title: Boom analytics: exploring data-centric, declarative programming for the cloud

abstract: Building and debugging distributed software remains extremely difficult. We conjecture that by adopting a data-centric approach to system design and by employing declarative programming languages, a broad range of distributed software can be recast naturally in a data-parallel programming model. Our hope is that this model can significantly raise the level of abstraction for programmers, improving code simplicity, speed of development, ease of software evolution, and program correctness. This paper presents our experience with an initial large-scale experiment in this direction. First, we used the Overlog language to implement a "Big Data" analytics stack that is API-compatible with Hadoop and HDFS and provides comparable performance. Second, we extended the system with complex distributed features not yet available in Hadoop, including high availability, scalability, and unique monitoring and debugging facilities. We present both quantitative and anecdotal results from our experience, providing some concrete evidence that both data-centric design and declarative languages can substantially simplify distributed systems programming.


title: Integrating software development security activities with agile methodologies

abstract: Because of several vulnerabilities in software products and high amount of damage caused by them, software developers are enforced to produce more secure systems. Software grows up through its life cycle, so software development methodologies should pay special attention to security aspects of the product. This paper focuses on agile methodologies in order to equip them with security activities. We can restrain reduction of agile nature of organization's current process by means of agility measurement and applying an efficient activity integration algorithm with a tunable parameter named agility reduction tolerance (ART). Using this approach, method engineer of the project can enhance his agile software development process with security features to increase product's trustworthiness.


RASR – The RWTH Aachen University Open Source Speech Recognition Toolkit


title: In-line pseudoelliptic TE01d mode dielectric resonator filters

abstract: A new configuration for dielectric resonator filters with in-line structure and pseudoelliptic response is presented in this paper. The proposed concept is realized through the use of TE 01δ single-mode resonators with different orientations cascaded along an evanescent mode waveguide. By exploiting a pair of orthogonal evanescent modes that can alternatively by-pass or excite the resonators, cross-coupling between nonadjacent pucks can be established and properly controlled. Pseudoelliptic filters can indeed be designed without using cumbersome cross-coupled architectures or reduced spurious performance dual-mode resonators. The experimental result of a 6th order filter with two transmission zeros in the lower stopband validates the proposed approach.


title: LOCALIZATION OF A MOBILE ROBOT BASED IN ODOMETRY AND NATURAL LANDMARKS USING EXTENDED KALMAN FILTER

abstract: SANTANA, Andre M.; SOUZA, Anderson A. S.; BRITTO, Ricardo S.; ALSINA, Pablo J.; MEDEIROS, Adelardo A. D. Localization of a mobile robot based on odometry and natural landmarks using extended Kalman Filter. In: INTERNATIONAL CONFERENCE ON INFORMATICS IN CONTROL, AUTOMATION AND ROBOTICS, 5., 2008, Funchal, Portugal. Proceedings... Funchal, Portugal: ICINCO, 2008.


title: Low-Cost Wideband and High-Gain Slotted Cavity Antenna Using High-Order Modes for Millimeter-Wave Application

abstract: A novel single-fed low-cost wideband and high-gain slotted cavity antenna based on substrate integrated waveguide (SIW) technology using high-order cavity modes is demonstrated in this paper. High-order resonant modes ( ${\text T}{{\text E}_{{130}}}$ , ${\text T}{{\text E}_{{310}}}$ , ${\text T}{{\text E}_{{330}}}$ ) inside the cavity are simply excited by a coaxial probe which is located at the center of the antenna. Energy is coupled out of the cavity by a $3 \times 3$ slot array etched on the top surface of the cavity. Two antennas with different polarizations are designed and tested. Measured results show that the linearly polarized prototype achieves an impedance bandwidth $\left(\vert{{\text S}_{{\text 11}}}\vert of $> {26}\% $ (28 to 36.6 GHz), and a 1-dB gain bandwidth of 14.1% (30.3 to 34.9 GHz). In addition, a measured maximum gain of 13.8 dBi and radiation efficiency of 92% are obtained. To generate circularly polarized radiation, a rotated dipole array is placed in front of the proposed linearly polarized antenna. Measured results show that the circularly polarized antenna exhibits a common bandwidth (10-dB return loss bandwidth, 3-dB axial ratio bandwidth, and 1-dB gain bandwidth) of 11%.


Functional considerations in the fitting of above-knee prostheses.


title: Ant colony based constrained workflow scheduling for heterogeneous computing systems

abstract: The problem of constrained workflow scheduling on heterogeneous computing systems has been of major interest in the recent years. The user requirements are described by defining constraints on the workflow makespan and/or its execution cost. The uncertainty in the activity execution path and the dynamicity in the resource workload may cause some run-time changes of the makespan or cost. To prohibit run-time constraint violation, the system needs robust schedules. In this paper, probability of violation (POV) of constraints is proposed as a criterion for the schedule robustness. An ant colony system is then used to minimize an aggregation of violation of constraints and the POV. Simulation results on real world workflows show the effectiveness of the proposed method in finding feasible schedules. The results also indicate that the proposed method decreases the POV, as well as the expected penalty at run-time.


PEDESTRIAN AND NON-MOTOR VEHICLE DETECTION SYSTEMS WITH LIDAR FOR AUTONOMOUS VEHICLES


title: Paper-Based Inkjet-Printed Tri-Band U-Slot Monopole Antenna for Wireless Applications

abstract: Realization of a U-slot tri-band monopole antenna on a low-cost paper substrate using inkjet-printed technology is presented for the first time. The U-shaped slot is optimized to enhance the bandwidth and to achieve tri-band operation of 1.57, 3.2, and 5 GHz with measured impedance bandwidths of 3.21%, 28.1%, and 36%, respectively. The antenna is fabricated through a metallic nanoparticle ink on a standard commercial paper. Thus, the antenna can be used to cover the GPS, WiMAX, HiperLAN/2, and WLAN. The antenna has a compact size of 12 × 37.3 × 0.44 mm3, leaving enough space for the driving electronics on the paper substrate. The impedance bandwidth, current distributions, radiation patterns, gain, and efficiency of the antenna have been studied through computer simulations and measurements.


title: Your Exploit is Mine: Automatic Shellcode Transplant for Remote Exploits

abstract: Developing a remote exploit is not easy. It requires a comprehensive understanding of a vulnerability and delicate techniques to bypass defense mechanisms. As a result, attackers may prefer to reuse an existing exploit and make necessary changes over developing a new exploit from scratch. One such adaptation is the replacement of the original shellcode (i.e., the attacker-injected code that is executed as the final step of the exploit) in the original exploit with a replacement shellcode, resulting in a modified exploit that carries out the actions desired by the attacker as opposed to the original exploit author. We call this a shellcode transplant. Current automated shellcode placement methods are insufficient because they over-constrain the replacement shellcode, and so cannot be used to achieve shellcode transplant. For example, these systems consider the shellcode as an integrated memory chunk and require that the execution path of the modified exploit must be same as the original one. To resolve these issues, we present ShellSwap, a system that uses symbolic tracing, with a combination of shellcode layout remediation and path kneading to achieve shellcode transplant. We evaluated the ShellSwap system on a combination of 20 exploits and 5 pieces of shellcode that are independently developed and different from the original exploit. Among the 100 test cases, our system successfully generated 88% of the exploits.


title: Millibot Trains for Enhanced Mobility

abstract: The objective of this work is to enhance the mobility of small mobile robots by enabling them to link into a train configuration capable of crossing relatively large obstacles. In particular, we are building on Millibots, semiautonomous, tracked mobile sensing/communication platforms at the 5-cm scale previously developed at Carnegie Mellon University. The Millibot Train concept provides couplers that allow the Millibot modules to engage/disengage under computer control and joint actuators that allow lifting of one module by another and control of the whole train shape in two dimensions. A manually configurable train prototype demonstrated the ability to climb standard stairs and vertical steps nearly half the train length. A fully functional module with powered joints has been developed and several have been built and tested. Construction of a set of six modules is well underway and will allow testing of the complete train in the near future. This paper focuses on the development, design, and construction of the electromechanical hardware for the Millibot Train.


title: Can Neural Networks Understand Logical Entailment?

abstract: We introduce a new dataset of logical entailments for the purpose of measuring models' ability to capture and exploit the structure of logical expressions against an entailment prediction task. We use this task to compare a series of architectures which are ubiquitous in the sequence-processing literature, in addition to a new model class—PossibleWorldNets—which computes entailment as a "convolution over possible worlds". Results show that convolutional networks present the wrong inductive bias for this class of problems relative to LSTM RNNs, tree-structured neural networks outperform LSTM RNNs due to their enhanced ability to exploit the syntax of logic, and PossibleWorldNets outperform all benchmarks.


title: The Cost of Digital Advertisement: Comparing User and Advertiser Views

abstract: Digital advertisements are delivered in the form of static images, animations or videos, and their purpose is to deliver a message to desktop or mobile users and promote a product, a service or an idea. Thus, the message’s promoter, or advertiser, pays a monetary cost to buy ad-space in a content provider’s medium (e.g., website) to place their advertisement in the consumer’s display. However, is it only the advertiser who pays for the advertisement delivery? Unlike traditional advertisements in mediums such as newspapers, TVs or radio, in the digital world, the end-users are also paying a cost for the advertisement delivery. Whilst the cost on the advertiser’s side is clearly monetary, on the end-user, it includes both quantifiable costs, such as network requests and transferred bytes, and qualitative costs such as privacy loss to the ad ecosystem. In this study, we aim to increase user awareness regarding the hidden costs of digital advertisement in mobile devices, and compare the user and advertiser views. Specifically, we built OpenDAMP, a transparency tool that passively analyzes users’ web traffic and estimates the costs of advertising for both users and advertisers. Using OpenDAMP, we measure the ad-related costs from both sides in a large, year-long dataset of 1270 real mobile users. Juxtaposing the costs of both sides, we identify a clear imbalance: the advertisers pay several times less to deliver ads to end-users, than the cost paid by the users to download these ads. In addition, the majority of users experience a significant privacy loss to the ad-ecosystem, through the received personalized advertisements and their delivery mechanics.


title: A Spatiotemporal Analysis of the Global Diffusion of ISO 9000 and ISO 14000 Certification

abstract: We study the global diffusion of ISO 9000 and ISO 14000 certification using a network diffusion framework. We start by investigating the presence and nature of contagion effects by defining alternative cross-country networks and testing their relative strength. Second, we study how the rate of diffusion differs between the two standards and between early-and later-adopting countries. Third, we identify which countries had more influence on diffusion than others. Empirically, we build a diffusion model which includes several possible cross-country contagion effects and then use Bayesian methods for estimation and model selection. Using country by year data for 56 countries and nine years, we find that accounting for cross-country influences improves both the fit and the prediction accuracy of our models. However, the specific cross-country contagion mechanism is different across the two standards. Diffusion of ISO 9000 is driven primarily by geography and bilateral trade relations, whereas that of ISO 14000 is driven primarily by geography and cultural similarity. We also find that the diffusion rate of ISO standards is higher for later-adopting countries and for the later ISO 14000 standard. We discuss several implications of our findings for the global diffusion of management standards.


title: Collaboration over time: characterizing and modeling network evolution

abstract: A formal type of scientific and academic collaboration is coauthorship which can be represented by a coauthorship network. Coauthorship networks are among some of the largest social networks and offer us the opportunity to study the mechanisms underlying large-scale real world networks. We construct such a network for the Computer Science field covering research collaborations from 1980 to 2005, based on a large dataset of 451,305 papers authored by 283,174 distinct researchers. By mining this network, we first present a comprehensive study of the network statistical properties for a longitudinal network at the overall network level as well as for the intermediate community level. Major observations are that the database community is the best connected while the AI community is the most assortative, and that the Computer Science field as a whole shows a collaboration pattern more similar to Mathematics than to Biology. Moreover, the small world phenomenon and the scale-free degree distribution accompany the growth of the network. To study the individual collaborations, we propose a novel stochastic model, Stochastic Poisson model with Optimization Tree (S pot )to efficiently predict any increment of collaboration based on the local neighborhood structure. S pot models the non-stationary Poisson process by maximizing the log-likelihood with a tree structure. Empirical results show that S pot outperforms Support Vector Regression by better fitting collaboration records and predicting the rate of collaboration


title: Dual-band circularly polarized antennas for GNSS remote sensing onboard small satellites

abstract: Dual-band planar antennas for Global Navigation Satellite System (GNSS) based remote sensing are presented. The antenna designed can cover both L1 and L2 bands, centred at 1.575 GHz and 1.227 GHz, respectively. They will be used onboard satellites remote sensing satellites as part of an instrument to measure ocean roughness by GNSS reflectometry, and the meteorology of the atmosphere by radio occultation amongst other applications. Two antennas (Zenith and Nadir) are presented focussing on different requirements. The Zenith antenna points towards space to receive direct GNSS signals for navigation and is a single-element patch antenna. The Nadir Antenna points towards the earth to pick up reflected signals, and is a dual-band array antenna with a higher gain. Simulation and Measurement results of both antennas are presented.


title: The Human Gene Mutation Database (HGMD) and its exploitation in the fields of personalized genomics and molecular evolution.

abstract: The Human Gene Mutation Database (HGMD) constitutes a comprehensive core collection of data on germ-line mutations in nuclear genes underlying or associated with human inherited disease (http://www.hgmd.org). Data cataloged include single-base-pair substitutions in coding, regulatory, and splicing-relevant regions, micro-deletions and micro-insertions, indels, and triplet repeat expansions, as well as gross gene deletions, insertions, duplications, and complex rearrangements. Each mutation is entered into HGMD only once, in order to avoid confusion between recurrent and identical-by-descent lesions. By March 2012, the database contained in excess of 123,600 different lesions (HGMD Professional release 2012.1) detected in 4,514 different nuclear genes, with new entries currently accumulating at a rate in excess of 10,000 per annum. ∼6,000 of these entries constitute disease-associated and functional polymorphisms. HGMD also includes cDNA reference sequences for more than 98% of the listed genes.


title: Deep Learning for Semantic Part Segmentation with High-Level Guidance

abstract: In this work we address the task of segmenting an object into its parts, or semantic part segmentation. We start by adapting a state-of-the-art semantic segmentation system to this task, and show that a combination of a fully-convolutional Deep CNN system coupled with Dense CRF labelling provides excellent results for a broad range of object categories. Still, this approach remains agnostic to high-level constraints between object parts. We introduce such prior information by means of the Restricted Boltzmann Machine, adapted to our task and train our model in an discriminative fashion, as a hidden CRF, demonstrating that prior information can yield additional improvements. We also investigate the performance of our approach ``in the wild'', without information concerning the objects' bounding boxes, using an object detector to guide a multi-scale segmentation scheme. We evaluate the performance of our approach on the Penn-Fudan and LFW datasets for the tasks of pedestrian parsing and face labelling respectively. We show superior performance with respect to competitive methods that have been extensively engineered on these benchmarks, as well as realistic qualitative results on part segmentation, even for occluded or deformable objects. We also provide quantitative and extensive qualitative results on three classes from the PASCAL Parts dataset. Finally, we show that our multi-scale segmentation scheme can boost accuracy, recovering segmentations for finer parts.


title: Who, where, when and what: discover spatio-temporal topics for twitter users

abstract: Micro-blogging services, such as Twitter, and location-based social network applications have generated short text messages associated with geographic information, posting time, and user ids. The availability of such data received from users offers a good opportunity to study the user's spatial-temporal behavior and preference. In this paper, we propose a probabilistic model W4 (short for Who+Where+When+What) to exploit such data to discover individual users' mobility behaviors from spatial, temporal and activity aspects. To the best of our knowledge, our work offers the first solution to jointly model individual user's mobility behavior from the three aspects. Our model has a variety of applications, such as user profiling and location prediction; it can be employed to answer questions such as ``Can we infer the location of a user given a tweet posted by the user and the posting time?" Experimental results on two real-world datasets show that the proposed model is effective in discovering users' spatial-temporal topics, and outperforms state-of-the-art baselines significantly for the task of location prediction for tweets.


title: The impact of the online and offline features on the user acceptance of Internet shopping malls

abstract: Abstract Internet shopping mall has the dual nature of Web-based application system and traditional shopping mall. This paper explores online and offline features of Internet shopping malls and their relationships with the acceptance behaviors of customers. The results from a Web survey of 932 users show that the technology acceptance model (TAM) is valid in predicting the acceptance of the Internet shopping malls and that online and offline features have positive effects on the user acceptance. Both online and offline features have greater effects on the usefulness, attitude, and intention to use than either online or offline features separately. This study provides a domain-specific, integrative approach in evaluating the quality and antecedents of user acceptance for Internet shopping malls.


title: Sensory Processing Problems in Children with ADHD, a Systematic Review

abstract: One of the most common psychiatric disorders in children is attention deficit hyperactivity disorder (ADHD). Its course and outcome are heterogeneous. Sensory processing problems impact the nature of response to daily events. ADHD and sensory problems may occur together and interact. No published review article about sensory processing problems in children with ADHD were found. A systematic search, conducted on Pub-Med (up to January 2010), and Google Scholar, yielded 255 abstracts on sensory processing problems in children including 11 studies about sensory problems in children with ADHD. Sensory processing problems in children with ADHD is not a well studied area. Sensory processing problems in children with ADHD are more common than in typically developing children. Findings do not support that ADHD subtypes are distinct disorders with regard to sensory processing problems. However, co-morbidity with oppositional defiant disorder and anxiety are predictors of more severe sensory processing problems in children with ADHD.


title: MPPT design for photovoltaic system using backstepping control with boost converter

abstract: The energy generated using a photovoltaic (PV) are depends mainly on meteorological factors such as temperature and solar radiation. Considering the high cost and the low efficiency of a PV system, it must operate at maximum power point (MPP) which changes with the load variations and weather conditions. This article presents the conception of a nonlinear controller for the DC-DC converter to retrieve a maximum power from photovoltaic systems (PV). This is affected via the control of duty cycle of the converter. The backstepping control is used to design the controller. The incremental conductance algorithm provides the output reference voltage PV to the backstepping controller. An integral Action is added in order to render the controller robust against changes of parameters and perturbations. The proposed controller performance is assessed by numerical simulations under changing atmospheric conditions.


CHILDREN WITH AUTISM GROW UP Use of the DISCO ( Diagnostic Interview for Social and COmmunication disorders ) in population cohorts


title: Compact Design of Triple-Band Circularly Polarized Quadrifilar Helix Antennas

abstract: This letter introduces the compact design of triple-band circularly polarized quadrifilar helix antennas (QHAs) possessing the characteristics of wide beam, high gain at the low elevation, and high stability of the phase center. The available triple-band operation is achieved through the incorporation of a UHF-band QHA and an L/S-band QHA, which are assembled in a “piggyback” fashion. The UHF-band QHA is fed by a compact microstrip feed network, while the L/S-band QHA is fed by a split coax balun. The measured results coincide well with the simulated ones, which verified the effectiveness of the proposed compact design .


title: A polynomially solvable special case of the unbounded knapsack problem

abstract: We consider a special case of the unbounded knapsack problem that is characterized by a set of simple inequalities that relate item weights to item costs. We present an algorithm for this special case with time complexity linear in the number of items.


title: Genones: Generalized Mixture Tying in Continuous Hidden Markov Model-Based Speech Recognizers

abstract: An algorithm is proposed that achieves a good tradeoff between modeling resolution and robustness by using a new, general scheme for tying of mixture components in continuous mixture-density hidden Markov model (HMM)-based speech recognizers. The sets of HMM states that share the same mixture components are determined automatically using agglomerative clustering techniques. Experimental results on ARPA's Wall Street Journal corpus show that this scheme reduces errors by 25% over typical tied-mixture systems. New fast algorithms for computing Gaussian likelihoods-the-most time-consuming aspect of continuous-density HMM systems-are also presented. These new algorithms-significantly reduce the number of Gaussian densities that are evaluated with little or no impact on speech recognition accuracy.


Instrument Title : The Psychological Climate for Sexual Harassment ( PCSH ) Questionnaire Instrument


title: Acute idiopathic gastric dilatation with gastric necrosis in individuals with Prader-Willi syndrome

abstract: Individuals with Prader-Willi syndrome (PWS) have excessive appetite with the ability to consume large quantities of food. Absence of vomiting and a high pain threshold are considered manifestations of the disorder. We present 6 patients with PWS with acute dramatic gastric distention. In 3 young adult women with vomiting and apparent gastroenteritis, clinical course progressed rapidly to massive gastric dilatation with subsequent gastric necrosis. One individual died of overwhelming sepsis and disseminated intravascular coagulation. In 2 children, gastric dilatation resolved spontaneously. Gastrectomy specimens-in 2 cases subtotal and distal, in the other with accompanying partial duodenectomy and pancreatectomy-showed similar changes. All cases demonstrated signs of ischaemic gastroenteritis. All specimens showed diffuse mucosal infarction with multifocal transmural necrosis. Vascular dilatation and small bifrin thrombi were apparent within the infarcted areas. These 6 women with PWS had acute idiopathic gastric dilatation. It is possible that a predisposition to acute gastric dilatation may be related to abnormal gastric homeostasis on a genetic basis. Understanding the mechanisms responsible for this event could increase the understanding of gastrointestinal and appetite regulation in individuals with PWS.


title: The Influences of Emotion on Learning and Memory

abstract: Emotion has a substantial influence on the cognitive processes in humans, including perception, attention, learning, memory, reasoning, and problem solving. Emotion has a particularly strong influence on attention, especially modulating the selectivity of attention as well as motivating action and behavior. This attentional and executive control is intimately linked to learning processes, as intrinsically limited attentional capacities are better focused on relevant information. Emotion also facilitates encoding and helps retrieval of information efficiently. However, the effects of emotion on learning and memory are not always univalent, as studies have reported that emotion either enhances or impairs learning and long-term memory retention, depending on a range of factors. Recent neuroimaging findings have indicated that the amygdala and prefrontal cortex cooperate with the medial temporal lobe in an integrated manner that affords (i) the amygdala modulating memory consolidation; (ii) the prefrontal cortex mediating memory encoding and formation; and (iii) the hippocampus for successful learning and long-term memory retention. We also review the nested hierarchies of circular emotional control and cognitive regulation (bottom-up and top-down influences) within the brain to achieve optimal integration of emotional and cognitive processing. This review highlights a basic evolutionary approach to emotion to understand the effects of emotion on learning and memory and the functional roles played by various brain regions and their mutual interactions in relation to emotional processing. We also summarize the current state of knowledge on the impact of emotion on memory and map implications for educational settings. In addition to elucidating the memory-enhancing effects of emotion, neuroimaging findings extend our understanding of emotional influences on learning and memory processes; this knowledge may be useful for the design of effective educational curricula to provide a conducive learning environment for both traditional “live” learning in classrooms and “virtual” learning through online-based educational technologies.


title: Beyond the One Step Greedy Approach in Reinforcement Learning

abstract: The famous Policy Iteration algorithm alternates between policy improvement and policy evaluation. Implementations of this algorithm with several variants of the latter evaluation stage, e.g, $n$-step and trace-based returns, have been analyzed in previous works. However, the case of multiple-step lookahead policy improvement, despite the recent increase in empirical evidence of its strength, has to our knowledge not been carefully analyzed yet. In this work, we introduce the first such analysis. Namely, we formulate variants of multiple-step policy improvement, derive new algorithms using these definitions and prove their convergence. Moreover, we show that recent prominent Reinforcement Learning algorithms are, in fact, instances of our framework. We thus shed light on their empirical success and give a recipe for deriving new algorithms for future study.


title: Towards a reduced meat diet: Mindset and motivation of young vegetarians, low, medium and high meat-eaters

abstract: Abstract This study provides insight into differences and similarities in the mindset and motivation of four dietary groups (young self-declared vegetarians, low, medium and high meat-eaters) to support the development of strategies for a general transition to a less meat-based diet. The paper highlights the value of the identity concept for our understanding of both vegetarians and meat eaters. The analysis involves a comparison of the four dietary groups focusing on the strength and the profile of their food-related motivation and their reasons for and against frequent meat eating. To check for the generalizability of the results, the analyses were performed in two samples of adults (aged 18–35) in the Netherlands (native Dutch, n = 357, and second generation Chinese Dutch, n = 350). In both samples, the vegetarians had the same level of food-related motivation as the other groups, but a different motivational profile and distinctive, taste- and animal-welfare related reasons to justify their abstinence from eating meat. The low and medium meat-eaters often considered health a reason to eat meat as well as to moderate meat eating, plus they liked to vary their meals. In these aspects they were different from both the vegetarians and the high meat-eaters. The findings are relevant for (non) governmental organizations that aim to influence dietary choices, as well as for businesses that operate in the market of meat substitutes.


title: The absorption of radioactive strontium by plants under field conditions in the United Kingdom

abstract: Abstract The absorption of radioactive strontium by crop plants has been investigated in field experiments on six soils which represent a wide range of arable land in Britain. 89 Sr was applied as a spray, and cultivation procedures were employed to place it at different depths below the soil surface. Three test crops were grown in each experiment. With shallow-rooted crops, for example, a grass-clover pasture mixture, ploughing to the depth of 12 in. caused the absorption of 89 Sr and the ratio of 89 Sr to Ca in plants, to be on the average, less than 30 per cent of that observed when the 89 Sr was left on the surface. Only small effects of placement, however, occurred with deeper rooted species. When established pasture was contaminated the level of 89 Sr in the tissues formed subsequently was up to 5 times higher than in grass sown on contaminated ground. The effects of the Ca status of the soil and of added lime were examined. The 89 Sr content of plants and the ratio of 89 Sr to Ca were highest when the soil was low in Ca (2 milli-equivalents Ca per 100 g extracted with N. ammonium acetate). Relatively small differences, however, occurred when the soils contained more than 7 milli-equivalents Ca. The effect of added lime was variable and differences between experiments cannot be attributed entirely to differences in soil calcium.


title: Tjänstens triad: Från ömsesidig harmoni till dialektisk spänning i tjänstemöten

abstract: Models of service encounters are often fraught by reductionism, describing business relationships as mathematical combinations of dyadic constellations. Metaphors of ideal social relationships (marriages or friendships) are highlighted to stress normative aspects of equal, balanced and long-term business partnerships. However, these approaches are limited in their analytical sensitivity, as they cannot address the complexity of multipart relationships, where meanings, roles and relationships are continuously constructed and reconstructed. In order to understand the ambivalent quality of business interactions, this article analyses the corporate travel market by applying Georg Simmel’s depiction of the triad as a specific social form. Triadic constellations and more complex service networks involve dialectic tensions, simultaneously exhibiting for example loyalty and disloyalty, trust and distrust, empowerment and disempowerment. It is argued that a qualitative methodology is more adequate approach to grasp such dynamic and contextual social realities, because (opposed to a quantitative approach) it is not confined to operate with mutually exclusive analytical categories.


title: Review of Design of Speech Recognition and Text Analytics based Digital Banking Customer Interface and Future Directions of Technology Adoption

abstract: Banking is one of the most significant adopters of cutting-edge information technologies. Since its modern era beginning in the form of paper based accounting maintained in the branch, adoption of computerized system made it possible to centralize the processing in data centre and improve customer experience by making a more available and efficient system. The latest twist in this evolution is adoption of natural language processing and speech recognition in the user interface between the human and the system and use of machine learning and advanced analytics, in general, for backend processing as well. The paper reviews the progress of technology adoption in the field and comments on the maturity level of solutions involving less studied or low-resource languages like Hindi and also other Indian, regional languages. Furthermore, it also provides an analysis from a prototype built by us. The future directions of this area are also highlighted.


title: 3D free-form object recognition in range images using local surface patches

abstract: This paper introduces an integrated local surface descriptor for surface representation and 3D object recognition. A local surface descriptor is characterized by its centroid, its local surface type and a 2D histogram. The 2D histogram shows the frequency of occurrence of shape index values vs. the angles between the normal of reference feature point and that of its neighbors. Instead of calculating local surface descriptors for all the 3D surface points, they are calculated only for feature points that are in areas with large shape variation. In order to speed up the retrieval of surface descriptors and to deal with a large set of objects, the local surface patches of models are indexed into a hash table. Given a set of test local surface patches, votes are cast for models containing similar surface descriptors. Based on potential corresponding local surface patches candidate models are hypothesized. Verification is performed by running the Iterative Closest Point (ICP) algorithm to align models with the test data for the most likely models occurring in a scene. Experimental results with real range data are presented to demonstrate and compare the effectiveness and efficiency of the proposed approach with the spin image and the spherical spin image representations.


title: Fill and Transfer: A Simple Physics-Based Approach for Containability Reasoning

abstract: The visual perception of object affordances has emerged as a useful ingredient for building powerful computer vision and robotic applications [31]. In this paper we introduce a novel approach to reason about liquid containability - the affordance of containing liquid. Our approach analyzes container objects based on two simple physical processes: the Fill and Transfer of liquid. First, it reasons about whether a given 3D object is a liquid container and its best filling direction. Second, it proposes directions to transfer its contained liquid to the outside while avoiding spillage. We compare our simplified model with a common fluid dynamics simulation and demonstrate that our algorithm makes human-like choices about the best directions to fill containers and transfer liquid from them. We apply our approach to reason about the containability of several real-world objects acquired using a consumer-grade depth camera.


Rock-fill Dam Construction Quality Evaluation Based on Combination Model


title: Corporate residence fraud detection

abstract: With the globalisation of the world's economies and ever-evolving financial structures, fraud has become one of the main dissipaters of government wealth and perhaps even a major contributor in the slowing down of economies in general. Although corporate residence fraud is known to be a major factor, data availability and high sensitivity have caused this domain to be largely untouched by academia. The current Belgian government has pledged to tackle this issue at large by using a variety of in-house approaches and cooperations with institutions such as academia, the ultimate goal being a fair and efficient taxation system. This is the first data mining application specifically aimed at finding corporate residence fraud, where we show the predictive value of using both structured and fine-grained invoicing data. We further describe the problems involved in building such a fraud detection system, which are mainly data-related (e.g. data asymmetry, quality, volume, variety and velocity) and deployment-related (e.g. the need for explanations of the predictions made).


title: Sensor modality fusion with CNNs for UGV autonomous driving in indoor environments

abstract: We present a novel end-to-end learning framework to enable ground vehicles to autonomously navigate unknown environments by fusing raw pixels from cameras and depth measurements from a LiDAR. A deep neural network architecture is introduced to effectively perform modality fusion and reliably predict steering commands even in the presence of sensor failures. The proposed network is trained on our own dataset, from LiDAR and a camera mounted on a UGV taken in an indoor corridor environment. Comprehensive experimental evaluation to demonstrate the robustness of our network architecture is performed to show that the proposed deep learning neural network is able to autonomously navigate in the corridor environment. Furthermore, we demonstrate that the fusion of the camera and LiDAR modalities provides further benefits beyond robustness to sensor failures. Specifically, the multimodal fused system shows a potential to navigate around static and dynamic obstacles and to handle changes in environment geometry without being trained for these tasks.


title: Geometric control of particle manipulation in a two-dimensional fluid

abstract: Manipulation of particles suspended in fluids is crucial for many applications, such as precision machining, chemical processes, bio-engineering, and self-feeding of microorganisms. In this paper, we study the problem of particle manipulation by cyclic fluid boundary excitations from a geometric-control viewpoint. We focus on the simplified problem of manipulating a single particle by generating controlled cyclic motion of a circular rigid body in a two-dimensional perfect fluid. We show that the drift in the particle location after one cyclic motion of the body can be interpreted as the geometric phase of a connection induced by the system's hydrodynamics. We then formulate the problem as a control system, and derive a geometric criterion for its nonlinear controllability. Moreover, by exploiting the geometric structure of the system, we explicitly construct a feedback-based gait that results in attraction of the particle towards the rigid body. We argue that our gait is robust and model-independent, and demonstrate it in both perfect fluid and Stokes fluid.


title: An Image Preprocessing Algorithm for Illumination Invariant Face Recognition

abstract: Face recognition algorithms have to deal with significant amounts of illumination variations between gallery and probe images. State-of-the-art commercial face recognition algorithms still struggle with this problem. We propose a new image preprocessing algorithm that compensates for illumination variations in images. From a single brightness image the algorithm first estimates the illumination field and then compensates for it to mostly recover the scene reflectance. Unlike previously proposed approaches for illumination compensation, our algorithm does not require any training steps, knowledge of 3D face models or reflective surface models. We apply the algorithm to face images prior to recognition. We demonstrate large performance improvements with several standard face recognition algorithms across multiple, publicly available face databases.


title: Adaptive Synthetic-Nominal (ADASYN-N) and Adaptive Synthetic-KNN (ADASYN-KNN) for Multiclass Imbalance Learning on Laboratory Test Data

abstract: Annually about 1,500 cases of cervical cancer are found in Indonesia, which made Indonesia as the country with the highest number of cervical cancer cases in the world. Cervical cancer screening and HPV testing are done with a Pap smear test. However, this examination requires a lot of time, costly and highly susceptible bias of the observer during the process of investigation and analysis. To overcome these problems, several studies have modeled the machine learning with a variety of approaches have been made. However, these studies are constrained by the limitation of the data amounts and the imbalanced data that caused by the different ratio of each case. This can lead to errors in the classification of the minority due to the tendency of the classification results that focus on the majority class. This study addressed the handling imbalance data on classification of cases Pap test results using the method of over-sampling. ADASYN-N and ADASYN-KNN algorithms were proposed as a development of ADASYN algorithm to handle datasets with nominal data types. This study included SMOTE-N algorithm to deal with the problem as comparison algorithm. As the results, ADASYN-KNN with the preference “0” gave the highest accuracy, precision, recall, and f-score of 95.38%; 95.583%; 95.383%; and 95.283%. The highest ROC area value was obtained with the ADASYN-KNN with preference “1” of 99.183%.


title: A Review of Localization and Tracking Algorithms in Wireless Sensor Networks

abstract: In this paper, a comprehensive survey of the pioneer as well as the state of-the-art localization and tracking methods in the wireless sensor networks is presented. Localization is mostly applicable for the static sensor nodes, whereas, tracking for the mobile sensor nodes. The localization algorithms are broadly classified as range-based and range-free methods. The estimated range (distance) between an anchor and an unknown node is highly erroneous in an indoor scenario. This limitation can be handled up to a large extent by employing a large number of existing access points (APs) in the range free localization method. Recent works emphasize on the use multi-sensor data like magnetic, inertial, compass, gyroscope, ultrasound, infrared, visual and/or odometer to improve the localization accuracy further. Additionally, tracking method does the future prediction of location based on the past location history. A smooth trajectory is noted even if some of the received measurements are erroneous. Real experimental set-ups such as National Instruments (NI) wireless sensor nodes, Crossbow motes and hand-held devices for carrying out the localization and tracking are also highlighted herein.


title: Operational Models of Infrastructure Resilience

abstract: The authors propose a definition of infrastructure resilience that is tied to the operation (or function) of an infrastructure as a system of interacting components and that can be objectively evaluated using quantitative models. Specifically, for any particular system, the authors use quantitative models of system operation to represent the decisions of an infrastructure operator who guides the behavior of the system as a whole, even in the presence of disruptions. Modeling infrastructure operation in this way makes it possible to systematically evaluate the consequences associated with the loss of infrastructure components, and leads to a precise notion of “operational resilience” that facilitates model verification, validation, and reproducible results. Using a simple example of a notional infrastructure, the authors demonstrate how to use these models for (1) assessing the operational resilience of an infrastructure system, (2) identifying critical vulnerabilities that threaten its continued function, and (3) advising policymakers on investments to improve resilience.


Teaching geologic events to understand geologic time


title: DEVELOPMENT OF A DYNAMIC SIMULATION TOOL FOR THE EXOMARS ROVER

abstract: Future planetary missions, including the 2011 European Space Agency (ESA) ExoMars mission, will require rovers to travel further, faster, and over more demanding terrain than has been encountered to date. To improve overall mobility, advances need to be made in autonomous navigation, power collection, and locomotion. In this paper we focus on the locomotion problem and discuss the development of a planetary rover chassis simulation tool that allows us to study key locomotion test cases such as slope climbing in loose soil. We have also constructed rover wheels and obtained experimental data to validate the wheel-soil interaction module. The main conclusion is that to fully validate such a complex simulation, experimental data from a full rover chassis is required. This is a first step in an on-going effort to validate the simulation with experimental data obtained from a full rover prototype.


title: Design of a high-voltage differential amplifier based on stacked low-voltage standard CMOS with different input stages

abstract: This paper presents the design of a high-voltage differential amplifier using six different pre-input stage circuits to reduce high-voltage input levels to low-voltage signals. The proposed circuits are designed using 65 nm CMOS process technology with a nominal voltage of 2.5 V and a supply voltage of 5 V. The designs are based on stacked low-voltage standard CMOS transistors. The different pre-input stage circuits are compared to each other in terms of their circuit description, drawbacks, advantages and simulation results. The principle of the five designed pre-input stage circuits can be applied to higher voltage range input signals as well.


title: Estimating the size, cost, and types of technical debt

abstract: This study summarizes results of a study of Technical Debt across 745 business applications comprising 365 million lines of code collected from 160 companies in 10 industry segments. These applications were submitted to a static analysis that evaluates quality within and across application layers that may be coded in different languages. The analysis consists of evaluating the application against a repository of over 1200 rules of good architectural and coding practice. A formula for estimating Technical Debt with adjustable parameters is presented. Results are presented for Technical Debt across the entire sample as well as for different programming languages and quality factors.


title: Mitochondria: In Sickness and in Health

abstract: Mitochondria perform diverse yet interconnected functions, producing ATP and many biosynthetic intermediates while also contributing to cellular stress responses such as autophagy and apoptosis. Mitochondria form a dynamic, interconnected network that is intimately integrated with other cellular compartments. In addition, mitochondrial functions extend beyond the boundaries of the cell and influence an organism's physiology by regulating communication between cells and tissues. It is therefore not surprising that mitochondrial dysfunction has emerged as a key factor in a myriad of diseases, including neurodegenerative and metabolic disorders. We provide a current view of how mitochondrial functions impinge on health and disease.


title: Attendance dynamics at work: the antecedents and correlates of presenteeism, absenteeism, and productivity loss.

abstract: Presenteeism is attending work when ill. This study examined the antecedents and correlates of presenteeism, absenteeism, and productivity loss attributed to presenteeism. Predictors included work context, personal characteristics, and work experiences. Business school graduates employed in a variet


title: Political Knowledge, Political Engagement, and Civic Education

abstract: After decades of neglect, civic education is back on the agenda of political science in the United States. Despite huge increases in the formal educational attainment of the US population during the past 50 years, levels of political knowledge have barely budged. Today's college graduates know no more about politics than did high school graduates in 1950. Recent research indicates that levels of political knowledge affect the acceptance of democratic principles, attitudes toward specific issues, and political participation. There is evidence that political participation is in part a positional good and is shaped by relative as well as absolute levels of educational attainment. Contrary to findings from 30 years ago, recent research suggests that traditional classroom-based civic education can significantly raise political knowledge. Service learning—a combination of community-based civic experience and systematic classroom reflection on that experience—is a promising innovation, but program evaluations ha...


title: Modeling wine preferences by data mining from physicochemical properties

abstract: We propose a data mining approach to predict human wine taste preferences that is based on easily available analytical tests at the certification step. A large dataset (when compared to other studies in this domain) is considered, with white and red vinho verde samples (from Portugal). Three regression techniques were applied, under a computationally efficient procedure that performs simultaneous variable and model selection. The support vector machine achieved promising results, outperforming the multiple regression and neural network methods. Such model is useful to support the oenologist wine tasting evaluations and improve wine production. Furthermore, similar techniques can help in target marketing by modeling consumer tastes from niche markets.


title: RFID technology and its applications in Internet of Things (IoT)

abstract: Radio frequency identification system (RFID) is an automatic technology and aids machines or computers to identify objects, record metadata or control individual target through radio waves. Connecting RFID reader to the terminal of Internet, the readers can identify, track and monitor the objects attached with tags globally, automatically, and in real time, if needed. This is the so-called Internet of Things (IoT). RFID is often seen as a prerequisite for the IoT. This paper introduces the technologies of RFID and IoT, discusses the applications and challenges of RFID technology used in IoT.


title: Dynamic programming algorithm optimization for spoken word recognition

abstract: This paper reports on an optimum dynamic progxamming (DP) based time-normalization algorithm for spoken word recognition. First, a general principle of time-normalization is given using time-warping function. Then, two time-normalized distance definitions, called symmetric and asymmetric forms, are derived from the principle. These two forms are compared with each other through theoretical discussions and experimental studies. The symmetric form algorithm superiority is established. A new technique, called slope constraint, is successfully introduced, in which the warping function slope is restricted so as to improve discrimination between words in different categories. The effective slope constraint characteristic is qualitatively analyzed, and the optimum slope constraint condition is determined through experiments. The optimized algorithm is then extensively subjected to experimental comparison with various DP-algorithms, previously applied to spoken word recognition by different research groups. The experiment shows that the present algorithm gives no more than about two-thirds errors, even compared to the best conventional algorithm.


title: On the power of quantum computation

abstract: This paper surveys the use of the ‘hybrid argument’ to prove that quantum corrections are insensitive to small perturbations. This property of quantum computations is used to establish that quantum circuits are robust against inaccuracy in the implementation of its elementary gates. The insensitivity to small perturbations is also used to establish lower–bounds, including showing that relative to an oracle, the class NP requires exponential time on a quantum computer; and that quantum algorithms are polynomially related to deterministic algorithms in the black–box model.


Eye Tracking Based Control System for Natural Human-Computer Interaction


title: Quasi-cyclic LDPC codes: an algebraic construction

abstract: This paper presents two new large classes of QC-LDPC codes, one binary and one non-binary. Codes in these two classes are constructed by array dispersions of row-distance constrained matrices formed based on additive subgroups of finite fields. Experimental results show that codes constructed perform very well over the AWGN channel with iterative decoding based on belief propagation. Codes of a subclass of the class of binary codes have large minimum distances comparable to finite geometry LDPC codes and they offer effective tradeoff between error performance and decoding complexity when decoded with low-complexity reliability-based iterative decoding algorithms such as binary message passing decoding algorithms. Non-binary codes decoded with a Fast-Fourier Transform based sum-product algorithm achieve significantly large coding gains over Reed-Solomon codes of the same lengths and rates decoded with either the hard-decision Berlekamp-Massey algorithm or the algebraic soft-decision Kotter-Vardy algorithm. They have potential to replace Reed-Solomon codes in some communication or storage systems where combinations of random and bursts of errors (or erasures) occur.


title: Worldwide prevalence of physical inactivity and its association with human development index in 76 countries.

abstract: OBJECTIVE ::: To describe the worldwide prevalence of physical inactivity and to analyze its association with development level of each country. ::: ::: ::: METHODS ::: Pooled analysis of three multicenter studies, conducted between 2002 and 2004, which investigated the prevalence of physical inactivity in 76 countries, and comprised almost 300,000 individuals aged 15 years or older. Each study used the International Physical Activity Questionnaire to assess physical inactivity. The level of development of each country was analyzed by the Human Development Index (HDI). ::: ::: ::: RESULTS ::: The crude worldwide prevalence of physical inactivity was 21.4% (95%CI 18.4-24.3), being higher among women (mean=23.7%, 95%CI 20.4-27.1) than men (mean=18.9%, 95%CI 16.2-21.7). It ranged from 2.6% (in Comoros) to 62.3% (in Mauritania), with a median equal to 18%. After weighting for the total population of each country, the worldwide prevalence of physical inactivity was 17.4% (95%CI 15.1-19.7). There was a positive association between HDI and prevalence of physical inactivity (rho=0.27). Less developed countries showed the lowest prevalence of physical inactivity (18.7%), while physical inactivity was more prevalent among the most developed countries (27.8%). ::: ::: ::: CONCLUSIONS ::: One out of five adults around the world is physically inactive. Physical inactivity was more prevalent among wealthier and urban countries, and among women and elderly individuals.


title: An assessment of the potential health impacts of food reformulation

abstract: Background/Objectives: Policies focused on food quality are intended to facilitate healthy choices by consumers, even those who are not fully informed about the links between food consumption and health. The goal of this paper is to evaluate the potential impact of such a food reformulation scenario on health outcomes. Subjects/Methods: We first created reformulation scenarios adapted to the French characteristics of foods. After computing the changes in the nutrient intakes of representative consumers, we determined the health effects of these changes. To do so, we used the DIETRON health assessment model, which calculates the number of deaths avoided by changes in food and nutrient intakes. Results: Depending on the reformulation scenario, the total impact of reformulation varies between 2408 and 3597 avoided deaths per year, which amounts to a 3.7–5.5% reduction in mortality linked to diseases considered in the DIETRON model. The impacts are much higher for men than for women and much higher for low-income categories than for high-income categories. These differences result from the differences in consumption patterns and initial disease prevalence among the various income categories. Conclusions: Even without any changes in consumers’ behaviors, realistic food reformulation may have significant health outcomes.


title: Simulation and hardware design of a flyback converter for solar energy powered DC loads

abstract: In the present day situation as the energy sources are becoming extinct to generate electricity, it has become a necessity to depend on the renewable energy sources to generate the power and convert it according to the type of appliances that are in use either AC or DC loads. This paper gives brief idea on the hardware implementation of the flyback converter.


Time series forecasting used for real-time anomaly detection on websites author :


title: A Multiobjective Optimization Algorithm for LSP Setup in Diffserv and MPLS Networks

abstract: In this paper, a multiobjective optimization QoS-aware path selection algorithm is proposed for LSPs setup in DiffServ-MPLS networks. We simultaneously optimize multiple QoS objectives by a genetic algorithm in conjunction with concept of Pareto dominance. Simulation demonstrates that the proposed algorithm is capable of discovering a set of QoS-aware near optimal paths within in a few iterations. In addition, simulation results also very promising indicates scalability of the algorithm with increasing number of network nodes.


title: The Cancer Stem Cell Niche: How Essential Is the Niche in Regulating Stemness of Tumor Cells?

abstract: Cancer stem cells (CSCs) are tumor cells that have the principal properties of self-renewal, clonal tumor initiation capacity, and clonal long-term repopulation potential. CSCs reside in niches, which are anatomically distinct regions within the tumor microenvironment. These niches maintain the principle properties of CSCs, preserve their phenotypic plasticity, protect them from the immune system, and facilitate their metastatic potential. In this perspective, we focus on the CSC niche and discuss its contribution to tumor initiation and progression. Since CSCs survive many commonly employed cancer therapies, we examine the prospects of targeting the niche components as preferable therapeutic targets.


title: Conflict in Comments: Learning but Lowering Perceptions, with Limits

abstract: Prior work and perception theory suggests that when exposed to discussion related to a particular piece of crowdsourced text content, readers generally perceive that content to be of lower quality than readers who do not see those comments, and that the effect is stronger if the comments display conflict. This paper presents a controlled experiment with over 1000 participants testing to see if this effect carries over to other documents from the same platform, including those with similar content or by the same author. Although we do generally find that perceived quality of the commented-on document is affected, effects do not carry over to the second item and readers are able to judge the second in isolation from the comment on the first. We confirm a prior finding about the negative effects conflict can have on perceived quality but note that readers report learning more from constructive conflict comments.


title: Robust Unit Commitment With Wind Power and Pumped Storage Hydro

abstract: As renewable energy increasingly penetrates into power grid systems, new challenges arise for system operators to keep the systems reliable under uncertain circumstances, while ensuring high utilization of renewable energy. With the naturally intermittent renewable energy, such as wind energy, playing more important roles, system robustness becomes a must. In this paper, we propose a robust optimization approach to accommodate wind output uncertainty, with the objective of providing a robust unit commitment schedule for the thermal generators in the day-ahead market that minimizes the total cost under the worst wind power output scenario. Robust optimization models the randomness using an uncertainty set which includes the worst-case scenario, and protects this scenario under the minimal increment of costs. In our approach, the power system will be more reliable because the worst-case scenario has been considered. In addition, we introduce a variable to control the conservatism of our model, by which we can avoid over-protection. By considering pumped-storage units, the total cost is reduced significantly.


title: Differential evolution versus genetic algorithms in multiobjective optimization

abstract: This paper presents a comprehensive comparison between the performance of state-of-the-art genetic algorithms NSGA-II, SPEA2 and IBEA and their differential evolution based variants DEMONS-II, DEMOSP2 and DEMOIB. Experimental results on 16 numerical multi-objective test problems show that on the majority of problems, the algorithms based on differential evolution perform significantly better than the corresponding genetic algorithms with regard to applied quality indicators. This suggests that in numerical multiobjective optimization, differential evolution explores the decision space more efficiently than genetic algorithms.


title: Training Small Networks for Scene Classification of Remote Sensing Images via Knowledge Distillation

abstract: Scene classification, aiming to identify the land-cover categories of remotely sensed image patches, is now a fundamental task in the remote sensing image analysis field. Deep-learning-model-based algorithms are widely applied in scene classification and achieve remarkable performance, but these high-level methods are computationally expensive and time-consuming. Consequently in this paper, we introduce a knowledge distillation framework, currently a mainstream model compression method, into remote sensing scene classification to improve the performance of smaller and shallower network models. Our knowledge distillation training method makes the high-temperature softmax output of a small and shallow student model match the large and deep teacher model. In our experiments, we evaluate knowledge distillation training method for remote sensing scene classification on four public datasets: AID dataset, UCMerced dataset, NWPU-RESISC dataset, and EuroSAT dataset. Results show that our proposed training method was effective and increased overall accuracy (3% in AID experiments, 5% in UCMerced experiments, 1% in NWPU-RESISC and EuroSAT experiments) for small and shallow models. We further explored the performance of the student model on small and unbalanced datasets. Our findings indicate that knowledge distillation can improve the performance of small network models on datasets with lower spatial resolution images, numerous categories, as well as fewer training samples.


title: Developing Mental Toughness: Lessons from Paralympians

abstract: Abstract Mental toughness (MT) is a key psychological variable related to perseverance and success in performance domains. MT and its development has been explored across a range of contexts and across different sports, but no research to date has examined MT in relation to Paralympic athletes. We sought to understand the lived-experiences of mentally tough Paralympians, aiming to conceptualize MT in a Paralympic context and investigate its development. Ten Paralympic athletes were interviewed using in-depth, semi-structured interviews. The verbatim transcripts of the interviews served as the data for an interpretative phenomenological analysis. Three broad themes and several subthemes emerged in conceptualizing Paralympian MT: characteristics (determination, defiance, pragmatic, optimistic, resilient, self-belief and independence and autonomy), cognitions (normalization, sense of escape, non-acceptance of constraints, influence perception and connection) and cognitive strategies (rational thinking, goal setting, pain management and control). In understanding MT development, two broad themes and several subthemes emerged: formative experiences (challenge, classification, setbacks, critical incident, trauma and recovery, sustained commitment, development of mind-set and perspective during challenge, failure, and acceptance), and support and coping resources (social support and significant others, external shaping, social support, overcoming problems, social comparison and reflective practice). The findings suggest that Paralympians benefited from exposure to highly demanding situations in a supportive environment and this helped develop mentally tough characteristics and behaviors and individualized cognitive coping strategies. Our findings highlight the association between the adaptive development of personal characteristics by overcoming physical and mental setbacks over a sustained time period. Overall, the findings suggest that to develop mentally tough characteristics and behaviors, athletes in general could benefit from exposure to highly demanding situations in a supportive environment.


title: Ultrasound evaluation of the fetal nasal bone: what is the most appropriate first-trimester cut-off point for aneuploidy screening?

abstract: Purpose ::: To establish fetal nasal bone length cut-off points for first trimester aneuploidy screening based on a normal curve of a Brazilian population.


title: An Online RFID Localization in the Manufacturing Shopfloor

abstract: Radio Frequency Identification technology has gained popularity for cheap and easy deployment. In the realm of manufacturing shopfloor, it can be used to track the location of manufacturing objects to achieve better efficiency. The underlying challenge of localization lies in the nonstationary characteristics of manufacturing shopfloor which calls for an adaptive life-long learning strategy in order to arrive at accurate localization results. This paper presents an evolving model based on a novel evolving intelligent system, namely evolving Type-2 Quantum Fuzzy Neural Network (eT2QFNN), which features an interval type-2 quantum fuzzy set with uncertain jump positions. The quantum fuzzy set possesses a graded membership degree which enables better identification of overlaps between classes. The eT2QFNN works fully in the evolving mode, where all parameters including the number of rules are automatically adjusted and generated on the fly. The parameter adjustment scenario relies on decoupled extended Kalman filter method. Our numerical study shows that eT2QFNN is able to deliver comparable accuracy compared to state-of-the-art algorithms.


title: Graph Capsule Convolutional Neural Networks

abstract: Graph Convolutional Neural Networks (GCNNs) are the most recent exciting advancement in deep learning field and their applications are quickly spreading in multi-cross-domains including bioinformatics, chemoinformatics, social networks, natural language processing and computer vision. In this paper, we expose and tackle some of the basic weaknesses of a GCNN model with a capsule idea presented in \cite{hinton2011transforming} and propose our Graph Capsule Network (GCAPS-CNN) model. In addition, we design our GCAPS-CNN model to solve especially graph classification problem which current GCNN models find challenging. Through extensive experiments, we show that our proposed Graph Capsule Network can significantly outperforms both the existing state-of-art deep learning methods and graph kernels on graph classification benchmark datasets.


title: IMU Self-Calibration Using Factorization

abstract: This paper presents a convenient self-calibration method for an inertial measurement unit (IMU) using matrix factorization. Using limited information about applied loads (accelerations or angular rates) available from natural references, the proposed method can linearly solve all the parameters of an IMU in any configuration of its inertial components. Our factorization-based calibration method exploits the bilinear form of an IMU measurement, which is the product of intrinsic calibration parameters and exerted loads. For a redundant IMU, we prove that partial knowledge of the loads, such as magnitude, can produce a linear solution space for a proper decomposition of the measurement. Theoretical analysis on this linear space reveals that a 1-D null space should be considered when load magnitudes are all equal (e.g., gravity loads). Degenerate load distributions are also geometrically identified to avoid singular measurement collection. Since a triad IMU has a lower number of sensor components than a 4-D parameter space, we propose an iterative factorization in which only initial bias is required. A wide convergence region of the bias can provide an automatic setting of the initial bias as the mean of the measurements. Performance of the proposed method is evaluated with respect to various noise levels and constraint types. Self-calibration capability is demonstrated using natural references, which are gravity for accelerometers and image stream from an attached camera for gyroscopes. Calibration results are globally optimal and identical to those of nonlinear optimization.


title: Multiple Solutions of Optimal PMU Placement Using Exponential Binary PSO Algorithm for Smart Grid Applications

abstract: For smart grid execution, one of the most important requirements is fast, precise, and efficient synchronized measurements, which are possible by phasor measurement unit (PMU). To achieve fully observable network with the least number of PMUs, optimal placement of PMU (OPP) is crucial. In trying to achieve OPP, priority may be given at critical buses, generator buses, or buses that are meant for future extension. Also, different applications will have to be kept in view while prioritizing PMU placement. Hence, OPP with multiple solutions (MSs) can offer better flexibility for different placement strategies as it can meet the best solution based on the requirements. To provide MSs, an effective exponential binary particle swarm optimization (EBPSO) algorithm is developed. In this algorithm, a nonlinear inertia-weight-coefficient is used to improve the searching capability. To incorporate previous position of particle, two innovative mathematical equations that can update particle's position are formulated. For quick and reliable convergence, two useful filtration techniques that can facilitate MSs are applied. Single mutation operator is conditionally applied to avoid stagnation. The EBPSO algorithm is so developed that it can provide MSs for various practical contingencies, such as single PMU outage and single line outage for different systems.


title: A High Precision Multi-Cell Battery Voltage Detecting Circuit for Battery Management Systems

abstract: This paper presents a high precision direct multi-cell Battery Voltage Detecting Circuit (BVDC) for Battery Management Systems (BMS) in electric vehicles. BVDC in BMS must be able to accommodate direct voltage input up to tens of volts from series connected batteries, fulfil the precision needs by battery state of charge estimation algorithm. The BVDC circuit is designed with a 0.5μm 1-poly 3-metal high voltage (HV) 60V Bipolar-CMOS-DMOS (BCD) semiconductor process. System optimization design of HV multiplexer and incremental sigma-delta analog-to-digital converter (ADC) in BVDC circuit eliminates the need of amplifier-resistor based level shift, and any static current through HV signal path, which greatly improved the conversion accuracy. Post layout extracted simulation result shows that the typical conversion error is less than 0.3mV@1MHz for series connected batteries without extra calibration.


title: A CMOS voltage reference based on weighted difference of gate-source voltages between PMOS and NMOS transistors for low dropout regulators

abstract: A CMOS voltage reference, which takes advantage of weighted difference of the gate-source voltages between a PMOS and an NMOS transistor operating in saturation region, is presented in this paper. The reference has been implemented in a standard 0.6-µm CMOS process (V thn ≈ |V thp | ≈ 0.9 V@0° C) and gives a temperature coefficient of not greater than 62 ppm/°C from 0 to 100° C without trimming, while consuming a maximum of 9.7 µA with a minimum supply of 1.4 V. The worst-case line regulation is ±0.17 %/V. The occupied chip area is 0.055 mm2. The proposed reference has been applied to a 10-mA CMOS low dropout regulator, and a temperature coefficient of 94 ppm/°C is achieved when the regulator delivers maximum load current.


title: Bitcoin Beacon

abstract: We examine a protocol $\pi_{\text{beacon}}$ that outputs unpredictable and publicly verifiable randomness, meaning that the output is unknown at the time that $\pi_{\text{beacon}}$ starts, yet everyone can verify that the output is close to uniform after $\pi_{\text{beacon}}$ terminates. We show that $\pi_{\text{beacon}}$ can be instantiated via Bitcoin under sensible assumptions; in particular we consider an adversary with an arbitrarily large initial budget who may not operate at a loss indefinitely. In case the adversary has an infinite budget, we provide an impossibility result that stems from the similarity between the Bitcoin model and Santha-Vazirani sources. We also give a hybrid protocol that combines trusted parties and a Bitcoin-based beacon.


title: What next?: a half-dozen data management research goals for big data and the cloud

abstract: In this short paper, I describe six data management research challenges relevant for Big Data and the Cloud. Although some of these problems are not new, their importance is amplified by Big Data and Cloud Computing.


title: Tracking an RGB-D Camera Using Points and Planes

abstract: Planes are dominant in most indoor and outdoor scenes and the development of a hybrid algorithm that incorporates both point and plane features provides numerous advantages. In this regard, we present a tracking algorithm for RGB-D cameras using both points and planes as primitives. We show how to extend the standard prediction-and-correction framework to include planes in addition to points. By fitting planes, we implicitly take care of the noise in the depth data that is typical in many commercially available 3D sensors. In comparison with the techniques that use only points, our tracking algorithm has fewer failure modes, and our reconstructed model is compact and more accurate. The tracking algorithm is supported by relocalization and bundle adjustment processes to demonstrate a real-time simultaneous localization and mapping (SLAM) system using a hand-held or robot-mounted RGB-D camera. Our experiments show large-scale indoor reconstruction results as point-based and plane-based 3D models, and demonstrate an improvement over the point-based tracking algorithms using a benchmark for RGB-D cameras.


title: Fast Resampling of 3D Point Clouds via Graphs

abstract: To reduce the cost of storing, processing, and visualizing a large-scale point cloud, we propose a randomized resampling strategy that selects a representative subset of points while preserving application-dependent features. The strategy is based on graphs, which can represent underlying surfaces and lend themselves well to efficient computation. We use a general feature-extraction operator to represent application-dependent features and propose a general reconstruction error to evaluate the quality of resampling; by minimizing the error, we obtain a general form of optimal resampling distribution. The proposed resampling distribution is guaranteed to be shift-, rotation- and scale-invariant in the three-dimensional space. We then specify the feature-extraction operator to be a graph filter and study specific resampling strategies based on all-pass, low-pass, high-pass graph filtering and graph filter banks. We validate the proposed methods on three applications: Large-scale visualization, accurate registration, and robust shape modeling demonstrating the effectiveness and efficiency of the proposed resampling methods.


title: HyperLex: A Large-Scale Evaluation of Graded Lexical Entailment

abstract: We introduce HyperLex-a data set and evaluation resource that quantifies the extent of the semantic category membership, that is, type-of relation, also known as hyponymy-hypernymy or lexical entailment LE relation between 2,616 concept pairs. Cognitive psychology research has established that typicality and category/class membership are computed in human semantic memory as a gradual rather than binary relation. Nevertheless, most NLP research and existing large-scale inventories of concept category membership WordNet, DBPedia, etc. treat category membership and LE as binary. To address this, we asked hundreds of native English speakers to indicate typicality and strength of category membership between a diverse range of concept pairs on a crowdsourcing platform. Our results confirm that category membership and LE are indeed more gradual than binary. We then compare these human judgments with the predictions of automatic systems, which reveals a huge gap between human performance and state-of-the-art LE, distributional and representation learning models, and substantial differences between the models themselves. We discuss a pathway for improving semantic models to overcome this discrepancy, and indicate future application areas for improved graded LE systems.


title: Recognition of normal–abnormal phonocardiographic signals using deep convolutional neural networks and mel-frequency spectral coefficients

abstract: Intensive care unit patients are heavily monitored, and several clinically-relevant parameters are routinely extracted from high resolution signals. Objective: The goal of the 2016 PhysioNet/CinC Challenge was to encourage the creation of an intelligent system that fused information from different phonocardiographic signals to create a robust set of normal/abnormal signal detections. Approach: Deep convolutional neural networks and mel-frequency spectral coefficients were used for recognition of normal–abnormal phonocardiographic signals of the human heart. This technique was developed using the PhysioNet.org Heart Sound database and was submitted for scoring on the challenge test set. Main results: The current entry for the proposed approach obtained an overall score of 84.15% in the last phase of the challenge, which provided the sixth official score and differs from the best score of 86.02% by just 1.87%.


title: NeuroStylist: Neural Compatibility Modeling for Clothing Matching

abstract: Nowadays, as a beauty-enhancing product, clothing plays an important role in human's social life. In fact, the key to a proper outfit usually lies in the harmonious clothing matching. Nevertheless, not everyone is good at clothing matching. Fortunately, with the proliferation of fashion-oriented online communities, fashion experts can publicly share their fashion tips by showcasing their outfit compositions, where each fashion item (e.g., a top or bottom) usually has an image and context metadata (e.g., title and category). Such rich fashion data offer us a new opportunity to investigate the code in clothing matching. However, challenges co-exist with opportunities. The first challenge lies in the complicated factors, such as color, material and shape, that affect the compatibility of fashion items. Second, as each fashion item involves multiple modalities (i.e., image and text), how to cope with the heterogeneous multi-modal data also poses a great challenge. Third, our pilot study shows that the composition relation between fashion items is rather sparse, which makes traditional matrix factorization methods not applicable. Towards this end, in this work, we propose a content-based neural scheme to model the compatibility between fashion items based on the Bayesian personalized ranking (BPR) framework. The scheme is able to jointly model the coherent relation between modalities of items and their implicit matching preference. Experiments verify the effectiveness of our scheme, and we deliver deep insights that can benefit future research.


title: Automatic personality assessment: A systematic review

abstract: Personality Assessment is an emerging research area. In recent years, the interest of the scientific community towards personality assessment has grown incredibly. Personality is a psychological model that can be used to explain the wide variety of human behaviors with the help of individual characteristics. The applications of personality assessment range from behavior analysis to disease diagnosis, counseling, employee recruitment, social network analysis, security systems, mood prediction, and many others. Automatic personality assessment consists of the automatic classification of users' personality traits from the data such as video, speech and text. Despite growing number of works in personality assessment, it is still very difficult to say what the current state-of-the-art is. The objective of this survey paper is to discuss various approaches used for personality assessment and to present current state-of-art related to it. It also provides guidelines for further research.


title: Proceedings, The Fourth International Symposium on Combinatorial Search (SoCS-2011) A Preliminary Evaluation of Machine Learning in Algorithm Selection for Search Problems

abstract: Machine learning is an established method of selecting algorithms to solve hard search problems. Despite this, to date no systematic comparison and evaluation of the different techniques has been performed and the performance of existing systems has not been critically compared to other approaches. We compare machine learning techniques for algorithm selection on real-world data sets of hard search problems. In addition to well-established approaches, for the first time we also apply statistical relational learning to this problem. We demonstrate that most machine learning techniques and existing systems perform less well than one might expect. To guide practitioners, we close by giving clear recommendations as to which machine learning techniques are likely to perform well based on our experiments.


title: Tetrode recordings in the cerebellar cortex

abstract: Multi-unit recordings with tetrodes have been used in brain studies for many years, but surprisingly, scarcely in the cerebellum. The cerebellum is subdivided in multiple small functional zones. Understanding the proper features of the cerebellar computations requires a characterization of neuronal activity within each area. By allowing simultaneous recordings of neighboring cells, tetrodes provide a helpful technique to study the dynamics of the cerebellar local networks. Here, we discuss experimental configurations to optimize such recordings and demonstrate their use in the different layers of the cerebellar cortex. We show that tetrodes can also be used to perform simultaneous recordings from neighboring units in freely moving rats using a custom-made drive, thus permitting studies of cerebellar network dynamics in a large variety of behavioral conditions.


title: Determination of persistent tetracycline residues in soil fertilized with liquid manure by high-performance liquid chromatography with electrospray ionization tandem mass spectrometry.

abstract: Little is known about the occurrence and the fate of veterinary drugs in the environment. Therefore, a liquid chromatography/tandem mass spectrometry method was developed and employed to investigate in detail the distribution and persistence of the frequently used tetracyclines and tylosin in a field fertilized with liquid manure on April 2000 and April 2001; soil sampling was performed in May 2000, November 2000, and May 2001. We detected 4.0 mg/kg tetracycline and 0.1 mg/kg chlortetracycline in the liquid manure of April 2000, as well as comparable amounts in the liquid manure of April 2001. In the soil samples of May 2001, the highest average concentrations of 86.2 (0-10 cm), 198.7 (10-20 cm), and 171.7 microg/kg (20-30 cm) tetracycline and 4.6-7.3 micro/kg chlortetracycline (all three sublayers) were found. At soil depths between 30 and 90 cm, as well as in soil or groundwater, tetracyclines could not be detected. In addition, oxytetracycline and tylosin could not be detected in any sample investigated. We conclude that tetracyclines enter the environment in significant concentrations via repeated fertilizations with liquid manure, build up persistent residues, and accumulate in soil. Therefore, tetracyclines may have a potential risk and investigations on the environmental effects of these antibiotics are necessary.


title: A high-power-density, high-speed gate driver for a 10 kV SiC MOSFET module

abstract: Several high-voltage silicon carbide (SiC) devices have been demonstrated over the past few years, and the latest-generation devices are showing even faster switching, and greater current densities. However, there are no commercial gate drivers that are suitable for these high-voltage, high-speed devices. Consequently, there has been a great research effort into the development of gate drivers for high-voltage SiC transistors. This work presents the first detailed report on the design and testing of a high-power-density, high-speed, and high-noise-immunity gate drive for a high-current, 10 kV SiC MOSFET module.


Improving Question Answering Sentence Selection by Rank Propagation


Plans and Situated Actions: The Problem of Human Machine Communication.


title: Beyond Geometric Path Planning: Learning Context-Driven Trajectory Preferences via Sub-optimal Feedback

abstract: We consider the problem of learning preferences over trajectories for mobile manipulators such as personal robots and assembly line robots. The preferences we learn are more intricate than those arising from simple geometric constraints on robot’s trajectory, such as distance of the robot from human etc. Our preferences are rather governed by the surrounding context of various objects and human interactions in the environment. Such preferences makes the problem challenging because the criterion of defining a good trajectory now varies with the task, with the environment and across the users. Furthermore, demonstrating optimal trajectories (e.g., learning from expert’s demonstrations) is often challenging and non-intuitive on high degrees of freedom manipulators. In this work, we propose an approach that requires a non-expert user to only incrementally improve the trajectory currently proposed by the robot. We implement our algorithm on two high degree-of-freedom robots, PR2 and Baxter, and present three intuitive mechanisms for providing such incremental feedback. In our experimental evaluation we consider two context rich settings—household chores and grocery store checkout—and show that users are able to train the robot with just a few feedbacks (taking only a few minutes). Despite receiving sub-optimal feedback from non-expert users, our algorithm enjoys theoretical bounds on regret that match the asymptotic rates of optimal trajectory algorithms.


title: Mindfulness and De-Automatization

abstract: Some maladaptive thought processes are characterized by reflexive and habitual patterns of cognitive and emotional reactivity. We review theoretical and empirical work suggesting that mindfulness—a state of nonjudgmental awareness of the present moment—can facilitate the discontinuation of such automatic mental operations. We propose a framework that suggests a series of more specific mechanisms supporting the de-automatizing function of mindfulness. Four related but distinct elements of mindfulness (awareness, attention, focus on the present, and acceptance) can each contribute to de-automatization through subsequent processes, including discontinuing automatic inference, enhancing cognitive control capacity, facilitating metacognitive insight, and preventing suppression or thought distortion. De-automatizing can, in turn, allow enhancement of adaptive self-control ability and increased well-being.


title: Trends and Challenges in CMOS Design for Emerging 60 GHz WPAN Applications

abstract: The extensive growth of wireless communications industry is creating a big market opportunity. Wireless operators are currently searching for new solutions which would be implemented into the existing wireless communication networks to provide the broader bandwidth, the better quality and new value-added services. In the last decade, most commercial efforts were focused on the 1-10 GHz spectrum for voice and data applications for mobile phones and portable computers (Niknejad & Hashemi, 2008). Nowadays, the interest is growing in applications that use high rate wireless communications. Multigigabit-per-second communication requires a very large bandwidth. The Ultra-Wide Band (UWB) technology was basically used for this issue. However, this technology has some shortcomings including problems with interference and a limited data rate. Furthermore, the 3–5 GHz spectrum is relatively crowded with many interferers appearing in the WiFi bands (Niknejad & Hashemi, 2008). The use of millimeter wave frequency band is considered the most promising technology for broadband wireless. In 2001, the Federal Communications Commission (FCC) released a set of rules governing the use of spectrum between 57 and 66 GHz (Baldwin, 2007). Hence, a large bandwidth coupled with high allowable transmit power equals high possible data rates. Traditionally the implementation of 60 GHz radio technology required expensive technologies based on III-V compound semiconductors such as InP and GaAs (Smulders et al., 2007). The rapid progress of CMOS technology has enabled its application in millimeter wave applications. Currently, the transistors became small enough, consequently fast enough. As a result, the CMOS technology has become one of the most attractive choices in implementing 60 GHz radio due to its low cost and high level of integration (Doan et al., 2005). Despite the advantages of CMOS technology, the design of 60 GHz CMOS transceiver exhibits several challenges and difficulties that the designers must overcome. This chapter aims to explore the potential of the 60 GHz band in the use for emergent generation multi-gigabit wireless applications. The chapter presents a quick overview of the state-of-the-art of 60 GHz radio technology and its potentials to provide for high data rate and short range wireless communications. The chapter is organized as follows. Section 2 presents an overview about 60 GHz band. The advantages are presented to highlight the performance characteristics of this band. The opportunities of the physical layer of the IEEE


title: BUILDING THE NEXT GENERATION OF CYBER SECURITY PROFESSIONALS

abstract: Cyber security is an area of strategic and policy interest to governments and enterprises globally, which results in an increase in the demand for cyber security professionals. However, there is a lack of education based on sound theories, standards and practices. In this paper, we adapted the Situational Crime Prevention Theory and the NICE National Cybersecurity Workforce Framework in the design and delivery of our courses, particularly in the Cyber Security Exercise (CSE) which forms an integral part of the courses. The CSE is an attack/defence environment where students are grouped and given a virtual machine with which to host a number of services (e.g. HTTP(S), FTP and SSH) for access by other groups. The CSE is designed to mirror real-world environments where the students’ skills will be applied. An overview of the CSE architecture was also provided for readers interested in replicating the exercise in their institutions. Based on student assessment and feedback, we found that our approach was useful in transferring theoretical knowledge to practical skills suitable for the cyber security workforce.


title: Efficient Mean‐shift Clustering Using Gaussian KD‐Tree

abstract: Mean shift is a popular approach for data clustering, however, the high computational complexity of the mean shift procedure limits its practical applications in high dimensional and large data set clustering. In this paper, we propose an efficient method that allows mean shift clustering performed on large data set containing tens of millions of points at interactive rate. The key in our method is a new scheme for approximating mean shift procedure using a greatly reduced feature space. This reduced feature space is adaptive clustering of the original data set, and is generated by applying adaptive KD-tree in a high-dimensional affinity space. The proposed method significantly reduces the computational cost while obtaining almost the same clustering results as the standard mean shift procedure. We present several kinds of data clustering applications to illustrate the efficiency of the proposed method, including image and video segmentation, static geometry model and time-varying sequences segmentation.


title: 3D Bounding Box Estimation Using Deep Learning and Geometry

abstract: We present a method for 3D object detection and pose estimation from a single image. In contrast to current techniques that only regress the 3D orientation of an object, our method first regresses relatively stable 3D object properties using a deep convolutional neural network and then combines these estimates with geometric constraints provided by a 2D object bounding box to produce a complete 3D bounding box. The first network output estimates the 3D object orientation using a novel hybrid discrete-continuous loss, which significantly outperforms the L2 loss. The second output regresses the 3D object dimensions, which have relatively little variance compared to alternatives and can often be predicted for many object types. These estimates, combined with the geometric constraints on translation imposed by the 2D bounding box, enable us to recover a stable and accurate 3D object pose. We evaluate our method on the challenging KITTI object detection benchmark [2] both on the official metric of 3D orientation estimation and also on the accuracy of the obtained 3D bounding boxes. Although conceptually simple, our method outperforms more complex and computationally expensive approaches that leverage semantic segmentation, instance level segmentation and flat ground priors [4] and sub-category detection [23][24]. Our discrete-continuous loss also produces state of the art results for 3D viewpoint estimation on the Pascal 3D+ dataset[26].


title: NeatMap - non-clustering heat map alternatives in R

abstract: BackgroundThe clustered heat map is the most popular means of visualizing genomic data. It compactly displays a large amount of data in an intuitive format that facilitates the detection of hidden structures and relations in the data. However, it is hampered by its use of cluster analysis which does not always respect the intrinsic relations in the data, often requiring non-standardized reordering of rows/columns to be performed post-clustering. This sometimes leads to uninformative and/or misleading conclusions. Often it is more informative to use dimension-reduction algorithms (such as Principal Component Analysis and Multi-Dimensional Scaling) which respect the topology inherent in the data. Yet, despite their proven utility in the analysis of biological data, they are not as widely used. This is at least partially due to the lack of user-friendly visualization methods with the visceral impact of the heat map.ResultsNeatMap is an R package designed to meet this need. NeatMap offers a variety of novel plots (in 2 and 3 dimensions) to be used in conjunction with these dimension-reduction techniques. Like the heat map, but unlike traditional displays of such results, it allows the entire dataset to be displayed while visualizing relations between elements. It also allows superimposition of cluster analysis results for mutual validation. NeatMap is shown to be more informative than the traditional heat map with the help of two well-known microarray datasets.ConclusionsNeatMap thus preserves many of the strengths of the clustered heat map while addressing some of its deficiencies. It is hoped that NeatMap will spur the adoption of non-clustering dimension-reduction algorithms.