title: Measures of entropy and complexity in altered states of consciousness

abstract: Quantification of complexity in neurophysiological signals has been studied using different methods, especially those from information or dynamical system theory. These studies have revealed a dependence on different states of consciousness, and in particular that wakefulness is characterized by a greater complexity of brain signals, perhaps due to the necessity for the brain to handle varied sensorimotor information. Thus, these frameworks are very useful in attempts to quantify cognitive states. We set out to analyze different types of signals obtained from scalp electroencephalography (EEG), intracranial EEG and magnetoencephalography recording in subjects during different states of consciousness: resting wakefulness, different sleep stages and epileptic seizures. The signals were analyzed using a statistical (permutation entropy) and a deterministic (permutation Lempel–Ziv complexity) analytical method. The results are presented in complexity versus entropy graphs, showing that the values of entropy and complexity of the signals tend to be greatest when the subjects are in fully alert states, falling in states with loss of awareness or consciousness. These findings were robust for all three types of recordings. We propose that the investigation of the structure of cognition using the frameworks of complexity will reveal mechanistic aspects of brain dynamics associated not only with altered states of consciousness but also with normal and pathological conditions.


title: OPTIMIS : A holistic approach to cloud service provisioning

abstract: We present fundamental challenges for scalable and dependable service platforms and architectures that enable flexible and dynamic provisioning of cloud services. Our findings are incorporated in a toolkit targeting the cloud service and infrastructure providers. The innovations behind the toolkit are aimed at optimizing the whole service life cycle, including service construction, deployment, and operation, on a basis of aspects such as trust, risk, eco-efficiency and cost. Notably, adaptive self-preservation is crucial to meet predicted and unforeseen changes in resource requirements. By addressing the whole service life cycle, taking into account several cloud architectures, and by taking a holistic approach to sustainable service provisioning, the toolkit aims to provide a foundation for a reliable, sustainable, and trustful cloud computing industry.


title: The dissociation of color from form and function knowledge

abstract: We report on two brain-damaged subjects who exhibit the uncommon pattern of loss of object color knowledge, but spared color perception and naming. The subject P.C.O., as in previously reported patients, is also impaired in processing other perceptual and functional properties of objects. I.O.C., in contrast, is the first subject on record to have impaired object color knowledge, but spared knowledge of object form, size and function. This pattern of performance is consistent with the view that semantic information about color and other perceptual properties of objects is grounded in modality-specific systems. Lesion analysis suggests that such grounding requires the integrity of the mesial temporal regions of the left hemisphere.


title: Speeding up Scoring Module of Mass Spectrometry Based Protein Identification by GPU

abstract: Database searching is a main method for protein identification in shotgun proteomics, and many research efforts are dedicated to improving its effectiveness. However, the efficiency of database searching is facing a serious challenge, due to the ever fast growth of protein and peptide databases resulted from genome translations, enzymatic digestions, and post-translational modifications (PTMs). On the other hand, as a general-purpose and high performance parallel hardware, Graphics Processing Units (GPUs) develop continuously and provide another promising platform for parallelizing database searching based protein identification. It becomes very important to study how to speed up database search engines by GPUs for protein identification. In this paper, we mainly utilize GPUs to accelerate the scoring module, which is the most time-consuming component. Specifically, we study two popular scoring method: spectral dot product based method, which is used by X!Tandem, and kernel spectral dot product, which is used by pFind.


title: Composite Indicators Visualization: Exploration of Multivariate Temporal Changes using Radial Visualization

abstract: We present a work in progress on visualization of a popular Measurement and Benchmarking method, namely Composite Indicators (CIs). Composite indicators are increasingly recognized as an important tool in policy analysis and public communication and are often used to reflect performances of different units (e.g., countries, regions or organizations). Existing CI visualizations introduce a variety of solutions for the complex challenge of presenting multi-dimensional, time-oriented hierarchic data. Radial visualizations are popular among these solutions because of CI’s multi-dimensionality, although different coding is in use. In our work, we aim to shed light on radial presentations for multivariate temporal changes, hoping to produce relevant guidelines for CI visualizations. We introduce some radial design options with static and dynamic time coding, as we explore the design space. In future work, we plan to formally implement, and deploy selected designs.


title: CMOS based design and simulation of ternary full adder and Ternary coded Decimal (TCD) adder circuit

abstract: A number system with radix-3 is referred to as Ternary system. With a lot of new ternary circuits being proposed as an alternative to the digital logic, we take a step further, in this paper we propose to design a Ternary coded Decimal (TCD) adder circuit based on CMOS technology. Unlike a Ternary adder, the TCD adder uses 3-bit Ternary coded Decimal (TCD) number as input and the resulting sum will also be in TCD form. The design of this TCD adder is based on a new custom circuit for ternary adder which is optimized for less number of transistors. In the due course we will also highlight how non-conventional, custom designed circuits provide area advantage compared to the Ternary k-map based methods.


title: SPARC - Sorted ASP with Consistency Restoring Rules

abstract: This is a preliminary report on the work aimed at making CR-Prolog -- a version of ASP with consistency restoring rules -- more suitable for use in teaching and large applications. First we describe a sorted version of CR-Prolog called SPARC. Second, we translate a basic version of the CR-Prolog into the language of DLV and compare the performance with the state of the art CR-Prolog solver. The results form the foundation for future more efficient and user friendly implementation of SPARC and shed some light on the relationship between two useful knowledge representation constructs: consistency restoring rules and weak constraints of DLV.


title: Two cortical systems for memory-guided behaviour

abstract: The roles of the perirhinal, parahippocampal and retrosplenial cortices in memory are not well understood. Reviewing studies in rodents, monkeys and humans, Ranganath and Ritchey suggest that these areas are core components of two cortical networks that support different types of memory and different aspects of cognition.


An Artificial Neural Network based Intrusion Detection System and Classification of Attacks


title: Natural history and evolutionary principles of gene duplication in fungi

abstract: Gene duplication and loss is a powerful source of functional innovation. However, the general principles that govern this process are still largely unknown. With the growing number of sequenced genomes, it is now possible to examine these events in a comprehensive and unbiased manner. Here, we develop a procedure that resolves the evolutionary history of all genes in a large group of species. We apply our procedure to seventeen fungal genomes to create a genome-wide catalogue of gene trees that determine precise orthology and paralogy relations across these species. We show that gene duplication and loss is highly constrained by the functional properties and interacting partners of genes. In particular, stress-related genes exhibit many duplications and losses, whereas growth-related genes show selection against such changes. Whole-genome duplication circumvents this constraint and relaxes the dichotomy, resulting in an expanded functional scope of gene duplication. By characterizing the functional fate of duplicate genes we show that duplicated genes rarely diverge with respect to biochemical function, but typically diverge with respect to regulatory control. Surprisingly, paralogous modules of genes rarely arise, even after whole-genome duplication. Rather, gene duplication may drive the modularization of functional networks through specialization, thereby disentangling cellular systems. Gene duplication and loss is a key mechanism of evolutionary innovation, but little is known about the evolutionary principles that govern it. SYNERGY is a newly developed algorithm that uses sequence similarity and species phylogeny to map the evolutionary history of gene duplication and loss in a group of species. This strategy has been applied to the genomes of 17 Ascomycota fungi (including the popular model organisms S. cerevisiae and S. pombe) to determine the role of gene duplication and loss in the yeast lineage. One inference from the results is the finding that gene duplication may simplify a system, rather than make it more complex, by driving the specialization and modularization of pathways. A new method, SYNERGY, has been developed to systematically map the detailed evolutionary history of gene duplication and loss. This strategy was applied to the genomes of 17 Ascomycota fungal species to provide a comprehensive overview of gene duplication and loss in the yeast lineage and the biological constraints that govern gene evolution.


title: Polarimetric SAR Image Classification Using Deep Convolutional Neural Networks

abstract: Deep convolutional neural networks have achieved great success in computer vision and many other areas. They automatically extract translational-invariant spatial features and integrate with neural network-based classifier. This letter investigates the suitability and potential of deep convolutional neural network in supervised classification of polarimetric synthetic aperture radar (POLSAR) images. The multilooked POLSAR data in the format of coherency or covariance matrix is first converted into a normalized 6-D real feature vector. The six-channel real image is then fed into a four-layer convolutional neural network tailored for POLSAR classification. With two cascaded convolutional layers, the designed deep neural network can automatically learn hierarchical polarimetric spatial features from the data. Two experiments are presented using the AIRSAR data of San Francisco, CA, and Flevoland, The Netherlands. Classification result of the San Francisco case shows that slant built-up areas, which are conventionally mixed with vegetated area in polarimetric feature space, can now be successfully distinguished after taking into account spatial features. Quantitative analysis with respect to ground truth information available for the Flevoland test site shows that the proposed method achieves an accuracy of 92.46% in classifying the considered 15 classes. Such results are comparable with the state of the art.


title: Generalized Projection-Based M-Estimator

abstract: We propose a novel robust estimation algorithm - the generalized projection-based M-estimator (gpbM), which does not require the user to specify any scale parameters. The algorithm is general and can handle heteroscedastic data with multiple linear constraints for single and multicarrier problems. The gpbM has three distinct stages - scale estimation, robust model estimation, and inlier/outlier dichotomy. In contrast, in its predecessor pbM, each model hypotheses was associated with a different scale estimate. For data containing multiple inlier structures with generally different noise covariances, the estimator iteratively determines one structure at a time. The model estimation can be further optimized by using Grassmann manifold theory. We present several homoscedastic and heteroscedastic synthetic and real-world computer vision problems with single and multiple carriers.


title: Contextual-based Image Inpainting: Infer, Match, and Translate

abstract: We study the task of image inpainting, which is to fill in the missing region of an incomplete image with plausible contents. To this end, we propose a learning-based approach to generate visually coherent completion given a high-resolution image with missing components. In order to overcome the difficulty to directly learn the distribution of high-dimensional image data, we divide the task into inference and translation as two separate steps and model each step with a deep neural network. We also use simple heuristics to guide the propagation of local textures from the boundary to the hole. We show that, by using such techniques, inpainting reduces to the problem of learning two image-feature translation functions in much smaller space and hence easier to train. We evaluate our method on several public datasets and show that we generate results of better visual quality than previous state-of-the-art methods.


title: Prob-Maxn: Playing N-Player Games with Opponent Models

abstract: Much of the work on opponent modeling for game tree search has been unsuccessful. In two-player, zero-sum games, the gains from opponent modeling are often outweighed by the cost of modeling. Opponent modeling solutions simply cannot search as deep as the highly optimized minimax search with alpha-beta pruning. Recent work has begun to look at the need for opponent modeling in n-player or general-sum games. We introduce a probabilistic approach to opponent modeling in n-player games called prob-maxn, which can robustly adapt to unknown opponents. We implement prob-maxn in the game of Spades, showing that prob-maxn is highly effective in practice, beating out the maxn and soft-max n algorithms when faced with unknown opponents.


title: Natural language processing: an introduction

abstract: Objectives To provide an overview and tutorial of natural language processing (NLP) and modern NLP-system design. Target audience This tutorial targets the medical informatics generalist who has limited acquaintance with the principles behind NLP and/or limited knowledge of the current state of the art. Scope We describe the historical evolution of NLP, and summarize common NLP sub-problems in this extensive field. We then provide a synopsis of selected highlights of medical NLP efforts. After providing a brief description of common machine-learning approaches that are being used for diverse NLP sub-problems, we discuss how modern NLP architectures are designed, with a summary of the Apache Foundation’s Unstructured Information Management Architecture. We finally consider possible future directions for NLP, and reflect on the possible impact of IBM Watson on the medical field.


title: Optimal coordination of directional overcurrent relays using hybrid BBO/DE algorithm and considering double primary relays strategy

abstract: Finding optimal settings of directional overcurrent relays (DOCRs) has been extensively solved by many scenarios with adopting different traditional and modern optimization algorithms. For the sack of simplicity, all the conducted studies in the literature are restricted on a condition that all DOCRs are numerical, static, or electromechanical. There is a fact that when jumping from electromechanical or static relays to numerical relays the former devices could be used as a second wall of protection instead of just throwing them. With a very extreme condition, which may not happen in the real-world applications, all buses will have double primary DOCRs, which where act as backup DOCRs for other buses. That is, the dimension of any given problem is duplicated and becomes very hard to be feasibly and optimally solved. This paper covers this uncommon scenario, and a new hybrid algorithm with some additional sub-algorithms is developed to solve the IEEE 6-bus test system. Moreover, both numerical with electromechanical DOCRs and numerical with static DOCRs are considered in this study.


title: Fast Simulation of Dynamic Ultrasound Images Using the GPU

abstract: Simulated ultrasound data is a valuable tool for development and validation of quantitative image analysis methods in echocardiography. Unfortunately, simulation time can become prohibitive for phantoms consisting of a large number of point scatterers. The COLE algorithm by Gao et al. is a fast convolution-based simulator that trades simulation accuracy for improved speed. We present highly efficient parallelized CPU and GPU implementations of the COLE algorithm with an emphasis on dynamic simulations involving moving point scatterers. We argue that it is crucial to minimize the amount of data transfers from the CPU to achieve good performance on the GPU. We achieve this by storing the complete trajectories of the dynamic point scatterers as spline curves in the GPU memory. This leads to good efficiency when simulating sequences consisting of a large number of frames, such as B-mode and tissue Doppler data for a full cardiac cycle. In addition, we propose a phase-based subsample delay technique that efficiently eliminates flickering artifacts seen in B-mode sequences when COLE is used without enough temporal oversampling. To assess the performance, we used a laptop computer and a desktop computer, each equipped with a multicore Intel CPU and an NVIDIA GPU. Running the simulator on a high-end TITAN X GPU, we observed two orders of magnitude speedup compared to the parallel CPU version, three orders of magnitude speedup compared to simulation times reported by Gao et al. in their paper on COLE, and a speedup of 27000 times compared to the multithreaded version of Field II, using numbers reported in a paper by Jensen. We hope that by releasing the simulator as an open-source project we will encourage its use and further development.


title: Multiscale CNNs for Brain Tumor Segmentation and Diagnosis

abstract: Early brain tumor detection and diagnosis are critical to clinics. Thus segmentation of focused tumor area needs to be accurate, efficient, and robust. In this paper, we propose an automatic brain tumor segmentation method based on Convolutional Neural Networks (CNNs). Traditional CNNs focus only on local features and ignore global region features, which are both important for pixel classification and recognition. Besides, brain tumor can appear in any place of the brain and be any size and shape in patients. We design a three-stream framework named as multiscale CNNs which could automatically detect the optimum top-three scales of the image sizes and combine information from different scales of the regions around that pixel. Datasets provided by Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized by MICCAI 2013 are utilized for both training and testing. The designed multiscale CNNs framework also combines multimodal features from T1, T1-enhanced, T2, and FLAIR MRI images. By comparison with traditional CNNs and the best two methods in BRATS 2012 and 2013, our framework shows advances in brain tumor segmentation accuracy and robustness.


title: Evaluation of parameters that affect the 4-dimethylaminocinnamaldehyde assay for flavanols and proanthocyanidins.

abstract: Proanthocyanidins are widely distributed in nature and represent the most abundant flavonoids consumed in the diet. Recent attention has been given to these compounds because of their health-promoting properties toward chronic diseases. Because of their large degrees of chemical variation and stereochemistry, isolation/quantification is difficult. The 4-dimethylaminocinnamaldehyde (DMAC) spectrophotometric assay has become increasingly popular as a rapid technique to quantify the amount of proanthocyanidins present in foods and beverages; however, there is no current industry standard. In this study, several parameters affecting the DMAC reaction with catechin were examined. Effects of acid nature (hydrochloric acid [HCl] and sulfuric acid [H(2)SO(4)]) and concentration (2 to 10 N), temperature (5 to 45 °C), reaction time (2 to 35 min), sample water content (1 to 100%), DMAC concentration (1 to 3%), and interference of 8 different substances were evaluated. A mixture of 2% DMAC in methanol (w/v) in 6 N H(2)SO(4) (50:50 v/v) showed the highest slope in the standard curve when allowed to react for 15 to 35 min prior to analysis. For samples containing high concentrations of oligomeric proanthocyanidins, a reaction time of 20 to 35 min is recommended. The reaction of catechin with DMAC at constant room temperature (21 to 25 °C), with a sample water content of less than 1% was found to increase reproducibility and better assess the amount of catechin. Sample water contents higher than 1% showed significant bleaching effects on the color produced, when catechin concentrations were high (lower levels showed no significant differences). None of the substances examined in this study interfered with the assay; however, other compounds present in various food matrices may have an effect on color development.


title: Distributional Adversarial Networks

abstract: We propose a framework for adversarial training that relies on a sample rather than a single sample point as the fundamental unit of discrimination. Inspired by discrepancy measures and two-sample tests between probability distributions, we propose two such distributional adversaries that operate and predict on samples, and show how they can be easily implemented on top of existing models. Various experimental results show that generators trained with our distributional adversaries are much more stable and are remarkably less prone to mode collapse than traditional models trained with pointwise prediction discriminators. The application of our framework to domain adaptation also results in considerable improvement over recent state-of-the-art.


title: Hippocampal structural asymmetry in unsuccessful psychopaths

abstract: Abstract Background Structural and functional hippocampal abnormalities have been previously reported in institutionalized psychopathic and aggressive populations. This study assessed whether prior findings of a right greater than left (R > L) functional asymmetry in caught violent offenders generalize to the structural domain in unsuccessful, caught psychopaths. Methods Left and right hippocampal volumes were assessed using structural magnetic resonance imaging (MRI) in 23 control subjects, 16 unsuccessful psychopaths, and 12 successful (uncaught) community psychopaths and transformed into standardized space. Results Unsuccessful psychopaths showed an exaggerated structural hippocampal asymmetry (R > L) relative both to successful psychopaths and control subjects ( p Conclusions Atypical anterior hippocampal asymmetries in unsuccessful psychopaths may reflect an underlying neurodevelopmental abnormality that disrupts hippocampal-prefrontal circuitry, resulting in affect dysregulation, poor contextual fear conditioning, and insensitivity to cues predicting capture.


title: ADVISE: Symbolism and External Knowledge for Decoding Advertisements

abstract: In order to convey the most content in their limited space, advertisements embed references to outside knowledge via symbolism. For example, a motorcycle stands for adventure (a positive property the ad wants associated with the product being sold), and a gun stands for danger (a negative property to dissuade viewers from undesirable behaviors). We show how to use symbolic references to better understand the meaning of an ad. We further show how anchoring ad understanding in general-purpose object recognition and image captioning can further improve results. We formulate the ad understanding task as matching the ad image to human-generated statements that describe the action that the ad prompts and the rationale it provides for this action. We greatly outperform the state of the art in this task. We also show additional applications of our learned representations for ranking the slogans of ads, and clustering ads according to their topic.


title: Design of an electric series elastic actuated joint for robotic gait rehabilitation training

abstract: Robotic gait rehabilitation is at least as effective as conventional gait training in stroke survivors. Patients must be assisted as needed in order to improve affected gait patterns. The combination of impedance control and series elastic actuation is a viable actuation principle to be used for human robot interaction. Here, a new promising electric series elastic actuated joint is developed. The large torque bandwidth limit at 100 Nm is 6.9 Hz. With a total weight of 3.175 kg it is possible to directly mount the actuator on the exoskeleton frame. The actuator is capable of providing sufficient torque at normal walking speed. Full patient assistance during gait and free motions without impeding the gait pattern are possible. The actuator allows isometric measurements up to 100 Nm and the patient's progress in robotic rehabilitation can be evaluated.


title: Guiding goal modeling using scenarios

abstract: Even though goal modeling is an effective approach to requirements engineering, it is known to present a number of difficulties in practice. The paper discusses these difficulties and proposes to couple goal modeling and scenario authoring to overcome them. Whereas existing techniques use scenarios to concretize goals, we use them to discover goals. Our proposal is to define enactable rules which form the basis of a software environment called L'Ecritoire to guide the requirements elicitation process through interleaved goal modeling and scenario authoring. The focus of the paper is on the discovery of goals from scenarios. The discovery process is centered around the notion of a requirement chunk (RC) which is a pair . The paper presents the notion of RC, the rules to support the discovery of RCs and illustrates the application of the approach within L'Ecritoire using the ATM example. It also evaluates the potential practical benefits expected from the use of the approach.


Results of the WNUT16 Named Entity Recognition Shared Task


Clara: Generating Polyphonic and Multi-Instrument Music Using an AWD-LSTM Architecture


title: Strategic Approach for Successful Implementation of Management Information System for Manufacturing Industry

abstract: Purpose - To focus on challenges & strategic approaches in implementing „Management Information System (MIS)‟ for process equipment (For Ex: Pressure Vessels, Boilers, Heat Exchangers, Agitators, Dryers, Storage Containers) manufacturing industry. Approach - Case study Findings - There was about 20% of productivity improvement after successful implementation of MIS for various divisions in the organization. Practical implications - One can understand better in the context of process equipment manufacturing industry, the concepts of management information system, its impact on productivity & financials. The strategic approach indicated here will help organizations, before introducing MIS, to take appropriate actions in efficient implementation. Originality/value – This study will serve as a point of reference for future studies in this area of concern because of the inadequate academic literature and empirical findings relating to implementation of MIS in process equipment manufacturing industry.


title: A framework for scheduling DRAM memory accesses for multi-core mixed-time critical systems

abstract: Mixed-time critical systems are real-time systems that accommodate both hard real-time (HRT) and soft realtime (SRT) tasks. HRT tasks mandate a gurantee on the worstcase latency, while SRT tasks have average-case bandwidth (BW) demands. Memory requests in mixed-time critical systems usually have different transaction sizes based on whether the issuer task is HRT or SRT. For example, HRT tasks often issue requests with a cache line size. On the other side, SRT tasks may issue requests with a size of KBs. Requests from multimedia cores, cores controlling network interfaces and direct memory accesses (DMAs) are obvious examples of these large-size requests. Based on these observations, we promote in this work a new approach to schedule memory requests. This approach retains locality within large-size requests to minimize the worst-case latency, while maintaining the average-case BW as high as required. To achieve this target, we introduce a novel and compact time-division-multiplexing scheduler that is adequate for mixed-time critical systems. We also present a novel framework that constructs optimal offchip DRAM memory controller schedules for multi-core mixedtime critical systems. These schedules are loaded to the memory controller during boot-time. Based on the proposed schedule, we provide a detailed static analysis that guarantees predictability. We compare the proposed controller against state-of-the-art realtime memory controllers using synthetic experiments as well as a practical use-case from multimedia systems.


title: UHF RFID PIFA array tag antenna for human body applications

abstract: This paper reports a four-element array tag antenna close to a human body for UHF Radio frequency identification (RFID) applications. The four-element array is based on PIFA grounded by vias, which can enhance the directive gain. The array antenna is fed by a four-port microstrip-line power divider. The input impedance of the power divider is designed to match with that of a Monza® 4 microchip. The parametric analysis of conjugate matching was performed and prototypes were fabricated to verify the simulated results. Experimental tests show that the maximum reading range achieved by an RFID tag equipped with the array antenna achieves about 3.9 m when the tag was mounted on a human body.


title: The so-called renormalization group method applied to the specific prime number logarithmic decrease

abstract: A so-called Renormalization Group (RG) analysis is performed in order to shed some light on why the density of prime numbers in $\Bbb N^*$ decreases like the single power of the inverse neperian logarithm.


title: Performance Analysis of Sensing-Based Semi-Persistent Scheduling in C-V2X Networks

abstract: The 3rd Generation Partnership Project released the cellular vehicular-to-everything (C-V2X) specifications as part of the LTE framework in Release 14. C-V2X is the alternative to dedicated short range communications and both are specifically designed for V2X control signaling. C-V2X extends the device-to-device specifications by adding two more modes of operation targeting the vehicular environment in coverage and out of coverage of LTE base stations. Vehicle-to-vehicle communications (V2V) is established with Mode 4, where the devices schedule their transmissions in a distributed way employing sensing-based semi-persistent scheduling (SPS). Research is needed to assess the performance of SPS, especially in congested radio environments. This paper presents the first open-source C-V2X simulator that enables such research. The simulator is implemented in ns-3. We analyze the effect of the Mode 4 resource pool configuration and some of the key SPS parameters on the scheduling performance and find that the resource reservation interval significantly influences packet data rate performance, whereas resource reselection probability has little effect in dense vehicular highway scenarios. Our results show that proper configuration of scheduling parameters can significantly improve performance. We conclude that research on congestion control mechanisms is needed to further enhance the SPS performance for many practical use cases.


title: Compression of concordances in full-text retrieval systems

abstract: The concordance of a full-text information retrieval system contains for every different word W of the data base, a list L(W) of “coordinates”, each of which describes the exact location of an occurrence of W in the text. The concordance should be compressed, not only for the savings in storage space, but also in order to reduce the number of I/O operations, since the file is usually kept in secondary memory. Several methods are presented, which efficiently compress concordances of large fulltext retrieval systems. The methods were tested on the concordance of the Responsa Retrieval Project and yield savings of up to 49% relative to the non-compressed file; this is a relative improvement of about 27% over the currently used prefix-omission compression technique.


title: FractalNet: Ultra-Deep Neural Networks without Residuals

abstract: We introduce a design strategy for neural network macro-architecture based on self-similarity. Repeated application of a simple expansion rule generates deep networks whose structural layouts are precisely truncated fractals. These networks contain interacting subpaths of different lengths, but do not include any pass-through or residual connections; every internal signal is transformed by a filter and nonlinearity before being seen by subsequent layers. In experiments, fractal networks match the excellent performance of standard residual networks on both CIFAR and ImageNet classification tasks, thereby demonstrating that residual representations may not be fundamental to the success of extremely deep convolutional neural networks. Rather, the key may be the ability to transition, during training, from effectively shallow to deep. We note similarities with student-teacher behavior and develop drop-path, a natural extension of dropout, to regularize co-adaptation of subpaths in fractal architectures. Such regularization allows extraction of high-performance fixed-depth subnetworks. Additionally, fractal networks exhibit an anytime property: shallow subnetworks provide a quick answer, while deeper subnetworks, with higher latency, provide a more accurate answer.


title: Cluster Ensembles – A Knowledge Reuse Framework for Combining Multiple Partitions

abstract: This paper introduces the problem of combining multiple partitionings of a set of objects into a single consolidated clustering without accessing the features or algorithms that determined these partitionings. We first identify several application scenarios for the resultant 'knowledge reuse' framework that we call cluster ensembles. The cluster ensemble problem is then formalized as a combinatorial optimization problem in terms of shared mutual information. In addition to a direct maximization approach, we propose three effective and efficient techniques for obtaining high-quality combiners (consensus functions). The first combiner induces a similarity measure from the partitionings and then reclusters the objects. The second combiner is based on hypergraph partitioning. The third one collapses groups of clusters into meta-clusters which then compete for each object to determine the combined clustering. Due to the low computational costs of our techniques, it is quite feasible to use a supra-consensus function that evaluates all three approaches against the objective function and picks the best solution for a given situation. We evaluate the effectiveness of cluster ensembles in three qualitatively different application scenarios: (i) where the original clusters were formed based on non-identical sets of features, (ii) where the original clustering algorithms worked on non-identical sets of objects, and (iii) where a common data-set is used and the main purpose of combining multiple clusterings is to improve the quality and robustness of the solution. Promising results are obtained in all three situations for synthetic as well as real data-sets.


title: Perpetual environmentally powered sensor networks

abstract: Environmental energy is an attractive power source for low power wireless sensor networks. We present Prometheus, a system that intelligently manages energy transfer for perpetual operation without human intervention or servicing. Combining positive attributes of different energy storage elements and leveraging the intelligence of the microprocessor, we introduce an efficient multi-stage energy transfer system that reduces the common limitations of single energy storage systems to achieve near perpetual operation. We present our design choices, tradeoffs, circuit evaluations, performance analysis, and models. We discuss the relationships between system components and identify optimal hardware choices to meet an application's needs. Finally we present our implementation of a real system that uses solar energy to power Berkeley's Telos Mote. Our analysis predicts the system will operate for 43 years under 1% load, 4 years under 10% load, and 1 year under 100% load. Our implementation uses a two stage storage system consisting of supercapacitors (primary buffer) and a lithium rechargeable battery (secondary buffer). The mote has full knowledge of power levels and intelligently manages energy transfer to maximize lifetime.


Recent Advances in VJFETs at SemiSouth


Extending Ontolingua for Representing Control Knowledge


title: Look-ahead before you leap: end-to-end active recognition by forecasting the effect of motion

abstract: Visual recognition systems mounted on autonomous moving agents face the challenge of unconstrained data, but simultaneously have the opportunity to improve their performance by moving to acquire new views of test data. In this work, we first show how a recurrent neural network-based system may be trained to perform end-to-end learning of motion policies suited for this "active recognition" setting. Further, we hypothesize that active vision requires an agent to have the capacity to reason about the effects of its motions on its view of the world. To verify this hypothesis, we attempt to induce this capacity in our active recognition pipeline, by simultaneously learning to forecast the effects of the agent's motions on its internal representation of the environment conditional on all past views. Results across two challenging datasets confirm both that our end-to-end system successfully learns meaningful policies for active category recognition, and that "learning to look ahead" further boosts recognition performance.


title: Spatial Computing and Social Media in the Context of Disaster Management

abstract: The growing trend of using smartphones and other GPS-enabled devices has provided new opportunities for developing spatial computing applications and technologies in unanticipated and unprecedented ways. Some capabilities of today's smartphones highlight the potential of citizen sensors to enable the next generation of geoinformatics. One promising application area for this is social media and its application to disaster management. Dynamic, real-time incident information collected from onsite human responders about the extent of damage, the evolution of the event, the community's needs, and responders' ability to deal with the situation, combined with information from the larger emergency management community, could lead to more accurate and real-time situational awareness. This would enable informed decisions, better resource allocation and thus a better response and outcome to the total crisis. In this context, the US Department of Homeland Security's Science & Technology Directorate (DHS-S&T) has initiated the Social Media Alert and Response to Threats to Citizens" (SMART-C) program, which aims to develop citizen participatory sensing capabilities for decision support throughout the disaster life cycle via a multitude of devices and modalities. Here, the authors provide an overview of the envisioned SMART-C system's capabilities and discuss some of the interesting and unique challenges that arise due to the combination of spatial computing and social media within the context of disaster management.


title: Relaxed collaborative representation for pattern classification

abstract: Regularized linear representation learning has led to interesting results in image classification, while how the object should be represented is a critical issue to be investigated. Considering the fact that the different features in a sample should contribute differently to the pattern representation and classification, in this paper we present a novel relaxed collaborative representation (RCR) model to effectively exploit the similarity and distinctiveness of features. In RCR, each feature vector is coded on its associated dictionary to allow flexibility of feature coding, while the variance of coding vectors is minimized to address the similarity among features. In addition, the distinctiveness of different features is exploited by weighting its distance to other features in the coding domain. The proposed RCR is simple, while our extensive experimental results on benchmark image databases (e.g., various face and flower databases) show that it is very competitive with state-of-the-art image classification methods.


title: Black-box Testing of First-Order Logic Ontologies Using WordNet

abstract: Artificial Intelligence aims to provide computer programs with commonsense knowledge to reason about our world. This paper offers a new practical approach towards automated commonsense reasoning with first-order logic (FOL) ontologies. We propose a new black-box testing methodology of FOL SUMO-based ontologies by exploiting WordNet and its mapping into SUMO. Our proposal includes a method for the (semi-)automatic creation of a very large benchmark of competency questions and a procedure for its automated evaluation by using automated theorem provers (ATPs). Applying different quality criteria, our testing proposal enables a successful evaluation of a) the competency of several translations of SUMO into FOL and b) the performance of various automated ATPs. Finally, we also provide a fine-grained and complete analysis of the commonsense reasoning competency of current FOL SUMO-based ontologies.


title: Different voltage-dependent thresholds for inducing long-term depression and long-term potentiation in slices of rat visual cortex

abstract: IN the hippocampus and neocortex, high-frequency (tetanic) stimu-lation of an afferent pathway leads to long-term potentiation (LTP) of synaptic transmission1–5. In the hippocampus it has recently been shown that long-term depression (LTD) of excitatory transmission can also be induced by certain combinations of synaptic activation6,7. In most hippocampal8 and all neocortical pathways4,9–11 studied so far, the induction of LTP requires the activation of JV-methyl-D-aspartate (NMDA) receptorgated conductances. Here we report that LTD can occur in neurons of slices of the rat visual cortex and that the same tetanic stimulation can induce either LTP or LTD depending on the level of depolarization of the postsynaptic neuron. By applying intracellular current injections or pharmacological disinhibition to modify the depolarizing response of the postsynaptic neuron to tetanic stimulation, we show that the mechanisms of induction of LTD and LTP are both postsynaptic. LTD is obtained if postsynaptic depolarization exceeds a critical level but remains below a threshold related to NMDA receptorgated conductances, whereas LTP is induced if this second threshold is reached.


title: A Supervised Learning Method for Prediction Citation Count of Scientists in Citation Networks

abstract: Predicting the future impacts of scientists and their publications is an important task for future scientific studies. Studies predicting scientific effects in the academia are mostly based on estimating citation numbers of publications. However, there are few studies focusing on predicting the impact of scientists. Determining the potential influence of scientists is useful for future scientific studies and organizations. In this respect, we propose a new approach for predicting future impacts of scientists by considering the number of citations that each scientist individually has. A great number of studies predicting citation count of publications use content based information such as title, abstract and keywords of publication, institution of scientists, journal impact factor of publisher, etc. and they don't benefit from graph structures in citation networks. However, citations among scientific publications create more complex networks which have the potential for mining scientific impacts. So, we formulated the problem of predicting future citation counts of a given scientist, as a link prediction problem for directed, weighted and dynamic citation networks. The proposed link prediction method is able to predict links and its weights. Firstly, we introduce a dynamic metric based on increasing and decreasing trends during transitions time frames in citation networks. Then, we use the introduced dynamic proximity metric with some simple topological features in classifiers to predict citation count. Experiments are conducted on 12 sampled datasets which are extracted from two citation networks. The experimental results show that the proposed approach performs well when considering the problem of prediction links with its weights is firstly addressed in the context of link prediction problem in directed, weighted and dynamic networks.


title: Multi-Ray Channel Modeling and Wideband Characterization for Wireless Communications in the Terahertz Band

abstract: Terahertz (0.06–10 THz) Band communication is envisioned as a key technology for satisfying the increasing demand for ultra-high-speed wireless links. In this paper, first, a unified multi-ray channel model in the THz Band is developed based on ray tracing techniques, which incorporates the propagation models for the line-of-sight, reflected, scattered, and diffracted paths. The developed theoretical model is validated with the experimental measurements (0.06–1 THz) from the literature. Then, using the developed propagation models, an in-depth analysis on the THz channel characteristics is carried out. In particular, the distance-varying and frequency-selective nature of the Terahertz channel is analyzed. Moreover, the coherence bandwidth and the significance of the delay spread are studied. Furthermore, the wideband channel capacity using flat and water-filling power allocation strategies is characterized. Additionally, the temporal broadening effects of the Terahertz channel are studied. Finally, distance-adaptive and multi-carrier transmissions are suggested to best benefit from the unique relationship between distance and bandwidth. The provided analysis lays out the foundation for reliable and efficient ultra-high-speed wireless communications in the (0.06–10) THz Band.


title: An approach combined Response Surface Method and Particle Swarm Optimization to ship multidisciplinary design and optimization

abstract: Ship design is a complex endeavor requiring the successful coordination of many different disciplines. According to various disciplines requirements, how to get a balanced performance is imperative in ship design. Thus, a all-in-one Multidisciplinary Design Optimization (MDO) approach is proposed to get the optimum performance of the ship considering three disciplines, structure; cargo loads and power of propulsion. In this research a Latin Hypercube Sampling (LHS) is employed to explore the design space and to sample data for covering the design space. For the purpose of reducing the calculation and saving the develop time, a quadratic Response Surface Method (RSM) is adopted as an approximation model for solving the system design problems. Particle Swarm Optimization (PSO) is introduced to search the appropriate design result in MDO in ship design. Finally, the validity of the proposed approach is proven by a case study of a bulk carrier.


title: Boosting Response Aware Model-Based Collaborative Filtering

abstract: Recommender systems are promising for providing personalized favorite services. Collaborative filtering (CF) technologies, making prediction of users’ preference based on users’ previous behaviors, have become one of the most successful techniques to build modern recommender systems. Several challenging issues occur in previously proposed CF methods: 1) most CF methods ignore users’ response patterns and may yield biased parameter estimation and suboptimal performance; 2) some CF methods adopt heuristic weight settings, which lacks a systematical implementation; and 3) the multinomial mixture models may weaken the computational ability of matrix factorization for generating the data matrix, thus increasing the computational cost of training. To resolve these issues, we incorporate users’ response models into the probabilistic matrix factorization (PMF), a popular matrix factorization CF model, to establish the response aware probabilistic matrix factorization (RAPMF) framework. More specifically, we make the assumption on the user response as a Bernoulli distribution which is parameterized by the rating scores for the observed ratings while as a step function for the unobserved ratings. Moreover, we speed up the algorithm by a mini-batch implementation and a crafting scheduling policy. Finally, we design different experimental protocols and conduct systematical empirical evaluation on both synthetic and real-world datasets to demonstrate the merits of the proposed RAPMF and its mini-batch implementation.


title: Geometric and Illumination Invariants for Object Recognition

abstract: We propose invariant formulations that can potentially be combined into a single system. In particular, we describe a framework for computing invariant features which are insensitive to rigid motion, affine transform, changes of parameterization and scene illumination, perspective transform, and view point change. This is unlike most current research on image invariants which concentrates on either geometric or illumination invariants exclusively. The formulations are widely applicable to many popular basis representations, such as wavelets, short-time Fourier analysis, and splines. Exploiting formulations that examine information about shape and color at different resolution levels, the new approach is neither strictly global nor local. It enables a quasi-localized, hierarchical shape analysis which is rarely found in other known invariant techniques, such as global invariants. Furthermore, it does not require estimating high-order derivatives in computing invariants (unlike local invariants), whence is more robust. We provide results of numerous experiments on both synthetic and real data to demonstrate the validity and flexibility of the proposed framework.


title: Performance modeling for systematic performance tuning

abstract: The performance of parallel scientific applications depends on many factors which are determined by the execution environment and the parallel application. Especially on large parallel systems, it is too expensive to explore the solution space with series of experiments. Deriving analytical models for applications and platforms allow estimating and extrapolating their execution performance, bottlenecks, and the potential impact of optimization options. We propose to use such "performance modeling" techniques beginning from the application design process throughout the whole software development cycle and also during the lifetime of supercomputer systems. Such models help to guide supercomputer system design and re-engineering efforts to adopt applications to changing platforms and allow users to estimate costs to solve a particular problem. Models can often be built with the help of well-known performance profiling tools. We discuss how we successfully used modeling throughout the proposal, initial testing, and beginning deployment phase of the Blue Waters supercomputer system.


title: Knowledge management and organizational performance in the service industry: The role of transformational leadership beyond the effects of transactional leadership

abstract: This study examines a comprehensive model comprising of various relationships between transformational and transactional leadership, knowledge management (KM) process, and organizational performance. Data are collected from human resource managers and general managers working in 119 service firms. Exploratory factor analysis and hierarchical regression analysis are used to analyze the proposed hypotheses. The results indicate that transformational leadership has strong and positive effects on KM process and organizational performance after controlling for the effects of transactional leadership. Further, KM process partially mediates the relationship between transformational leadership and organizational performance after controlling for the effects of transactional leadership. Implications and directions for future research are also discussed.


title: Pronominal Anaphora Resolution Algorithm in Myanmar Text

abstract: One way to increase the coherence of summary generation system is to derive first the discourse structure of the text and to guide the selection of the sentences. These sentences to be included into the summary are considered by a score according to both the relevance of the sentence in a discourse tree and the coherence of the text, as given by solving anaphoric references. Anaphoric references resolution is a common phenomenon in natural language processing, and has correspondingly received a significant amount of attention in the literature. In this paper, we present a Myanmar pronominal anaphora resolution algorithm which bases on Hobbs' algorithm that works only the surface syntax of sentences in a given text. The purpose of this paper is to implement pronominal anaphora resolution system on computer in java. This study shows that Myanmar anaphora resolution algorithm perform on two kinds of data sets and corresponding results are obtained a substantial accuracy 80% that can be an acceptable resolution performance for Myanmar. In addition, this pronominal anaphora resolution algorithm will be used for summary generation system in Myanmar.


title: Measuring e‐government service value with the E‐GOVSQUAL‐RISK model

abstract: Purpose – The purpose of this paper is to study the e‐government service quality and risk perceptions of personal income taxpayers on e‐government service value.Design/methodology/approach – The study uses qualitative in‐depth interview and content analysis to explore the determinants of e‐government service quality and risk dimensions of e‐government service value.Findings – The findings suggest that perceived value of e‐government service is e‐government service quality, which consists of service design, web site design, technical support, and customer support quality. On the other hand, the three perceived risk concerns are performance, privacy, and financial audit risk.Research limitations/implications – The study interviews the small samples of income taxpayers to develop the determinants of e‐government service value, future studies should utilize a quantitative study to strengthen the results. Future researchers could also expand the results to other groups of taxpayers (e.g. corporate tax) to expl...


title: Entity-Focused Sentence Simplification for Relation Extraction

abstract: Relations between entities in text have been widely researched in the natural language processing and information-extraction communities. The region connecting a pair of entities (in a parsed sentence) is often used to construct kernels or feature vectors that can recognize and extract interesting relations. Such regions are useful, but they can also incorporate unnecessary distracting information. In this paper, we propose a rule-based method to remove the information that is unnecessary for relation extraction. Protein-protein interaction (PPI) is used as an example relation extraction problem. A dozen simple rules are defined on output from a deep parser. Each rule specifically examines the entities in one target interaction pair. These simple rules were tested using several PPI corpora. The PPI extraction performance was improved on all the PPI corpora.


title: RoomNet: End-to-End Room Layout Estimation

abstract: This paper focuses on the task of room layout estimation from a monocular RGB image. Prior works break the problem into two sub-tasks: semantic segmentation of floor, walls, ceiling to produce layout hypotheses, followed by an iterative optimization step to rank these hypotheses. In contrast, we adopt a more direct formulation of this problem as one of estimating an ordered set of room layout keypoints. The room layout and the corresponding segmentation is completely specified given the locations of these ordered keypoints. We predict the locations of the room layout keypoints using RoomNet, an end-to-end trainable encoder-decoder network. On the challenging benchmark datasets Hedau and LSUN, we achieve state-of-the-art performance along with 200× to 600× speedup compared to the most recent work. Additionally, we present optional extensions to the RoomNet architecture such as including recurrent computations and memory units to refine the keypoint locations under the same parametric capacity.


title: Scenarios-based energy dispatching of PVG/Battery/Grid-connected installation

abstract: In most grid-connected photovoltaic standard installations, the generated energy is consumed by the user and in case of excess or lack the grid intervene to either absorb energy or cover the need. Such strategy involves limitations as blackout while grid failure and grid pollution with inverters harmonics. This paper proposes a novel approach that considers the grid as a complementary source to avoid the grid-connected photovoltaic system problems. An algorithm is developed to manage energy flows of a PVG/Battery/Grid installation by minimizing the grid connection for load supply. In addition, a continuous supply of the load is guaranteed even while grid failure. The proposed strategy aims to increase the self-consumption, to avoid grid pollution and to keep saving the battery from an excessive charge and discharge. Obtained results confirm a continuous load supply during stable day as well as perturbed day and an improvement of the installation self-consumption.


title: LFIQ: Latent fingerprint image quality

abstract: Latent fingerprint images are typically obtained under non-ideal acquisition conditions, resulting in incomplete or distorted impression of a finger, and ridge structure corrupted by background noise. This necessitates involving latent experts in latent fingerprint examination, including assessing the value of a latent print as forensic evidence. However, it is now generally agreed that human factors (e.g., human visual perception, expertise of latent examiners, workload, etc.) can significantly affect the reliability and consistency of the value determinations made by latent examiners. We propose an objective quality measure for latent fingerprints, called Latent Fingerprint Image Quality (LFIQ), that can be effectively used to distinguish latent fingerprints of good quality, which do not require any human intervention, and to compensate for the subjective nature of value determination by latent examiners. We investigate several factors that determine the latent quality: (i) ridge quality based on ridge clarity and connectivity of good ridge structures, (ii) minutiae reliability based on a minutiae dictionary learnt from high quality minutia patches, and (iii) position of the finger by detecting a reference point. The proposed LFIQ metric is based on triangulation of minutiae incorporating the above three factors. Experimental results show that (i) the proposed LFIQ is a good predictor of the latent matching performance by AFIS and (ii) it is also correlated with value determination by latent examiners.


title: First evidence of histological and histochemical intraspecific differences in salivary glands in Brown Norway and albino Wistar rats*

abstract: The size of salivary glands, acinar structure, and secretion particularities differ from one species to another and are influenced by diet and environment. In order to investigate whether there are histological or histochemical differences in the salivary glands of animals from the same species, bred in identical conditions of environment and diet, we assessed two laboratory rat strains. The salivary glands from two rat strains were processed for histological and histochemical investigations. Histologically, we noticed differences in the parotid gland: seromucous acini were present in Brown Norway rat and typical serous ones in Wistar rat. Histochemically, we observed that cells in the parotid gland present a moderate PAS-positive reaction only in Wistar rat. In the mandibular gland, the size and shape of the acini were similar, whereas the cytoplasm of acinar and granular duct cells was PAS-positive in both rat strains, but with different intensities. In the sublingual gland, both PAS and Alcian blue reactions were positive in the two rat strains, with a higher intensity in the Brown Norway strain. Our study highlighted intraspecific histological and histochemical differences in salivary glands from two rat strains bred and fed in identical conditions.


title: A deep architecture for unified aesthetic prediction

abstract: Image aesthetics has become an important criterion for visual content curation on social media sites and media content repositories. Previous work on aesthetic prediction models in the computer vision community has focused on aesthetic score prediction or binary image labeling. However, raw aesthetic annotations are in the form of score histograms and provide richer and more precise information than binary labels or mean scores. Consequently, in this work we focus on the rarely-studied problem of predicting aesthetic score distributions and propose a novel architecture and training procedure for our model. Our model achieves state-of-the-art results on the standard AVA large-scale benchmark dataset for three tasks: (i) aesthetic quality classification; (ii) aesthetic score regression; and (iii) aesthetic score distribution prediction, all while using one model trained only for the distribution prediction task. We also introduce a method to modify an image such that its predicted aesthetics changes, and use this modification to gain insight into our model.


title: A Domain-Independent Algorithm for Plan Adaptation

abstract: The paradigms of transformational planning, case-based planning, and plan debugging all involve a process known as plan adaptation -- modifying or repairing an old plan so it solves a new problem. In this paper we provide a domain-independent algorithm for plan adaptation, demonstrate that it is sound, complete, and systematic, and compare it to other adaptation algorithms in the literature. ::: ::: Our approach is based on a view of planning as searching a graph of partial plans. Generative planning starts at the graph's root and moves from node to node using plan-refinement operators. In planning by adaptation, a library plan--an arbitrary node in the plan graph--is the starting point for the search, and the plan-adaptation algorithm can apply both the same refinement operators available to a generative planner and can also retract constraints and steps from the plan. Our algorithm's completeness ensures that the adaptation algorithm will eventually search the entire graph and its systematicity ensures that it will do so without redundantly searching any parts of the graph.


title: Text Clustering with Feature Selection by Using Statistical Data

abstract: Feature selection is an important method for improving the efficiency and accuracy of text categorization algorithms by removing redundant and irrelevant terms from the corpus. In this paper, we propose a new supervised feature selection method, named CHIR, which is based on the chi2 statistic and new statistical data that can measure the positive term-category dependency. We also propose a new text clustering algorithm, named text clustering with feature selection (TCFS). TCFS can incorporate CHIR to identify relevant features (i.e., terms) iteratively, and the clustering becomes a learning process. We compared TCFS and the K-means clustering algorithm in combination with different feature selection methods for various real data sets. Our experimental results show that TCFS with CHIR has better clustering accuracy in terms of the F-measure and the purity.


title: Efficient dense scene flow from sparse or dense stereo data

abstract: This paper presents a technique for estimating the three-dimensional velocity vector field that describes the motion of each visible scene point (scene flow). The technique presented uses two consecutive image pairs from a stereo sequence. The main contribution is to decouple the position and velocity estimation steps, and to estimate dense velocities using a variational approach. We enforce the scene flow to yield consistent displacement vectors in the left and right images. The decoupling strategy has two main advantages: Firstly, we are independent in choosing a disparity estimation technique, which can yield either sparse or dense correspondences, and secondly, we can achieve frame rates of 5 fps on standard consumer hardware. The approach provides dense velocity estimates with accurate results at distances up to 50 meters.


title: High frequency integrated Point of Load (POL) module with PCB embedded inductor substrate

abstract: In this paper, a novel alloy flake composite material is used to demonstrate the PCB integrated magnetic component with low temperature fabrication process. The most important benefit of the alloy flake composite core is easy to be patterned into any desired shape for integration. Compared with the traditional flake composite, the permeability and core loss of the new flake composite are improved prominently, by doing some lateral alignment of the flake and increasing the volume ratio of the alloy. The layerwise magnetic core is sandwiched into multilayer PCB using conventional PCB laminating technique. It has been proved that the manufacturing process, such as laminating, cutting and drilling, has very little impact on the magnetic properties of the flake core. Based on a simple 4-layer PCB substrate with embedded core, the megahertz 3D integrated Point of Load (POL) modules are built, which achieve more than 700 W/in3 power density. The PCB modules survive after hundreds of thermal variation cycles, validating the reliability and compatibility of the alloy flake composite material with PCB integration. In addition, the application of standard PCB process reduces the cost for manufacturing such integrated modules due to the easy automation and low temperature process.


Mapping Dialectal Variation by Querying Social Media


title: Six Challenges for Neural Machine Translation

abstract: We explore six challenges for neural machine translation: domain mismatch, amount of training data, rare words, long sentences, word alignment, and beam search. We show both deficiencies and improvements over the quality of phrase-based statistical machine translation.


title: Focusing bistatic synthetic aperture radar using dip move out

abstract: The appearance of new synthetic aperture radar (SAR) acquisition techniques based on opportunity sources enhances interest in bistatic geometries. In seismic data acquisition, each source is currently accompanied by up to 10 000 receivers, and in the last two decades, the bistatic geometry has been carefully studied by scores of authors. Rather then introducing new focusing techniques, within the first-order Born approximation (no multiple reflections), seismic bistatic acquisitions are transformed into monostatic ones using a simple operator named "dip move out" (DMO). In essence, the elliptical locus of the reflectors corresponding to a spike in the bistatic survey is forward modeled as if observed in a monostatic one. The outcome of the model, the so-called smile, is a short operator, slowly time varying but space stationary. To transform a bistatic survey into a monostatic one, it is enough to convolve the initial dataset with this smile. Based on the well-known similarity between seismic and SAR surveys, DMO is first described in its simple geometric understanding and is then used in the SAR case. The same processing that is being used for movement compensation can be applied to the bistatic to monostatic survey transformation. Synthetic examples are also provided.


title: [The hemodynamic and oxygen dynamic effects of mechanical ventilation in chronic cor pulmonale].

abstract: Observation by using Swan-Ganz catheter on hemodynamic and oxygen dynamic changes before and after assisted volume controlled mechanical ventilation (MV) was carried out in 11 cases of chronic cor pulmonale complicated with pulmonary encephalopathy. Pulmonary vascular resistance and pulmonary artery pressure were significantly decreased by the relief of hypoxia pulmonary vasoconstriction after MV. Because of the relax of sympathetic tension, the heart rate, arterial blood pressure, pulmonary capillary wedge pressure and central venous pressure reduced simultaneously. Left and right ventricular stroke work decreased after MV, without significant change in cardiac output. Venous admixture (Qs/Qt) was markedly reduced by MV and oxygen supply-demand ratio raised. It is thus shown that marked improvement of hemodynamic and oxygen dynamic status can be induced by mechanical ventilation at an opportune moment.


title: Towards Knowledge Discovery in Big Data

abstract: Analytics-as-a-Service (AaaS) has become indispensable because it affords stakeholders to discover knowledge in Big Data. Previously, data stored in data warehouses follow some schema and standardization which leads to efficient data mining. However, the Big Data epoch has witnessed the rise of structured, semi-structured, and unstructured data, a trend that motivated enterprises to employ the NoSQL data storages to accommodate the high-dimensional data. Unfortunately, the existing data mining techniques which are designed for schema-oriented storages are non-applicable to the unstructured data style. Thus, the AaaS though still in its infancy, is gaining widespread attention for its ability to provide novel ways and opportunities to mine the heterogeneous data. In this paper, we discuss our AaaS tool that performs terms and topics extraction and organization from unstructured data sources such as NoSQL databases, textual contents (e.g., websites), and structured sources (e.g. SQL). The tool is built on methodologies such as tagging, filtering, association maps, and adaptable dictionary. The evaluation of the tool shows high accuracy in the mining process.


title: Students’ approaches to groupwork in a blended course, associations with perceptions of the online environment and academic achievement – when is learning engaged?

abstract: University teachers provided first year Arts students with hundreds of cinematic images online to analyse as a key part of their predominantly face-to-face undergraduate course. This qualitative study investigates the extent to which the groups engaged in learning involving their analysis of the images and how this was related to their perception of the ICT-mediated environment. Interviews and questionnaires completed by students revealed that the extent of engaged learning was related to the quality of the approach to groupwork reported by the students, the quality of their approach to the analysis of the images and their perceptions of key aspects of the online environment which provided the images. The findings have implications for the design and approach to teaching best suited for students involved in groupwork and the use of ICT resources provided to promote engaged experiences of learning.


title: Formulation and characterization of caffeine biodegradable chewing gum delivery system for alertness using plasticized poly (D,L-lactic acid) as gum base

abstract: Purpose: To formulate and evaluate biodegradable plasticized poly(D,L-lactic acid) as a base for caffeine-medicated chewing gums (MCGs) to increase alertness. Methods: Biodegradable plasticized poly(D,L-lactic acid) caffeine-MCGs were formulated with a fixed concentration (15 % w/w) of different plasticizers for different formulations. Substances used as plasticizers were triacetin, stearic acid, PEG-600, tributyl citrate, soya oil, sunflower oil, glycerol, triethyl citrate, PEG-4000, and castor oil. The characteristics of the gum formulations were examined using texture profile analysis (TPA), and also evaluated for biodegradation, microstructure`, in vitro drug release, and sensory features. Results: The MCG-1 and MCG-7 formulations with triacetin and glycerol as plasticizers, respectively, exhibited a biodegradation score of 1 and 2, respectively, indicating considerable biodegradation. The formulation with triacetin as a plasticizer exhibited TPA values of 3750.52 g, -51.13 g.s, 1580.88, 1526.23, and 0.364 for hardness, adhesiveness, gumminess, chewiness, and cohesiveness, respectively; these values are similar to those of the Military Energy Gum. The microstructure of the gum base was characterized using scanning electron microscopy to determine surface properties. The in vitro drug release was determined as 97 % after a mean chewing time of 15 min by using a specially designed in vitro chewing machine. Conclusion: Plasticized poly (D,L-lactic acid) gum formulation is suitable for the delivery of caffeine and can be used as an alternative MCG for effective management of fatigue.


title: Acute calcific tendinitis of peroneus longus

abstract: We present a case of acute painful calcific tendinitis in the peroneus longus tendon which responded dramatically to a single steroid injection. Calcific tendinitis is most commonly seen in the supraspinatus tendon ; less common sites include the wrist, elbow, deltoid insertion, gluteus maximus, knee, neck and forefoot (Hayes et al 1987). To our knowledge acute calcific tendinitis in peroneus longus has not previously been reported. r


title: The Effect of Aesthetic on the Usability of Data Visualization

abstract: Aesthetic seems currently under represented in most current data visualization evaluation methodologies. This paper investigates the results of an online survey of 285 participants, measuring both perceived aesthetic as well as the efficiency and effectiveness of retrieval tasks across a set of 11 different data visualization techniques. The data visualizations represent an identical hierarchical dataset, which has been normalized in terms of color, typography and layout balance. This study measured parameters such as speed of completion, accuracy rate, task abandonment and latency of erroneous response. Our findings demonstrate a correlation between latency in task abandonment and erroneous response time in relation to visualization's perceived aesthetic. These results support the need for an increased recognition for aesthetic in the typical evaluation process of data visualization techniques.


title: AI tools for software development effort estimation

abstract: Software development involves a number of interrelated factors which affect development effort and productivity. Since many of these relationships are not well understood, accurate estimation of software development time and effort is a difficult problem. Most estimation models in use or proposed in the literature are based on regression techniques. This paper examines the potential of two artificial intelligence approaches, viz. artificial neural networks and case-based reasoning, for creating development effort estimation models. Artificial neural networks can provide accurate estimates when there are complex relationships between variables and where the input data is distorted by high noise levels. Case-based reasoning solves problems by adapting solutions from old problems that are similar to the current problem. This research examines both the performance of backpropagation artificial neural networks in estimating software development effort and the potential of case-based reasoning for development estimation using the same dataset.


A study on Deep Machine Learning Algorithms for diagnosis of diseases


title: TGIF: A New Dataset and Benchmark on Animated GIF Description

abstract: With the recent popularity of animated GIFs on social media, there is need for ways to index them with rich metadata. To advance research on animated GIF understanding, we collected a new dataset, Tumblr GIF (TGIF), with 100K animated GIFs from Tumblr and 120K natural language descriptions obtained via crowdsourcing. The motivation for this work is to develop a testbed for image sequence description systems, where the task is to generate natural language descriptions for animated GIFs or video clips. To ensure a high quality dataset, we developed a series of novel quality controls to validate free-form text input from crowdworkers. We show that there is unambiguous association between visual content and natural language descriptions in our dataset, making it an ideal benchmark for the visual content captioning task. We perform extensive statistical analyses to compare our dataset to existing image and video description datasets. Next, we provide baseline results on the animated GIF description task, using three representative techniques: nearest neighbor, statistical machine translation, and recurrent neural networks. Finally, we show that models fine-tuned from our animated GIF description dataset can be helpful for automatic movie description.


title: Adaptive Security for Threshold Cryptosystems

abstract: We present adaptively-secure efficient solutions to several central problems in the area of threshold cryptography. We prove these solutions to withstand adaptive attackers that choose parties for corruption at any time during the run of the protocol. In contrast, all previously known efficient protocols for these problems were proven secure only against less realistic static adversaries that choose and fix the subset of corrupted parties before the start of the protocol run. Specifically, we provide adaptively-secure solutions for distributed key generation in discrete-log based cryptosystems, and for the problem of distributed generation of DSS signatures (threshold DSS). We also show how to transform existent static solutions for threshold RSA and proactive schemes to withstand the stronger adaptive attackers. In doing so, we introduce several techniques for the design and analysis of adaptivelysecure protocols that may well find further applications.


title: An FPGA implementation of incremental clustering for radar pulse deinterleaving

abstract: Incremental clustering is the unsupervised classification of dynamic streaming data samples into related groups called clusters. The process considers each data point only once so it is applicable to real-time problems requiring low latency solutions. One such application is the deinterleaving of radar pulse streams in an electronic warfare (EW) systems. Given a single stream of combined radar signals deinterleaving attempts to identify individual radar emitters based on characteristics of the received signal. This thesis focuses on implementing an incremental clustering algorithm on a fieldprogrammable gate array (FPGA) for the purposes of radar pulse deinterleaving. We introduce ICED, an algorithm for the I ncremental C lustering of Evolving Data, and discuss the details of implementing it in an FPGA. Experimental results show the applicability of the algorithm to the real-time requirements of EW pulse deinterleaving. The resulting design provides a 16 cluster implementation that consumes 70% of a Xilinx Virtex-5 SX95T FPGA and requires a processing latency of 420ns, resulting in a 39x speedup over software.


title: Porphyromonas gingivalis suppresses adaptive immunity in periodontitis, atherosclerosis, and Alzheimer’s disease

abstract: Porphyromonas gingivalis , a keystone pathogen in chronic periodontitis, has been found to associate with remote body organ inflammatory pathologies, including atherosclerosis and Alzheimer’s disease (AD). Although P. gingivalis has a plethora of virulence factors, much of its pathogenicity is surprisingly related to the overall immunosuppression of the host. This review focuses on P. gingivalis aiding suppression of the host’s adaptive immune system involving manipulation of cellular immunological responses, specifically T cells and B cells in periodontitis and related conditions. In periodontitis, this bacterium inhibits the synthesis of IL-2 and increases humoral responses. This reduces the inflammatory responses related to T- and B-cell activation, and subsequent IFN-γ secretion by a subset of T cells. The T cells further suppress upregulation of programmed cell death-1 (PD-1)-receptor on CD + cells and its ligand PD-L1 on CD11b + -subset of T cells. IL-2 downregulates genes regulated by immune response and induces a cytokine pattern in which the Th17 lineage is favored, thereby modulating the Th17/T-regulatory cell (Treg) imbalance. The suppression of IFN-γ-stimulated release of interferon-inducible protein-10 (IP-10) chemokine ligands [ITAC (CXCL11) and Mig (CXCL9)] by P. gingivalis capsular serotypes triggers distinct T cell responses and contributes to local immune evasion by release of its outer membrane vesicles. In atherosclerosis, P. gingivalis reduces Tregs, transforms growth factor beta-1 (TGFβ-1), and causes imbalance in the Th17 lineage of the Treg population. In AD, P. gingivalis may affect the blood–brain barrier permeability and inhibit local IFN-γ response by preventing entry of immune cells into the brain. The scarcity of adaptive immune cells in AD neuropathology implies P. gingivalis infection of the brain likely causing impaired clearance of insoluble amyloid and inducing immunosuppression. By the effective manipulation of the armory of adaptive immune suppression through a plethora of virulence factors, P. gingivalis may act as a keystone organism in periodontitis and in related systemic diseases and other remote body inflammatory pathologies. Keywords: P. gingivalis; adaptive immunity; suppression; periodontitis; atherosclerosis; Alzheimer’s disease (Published: 22 November 2016) Citation: Journal of Oral Microbiology 2016, 8: 33029 - http://dx.doi.org/10.3402/jom.v8.33029


title: Active thermal management for Interior Permanent Magnet Synchronous Machine (IPMSM) drives

abstract: This paper proposes an active temperature management scheme for Interior Permanent Magnet Synchronous Machine (IPMSM) drives based on the model predictive control concept. The proposed control scheme can adaptively set torque limit based on thermal state of the machine in order to limit the machine winding and end-winding temperatures. A temperature feedback loop is also adopted to compensate the error in the machine thermal model. The proposed control scheme is assessed by experiments on a laboratory machine drive system and simulated for traction drives over World-wide harmonized Light duty Test Cycle (WLTC) cycles. Compared with conventional traction control scheme, the proposed scheme can effectively reduce peak temperature and hence thermal stress of the machine.


title: Globally Consistent Wrinkle-Aware Shading of Line Drawings

abstract: Shading is a tedious process for artists involved in 2D cartoon and manga production given the volume of contents that the artists have to prepare regularly over tight schedule. While we can automate shading production with the presence of geometry, it is impractical for artists to model the geometry for every single drawing. In this work, we aim to automate shading generation by analyzing the local shapes, connections, and spatial arrangement of wrinkle strokes in a clean line drawing. By this, artists can focus more on the design rather than the tedious manual editing work, and experiment with different shading effects under different conditions. To achieve this, we have made three key technical contributions. First, we model five perceptual cues by exploring relevant psychological principles to estimate the local depth profile around strokes. Second, we formulate stroke interpretation as a global optimization model that simultaneously balances different interpretations suggested by the perceptual cues and minimizes the interpretation discrepancy. Lastly, we develop a wrinkle-aware inflation method to generate a height field for the surface to support the shading region computation. In particular, we enable the generation of two commonly-used shading styles: 3D-like soft shading and manga-style flat shading.


title: Kansei Engineering: A new ergonomic consumer-oriented technology for product development

abstract: Abstract Kansei Engineering was developed as a consumer-oriented technology for new product development. It is defined as “translating technology of a consumer's feeling and image for a product into design elements”. Kansei Engineering (KE) technology is classified into three types, KE Type I, II, and III. KE Type I is a category classification on the new product toward the design elements. Type II utilizes the current computer technologies such as Expert System, Neural Network Model and Genetic Algorithm. Type III is a model using a mathematical structure. Kansei Engineering has permeated Japanese industries, including automotive, electrical appliance, construction, clothing and so forth. The successful companies using Kansei Engineering benefited from good sales regarding the new consumer-oriented products. Relevance to industry Kansei Engineering is utilized in the automotive, electrical appliance, construction, clothing and other industries. This paper provides help to potential product designers in these industries.


title: Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices

abstract: Artificial Intelligence (AI) has long promised to increase healthcare affordability, quality and accessibility but FDA, until recently, had never authorized an autonomous AI diagnostic system. This pivotal trial of an AI system to detect diabetic retinopathy (DR) in people with diabetes enrolled 900 subjects, with no history of DR at primary care clinics, by comparing to Wisconsin Fundus Photograph Reading Center (FPRC) widefield stereoscopic photography and macular Optical Coherence Tomography (OCT), by FPRC certified photographers, and FPRC grading of Early Treatment Diabetic Retinopathy Study Severity Scale (ETDRS) and Diabetic Macular Edema (DME). More than mild DR (mtmDR) was defined as ETDRS level 35 or higher, and/or DME, in at least one eye. AI system operators underwent a standardized training protocol before study start. Median age was 59 years (range, 22-84 years); among participants, 47.5% of participants were male; 16.1% were Hispanic, 83.3% not Hispanic; 28.6% African American and 63.4% were not; 198 (23.8%) had mtmDR. The AI system exceeded all pre-specified superiority endpoints at sensitivity of 87.2% (95% CI, 81.8-91.2%) (>85%), specificity of 90.7% (95% CI, 88.3-92.7%) (>82.5%), and imageability rate of 96.1% (95% CI, 94.6-97.3%), demonstrating AI's ability to bring specialty-level diagnostics to primary care settings. Based on these results, FDA authorized the system for use by health care providers to detect more than mild DR and diabetic macular edema, making it, the first FDA authorized autonomous AI diagnostic system in any field of medicine, with the potential to help prevent vision loss in thousands of people with diabetes annually. ClinicalTrials.gov NCT02963441.


title: Joint Domain Alignment and Discriminative Feature Learning for Unsupervised Deep Domain Adaptation

abstract: Recently, considerable effort has been devoted to deep domain adaptation in computer vision and machine learning communities. However, most of existing work only concentrates on learning shared feature representation by minimizing the distribution discrepancy across different domains. Due to the fact that all the domain alignment approaches can only reduce, but not remove the domain shift, target domain samples distributed near the edge of the clusters, or far from their corresponding class centers are easily to be misclassified by the hyperplane learned from the source domain. To alleviate this issue, we propose to joint domain alignment and discriminative feature learning, which could benefit both domain alignment and final classification. Specifically, an instance-based discriminative feature learning method and a center-based discriminative feature learning method are proposed, both of which guarantee the domain invariant features with better intra-class compactness and inter-class separability. Extensive experiments show that learning the discriminative features in the shared feature space can significantly boost the performance of deep domain adaptation methods.


title: Cross-sectional analysis of the mandibular lingual concavity using cone beam computed tomography.

abstract: OBJECTIVES ::: To study the prevalence and the degree of lingual concavity in the edentulous first molar region from cone beam computed tomography (CBCT) scans of the mandibles. ::: ::: ::: MATERIAL AND METHODS ::: Qualified cross-sectional images in mandibular first molar edentulous region taken from CBCT were selected. The mandible morphology 2 mm above the inferior alveolar canal (IAC) was classified into the convex (C), parallel (P) and undercut (U) type, based on the presence of lingual concavity and the shape of alveolar ridge. The prevalence of each group was determined. Subsequently, the lingual concavity characters, including the depth, the angulation and the vertical location were determined by the measurements of selected anatomic landmarks. ::: ::: ::: RESULTS ::: One hundred and three subjects (mean age 51 with a range of 23.7-70.4 years) were studied. The U type was the most prevalent, accounting for 66% of the study population. The mean undercut depth and angulation at the level 2 mm above IAC were on average 2.4 mm and 57.7°. The mean vertical distances from the most prominent point (P) of the lingual concavity to the cemento-enamel junction of second premolar and the inferior border of the mandible were 11.7 and 14.9 mm, respectively. ::: ::: ::: CONCLUSIONS ::: The anatomic location and the degree of the lingual concavity presented in this article add more information in implant treatment planning in the mandibular first molar edentulous region.


title: Authoring Narrative Visualizations with Ellipsis

abstract: Data visualization is now a popular medium for journalistic storytelling. However, current visualization tools either lack support for storytelling or require significant technical expertise. Informed by interviews with journalists, we introduce a model of storytelling abstractions that includes state-based scene structure, dynamic annotations and decoupled coordination of multiple visualization components. We instantiate our model in Ellipsis: a system that combines a domain-specific language DSL for storytelling with a graphical interface for story authoring. User interactions are automatically translated into statements in the Ellipsis DSL. By enabling storytelling without programming, the Ellipsis interface lowers the threshold for authoring narrative visualizations. We evaluate Ellipsis through example applications and user studies with award-winning journalists. Study participants find Ellipsis to be a valuable prototyping tool that can empower journalists in the creation of interactive narratives.


title: Numerical representations in primates.

abstract: Research has demonstrated that human infants and nonhuman primates have a rudimentary numerical system that enables them to count objects or events. More recently, however, studies using a preferential looking paradigm have suggested that preverbal human infants are capable of simple arithmetical operations, such as adding and subtracting a small number of visually presented objects. These findings implicate a relatively sophisticated representational system in the absence of language. To explore the evolutionary origins of this capacity, we present data from an experiment with wild rhesus monkeys (Macaca mulatta) that methodologically mirrors those conducted on human infants. Results suggest that rhesus monkeys detect additive and subtractive changes in the number of objects present in their visual field. Given the methodological and empirical similarities, it appears that nonhuman primates such as rhesus monkeys may also have access to arithmetical representations, although alternative explanations must be considered for both primate species.


title: Radio-Aware Service-Level Scheduling to Minimize Downlink Traffic Delay Through Mobile Edge Computing

abstract: One of the most challenging problems in mobile broadband networks is how to assign the available radio resources among the different mobile users. Traditionally, research proposals are either specific to some type of traffic or deal with computationally intensive algorithms aimed at optimizing the delivery of general purpose traffic. Consequently, commercial networks do not incorporate these mechanisms due to the limited hardware resources at the mobile edge. Emerging 5G architectures introduce cloud computing principles to add flexible computational resources to Radio Access Networks. This paper makes use of the Mobile Edge Computing concepts to introduce a new element, denoted as Mobile Edge Scheduler, aimed at minimizing the mean delay of general traffic flows in the LTE downlink. This element runs close to the eNodeB element and implements a novel flow-aware and channel-aware scheduling policy in order to accommodate the transmissions to the available channel quality of end users.


title: Meta-Learning with Hessian-Free Approach in Deep Neural Nets Training

abstract: Meta-learning is a promising method to achieve efficient training method towards deep neural net and has been attracting increases interests in recent years. But most of the current methods are still not capable to train complex neuron net model with long-time training process. In this paper, a novel second-order meta-optimizer, named Meta-learning with Hessian-Free(MLHF) approach, is proposed based on the Hessian-Free approach. Two recurrent neural networks are established to generate the damping and the precondition matrix of this Hessian-Free framework. A series of techniques to meta-train the MLHF towards stable and reinforce the meta-training of this optimizer, including the gradient calculation of $H$. Numerical experiments on deep convolution neural nets, including CUDA-convnet and ResNet18(v2), with datasets of CIFAR10 and ILSVRC2012, indicate that the MLHF shows good and continuous training performance during the whole long-time training process, i.e., both the rapid-decreasing early stage and the steadily-deceasing later stage, and so is a promising meta-learning framework towards elevating the training efficiency in real-world deep neural nets.


Discovery of Frequent Episodes in Event Sequences


title: An Experimental Survey on Correlation Filter-based Tracking

abstract: Over these years, Correlation Filter-based Trackers (CFTs) have aroused increasing interests in the field of visual object tracking, and have achieved extremely compelling results in different competitions and benchmarks. In this paper, our goal is to review the developments of CFTs with extensive experimental results. 11 trackers are surveyed in our work, based on which a general framework is summarized. Furthermore, we investigate different training schemes for correlation filters, and also discuss various effective improvements that have been made recently. Comprehensive experiments have been conducted to evaluate the effectiveness and efficiency of the surveyed CFTs, and comparisons have been made with other competing trackers. The experimental results have shown that state-of-art performance, in terms of robustness, speed and accuracy, can be achieved by several recent CFTs, such as MUSTer and SAMF. We find that further improvements for correlation filter-based tracking can be made on estimating scales, applying part-based tracking strategy and cooperating with long-term tracking methods.


title: A constant-factor approximation algorithm for the k MST problem (extended abstract)

abstract: A process for producing collagen sponge which is insoluble but highly swellable in water, the sponge having a velour-like surface and being particularly suited for medical and cosmetic applications.


title: BUSINESS INTELLIGENCE AND NOSQL DATABASES

abstract: NoSQL databases become more and more popular, not only in typical Internet applications. They allow to store large volumes of data (so called big data), while ensuring fast retrieving and fast appending. The main disadvantage of NoSQL databases is that they do not use relational model of data and usually do not offer any declarative query language similar to SQL. This raises the question how NoSQL databases can be used for OLAP processing and other Business Intelligence tasks. In the paper the author presents the most common types of NoSQL databases, describes MapReduce paradigm and discusses models of OLAP processing for such databases. Finally some preliminary results of aggregation performance in non-relational environment are presented.


title: Collection tree protocol

abstract: This paper presents and evaluates two principles for wireless routing protocols. The first is datapath validation: data traffic quickly discovers and fixes routing inconsistencies. The second is adaptive beaconing: extending the Trickle algorithm to routing control traffic reduces route repair latency and sends fewer beacons. We evaluate datapath validation and adaptive beaconing in CTP Noe, a sensor network tree collection protocol. We use 12 different testbeds ranging in size from 20--310 nodes, comprising seven platforms, and six different link layers, on both interference-free and interference-prone channels. In all cases, CTP Noe delivers > 90% of packets. Many experiments achieve 99.9%. Compared to standard beaconing, CTP Noe sends 73% fewer beacons while reducing topology repair latency by 99.8%. Finally, when using low-power link layers, CTP Noe has duty cycles of 3% while supporting aggregate loads of 30 packets/minute.