title: Effect of solvents extraction on total phenolics and antioxidant activity of extracts from flaxseed (Linum usitatissimum L.)

abstract: Background. Plant origin food ingredients are the main source of very potent antioxidants. Tocopherols, the main oilseeds natural antioxidants are very potent and when implemented into cell membranes are able to scavenge large number of free radicals. Among plant antioxidants are mainly phenolics, large and diversifi ed group of chemical compounds with different radical scavenging potential. Material and methods. Defatted fl axseed meals were extracted with pure alcohols and its mixture with water. Acquired extracts were analysed for the content of phenolics and fl avonoids using colorimetric procedures. Antioxidative capacity was assessed by utilizing: DPPH stable free radicals; inhibition of linoleic acid oxidation and reducing power of components. Results. Investigation was conducted on two different batches of fl axseed, assessing antioxidant capacity of compounds extracted with different polarity solvents and extracts were tested for antioxidant activity with different methods. The highest yield of extraction was achieved with 80% methanol but the extract did not contain the highest amount of phenolics and fl avonoids. When 80% ethanol was used for extraction the highest amount of fl avonoids was detected and also the best antioxidant capacity. Conclusions. The results clearly showed that utilization of polar solvent enable extraction of signifi cant amounts of phenolics and fl avonoids. Those components were the most potent antioxidants present in these extracts. Content of these compounds correlated well with results from applied methods for antioxidant assessment.


title: One Sense Per Collocation

abstract: Previous work [Gale, Church and Yarowsky, 1992] showed that with high probability a polysemous word has one sense per discourse. In this paper we show that for certain definitions of collocation, a polysemous word exhibits essentially only one sense per collocation. We test this empirical hypothesis for several definitions of sense and collocation, and discover that it holds with 90--99% accuracy for binary ambiguities. We utilize this property in a disambiguation algorithm that achieves precision of 92% using combined models of very local context.


title: Studying the Maximum Instantaneous Non-Synchronous Generation in an Island System—Frequency Stability Challenges in Ireland

abstract: Synchronous island power systems, such as the combined Ireland and Northern Ireland power system, are facing increasing penetrations of renewable generation. As part of a wider suite of studies, performed in conjunction with the transmission system operators (TSOs) of the All-Island system (AIS), the frequency stability challenges at high and ultra-high wind penetrations were examined. The impact of both largest infeed loss and network fault induced wind turbine active power dips was examined: the latter contingency potentially representing a fundamental change in frequency stability risk. A system non-synchronous penetration (SNSP) ratio was defined to help identify system operational limits. A wide range of system conditions were studied, with results showing that measures such as altering ROCOF protection and enabling emulated inertia measures were most effective in reducing the frequency stability risk of a future Ireland system.


title: Designing a Measurement Method for the Portability Non-functional Requirement

abstract: This paper proposes a measurement method to measure the non-functional portability software requirement. The proposed method complies with the COSMIC ISO 19761 measurement method standard. The details of the design steps are presented and explained. This paper also includes an analysis and possible utilizations of the method results into the various estimation models. Applying the proposed method can be beneficial to estimate the development time and/or the quality assurance efforts to develop/certify portable software on the various environments supported.


title: AN ELECTROENCEPHALOGRAPHIC STUDY ON THE ZEN MEDITATION (ZAZEN)

abstract: SUMMARY ::: Zen meditation (ZAZEN) is a spiritual exercise held in the Zen sect of Buddhism. Apart from its religious significance, the training of Zen meditation produces changes not only in the mind but also in the body—these influences are of interest to scientific studies, from the stand point of psychology and physiology. ::: ::: In the present study the EEG changes accompanied with Zen meditation have been revealed and described in detail. The authors discussed further these electro-graphic changes in relation to the consciousness with its underlying neurophysiological background, comparing with that of the hypnotic trance and sleep. ::: ::: In our study, 48 priests and disciples of Zen sects of Buddhism were selected as the subjects and their EEGs were continuously recorded before, during and after Zen meditation. The following results were obtained; ::: ::: 1The appearance of alpha waves were observed, without regard to opened eyes, within 50 sec. after the beginning of Zen meditation. These alpha waves continued to appear, and their amplitudes increased. And as Zen meditation progressed, the decrease of the alpha frequency was gradually manifested at the later stage. Further the rhythmical theta train with the amplitude modulated alpha-background was observed in some records of the priests. These EEG changes could be classified into 4 stages; the appearance of alpha waves (stage I), an increase of alpha amplitude (stage II), a decrease of alpha frequency (stage III) and the appearance of rhythmical theta train (stage IV). ::: 2These 4 stages of EEG changes were parallel with the disciples' mental states, which were evaluated by a Zen master, and disciples' years spent in Zen training. ::: 3These electrographic changes were also compared with that of the hypnotic trance and sleep. From the electroencephalographic point of view, the changes of stages I, II and III could not be clearly differentiated from those seen in hypnagogic state or the hypnotic sleep, though the changes during Zen meditation were more persistent and did not turn into deeper sleep pattern. The rhythmical theta train is suppressed by click stimulation and turns into a desynchronized pattern, whereas the drowsy pattern turns into alpha waves (the alpha arousal reaction). ::: 4The alpha blocking to the repeated click stimuli with regular intervals was also examined in Zen meditation with opened eyes and the ordinary conditions of control subjects with closed eyes. The former showed a fairly constant blocking time (3–5 sec.) to every stimuli repeated 20 times and the habituation was not recognized. On the other hand, in control subjects the habituation of alpha waves occurred very quickly. This alpha blocking, which is less susceptible to habituation, is of importance to consider the neurophysiological basis of the mental state during Zen meditation. ::: ::: ::: These electroencephalographic findings lead to the following conclusions; In Zen meditation, the slowing of EEG pattern is confirmed on the one hand, and the dehabituation of the alpha blocking on the other. These indicate the specific change of consciousness. The authors further discussed the state of mind during Zen meditation from the psychophysiological point of view.


title: Scaling up Dynamic Topic Models

abstract: Dynamic topic models (DTMs) are very effective in discovering topics and capturing their evolution trends in time series data. To do posterior inference of DTMs, existing methods are all batch algorithms that scan the full dataset before each update of the model and make inexact variational approximations with mean-field assumptions. Due to a lack of a more scalable inference algorithm, despite the usefulness, DTMs have not captured large topic dynamics. This paper fills this research void, and presents a fast and parallelizable inference algorithm using Gibbs Sampling with Stochastic Gradient Langevin Dynamics that does not make any unwarranted assumptions. We also present a Metropolis-Hastings based $O(1)$ sampler for topic assignments for each word token. In a distributed environment, our algorithm requires very little communication between workers during sampling (almost embarrassingly parallel) and scales up to large-scale applications. We are able to learn the largest Dynamic Topic Model to our knowledge, and learned the dynamics of 1,000 topics from 2.6 million documents in less than half an hour, and our empirical results show that our algorithm is not only orders of magnitude faster than the baselines but also achieves lower perplexity.


title: A comparative study of three social networking websites

abstract: Recently, the number of social networking sites is rapidly increasing, and the number of users joining these sites is dramatically increasing as well. This paper aims at comprehensively comparing three social networking sites, and provides an in-depth analysis. We compare three of the most popular social networking sites, i.e., Facebook, Twitter and MySpace. Specifically, we evaluate those social networking sites based on four criteria (i.e., navigation, interactivity, source credibility and intelligence). For each criterion, we propose a list of measures for the comparison. The comparison essentially explores the differences and commonalities among those social networking sites. Based on the analysis of the comparison, a user study is conducted to evaluate the three websites.


title: A Filtering Dual-Polarized Antenna Subarray Targeting for Base Stations in Millimeter-Wave 5G Wireless Communications

abstract: A $2 \times 2$ dual-polarized antenna subarray with filtering responses is proposed in this paper. This antenna subarray is a multilayered 3-D geometry, including a dual-path $1 \times 4$ feeding network and four cavity-backed slot antennas. The isolation performance between two input ports is greatly improved by a novel method, which only needs to modify several vias in a square resonator. Cavities in the feeding network are properly arranged and coupled using different coupling structures, so that the operation modes in each cavity for different paths can always remain orthogonal, which enables the subarray to exhibit not only filtering functions (in both reflection coefficients and gain responses), but also a low cross-polarization level. A prototype is fabricated with a center frequency of 37 GHz and a bandwidth of 600 MHz for demonstration. Good agreement is achieved between simulation and measurement, for both $S$ -parameter and far-field results. The proposed filtering dual-polarized antenna array is very suitable to be employed as the subarray in millimeter-wave 5G base stations to reduce the complexity and integration loss of such beamforming systems.


Virus taxonomy in the age of metagenomics


title: Multiple Target Tracking with RF Sensor Networks

abstract: RF sensor networks are wireless networks that can localize and track people (or targets) without needing them to carry or wear any electronic device. They use the change in the received signal strength (RSS) of the links due to the movements of people to infer their locations. In this paper, we consider real-time multiple target tracking with RF sensor networks. We apply radio tomographic imaging (RTI), which generates images of the change in the propagation field, as if they were frames of a video. Our RTI method uses RSS measurements on multiple frequency channels on each link, combining them with a fade level-based weighted average. We introduce methods, inspired by machine vision and adapted to the peculiarities of RTI, that enable accurate and real-time multiple target tracking. Several tests are performed in an open environment, a one-bedroom apartment, and a cluttered office environment. The results demonstrate that the system is capable of accurately tracking in real-time up to four targets in cluttered indoor environments, even when their trajectories intersect multiple times, without mis-estimating the number of targets found in the monitored area. The highest average tracking error measured in the tests is 0.45 m with two targets, 0.46 m with three targets, and 0.55 m with four targets.


title: Speaker verification using adapted Gaussian mixture models

abstract: Reynolds, Douglas A., Quatieri, Thomas F., and Dunn, Robert B., Speaker Verification Using Adapted Gaussian Mixture Models, Digital Signal Processing10(2000), 19?41.In this paper we describe the major elements of MIT Lincoln Laboratory's Gaussian mixture model (GMM)-based speaker verification system used successfully in several NIST Speaker Recognition Evaluations (SREs). The system is built around the likelihood ratio test for verification, using simple but effective GMMs for likelihood functions, a universal background model (UBM) for alternative speaker representation, and a form of Bayesian adaptation to derive speaker models from the UBM. The development and use of a handset detector and score normalization to greatly improve verification performance is also described and discussed. Finally, representative performance benchmarks and system behavior experiments on NIST SRE corpora are presented.


title: A pragmatic view of knowledge and boundaries: Boundary objects in new product development

abstract: This study explores the premise that knowledge in new product development proves both a barrier to and a source of innovation. To understand the problematic nature of knowledge and the boundaries that result, an ethnographic study was used to understand how knowledge is structured differently across the four primary functions that are dependent on each other in the creation and production of a high-volume product. A pragmatic view of 'knowledge in practice' is developed, describing knowledge as localized, embedded, and invested within a function and how, when working across functions, consequences often arise that generate problematic knowledge boundaries. The use of a boundary object is then described as a means of representing, learning about, and transforming knowledge to resolve the consequences that exist at a given boundary. Finally, this pragmatic view of knowledge and boundaries is proposed as a framework to revisit the differentiation and integration of knowledge.


title: Neural Paraphrase Identification of Questions with Noisy Pretraining

abstract: We present a solution to the problem of paraphrase identification of questions. We focus on a recent dataset of question pairs annotated with binary paraphrase labels and show that a variant of the decomposable attention model (Parikh et al., 2016) results in accurate performance on this task, while being far simpler than many competing neural architectures. Furthermore, when the model is pretrained on a noisy dataset of automatically collected question paraphrases, it obtains the best reported performance on the dataset.


A possible role of the Infant/Toddler Sensory Profile in screening for autism: a proof-of-concept study in the specific sample of prematurely born children with birth weights <1,500 g


Identification and Implementation of Authentication and Authorization Patterns in the Spring Security Framework


title: A data mining approach for database intrusion detection

abstract: In this paper we proposed a data mining approach for detecting malicious transactions in a Database System. Our approach concentrates on mining data dependencies among data items in the database. A data dependency miner is designed for mining data correlations from the database log. The transactions not compliant to the data dependencies mined are identified as malicious transactions. The experiment illustrates that the proposed method works effectively for detecting malicious transactions provided certain data dependencies exist in the database.


title: Learning from demonstration and adaptation of biped locomotion

abstract: In this paper, we introduce a framework for learning biped locomotion using dynamical movement primitives based on non-linear oscillators. Our ultimate goal is to establish a design principle of a controller in order to achieve natural human-like locomotion. We suggest dynamical movement primitives as a central pattern generator (CPG) of a biped robot, an approach we have previously proposed for learning and encoding complex human movements. Demonstrated trajectories are learned through movement primitives by locally weighted regression, and the frequency of the learned trajectories is adjusted automatically by a novel frequency adaptation algorithm based on phase resetting and entrainment of coupled oscillators. Numerical simulations and experimental implementation on a physical robot demonstrate the effectiveness of the proposed locomotion controller. © 2004 Elsevier B.V. All rights reserved.


Improving model-based RL with Adaptive Rollout using Uncertainty Estimation


title: Investigation of the Lack of Common Understanding in the Discipline of Enterprise Architecture : A Systematic Mapping Study

abstract: The number of publications, along with the organization of new conferences are a couple of the relevant elements that usually indicate the progress of an area of study over the years. This is definitely true in the case of the Enterprise Architecture (EA) discipline, which went from having its first journal article published in 1989 to over two hundred published articles by 2015. But in spite of this evolution, EA is still suffering from a considerable lack of common understanding. It has become very important to investigate the current state of affairs concerning the EA discipline through its relevant publications in order to shed some light on this challenge. 171 journal papers published between 1990 and 2015 were systematically selected and examined in order to accomplish this investigation. The quantitative and qualitative findings of this examination show that EA is a young discipline which raises a growing interest in recent years. This examination also confirms the lack of common understanding in EA, which can be observed in the different descriptions of the term "enterprise architecture," and in the diversity of perspective with regards to the whole discipline. Several issues related to this lack has been reported, such as multidisciplinary issue, language issue, structure of research and mode of observation issues. The major issue concerns the absence of enough research to shed some light on this challenge. In addition to this investigation, helpful directions for future research in this area was proposed.


title: Building Energy Management Systems — Optimization of comfort and energy use

abstract: Building Energy Management Systems (BEMS) have been introduced in the built environment as a mechanism to increase the energy efficiency while maintaining the required comfort levels. BEMS also offers promising flexibility for Demand Side Management and Demand Responses (DSM&DR) to interact with Smart Grids. Through the use of intelligent control techniques, BEMS can be optimized to exploit the built environment' flexibility while ensuring the minimum comfort levels required by the building's user. This paper presents an analysis framework for BEMS based on a state of the art literature review. Furthermore, Simscape is introduced as a simulation tool that can model effectively sub-systems in the BEMS.


title: Luxury Retail Management: How the World's Top Brands Provide Quality Product and Service Support

abstract: Introduction vii Chapter 1 Luxury and Brand Power 1 Chapter 2 The Distribution Models of Luxury 15 Chapter 3 The Different Outlets of Luxury Distribution 37 Chapter 4 The Internet as a Channel of Distribution 63 Chapter 5 Luxury Store Location 83 Chapter 6 Luxury Store Concept and Design 119 Chapter 7 Luxury Store Economics 139 Chapter 8 Luxury Retail Pricing 169 Chapter 9 Customer In-Store Behavior 185 Chapter 10 The Importance of Stores in Customer Relationship Building 195 Chapter 11 Building Loyalty in Luxury Brands 209 Chapter 12 Advertising and Communication 229 Chapter 13 The Future of Luxury Brand Retailing 245 Appendix Managing a Store Toolbox 257 Bibliography 291 About the Authors 295 Index 297


title: Cross-domain ranking via latent space learning

abstract: We study the problem of cross-domain ranking, which addresses learning to rank objects from multiple interrelated domains. In many applications, we may have multiple interrelated domains, some of them with a large amount of training data and others with very little. We often wish to utilize the training data from all these related domains to help improve ranking performance. In this paper, we present a unified model: BayCDR for cross-domain ranking. BayCDR uses a latent space to measure the correlation between different domains, and learns the ranking functions from the interrelated domains via the latent space by a Bayesian model, where each ranking function is based on a weighted average model. An efficient learning algorithm based on variational inference and a generalization bound has been developed. To scale up to handle real large data, we also present a learning algorithm under the Map-Reduce programming model. Finally, we demonstrate the effectiveness and efficiency of BayCDR on large datasets.


title: Stacked Sparse Autoencoder-Based Deep Network for Fault Diagnosis of Rotating Machinery

abstract: As a breakthrough in the field of machine fault diagnosis, deep learning has great potential to extract more abstract and discriminative features automatically without much prior knowledge compared with other methods, such as the signal processing and analysis-based methods and machine learning methods with shallow architectures. One of the most important aspects in measuring the extracted features is whether they can explore more information of the inputs and avoid redundancy to be representative. Thus, a stacked sparse autoencoder (SAE)-based machine fault diagnosis method is proposed in this paper. The penalty term of the SAE can help mine essential information and avoid redundancy. To help the constructed diagnosis network further mine more abstract and representative high-level features, the collected non-stationary and transient signals are preprocessed with ensemble empirical mode decomposition and autoregressive (AR) models to obtain AR parameters, which are extracted based on the intrinsic mode functions (IMFs) and regarded as the low-level features for the inputs of the proposed diagnosis network. Only the first four IMFs are considered, because fault information is mainly reflected in high-frequency IMFs. Experiments and comparisons are complemented to validate the superiority of the presented diagnosis network. Results fully demonstrate that the stacked SAE-based diagnosis method can extract more discriminative high-level features and has a better performance in rotating machinery fault diagnosis compared with the traditional machine learning methods with shallow architectures.


title: A Neural Network Approach to Missing Marker Reconstruction.

abstract: Optical motion capture systems have become a widely used technology in various fields, such as augmented reality, robotics, movie production, etc. Such systems use a large number of cameras to triangulate the position of optical markers. These are then used to reconstruct the motion of rigid objects or human articulated bodies, to which the markers are attached. ::: The marker positions are estimated with high accuracy. However, especially when tracking articulated bodies, a fraction of the markers in each timestep is missing from the reconstruction. ::: In this paper, we propose to use a neural network approach to learn how human motion is temporally and spatially correlated, and reconstruct missing markers positions through this model. We experiment with two different models, one LSTM-based and one window-based. Experiments on the CMU Mocap dataset show that we outperform the state of the art by 20\% - 400\%.


title: Context-Attentive Embeddings for Improved Sentence Representations

abstract: While one of the first steps in many NLP systems is selecting what embeddings to use, we argue that such a step is better left for neural networks to figure out by themselves. To that end, we introduce a novel, straightforward yet highly effective method for combining multiple types of word embeddings in a single model, leading to state-of-the-art performance within the same model class on a variety of tasks. We subsequently show how the technique can be used to shed new insight into the usage of word embeddings in NLP systems.


title: Platelet-rich fibrin (PRF): a second-generation platelet concentrate. Part I: technological concepts and evolution.

abstract: Platelet-rich fibrin (PRF) belongs to a new generation of platelet concentrates geared to simplified preparation without biochemical blood handling. In this initial article, we describe the conceptual and technical evolution from fibrin glues to platelet concentrates. This retrospective analysis is necessary for the understanding of fibrin technologies and the evaluation of the biochemical properties of 3 generations of surgical additives, respectively fibrin adhesives, concentrated platelet-rich plasma (cPRP) and PRF. Indeed, the 3-dimensional fibrin architecture is deeply dependent on artificial clinical polymerization processes, such as massive bovine thrombin addition. Currently, the slow polymerization during PRF preparation seems to generate a fibrin network very similar to the natural one. Such a network leads to a more efficient cell migration and proliferation and thus cicatrization.


title: A survey of PEV impacts on electric utilities

abstract: For the past few years, plug-in electric vehicle (PEV) technology development has gained immense popularity. Recent studies show that if PEVs displaced half of all vehicles on the road, they would require only an 8% increase in electricity generation. Results similar to this help encourage the continuing development of PEVs. Despite this small increase in generation, uncontrolled charging, especially during on-peak summer hours, could overload the current power grid. This paper provides a narrative literature survey of the development and impact of PEVs. Subjects cover PEV industry trends, charge and discharge scenarios, and impacts on distribution systems. Some concluding remarks are summarized.


title: Managing the requirements flow from strategy to release in large-scale agile development: a case study at Ericsson

abstract: In a large organization, informal communication and simple backlogs are not sufficient for the management of requirements and development work. Many large organizations are struggling to successfully adopt agile methods, but there is still little scientific knowledge on requirements management in large-scale agile development organizations. We present an in-depth study of an Ericsson telecommunications node development organization which employs a large scale agile method to develop telecommunications system software. We describe how the requirements flow from strategy to release, and related benefits and problems. Data was collected by 43 interviews, which were analyzed qualitatively. The requirements management was done in three different processes, each of which had a different process model, purpose and planning horizon. The release project management process was plan-driven, feature development process was continuous and implementation management process was agile. The perceived benefits included reduced development lead time, increased flexibility, increased planning efficiency, increased developer motivation and improved communication effectiveness. The recognized problems included difficulties in balancing planning effort, overcommitment, insufficient understanding of the development team autonomy, defining the product owner role, balancing team specialization, organizing system-level work and growing technical debt. The study indicates that agile development methods can be successfully employed in organizations where the higher level planning processes are not agile. Combining agile methods with a flexible feature development process can bring many benefits, but large-scale software development seems to require specialist roles and significant coordination effort.


title: A connection between a convex programming problem and the LYM property on perfect graphs

abstract: We derive three equivalent conditions on a perfect graph concerning the optimal solution of a convex programming problem, the length-width inequality, and the simultaneous vertex covering by cliques and anticliques. By combining proof techniques including Lagrangian dual, Dilworth's Theorem, and Kuhn-Tucker Theorem, we establish a strong connection between the three topics. This provides new insights into the structure of perfect graphs. The famous Lubell-Yamamoto-Meschalkin (LYM) Property or Sperner Property for partially ordered sets is a specialization of our results to a subclass of perfect graphs.


title: Women’s perceptions of contraceptive efficacy and safety

abstract: Adoption of contraceptive implants and intrauterine devices has been less than might be expected given their superior efficacy and convenience. The purpose of this study was to assess knowledge and beliefs held by women, which may influence their contraceptive choices and theirongoing utilization of contraceptive methods. English speaking, nonpregnant, reproductive-age women, who were not surgically sterilized, were individually interviewed to obtain limited demographic characteristics and to assess their knowledge about the efficacy of various contraceptive methods in typical use and about the relative safety of oral contraceptives. A convenience sample of 500 women aged 18–45 years, with education levels that ranged from middle school to postdoctoral level was interviewed. The efficacy in typical use of both combined oral contraceptives and male condoms was correctly estimated by 2.2%; over two-thirds of women significantly overestimated the efficacy of each of those methods in typical use. Oral contraceptives were thought to be at least as hazardous to a woman’s health as pregnancy by 56% of women. The majority of reproductive aged women surveyed substantially overestimated the efficacy of the two most popular contraceptive methods, often saying that they were 99% effective. Women with higher education levels were most likely to overestimate efficacy of oral contraceptives. Women of all ages and education levels significantly overestimated the health hazards of oral contraceptives compared to pregnancy. Overestimation of effectiveness of these methods of contraception, may contribute to lower adoption of implants and intrauterine devices. When individualizing patient counselling, misperceptions must be identified and addressed with women of all educational backgrounds. Not applicable.


title: Omnidirectional Coverage for Device-Free Passive Human Detection

abstract: Device-free Passive (DfP) human detection acts as a key enabler for emerging location-based services such as smart space, human-computer interaction, and asset security. A primary concern in devising scenario-tailored detecting systems is coverage of their monitoring units. While disk-like coverage facilitates topology control, simplifies deployment analysis, and is crucial for proximity-based applications, conventional monitoring units demonstrate directional coverage due to the underlying transmitter-receiver link architecture. To achieve omnidirectional coverage under such link-centric architecture, we propose the concept of omnidirectional passive human detection. The rationale is to exploit the rich multipath effect to blur the directional coverage. We harness PHY layer features to robustly capture the fine-grained multipath characteristics and virtually tune the shape of the coverage of the monitoring unit, which is previously prohibited with mere MAC layer RSSI. We design a fingerprinting scheme and a threshold-based scheme with off-the-shelf WiFi infrastructure and evaluate both schemes in typical clustered indoor scenarios. Experimental results demonstrate an average false positive of 8 percent and an average false negative of 7 percent for fingerprinting in detecting human presence in 4 directions. And both average false positive and false negative remain around 10 percent even with threshold-based methods.


title: Detection of cracks in computer tomography images of logs

abstract: Computer Tomography (CT) is being increasingly employed for automated detection and localization of internal defects in logs prior to their sawing. Reliable detection and localization of cracks in CT images of logs is particularly important from the viewpoint of lumber production planning since the presence of cracks substantially reduces the value and also compromises the structural strength of the resulting lumber. A crack is hard to detect in a cross-sectional CT image of a log because it has geometric properties and grayscale values that are similar to those associated with the ring structure of the log. In this paper, a method for crack detection is presented, which exploits the fact that the line defining the crack makes a significant non-zero angle with the log ring structure. Sobel-like operators are used to extract both, the line defining the crack and the contours corresponding to the grayscale valleys between two neighboring rings. Fork detection and grouping methods are subsequently employed to localize the actual crack line using a RANSAC-based line fitting procedure. Experimental results show the advantages of the proposed technique for crack detection when compared to techniques that employ straightforward grayscale histogram-based thresholding.


title: A Review of Brain-Computer Interface Games and an Opinion Survey from Researchers, Developers and Users

abstract: In recent years, research on Brain-Computer Interface (BCI) technology for healthy users has attracted considerable interest, and BCI games are especially popular. This study reviews the current status of, and describes future directions, in the field of BCI games. To this end, we conducted a literature search and found that BCI control paradigms using electroencephalographic signals (motor imagery, P300, steady state visual evoked potential and passive approach reading mental state) have been the primary focus of research. We also conducted a survey of nearly three hundred participants that included researchers, game developers and users around the world. From this survey, we found that all three groups (researchers, developers and users) agreed on the significant influence and applicability of BCI and BCI games, and they all selected prostheses, rehabilitation and games as the most promising BCI applications. User and developer groups tended to give low priority to passive BCI and the whole head sensor array. Developers gave higher priorities to “the easiness of playing” and the “development platform” as important elements for BCI games and the market. Based on our assessment, we discuss the critical point at which BCI games will be able to progress from their current stage to widespread marketing to consumers. In conclusion, we propose three critical elements important for expansion of the BCI game market: standards, gameplay and appropriate integration.


title: Predicting the severity of a reported bug

abstract: The severity of a reported bug is a critical factor in deciding how soon it needs to be fixed. Unfortunately, while clear guidelines exist on how to assign the severity of a bug, it remains an inherent manual process left to the person reporting the bug. In this paper we investigate whether we can accurately predict the severity of a reported bug by analyzing its textual description using text mining algorithms. Based on three cases drawn from the open-source community (Mozilla, Eclipse and GNOME), we conclude that given a training set of sufficient size (approximately 500 reports per severity), it is possible to predict the severity with a reasonable accuracy (both precision and recall vary between 0.65–0.75 with Mozilla and Eclipse; 0.70–0.85 in the case of GNOME).


title: Food, livestock production, energy, climate change, and health

abstract: Food provides energy and nutrients, but its acquisition requires energy expenditure. In post-hunter-gatherer societies, extra-somatic energy has greatly expanded and intensified the catching, gathering, and production of food. Modern relations between energy, food, and health are very complex, raising serious, high-level policy challenges. Together with persistent widespread under-nutrition, over-nutrition (and sedentarism) is causing obesity and associated serious health consequences. Worldwide, agricultural activity, especially livestock production, accounts for about a fifth of total greenhouse-gas emissions, thus contributing to climate change and its adverse health consequences, including the threat to food yields in many regions. Particular policy attention should be paid to the health risks posed by the rapid worldwide growth in meat consumption, both by exacerbating climate change and by directly contributing to certain diseases. To prevent increased greenhouse-gas emissions from this production sector, both the average worldwide consumption level of animal products and the intensity of emissions from livestock production must be reduced. An international contraction and convergence strategy offers a feasible route to such a goal. The current global average meat consumption is 100 g per person per day, with about a ten-fold variation between high-consuming and low-consuming populations. 90 g per day is proposed as a working global target, shared more evenly, with not more than 50 g per day coming from red meat from ruminants (ie, cattle, sheep, goats, and other digastric grazers).


title: Differential Emotions and the Stock Market - The Case of Company-specific Trading

abstract: Practitioners and researchers alike increasingly use social media messages as an additional source of information to analyse stock price movements. In this regard, previous preliminary findings demonstrate the incremental value of considering the multi-dimensional structure of human emotions in sentiment analysis instead of the predominant assessment of the binary positive-negative valence of emotions. Therefore, based on emotion theory and an established sentiment lexicon, we develop and apply an open source dictionary for the analysis of seven different emotions (affection, happiness, satisfaction, fear, anger, depression, and contempt).To investigate the connection between the differential emotions and stock movements we analyse approximately 5.5 million Twitter messages on 33 S&P 100 companies and their respective NYSE stock prices from Yahoo!Finance over a period of three months. Subsequently, we conduct a lagged fixed-effects panel regression on the daily closing value differences. The results generally support the assumption of the necessity of considering a more differentiated sentiment. Moreover, comparing positive and negative valence, we find that only the average negative emotionality strength has a significant connection with company-specific stock price movements. The emotion specific analysis reveals that an increase in depression and happiness strength isassociated with a significant decrease in company-specific stock prices.


title: The prevalence and correlates of depression, anxiety, and stress in a sample of college students.

abstract: Abstract Background Over the past four years, the Franciscan University Counseling Center has reported a 231% increase in yearly visits, as well as a 173% increase in total yearly clients. This trend has been observed at many universities as mental health issues pose significant problems for many college students. The objective of this study was to investigate potential correlates of depression, anxiety, and stress in a sample of college students. Methods The final analyzed sample consisted of 374 undergraduate students between the ages of 18 and 24 attending Franciscan University, Steubenville, Ohio. Subjects completed a survey consisting of demographic questions, a section instructing participants to rate the level of concern associated with challenges pertinent to daily life (e.g. academics, family, sleep), and the 21 question version of the Depression Anxiety Stress Scale (DASS21). Results The results indicated that the top three concerns were academic performance, pressure to succeed, and post-graduation plans. Demographically, the most stressed, anxious, and depressed students were transfers, upperclassmen, and those living off-campus. Conclusions With the propensity for mental health issues to hinder the success of college students, it is vital that colleges continually evaluate the mental health of their students and tailor treatment programs to specifically target their needs.


title: Stateline - the Sting of High-Stakes Testing and Accountability

abstract: IN THE short-term, simplistic world where many of our education policy decisions are made, high-stakes testing has again become the instrument of reform. Back in the late 1970s and early 1980s, minimum-competency testing (MCT) for high school graduation, grade promotion, and remediation was mandated by 35 states in just a few short years. Back in those days, the legislation made students the focal point of the rewards and sanctions. The test was held out as a fix for the abuses of social promotion that resulted in a meaningless high school diploma. For a few students, such mandates probably helped. These were the ones possessed of some ability in reading and math but carrying those skills around on a set of lazy bones. Strong and carefully thought-out remediation programs probably saved other students. But in the case of the masses of low-performing students, it was soon discovered that MCT couldn't teach anyone to read. On the other hand, the MCT mandate probably made the difference in some close elections, where a dose of strong medicine always looks like the right cure. Even in states like Colorado ' where legislators passed a law mandating that districts using MCT had to provide remediation during the school day, give students two opportunities a year to take the test, and put the pass/fail score on each student's report card ' the impact of the program was not registering with students. Phi Delta Kappa commissioned two newspaper reporters, Jimmy Covington and Bob Frahm, to visit states and schools and write a book about the impact of MCT. Frahm's visit to the Denver schools found students who had failed the test several times but didn't even know they had to pass it before high school graduation. This time around, state legislators are going to use school report card grades that are based on student test scores to identify school buildings and districts that are failing and then close them down or give a voucher to parents. As history repeats itself, one has to wonder why the benefits and risks of high-stakes testing aren't better understood by legislators before they take action. Of course, anyone raising questions is usually labeled as a loyal defender of the status quo. Nevertheless, a few voices, some of them well known, are giving it a try. And, as usual, enough good and bad news is present so that both sides can claim victory. The Texas Record The Texas Assessment of Academic Skills (TAAS) has produced some achievement gains since 1994. As a result, it will probably be a central focus of the education plank of the Republican Party when the party's presumptive choice, George W. Bush, is nominated for President. The percentage of students in grades 3 through 8 and in grade 10 who have scored at the 70% level or higher has risen from 53% to 78% between 1994 and 1999. Meanwhile, scores on the National Assessment of Educational Progress (NAEP) for Texas students have also been on the rise. Between 1990 and 1997, Texas outpaced nearly every other state in gains on NAEP tests. In 1999, black students in Texas ranked first and Hispanic students in Texas ranked second on the NAEP writing assessment, when compared to their counterparts in other states. Meanwhile, the Texas Education Agency has received support from a San Antonio federal court decision that ruled that any adverse impact from the test did not outweigh its positive impact. Judge Ed Prado said in his ruling that 'the receipt of an education that does not meet some minimal standards is an adverse impact just as surely as failure to receive a diploma.' The plaintiffs (the Mexican American Legal Defense and Education Fund) had presented strong evidence that the test was encouraging minorities to drop out. Another charge leveled against TAAS has come from the Tax Research Association (TRA) of Houston and Harris counties. Since 1995, these counties have charged that the large test-score gains are the result of excluding large numbers of students from taking the test. …


title: A test structure for characterizing local device mismatches

abstract: We present a test structure for statistical characterization of local device mismatches. The structure contains densely populated SRAM devices arranged in an addressable manner. Measurements on a test chip fabricated in an advanced 65 nm process show little spatial correlation. We vary the nominal threshold voltage of the devices by changing the threshold-adjust implantations and observe that the ratio of standard deviation to mean gets worse with threshold scaling. The large variations observed in the extracted threshold voltage statistics indicate that the random doping fluctuation is the likely reason behind mismatch in the adjacent devices


Infrared Spectra of Chemisorbed Olefins and Acetylene


title: Learning to Represent Words in Context with Multilingual Supervision

abstract: We present a neural network architecture based on bidirectional LSTMs to compute representations of words in the sentential contexts. These context-sensitive word representations are suitable for, e.g., distinguishing different word senses and other context-modulated variations in meaning. To learn the parameters of our model, we use cross-lingual supervision, hypothesizing that a good representation of a word in context will be one that is sufficient for selecting the correct translation into a second language. We evaluate the quality of our representations as features in three downstream tasks: prediction of semantic supersenses (which assign nouns and verbs into a few dozen semantic classes), low resource machine translation, and a lexical substitution task, and obtain state-of-the-art results on all of these.


title: Cross-system user modeling and personalization on the Social Web

abstract: In order to adapt functionality to their individual users, systems need information about these users. The Social Web provides opportunities to gather user data from outside the system itself. Aggregated user data may be useful to address cold-start problems as well as sparse user profiles, but this depends on the nature of individual user profiles distributed on the Social Web. For example, does it make sense to re-use Flickr profiles to recommend bookmarks in Delicious? In this article, we study distributed form-based and tag-based user profiles, based on a large dataset aggregated from the Social Web. We analyze the completeness, consistency and replication of form-based profiles, which users explicitly create by filling out forms at Social Web systems such as Twitter, Facebook and LinkedIn. We also investigate tag-based profiles, which result from social tagging activities in systems such as Flickr, Delicious and StumbleUpon: to what extent do tag-based profiles overlap between different systems, what are the benefits of aggregating tag-based profiles. Based on these insights, we developed and evaluated the performance of several cross-system user modeling strategies in the context of recommender systems. The evaluation results show that the proposed methods solve the cold-start problem and improve recommendation quality significantly, even beyond the cold-start.


DHER: HINDSIGHT EXPERIENCE REPLAY


title: Discovering similar multidimensional trajectories

abstract: We investigate techniques for analysis and retrieval of object trajectories in two or three dimensional space. Such data usually contain a large amount of noise, that has made previously used metrics fail. Therefore, we formalize non-metric similarity functions based on the longest common subsequence (LCSS), which are very robust to noise and furthermore provide an intuitive notion of similarity between trajectories by giving more weight to similar portions of the sequences. Stretching of sequences in time is allowed, as well as global translation of the sequences in space. Efficient approximate algorithms that compute these similarity measures are also provided. We compare these new methods to the widely used Euclidean and time warping distance functions (for real and synthetic data) and show the superiority of our approach, especially in the strong presence of noise. We prove a weaker version of the triangle inequality and employ it in an indexing structure to answer nearest neighbor queries. Finally, we present experimental results that validate the accuracy and efficiency of our approach.


A Characterization Study of Arabic Twitter Data with a Benchmarking for State-of-the-Art Opinion Mining Models


title: Extract highlights from baseball game video with hidden Markov models

abstract: We describe a statistical method to detect highlights in a baseball game video. The input video is first segmented into scene shots, within which the camera motion is continuous. Our approach is based on the observations that (1) most highlights in baseball games are composed of certain types of scene shots and (2) those scene shots exhibit special transition context in time. To exploit those two observations, we first build statistical models for each type of scene shots with products of histograms, and then for each type of highlight a hidden Markov model is learned to represent the context of transition in the time domain. A probabilistic model can be obtained by combining the two, which is used for highlight detection and classification. Satisfactory results have been achieved on initial experimental results.


title: Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts

abstract: Neural-based multi-task learning has been successfully used in many real-world large-scale applications such as recommendation systems. For example, in movie recommendations, beyond providing users movies which they tend to purchase and watch, the system might also optimize for users liking the movies afterwards. With multi-task learning, we aim to build a single model that learns these multiple goals and tasks simultaneously. However, the prediction quality of commonly used multi-task models is often sensitive to the relationships between tasks. It is therefore important to study the modeling tradeoffs between task-specific objectives and inter-task relationships. In this work, we propose a novel multi-task learning approach, Multi-gate Mixture-of-Experts (MMoE), which explicitly learns to model task relationships from data. We adapt the Mixture-of-Experts (MoE) structure to multi-task learning by sharing the expert submodels across all tasks, while also having a gating network trained to optimize each task. To validate our approach on data with different levels of task relatedness, we first apply it to a synthetic dataset where we control the task relatedness. We show that the proposed approach performs better than baseline methods when the tasks are less related. We also show that the MMoE structure results in an additional trainability benefit, depending on different levels of randomness in the training data and model initialization. Furthermore, we demonstrate the performance improvements by MMoE on real tasks including a binary classification benchmark, and a large-scale content recommendation system at Google.


title: STING agonist therapy in combination with PD-1 immune checkpoint blockade enhances response to carboplatin chemotherapy in high-grade serous ovarian cancer

abstract: High-grade serous carcinoma (HGSC) of the ovary is predominantly diagnosed at late stages and primarily treated with debulking surgery followed by platinum/taxane-based chemotherapy. Although certain patients benefit significantly from currently used chemotherapy, there are patients who either do not respond or have an inadequate duration of response. We previously showed that tumours from chemoresistant patients have an immunosuppressed pre-existing tumour immune microenvironment with decreased expression of Type I Interferon (IFN1) genes. Efficacy of a ‘STimulator of INterferon Genes’ agonist was evaluated in combination with carboplatin chemotherapy and PD-1 immune checkpoint blockade therapy in the ID8-Trp53−/− immunocompetent murine model of HGSC. Treatment with STING agonist led to decreased ascites accumulation and decreased tumour burden. Survival of mice treated with a combination of carboplatin, STING agonist and anti-PD-1 antibody was the longest. Tumour immune transcriptomic profiling revealed higher IFN response, antigen presentation and MHC II genes in tumours from STING agonist-treated mice compared to vehicle controls. Flow cytometry analysis revealed significantly higher intra-tumoural PD-1+ and CD69+CD62L−, CD8+ T cells in STING agonist-treated mice. These findings will enable rational design of clinical trials aimed at combinatorial approaches to improve chemotherapy response and survival in HGSC patients.


title: RoboADS: Anomaly Detection Against Sensor and Actuator Misbehaviors in Mobile Robots

abstract: Mobile robots such as unmanned vehicles integrate heterogeneous capabilities in sensing, computation, and control. They are representative cyber-physical systems where the cyberspace and the physical world are strongly coupled. However, the safety of mobile robots is significantly threatened by cyber/physical attacks and software/hardware failures. These threats can thwart normal robot operations and cause robot misbehaviors. In this paper, we propose a novel anomaly detection system, which leverages physical dynamics of mobile robots to detect misbehaviors in sensors and actuators. We explore issues raised in real-world implementations, e.g., distinctive robot dynamic models, sensor quantity and quality, decision parameters, etc., for practicality purposes. We implement the detection system on two types of mobile robots and evaluate the detection performance against various misbehavior scenarios, including signal interference, sensor spoofing, logic bomb and physical jamming. The experiments show detection effectiveness and small detection delays.


title: High participation rate among 25 721 patients with broad age range in a hospital-based research project involving whole-genome sequencing – the Lausanne Institutional Biobank

abstract: AIMS ::: We aimed to evaluate the interest of adult inpatients and selected outpatients in engaging in a large, real-life, hospital-based, genomic medicine research project and in receiving clinically actionable incidental findings. ::: ::: ::: METHODS ::: Within the framework of the cross-sectional Institutional Biobank of Lausanne, Switzerland, a total of 25721 patients of the CHUV University Hospital were systematically invited to grant researchers access to their biomedical data and to donate blood for future analyses, including whole-genome sequencing. Multivariable logistic regression analysis was used to identify personal factors, including age, gender, religion, ethnicity, citizenship, education level and mode of admission, associated with willingness to participate in this genomic research project and with interest in receiving clinically actionable incidental findings. ::: ::: ::: RESULTS ::: The overall participation rate was 79% (20343/25721). Participation rate declined progressively with age, averaging 83%, 75%, 67% and 62% in patients aged <64 years (n = 13108), ≥64 years (n = 12613), ≥80 years (n = 4557) and ≥90 years (n = 1050), respectively. Factors associated with participation substantially differed between age strata. Patients less likely to participate included women (odds ratio 0.86, [95% confidence interval 0.79-0.95] and 0.78 [0.71-0.85] before and after age 64, respectively), non-Swiss (0.81 [0.74-0.90] and 0.58 [0.52-0.65]) and those admitted through the emergency ward (0.88 [0.79-0.98] and 0.66 [0.60-0.73]). Religion and marital status were associated with participation among patients aged <64 years. A total of 19 018 (93%) participants were willing to be re-contacted for incidental findings. A high education level was associated with higher participation rate, but not with higher willingness to receive incidental findings within the population who had agreed to participate. ::: ::: ::: CONCLUSION ::: A large proportion of adult patients, even among the elderly, are willing to actively participate and receive incidental findings in this systematic hospital-based precision and genomic medicine research program with broad consent.


title: Posterior component separation with transversus abdominis release successfully addresses recurrent ventral hernias following anterior component separation

abstract: PURPOSE ::: Anterior component separation (ACS) with external oblique release for ventral hernia repair has a recurrence rate up to 32%. Hernia recurrence after prior ACS represents a complex surgical challenge. In this context, we report our experience utilizing posterior component separation with transversus abdominis muscle release (PCS/TAR) and retromuscular mesh reinforcement. ::: ::: ::: METHODS ::: Patients with a history of recurrent hernia following ACS repaired with PCS/TAR were retrospectively identified from prospective databases collected at two large academic institutions. Patient demographics, hernia characteristics (using CT scan) and outcomes were evaluated. ::: ::: ::: RESULTS ::: Twenty-nine patients with a history of ACS developed 22 (76%) midline, 3 (10%) lateral and 4 (14%) concomitant recurrences. Contamination was present in 11 (38%) of cases. All were repaired utilizing a PCS/TAR with retromuscular mesh placement (83% synthetic, 17% biologic) and fascial closure. Wound morbidity consisted of 13 (45%) surgical site occurrences including 8 (28%) surgical site infections. Five (17%) patients required 90-day readmission, and two (7%) were related to wound morbidity. One organ space infection with frank spillage of stool resulted in the only instance of mesh excision. This case also represents the only instance of recurrence (3%) with a mean follow-up of 11 (range 3-36) months. ::: ::: ::: CONCLUSION ::: Patients with a history of an ACS who develop a recurrence represent a challenging clinical scenario with limited options for surgical repair. A PCS/TAR hernia repair achieves acceptable outcomes and may in fact be the best approach available.


title: Why Is (111) Silicon a Better Mechanical Material for MEMS: Torsion Case

abstract: This paper shows that using the Finite Element Method (FEM), the torsional stiffness of silicon varies by the least amount on silicon (111) with respect to crystallographic directions, when compared to silicon (100) and (110). The used simulator is ANSYS 5.7 with the element type of Solid 64. As a simulation model, we use a simple torsion system, in which a rotational inertia is attached to the center of clamped-clamped beam with a rectangular cross-section. From the results of the modal analysis, the torsional stiffness is derived using the formula between the natural frequency and the torsional stiffness. Simulation results show that the maximum variations of the torsional stiffness on silicon (111), (100) and (110) are 2.3%, 26.5%, and 31.2%, respectively. This implies that on and silicon wafers, substantially different physical dimensions are necessary for devices with the same torsional characteristics, but with different orientations. Therefore, silicon wafers represent a more suitable substrate to design and fabricate torsional micro and nano systems.Copyright © 2003 by ASME


title: Healing the community to heal the individual

abstract: OBJECTIVE To understand the development of culturally based and community-based alcohol and substance abuse treatment programs for aboriginal patients in an international context. ::: ::: SOURCES OF INFORMATION MEDLINE, HealthSTAR, and PsycINFO databases and government documents were searched from 1975 to 2007. MeSH headings included the following: Indians, North American, Pacific ancestry group, aboriginal, substance-related disorders, alcoholism, addictive behaviour, community health service, and indigenous health . The search produced 150 articles, 34 of which were relevant; most of the literature comprised opinion pieces and program descriptions (level III evidence). ::: ::: MAIN MESSAGE Substance abuse in some aboriginal communities is a complex problem requiring culturally appropriate, multidimensional approaches. One promising perspective supports community-based programs or community mobile treatment. These programs ideally cover prevention, harm reduction, treatment, and aftercare. They often eliminate the need for people to leave their remote communities. They become focuses of community development, as the communities become the treatment facilities. Success requires solutions developed within communities, strong community interest and engagement, leadership, and sustainable funding. ::: ::: CONCLUSION Community-based addictions programs are appropriate alternatives to treatment at distant residential addictions facilities. The key components of success appear to be strong leadership in this area; strong community-member engagement; funding for programming and organizing; and the ability to develop infrastructure for long-term program sustainability. Programs require increased documentation of their inroads in this developing field.


title: Secured data processing, notification and transmission in a human-vehicle interaction system

abstract: Project CASA (Car Safety Apps) is a joint project of a consortium composed of 5 industrial and one academic partners in France with an aim of building an Android app that promotes safe and green driving, and secured data transmission. In this paper, we focus on the security aspects and features of our project. We present that the multimodal data processing of our work is safe and secured. The driver notification and alert messages are intended to keep the driver safe from road accidents. The data transmission using LiFi, instead of WiFi, makes the communication secured due to the absence of radio wave' interference with other road users and the absence of electromagnetic interference makes it a green technology. The contributions of our work are as follows: (i) generation of assistive messages that are suitable for safe and green driving, and (ii) secured and green data transmission.


title: Forward Stability of ResNet and Its Variants

abstract: The residual neural network (ResNet) is a popular deep network architecture which has the ability to obtain high-accuracy results on several image processing problems. In order to analyze the behavior and structure of ResNet, recent work has been on establishing connections between ResNets and continuous-time optimal control problems. ::: In this work, we show that the post-activation ResNet is related to an optimal control problem with differential inclusions and provide continuous-time stability results for the differential inclusion associated with ResNet. Motivated by the stability conditions, we show that alterations of either the architecture or the optimization problem can generate variants of ResNet which improves the theoretical stability bounds. In addition, we establish stability bounds for the full (discrete) network associated with two variants of ResNet, in particular, bounds on the growth of the features and a measure of the sensitivity of the features with respect to perturbations. These results also help to show the relationship between the depth, regularization, and stability of the feature space. Computational experiments on the proposed variants show that the accuracy of ResNet is preserved and that the accuracy seems to be monotone with respect to the depth and various corruptions.


title: Characterization of 8T SRAM cells using 16 nm FinFET technology

abstract: SRAMs are faster and more reliable which are often used as memory cache in digital processors for high speed operation. Conventional 6T SRAM cell suffers from access transistor sizing conflict resulting in a trade-off between read stability and write ability. This paper presents the characterization of four 8T SRAM cell structures - Conventional 8T SRAM cell, Single Ended 8T SRAM cell, 8T SRAM cell using two conducting p-type transistors and 8T SRAM cell using transmission gate between the cross-coupled inverters using 16 nm FinFET technology at a supply voltage 0.85 V. The stability performance parameters RSNM and WSNM are analyzed. HSPICE simulation results show that FinFET based SRAM designs provide better performance compared to CMOS based SRAM designs at technology nodes below 32 nm. Conventional 8T SRAM cell provides better RSNM and WSNM of 261.81 mV and 273.58 mV compared to the other structures which either degrade RSNM or WSNM or depend on proper transistor sizing for successful operation of the SRAM cell.


Probabilistic methods for mobile robot mapping


title: Learning semantic representations using convolutional neural networks for web search

abstract: This paper presents a series of new latent semantic models based on a convolutional neural network (CNN) to learn low-dimensional semantic vectors for search queries and Web documents. By using the convolution-max pooling operation, local contextual information at the word n-gram level is modeled first. Then, salient local fea-tures in a word sequence are combined to form a global feature vector. Finally, the high-level semantic information of the word sequence is extracted to form a global vector representation. The proposed models are trained on clickthrough data by maximizing the conditional likelihood of clicked documents given a query, us-ing stochastic gradient ascent. The new models are evaluated on a Web document ranking task using a large-scale, real-world data set. Results show that our model significantly outperforms other se-mantic models, which were state-of-the-art in retrieval performance prior to this work.


title: Incremental graph pattern matching

abstract: Graph pattern matching is commonly used in a variety of emerging applications such as social network analysis. These applications highlight the need for studying the following two issues. First, graph pattern matching is traditionally defined in terms of subgraph isomorphism or graph simulation. These notions, however, often impose too strong a topological constraint on graphs to identify meaningful matches. Second, in practice a graph is typically large, and is frequently updated with small changes. It is often prohibitively expensive to recompute matches starting from scratch via batch algorithms when the graph is updated. This article studies these two issues. (1) We propose to define graph pattern matching based on a notion of bounded simulation, which extends graph simulation by specifying the connectivity of nodes in a graph within a predefined number of hops. We show that bounded simulation is able to find sensible matches that the traditional matching notions fail to catch. We also show that matching via bounded simulation is in cubic time, by giving such an algorithm. (2) We provide an account of results on incremental graph pattern matching, for matching defined with graph simulation, bounded simulation, and subgraph isomorphism. We show that the incremental matching problem is unbounded, that is, its cost is not determined alone by the size of the changes in the input and output, for all these matching notions. Nonetheless, when matching is defined in terms of simulation or bounded simulation, incremental matching is semibounded, that is, its worst-time complexity is bounded by a polynomial in the size of the changes in the input, output, and auxiliary information that is necessarily maintained to reuse previous computation, and the size of graph patterns. We also develop incremental matching algorithms for graph simulation and bounded simulation, by minimizing unnecessary recomputation. In contrast, matching based on subgraph isomorphism is neither bounded nor semibounded. (3) We experimentally verify the effectiveness and efficiency of these algorithms, and show that: (a) the revised notion of graph pattern matching allows us to identify communities commonly found in real-life networks, and (b) the incremental algorithms substantially outperform their batch counterparts in response to small changes. These suggest a promising framework for real-life graph pattern matching.


title: Human-robot interaction design: Understanding user needs and requirements

abstract: Goal-Directed Task Analysis is being applied to assessing the needs and requirements for developing novel techniques and tools to permit a small number of humans to supervise large robotic teams. The paper presents a preliminary overall goal hierarchy as well as a preliminary communication goal hierarchy based upon the initial analysis. This research involves working directly with the Nashville Metro Police department's Bomb Squad and the Nashville Metro Fire Department's HAZMAT team. The focus is the assessment of actual user needs and requirements employing well-defined user centered design practices in combination with Goal-Directed Task Analysis. The resulting Goal- Decision-SA structure will be employed to develop potential human-robot interaction designs for further qualitative and qualitative evaluation. The objective is to successfully integrate robots into the Chemical, Biological, Radiological, Nuclear and Explosive device search and rescue tasks such that the robots complement and augment the c...


title: Calliphora vicina (Diptera: Calliphoridae) pupae: a timeline of external morphological development and a new age and PMI estimation tool

abstract: The minimum postmortem interval (PMI(min)) is commonly estimated using calliphorid larvae, for which there are established age estimation methods based on morphological and development data. Despite the increased duration and sedentary nature of the pupal stage of the blowfly, morphological age estimation methods are poorly documented and infrequently used for PMI determination. The aim of this study was to develop a timeline of metamorphosis, focusing on the development of external morphology (within the puparium), to provide a means of age and PMI estimation for Calliphora vicina (Rob-Desvoidy) pupae. Under controlled conditions, 1,494 pupae were reared and sampled at regular time intervals. After puparium removal, observations of 23 external metamorphic developments were correlated to age in accumulated degree hours (ADH). Two age estimation methods were developed based on (1) the combination of possible age ranges observed for each characteristic and (2) regression analyses to generate age estimation equations employing all 23 characteristics observed and a subset of ten characteristics most significantly correlated with age. Blind sample analysis indicated that, using the combination of both methods, pupal age could be estimated to within ±500 ADH with 95% reliability.


title: Compact doublet structure for Quasi-elliptical filters using stereolitographic 3D printing

abstract: In this paper a doublet consisting of a pair of mushroom-shaped posts transversally positioned into an evanescent waveguide is presented. An ultra-compact second-order filter having a response with two transmission zeros has then been designed, manufactured and measured. Additive Manufacturing (AM) technology based on stereolithography (SLA) has been exploited for the filter manufacturing. The resulting structure shows excellent response thanks to the tight tolerances obtained in the AM process and to the very good surface conductivity obtained in the metallization process.


title: MOS charge pumps for low-voltage operation

abstract: New MOS charge pumps utilizing the charge transfer switches (CTSs) to direct charge flow and generate boosted output voltage are described. Using the internal boosted voltage to backward control the CTS of a previous stage yields charge pumps that are suitable for low-voltage operation. Applying dynamic control to the CTSs can eliminate the reverse charge sharing phenomenon and further improve the voltage pumping gain. The limitation imposed by the diode-configured output stage can be mitigated by pumping it with a clock of enhanced voltage amplitude. Using the new circuit techniques, a 1.2-V-to-3.5-V charge pump and a 2-V-to-16-V charge pump are demonstrated.


title: Emotion Detection from Natural Walking

abstract: Emotion identification, which aims to determine a person's affective state automatically, has immense potential value in many areas, such as action tendency, health care, psychological detection and human-computer robot interaction. In this paper, we propose a novel method for identifying emotion from natural walking. After obtaining the three-axis acceleration data of wrist and ankle recorded by smartphone, we run a moving average filter window with different length w, then cut actual data into slices. 114 features are extracted from each slice, and Principal Component Analysis PCA is used for feature selection. We train SVM, Decision Tree, Multilayerperception, Random Tree and Random Forest classifiers, and compare the accuracy of emotion identification using different datasets wrist vs. ankle on different models. Results show that acceleration data from ankle has better performance in emotion identification than wrist. Among these models, SVM has the highest accuracy of 90.31i¾ź% for identifying anger vs. neutral, 89.76i¾ź% for happy vs. neutral, and 87.10i¾ź% for anger vs. happy. The model for identifying anger/neutral/happy yields the best accuracy of 85i¾ź%-78i¾ź%-78i¾ź%. The results show that we could identify people's emotional states through the gait of walking with high accuracy.


title: IMPLEMENTING KNOWLEDGE MANAGEMENT SYSTEMS IN PUBLIC SECTOR ORGANISATIONS: A CASE STUDY OF CRITICAL

abstract: In 2006, the Government Department participating in this study deployed its Knowledge Management System (KMS): this case study describes the lessons learned by this public sector organisation in its journey towards becoming a knowledge organisation. Extant research on the implementation of KMS in organisations operating in the private sector posits a range of factors that are correlated with successful KMS implementation. This paper first outlines these factors and then employs them as an integrative analytic framework to evaluate the critical success factors (CSFs) for KMS implementation in this government organisation. The study supports the empirical fidelity of the factors presented in the framework. Yet despite the successful attainment of departmental specific CSFs, regulative influences from the institutional environment, which lay outside the locus of control of the department, led to sub-optimal outcomes in the use of the KMS in the short-term. Hence, government organisations need to understand and take into account such influences if they are to successfully implement KMS.


title: Hybrid driving-stepping locomotion with the wheeled-legged robot Momaro

abstract: Locomotion in uneven terrain is important for a wide range of robotic applications, including Search&Rescue operations. Our mobile manipulation robot Momaro features a unique locomotion design consisting of four legs ending in pairs of steerable wheels, allowing the robot to omnidirectionally drive on sufficiently even terrain, step over obstacles, and also to overcome height differences by climbing. We demonstrate the feasibility and usefulness of this design on the example of the DARPA Robotics Challenge, where our team NimbRo Rescue solved seven out of eight tasks in only 34 minutes. We also introduce a method for semi-autonomous execution of weight-shifting and stepping actions based on a 2D heightmap generated from 3D laser data.


title: The Rise of the Skilled City

abstract: For more than a century, educated cities have grown more quickly than comparable cities with less human capital. This fact survives a battery of other control variables, metropolitan area fixed effects and tests for reverse causality. We also find that skilled cities are growing because they are becoming more economically productive (relative to less skilled cities), not because these cities are becoming more attractive places to live. Most surprisingly, we find evidence suggesting that the skills-city growth connection occurs mainly in declining areas and occurs in large part because skilled cities are better at adapting to economic shocks. As in Schultz (1964), skills appear to permit adaptation.


title: Privacy? I Can’t Even! Making a Case for User-Tailored Privacy

abstract: How can we help users balance the benefits and risks of information disclosure online? A user-tailored privacy approach makes privacy decisions less burdensome by giving users the right kind of information and the right amount of control so as to be useful but not overwhelming or misleading.


title: Incremental tessellation of trimmed parametric surfaces

abstract: This paper presents an incremental approach for tessellating trimmed parametric surfaces. A database is built to keep the tessellation of a CAD model. With the topological information provided, the database is efficiently updated when the model is modified. This enhances the tessellation process as only a slight change of the existing tessellation is required. The tessellation which involves the insertion and removal of trimlines is performed completely in the parametric space. The topological information also facilitates the triangles classification and the merging of boundaries between surfaces. The algorithm is particularly suitable for the generation of a valid triangulation of a complex CAD model for many downstream applications, e.g. in rapid prototyping (RP).


title: Cancelable features using log-Gabor filters for biometric authentication

abstract: Wide spread use of biometric based authentication requires security of biometric data against identity thefts. Cancelable biometrics is a recent approach to address the concerns regarding privacy of biometric data, public confidence, and acceptance of biometric systems. This work proposes a template protection approach which generates revocable binary features from phase and magnitude patterns of log-Gabor filters. Multi-level transformations are applied at signal and feature level to distort the biometric data using user specific tokenized variables which are observed to provide better performance and security against information leakage under correlation attacks. A thorough analysis is performed to study the performance, non-invertibility, and changeability of the proposed approach under stolen token scenario on multiple biometric modalities. It is revealed that generated templates are non-invertible, easy to revoke, and also deliver good performance.


title: [Study on the esophageal cancer incidence and mortality rate from 1974-2002 in Cixian, China].

abstract: OBJECTIVE ::: To describe the incidence and mortality rates of esophageal cancer from 1974-2002 in Cixian county of Hebei province. Basic information on comparative geographical, epidemiological, and clinical research was collected. ::: ::: ::: METHODS ::: In early 1970s, cancer registry system in Cixian was established, collecting information on all the esophageal cancer cases in Cixian. Data was checked manually, then computerized, coded and analyzed using the software--SPSS 11.5. ::: ::: ::: RESULTS ::: From 1974 to 2002, there were 18 471 esophageal cancer cases in Cixian, with 11 068 males and 7403 females, respectively. The age standardized incidence rate (ASR) for males was 208.77 per 100,000, while 120.47 per 100,000 for females. The trend of incidence rate of esophageal cancer had decreased during the 29 years from 1974 to 2002 (trend chi(2) = 19.94, P < 0.001). From 25 years of age onward, the incidence rates of the lower age groups declined with the increase of age. As for geographic distribution, the incidence rate in mountainous areas and hilly areas showed a significant declining trend in mountainous areas, chi(2) = 195.00, P < 0.001; hilly areas, chi(2) = 46.08, P < 0.001. The esophageal cancer incidence in plain areas remained steady, but had a slight increase in recent years. From 1969 to 2002, there were 18,736 cases died of esophageal cancer with 11 598 males and 7138 females. The ASR for male was 127.17 per 100,000 and 101.57 per 100,000 for female. Compared with the year 1969, the mortality rate of esophageal cancer in 2002 had a 37.96% decline. The proportion of esophageal cancer among malignant tumors in different decades decreased significantly. ::: ::: ::: CONCLUSION ::: The trend of the incidence rate of esophageal cancer had been decreasing for the last 29 years. The incidence rate in mountainous areas and hilly areas showed a declining trend while in the plain areas it remained steady but having slight increase in the recent years. The mortality rate of esophageal cancer had a significant decrease from 1969 to 2002.


title: Towards an easy to use authoring tool for interactive non-linear video

abstract: With growing bandwidths in the Internet and seemingly unlimited storage capacities on web servers, media became more and more important in the daily use of the World Wide Web. While about ten years ago only text and images with small file sizes (and as a result small resolutions) could be used, it is possible to watch high quality multimedia presentations nowadays. But those rarely exist because of tedious to learn authoring tools. A specialization to one main medium, in our case video, allows creating efficient authoring tools using well known paradigms. This work introduces an authoring tool called SIVA Producer. An iterative process for improving the usability of the authoring tool is described. Furthermore, a distinction of the terms "interactive video", "annotated video", "non-linear video" and "hypervideo" is given.


title: Psilocybin and Obsessive Compulsive Disorder

abstract: AbstractObsessive Compulsive Disorder (OCD) is a psychiatric disorder with considerable morbidity and mortality. This condition disables many individuals and is often refractory to treatment. Research suggests that serotonin plays a role in OCD symptom reduction. We present a case of an individual who successfully used psilocybin, a serotonergic agent, to reduce the core symptoms of OCD for several years. Although not endorsing this form of treatment, we feel that the successful use of this agent highlights the role of serotonergic factors in OCD and the need for further, legitimate research into the value of psilocybin in the treatment of anxiety disorders.


title: A Delicious Recipe Analysis Framework for Exploring Multi-Modal Recipes with Various Attributes

abstract: Human beings have developed a diverse food culture. Many factors like ingredients, visual appearance, courses (e.g., breakfast and lunch), flavor and geographical regions affect our food perception and choice. In this work, we focus on multi-dimensional food analysis based on these food factors to benefit various applications like summary and recommendation. For that solution, we propose a delicious recipe analysis framework to incorporate various types of continuous and discrete attribute features and multi-modal information from recipes. First, we develop a Multi-Attribute Theme Modeling (MATM) method, which can incorporate arbitrary types of attribute features to jointly model them and the textual content. We then utilize a multi-modal embedding method to build the correlation between the learned textual theme features from MATM and visual features from the deep learning network. By learning attribute-theme relations and multi-modal correlation, we are able to fulfill different applications, including (1) flavor analysis and comparison for better understanding the flavor patterns from different dimensions, such as the region and course, (2) region-oriented multi-dimensional food summary with both multi-modal and multi-attribute information and (3) multi-attribute oriented recipe recommendation. Furthermore, our proposed framework is flexible and enables easy incorporation of arbitrary types of attributes and modalities. Qualitative and quantitative evaluation results have validated the effectiveness of the proposed method and framework on the collected Yummly dataset.


title: The BLAST Sequence Analysis Tool

abstract: Summary The comparison of nucleotide or protein sequences from the same or different organisms is a very powerful tool in molecular biology. By finding similarities between sequences, scientists can infer the function of newly sequenced genes, predict new members of gene families, and explore evolutionary relationships. Now that whole genomes are being sequenced, sequence similarity searching can be used to predict the location and function of protein-coding and transcriptionregulation regions in genomic DNA. Basic Local Alignment Search Tool (BLAST) (1, 2) is the tool most frequently used for calculating sequence similarity. BLAST comes in variations for use with different query sequences against different databases. All BLAST applications, as well as information on which BLAST program to use and other help documentation, are listed on the BLAST homepage [http://www.ncbi. nlm.nih.gov/BLAST/]. This chapter will focus more on how BLAST works, its output, and how both the output and program itself can be further manipulated or customized, rather than on how to use BLAST [http://www.ncbi.nlm.nih.gov/Education/BLASTinfo/information3.html] or interpret BLAST results.


title: Co-design in the wild: a case study on meme creation tools

abstract: The internet meme has become a vital form of self-expression in social communities throughout the Internet. The tools facilitating meme-creation, specifically image macros, have been little-studied but endow non-technical users with the ability to create the multi-layered graphics typical to such memes. The use of these creativity tools provides a unique setting in which to explore the concept of co-design, wherein tools are shaped in response to emergent user needs. Users and designers of meme-creation tools have evolved ways of collaborating and communicating over time, in a fully naturalistic setting. This study explores these processes through survey and interviews of tool designer/developers and an analysis of users' design ideas generated over time. The study finds that, while co-design may be commonly practiced today, these activities raise numerous challenges to participatory design, including: creating trust between designers and users, managing unwieldy system growth, and supporting features specific to aspiring end-user crafters.


title: Reflections on the Efficient Market Hypothesis: 30 Years Later

abstract: In recent years financial economists have increasingly questioned the efficient market hypothesis. But surely if market prices were often irrational and if market returns were as predictable as some critics have claimed, then professionally managed investment funds should easily be able to outdistance a passive index fund. This paper shows that professional investment managers, both in The U.S. and abroad, do not outperform their index benchmarks and provides evidence that by and large market prices do seem to reflect all available information.


title: Reparative inflammation takes charge of tissue regeneration

abstract: Inflammation underlies many chronic and degenerative diseases, but it also mitigates infections, clears damaged cells and initiates tissue repair. Many of the mechanisms that link inflammation to damage repair and regeneration in mammals are conserved in lower organisms, indicating that it is an evolutionarily important process. Recent insights have shed light on the cellular and molecular processes through which conventional inflammatory cytokines and Wnt factors control mammalian tissue repair and regeneration. This is particularly important for regeneration in the gastrointestinal system, especially for intestine and liver tissues in which aberrant and deregulated repair results in severe pathologies.


title: Software for advanced HRV analysis

abstract: A computer program for advanced heart rate variability (HRV) analysis is presented. The program calculates all the commonly used time- and frequency-domain measures of HRV as well as the nonlinear Poincare plot. In frequency-domain analysis parametric and nonparametric spectrum estimates are calculated. The program generates an informative printable report sheet which can be exported to various file formats including the portable document format (PDF). Results can also be saved as an ASCII file from which they can be imported to a spreadsheet program such as the Microsoft® Excel®. Together with a modern heart rate monitor capable of recording RR intervals this freely distributed program forms a complete low-cost HRV measuring and analysis system.


title: Demographic Prediction of Mobile User from Phone Usage

abstract: In this paper, we describe how we use the mobile phone usage of users to predict their demographic attributes. Using call log, visited GSM cells information, visited Bluetooth devices, visited Wireless LAN devices, accelerometer data, and so on, we predict the gender, age, marital status, job and number of people in household of users. The accuracy of developed classifiers for these classification problems ranges from 45-87% depending upon the particular classification problem.


title: Video delivery over heterogeneous cellular networks: Optimizing cost and performance

abstract: Video delivery to mobile users is one of the largest challenges that network operators face today. In this work we consider a heterogeneous cellular network with storage capable small-cell base stations, and study this problem for pre-stored video files that can be encoded with two different schemes, namely versions or layers, in various qualities. We introduce a framework for the joint derivation of video caching and routing policies for users with different quality requirements. This allows the operator to optimize a balanced objective of incurred servicing cost, and users experienced delay, according to his priorities. The numerical results indicate that versions and layers may have different impact on the delay and servicing cost, depending on the diversity of users’ demand, and that the cost-delay trade off is affected by the network’s load.


IMPACT: an interactive natural-motion-picture dedicated multimedia authoring system


title: IMR: High-Performance Low-Cost Multi-Ring NoCs

abstract: A ring topology is a common solution of network-on-chip (NoC) in industry, but is frequently criticized to have poor scalability. In this paper, we present a novel type of multi-ring NoC called isolated multi-ring (IMR), which can even support chip multi-processors (CMPs) with 1,024 cores. In IMR, any pair of cores are connected via at least one isolated ring, so that each packet can reach the destination without transferring from one ring to another. Therefore, IMR no longer needs expensive routers as mesh, which not only enhances the network performance but also reduces hardware overheads. We utilize simulated evolution to design optimized IMR topologies. We compare these IMR topologies against nine representative NoCs (e.g., traditional mesh, multi mesh, low-cost mesh, Express-virtual-channels mesh (EVC), torus ring, and hierarchical ring). We observe from experiments that IMR significantly outperforms its competitors in both saturation throughput and latency across all scenarios considered. For example, in a 16 $\times$ 16 CMP, IMR improves the saturation throughput of a state-of-the-art mesh (EVC) by 265.29 percent on average, and reduces the average packet latency on SPLASH-2 application traces by 71.58 percent, while consuming 5.08 percent less area and 9.76 percent less power. In a 32 $\times$ 32 CMP, IMR averagely improves the saturation throughput of EVC by 191.58 percent, and averagely reduces the packet latency on SPLASH-2 application traces by 23.09 percent, while consuming 2.86 percent less area and 10.81 percent less power.


title: Smart phone based blood pressure indicator

abstract: In this paper, we propose a methodology to estimate the range of human blood pressure (BP) using Photoplethysmography (PPG). 12 time domain features and 7 frequency domain features are pointed out and extracted from the PPG signal. A feature selection algorithm based on Maximal Information Coefficient (MIC) is presented to reduce the dimensionality of the feature set to effective ones, thereby cutting down resource requirements. Support Vector Machine (SVM) is used to classify the BP values into separate bins. The proposed methodology is validated and tested on a standard benchmark clean dataset as well as phone captured noisy dataset to justify its robustness and efficiency. Apart from a commending performance improvement, BP estimation is achieved with minimal features and processing, making the algorithm light weight for porting on smart phones.


title: Optimal Siting and Sizing of Distributed Generators in Distribution Systems Considering Uncertainties

abstract: Some uncertainties, such as the uncertain output power of a plug-in electric vehicle (PEV) due to its stochastic charging and discharging schedule, that of a wind generation unit due to the stochastic wind speed, and that of a solar generating source due to the stochastic illumination intensity, volatile fuel prices, and future uncertain load growth could lead to some risks in determining the optimal siting and sizing of distributed generators (DGs) in distribution system planning. Given this background, under the chance constrained programming (CCP) framework, a new method is presented to handle these uncertainties in the optimal siting and sizing of DGs. First, a mathematical model of CCP is developed with the minimization of the DGs' investment cost, operating cost, maintenance cost, network loss cost, as well as the capacity adequacy cost as the objective, security limitations as constraints, and the siting and sizing of DGs as optimization variables. Then, a Monte Carlo simulation-embedded genetic-algorithm-based approach is employed to solve the developed CCP model. Finally, the IEEE 37-node test feeder is used to verify the feasibility and effectiveness of the developed model and method, and the test results have demonstrated that the voltage profile and power-supply reliability for customers can be significantly improved and the network loss substantially reduced.


title: Mining customer product ratings for personalized marketing

abstract: Abstract With the increasing popularity of Internet commerce, a wealth of information about the customers can now be readily acquired on-line. An important example is the customers' preference ratings for the various products offered by the company. Successful mining of these ratings can thus allow the company's direct marketing campaigns to provide automatic product recommendations. In general, these recommender systems are based on two complementary techniques. Content-based systems match customer interests with information about the products, while collaborative systems utilize preference ratings from the other customers. In this paper, we address some issues faced by these systems, and study how recent machine learning algorithms, namely the support vector machine and the latent class model, can be used to alleviate these problems.


title: Intrusion Detection Systems- Analysis and Containment of False Positives Alerts

abstract: dependence on information technology became critical and IT infrastructure, critical data, intangible intellectual property are vulnerable to threats and attacks. Organizations install Intrusion Detection Systems (IDS) to alert suspicious traffic or activity. IDS generate a large number of alerts and most of them are false positive as the behavior construe for partial attack pattern or lack of environment knowledge. Monitoring and identifying risky alerts is a major concern to security administrator. The present work is to design an operational model for minimization of false positive alarms, including recurring alarms by security administrator. The architecture, design and performance of model in minimization of false positives in IDS are explored and the experimental results are presented with reference to lab environment.


title: NIRS-Based Hyperscanning Reveals Inter-brain Neural Synchronization during Cooperative Jenga Game with Face-to-Face Communication

abstract: Functional near-infrared spectroscopy (fNIRS) is an increasingly popular technology for studying social cognition. In particular, fNIRS permits simultaneous measurement of hemodynamic activity in two or more individuals interacting in a naturalistic setting. Here, we used fNIRS hyperscanning to study social cognition and communication in human dyads engaged in cooperative and non-cooperative interaction while they played the game of Jenga™. Novel methods were developed to identify synchronized channels for each dyad and a structural node-based spatial registration approach was utilized for inter-dyad analyses. Strong inter-brain neural synchrony (INS) was observed in the posterior region of the right middle and superior frontal gyrus, in particular Brodmann area 8, during cooperative and obstructive interaction. This synchrony was not observed during the parallel game play condition and the dialogue section, suggesting that BA8 was involved in goal-oriented social interaction such as complex interactive movements and social decision-making. INS was also observed in the dorsomedial prefrontal region (dmPFC), in particular Brodmann 9, during cooperative interaction only. These additional findings suggest that BA9 may be particularly engaged when theory-of-mind is required for cooperative social interaction. The new methods described here have the potential to significantly extend fNIRS applications to social cognitive research.