title: Combined ivermectin and doxycycline treatment has microfilaricidal and adulticidal activity against Dirofilaria immitis in experimentally infected dogs.

text: There is still a pressing need for effective adulticide treatment for human and animal filarial infections. Like many filarial nematodes, Dirofilaria immitis, the causative agent of canine heartworm disease, harbours the bacterial endosymbiont Wolbachia, which has been shown to be essential for worm development, fecundity and survival. Here the authors report the effect of different treatment regimens in dogs experimentally infected with adult D. immitis on microfilariemia, antigenemia, worm recovery and Wolbachia content. Treatment with ivermectin (IVM; 6 microg/kg per os weekly) combined with doxycycline (DOXY; 10 mg/kg/day orally from Weeks 0-6, 10-12, 16-18, 22-26 and 28-34) resulted in a significantly faster decrease of circulating microfilariae and higher adulticidal activity compared with either IVM or DOXY alone. Quantitative PCR analysis of ftsZ (Wolbachia DNA) and 18S rDNA (nematode DNA) absolute copy numbers showed significant decreases in Wolbachia content compared with controls in worms recovered from DOXY-treated dogs that were not, however, associated with worm death. Worms from IVM/DOXY-treated dogs, on the other hand, had Wolbachia/nematode DNA ratios similar to those of control worms, suggesting a loss of both Wolbachia and nematode DNA as indicated by absolute copy number values. Histology and transmission electron microscopy of worms recovered from the IVM/DOXY combination group showed complete loss of uterine content in females and immunohistochemistry for Wolbachia was negative. Results indicate that the combination of these two drugs causes adult worm death. This could have important implications for control of human and animal filarial infections.


title: Landing Strategies in Honeybees and Applications to Uninhabited Airborne Vehicles

text: An application of insect visuomotor behavior to automatic control of landing is explored. Insects, being perhaps more reliant on image motion cues than mammals or higher vertebrates, are proving to be an excellent organism in which to investigate how information on optic flow is exploited to guide locomotion and navigation. We have observed how bees perform grazing landings on a flat surface and have deduced the algorithmic basis for the behavior. A smooth landing is achieved by a surprisingly simple and elegant strategy: image velocity is held constant as the surface is approached, thus automatically ensuring that flight speed is close to zero at touchdown. No explicit knowledge of flight speed or height above the ground is necessary. The feasibility of this landing strategy was tested by implementation in a robotic gantry. We also outline our current efforts at exploring the applicability of this and related techniques to the guidance of uninhabited airborne vehicles (UAVs). Aspects of the algorithm were tested on a small UAV using real imagery to control


title: A New Approach for Resource Scheduling with Deep Reinforcement Learning

text: With the rapid development of deep learning, deep reinforcement learning (DRL) began to appear in the field of resource scheduling in recent years. Based on the previous research on DRL in the literature, we introduce online resource scheduling algorithm DeepRM2 and the offline resource scheduling algorithm DeepRM_Off. Compared with the state-of-the-art DRL algorithm DeepRM and heuristic algorithms, our proposed algorithms have faster convergence speed and better scheduling efficiency with regarding to average slowdown time, job completion time and rewards.


title: COTS-based software development: Processes and open issues

text: The work described in this paper is an investigation of COTS-based software development within a particular NASA environment, with an emphasis on the processes used. Fifteen projects using a COTS-based approach were studied and their actual process was documented. This process is evaluated to identify essential differences in comparison to traditional software development. The main differences, and the activities for which projects require more guidance, are requirements definition and COTS selection, high level design, integration and testing. Starting from these empirical observations, a new process and set of guidelines for COTS-based development are developed and briefly presented.


title: UML CLASS DIAGRAM OR ENTITY RELATIONSHIP DIAGRAM? AN OBJECT-RELATIONAL CONCEPTUAL IMPEDANCE MISMATCH

text: It is now nearly 30 years since Peter Chen’s watershed paper “The Entity-Relationship Model – towards a Unified View of Data”. [1] The entity relationship model and variations and extensions to it have been taught in colleges and universities for many years. In his original paper Peter Chen looked at converting his new ER model to the then existing data structure diagrams for the Network model. In recent years there has been a tendency to use a Unified Modelling Language (UML) class diagram for conceptual modeling for relational databases, and several popular course text books use UML notation to some degree [2] [3]. However Object and Relational technology are based on different paradigms. In the paper we argue that the UML class diagram is more of a logical model (implementation specific). ER Diagrams on the other hand, are at a conceptual level of database design dealing with the main items and their relationships and not with implementation specific detail. UML focuses on OOAD (Object Oriented Analysis and Design) and is navigational and program dependent whereas the relational model is set based and exhibits data independence. The ER model provides a well-established set of mapping rules for mapping to a relational model. In this paper we look specifically at the areas which can cause problems for the novice database designer due to this conceptual mismatch of two different paradigms. Firstly, transferring the mapping of a weak entity from an Entity Relationship model to UML and secondly the representation of structural constraints between objects. We look at the mixture of notations which students mistakenly use when modeling. This is often the result of different notations being used on different courses throughout their degree. Several of the popular text books at the moment use either a variation of ER, UML, or both for teaching database modeling. At the moment if a student picks up a text book they could be faced with either; one of the many ER variations, UML, UML and a variation of ER both covered separately, or UML and ER merged together. We regard this problem as a conceptual impedance mismatch. This problem is documented in [21] who have produced a catalogue of impedance mismatch problems between object-relational and relational paradigms. We regard the problems of using UML class diagrams for relational database design as a conceptual impedance mismatch as the Entity Relationship model does not have the structures in the model to deal with Object Oriented concepts


title: Controlling Physically Based Virtual Musical Instruments Using The Gloves

text: In this paper we propose an empirical method to develop mapping strategies between a gestural-based interface (the Gloves) and physically based sound synthesis models. An experiment was conducted to investigate which gestures listeners associate with sounds synthesised using physical models, corresponding to three categories of sound: sustained, iterative and impulsive. The results of the experiment show that listeners perform similar gestures when controlling sounds from the different categories. We used such gestures in order to create the mapping strategy between the Gloves and the physically based synthesis engine.


title: Conservative treatment of fractures of the thoracolumbar spine

text: We reviewed 48 patients with thoracolumbar fractures treated conservatively between 1988 and 1999. The average follow-up was 77.5 (31–137) months and average patient age (23 women, 25 men) was 46 (18–76) years. Twenty-nine patients suffered a fall from a height and 13 patients were injured in traffic accidents. Thirty-two patients had compression-type fractures and 16 burst-type fractures. There were no neurological deficits. Twenty-nine patients were treated by orthosis, 13 by body cast and six by bed rest. In addition to pain and functional scoring, we measured a number of radiographic parameters at the time of admission and at latest follow-up and compared the values. In patients with compression fractures there were significant changes in scoliosis angle and wedging index (p<0.05). The mean pain score was 1.66 and mean functional score 1.03. In patients with burst fractures, vertebral index, wedging index and height loss increased after treatment (p<0.05). The mean pain score was 1.26 and functional score 0.93. Compression fractures with kyphosis angle <30° are supposed to be stable and can be treated conservatively. If the kyphosis angle is more than 30°, magnetic resonance imaging (MRI) should be performed, and if the posterior ligamentous complex is damaged, surgery should be considered. In burst fractures, MRI should always be performed and conservative treatment should only be considered if there is no neurological deficit and the ligaments are intact. Nous avons examiné 48 malades avec une fracture dorsolombaire traitée d’une manière conservatrice entre 1988 et 1999. Le suivi moyen était de 77,5 (31–137) mois et l’âge moyen des malades (23 femmes, 25 hommes) était de 46 (18–76) ans. Vingt-neuf malades avaient fait une chute de grande hauteur et 13 malades ont été blessés dans un accident de la circulation. Trente-deux malades avaient des fractures du type compression et 16 fractures du type explosion. Il n’y avait pas de déficit neurologique. Vingt-neuf malades ont été traités par orthèse, 13 par corset plâtré et six par repos au lit. En plus des scores douleur et des scores fonctionnels, nous avons comparé plusieurs paramètres radiographiques entre l’admission et le plus long recul. Chez les malades avec fracture-compression il y avait des changements significatifs dans l’angle de la scoliose et l’index angulaire (p<0.05). Le score moyen de la douleur était 1.66 et le score fonctionnel moyen 1.03. Chez les malades avec fracture-explosion, l’index vertébral, l’index angulaire, et la perte de hauteur ont augmenté après traitement (p<0.05). Le score moyen de la douleur était 1.26 et le score fonctionnel 0.93. Les fractures de type de compression avec angle de cyphose <30° sont supposés être stables et sont traités d’une manière conservatrice. Si l’angle de la cyphose est de plus de 30°, une IRM devrait être exécutée et si le complexe ligamentaire postérieur est endommagé, la chirurgie devrait être envisagée. Dans les fractures du type de explosion, une IMR devrait toujours être exécutée et le traitement conservateur devrait être retenu seulement s’il n’y a aucun déficit neurologique et si les ligaments sont intacts.


title: The role of rumination in depressive disorders and mixed anxiety/depressive symptoms.

text: Several studies have shown that people who engage in ruminative responses to depressive symptoms have higher levels of depressive symptoms over time, after accounting for baseline levels of depressive symptoms. The analyses reported here showed that rumination also predicted depressive disorders, including new onsets of depressive episodes. Rumination predicted chronicity of depressive disorders before accounting for the effects of baseline depressive symptoms but not after accounting for the effects of baseline depressive symptoms. Rumination also predicted anxiety symptoms and may be particularly characteristic of people with mixed anxiety/depressive symptoms.


title: A direct approach to estimating surfaces in tomographic data

text: Under ideal circumstances, the inverse of the radon transform is computable, and sequences of measured projections are sufficient to obtain accurate estimates of volume densities. In situations where the sinogram data is incomplete, the radon transform is noninvertable, and attempts to reconstruct greyscale density values result in reconstruction artifacts that can undermine the effectiveness of subsequent processing. This paper presents a direct approach to the segmentation of incomplete tomographic data. The strategy is to impose a fairly simple model on the data, and treat segmentation as a problem of estimating the interface between two substances of somewhat homogeneous density. The segmentation is achieved by simultaneously deforming a surface model and updating density parameters in order to achieve a best fit between the projected model and the measured sinograms. The deformation is implemented with level-set surface models, calculated at the resolution of the input data. Relative to previous work, this paper makes several contributions. First is a proper derivation of the deformation of surface models that move according to a gradient descent on a likelihood measure. We also present a series of computational innovations that make this direct surface-fitting approach feasible with state-of-the-art computers. Another contribution is the demonstration of the effectiveness of this approach on under-constrained tomographic problems, using both simulated and real datasets.


title: Adaptive Simulation-based Training of AI Decision-makers using Bayesian Optimization

text: This work studies how an AI-controlled dog-fighting agent with tunable decisionmaking parameters can learn to optimize performance against an intelligent adversary, as measured by a stochastic objective function evaluated on simulated combat engagements. Gaussian process Bayesian optimization (GPBO) techniques are developed to automatically learn global Gaussian Process (GP) surrogate models, which provide statistical performance predictions in both explored and unexplored areas of the parameter space. This allows a learning engine to sample full-combat simulations at parameter values that are most likely to optimize performance and also provide highly informative data points for improving future predictions. However, standard GPBO methods do not provide a reliable surrogate model for the highly volatile objective functions found in aerial combat, and thus do not reliably identify global maxima. These issues are addressed by novel Repeat Sampling (RS) and Hybrid Repeat/Multipoint Sampling (HRMS) techniques. Simulation studies show that HRMS improves the accuracy of GP surrogate models, allowing AI decision-makers to more accurately predict performance and efficiently tune parameters.


title: Folded cascode OTA design for wide band applications

text: This paper deals with design and optimization of a folded cascode operational transconductance amplifier. First, a detailed description of an optimum OTA topology is done in order to optimize MOS transistor sizing. Second, the design of folded cascode OTA, which works for frequencies that lead to a base band circuit design for RF application, is based on transistor sizing methodology. Third, folded cascode OTAs generally find several applications that are well developed. Simulation results are performed using SPICE software and BSIM3V3 model for CMOS 0.35mum process, show that the designed folded cascode OTA has a 85dB DC gain and provides a gain bandwidth product of around 332MHz


title: On visual similarity based 2D drawing retrieval

text: A large amount of 2D drawings have been produced in engineering fields. To reuse and share the available drawings efficiently, we propose two methods in this paper, namely 2.5D spherical harmonics transformation and 2D shape histogram, to retrieve 2D drawings by measuring their shape similarity. The first approach represents a drawing as a spherical function by transforming it from a 2D space into a 3D space. Then a fast spherical harmonics transformation is employed to get a rotation invariant descriptor. The second statistics-based approach represents the shape of a 2D drawing using a distance distribution between two randomly sampled points. To allow users to interactively emphasize certain local shapes that they are interested in, we have adopted a flexible sampling strategy by specifying a bias sampling density upon these local shapes. The two proposed methods have many valuable properties, including transform invariance, efficiency, and robustness. In addition, their insensitivity to noise allows for the user’s causal input, thus supporting a freehand sketch-based retrieval user interface. Experiments show that a better performance can be achieved by combining them together using weights. q 2005 Elsevier Ltd. All rights reserved.


title: Paraphrasing for Style

text: We present initial investigation into the task of paraphrasing language while targeting a particular writing style. The plays of William Shakespeare and their modern translations are used as a testbed for evaluating paraphrase systems targeting a specific style of writing. We show that even with a relatively small amount of parallel training data, it is possible to learn paraphrase models which capture stylistic phenomena, and these models outperform baselines based on dictionaries and out-of-domain parallel text. In addition we present an initial investigation into automatic evaluation metrics for paraphrasing writing style. To the best of our knowledge this is the first work to investigate the task of paraphrasing text with the goal of targeting a specific style of writing.


title: Measurements and Modelling of Base Station Power Consumption under Real Traffic Loads †

text: Base stations represent the main contributor to the energy consumption of a mobile cellular network. Since traffic load in mobile networks significantly varies during a working or weekend day, it is important to quantify the influence of these variations on the base station power consumption. Therefore, this paper investigates changes in the instantaneous power consumption of GSM (Global System for Mobile Communications) and UMTS (Universal Mobile Telecommunications System) base stations according to their respective traffic load. The real data in terms of the power consumption and traffic load have been obtained from continuous measurements performed on a fully operated base station site. Measurements show the existence of a direct relationship between base station traffic load and power consumption. According to this relationship, we develop a linear power consumption model for base stations of both technologies. This paper also gives an overview of the most important concepts which are being proposed to make cellular networks more energy-efficient.


title: Routing questions to appropriate answerers in community question answering services

text: Community Question Answering (CQA) service provides a platform for increasing number of users to ask and answer for their own needs but unanswered questions still exist within a fixed period. To address this, the paper aims to route questions to the right answerers who have a top rank in accordance of their previous answering performance. In order to rank the answerers, we propose a framework called Question Routing (QR) which consists of four phases: (1) performance profiling, (2) expertise estimation, (3) availability estimation, and (4) answerer ranking. Applying the framework, we conduct experiments with Yahoo! Answers dataset and the results demonstrate that on average each of 1,713 testing questions obtains at least one answer if it is routed to the top 20 ranked answerers.


title: Punctuation Prediction with Transition-based Parsing

text: Punctuations are not available in automatic speech recognition outputs, which could create barriers to many subsequent text processing tasks. This paper proposes a novel method to predict punctuation symbols for the stream of words in transcribed speech texts. Our method jointly performs parsing and punctuation prediction by integrating a rich set of syntactic features when processing words from left to right. It can exploit a global view to capture long-range dependencies for punctuation prediction with linear complexity. The experimental results on the test data sets of IWSLT and TDT4 show that our method can achieve high-level performance in punctuation prediction over the stream of words in transcribed speech text.


title: A circularly polarized planar antenna modified for passive UHF RFID

text: The majority of RFID tags are linearly polarized dipole antennas, but a few use a planar, dual-dipole antenna that facilitates circular polarization, but requires a three-terminal IC. In this paper, we present a novel way to achieve circular polarization with a planar antenna using a two-terminal IC. We present an intuitive methodology for design, and perform experiments that validate circular polarization. The results show that the tag exhibits strong circular polarization, but the precise axial ratio of the tag remains uncertain due to lack of precision in the experimental system.


title: The art of UHF RFID antenna design: impedance-matching and size-reduction techniques

text: Radio-frequency identification technology, based on the reader/tag paradigm, is quickly permeating several aspects of everyday life. The electromagnetic research mainly concerns the design of tag antennas having high efficiency and small size, and suited to complex impedance matching to the embedded electronics. Starting from the available but fragmented open literature, this paper presents a homogeneous survey of relevant methodologies for the design of UHF passive tag antennas. Particular care is taken to illustrate, within a common framework, the basic concepts of the most-used design layouts. The design techniques are illustrated by means of many noncommercial examples.


title: Novel Compact Circularly Polarized Square Microstrip Antenna

text: A novel compact circular-polarization (CP) operation of the square microstrip antenna with four slits and a pair of truncated corners is proposed and investigated. Experimental results show that the proposed compact CP design can have an antenna-size reduction of about 36% as compared to the conventional corner-truncated square microstrip antenna at a given operating frequency. Also, the required size of the truncated corners for CP operation is much greater than that for the conventional CP design using a simple square microstrip patch, providing a relaxed manufacturing tolerance for the proposed compact CP design. Details of the experimental results are presented and discussed.


title: The history of RFID

text: Radio frequency identification (RFID) is an integral part of our life, which increases productivity and convenience. It is the term coined for short-range radio technology used to communicate mainly digital information between a stationary location and a movable object or between movable objects. This RFID system uses the principle of modulated backscatter where it can transfer the data from the tag to the reader. The tag generally reads its internal memory of stored data and changes the loading on the tag antenna in a coded manner corresponding to the stored data. RFID is a technology, which spans systems engineering, software development, encryption etc., and thus there are many engineers involved in the development and application of RFID and at present the shortage of technical and business people trained in RFID is hampering the growth of the industry.


title: Software Cost Estimation Meets Software Diversity

text: The previous goal of having a one-size-fits-all software cost (and schedule) estimation model is no longer achievable. Sources of wide variation in the nature of software development and evolution processes, products, properties, and personnel (PPPPs) require a variety of estimation models and methods best fitting their situations. This Technical Briefing will provide a short history of pattern-breaking changes in software estimation methods, a summary of the sources of variation in software PPPPs and their estimation implications, a summary of the types of estimation methods being widely used or emerging, a summary of the best estimation-types for the various PPPP-types, and a process for guiding an organization's choices of estimation methods as their PPPP-types evolve.


title: Balance equilibrium manifold and control of rider-bikebot systems

text: Postural balance control of machines is one of the most important human motor skills. We present a balance equilibrium manifold (BEM) concept to study how a human rider balances a bicycle-like robot (i.e., bikebot) while maintaining tracking a desired trajectory. The BEM is built on the external-internal convertible (EIC) structure of the rider-bikebot dynamics to capture the balance properties of the rider-bikebot systems. We analyze the contribution of the upper-body movements and the steering inputs to the task of balance control of the systems. The results reveal that steering actuation is more effective than upper-body movement for balance task. A performance metric is also introduced to quantify the balance motor skills using the BEM. Extensive experiments are conducted to validate and demonstrate the analyses and the proposed balance skill metrics.


title: The Adaptive Markets Hypothesis Market efficiency from an evolutionary perspective

text: 30TH ANNIVERSARY ISSUE 2004 THE JOURNAL OF PORTFOLIO MANAGEMENT 15 The 30th anniversary of The Journal of Portfolio Management is a milestone in the rich intellectual history of modern finance, firmly establishing the relevance of quantitative models and scientific inquiry in the practice of financial management. One of the most enduring ideas from this intellectual history is the Efficient Markets Hypothesis (EMH), a deceptively simple notion that has become a lightning rod for its disciples and the proponents of behavioral economics and finance. In its purest form, the EMH obviates active portfolio management, calling into question the very motivation for portfolio research. It is only fitting that we revisit this groundbreaking idea after three very successful decades of this Journal. In this article, I review the current state of the controversy surrounding the EMH and propose a new perspective that reconciles the two opposing schools of thought. The proposed reconciliation, which I call the Adaptive Markets Hypothesis (AMH), is based on an evolutionary approach to economic interactions, as well as some recent research in the cognitive neurosciences that has been transforming and revitalizing the intersection of psychology and economics. Although some of these ideas have not yet been fully articulated within a rigorous quantitative framework, long time students of the EMH and seasoned practitioners will no doubt recognize immediately the possibilities generated by this new perspective. Only time will tell whether its potential will be fulfilled. I begin with a brief review of the classic version of the EMH, and then summarize the most significant criticisms leveled against it by psychologists and behavioral economists. I argue that the sources of this controversy can ANDREW W. LO is Harris & Harris Group Professor at the MIT Sloan School of Management, and chief scientific officer at AlphaSimplex Group, LLC, in Cambridge, MA. The Adaptive Markets Hypothesis


title: The distance function effect on k-nearest neighbor classification for medical datasets

text:
INTRODUCTION
K-nearest neighbor (k-NN) classification is conventional non-parametric classifier, which has been used as the baseline classifier in many pattern classification problems. It is based on measuring the distances between the test data and each of the training data to decide the final classification output.

CASE DESCRIPTION
Since the Euclidean distance function is the most widely used distance metric in k-NN, no study examines the classification performance of k-NN by different distance functions, especially for various medical domain problems. Therefore, the aim of this paper is to investigate whether the distance function can affect the k-NN performance over different medical datasets. Our experiments are based on three different types of medical datasets containing categorical, numerical, and mixed types of data and four different distance functions including Euclidean, cosine, Chi square, and Minkowsky are used during k-NN classification individually.

DISCUSSION AND EVALUATION
The experimental results show that using the Chi square distance function is the best choice for the three different types of datasets. However, using the cosine and Euclidean (and Minkowsky) distance function perform the worst over the mixed type of datasets.

CONCLUSIONS
In this paper, we demonstrate that the chosen distance function can affect the classification accuracy of the k-NN classifier. For the medical domain datasets including the categorical, numerical, and mixed types of data, K-NN based on the Chi square distance function performs the best.


title: Application of Gradient Boosting through SAS Enterprise Miner TM 12 . 3 to Classify Human Activities

text: Using smart clothing with wearable medical sensors integrated to keep track of human health is now attracting many researchers. However, body movement caused by daily human activities inserts artificial noise into physiological data signals, which affects the output of a health monitoring/alert system. To overcome this problem, recognizing human activities, determining relationship between activities and physiological signals, and removing noise from the collected signals are essential steps. This paper focuses on the first step, which is human activity recognition. Our research shows that no other study used SAS® for classifying human activities. For this study, two data sets were collected from an open repository. Both data sets have 561 input variables and one nominal target variable with four levels. Principal component analysis along with other variable reduction and selection techniques were applied to reduce dimensionality in the input space. Several modeling techniques with different optimization parameters were used to classify human activity. The gradient boosting model was selected as the best model based on a test misclassification rate of 0.1233. That is, 87.67% of total events were classified correctly.


title: Bioavailability and bioefficacy of polyphenols in humans. I. Review of 97 bioavailability studies.

text: Polyphenols are abundant micronutrients in our diet, and evidence for their role in the prevention of degenerative diseases is emerging. Bioavailability differs greatly from one polyphenol to another, so that the most abundant polyphenols in our diet are not necessarily those leading to the highest concentrations of active metabolites in target tissues. Mean values for the maximal plasma concentration, the time to reach the maximal plasma concentration, the area under the plasma concentration-time curve, the elimination half-life, and the relative urinary excretion were calculated for 18 major polyphenols. We used data from 97 studies that investigated the kinetics and extent of polyphenol absorption among adults, after ingestion of a single dose of polyphenol provided as pure compound, plant extract, or whole food/beverage. The metabolites present in blood, resulting from digestive and hepatic activity, usually differ from the native compounds. The nature of the known metabolites is described when data are available. The plasma concentrations of total metabolites ranged from 0 to 4 mumol/L with an intake of 50 mg aglycone equivalents, and the relative urinary excretion ranged from 0.3% to 43% of the ingested dose, depending on the polyphenol. Gallic acid and isoflavones are the most well-absorbed polyphenols, followed by catechins, flavanones, and quercetin glucosides, but with different kinetics. The least well-absorbed polyphenols are the proanthocyanidins, the galloylated tea catechins, and the anthocyanins. Data are still too limited for assessment of hydroxycinnamic acids and other polyphenols. These data may be useful for the design and interpretation of intervention studies investigating the health effects of polyphenols.


title: Operative strategy in postero-medial fracture-dislocation of the proximal tibia.

text:
OBJECTIVE
In 1981, Moore introduced a new classification for dislocation-type fractures caused by high-energy mechanisms. The most common medial Moore-type fractures are entire condyle fractures with the avulsion of the median eminence (tibial anterior cruciate ligament (ACL) insertion). They are usually associated with a posterolateral depression of the tibial plateau and an injury of the lateral menisco-tibial capsule. This uniform injury of the knee is increasingly observed in the recent years after skiing injuries due to the high-speed carving technique. This uprising technique uses shorter skis with more sidecut allowing much higher curve speeds and increases the forces on the knee joint. The aim of this study was to describe the injury pattern, our developed operative approach for reconstruction and outcome.

METHODS
A total of 28 patients with 29 postero-medial fracture dislocation of the proximal tibia from 2001 until 2009 were analysed. Clinical and radiological follow-up was performed after 4 years on average (1 year in minimum). Evaluation criteria included the Lysholm score for everyday knee function and the Tegner score evaluating the activity level. All fractures were stabilised post primarily. The surgical main approach was medial. First, the exposure of the entire medial condyle fracture was performed following the fracture line to the articular border. The posterolateral impaction was addressed directly through the main fracture gap from anteromedial to posterolateral. Small plateau fragments were removed, larger fragments reduced and preliminarily fixed with separate K-wire(s). The postero-medial part of the condyle was then prepared for main reduction and application of a buttress T-plate in a postero-medial position, preserving the pes anserinus and medial collateral ligament. In addition, a parapatellar medial mini-arthrotomy through the same main medial approach was performed. Through this mini-arthrotomy, the avulsed anterior eminence with attached distal ACL is retained by a transosseous suture back to the tibia.

RESULTS
We could evaluate 26 patients (93%); two patients were lost to follow-up due to foreign residence. Median age was 51 years (20-77 years). The fractures were treated post primarily at an average of 4 days; in 18 cases a two-staged procedure with initial knee-spanning external fixator was used. All fractures healed without secondary displacement or infection. As many as 25 patients showed none to moderate osteoarthritis after a median of 4 years. One patient showed a severe osteoarthritis after 8 years. All patients judge the clinical result as good to excellent. The Lysholm score reached 95 (75-100) of maximal 100 points and the Tegner activity score 5 (3-7) of maximal 10 points (competitive sports). The patients achieved a median flexion of 135° (100-145°).

CONCLUSION
In our view, it is crucial to recognise the different components of the injury in the typical postero-medial fracture dislocation of the proximal tibia. The described larger medial approach for this type of medial fracture dislocation allows repairing most of the injured aspects of the tibial head, namely the medial condyle with postero-medial buttressing, the distal insertion of the ACL and the posterolateral impaction of the plateau.


title: Quantum Algorithm Implementations for Beginners

text: Patrick J. Coles, Stephan Eidenbenz,∗ Scott Pakin, Adetokunbo Adedoyin, John Ambrosiano, Petr Anisimov, William Casper, Gopinath Chennupati, Carleton Coffrin, Hristo Djidjev, David Gunter, Satish Karra, Nathan Lemons, Shizeng Lin, Andrey Lokhov, Alexander Malyzhenkov, David Mascarenas, Susan Mniszewski, Balu Nadiga, Dan O’Malley, Diane Oyen, Lakshman Prasad, Randy Roberts, Phil Romero, Nandakishore Santhi, Nikolai Sinitsyn, Pieter Swart, Marc Vuffray, Jim Wendelberger, Boram Yoon, Richard Zamora, and Wei Zhu Los Alamos National Laboratory, Los Alamos, New Mexico, USA


title: Alana: Social Dialogue using an Ensemble Model and a Ranker trained on User Feedback

text: We describe our Alexa prize system (called ‘Alana’) which consists of an ensemble of bots, combining rule-based and machine learning systems, and using a contextual ranking mechanism to choose system responses. This paper reports on the version of the system developed and evaluated in the semi-finals of the competition (i.e. up to 15 August 2017), but not on subsequent enhancements. The ranker for this system was trained on real user feedback received during the competition, where we address the problem of how to train on the noisy and sparse feedback obtained during the competition. In order to avoid initial problems of inappropriate and boring utterances coming from big datasets such as Reddit and Twitter, we later focussed on ‘clean’ data sources such as news and facts. We report on experiments with different ranking functions and versions of our NewsBot. We find that a multiturn news strategy is beneficial, and that a ranker trained on the ratings feedback from users is also effective. Our system continuously improved using the data gathered over the course over the competition (1 July – 15 August) . Our final user score (averaged user rating over the whole semi-finals period) was 3.12, and we achieved 3.3 for the averaged user rating over the last week of the semi-finals (8-15 August 2017). We were also able to achieve long dialogues (average 10.7 turns) during the competition period. In subsequent weeks, after the end of the semi-final competition, we have achieved our highest scores of 3.52 (daily average, 18th October), 3.45 (weekly average on 23 and 24 October), and average dialogue lengths of 14.6 turns (1 October), and median dialogue length of 2.25 minutes (average for 7 days on 10th October).


title: CBAM: Convolutional Block Attention Module

text: We propose Convolutional Block Attention Module (CBAM), a simple yet effective attention module for feed-forward convolutional neural networks. Given an intermediate feature map, our module sequentially infers attention maps along two separate dimensions, channel and spatial, then the attention maps are multiplied to the input feature map for adaptive feature refinement. Because CBAM is a lightweight and general module, it can be integrated into any CNN architectures seamlessly with negligible overheads and is end-to-end trainable along with base CNNs. We validate our CBAM through extensive experiments on ImageNet-1K, MS COCO detection, and VOC 2007 detection datasets. Our experiments show consistent improvements in classification and detection performances with various models, demonstrating the wide applicability of CBAM. The code and models will be publicly available.


title: Use of assistance and therapy dogs for children with autism spectrum disorders: a critical review of the current evidence.

text:
BACKGROUND
Autism spectrum disorders (ASD) are characterized by deficits in social reciprocity and communication, and by unusually restricted, repetitive behaviors. Intervention strategies based on the exploitation of the emotional aspects of human-dog relationships hold the potential to overcome the difficulty of subjects with ASD to relate and interact effectively with others, targeting core symptoms of this disorder.

METHODS
This review summarizes the results of six published studies on the effects of brief interactions with dogs and the effects of introducing dogs in families with a child diagnosed with ASD, with an emphasis on social behaviors and language use. Furthermore, the possible mechanisms responsible for the beneficial effects observed are discussed.

CONCLUSIONS
Although the studies described here are encouraging, further research with better designs and using larger samples is needed to strengthen translation of such interventions to the clinic. In addition, potential applications of analyzing child-dog interactions are highlighted to screen for early signs of the disorder.


title: Neurodegenerative Diseases Monitoring ( NDM ) main Challenges , Tendencies , and Enabling Big Data Technologies : A Survey

text: Evidence-based health monitoring has been recognized in the past few years as a very prominent solution to cope with continuous monitoring of chronic diseases such as neurodegenerative diseases for example: epilepsy. This has reduced the burden and cost for healthcare agencies and has led to efficient disease tracking, diagnosis, and intervention. Monitoring these diseases requires a long and continuous EEG signals recordings, pre-processing, analytics, and visualization. This will require a comprehensive solution to handle the complexity and time sensitivity of continuous monitoring. Several neurodegenerative disease monitoring (NDM) architectures have been proposed in the literature, however, they diverge on different aspects, such as the way they handle the monitoring processes, and the techniques they used to process, classify, and analyze the data. In this paper, we aim to bridge the gap between these existing NDM solutions. We provide first an overview of a standard NDM system, its main components, and requirements. We then survey and classify the exiting NDM solutions features, and characteristics. Afterwards, we provide a thorough evaluation of existing NDM solutions and we discuss the remaining key research challenges that have to be addressed. Finally, we propose and describe a generic NDM framework incorporating new technologies mainly the Cloud and Big Data to efficiently handle data intensive related processes. We aim by this work to serve researchers in this filed with useful information on NDM and provide direction for future research advancements. KeywordsNeurodegenerative Diseases Monitoring, Brain Informatics, Neuro Informatics, Big data, EEG, Seizure Detection


title: The Malmo Platform for Artificial Intelligence Experimentation

text: We present Project Malmo – an AI experimentation platform built on top of the popular computer game Minecraft, and designed to support fundamental research in artificial intelligence. As the AI research community pushes for artificial general intelligence (AGI), experimentation platforms are needed that support the development of flexible agents that learn to solve diverse tasks in complex environments. Minecraft is an ideal foundation for such a platform, as it exposes agents to complex 3D worlds, coupled with infinitely varied game-play. Project Malmo provides a sophisticated abstraction layer on top of Minecraft that supports a wide range of experimentation scenarios, ranging from navigation and survival to collaboration and problem solving tasks. In this demo we present the Malmo platform and its capabilities. The platform is publicly released as open source software at IJCAI, to support openness and collaboration in AI research.


title: A multidisciplinary approach towards computational thinking for science majors

text: This paper describes the development and initial evaluation of a new course ``Introduction to Computational Thinking'' taken by science majors to fulfill a college computing requirement. The course was developed by computer science faculty in collaboration with science faculty and it focuses on the role of computing and computational principles in scientific inquiry. It uses Python and Python libraries to teach computational thinking via basic programming concepts, data management concepts, simulation, and visualization. Problems with a computational aspect are drawn from different scientific disciplines and are complemented with lectures from faculty in those areas. Our initial evaluation indicates that the problem-driven approach focused on scientific discovery and computational principles increases the student's interest in computing.


title: Design of a Direct-Driven Linear Actuator for a High-Speed Quadruped Robot, Cheetaroid-I

text: Several legged robots have been developed in recent years and proved that they are an effective transportation system on an uneven terrain. In particular, quadruped robots are regarded as a new trend in robotics due to their superior gait stability and robustness to disturbances. More recently, many robotics researchers are making their best efforts to improve the locomotion speed, as well as the stability and robustness, of quadruped robots. The high-speed locomotion creates various challenges in the development of actuators, mechanical design, and control algorithms of the robot. In this paper, a linear actuation system for the high-speed locomotion of a quadruped robot is introduced. The proposed actuator is designed based on the principle of brushed direct-current electric motor systems. For the minimal impedance and improved power capacity, the actuator is designed with dual layers of cores, which are aligned parallel to permanent magnets. The mechanical and electrical properties of the actuation system, such as back-drivability, controllability, and response time, are verified by experimental results. A robotic leg, which is the rear leg of a Cheetah-like robot, is designed with the proposed actuator, and experimental results for trajectory tracking performance are presented.


title: Relationship Between Awareness, Knowledge and Attitudes Towards Environmental Education Among Secondary School Students in Malaysia

text: The importance of environmental education (EE) is well known globally among societies. Environmental education is gradually promoted as a sustainable tool in protection of the environment Environmental education is found across school curriculums in Malaysia. The objectives of the curriculum are environmental attitude, knowledge and awareness (AKA) where has been investigated in the current study. The study was conducted to identify the relationship between environmental awareness, knowledge and attitude among secondary school students. The survey was conducted on 470 respondents who were in Form Four (16 years old) in Kajang city, Selangor, Malaysia. An instrument which included (48 questions) was employed to investigate the relationship between awareness, knowledge and attitude. The results of Person Correlation showed a significant but weak relationship between awareness and knowledge on environmental issues while there was high relationship observed between awareness and attitudes among respondents. Moreover, the statistical test showed a negligible relationship between knowledge and attitude among students about environment. The study concluded that a high level of awareness and knowledge plus positive attitude of students may come have been achieved from the families of respondents, teachers, media, private reading and school curriculums regarding the environment that increases the environmental view among students as well as overall in the society. The study recommended that environmental education subject necessarily might be considered as an independent syllabus in Malaysian education system.


title: A First-Estimates Jacobian EKF for Improving SLAM Consistency

text: In this work, we study the inconsistency of EKF-based SLAM from the perspective of observability. We analytically prove that when the Jacobians of the system and measurement models are evaluated at the latest state estimates during every time step, the linearized error-state system employed in the EKF has observable subspace of dimension higher than that of the actual, nonlinear, SLAM system. As a result, the covariance estimates of the EKF undergo reduction in directions of the state space where no information is available, which is a primary cause of the inconsistency. Furthermore, a new “First-Estimates Jacobian” (FEJ) EKF is proposed to improve the estimator’s consistency during SLAM. The proposed algorithm performs better in terms of consistency, because when the filter Jacobians are calculated using the first-ever available estimates for each state variable, the error-state system model has an observable subspace of the same dimension as the underlying nonlinear SLAM system. The theoretical analysis is validated through both simulations and experiments.


title: Computer security in the real world

text: Most computers today are insecure because security is costly in terms of user inconvenience and foregone features, and people are unwilling to pay the price. Real-world security depends more on punishment than on locks, but it's hard to even find network attackers, much less punish them. The basic elements of security are authentication, authorization, and auditing: the gold standard. The idea of one principal speaking for another is the key to doing these uniformly across the Internet.


title: A review of argumentation for the Social Semantic Web

text: Argumentation represents the study of views and opinions that humans express with the goal of reaching a conclusion through logical reasoning. Since the 1950’s, several models have been proposed to capture the essence of informal argumentation in different settings. With the emergence of the Web, and then the Semantic Web, this modeling shifted towards ontologies, while from the development perspective, we witnessed an important increase in Web 2.0 human-centered collaborative deliberation tools. Through a review of more than 150 scholarly papers, this article provides a comprehensive and comparative overview of approaches to modeling argumentation for the Social Semantic Web. We start from theoretical foundational models and investigate how they have influenced Social Web tools. We also look into Semantic Web argumentation models. Finally we end with Social Web tools for argumentation, including online applications combining Web 2.0 and Semantic Web technologies, following the path to a global World Wide Argument Web.


title: Discriminating Gender on Twitter

text: Accurate prediction of demographic attributes from social media and other informal online content is valuable for marketing, personalization, and legal investigation. This paper describes the construction of a large, multilingual dataset labeled with gender, and investigates statistical models for determining the gender of uncharacterized Twitter users. We explore several different classifier types on this dataset. We show the degree to which classifier accuracy varies based on tweet volumes as well as when various kinds of profile metadata are included in the models. We also perform a large-scale human assessment using Amazon Mechanical Turk. Our methods significantly out-perform both baseline models and almost all humans on the same task.


title: Embedded Vision System for Real-Time Object Tracking using an Asynchronous Transient Vision Sensor

text: This paper presents an embedded vision system for object tracking applications based on a 128times128 pixel CMOS temporal contrast vision sensor. This imager asynchronously responds to relative illumination intensity changes in the visual scene, exhibiting a usable dynamic range of 120 dB and a latency of under 100 mus. The information is encoded in the form of address-event representation (AER) data. An algorithm for object tracking with 1 millisecond timestamp resolution of the AER data stream is presented. As a real-world application example, vehicle tracking for a traffic-monitoring is demonstrated in real time. The potential of the proposed algorithm for people tracking is also shown. Due to the efficient data pre-processing in the imager chip focal plane, the embedded vision system can be implemented using a low-cost, low-power digital signal processor


title: Deformable Image Registration: A Survey

text: Deformable image registration is a fundamental task in medical image processing. Among its most important applications, one may cite: i) multi-modality fusion, where information acquired by di erent imaging devices or protocols is fused to facilitate diagnosis and treatment planning; ii) longitudinal studies, where the development of phenomenon is studied over time; and iii) population modeling towards studying normal anatomical variability. In this research report, we attempt to give an overview of the deformable registration methods, putting emphasis on the most recent advances in the domain. Additional emphasis has been given to techniques applied to medical images. In order to study in depth image registration methods, their main components are identi ed and then studied independently. The most recent techniques are presented in a principled fashion. Key-words: Deformable registration, bibliographical review ∗ Center for Visual Computing, Department of Applied Mathematics, Ecole Centrale de Paris, Equipe GALEN, INRIA Saclay Étude Bibliographique sur le Recalage Déformable d'Images Résumé : Le recalage déformable d'images est une des tâches les plus fondamentales dans l'imagerie médicale. Parmi ses applications les plus importantes, on compte: i) la fusion d' information provenant des di érents types de modalités a n de faciliter le diagnostic et la plani cation du traitement; ii) les études longitudinales, oú le d'eveloppement d'un phénomène est etudié en fonction du temps; et iii) la modélisation de la variabilité anatomique normale d'une population. Dans ce rapport de recherche, nous essayons de donner un aperçu des di érentes méthodes du recalage déformables, en mettant l'accent sur les avancées les plus récentes du domaine. Nous avons particuliérement insisté sur les techniques appliquées aux images médicales. A n d'étudier en profondeur les méthodes du recalage d'images, leurs composants principales sont d'abord identi és puis étudiées de manière indeépendante, les techniques les plus récentes étant classi ées en suivant un schéma logique déterminé. Mots-clés : recalage déformable, étude bibliographique Deformable Image Registration: A Survey 3


title: Edge-Oriented Computing Paradigms: A Survey on Architecture Design and System Management

text: While cloud computing has brought paradigm shifts to computing services, researchers and developers have also found some problems inherent to its nature such as bandwidth bottleneck, communication overhead, and location blindness. The concept of fog/edge computing is therefore coined to extend the services from the core in cloud data centers to the edge of the network. In recent years, many systems are proposed to better serve ubiquitous smart devices closer to the user. This article provides a complete and up-to-date review of edge-oriented computing systems by encapsulating relevant proposals on their architecture features, management approaches, and design objectives.


title: Computation and Computational Thinking

text: ion of reality in such a way that the neglected details in the model make it executable by a machine.” [2] As we shall see, finding or devising appropriate models of computation to formulate problems is a central and often nontrivial part of computational thinking.


title: Autopoiesis, Structural Coupling and Cognition: A history of these and other notions in the biology of cognition

text: My intent in this essay is to reflect on the history of some biological notions such as autopoiesis, structural coupling, and cognition, that I have developed since the early 1960's as a result of my work on visual perception and the organization of the living. No doubt I shall repeat things that I have said in other publications (Maturana & Varela, 1980, 1988), and I shall present notions"that once they are said appear as obvious truisms. Moreover, I shall refine or expand the meaning of such notions, or even modify them. Yet, in any case, the reader is not invited to attend to the truisms, or to what seems to be obvious, rather he or she is invited to attend to the consequences that those notions entail for the understanding of cognition as a biological process. After all, explanations or demonstrations always become self evident once they are understood and accepted, and the purpose of this essay is the expansion of understanding in all dimensions of human existence.


title: Integration of an On-line Kaldi Speech Recogniser to the Alex Dialogue Systems Framework

text: This paper describes the integration of an on-line Kaldi speech recogniser into the Alex Dialogue Systems Framework (ADSF). As the Kaldi OnlineLatgenRecogniser is written in C++, we first developed a Python wrapper for the recogniser so that the ADSF, written in Python, could interface with it. Training scripts for acoustic and language modelling were developed and integrated into ADSF, and acoustic and language models were build. Finally, optimal recogniser parameters were determined and evaluated. The dialogue system Alex with the new speech recogniser is evaluated on Public Transport Information (PTI) domain.


title: Improving deep neural network acoustic models using generalized maxout networks

text: Recently, maxout networks have brought significant improvements to various speech recognition and computer vision tasks. In this paper we introduce two new types of generalized maxout units, which we call p-norm and soft-maxout. We investigate their performance in Large Vocabulary Continuous Speech Recognition (LVCSR) tasks in various languages with 10 hours and 60 hours of data, and find that the p-norm generalization of maxout consistently performs well. Because, in our training setup, we sometimes see instability during training when training unbounded-output nonlinearities such as these, we also present a method to control that instability. This is the “normalization layer”, which is a nonlinearity that scales down all dimensions of its input in order to stop the average squared output from exceeding one. The performance of our proposed nonlinearities are compared with maxout, rectified linear units (ReLU), tanh units, and also with a discriminatively trained SGMM/HMM system, and our p-norm units with p equal to 2 are found to perform best.


title: Hypothesis spaces for minimum Bayes risk training in large vocabulary speech recognition

text: The Minimum Bayes Risk (MBR) framework has been a successful strategy for the training of hidden Markov models for large vocabulary speech recognition. Practical implementations of MBR must select an appropriate hypothesis space and loss function. The set of word sequences and a word-based Levenshtein distance may be assumed to be the optimal choice but use of phoneme-based criteria appears to be more successful. This paper compares the use of different hypothesis spaces and loss functions defined using the system constituents of word, phone, physical triphone, physical state and physical mixture component. For practical reasons the competing hypotheses are constrained by sampling. The impact of the sampling technique on the performance of MBR training is also examined.


title: Improving neural networks by preventing co-adaptation of feature detectors

text: When a large feedforward neural network is trained on a small training set, it typically performs poorly on held-out test data. This overfitting is greatly reduced by randomly omitting half of the feature detectors on each training case. This prevents complex co-adaptations in which a feature detector is only helpful in the context of several other specific feature detectors. Instead, each neuron learns to detect a feature that is generally helpful for producing the correct answer given the combinatorially large variety of internal contexts in which it must operate. Random “dropout” gives big improvements on many benchmark tasks and sets new records for speech and object recognition. A feedforward, artificial neural network uses layers of non-linear hidden units between its inputs and its outputs. By adapting the weights on the incoming connections of these hidden units it learns feature detectors that enable it to predict the correct output when given an input vector [15]. If the relationship between the input and the correct output is complicated and the network has enough hidden units to model it accurately, there will typically be many different settings of the weights that can model the training set almost perfectly, especially if there is only a limited amount of labeled training data. Each of these weight vectors will make different predictions on held-out test data and almost all of them will do worse on the test data than on the training data because the feature detectors have been tuned to work well together on the training data but not on the test data. Overfitting can be reduced by using “dropout” to prevent complex co-adaptations on the training data. On each presentation of each training case, each hidden unit is randomly omitted from the network with a probability of 0.5, so a hidden unit cannot rely on other hidden units being present. Another way to view the dropout procedure is as a very efficient way of performing model averaging with neural networks. A good way to reduce the error on the test set is to average the predictions produced by a very large number of different networks. The standard way to do this is to train many separate networks and then to apply each of these networks to the test data, but this is computationally expensive during both training and testing. Random dropout makes it possible to train a huge number of different networks in a reasonable time. There is almost certainly a different network for each presentation of each training case but all of these networks share the same weights for the hidden units that are present. We use the standard, stochastic gradient descent procedure for training the dropout neural networks on mini-batches of training cases, but we modify the penalty term that is normally used to prevent the weights from growing too large. Instead of penalizing the squared length (L2 norm) of the whole weight vector, we set an upper bound on the L2 norm of the incoming weight vector for each individual hidden unit. If a weight-update violates this constraint, we renormalize the weights of the hidden unit by division. Using a constraint rather than a penalty prevents weights from growing very large no matter how large the proposed weight-update is. This makes it possible to start with a


title: LIMSI$@$WMT'16: Machine Translation of News

text: This paper describes LIMSI’s submissions to the shared WMT’16 task “Translation of News”. We report results for Romanian-English in both directions, for English to Russian, as well as preliminary experiments on reordering to translate from English into German. Our submissions use mainly NCODE and MOSES along with continuous space models in a post-processing step. The main novelties of this year’s participation are the following: for the translation into Russian and Romanian, we have attempted to extend the output of the decoder with morphological variations and to use a CRF model to rescore this new search space; as for the translation into German, we have been experimenting with source-side pre-ordering based on a dependency structure allowing permutations in order to reproduce the tar-


title: Expanded Semantic Graph Representation for Matching Related Information of Interest across Free Text Documents

text: This research proposes an expanded semantic graph definition that serves as a basis for an expanded semantic graph representation and graph matching approach. This representation separates the content and context and adds a number of semantic structures that encapsulate inferred information. The expanded semantic graph approach facilitates finding additional matches, identifying and eliminating poor matches, and prioritizing matches based on how much new knowledge is provided. By focusing on information of interest, doing pre-processing, and reducing processing requirements, the approach is applicable to problems where related information of interest is sought across a massive body of free text documents. Key aspects of the approach include (1) expanding the nodes and edges through inference using DL-Safe rules, abductive hypotheses, and syntactic patterns, (2) separating semantic content into nodes and semantic context into edges, and (3) applying relatedness measures on a node, edge, and sub graph basis. Results from tests using a ground-truthed subset of a large dataset of law enforcement investigator emails illustrate the benefits of these approaches.


title: Blind Deconvolution Using Alternating Maximum a Posteriori Estimation with Heavy-Tailed Priors

text: Single image blind deconvolution aims to estimate the unknown blur from a single observed blurred image and recover the original sharp image. Such task is severely ill-posed and typical approaches involve some heuristic or other steps without clear mathematical explanation to arrive at an acceptable solution. We show that a straightforward maximum a posteriory estimation combined with very sparse priors and an efficient numerical method can produce results, which compete with much more complicated state-of-the-art methods.


title: A 3D object detection and pose estimation pipeline using RGB-D images

text: 3D object detection and pose estimation has been studied extensively in recent decades for its potential applications in robotics. However, there still remains challenges when we aim at detecting multiple objects while retaining low false positive rate in cluttered environments. This paper proposes a robust 3D object detection and pose estimation pipeline based on RGB-D images, which can detect multiple objects simultaneously while reducing false positives. Detection begins with template matching and yields a set of template matches. A clustering algorithm then groups templates of similar spatial location and produces multiple-object hypotheses. A scoring function evaluates the hypotheses using their associated templates and non-maximum suppression is adopted to remove duplicate results based on the scores. Finally, a combination of point cloud processing algorithms are used to compute objects' 3D poses. Existing object hypotheses are verified by computing the overlap between model and scene points. Experiments demonstrate that our approach provides competitive results comparable to the state-of-the-arts and can be applied to robot random bin-picking.


title: Mis problems and failures: a socio-technical perspective

text: Many of the problems arjd failures of Management Information Systems (MIS) and Management Science/Operations Research (MS/OR) projects have been attributed to organizational behavioral problems. The millions of dollars organizations spend on MIS and MS/OR development are of little benefit because systems continue to fail. Steps can be taken to understand and solve these behavioral probiems. This article argues that in most oases these behavioral problems are the result of inadequate designs. These bad designs are attributed to the way MIS systems designers view organizations, their members, and the function of an MIS within them. I.e., systems designers' frames of reference. These frames of reference cause faulty design choices and failures to perceive better design alternatives. Seven conditions are discussed which reflect current systems designers' points of view. The discussion of these conditions demonstrates the need to refrawe MIS design methodology within the Socio-Technicai Systems (STS) design approach and change systems designers' perspectives. The STS approach is introduced as a realistic view of organizations and a way to change them. This article is the first of two to appear in consecutive issues of the MIS Quarterly. The purpose of this first article is to demonstrate the need for the srs approaj:h. The second will present the basic concepts and principles of the STS methodology and how it can be utilized in the design of an MIS.


title: Dynamic Weighted Majority for Incremental Learning of Imbalanced Data Streams with Concept Drift

text: Concept drifts occurring in data streams will jeopardize the accuracy and stability of the online learning process. If the data stream is imbalanced, it will be even more challenging to detect and cure the concept drift. In the literature, these two problems have been intensively addressed separately, but have yet to be well studied when they occur together. In this paper, we propose a chunk-based incremental learning method called Dynamic Weighted Majority for Imbalance Learning (DWMIL) to deal with the data streams with concept drift and class imbalance problem. DWMIL utilizes an ensemble framework by dynamically weighting the base classifiers according to their performance on the current data chunk. Compared with the existing methods, its merits are four-fold: (1) it can keep stable for non-drifted streams and quickly adapt to the new concept; (2) it is totally incremental, i.e. no previous data needs to be stored; (3) it keeps a limited number of classifiers to ensure high efficiency; and (4) it is simple and needs only one thresholding parameter. Experiments on both synthetic and real data sets with concept drift show that DWMIL performs better than the state-of-the-art competitors, with less computational cost.


title: Android smartphone vulnerabilities: A survey

text: Round the globe mobile devices like Smartphone, PDAs & tablets are playing an essential role in every person day to day lives. Various operating systems such as android, ios, blackberry etc provides a platform for smart devices. Google's Android is a one of the most popular and user friendly open source software platform for mobile devices. Along with its convenience people are likely to download and install malicious applications developed to misguide the user which will create a security gap to penetrate for the attacker. Hackers are inclined to discover and exploit the new vulnerabilities which will bring forth with the latest version of Android. In this paper, we concentrate on examining and understanding the vulnerabilities exist in android operating system. We will also suggest a metadata model which gives the information of all the related terms required for vulnerability assessment. Here, analyzing data is extracted from Open Source Vulnerability Database (OSVDB) and National Vulnerability Database (NVD).


title: Kernel Region ( a ) Top Level ( b ) Compute Unit Level ( c ) Processing Element Level

text: OpenCL FPGA has recently gained great popularity with emerging needs for workload acceleration such as Convolutional Neural Network (CNN), which is the most popular deep learning architecture in the domain of computer vision. While OpenCL enhances the code portability and programmability of FPGA, it comes at the expense of performance. The key challenge is to optimize the OpenCL kernels to efficiently utilize the flexible hardware resources in FPGA. Simply optimizing the OpenCL kernel code through various compiler options turns out insufficient to achieve desirable performance for both compute-intensive and data-intensive workloads such as convolutional neural networks . In this paper, we first propose an analytical performance model and apply it to perform an in-depth analysis on the resource requirement of CNN classifier kernels and available resources on modern FPGAs. We identify that the key performance bottleneck is the onchip memory bandwidth. We propose a new kernel design to effectively address such bandwidth limitation and to provide an optimal balance between computation, on-chip, and off-chip memory access. As a case study, we further apply these techniques to design a CNN accelerator based on the VGG model. Finally, we evaluate the performance of our CNN accelerator using an Altera Arria 10 GX1150 board. We achieve 866 Gop/s floating point performance at 370MHz working frequency and 1.79 Top/s 16-bit fixed-point performance at 385MHz. To the best of our knowledge, our implementation achieves the best power efficiency and performance density compared to existing work.


title: Valgrind: a framework for heavyweight dynamic binary instrumentation

text:
Dynamic binary instrumentation (DBI) frameworks make it easy to build dynamic binary analysis (DBA) tools such as checkers and profilers. Much of the focus on DBI frameworks has been on performance; little attention has been paid to their capabilities. As a result, we believe the potential of DBI has not been fully exploited.
 In this paper we describe Valgrind, a DBI framework designed for building heavyweight DBA tools. We focus on its unique support for shadow values-a powerful but previously little-studied and difficult-to-implement DBA technique, which requires a tool to shadow every register and memory value with another value that describes it. This support accounts for several crucial design features that distinguish Valgrind from other DBI frameworks. Because of these features, lightweight tools built with Valgrind run comparatively slowly, but Valgrind can be used to build more interesting, heavyweight tools that are difficult or impossible to build with other DBI frameworks such as Pin and DynamoRIO.


title: Estimation of land surface temperature over Delhi using Landsat-7 ETM +

text: Land surface temperature (LST) is important factor in global change studies, in estimating radiation budgets in heat balance studies and as a control for climate models. The knowledge of surface temperature is important to a range of issues and themes in earth sciences central to urban climatology, global environmental change, and human-environment interactions. In the study an attempt has been made to estimate surface temperature over Delhi area using Landsat-7 ETM+ satellite data. The variability of these retrieved LSTs has been investigated with respect to different land use / land cover (LU/LC) types determined from the Landsat visible and NIR channels. The classification uncertainties over different land use land cover were assessed and it has been observed that the classification uncertainties were found to be lowest using Minimum Noise Fraction (MNF) components. The emissivity per pixel is retrieved directly from satellite data and has been estimated as narrow band emissivity at the satellite sensor channel in order to have least error in the surface temperature estimation. Strong correlation is observed between surface temperature with Normalized Difference Vegetation Index (NDVI) over different LU/LC classes and the relationship is moderate with fractional vegetation cover (FVC). A regression relation between these parameters has also been estimated indicating that surface temperatures can be predicted if NDVI values are known. The results suggest that the methodology is feasible to estimate NDVI, surface emissivity and surface temperature with reasonable accuracy over heterogeneous urban areas.


title: A Novel Fuzzy C-Means Clustering Algorithm for Image Thresholding

text: Image thresholding has played an important role in image segmentation. In this paper, we present a novel spatially weighted fuzzy c-means (SWFCM) clustering algorithm for image thresholding. The algorithm is formulated by incorporating the spatial neighborhood information into the standard FCM clustering algorithm. Two improved implementations of the k-nearest neighbor (k-NN) algorithm are introduced for calculating the weight in the SWFCM algorithm so as to improve the performance of image thresholding. To speed up the FCM algorithm, the iteration is carried out with the gray level histogram of image instead of the conventional whole data of image. The performance of the algorithm is compared with those of an existed fuzzy thresholding algorithm and widely applied between variance and entropy methods. Experimental results with synthetic and real images indicate the proposed approach is effective and efficient. In addition, due to the neighborhood model, the proposed method is more tolerant to noise.


title: MIMO-SAR: Opportunities and Pitfalls

text: This paper reviews advanced radar architectures that employ multiple transmit and multiple receive antennas to improve the performance of future synthetic aperture radar (SAR) systems. These advanced architectures have been dubbed multiple-input multiple-output SAR (MIMO-SAR) in analogy to MIMO communication systems. Considerable confusion arose, however, with regard to the selection of suitable waveforms for the simultaneous transmission via multiple channels. It is shown that the mere use of orthogonal waveforms is insufficient for the desired performance improvement in view of most SAR applications. As a solution to this fundamental MIMO-SAR challenge, a new class of short-term shift-orthogonal waveforms is introduced. The short-term shift orthogonality avoids mutual interferences from the radar echoes of closely spaced scatterers, while interferences from more distant scatterers are suppressed by digital beamforming on receive in elevation. Further insights can be gained by considering the data acquisition of a side-looking imaging radar in a 3-D information cube. It becomes evident that the suggested waveforms fill different subspaces that can be individually accessed by a multichannel receiver. For completeness, the new class of short-term shift-orthogonal waveforms is also compared to a recently proposed pair of orthogonal frequency-division multiplexing waveforms. It is shown that both sets of waveforms require essentially the same principle of range time to elevation angle conversion via a multichannel receiver in order to be applicable for MIMO-SAR imaging without interference.


title: Tools to support systematic reviews in software engineering: a cross-domain survey using semi-structured interviews

text: Background: A number of software tools are being developed to support systematic reviewers within the software engineering domain. However, at present, we are not sure which aspects of the review process can most usefully be supported by such tools or what characteristics of the tools are most important to reviewers. Aim: The aim of the study is to explore the scope and practice of tool support for systematic reviewers in other disciplines. Method: Researchers with experience of performing systematic reviews in Healthcare and the Social Sciences were surveyed. Qualitative data was collected through semi-structured interviews and data analysis followed an inductive approach. Results: 13 interviews were carried out. 21 software tools categorised into one of seven types were identified. Reference managers were the most commonly mentioned tools. Features considered particularly important by participants were support for multiple users, support for data extraction and support for tool maintenance. The features and importance levels identified by participants were compared with those proposed for tools to support systematic reviews in software engineering. Conclusions: Many problems faced by systematic reviewers in other disciplines are similar to those faced in software engineering. There is general consensus across domains that improved tools are needed.


title: DeepEmo: Learning and Enriching Pattern-Based Emotion Representations

text: We propose a graph-based mechanism to extract rich-emotion bearing patterns, which fosters a deeper analysis of online emotional expressions, from a corpus. The patterns are then enriched with word embeddings and evaluated through several emotion recognition tasks. Moreover, we conduct analysis on the emotion-oriented patterns to demonstrate its applicability and to explore its properties. Our experimental results demonstrate that the proposed techniques outperform most stateof-the-art emotion recognition techniques.


title: A low power and high speed CMOS Voltage-Controlled Ring Oscillator

text: In this paper a new Voltage Controlled Ring Oscillator is presented. The proposed VCO uses partial positive feedback in its delay cell, allowing the circuit to operate with single two stages, achieving high speed with reduced power consumption. The new VCO has a wide range of operation frequencies (0.2 at 2.1 GHz), good linearity between the output frequency and the control voltage, phase noise of -90 dBc/Hz at 100 kHz offset and it only consumes 7.01 mW in its central frequency of 1.2 GHz using a 3.3 V power supply. The circuit was fabricated in a 0.35 /spl mu/m CMOS-AMS process and occupies an area of 67.5/spl times/77.5 /spl mu/m/sup 2/.


title: Low correlation MIMO antenna for LTE 700MHz band

text: A key feature of upcoming 4G wireless communication standards is multiple-input-multiple-output (MIMO) technology. To make the best use of MIMO, the antenna correlation between adjacent antennas must be low (< 0.5). In this context, we propose a new correlation reduction technique suitable for closely spaced antennas (distance, d < λ/40). This technique reduces mutual coupling between antennas and concurrently uncorrelates the antennas' radiation characteristic by inducing negative group delay in the target frequency band. The validity of the technique is demonstrated with a USB dongle MIMO antenna designed for LTE 700MHz band. Measurement results show that the antenna correlation is reduced more than 37% using the proposed technique.


title: Design of fuzzy controller for induction heating using DSP

text: This paper presents the development of fuzzy controller for induction heating temperature control. Induction heating coil can be controlled by single phase sinusoidal pulse width modulation (SPWM) digital signal processor (DSP)-based inverter. DSP is used to store the required commands for generating the necessary waveforms to control the frequency of the inverter through proper design of switching pulses. The SPWM technique produces pure sinusoidal wave of voltage and current output. Fuzzy controller software has been developed in the C++ language. VisSim software is used to experiment with a DSP. The inverter is current-fed and SPWM is used for DC to AC conversion. SPWM normally cuts off high-order harmonic by low-pass filter to produce sine wave power. The LC low-pass filter with cutoff frequency at 300Hz is used. Since low frequency produces ripple to cause transformer and inductance magnetic saturation, the range of frequency is between 100 Hz and 300 Hz. The results of the experiment are presented and show the effectiveness and superiority of this temperature control system.


title: Trust in school : a pathway to inhibit teacher burnout ?

text: Purpose – The purpose of this paper is to consider trust as an important relational source in schools by exploring whether trust lowers teacher burnout. The authors examine how trust relationships with different school parties such as the principal relate to distinct dimensions of teacher burnout. The authors further analyze whether school-level trust additionally influences burnout. In doing this, the authors account for other teacher and school characteristics. Design/methodology/approach – The authors use quantitative data gathered during the 2008-2009 school year from 673 teachers across 58 elementary schools in Flanders (i.e. the northern Dutch-speaking region of Belgium). Because teacher and school characteristics are simultaneously related to burnout, multilevel modeling is applied. Findings – Trust can act as a buffer against teacher burnout. Teachers’ trust in students demonstrates the strongest association with burnout compared to trust in principals or colleagues. Exploring relationships of trust in distinct school parties with different burnout dimensions yield interesting additional insights such as the specific importance of teacher-principal trust for teachers’ emotional exhaustion. Burnout is further an individual teacher matter to which school-level factors are mainly unrelated. Research limitations/implications – Principals fulfill an important role in inhibiting emotional exhaustion among teachers. They are advised to create a school atmosphere that is conducive for different kinds of trust relationships to develop. Actions to strengthen trust and inhibit teacher burnout are necessary, although further qualitative and longitudinal research is desirable. Originality/value – This paper offers a unique contribution by examining trust in different school parties as a relational buffer against teacher burnout. It indicates that principals can affect teacher burnout and prevent emotional exhaustion by nurturing trusting relationships in school.


title: New Trends in Robotics for Agriculture: Integration and Assessment of a Real Fleet of Robots

text: Computer-based sensors and actuators such as global positioning systems, machine vision, and laser-based sensors have progressively been incorporated into mobile robots with the aim of configuring autonomous systems capable of shifting operator activities in agricultural tasks. However, the incorporation of many electronic systems into a robot impairs its reliability and increases its cost. Hardware minimization, as well as software minimization and ease of integration, is essential to obtain feasible robotic systems. A step forward in the application of automatic equipment in agriculture is the use of fleets of robots, in which a number of specialized robots collaborate to accomplish one or several agricultural tasks. This paper strives to develop a system architecture for both individual robots and robots working in fleets to improve reliability, decrease complexity and costs, and permit the integration of software from different developers. Several solutions are studied, from a fully distributed to a whole integrated architecture in which a central computer runs all processes. This work also studies diverse topologies for controlling fleets of robots and advances other prospective topologies. The architecture presented in this paper is being successfully applied in the RHEA fleet, which comprises three ground mobile units based on a commercial tractor chassis.


title: Contactless Sleep Apnea Detection on Smartphones

text:
We present a contactless solution for detecting sleep apnea events on smartphones. To achieve this, we introduce a novel system that monitors the minute chest and abdomen movements caused by breathing on smartphones. Our system works with the phone away from the subject and can simultaneously identify and track the fine-grained breathing movements from multiple subjects. We do this by transforming the phone into an active sonar system that emits frequency-modulated sound signals and listens to their reflections; our design monitors the minute changes to these reflections to extract the chest movements. Results from a home bedroom environment shows that our design operates efficiently at distances of up to a meter and works even with the subject under a blanket.
 Building on the above system, we develop algorithms that identify various sleep apnea events including obstructive apnea, central apnea, and hypopnea from the sonar reflections. We deploy our system at the UW Medicine Sleep Center at Harborview and perform a clinical study with 37 patients for a total of 296 hours. Our study demonstrates that the number of respiratory events identified by our system is highly correlated with the ground truth and has a correlation coefficient of 0.9957, 0.9860, and 0.9533 for central apnea, obstructive apnea and hypopnea respectively. Furthermore, the average error in computing of rate of apnea and hypopnea events is as low as 1.9 events/hr.


title: Doubly Stochastic Variational Inference for Deep Gaussian Processes

text: Gaussian processes (GPs) are a good choice for function approximation as they are flexible, robust to overfitting, and provide well-calibrated predictive uncertainty. Deep Gaussian processes (DGPs) are multi-layer generalizations of GPs, but inference in these models has proved challenging. Existing approaches to inference in DGP models assume approximate posteriors that force independence between the layers, and do not work well in practice. We present a doubly stochastic variational inference algorithm that does not force independence between layers. With our method of inference we demonstrate that a DGP model can be used effectively on data ranging in size from hundreds to a billion points. We provide strong empirical evidence that our inference scheme for DGPs works well in practice in both classification and regression.


title: An experimental investigation of model-based parameter optimisation: SPO and beyond

text: This work experimentally investigates model-based approaches for optimising the performance of parameterised randomised algorithms. We restrict our attention to procedures based on Gaussian process models, the most widely-studied family of models for this problem. We evaluated two approaches from the literature, and found that sequential parameter optimisation (SPO) [4] offered the most robust performance. We then investigated key design decisions within the SPO paradigm, characterising the performance consequences of each. Based on these findings, we propose a new version of SPO, dubbed SPO+, which extends SPO with a novel intensification procedure and log-transformed response values. Finally, in a domain for which performance results for other (model-free) parameter optimisation approaches are available, we demonstrate that SPO+ achieves state-of-the-art performance.


title: Neuroevolutionary reinforcement learning for generalized control of simulated helicopters

text: This article presents an extended case study in the application of neuroevolution to generalized simulated helicopter hovering, an important challenge problem for reinforcement learning. While neuroevolution is well suited to coping with the domain's complex transition dynamics and high-dimensional state and action spaces, the need to explore efficiently and learn on-line poses unusual challenges. We propose and evaluate several methods for three increasingly challenging variations of the task, including the method that won first place in the 2008 Reinforcement Learning Competition. The results demonstrate that (1) neuroevolution can be effective for complex on-line reinforcement learning tasks such as generalized helicopter hovering, (2) neuroevolution excels at finding effective helicopter hovering policies but not at learning helicopter models, (3) due to the difficulty of learning reliable models, model-based approaches to helicopter hovering are feasible only when domain expertise is available to aid the design of a suitable model representation and (4) recent advances in efficient resampling can enable neuroevolution to tackle more aggressively generalized reinforcement learning tasks.


title: Automatic Gait Optimization with Gaussian Process Regression

text: Gait optimization is a basic yet challenging problem for both quadrupedal and bipedal robots. Although techniques for automating the process exist, most involve local function optimization procedures that suffer from three key drawbacks. Local optimization techniques are naturally plagued by local optima, make no use of the expensive gait evaluations once a local step is taken, and do not explicitly model noise in gait evaluation. These drawbacks increase the need for a large number of gait evaluations, making optimization slow, data inefficient, and manually intensive. We present a Bayesian approach based on Gaussian process regression that addresses all three drawbacks. It uses a global search strategy based on a posterior model inferred from all of the individual noisy evaluations. We demonstrate the technique on a quadruped robot, using it to optimize two different criteria: speed and smoothness. We show in both cases our technique requires dramatically fewer gait evaluations than state-of-the-art local gradient approaches.


title: Efficient Global Optimization of Expensive Black-Box Functions

text: In many engineering optimization problems, the number of function evaluations is severely limited by time or cost. These problems pose a special challenge to the field of global optimization, since existing methods often require more function evaluations than can be comfortably afforded. One way to address this challenge is to fit response surfaces to data collected by evaluating the objective and constraint functions at a few points. These surfaces can then be used for visualization, tradeoff analysis, and optimization. In this paper, we introduce the reader to a response surface methodology that is especially good at modeling the nonlinear, multimodal functions that often occur in engineering. We then show how these approximating functions can be used to construct an efficient global optimization algorithm with a credible stopping rule. The key to using response surfaces for global optimization lies in balancing the need to exploit the approximating surface (by sampling where it is minimized) with the need to improve the approximation (by sampling where prediction error may be high). Striking this balance requires solving certain auxiliary problems which have previously been considered intractable, but we show how these computational obstacles can be overcome.


title: Inter-satellite links for cubesats

text: Realizing inter-satellite links is a must for ensuring the success of cubesat swarm missions. Nevertheless, it has hardly been considered until now. The communication systems for cubesats have to deal with a few peculiar demands regarding consumed power, geometry and throughput. Depending on the type of application, required data rates can go up to tens of megabits per second, while power consumption and physical size are limited by the platform. The proposed communication scheme will combine power-efficient modulation and channel coding with multiple access and spread spectrum techniques, enabling the deployment of multiple satellites. Apart from this, the antenna system has to be designed such that links can be established and maintained independent of the satellites' orientation. An electrically steerable radiation pattern is achieved by placing antennas on each face of the cube. Conformal beamforming provides the system with 5 dBi gain for any desired direction of transmission, eliminating the need for attitude control. Furthermore, using planar antennas reduces the complexity of the mechanical part as they require no deployment.


title: Transparent, conductive graphene electrodes for dye-sensitized solar cells.

text: Transparent, conductive, and ultrathin graphene films, as an alternative to the ubiquitously employed metal oxides window electrodes for solid-state dye-sensitized solar cells, are demonstrated. These graphene films are fabricated from exfoliated graphite oxide, followed by thermal reduction. The obtained films exhibit a high conductivity of 550 S/cm and a transparency of more than 70% over 1000-3000 nm. Furthermore, they show high chemical and thermal stabilities as well as an ultrasmooth surface with tunable wettability.


title: BUILDING GENERALIZABLE AGENTS

text: Towards bridging the gap between machine and human intelligence, it is of utmost importance to introduce environments that are visually realistic and rich in content. In such environments, one can evaluate and improve a crucial property of practical intelligent systems, namely generalization. In this work, we build House3D, a rich, extensible and efficient environment that contains 45,622 human-designed 3D scenes of houses, ranging from single-room studios to multistoreyed houses, equipped with a diverse set of fully labeled 3D objects, textures and scene layouts, based on the SUNCG dataset (Song et al., 2017). With an emphasis on semantic-level generalization, we study the task of concept-driven navigation, RoomNav, using a subset of houses in House3D. In RoomNav, an agent navigates towards a target specified by a semantic concept. To succeed, the agent learns to comprehend the scene it lives in by developing perception, understand the concept by mapping it to the correct semantics, and navigate to the target by obeying the underlying physical rules. We train RL agents with both continuous and discrete action spaces and show their ability to generalize in new unseen environments. In particular, we observe that (1) training is substantially harder on large house sets but results in better generalization, (2) using semantic signals (e.g. segmentation mask) boosts the generalization performance, and (3) gated networks on semantic input signal lead to improved training performance and generalization. We hope House3D1, including the analysis of the RoomNav task, serves as a building block towards designing practical intelligent systems and we wish it to be broadly adopted by the community.


title: Inferring the meaning of chord sequences via lyrics

text: This paper discusses how meanings associated with chord sequences can be inferred from word associations based on lyrics. The approach works by analyzing in-line chord annotations of lyrics to maintain co-occurrence statistics for chords and lyrics. This is analogous to the way parallel corpora are analyzed in order to infer translation lexicons. The result can benefit musical discovery systems by modeling how the chord structure complements the lyrics.


title: Handwritten-Digit Recognition by Hybrid Convolutional Neural Network based on HfO2 Memristive Spiking-Neuron

text: Although there is a huge progress in complementary-metal-oxide-semiconductor (CMOS) technology, construction of an artificial neural network using CMOS technology to realize the functionality comparable with that of human cerebral cortex containing 1010–1011 neurons is still of great challenge. Recently, phase change memristor neuron has been proposed to realize a human-brain level neural network operating at a high speed while consuming a small amount of power and having a high integration density. Although memristor neuron can be scaled down to nanometer, integration of 1010–1011 neurons still faces many problems in circuit complexity, chip area, power consumption, etc. In this work, we propose a CMOS compatible HfO2 memristor neuron that can be well integrated with silicon circuits. A hybrid Convolutional Neural Network (CNN) based on the HfO2 memristor neuron is proposed and constructed. In the hybrid CNN, one memristive neuron can behave as multiple physical neurons based on the Time Division Multiplexing Access (TDMA) technique. Handwritten digit recognition is demonstrated in the hybrid CNN with a memristive neuron acting as 784 physical neurons. This work paves the way towards substantially shrinking the amount of neurons required in hardware and realization of more complex or even human cerebral cortex level memristive neural networks.