title: Gamification. using game-design elements in non-gaming contexts

abstract: "Gamification" is an informal umbrella term for the use of video game elements in non-gaming systems to improve user experience (UX) and user engagement. The recent introduction of 'gamified' applications to large audiences promises new additions to the existing rich and diverse research on the heuristics, design patterns and dynamics of games and the positive UX they provide. However, what is lacking for a next step forward is the integration of this precise diversity of research endeavors. Therefore, this workshop brings together practitioners and researchers to develop a shared understanding of existing approaches and findings around the gamification of information systems, and identify key synergies, opportunities, and questions for future research.


title: Cloud-Scale Entity Resolution: Current State and Open Challenges

abstract: Entity resolution (ER) is a process to identify records in information systems, which refer to the same real-world entity. Because in the two recent decades the data volume has grown so large, parallel techniques are called upon to satisfy the ER requirements of high performance and scalability. The development of parallel ER has reached a relatively prosperous stage, and has found its way into several applications. In this work, we first comprehensively survey the state of the art of parallel ER approaches. From the comprehensive overview, we then extract the classification criteria of parallel ER, classify and compare these approaches based on these criteria. Finally, we identify open research questions and challenges and discuss potential solutions and further research potentials in this field.


title: Effective LHC measurements with matrix elements and machine learning

abstract: One major challenge for the legacy measurements at the LHC is that the likelihood function is not tractable when the collected data is high-dimensional and the detector response has to be modeled. We review how different analysis strategies solve this issue, including the traditional histogram approach used in most particle physics analyses, the Matrix Element Method, Optimal Observables, and modern techniques based on neural density estimation. We then discuss powerful new inference methods that use a combination of matrix element information and machine learning to accurately estimate the likelihood function. The MadMiner package automates all necessary data-processing steps. In first studies we find that these new techniques have the potential to substantially improve the sensitivity of the LHC legacy measurements.


title: Adaptive IO System (ADIOS)

abstract: ADIOS is a state of the art componentization of the IO system that has demonstrated impressive IO performance results on the Cray XT system at ORNL. ADIOS separates the selection and implementation of any particular IO routines from the scientific code offering unprecedented flexibility in the choices for processing and storing data. The API was modelled on F90 IO routines emphasizing simplicity and clarity using external metadata for richness. The metadata is described in a stand-along XML file that is parsed once on code startup and determines what IO routines and parameters are used by the client code for each grouping of data elements generated by the code. By employing this API, a simple change to an entry in the XML file changes the codes to use either synchronous MPI-IO, collective MPI-IO, parallel HDF5, pnetcdf, NULL (no output), or asynchronous transports such as the Rutgers DART implementation and the Georgia Tech DataTap method. Simply by restarting the code, the new IO routines selected in the XML file will be employed. Furthermore, we have been defining additional metadata tags to support in-situ visualization solely through changes in the XML metadata file. The power of this technique is demonstrated on the GTC, GTC_S, XGC1, S3D, Chimera, and Flash codes. We show that when these codes run on a large number of processors, they can sustain high I/O bandwidth when they write out their restart and analysis files.


title: MERO: A Statistical Approach for Hardware Trojan Detection

abstract: In order to ensure trusted in---field operation of integrated circuits, it is important to develop efficient low---cost techniques to detect malicious tampering (also referred to as Hardware Trojan ) that causes undesired change in functional behavior. Conventional post--- manufacturing testing, test generation algorithms and test coverage metrics cannot be readily extended to hardware Trojan detection. In this paper, we propose a test pattern generation technique based on multiple excitation of rare logic conditions at internal nodes. Such a statistical approach maximizes the probability of inserted Trojans getting triggered and detected by logic testing, while drastically reducing the number of vectors compared to a weighted random pattern based test generation. Moreover, the proposed test generation approach can be effective towards increasing the sensitivity of Trojan detection in existing side---channel approaches that monitor the impact of a Trojan circuit on power or current signature. Simulation results for a set of ISCAS benchmarks show that the proposed test generation approach can achieve comparable or better Trojan detection coverage with about 85% reduction in test length on average over random patterns.


title: Long-lasting hyperalgesia induced by fentanyl in rats: preventive effect of ketamine.

abstract: BACKGROUND ::: It has been reported that mu-opioid receptor activation leads to a sustained increase in glutamate synaptic effectiveness at the N-methyl-D-aspartate (NMDA) receptor level, a system associated with central hypersensitivity to pain. One hypothesis is that postoperative pain may result partly from the activation of NMDA pain facilitatory processes induced by opiate treatment per se. The authors tested here the effectiveness of the opiate analgesic fentanyl for eliciting a delayed enhancement in pain sensitivity. ::: ::: ::: METHODS ::: The consequences of four bolus injections (every 15 min) of fentanyl (20-100 microg/kg per injection, subcutaneously) on immediate (for several hours) and long-term (for several days) sensitivity to nociceptive stimuli in the rat (paw-pressure vocalization test) were evaluated. The effects of the combination of the NMDA-receptor antagonist ketamine (10 mg/kg, subcutaneously) with fentanyl also were assessed. ::: ::: ::: RESULTS ::: Fentanyl administration exhibited a biphasic time-dependent effect: first, an early response (for 2-5 h) associated with a marked increase in nociceptive threshold (analgesia), and second, a later response associated with sustained lowering of the nociceptive threshold (5 days for the longest effect) below the basal value (30% of decrease for the maximal effect) indicative of hyperalgesia. The higher the fentanyl dose used, the more pronounced was the fentanyl-induced hyperalgesia. Ketamine pretreatment, which had no analgesic effect on its own, enhanced the earlier response (analgesia) and prevented the development of long-lasting hyperalgesia. ::: ::: ::: CONCLUSIONS ::: Fentanyl activates NMDA pain facilitatory processes, which oppose analgesia and lead to long-lasting enhancement in pain sensitivity.


title: A Rapid and Easy Method for the Detection of Microbial Cellulases on Agar Plates Using Gram’s Iodine

abstract: Screening for cellulase-producing microorganisms is routinely done on carboxymethylcellulose (CMC) plates. The culture plates are flooded either with 1% hexadecyltrimethyl ammonium bromide or with 0.1% Congo red followed by 1 M NaCl. In both cases, it takes a minimum of 30 to 40 minutes to obtain the zone of hydrolysis after flooding, and the hydrolyzed area is not sharply discernible. An improved method is reported herein for the detection of extracellular cellulase production by microorganisms by way of plate assay. In this method, CMC plates were flooded with Gram's iodine instead of the reagents just mentioned. Gram's iodine formed a bluish-black complex with cellulose but not with hydrolyzed cellulose, giving a sharp and distinct zone around the cellulase-producing microbial colonies within 3 to 5 minutes. The new method is rapid and efficient; therefore, it can be easily performed for screening large numbers of microbial cultures of both bacteria and fungi. This is the first report on the use of Gram's iodine for the detection of cellulase production by microorganisms using plate assay.


title: Evolutionary many-objective optimization using ensemble fitness ranking

abstract: In this paper, a new framework, called ensemble fitness ranking (EFR), is proposed for evolutionary many-objective optimization that allows to work with different types of fitness functions and ensemble ranking schemes. The framework aims to rank the solutions in the population more appropriately by combing the ranking results from many simple individual rankers. As to the form of EFR, it can be regarded as an extension of average and maximum ranking methods which have been shown promising for many-objective problems. The significant change is that EFR adopts more general fitness functions instead of objective functions, which would make it easier for EFR to balance the convergence and diversity in many-objective optimization. In the experimental studies, the influence of several fitness functions and ensemble ranking schemes on the performance of EFR is fist investigated. Afterwards, EFR is compared with two state-of-the-art methods (MOEA/D and NSGA-III) on well-known test problems. The computational results show that EFR significantly outperforms MOEA/D and NSGA-III on most instances, especially for those having a high number of objectives.


title: Flexible a-IGZO TFT amplifier fabricated on a free standing polyimide foil operating at 1.2 MHz while bent to a radius of 5 mm

abstract: We present flexible common source and cascode amplifiers fabricated on a free-standing plastic foil, using amorphous-Indium-Gallium-Zinc-Oxide (a-IGZO) TFTs with minimum channel lengths of 2.5 μm. Amplifiers are operated at a supply voltage V DD of 5 V, and exhibit maximum cutoff frequencies f C of 1.2 MHz. The circuits stay fully operational while bent to a tensile radius of 5 mm, and after 1000 cycles of repeated bending and re-flattening. To our knowledge, these are the fastest flexible oxide semiconductor based amplifiers.


title: Energy-Optimized Driving with an Autonomous Vehicle in Urban Environments

abstract: A high percentage of deceleration and acceleration in urban traffic is caused by the stops at traffic lights. Reducing these required stops combined with an overall smoother traffic flow can reduce the negative effects on the environment in terms of less emissions, noise reduction, and energy consumption. In this paper a cooperative system developed in the research project KOLINE is introduced aiming at the reduction of delay and emissions by adjusting both the signal control and the vehicles' driving strategy. First, the overall system architecture including the wireless communication between traffic signals and vehicles is described. The paper focuses on the vehicles' reaction approaching a signalized intersection and presents a novel approach to optimize the driving strategy based on the traffic information received. The main feature of our approach is the optimization of both longitudinal and lateral guidance of the vehicle simultaneously. Therefore, the surroundings of the vehicles detected by the vehicles' sensors is combined with more precise information on the local traffic state such as tailback length or traffic volume for each lane and forms the basis for proper driving strategies. To handle the complexity of the optimization problem, a metaheuristic considering the possible actions of the vehicle is used to determine the optimal driving strategy in the current driving situation. The optimized strategy is finally realized by a test vehicle capable of driving autonomously in the dense traffic of urban environments.


If There's No Mark, There's No Crime: Help Seeking Experiences of Rural Women Living With Psychological Abuse


title: Design of zero-voltage-ripple buck dc-dc converter

abstract: This paper presents the design of a zero-voltage-ripple (ZVR) buck dc-dc converter. The circuit uses an autotransformer for ripple cancellation in the output voltage. The principle of operation is discussed in brief. The relationship between the currents through the magnetizing inductance and the auto-transformer windings is developed. The auxiliary inductance for ripple cancellation in series with the secondary winding is determined. The expression for the auxiliary inductance is derived. A laboratory prototype of a buck converter with supply voltage 12 V, output voltage voltage 5 V, switching frequency 500 kHz, and output power 10 W was built. Simulation and experimental results are provided validating the theoretical predictions.


Creativity , Motivation to Learn , Family Environment , and Giftedness : A Comparative Study 1


title: System-Aware Optimization for Machine Learning at Scale

abstract: Author(s): Smith, Virginia | Advisor(s): Jordan, Michael I; Culler, David | Abstract: New computing systems have emerged in response to the increasing size and complexity of modern datasets. For best performance, machine learning methods must be designed to closely align with the underlying properties of these systems.In this thesis, we illustrate the impact of system-aware machine learning through the lens of optimization, a crucial component in formulating and solving most machine learning problems. Classically, the performance of an optimization method is measured in terms of accuracy (i.e., does it realize the correct machine learning model?) and convergence rate (after how many iterations?). In modern computing regimes, however, it becomes critical to additionally consider a number of systems-related aspects for best overall performance. These aspects can range from low-level details, such as data structures or machine specifications, to higher-level concepts, such as the tradeoff between communication and computation.We propose a general optimization framework for machine learning, CoCoA, that gives careful consideration to systems parameters, often incorporating them directly into the method and theory. We illustrate the impact of CoCoA in two popular distributed regimes: the traditional cluster-computing environment, and the increasingly common setting of on-device (federated) learning. Our results indicate that by marrying systems-level parameters and optimization techniques, we can achieve orders-of-magnitude speedups for solving modern machine learning problems at scale. We corroborate these empirical results by providing theoretical guarantees that expose systems parameters to give further insight into empirical performance.


title: Diagnosis of Breast Cancer using Decision Tree Models and SVM

abstract: Breast cancer represents the second important cause of cancer deaths in women today and it is the most common type of cancer in women. Disease diagnosis is one of the applications where data mining tools are proving successful results. Data mining with decision trees is popular and effective data mining classification approach. Decision trees have the ability to generate understandable classification rules, which are very efficient tool for transfer knowledge to physicians and medical specialists. In fundamental truth, they provide trails to find rules that could be evaluated for separating the input samples into one of several groups without having to state the functional relationship directly. The objective of this paper is to examine the performance of recent invented decision tree modeling algorithms and compared with one that achieved by radial basis function kernel support vector machine (RBFSVM) on the diagnosis of breast cancer using cytological proven tumor dataset. Four models have been evaluated in decision tree: Chi-squared Automatic Interaction Detection (CHAID), Classification and Regression tree (CR classification accuracy, sensitivity, and specificity. General Terms Breast cancer, Data mining


title: A Survey of Caching Policies and Forwarding Mechanisms in Information-Centric Networking

abstract: Information-centric networking (ICN), an alternative to the host-centric model of the current Internet infrastructure, focuses on the distribution and retrieval of content instead of the transfer of information between specific endpoints. In order to achieve this, ICN is based on the paradigm of publish-subscribe and the concepts of naming and in-network caching. Current approaches to ICN employ caches within networks to minimize the latency of information retrieval. Content may be distributed either in caches along the delivery path(s), on-path caching or in any cache within a network, off-path caching. While approaches to off-path caching are comparable to traditional approaches for content replication and Web caching, approaches to on-path caching are specific to the ICN area. The purpose of this paper is to provide a review of the caching problem in ICN, with a focus on on-path caching. To this end, a detailed analysis of the existing caching policies and forwarding mechanisms that complement these policies is given. A number of criteria such as the caching model and level of operation and the evaluation parameters used in the evaluation of the existing caching policies are being employed to derive a taxonomy for on-path caching and highlight the trends and evaluation issues in this area. A discussion driven by the advantages and disadvantages of the existing caching policies and the challenges and open questions in on-path caching is finally being held.


title: Detecting and reading text in natural scenes

abstract: This paper gives an algorithm for detecting and reading text in natural images. The algorithm is intended for use by blind and visually impaired subjects walking through city scenes. We first obtain a dataset of city images taken by blind and normally sighted subjects. From this dataset, we manually label and extract the text regions. Next we perform statistical analysis of the text regions to determine which image features are reliable indicators of text and have low entropy (i.e. feature response is similar for all text images). We obtain weak classifiers by using joint probabilities for feature responses on and off text. These weak classifiers are used as input to an AdaBoost machine learning algorithm to train a strong classifier. In practice, we trained a cascade with 4 strong classifiers containing 79 features. An adaptive binarization and extension algorithm is applied to those regions selected by the cascade classifier. Commercial OCR software is used to read the text or reject it as a non-text region. The overall algorithm has a success rate of over 90% (evaluated by complete detection and reading of the text) on the test set and the unread text is typically small and distant from the viewer.


title: First Person Action Recognition Using Deep Learned Descriptors

abstract: We focus on the problem of wearer's action recognition in first person a.k.a. egocentric videos. This problem is more challenging than third person activity recognition due to unavailability of wearer's pose and sharp movements in the videos caused by the natural head motion of the wearer. Carefully crafted features based on hands and objects cues for the problem have been shown to be successful for limited targeted datasets. We propose convolutional neural networks (CNNs) for end to end learning and classification of wearer's actions. The proposed network makes use of egocentric cues by capturing hand pose, head motion and saliency map. It is compact. It can also be trained from relatively small number of labeled egocentric videos that are available. We show that the proposed network can generalize and give state of the art performance on various disparate egocentric action datasets.


title: Object-Scene Convolutional Neural Networks for event recognition in images

abstract: Event recognition from still images is of great importance for image understanding. However, compared with event recognition in videos, there are much fewer research works on event recognition in images. This paper addresses the issue of event recognition from images and proposes an effective method with deep neural networks. Specifically, we design a new architecture, called Object-Scene Convolutional Neural Network (OS-CNN). This architecture is decomposed into object net and scene net, which extract useful information for event understanding from the perspective of objects and scene context, respectively. Meanwhile, we investigate different network architectures for OS-CNN design, and adapt the deep (AlexNet) and very-deep (GoogLeNet) networks to the task of event recognition. Furthermore, we find that the deep and very-deep networks are complementary to each other. Finally, based on the proposed OS-CNN and comparative study of different network architectures, we come up with a solution of five-stream CNN for the track of cultural event recognition at the ChaLearn Looking at People (LAP) challenge 2015. Our method obtains the performance of 85.5% and ranks the 1st place in this challenge.


Efficacy of Guava Leaves ( Psidiumguajava ) Suspension on Growth , Antibacterial and Hematological Performances in E . coli Included Japanese Quail


title: A study on the stress identification using observed heart beat data

abstract: Scientist since early 19th has discussed a lot about stress theories such as “Theory of Emotion”, “General Adaptation Syndrome Model”, and “Theory of Cognitive Appraisal”. Lately there are several studies about stress recognition by analyzing physical phenomenon. Beside the physically seen phenomenon, there is also multiple reliable markers to indicate stress used in medical environment for example HRV (Heart Rate Variability) which use heart beat data as input. There are three type of HRV analysis, which results in several parameters. In order to classify the subject condition based on HRV parameters, machine learning method such as SVM, decision tree, and random forest are being implemented. Right before doing the machine learning, several studies conduct feature selection phase by using several correlation method. Various researches about stress had been discussed, however as their research methods and environments differ, the result of the best parameter for stress identification also differ.


title: Accelerated Convolutions for Efficient Multi-Scale Time to Contact Computation in Julia

abstract: Convolutions have long been regarded as fundamental to applied mathematics, physics and engineering. Their mathematical elegance allows for common tasks such as numerical differentiation to be computed efficiently on large data sets. Efficient computation of convolutions is critical to artificial intelligence in real-time applications, like machine vision, where convolutions must be continuously and efficiently computed on tens to hundreds of kilobytes per second. In this paper, we explore how convolutions are used in fundamental machine vision applications. We present an accelerated n-dimensional convolution package in the high performance computing language, Julia, and demonstrate its efficacy in solving the time to contact problem for machine vision. Results are measured against synthetically generated videos and quantitatively assessed according to their mean squared error from the ground truth. We achieve over an order of magnitude decrease in compute time and allocated memory for comparable machine vision applications. All code is packaged and integrated into the official Julia Package Manager to be used in various other scenarios.


title: Methanogenic Archaea and oral infections — ways to unravel the black box

abstract: Archaea, organisms that make up the third domain of cellular life are members of the human oral microflora. They are strikingly less diverse than oral bacteria and appear to be relatively rare with respect to their numerical abundance. Since they have been exclusively found in association with oral infections such as periodontitis and apical periodontitis and given their unique physiology and energy metabolism, it is highly plausible that they are more than just secondary colonizers of infected areas, but instead are actively involved in the overall poly-microbial infection process. Conversely, it is a highly challenging task to clearly demonstrate their possible active participation - mostly due to the difficulty to grow them in routine microbiology laboratories. This current review points out the importance for understanding the medical impact of methanogens and aims at devising strategies for elucidating the true function of archaea in the oral ecosystem. Keywords: methanogenic archaea; human microbial ecosystems; oral infections; interspecies hydrogen transfer (Published: 23 February 2011) Citation: Journal of Oral Microbiology 2011, 3 : 5940 - DOI: 10.3402/jom.v3i0.5940


title: Methods for biological data integration: perspectives and challenges

abstract: Rapid technological advances have led to the production of different types of biological data and enabled construction of complex networks with various types of interactions between diverse biologi...


title: Sketch Me That Shoe

abstract: We investigate the problem of fine-grained sketch-based image retrieval (SBIR), where free-hand human sketches are used as queries to perform instance-level retrieval of images. This is an extremely challenging task because (i) visual comparisons not only need to be fine-grained but also executed cross-domain, (ii) free-hand (finger) sketches are highly abstract, making fine-grained matching harder, and most importantly (iii) annotated cross-domain sketch-photo datasets required for training are scarce, challenging many state-of-the-art machine learning techniques. In this paper, for the first time, we address all these challenges, providing a step towards the capabilities that would underpin a commercial sketch-based image retrieval application. We introduce a new database of 1,432 sketchphoto pairs from two categories with 32,000 fine-grained triplet ranking annotations. We then develop a deep tripletranking model for instance-level SBIR with a novel data augmentation and staged pre-training strategy to alleviate the issue of insufficient fine-grained training data. Extensive experiments are carried out to contribute a variety of insights into the challenges of data sufficiency and over-fitting avoidance when training deep networks for finegrained cross-domain ranking tasks.


title: JADE: Adaptive Differential Evolution With Optional External Archive

abstract: A new differential evolution (DE) algorithm, JADE, is proposed to improve optimization performance by implementing a new mutation strategy ldquoDE/current-to-p bestrdquo with optional external archive and updating control parameters in an adaptive manner. The DE/current-to-pbest is a generalization of the classic ldquoDE/current-to-best,rdquo while the optional archive operation utilizes historical data to provide information of progress direction. Both operations diversify the population and improve the convergence performance. The parameter adaptation automatically updates the control parameters to appropriate values and avoids a user's prior knowledge of the relationship between the parameter settings and the characteristics of optimization problems. It is thus helpful to improve the robustness of the algorithm. Simulation results show that JADE is better than, or at least comparable to, other classic or adaptive DE algorithms, the canonical particle swarm optimization, and other evolutionary algorithms from the literature in terms of convergence performance for a set of 20 benchmark problems. JADE with an external archive shows promising results for relatively high dimensional problems. In addition, it clearly shows that there is no fixed control parameter setting suitable for various problems or even at different optimization stages of a single problem.


Association of 12 h shifts and nurses’ job satisfaction, burnout and intention to leave: findings from a cross-sectional study of 12 European countries


Big Data Analytics


title: A Nonlinear Primal-Dual Method for Total Variation-Based Image Restoration

abstract: We present a new method for solving total variation (TV) minimization problems in image restoration. The main idea is to remove some of the singularity caused by the nondifferentiability of the quantity $|\nabla u|$ in the definition of the TV-norm before we apply a linearization technique such as Newton's method. This is accomplished by introducing an additional variable for the flux quantity appearing in the gradient of the objective function, which can be interpreted as the normal vector to the level sets of the image u. Our method can be viewed as a primal-dual method as proposed by Conn and Overton [ A Primal-Dual Interior Point Method for Minimizing a Sum of Euclidean Norms, preprint, 1994] and Andersen [Ph. D. thesis, Odense University, Denmark, 1995] for the minimization of a sum of Euclidean norms. In addition to possessing local quadratic convergence, experimental results show that the new method seems to be globally convergent.


The UMD Machine Translation Systems at IWSLT 2015


title: Similarity of sentences through comparison of syntactic trees with pairs of similar words

abstract: The comparison of sentences has several applications and they can be addressed by different approaches. One of them is to compare a pair of sentences through analysis of syntactic trees. In this work we present a method to evaluate the similarity between a pair of sentences, through the comparison of syntactic trees. The method makes an exploration of branches of trees trying to find similar nodes between them, giving a degree of similarity by each comparison. The method is designed to take into account the type of each node and, the number of them on each tree and the nodes to match on trees compared, i.e. this proposal considers all of nodes and not only if one tree is contained into another tree.


title: Mean-variance and value at risk in multi-armed bandit problems

abstract: We study risk-averse multi-armed bandit problems under different risk measures. We consider three risk mitigation models. In the first model, the variations in the reward values obtained at different times are considered as risk and the objective is to minimize the mean-variance of the observed rewards. In the second and the third models, the quantity of interest is the total reward at the end of the time horizon, and the objective is to minimize the mean-variance and maximize the value at risk of the total reward, respectively. We develop risk-averse online learning policies and analyze their regret performance. We also provide tight lower bounds on regret under the model of mean-variance of observations.


title: Interactive Freeform Design of Tensegrity

abstract: In this paper, we propose a novel interactive method for flexibly designing tensegrity structures under valid force equilibriums. Unlike previous form-finding techniques that aim to obtain a unique solution by fixing some parameters such as the force densities and lengths of elements, our method provides a design system that allows a user to continuously interact with the form within a multidimensional solution space. First, a valid initial form is generated by converting a given polygon mesh surface into a strut-and-cable network that follows the original surface, and the form is then perturbed to attain an equilibrium state through a two-step optimization of both node coordinates and force densities. Then, the form can be freely transformed by using a standard 2D input device while the system updates the form on the fly based on the orthogonal projection of a deformation mode into the solution space. The system provides a flexible platform for designing a tensegrity form for use as a static architectural structure or a kinetic deployable system.


title: Reinforcement learning in the brain

abstract: Abstract A wealth of research focuses on the decision-making processes that animals and humans employ when selecting actions in the face of reward and punishment. Initially such work stemmed from psychological investigations of conditioned behavior, and explanations of these in terms of computational models. Increasingly, analysis at the computational level has drawn on ideas from reinforcement learning , which provide a normative framework within which decision-making can be analyzed. More recently, the fruits of these extensive lines of research have made contact with investigations into the neural basis of decision making. Converging evidence now links reinforcement learning to specific neural substrates, assigning them precise computational roles. Specifically, electrophysiological recordings in behaving animals and functional imaging of human decision-making have revealed in the brain the existence of a key reinforcement learning signal, the temporal difference reward prediction error. Here, we first introduce the formal reinforcement learning framework. We then review the multiple lines of evidence linking reinforcement learning to the function of dopaminergic neurons in the mammalian midbrain and to more recent data from human imaging experiments. We further extend the discussion to aspects of learning not associated with phasic dopamine signals, such as learning of goal-directed responding that may not be dopamine-dependent, and learning about the vigor (or rate) with which actions should be performed that has been linked to tonic aspects of dopaminergic signaling. We end with a brief discussion of some of the limitations of the reinforcement learning framework, highlighting questions for future research.


title: Resilience in Women Sexually Abused as Children

abstract: Research suggests that adult women who have experienced childhood sexual abuse (CSA) vary on whether and how they experience long-term effects. According to current literature, the variation appears to be due to both risk and protective factors. In addition, the variation also appears to occur in connection with certain factors that function to help buffer the effects of CSA. The purpose of this study is to identify the protective factors that best predict resilience in a sample of 134 female adults who are members of AMAC (Adults Molested as Children). The clinical implications of these findings are discussed.


title: A literature review of UAV 3D path planning

abstract: 3D path planning of unmanned aerial vehicle (UAV) targets at finding an optimal and collision free path in a 3D cluttered environment while taking into account the geometric, physical and temporal constraints. Although a lot of works have been done to solve UAV 3D path planning problem, there lacks a comprehensive survey on this topic, let alone the recently published works that focus on this field. This paper analyses the most successful UAV 3D path planning algorithms that developed in recent years. This paper classifies the UAV 3D path planning methods into five categories, sampling-based algorithms, node-based algorithms, mathematical model based algorithms, Bio-inspired algorithms, and multi-fusion based algorithms. For each category a critical analysis and comparison is given. Furthermore a comprehensive applicable analysis for each kind of method is presented after considering its working mechanism and time complexity.


title: Tracking curved regularized optimization solution paths

abstract: A protective coating applicator tool for application of coating material to the interior of well casing.


title: Energy-efficient resource allocation in OFDMA systems with large numbers of base station antennas

abstract: In this paper, resource allocation for energy efficient communication in space division multiple access (SDMA) downlink networks with large numbers of transmit antennas is studied. The considered problem is modeled as a non-convex optimization problem which takes into account the circuit power consumption and a minimum required data rate. By exploiting the properties of fractional programming, the considered non-convex optimization problem in fractional form is transformed into an equivalent optimization problem in subtractive form, which enables the derivation of an efficient iterative resource allocation algorithm. The optimal power allocation solution for each iteration is derived based on a low complexity user selection policy for maximization of the energy efficiency of data transmission (bit/Joule delivered to the users). Simulation results illustrate that the proposed iterative resource allocation algorithm converges in a small number of iterations and unveil the trade-off between energy efficiency and the number of antennas.


title: A Bayesian Perspective on Generalization and Stochastic Gradient Descent

abstract: This paper tackles two related questions at the heart of machine learning; how can we predict if a minimum will generalize to the test set, and why does stochastic gradient descent find minima that generalize well? Our work is inspired by Zhang et al. (2017), who showed deep networks can easily memorize randomly labeled training data, despite generalizing well when shown real labels of the same inputs. We show here that the same phenomenon occurs in small linear models. These observations are explained by evaluating the Bayesian evidence, which penalizes sharp minima but is invariant to model parameterization. We also explore the"generalization gap"between small and large batch training, identifying an optimum batch size which maximizes the test set accuracy. Interpreting stochastic gradient descent as a stochastic differential equation, we identify a"noise scale"$g = \epsilon (\frac{N}{B} - 1) \approx \epsilon N/B$, where $\epsilon$ is the learning rate, $N$ training set size and $B$ batch size. Consequently the optimum batch size is proportional to the learning rate and the training set size, $B_{opt} \propto \epsilon N$. We verify these predictions empirically.


title: Deep Learning the City : Quantifying Urban Perception At A Global Scale

abstract: Computer vision methods that quantify the perception of urban environment are increasingly being used to study the relationship between a city’s physical appearance and the behavior and health of its residents. Yet, the throughput of current methods is too limited to quantify the perception of cities across the world. To tackle this challenge, we introduce a new crowdsourced dataset containing 110,988 images from 56 cities, and 1,170,000 pairwise comparisons provided by 81,630 online volunteers along six perceptual attributes: safe, lively, boring, wealthy, depressing, and beautiful. Using this data, we train a Siamese-like convolutional neural architecture, which learns from a joint classification and ranking loss, to predict human judgments of pairwise image comparisons. Our results show that crowdsourcing combined with neural networks can produce urban perception data at the global scale.


title: voluntary, mimicry of emotional facial expressions in autism

abstract: Humans, infants and adults alike, automatically mimic a variety of behaviors. Such mimicry facilitates social functioning, including establishment of interpersonal rapport and understanding of other minds. This fundamental social process may thus be impaired in disorders such as autism characterized by socio-emotional and communicative deficits. We examined automatic and voluntary mimicry of emotional facial expression among adolescents and adults with autistic spectrum disorders (ASD) and a typical sample matched on age, gender and verbal intelligence. Participants viewed pictures of happy and angry expressions while the activity over their cheek and brow muscle region was monitored with electromyography (EMG). ASD participants did not automatically mimic facial expressions whereas the typically developing participants did. However, both groups showed evidence of successful voluntary mimicry. The data suggest that autism is associated with an impairment of a basic automatic social-emotion process. Results have implications for understanding typical and atypical social cognition.


title: Tactical pattern recognition in soccer games by means of special self-organizing maps.

abstract: Abstract Increasing amounts of data are collected in sports due to technological progress. From a typical soccer game, for instance, the positions of the 22 players and the ball can be recorded 25 times per second, resulting in approximately 135.000 datasets. Without computational assistance it is almost impossible to extract relevant information from the complete data. This contribution introduces a hierarchical architecture of artificial neural networks to find tactical patterns in those positional data. The results from the classification using the hierarchical setup were compared to the results gained by an expert manually classifying the different categories. Short and long game initiations can be detected with relative high accuracy leading to the conclusion that the hierarchical architecture is capable of recognizing different tactical patterns and variations in these patterns. Remaining problems are discussed and ideas concerning further improvements of classification are indicated.


title: Ranking-Oriented Collaborative Filtering: A Listwise Approach

abstract: Collaborative filtering (CF) is one of the most effective techniques in recommender systems, which can be either rating oriented or ranking oriented. Ranking-oriented CF algorithms demonstrated significant performance gains in terms of ranking accuracy, being able to estimate a precise preference ranking of items for each user rather than the absolute ratings (as rating-oriented CF algorithms do). Conventional memory-based ranking-oriented CF can be referred to as pairwise algorithms. They represent each user as a set of preferences on each pair of items for similarity calculations and predictions. In this study, we propose ListCF, a novel listwise CF paradigm that seeks improvement in both accuracy and efficiency in comparison with pairwise CF. In ListCF, each user is represented as a probability distribution of the permutations over rated items based on the Plackett-Luce model, and the similarity between users is measured based on the Kullback--Leibler divergence between their probability distributions over the set of commonly rated items. Given a target user and the most similar users, ListCF directly predicts a total order of items for each user based on similar users’ probability distributions over permutations of the items. Besides, we also reveal insightful connections among pointwise, pairwise, and listwise CF algorithms from the perspective of the matrix representations. In addition, to make our algorithm more scalable and adaptive, we present an incremental algorithm for ListCF, which allows incrementally updating the similarities between users when certain user submits a new rating or updates an existing rating. Extensive experiments on benchmark datasets in comparison with the state-of-the-art approaches demonstrate the promise of our approach.


title: Effnet: An Efficient Structure for Convolutional Neural Networks

abstract: With the ever increasing application of Convolutional Neural Networks to customer products the need emerges for models to efficiently run on embedded, mobile hardware. Slimmer models have therefore become a hot research topic with various approaches which vary from binary networks to revised convolution layers. We offer our contribution to the latter and propose a novel convolution block which significantly reduces the computational burden while surpassing the current state-of-the-art. Our model, dubbed EffNet, is optimised for models which are slim to begin with and is created to tackle issues in existing models such as MobileNet and ShuffleNet.


title: Self-oscillating flyback driver for power LEDs

abstract: The self-oscillating flyback converter is a popular circuit for cost-sensitive applications due to its simplicity and low component count. It is widely employed in mobile phone chargers and as the stand-by power source in off-line power supplies for data-processing equipment. However, this circuit was almost not explored for supplying power LEDs. This paper presents a self-oscillating flyback driver for supplying Power LEDs directly, with no additional circuit. A simplified mathematical model of the LED was used to characterize the self-oscillating converter for driving the power LEDs. With the proposed converter the LEDs manufacturing tolerances and drifts over temperature presents little to no influence over the LED average current. This is proved by using the LED electrical model on the analysis.


title: XSat: A Fast Floating-Point Satisfiability Solver

abstract: The Satisfiability Modulo Theory (SMT) problem over floating-point arithmetic is a major hurdle in applying SMT techniques to real-world floating-point code. Solving floating-point constraints is challenging in part because floating-point semantics is difficult to specify or abstract. State-of-the-art SMT solvers still often run into difficulties when solving complex, non-linear floating-point constraints.


title: Random Shuffling and Resets for the Non-stationary Stochastic Bandit Problem

abstract: We consider a non-stationary formulation of the stochastic multi-armed bandit where the rewards are no longer assumed to be identically distributed. For the best-arm identification task, we introduce a version of Successive Elimination based on random shuffling of the $K$ arms. We prove that under a novel and mild assumption on the mean gap $\Delta$, this simple but powerful modification achieves the same guarantees in term of sample complexity and cumulative regret than its original version, but in a much wider class of problems, as it is not anymore constrained to stationary distributions. We also show that the original {\sc Successive Elimination} fails to have controlled regret in this more general scenario, thus showing the benefit of shuffling. We then remove our mild assumption and adapt the algorithm to the best-arm identification task with switching arms. We adapt the definition of the sample complexity for that case and prove that, against an optimal policy with $N-1$ switches of the optimal arm, this new algorithm achieves an expected sample complexity of $O(\Delta^{-2}\sqrt{NK\delta^{-1} \log(K \delta^{-1})})$, where $\delta$ is the probability of failure of the algorithm, and an expected cumulative regret of $O(\Delta^{-1}{\sqrt{NTK \log (TK)}})$ after $T$ time steps.


title: Effect of ploidy and parental genome composition on expression of Oct-4 protein in mouse embryos.

abstract: Abstract The transcription factor Oct-4 is expressed in germ cells and also is considered as a marker for pluripotency of stem cells. We first examined dynamics of Oct-4 protein expression during preimplantation development using both Western blot analysis, and immunofluorescence staining. We show that intact Oct-4 protein is not detected in either ovulated mature oocytes, or in zygotes and 2–4-cell embryos, which are the only known totipotent cell types in mammals. This finding is unexpected, since Oct-4 has been proposed to play a role in the control of totipotency. The results suggest that Oct-4 is not indispensable for fertilization and early cleavage. Rather, expression of Oct-4 protein is first detected in the nuclei of 8–16 cell morula, increases in early blastocysts, and declines in late blastocysts, in which most Oct-4 protein is confined to the inner cell mass (ICM) region, consistent with previous findings. We further compared Oct-4 protein expression in diploid and tetraploid blastocysts derived from normal fertilization or parthenogenesis, as well as expression in diploid androgenetic blastocysts. Expression levels and localization of Oct-4 protein are similar in both diploid and tetraploid early blastocysts, regardless of whether blastocysts are derived from fertilization or parthenogenesis. Androgenetic diploid blastocysts also express similar levels of Oct-4. Late blastocysts generated by both fertilization and parthenogenesis show a similar pattern of Oct-4 expression, suggesting that paternal genome activation is not required for Oct-4 expression. Expression of Oct-4 protein does not differ between diploid and tetraploid embryos, indicating that tetraploidy does not influence Oct-4 expression. Thus, expression of Oct-4 protein is initiated at morula stage in preimplantation embryos and completely controlled by a mechanism activated in oocytes. Downregulation of Oct-4 expression coincides with differentiation of trophectoderm. Similar profiles of Oct-4 expression observed in embryos with different ploidy and genome composition, are suggestive of Oct-4 being necessary but not sufficient for developmental potency.


title: The mode of binding of pyridoxal 5′-phosphate in glycogen phosphorylase

abstract: Abstract The absorption, fluorescence and chemical properties of the pyridoxal 5′-phosphate (PLP) site in glycogen phosphorylase at pH 7.0 can be simulated by Schiff base derivatives of PLP in non-aqueous solvents. It is proposed that at neutral pH,PLP in phosphorylase is embedded in a hydrophobic microenvironment and bound to an e-amino group of a lysine residue in the protein through a hydrogen-bonded Schiff base structure.


Predicting Generated Story Quality with Quantitative Measures.


title: Contamination source identification in water distribution networks using convolutional neural network

abstract: Contamination source identification (CSI) is significant for water quality security and social stability when a contamination intrusion event occurs in water distribution systems (WDSs). However, in research, this is an extremely challenging task for many reasons, such as limited number of water quality sensors and their limitations in detecting contaminants. Hence, some researchers have introduced consumers’ complaint information as an alternative of sensors for CSI. But the problem with this approach is that the uncertainty of complaint delay time has a great impact on the identification accuracy. To address this issue, this study constructed complaint matrices to present the spatiotemporal characteristics of consumer complaints in an intrusion event and proposed a new methodology employing convolution neural network (CNN)—a deep learning algorithm—for the purpose of pattern recognition. CNN aimed to explore the inherent characteristics of complaint patterns corresponding to different contaminant intrusion nodes and to improve the performance of identifying the contamination source based on consumer complaint information. Two case studies illustrated methodology effectiveness in WDSs of various scales, even with the high uncertainties of complaint delay time. The comparison between CNN and a back-propagation artificial neural network algorithm demonstrates that the former framework possesses stronger robustness and higher accuracy for CSI.


title: Detection and removal of rain from videos

abstract: The visual effects of rain are complex. Rain consists of spatially distributed drops falling at high velocities. Each drop refracts and reflects the environment, producing sharp intensity changes in an image. A group of such falling drops creates a complex time varying signal in images and videos. In addition, due to the finite exposure time of the camera, intensities due to rain are motion blurred and hence depend on the background intensities. Thus, the visual manifestations of rain are a combination of both the dynamics of rain and the photometry of the environment. In this paper, we present the first comprehensive analysis of the visual effects of rain on an imaging system. We develop a correlation model that captures the dynamics of rain and a physics-based motion blur model that explains the photometry of rain. Based on these models, we develop efficient algorithms for detecting and removing rain from videos. The effectiveness of our algorithms is demonstrated using experiments on videos of complex scenes with moving objects and time-varying textures. The techniques described in this paper can be used in a wide range of applications including video surveillance, vision based navigation, video/movie editing and video indexing/retrieval.


title: Real-Time Head Pose Tracking with Online Face Template Reconstruction

abstract: We propose a real-time method to accurately track the human head pose in the 3-dimensional (3D) world. Using a RGB-Depth camera, a face template is reconstructed by fitting a 3D morphable face model, and the head pose is determined by registering this user-specific face template to the input depth video.


title: A comparison of accuracy of fall detection algorithms (threshold-based vs. machine learning) using waist-mounted tri-axial accelerometer signals from a comprehensive set of falls and non-fall trials

abstract: Falls are the leading cause of injury-related morbidity and mortality among older adults. Over 90 % of hip and wrist fractures and 60 % of traumatic brain injuries in older adults are due to falls. Another serious consequence of falls among older adults is the 'long lie' experienced by individuals who are unable to get up and remain on the ground for an extended period of time after a fall. Considerable research has been conducted over the past decade on the design of wearable sensor systems that can automatically detect falls and send an alert to care providers to reduce the frequency and severity of long lies. While most systems described to date incorporate threshold-based algorithms, machine learning algorithms may offer increased accuracy in detecting falls. In the current study, we compared the accuracy of these two approaches in detecting falls by conducting a comprehensive set of falling experiments with 10 young participants. Participants wore waist-mounted tri-axial accelerometers and simulated the most common causes of falls observed in older adults, along with near-falls and activities of daily living. The overall performance of five machine learning algorithms was greater than the performance of five threshold-based algorithms described in the literature, with support vector machines providing the highest combination of sensitivity and specificity.


title: Design and analysis of slot-pole combination for synchronous reluctance machine with concentrated windings for automotive applications

abstract: Synchronous Reluctance Machines (SyncRM), began to draw a lot of attention in last decade and presently receives a great deal of interest, since the cost of active materials is much lower than for a Permanent Magnet Synchronous Machine. For automotive applications a high torque and power density, low torque ripple and a wide speed range is required. The synchronous reluctance machine is still under development and intensive investigation is being carried out. This paper presents the influence of stator and rotor pole number combinations with a stator configuration which for cost reduction purposes uses only concentrate windings.


title: ECG pattern recognition and classification using non-linear transformations and neural networks: A review

abstract: The most widely used signal in clinical practice is the ECG. ECG conveys information regarding the electrical function of the heart, by altering the shape of its constituent waves, namely the P, QRS, and T waves. Thus, the required tasks of ECG processing are the reliable recognition of these waves, and the accurate measurement of clinically important parameters measured from the temporal distribution of the ECG constituent waves. In this paper, we shall review some current trends on ECG pattern recognition. In particular, we shall review non-linear transformations of the ECG, the use of principal component analysis (linear and non-linear), ways to map the transformed data into n-dimensional spaces, and the use of neural networks (NN) based techniques for ECG pattern recognition and classification. The problems we shall deal with are the QRS/PVC recognition and classification, the recognition of ischemic beats and episodes, and the detection of atrial fibrillation. Finally, a generalised approach to the classification problems in n-dimensional spaces will be presented using among others NN, radial basis function networks (RBFN) and non-linear principal component analysis (NLPCA) techniques. The performance measures of the sensitivity and specificity of these algorithms will also be presented using as training and testing data sets from the MIT-BIH and the European ST-T databases.


title: The Trench Power MOSFET—Part II: Application Specific VDMOS, LDMOS, Packaging, and Reliability

abstract: The technological development of application specific VDMOS and lateral trench power MOSFETs is described. Unlike general-purpose trench vertical DMOS, application specific trench DMOS comprise devices merged or optimized for a specific function or characteristic. Examples include the bidirectional lithium ion battery disconnect switch, the airbag squib driver with safety redundancy, the antilock breaking systems solenoid driver with repeated avalanche operation, and various forms of synchronous rectifiers (including integrated Schottky and pseudo-Schottky operation). Trench lateral DMOS include all implant quasi-vertical, lateral trench, and lateral trench charge balanced devices. Trench power MOSFET packaging addresses multichip surface mount, DrMOS, low inductance, and clip lead packages.


Link Prediction in Bipartite Networks-Predicting Yelp Reviews


title: Autonomous navigation for mobile service robots in urban pedestrian environments

abstract: This paper presents a fully autonomous navigation solution for urban, pedestrian environments. The task at hand, undertaken within the context of the European project URUS, was to enable two urban service robots, based on Segway RMP200 platforms and using planar lasers as primary sensors, to navigate around a known, large (10,000 m2), pedestrian-only environment with poor global positioning system coverage. Special consideration is given to the nature of our robots, highly mobile but two-wheeled, self-balancing, and inherently unstable. Our approach allows us to tackle locations with large variations in height, featuring ramps and staircases, thanks to a three-dimensional, map-based particle filter for localization and to surface traversability inference for low-level navigation. This solution was tested in two different urban settings, the experimental zone devised for the project, a university campus, and a very crowded public avenue, both located in the city of Barcelona, Spain. Our results total more than 6 km of autonomous navigation, with a success rate on go-to requests of nearly 99%. The paper presents our system, examines its overall performance, and discusses the lessons learned throughout development. © 2011 Wiley Periodicals, Inc. © 2011 Wiley Periodicals, Inc.


title: Learning Common and Specific Features for RGB-D Semantic Segmentation with Deconvolutional Networks

abstract: In this paper, we tackle the problem of RGB-D semantic segmentation of indoor images. We take advantage of deconvolutional networks which can predict pixel-wise class labels, and develop a new structure for deconvolution of multiple modalities. We propose a novel feature transformation network to bridge the convolutional networks and deconvolutional networks. In the feature transformation network, we correlate the two modalities by discovering common features between them, as well as characterize each modality by discovering modality specific features. With the common features, we not only closely correlate the two modalities, but also allow them to borrow features from each other to enhance the representation of shared information. With specific features, we capture the visual patterns that are only visible in one modality. The proposed network achieves competitive segmentation accuracy on NYU depth dataset V1 and V2.


title: Neurotrophic Properties of the Lion's Mane Medicinal Mushroom, Hericium erinaceus (Higher Basidiomycetes) from Malaysia

abstract: Neurotrophic factors are important in promoting the growth and differentiation of neurons. Nerve growth factor (NGF) is essential for the maintenance of the basal forebrain cholinergic system. Hericenones and erinacines isolated from the medicinal mushroom Hericium erinaceus can induce NGF synthesis in nerve cells. In this study, we evaluated the synergistic interaction between H. erinaceus aqueous extract and exogenous NGF on the neurite outgrowth stimulation of neuroblastoma-glioma cell NG108-15. The neuroprotective effect of the mushroom extract toward oxidative stress was also studied. Aqueous extract of H. erinaceus was shown to be non-cytotoxic to human lung fibroblast MRC-5 and NG108-15 cells. The combination of 10 ng/mL NGF with 1 μg/mL mushroom extract yielded the highest percentage increase of 60.6% neurite outgrowth. The extract contained neuroactive compounds that induced the secretion of extracellular NGF in NG108-15 cells, thereby promoting neurite outgrowth activity. However, the H. erinaceus extract failed to protect NG108-15 cells subjected to oxidative stress when applied in pre-treatment and co-treatment modes. In conclusion, the aqueous extract of H. erinaceus contained neuroactive compounds which induced NGF-synthesis and promoted neurite outgrowth in NG108-15 cells. The extract also enhanced the neurite outgrowth stimulation activity of NGF when applied in combination. The aqueous preparation of H. erinaceus had neurotrophic but not neuroprotective activities.


title: Statistical transliteration for english-arabic cross language information retrieval

abstract: Out of vocabulary (OOV) words are problematic for cross language information retrieval. One way to deal with OOV words when the two languages have different alphabets, is to transliterate the unknown words, that is, to render them in the orthography of the second language. In the present study, we present a simple statistical technique to train an English to Arabic transliteration model from pairs of names. We call this a selected n-gram model because a two-stage training procedure first learns which n-gram segments should be added to the unigram inventory for the source language, and then a second stage learns the translation model over this inventory. This technique requires no heuristics or linguistic knowledge of either language. We evaluate the statistically-trained model and a simpler hand-crafted model on a test set of named entities from the Arabic AFP corpus and demonstrate that they perform better than two online translation sources. We also explore the effectiveness of these systems on the TREC 2002 cross language IR task. We find that transliteration either of OOV named entities or of all OOV words is an effective approach for cross language IR.


title: Calibrating photon counts from a single image

abstract: Current methods for detector gain calibration require acquisition of tens of special calibration images. Here we propose a method that obtains the gain from the actual image for which the photon count is desired by quantifying out-of-band information. We show on simulation and experimental data that our much simpler procedure, which can be retroactively applied to any image, is comparable in precision to traditional gain calibration procedures.


title: Improving Similar Question Retrieval using a Novel Tripartite Neural Network based Approach

abstract: Collective intelligence of the crowds is distilled together in various Community Question Answering (CQA) Services such as Quora, Yahoo Answers, Stack Overflow forums, wherein users share their knowledge, providing both informational and experiential support to other users. As users often search for similar information, probabilities are high that for a new incoming question, there is a related question-answer pair existing in the CQA dataset. Therefore, an efficient technique for similar question identification is need of the hour. While data is not a bottleneck in this scenario, addressing the vocabulary diversity generated by a variety pool of users certainly is. This paper proposes a novel tripartite neural network based approach towards the similar question retrieval problem. The network takes inputs in the form of question-answer and new question triplet and learns internal representations from similarities among them. Our approach achieves classification performances upto 77% on a real world CQA dataset.We have also compared our method with two other baselines and found that it performs significantly better in handling the problem of vocabulary diversity and 'zero-lexical overlap' among questions.


title: Stopband-Expanded Low-Pass Filters Using Microstrip Coupled-Line Hairpin Units

abstract: In this letter, a class of stopband-expanded low-pass filters (LPFs) is proposed via attenuation poles of microstrip coupled-line hairpin unit. By centrally tap-connecting this coupled-line unit and adjusting its coupling strength, three attenuation poles can be excited above the desired low passband. A coupled-line-oriented closed-form model is then constructed to provide a physical insight into the excitation of these poles and to execute an efficient synthesis design of this LPF. Two prototype filters with single and two units are finally designed with the specified cutoff frequency at 2.5 GHz. Measured results of the fabricated filters evidently demonstrate good LPF performance, sharp roll-off skirt, and a wide upper-stopband of 3.2 to 11.8 GHz with the insertion loss higher than 20.0 dB.


title: Adaptive wavelet analysis of oscillations in the human peripheral blood flow

abstract: The main principles are outlined for spectral timing analysis of the peripheral blood flow oscillations obtained by laser Doppler flowmetry. The oscillations can be studied in a wide frequency range both in stationary and nonstationary conditions during functional tests. The potential of the method has been demonstrated in experiments with the reaction of the microvascular bed to transcutaneous iontophoretic introduction of acetylcholine chloride. The major advantage of the method over conventional wavelet analysis is a significant increase in the “effective” length of the signal analyzed, which allows correct analysis of low-frequency components in much shorter LDF recordings than those commonly used.


title: A Machine Learning Approach to Routing

abstract: Can ideas and techniques from machine learning be leveraged to automatically generate"good"routing configurations? We investigate the power of data-driven routing protocols. Our results suggest that applying ideas and techniques from deep reinforcement learning to this context yields high performance, motivating further research along these lines.


title: Disorders of consciousness: coma, vegetative and minimally conscious states

abstract: Consciousness can be defined by two components: arousal and awareness. Disorders of consciousness (DOC) are characterized by a disrupted relationship between these two components. Coma is described by the absence of arousal and, hence, of awareness whereas the vegetative state is defined by recovery of arousal in the absence of any sign of awareness. In the minimally conscious state, patients show preserved arousal level and exhibit discernible but fluctuating signs of awareness. The study of DOC offers unique insights to the neural correlates of consciousness. We here review the challenges posed by the clinical examination of DOC patients and discuss the contribution of functional neuroimaging and electrophysiological techniques to the bedside assessment of consciousness. These studies raise important issues not only from a clinical and ethical perspective (i.e. diagnosis, prognosis and management of DOC patients) but also from a neuroscientific standpoint, as they enrich our current understanding of the emergence and function of the conscious mind.


title: Combining classifiers to identify online databases

abstract: We address the problem of identifying the domain of onlinedatabases. More precisely, given a set F of Web forms automaticallygathered by a focused crawler and an online databasedomain D, our goal is to select from F only the formsthat are entry points to databases in D. Having a set ofWebforms that serve as entry points to similar online databasesis a requirement for many applications and techniques thataim to extract and integrate hidden-Web information, suchas meta-searchers, online database directories, hidden-Webcrawlers, and form-schema matching and merging.We propose a new strategy that automatically and accuratelyclassifies online databases based on features that canbe easily extracted from Web forms. By judiciously partitioningthe space of form features, this strategy allows theuse of simpler classifiers that can be constructed using learningtechniques that are better suited for the features of eachpartition. Experiments using real Web data in a representativeset of domains show that the use of different classifiersleads to high accuracy, precision and recall. This indicatesthat our modular classifier composition provides an effectiveand scalable solution for classifying online databases.


title: Channel Modeling and Capacity Analysis for Electromagnetic Wireless Nanonetworks in the Terahertz Band

abstract: Nanotechnologies promise new solutions for several applications in the biomedical, industrial and military fields. At the nanoscale, a nanomachine is considered as the most basic functional unit which is able to perform very simple tasks. Communication among nanomachines will allow them to accomplish more complex functions in a distributed manner. In this paper, the state of the art in molecular electronics is reviewed to motivate the study of the Terahertz Band (0.1-10.0 THz) for electromagnetic (EM) communication among nano-devices. A new propagation model for EM communications in the Terahertz Band is developed based on radiative transfer theory and in light of molecular absorption. This model accounts for the total path loss and the molecular absorption noise that a wave in the Terahertz Band suffers when propagating over very short distances. Finally, the channel capacity of the Terahertz Band is investigated by using this model for different power allocation schemes, including a scheme based on the transmission of femtosecond-long pulses. The results show that for very short transmission distances, in the order of several tens of millimeters, the Terahertz channel supports very large bit-rates, up to few terabits per second, which enables a radically different communication paradigm for nanonetworks.


title: Using Qualitative Research to Inform Development of Professional Guidelines: A Case Study of the Society of Critical Care Medicine Family-Centered Care Guidelines

abstract: OBJECTIVES ::: To explore the importance, challenges, and opportunities using qualitative research to enhance development of clinical practice guidelines, using recent guidelines for family-centered care in the ICU as an example. ::: ::: ::: METHODS ::: In developing the Society of Critical Care Medicine guidelines for family-centered care in the neonatal ICU, PICU, and adult ICU, we developed an innovative adaptation of the Grading of Recommendations, Assessments, Development and Evaluations approach to explicitly incorporate qualitative research. Using Grading of Recommendations, Assessments, Development and Evaluations and the Council of Medical Specialty Societies principles, we conducted a systematic review of qualitative research to establish family-centered domains and outcomes. Thematic analyses were undertaken on study findings and used to support Population, Intervention, Comparison, Outcome question development. ::: ::: ::: RESULTS ::: We identified and employed three approaches using qualitative research in these guidelines. First, previously published qualitative research was used to identify important domains for the Population, Intervention, Comparison, Outcome questions. Second, this qualitative research was used to identify and prioritize key outcomes to be evaluated. Finally, we used qualitative methods, member checking with patients and families, to validate the process and outcome of the guideline development. ::: ::: ::: CONCLUSIONS ::: In this, a novel report, we provide direction for standardizing the use of qualitative evidence in future guidelines. Recommendations are made to incorporate qualitative literature review and appraisal, include qualitative methodologists in guideline taskforce teams, and develop training for evaluation of qualitative research into guideline development procedures. Effective methods of involving patients and families as members of guideline development represent opportunities for future work.


title: A Fast and Accurate Unconstrained Face Detector

abstract: We propose a method to address challenges in unconstrained face detection, such as arbitrary pose variations and occlusions. First, a new image feature called Normalized Pixel Difference (NPD) is proposed. NPD feature is computed as the difference to sum ratio between two pixel values, inspired by the Weber Fraction in experimental psychology. The new feature is scale invariant, bounded, and is able to reconstruct the original image. Second, we propose a deep quadratic tree to learn the optimal subset of NPD features and their combinations, so that complex face manifolds can be partitioned by the learned rules. This way, only a single soft-cascade classifier is needed to handle unconstrained face detection. Furthermore, we show that the NPD features can be efficiently obtained from a look up table, and the detection template can be easily scaled, making the proposed face detector very fast. Experimental results on three public face datasets (FDDB, GENKI, and CMU-MIT) show that the proposed method achieves state-of-the-art performance in detecting unconstrained faces with arbitrary pose variations and occlusions in cluttered scenes.


title: Inferring Strange Behavior from Connectivity Pattern in Social Networks

abstract: Given a multimillion-node social network, how can we summarize connectivity pattern from the data, and how can we find unexpected user behavior? In this paper we study a complete graph from a large who-follows-whom network and spot lockstep behavior that large groups of followers connect to the same groups of followees. Our first contribution is that we study strange patterns on the adjacency matrix and in the spectral subspaces with respect to several flavors of lockstep. We discover that (a) the lockstep behavior on the graph shapes dense “block” in its adjacency matrix and creates “ray” in spectral subspaces, and (b) partially overlapping of the behavior shapes “staircase” in the matrix and creates “pearl” in the subspaces. The second contribution is that we provide a fast algorithm, using the discovery as a guide for practitioners, to detect users who offer the lockstep behavior. We demonstrate that our approach is effective on both synthetic and real data.


title: Group sparse coding with a laplacian scale mixture prior

abstract: We propose a class of sparse coding models that utilizes a Laplacian Scale Mixture (LSM) prior to model dependencies among coefficients. Each coefficient is modeled as a Laplacian distribution with a variable scale parameter, with a Gamma distribution prior over the scale parameter. We show that, due to the conjugacy of the Gamma prior, it is possible to derive efficient inference procedures for both the coefficients and the scale parameter. When the scale parameters of a group of coefficients are combined into a single variable, it is possible to describe the dependencies that occur due to common amplitude fluctuations among coefficients, which have been shown to constitute a large fraction of the redundancy in natural images [1]. We show that, as a consequence of this group sparse coding, the resulting inference of the coefficients follows a divisive normalization rule, and that this may be efficiently implemented in a network architecture similar to that which has been proposed to occur in primary visual cortex. We also demonstrate improvements in image coding and compressive sensing recovery using the LSM model.


title: A grey wolf optimization based MPPT for PV system under changing insolation level

abstract: This paper presents a new maximum power extraction algorithm for a photovoltaic (PV) system under any variation in insolation. The new algorithm has the capability to reach global peak (GP) under any weather conditions. The main advantage of the proposed algorithm is the substitution of PI controller with direct duty cycle control scheme. To confirm the efficacy of the GWO based MPPT, it has been compared with PSO and P&O based MPPTs. The proposed technique is implemented using MATLAB/SIMULINK software followed by an experimentation developed in the laboratory to verify the effectiveness of GWO based MPPT. The results obtained ensure that GWO-MPPT tracks with an efficiency of 99.8% whereas P&O and PSO tracks with an efficiency of 31.3% and 99.7%.


title: Political hashtag hijacking in the U.S.

abstract: We study the change in polarization of hashtags on Twitter over time and show that certain jumps in polarity are caused by "hijackers" engaged in a particular type of hashtag war.


Conceptualization of Relational Assurance Mechanisms - A Literature Review on Relational Assurance Mechanisms, Their Antecedents and Effects


title: Mesh-based Autoencoders for Localized Deformation Component Analysis

abstract: Spatially localized deformation components are very useful for shape analysis and synthesis in 3D geometry processing. Several methods have recently been developed, with an aim to extract intuitive and interpretable deformation components. However, these techniques suffer from fundamental limitations especially for meshes with noise or large-scale deformations, and may not always be able to identify important deformation components. In this paper we propose a novel mesh-based autoencoder architecture that is able to cope with meshes with irregular topology. We introduce sparse regularization in this framework, which along with convolutional operations, helps localize deformations. Our framework is capable of extracting localized deformation components from mesh data sets with large-scale deformations and is robust to noise. It also provides a nonlinear approach to reconstruction of meshes using the extracted basis, which is more effective than the current linear combination approach. Extensive experiments show that our method outperforms state-of-the-art methods in both qualitative and quantitative evaluations.


title: Restaurant Revenue Management at Chevys: Determining the Best Table Mix

abstract: Revenue management has been used in a variety of industries and generally takes the form of managing demand by manipulating length of customer usage and price. Supply mix is rarely considered, although it can have considerable impact on revenue. In this research, we focused on developing an optimal supply mix, specifically on determining the supply mix that would maximize revenue. We used data from a Chevys restaurant, part of a large chain of Mexican restaurants, in conjunction with a simulation model to evaluate and enumerate all possible supply (table) mixes. Compared to the restaurant's existing table mix, the optimal mix is capable of handling a 30% increase in customer volume without increasing waiting times beyond their original levels. While our study was in a restaurant context, the results of this research are applicable to other service businesses.


title: Multiobjective Optimization for Stiffness and Position Control in a Soft Robot Arm Module

abstract: The central concept of this letter is to develop an assistive manipulator that can automate the bathing task for elderly citizens. We propose to exploit principles of soft robotic technologies to design and control a compliant system to ensure safe human–robot interaction, a primary requirement for the task. The overall system is intended to be modular with a proximal segment that provides structural integrity to overcome gravitational challenges and a distal segment to perform the main bathing activities. The focus of this letter is on the design and control of the latter module. The design comprises of alternating tendons and pneumatics in a radial arrangement, which enables elongation, contraction, and omnidirectional bending. Additionally, a synergetic coactivation of cables and tendons in a given configuration allows for stiffness modulation, which is necessary to facilitate washing and scrubbing. The novelty of the work is twofold: 1) Three base cases of antagonistic actuation are identified that enable stiffness variation. Each category is then experimentally characterized by the application of an external force that imposes a linear displacement at the tip in both axial and lateral directions. 2) The development of a novel algorithm based on cooperative multiagent reinforcement learning that simultaneously optimizes stiffness and position. The results highlight the effectiveness of the design and control to contribute toward the development of the assistive device.


title: Point convolutional neural networks by extension operators

abstract: This paper presents Point Convolutional Neural Networks (PCNN): a novel framework for applying convolutional neural networks to point clouds.


title: A design methodology for reliable software systems

abstract: Any user of a computer system is aware that current systems are unreliable because of errors in their software components. While system designers and implementers recognize the need for reliable software, they have been unable to produce it. For example, operating systems such as OS/360 are released to the public with hundreds of errors still in them.


title: Gestural control of sound synthesis

abstract: This paper provides a review of gestural control of sound synthesis in the context of the design and evaluation of digital musical instruments. It discusses research in various areas related to this field and equally focuses on four main topics: analysis of music performers' gestures, gestural capture technologies, real-time sound synthesis methods, and strategies for mapping gesture variables to sound synthesis input parameters. Finally, this approach is illustrated by presenting an application of this research to the control of digital audio effects.


title: Subcallosal cingulate deep brain stimulation for treatment-resistant depression: a multisite, randomised, sham-controlled trial

abstract: Summary Background Deep brain stimulation (DBS) of the subcallosal cingulate white matter has shown promise as an intervention for patients with chronic, unremitting depression. To test the safety and efficacy of DBS for treatment-resistant depression, a prospective, randomised, sham-controlled trial was conducted. Methods Participants with treatment-resistant depression were implanted with a DBS system targeting bilateral subcallosal cingulate white matter and randomised to 6 months of active or sham DBS, followed by 6 months of open-label subcallosal cingulate DBS. Randomisation was computer generated with a block size of three at each site before the site started the study. The primary outcome was frequency of response (defined as a 40% or greater reduction in depression severity from baseline) averaged over months 4–6 of the double-blind phase. A futility analysis was performed when approximately half of the proposed sample received DBS implantation and completed the double-blind phase. At the conclusion of the 12-month study, a subset of patients were followed up for up to 24 months. The study is registered at ClinicalTrials.gov, number NCT00617162. Findings Before the futility analysis, 90 participants were randomly assigned to active (n=60) or sham (n=30) stimulation between April 10, 2008, and Nov 21, 2012. Both groups showed improvement, but there was no statistically significant difference in response during the double-blind, sham-controlled phase (12 [20%] patients in the stimulation group vs five [17%] patients in the control group). 28 patients experienced 40 serious adverse events; eight of these (in seven patients) were deemed to be related to the study device or surgery. Interpretation This study confirmed the safety and feasibility of subcallosal cingulate DBS as a treatment for treatment-resistant depression but did not show statistically significant antidepressant efficacy in a 6-month double-blind, sham-controlled trial. Future studies are needed to investigate factors such as clinical features or electrode placement that might improve efficacy. Funding Abbott (previously St Jude Medical).


title: Polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDD/PCDF) in food samples collected in southern Mississippi, USA.

abstract: In 1994, we analyzed 43 foodstuff samples from local supermarkets in southern Mississippi, USA, for PCDD/PCDF. 2,3,7,8-Cl4DD could be quantified in 31 of these samples. On a lipid basis, levels in meat (0.53-1.10 pg I-TEQ/g) and dairy products (0.42-1.10 pg I-TEQ/g) were slightly lower than those reported from other industrialized countries. While levels in dairy samples from the United States and Europe are comparable, there is a difference in the contribution of individual congeners to the I-TEQ: for example, in milk samples from Germany approximately 40% of the I-TEQ is due to the presence of 2,3, 4,7,8-Cl5DF while in the Mississippi samples this congener only contributes 16%. The highest concentrations of PCDD/PCDF in our study were detected in the farm-raised catfish (10.2-27.8 pg I-TEQ/g). A unique finding was that in addition to the 2,3,7,8-substituted PCDD/PCDF the catfish samples contained many non-2,3,7,8-substituted congeners. This is unusual because vertebrate animals selectively eliminate or metabolize the non-2,3,7,8-substituted congeners.


title: Sparse Bayesian learning-based channel estimation in millimeter wave hybrid MIMO systems

abstract: This paper develops a novel sparse Bayesian learning (SBL)-based multiple-input multiple-output (MIMO) channel estimation technique for hybrid millimeter wave (mmWave) wireless systems by exploiting spatial sparsity in the wireless channels arising from the highly directional nature of propagation. The spatially sparse MIMO channel is modeled in terms of the basis array response matrices corresponding to the quantized directional cosines at the transmit and receive antenna arrays followed by the development of an expectation maximization (EM)-based sparse Bayesian learning (SBL) channel estimation approach. Subsequently, an enhanced variant of the SBL scheme is proposed based on hard thresholding the associated hyperparameter estimates, which is observed to significantly improve the accuracy of channel estimation. The Bayesian Cramer-Rao bound (BCRB) is also derived to benchmark the accuracy of the proposed SBL-based channel estimation schemes. Finally, simulation results are presented to illustrate the performance improvement achieved in comparison to the existing state-of-the-art orthogonal matching pursuit (OMP)-based sparse mmWave channel estimation scheme.


title: Vasopressor use in free tissue transfer surgery

abstract: Objective: To document the frequency of vasopressor use in free tissue transfer surgery and to compare the incidence of flap complications and flap survival in patients receiving or not receiving intraoperative vasopressors. Study Design and Setting: Case series with chart review of free tissue transfers performed between 2004 and 2006 at a large-volume tertiary academic hospital. Results: Of 241 free flaps, 169 had data available for analysis. Flap survival was 96.5 percent. The rate of other flap complications was 29 percent. A total of 139 (82%) patients received vasopressors intraoperatively. Of these, four (2.9%) flap failures and 40 (29%) complications occurred. In the 30 (18%) patients who did not receive vasopressors, two (6.7%) flap failures and nine (30%) complications occurred. The most common reasons for flap failure were arterial thrombus (n = 3) and thrombosis of the venous pedicle (n = 3). Conclusion: Intraoperative vasopressors are used more frequently than previously realized but do not appear to increase overall flap failure and the incidence of complications. Intraoperative vasopressor use in free flap surgery may not be as harmful as previously feared.


title: AR Plants: Herbal Plant Mobile Application utilizing Augmented Reality

abstract: This paper intends to introduce a mobile application that provides information about herbal plants commonly found in the Philippines utilizing augmented reality. All information about herbal plants were provided by the Philippine Institute of Traditional and Alternative Health Care (PITAHC) including its characteristics, scientific names, vitamin content, uses, where to find it and procedure how to use it. For the development of the mobile application, we used Unity software and Vuforia SDK as it may provide many resources such as image converter, camera, tracker, video background renderer, application code, user-defined targets, and device databases. We also used the Vuforia QCAR algorithm that can identify the tracking points of the image target from the database and will be used for augmentation of 3D models. In evaluating the acceptability of AR Plants mobile application, a 5-point Likert-scaled questionnaire was designed and validated by IT experts. With the total average mean of 4.29, it shows that the mobile application is acceptable in terms suitability, accurateness, learnability, operability, time behavior, resource utilization, and recovery. With the use of multimedia content and AR technology, it can help the user to become aware, engage, and enhance his vision with useful information about the surrounding environment. For future works, we recommend to add additional kinds of herbal plants, provide a cloud database and to use a better algorithm for identifying real-life targets.


title: The smartphone as a medical device: Assessing enablers, benefits and challenges

abstract: Over one billion smartphones have now been shipped worldwide. These mobile devices feature multi-core CPUs and GPUs, megapixel cameras and an array of sensors. Smartphone sensors can now be processed to diagnose a wide variety of medical conditions including cough detection, irregular heartbeat detection, and lung function analysis. The ability to diagnose ailments in the convenience of patients' homes on smartphones that they already possess could lead to early detection, which could ultimately reduce healthcare costs. This paper reviews state-of-the-art examples, examines the technical issues involved in the use of the smartphone as a medical device, and outlines potential benefits and challenges. A case study of an Android smartphone app for wound detection and healing progress analysis by diabetes patients is also presented.


title: Leadership: Current Theories, Research, and Future Directions

abstract: This review examines recent theoretical and empirical developments in the leadership literature, beginning with topics that are currently receiving attention in terms of research, theory, and practice. We begin by examining authentic leadership and its development, followed by work that takes a cognitive science approach. We then examine new-genre leadership theories, complexity leadership, and leadership that is shared, collective, or distributed. We examine the role of relationships through our review of leader member exchange and the emerging work on followership. Finally, we examine work that has been done on substitutes for leadership, servant leadership, spirituality and leadership, cross-cultural leadership, and e-leadership. This structure has the benefit of creating a future focus as well as providing an interesting way to examine the development of the field. Each section ends with an identification of issues to be addressed in the future, in addition to the overall integration of the literature we provide at the end of the article.


title: Collaborative and secure sharing of healthcare data in multi-clouds

abstract: In healthcare, inter-organizational sharing and collaborative use of big data become increasingly important. The cloud-computing paradigm is expected to provide an environment perfectly matching the needs of collaborating healthcare workers. However, there are still many security and privacy challenges impeding the wide adoption of cloud computing in this domain. In this paper, we present a novel architecture and its implementation for inter-organizational data sharing, which provides a high level of security and privacy for patient data in semi-trusted cloud computing environments. This architecture features attribute-based encryption for selective access authorization and cryptographic secret sharing in order to disperse data across multiple clouds, reducing the adversarial capabilities of curious cloud providers. An implementation and evaluation by several experiments demonstrate the practical feasibility and good performance of our approach.


title: Large Scale Semi-Supervised Object Detection Using Visual and Semantic Knowledge Transfer

abstract: Deep CNN-based object detection systems have achieved remarkable success on several large-scale object detection benchmarks. However, training such detectors requires a large number of labeled bounding boxes, which are more difficult to obtain than image-level annotations. Previous work addresses this issue by transforming image-level classifiers into object detectors. This is done by modeling the differences between the two on categories with both imagelevel and bounding box annotations, and transferring this information to convert classifiers to detectors for categories without bounding box annotations. We improve this previous work by incorporating knowledge about object similarities from visual and semantic domains during the transfer process. The intuition behind our proposed method is that visually and semantically similar categories should exhibit more common transferable properties than dissimilar categories, e.g. a better detector would result by transforming the differences between a dog classifier and a dog detector onto the cat class, than would by transforming from the violin class. Experimental results on the challenging ILSVRC2013 detection dataset demonstrate that each of our proposed object similarity based knowledge transfer methods outperforms the baseline methods. We found strong evidence that visual similarity and semantic relatedness are complementary for the task, and when combined notably improve detection, achieving state-of-the-art detection performance in a semi-supervised setting.


title: Scaling of respiration to nitrogen in leaves, stems and roots of higher land plants

abstract: Using a database of 2510 measurements from 287 species, we assessed whether general relationships exist between mass-based dark respiration rate and nitrogen concentration for stems and roots, and if they do, whether they are similar to those for leaves. The results demonstrate strong respiration–nitrogen scaling relationships for all observations and for data averaged by species; for roots, stems and leaves examined separately; and for life-forms (woody, herbaceous plants) and phylogenetic groups (angiosperms, gymnosperms) considered separately. No consistent differences in the slopes of these log–log scaling relations were observed among organs or among plant groups, but respiration rates at any common nitrogen concentration were consistently lower on average in leaves than in stems or roots, indicating that organ-specific relationships should be used in models that simulate respiration based on tissue nitrogen concentrations. The results demonstrate both common and divergent aspects of tissuelevel respiration–nitrogen scaling for leaves, stems and roots across higher land plants, which are important in their own right and for their utility in modelling carbon fluxes at local to global scales.


title: Labelling strategies for hierarchical multi-label classification techniques

abstract: Many hierarchical multi-label classification systems predict a real valued score for every (instance, class) couple, with a higher score reflecting more confidence that the instance belongs to that class. These classifiers leave the conversion of these scores to an actual label set to the user, who applies a cut-off value to the scores. The predictive performance of these classifiers is usually evaluated using threshold independent measures like precision-recall curves. However, several applications require actual label sets, and thus an automatic labelling strategy.In this paper, we present and evaluate different alternatives to perform the actual labelling in hierarchical multi-label classification. We investigate the selection of both single and multiple thresholds. Despite the existence of multiple threshold selection strategies in non-hierarchical multi-label classification, they cannot be applied directly to the hierarchical context. The proposed strategies are implemented within two main approaches: optimisation of a certain performance measure of interest (such as F-measure or hierarchical loss), and simulating training set properties (such as class distribution or label cardinality) in the predictions. We assess the performance of the proposed labelling schemes on 10 datasets from different application domains. Our results show that selecting multiple thresholds may result in an efficient and effective solution for hierarchical multi-label problems. HighlightsWe select single or multiple thresholds for hierarchical multi-label classifiers.Selecting multiple thresholds often yield better label sets in lesser time.We show that optimising H-loss tends to favor empty label sets.Multiple threshold selection is preferred for micro F-measure and HMC-loss.Imitating training set properties is a competitive approach to optimise HMC-loss.