The intermediate reconstruction is fed into an off-the-shelf denoiser to obtain the final reconstructed image. On a standard dataset of images we show significant improvements in reconstruction results (both in terms of PSNR and time complexity) over state-of-the-art iterative CS reconstruction algorithms at various measurement rates. Further, through qualitative experiments on real data collected using our block single pixel camera (SPC), we show that our network is highly robust to sensor noise and can recover visually better quality images than competitive algorithms at extremely low sensing rates of 0.1 and 0.04. To demonstrate that our algorithm can recover semantically informative images even at a low measurement rate of 0.01, we present a very robust proof of concept real-time visual tracking application.


Laws Governing Business


title: An Augmented Reality-Based Mobile Learning System to Improve Students' Learning Achievements and Motivations in Natural Science Inquiry Activities

abstract: Introduction Recently, the advancement and popularity of handheld devices and sensing technologies has enabled researchers to implement more effective learning methods (Ogata, Li, Hou, Uosaki, El-Bishouty, & Yano, 2011). Several studies have reported the importance of conducting contextual learning and experiential learning in real-world environments, encouraging the use of mobile and sensing technologies in outdoor learning activities (Chu, Hwang, Tsai, & Tseng, 2010; Hung, Hwang, Lin, Wu, & Su, 2013; Yang, 2006). For example, Chu, Hwang, Huang, and Wu (2008) developed a learning system that guided students to learn about the characteristics and life cycle of plants on a school campus using mobile communication and RFID (Radio Frequency Identification). Most of mobile learning studies emphasize the adoption of digital learning aids in real-life scenarios (Sharples, Milrad, Arnedillo-Sanchez, & Vavoula, 2009; Ogata & Yano, 2004; Wong & Looi, 2011). However, regarding supplementary mobile learning aids, the interaction between digital learning aids and the actual environment needs to be emphasized to enable students to effectively manage and incorporate personal knowledge (Wu, Lee, Chang, & Liang, 2013). For example, it is expiated that students can select a virtual learning object from the actual environment using a mobile learning aid, which allows them to obtain a first-hand understanding of the learning environment and, subsequently, increases their learning motivations and experiences. Such a learning support technology is achievable through the use of Augmented Reality (AR), which combines human senses (e.g., sight, sound, and touch) with virtual objects to facilitate real-world environment interactions for users to achieve an authentic perception of the environment (Azuma, 1997). For example, users who employ mobile devices with AR facilities to seek a target building on a street are able to see additional information surrounding individual buildings when they browse the buildings via the camera of their mobile device. Researchers have documented the potential of employing such facilities to assist students in learning in real-world environments in comparisons with traditional instructions (Andujar, Mejias, & Marquez, 2011; Chen, Chi, Hung, & Kang, 2011; Kamarainen, Metcalf, Grotzer, Browne, Mazzuca, Tutwiler, & Dede, 2013; Platonov, Heibel, Meier, & Grollmann, 2006), which showed that AR technology contributed to improve academic achievement compared to traditional teaching methods. On the other hand, numerous educators have contended that computer technology cannot support the learning process entirely; instead, the primary function of computer technology involves a knowledge building tool for students (Jonassen, Carr, & Yueh, 1998). Effective learning strategies remain the most crucial factor for increasing learning motivation. Therefore, effective learning strategies supplemented with appropriate computer technology can greatly enhance learning motivation (Chu, Hwang, & Tsai, 2010; Jonassen, 1999; Hwang, Tsai, Chu, Kinshuk, & Chen, 2012; Jonassen et al., 1998). Previous studies have highlighted that inquiry-based learning strategies supplemented by computer technology in a scenario-based learning environment can effectively increase learning motivation (Shih, Chuang, & Hwang, 2010; Soloway & Wallace, 1997). Inquiry-based learning strategies are student-centric knowledge exploration activities; the teacher serves as a guide, employing structured methods that train and encourage students to learn proactively (Hwang, Wu, Zhuang, & Huang, 2013; Soloway & Wallace, 1997). When students acquire the methods for problem-solving, they use the obtained information to establish a hypothesis or to plan solutions to the problem (Looi, 1998). Consequently, in this study, an innovative learning approach is proposed to support inquiry-based learning activities with mobile AR. …


Isolation Enhancement of MIMO Antenna for Wireless Routers in LTE band Applications


title: Towards Building Forensics Enabled Cloud Through Secure Logging-as-a-Service

abstract: Collection and analysis of various logs (e.g., process logs, network logs) are fundamental activities in computer forensics. Ensuring the security of the activity logs is therefore crucial to ensure reliable forensics investigations. However, because of the black-box nature of clouds and the volatility and co-mingling of cloud data, providing the cloud logs to investigators while preserving users’ privacy and the integrity of logs is challenging. The current secure logging schemes, which consider the logger as trusted cannot be applied in clouds since there is a chance that cloud providers (logger) collude with malicious users or investigators to alter the logs. In this paper, we analyze the threats on cloud users’ activity logs considering the collusion between cloud users, providers, and investigators. Based on the threat model, we propose Secure-Logging-as-a-Service ( SecLaaS ), which preserves various logs generated for the activity of virtual machines running in clouds and ensures the confidentiality and integrity of such logs. Investigators or the court authority can only access these logs by the RESTful APIs provided by SecLaaS, which ensures confidentiality of logs. The integrity of the logs is ensured by hash-chain scheme and proofs of past logs published periodically by the cloud providers. In prior research, we used two accumulator schemes Bloom filter and RSA accumulator to build the proofs of past logs. In this paper, we propose a new accumulator scheme— Bloom-Tree , which performs better than the other two accumulators in terms of time and space requirement.


title: Personal Agricultural Income Taxation. A Comparative Analysis between Several New Member States

abstract: This paper investigates in a comparative way how the agricultural personal income is taxed in four New Member States (NMS): Bulgaria, Hungary, Poland and Romania. The objective of this research is to identify any agricultural tax concessions which can reveal possible competitive advantages between these countries. The legislative tax framework shows that all these countries offer to individual producers important tax concessions. Nevertheless, the tax concessions are not linked to the economical farm size. This measure is seen as a proposal of improving the legislative tax regulations.


A Model Experimental Study on the Falling Velocity of Ice Crystals


title: An adaptive road ROI determination algorithm for lane detection

abstract: Road conditions can provide important information for driving safety in driving assistance system. The input images usually include unnecessary information and road conditions need to be analyzed only in a region of interest (ROI) to reduce the amount of computation. In this paper, a vision-based road ROI determination algorithm is proposed to detect the road region using the positional information of a vanishing point and line segments. The line segments are detected using Hough Transform. The road ROI can be determined automatically and adaptively in every frame. The proposed method is applied to various video images from black boxes, and is verified to be robust.


title: Origins and evolution of antibiotic resistance.

abstract: The massive prescription of antibiotics and their non-regulated and extensive usage has resulted in the development of extensive antibiotic resistance in microorganisms; this has been of great clinical significance. Antibiotic resistance occurs not only by mutation of microbial genes which code for antibiotic uptake into cells or the binding sites for antibiotics, but mostly by the acquisition of heterologous resistance genes from external sources. The physical characteristics of the microbial community play a major role in gene exchange, but antimicrobial agents provide the selective pressure for the development of resistance and promote the transfer of resistance genes among bacteria. The control of antibiotic usage is essential to prevent the development of resistance to new antibiotics.


title: Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection

abstract: We describe a learning-based approach to hand-eye coordination for robotic grasping from monocular images. To learn hand-eye coordination for grasping, we trained a large convolutional neural network to predict the probability that task-space motion of the gripper will result in successful grasps, using only monocular camera images and independently of camera calibration or the current robot pose. This requires the network to observe the spatial relationship between the gripper and objects in the scene, thus learning hand-eye coordination. We then use this network to servo the gripper in real time to achieve successful grasps. To train our network, we collected over 800,000 grasp attempts over the course of two months, using between 6 and 14 robotic manipulators at any given time, with differences in camera placement and hardware. Our experimental evaluation demonstrates that our method achieves effective real-time control, can successfully grasp novel objects, and corrects mistakes by continuous servoing.