title: Modified Round Robin algorithm (MRR)

abstract: CPU Scheduling Algorithms form the basic and fundamental concepts of Operating Systems. A scheduling algorithm is used to produce effective switches between the processes present in the ready queue. Round Robin algorithm is one among the many CPU scheduling algorithms where every process executes for a given time quantum (TQ). Therefore the system efficiency and performance are completely dependent on the value of TQ. If this value is static in nature, then the CPU performance decreases. This paper uses the dynamic TQ to create an efficient Round Robin Algorithm. This goal was achieved by using the concept of median. The traditional RR algorithm does not take priority under the consideration, which further reduces the CPU performance. The proposed algorithm improves the efficiency of the traditional RR algorithm by considering all the factors like priority, burst time and arrival time of the processes to arrange them in the ready queue and generating TQ accordingly; which effectively reduces the Waiting time, Turn-around time, and the number of context switches. The paper also presents a detailed comparison between the proposed algorithm and existing algorithms.


title: Word Embedding in Small Corpora: A Case Study in Quran

abstract: Text is a complex set of words to carry the meaning and representations of words is the first step to perform linguistic processing and text comprehension. So far, many researches have been done on the semantic representations of words using neural networks in various areas of natural language processing using large text corpus from general domain. In the meantime, some efforts have been done to apply deep learning methods to represent the words of small corpus supporting the hypothesis that the bigger corpora doesn't necessarily provide better results in words representation. In this research the capability of word2vec for learning semantic representation of words in small corpus is investigated. Here, we consider Skip-gram and CBOW learning models with different values of hyper parameters. Two new data sets have been created to evaluate the model's performance on the small domain-specific Quranic corpus. First and second datasets are used to test the words categorization and word pairwise similarity respectively. Our results demonstrate that the best performance for skip-gram occurs with 30 numbers of iterations when the dimension is set to 7.


title: Preference Elicitation For Single Crossing Domain

abstract: Eliciting the preferences of a set of agents over a set of alternatives is a problem of fundamental importance in social choice theory. Prior work on this problem has studied the query complexity of preference elicitation for the unrestricted domain and for the domain of single peaked preferences. In this paper, we consider the domain of single crossing preference profiles and study the query complexity of preference elicitation under various settings. We consider two distinct situations: when an ordering of the voters with respect to which the profile is single crossing is known versus when it is unknown. We also consider different access models: when the votes can be accessed at random, as opposed to when they are coming in a pre-defined sequence. In the sequential access model, we distinguish two cases when the ordering is known: the first is that sequence in which the votes appear is also a single-crossing order, versus when it is not. The main contribution of our work is to provide polynomial time algorithms with low query complexity for preference elicitation in all the above six cases. Further, we show that the query complexities of our algorithms are optimal up to constant factors for all but one of the above six cases. We then present preference elicitation algorithms for profiles which are close to being single crossing under various notions of closeness, for example, single crossing width, minimum number of candidates | voters whose deletion makes a profile single crossing.


title: Compiler Support for Machine Independent Parallelization of Irregular Problems

abstract: Data-parallel languages, such as H scIGH P scERFORMANCE F scORTRAN or F scORTRAN D, provide a machine-independent data-parallel programming paradigm in which the applications programmer uses a dialect of a sequential language annotated with high-level data-distribution directives. Identifying parallelism in data-parallel applications typically is straightforward, but making efficient use of this parallelism for irregular applications, such as molecular dynamics or unstructured meshes, is a challenge due to the limited compile-time knowledge about data access patterns. ::: This dissertation establishes the thesis that spatial locality of the underlying problems can be used as a basis of compiler support for parallelizing such applications. The work done for supporting this thesis and for parallelizing applications in general can be divided into three parts, which correspond to different aspects of parallelizing compilers for different architectures. Value-based mappings express the spatial locality characteristics of an application and assist the compiler in computing a distribution with both a balanced computational workload and high data access locality. The G scIVE-N-T scAKE data-flow framework is an extension of Partial Redundancy Elimination particularly well suited to advanced code-placement tasks such as communication generation. Loop flattening is a code transformation to overcome SIMD specific control flow limitations when executing nested loops with varying inner loop bounds, which are typical for irregular problems. ::: To illustrate this thesis, the F scORTRAN 77D compiler at Rice University has been extended with value-based alignments and distributions, a communication placement mechanism based on the G scIVE-N-T scAKE data-flow framework, and general infras- tructure for handling irregular subscripts. This dissertation describes the techniques involved in these extensions and provides experimental results for various irregular applications compiled for a distributed-memory architecture.


title: A General Framework for Competitive Analysis in Wireless Telecommunications

abstract: The Telecommunications Act of 1996 sets forth extensive provisions to unbundle the local telecommunications network to encourage the development of a competitive market for local telephone. It would seem to have been an unstated premise of those statutory provisions and the Federal Communications Commission (FCC) rules interpreting them that the task of unbundling is one that should take place in a technological vacuum. Although the Telecommunications Act of 1996 ostensibly removed artificial regulatory distinctions based on the particular technology employed to produce a communications service, the administrative rulemakings and federal court litigation that have dominated the first three years of experience under the new statute have focused on the traditional wireline access network and have seemingly ignored the fact that, over the same period, wireless telecommunications has rapidly matured as a substitute for wireline access. If regulators were to acknowledge that development, the entire exercise of wireline unbundling could become irrelevant. Wireless local telephony already provides a substitute for wireline access. It is therefore highly pertinent for a symposium on interconnection, such as this one, to consider the FCC's policies that artificially constrain the market structure for wireless telecommunications services. The Supreme Court's 1999 decision in AT&T Corp. v. Iowa Utilities Board, reversed the FCC's unbundling rules for incumbent local exchange carriers to the extent that the agency failed to establish a reasonable standard for determining whether it is necessary to unbundle a particular element and whether the failure to unbundle that element would impair and entrant's ability to compete in the provision of local telecommunications services. In this Article, we propose a general framework for evaluating competition in wireless telecommunications. Although our analysis has immediate ramifications for wireless telecommunications policies-such as spectrum caps and mergers of wireless carriers-the same analysis can shed light on the question of whether, or for how long, it is necessary to mandate the unbundling of even the copper loop, which constitutes the element of the wireline network that is considered the least susceptible to duplication by competitors. If wireless is indeed an access substitute for wireline copper loops, and if wireless thus permits the competitive supply of bundled services that are satisfactory substitutes in consumers' minds for the typical bundle of services that consumers have until now demanded in conjunction with standard wireline access, then Congress, the FCC, the state public utilities commission, and the courts must ask: Is the great experiment of mandatory unbundling of telecommunications networks worth the candle? That consequential question emerges from the analysis that we employ to study a seemingly narrower issue of wireless telecommunications policy. By regulation, the FCC has limited to 45MHz the amount of commercial mobile radio services (CMRS) spectrum that may be licensed to a single entity within a particular geographic area. As the Commission stated in its 1998 notice of proposed rulemaking (NPRM) concerning possible relaxation of the spectrum cap, a single entity may acquire attributable interests in the licenses of broadband Personal Communications Service (PCS), cellular, and Specialized Mobile Radio (SMR) services that cumulatively do not exceed 45 MHz of spectrum within the same geographic area. We formulate, in this Article, a decision rule that would assist the Commission in deciding whether or not to retain the spectrum cap and, thereafter, in evaluating competition in wireless telecommunications generally. We employ decision-theoretic analysis to determine whether the expected costs of retaining the 45 MHz spectrum cap exceed the expected costs of removing it. The expected costs of removing the spectrum cap are negligible. The probability of either monopolization by a single firm or collusive pricing by a group of nationwide pricing plans and because capacity is a function of both spectrum and equipment. In contrast, the expected costs of retaining the spectrum cap are substantial as wireless services evolve from mobile voice to fixed voice and data applications. The probability that a single carrier would use more than 45MHz is nontrivial, because the growth in demand due to consumers' desire for bundled service offerings and the invasion of wireless carriers into fixed communications markets will together severely burden existing networks. In short, a cost-benefit analysis demonstrates that the spectrum cap should be abolished because the expected costs of retaining the spectrum cap vastly exceed the expected costs of removing it. The application of decision-theoretic analysis to the issue of spectrum cap policy can easily be generalized to deal with a broad range of competitive policy issues in the wireless industry. We restate the decision rule in terms that can be applied to numerous wireless policy issues. For example, regulators may have to decide whether newly merged firms should be forced to divest themselves of wireless properties in overlap territories. The issue of divestiture is treated in similar fashion to the spectrum cap analysis. Not surprisingly, many of the same factors that influence the spectrum cap analysis resurface in the merger analysis. In Part I of this Article, we explain our decision-theoretic rule for determining whether the spectrum cap should be retained. In Part II, we estimate the expected costs of removing the cap and describe the magnitude of those costs in qualitative terms. In Part III, we present the same analysis with respect to the expected costs of retaining the cap. In Part IV, we compare the expected costs of retaining and removing the spectrum cap. In Part V, we demonstrated the general applicability of our decision-theoretic approach to competitive policy in the wireless communications industry. We conclude by noting how the increasing substitutability of wireless and wireline services is blurring the definitions of relevant market in the telecommunications industry-a development that has direct implications for whether, and how much, to mandate unbundling of the incumbent wireline network.


title: A Natural Visible and Infrared Facial Expression Database for Expression Recognition and Emotion Inference

abstract: To date, most facial expression analysis has been based on visible and posed expression databases. Visible images, however, are easily affected by illumination variations, while posed expressions differ in appearance and timing from natural ones. In this paper, we propose and establish a natural visible and infrared facial expression database, which contains both spontaneous and posed expressions of more than 100 subjects, recorded simultaneously by a visible and an infrared thermal camera, with illumination provided from three different directions. The posed database includes the apex expressional images with and without glasses. As an elementary assessment of the usability of our spontaneous database for expression recognition and emotion inference, we conduct visible facial expression recognition using four typical methods, including the eigenface approach [principle component analysis (PCA)], the fisherface approach [PCA + linear discriminant analysis (LDA)], the Active Appearance Model (AAM), and the AAM-based + LDA. We also use PCA and PCA+LDA to recognize expressions from infrared thermal images. In addition, we analyze the relationship between facial temperature and emotion through statistical analysis. Our database is available for research purposes.


title: Deep learning based optimization in wireless network

abstract: With the development of wireless networks, the scale of network optimization problems is growing correspondingly. While algorithms have been designed to reduce complexity in solving these problems under given size, the approach of directly reducing the size of problem has not received much attention. This motivates us to investigate an innovative approach to reduce problem scale while maintaining the optimality of solution. Through analysis on the optimization solutions, we discover that part of the elements may not be involved in the solution, such as unscheduled links in the flow constrained optimization problem. The observation indicates that it is possible to reduce problem scale without affecting the solution by excluding the unused links from problem formulation. In order to identify the link usage before solving the problem, we exploit deep learning to find the latent relationship between flow information and link usage in optimal solution. Based on this, we further predict whether a link will be scheduled through link evaluation and eliminate unused link from formulation to reduce problem size. Numerical results demonstrate that the proposed method can reduce computation cost by at least 50% without affecting optimality, thus greatly improve the efficiency of solving large scale network optimization problems.


title: Design and optimization of edge computing distributed neural processor for biomedical rehabilitation with sensor fusion

abstract: Modern biomedical devices use sensor fusion techniques to improve the classification accuracy of motion intent of users for rehabilitation application. The design of motion classifier observes significant challenges due to the large number of channels and stringent communication latency requirement. This paper proposes an edge-computing distributed neural processor to effectively reduce the data traffic and physical wiring congestion. A special local and global networking architecture is introduced to significantly reduce traffic among multi-chips in edge computing. To optimize the design space of the features selected, a systematic design methodology is proposed. A novel mixed-signal feature extraction approach with assistance of neural network distortion recovery is also provided to significantly reduce the silicon area. A 12-channel 55nm CMOS test chip was implemented to demonstrate the proposed systematic design methodology. The measurement shows the test chip consumes only 20uW power, more than 10,000X less power than the current clinically used microprocessor and can perform edge-computing networking operation within 5ms time.


title: Industrial control systems security: What is happening?

abstract: Increasing awareness of ICS security issues has brought about a growing body of work in this area, including pioneering contributions based on realistic control system logs and network traces. This paper surveys the state of the art in ICS security research, including efforts of industrial researchers, highlighting the most interesting works. Research efforts are grouped into divergent areas, where we add “secure control” as a new category to capture security goals specific to control systems that differ from security goals in traditional IT systems.


title: The Extent of Possession of Knowledge Management Requirements Among the Principals of Public Secondary Schools in Mafraq Governorate

abstract: The aim of this study is to identify the extent of possession of knowledge management requirements among the principals of public secondary schools in Mafraq Governorate. In order to achieve the objectives of the study, the study population will consist of all school principals in Mafraq Governorate in the Hashemite Kingdom of Jordan for the academic year 2018, namely: (120) male and female principals at undergraduate levels and (150) male and female principals at postgraduate levels, i.e. (145) male principals, and (125) female principals, leading them to represent the study sample. To achieve the objectives of the study and answer its questions, the researcher formatted a 20-item questionnaire. ::: ::: The researcher conducted an explanation of the results using the descriptive approach, where he used the arithmetic means, the standard deviations and the T tests to determine the significance of the differences according to the variables of the study. The results showed that the extent of possession of knowledge management requirements among the principals of public secondary schools in Mafraq Governorate was in a high degree. The results also showed that there were statistically significant differences at the level of 0.05 or less due to the difference of academic qualification for the postgraduate qualification. The results also showed no statistically significant differences at the level of 0.05 or less due to the difference of gender. ::: ::: In the light of the previous results, the researcher recommended working to increase the training workshops for school principals and employees to make the process of knowledge management succeed and get use of it in the field of education, benefit from the information and communication revolution in the field of education, implement the knowledge management strategy as received from the Ministry of Education and train all concerned on its terms and contents, especially those with a bachelor's degree only, work to link schools to each other with electronic interacting networks for its significance and exchange of experiences, and raise the efficiency of staff in schools in general to be able to follow up everything that is new in the world of knowledge and technology.


title: Relationship between ethical work climate and nurses’ perception of organizational support, commitment, job satisfaction and turnover intent

abstract: Background:Healthcare organizations are now challenged to retain nurses’ generation and understand why they are leaving their nursing career prematurely. Acquiring knowledge about the effect of ethical work climate and level of perceived organizational support can help organizational leaders to deal effectively with dysfunctional behaviors and make a difference in enhancing nurses’ dedication, commitment, satisfaction, and loyalty to their organization.Purpose:This study aims to determine the relationship between ethical work climate, and perceived organizational support and nurses’ organizational commitment, job satisfaction, and turnover intention.Methods:A descriptive correlational research design was conducted in all inpatient care units at three major hospitals affiliated to different health sectors at Alexandria governorate. All nurses working in these previous hospitals were included in the study (N = 500). Ethical Climate Questionnaire, Survey of Perceived Organizational Support, Organizational Co...


title: Online Trust: The Influence of Perceived Company’s Reputation on Consumers’ Trust and the Effects of Trust on Intention for Online Transactions

abstract: Trust is a basic ingredient in the creation, the evolvement and the conservation of a long-term relationship between suppliers and buyers. It is also a key differentiator in defining the success or failure of many e-business companies, in order to endorse the importance of online trust. In this paper we present a study on online trust in the B2C context. More specifically, we focus on the issues of perceived company’s reputation, online trust and intention for online transactions. The aim of this study is to examine if there is a positive relationship between perceived company’s reputation and on- line trust, and between online trust and consumer’s intention for online transactions.


title: Cinematic Bokeh rendering for real scenes

abstract: In this work, we present a novel approach for rendering high-quality Bokeh and depth-of-field for real, non-computer graphics scenes by synthesizing a real lens and involving depth. Although this discipline is well-known to methods in computer graphics, their transition to real content is pending due to the lack of real scene geometry. Our approach utilizes a highly integrated RGBD camera as well as a lens Bokeh archive which is derived from high-quality lens point-spread function measurements. Our novel kernel stitching renderer which implicitly employs non-recursive ray tracing, incorporates both contributions. Thereby, we obtain spatially and depth-varying, photo-realistic and high-dynamic Bokeh shapes, as well as inherently realistic depth-of-field renderings per pixel without image-space splatting. This methodology allows for an increased creative choice in the depth-of-field setting without enforcing the final depth-of-field blur being burned-in while capturing. We explicitly show that our rendering methodology compares to real references of a high-quality reference lens.


title: Biodiversity and Appraisal on Mammal Resources of Lushan Nature Reserve in Jiangxi Province

abstract: The results showed as follows: 40 mammal species belong to 7 orders and 17 families were recorded in Lushan Nature Reserve in Jiangxi province.Among of them,there were 24 oriental realm species,4 palearctic realm species and 12 widespread species,1 species categorized as the first priority,and 8 species as the second priority in China;3 and 4 species were listed in appendixⅠand Ⅱ of the list of CITES respectively.9 species were ranked in the list of China Red Data Book of Endangered Animals,2 species were endemic to Chinese.The faunal feature illustrated that the oriental species were dominant.There were seven ecotypes,in which the species of ground and sub-ground were the richest whereas aquatic species were the least.The mammal resources were appraised by comparing the biodiversity with its historical resources and the surrounding other natural reserves.At last,some protective suggestions were honestly proposed.


title: Time-dependent trajectory regression on road networks via multi-task learning

abstract: Road travel costs are important knowledge hidden in large-scale GPS trajectory data sets, the discovery of which can benefit many applications such as intelligent route planning and automatic driving navigation. While there are previous studies which tackled this task by modeling it as a regression problem with spatial smoothness taken into account, they unreasonably assumed that the latent cost of each road remains unchanged over time. Other works on route planning and recommendation that have considered temporal factors simply assumed that the temporal dynamics be known in advance as a parametric function over time, which is not faithful to reality. To overcome these limitations, in this paper, we propose an extension to a previous static trajectory regression framework by learning the temporal dynamics of road travel costs in an innovative non-parametric manner which can effectively overcome the temporal sparsity problem. In particular, we unify multiple different trajectory regression problems in a multi-task framework by introducing a novel crosstask regularization which encourages temporal smoothness on the change of road travel costs. We then propose an efficient block coordinate descent method to solve the resulting problem by exploiting its separable structures and prove its convergence to global optimum. Experiments conducted on both synthetic and real data sets demonstrate the effectiveness of our method and its improved accuracy on travel time prediction.


title: Bayesian online classifiers for text classification and filtering

abstract: This paper explores the use of Bayesian online classifiers to classify text documents. Empirical results indicate that these classifiers are comparable with the best text classification systems. Furthermore, the online approach offers the advantage of continuous learning in the batch-adaptive text filtering task.


title: Penetration Testing == POMDP Solving?

abstract: Penetration Testing is a methodology for assessing network security, by generating and executing possible attacks. Doing so automatically allows for regular and systematic testing without a prohibitive amount of human labor. A key question then is how to generate the attacks. This is naturally formulated as a planning problem. Previous work (Lucangeli et al. 2010) used classical planning and hence ignores all the incomplete knowledge that characterizes hacking. More recent work (Sarraute et al. 2011) makes strong independence assumptions for the sake of scaling, and lacks a clear formal concept of what the attack planning problem actually is. Herein, we model that problem in terms of partially observable Markov decision processes (POMDP). This grounds penetration testing in a well-researched formalism, highlighting important aspects of this problem's nature. POMDPs allow to model information gathering as an integral part of the problem, thus providing for the first time a means to intelligently mix scanning actions with actual exploits.


title: Optimism, social support, and adjustment in African American women with breast cancer

abstract: Past studies show that optimism and social support are associated with better adjustment following breast cancer treatment. Most studies have examined these relationships in predominantly non-Hispanic White samples. The present study included 77 African American women treated for nonmetastatic breast cancer. Women completed measures of optimism, social support, and adjustment within 10-months of surgical treatment. In contrast to past studies, social support did not mediate the relationship between optimism and adjustment in this sample. Instead, social support was a moderator of the optimism-adjustment relationship, as it buffered the negative impact of low optimism on psychological distress, well-being, and psychosocial functioning. Women with high levels of social support experienced better adjustment even when optimism was low. In contrast, among women with high levels of optimism, increasing social support did not provide an added benefit. These data suggest that perceived social support is an important resource for women with low optimism.


title: Digital control of continuous-time switching systems with safety constraints

abstract: Relations between safe sets for a continuous-time switching system S and an appropriate discrete-time switching system associated with S are established. Exploiting these relations, the maximal controlled invariant set for the continuous-time switching system can be arbitrarily closely approximated by an appropriate controlled invariant set for the discretized switching system. Moreover, the safety control problem in a continuous time domain is reduced to a safety problem in a discrete time domain.


title: Long-term use of the Fogli temporal lift technique.

abstract: Background:The temporal lift by galeapexy is a short-scar lifting of the lateral third of the eyebrow and temporal region described in 2003 by Alain Fogli. The senior authors (A.M.V., P.L.T.) have been early adopters of this technique, albeit with some modifications.Methods:The technique was used in


title: Q & A: The Alzheimer's disease neuroimaging initiative

abstract: What is the Alzheimer’s Disease Neuroimaging Initiative and who funds it? The Alzheimer’s Disease Neuroimaging Initiative (ADNI) is a large, highly collaborative longitudinal cohort study aiming to elucidate biomarker trajectories in Alzheimer’s disease (AD). ADNI was initially funded in 2004, based on the urgent need to understand potential biomarkers better in order to facilitate drug development, and the large cost and effort required to conduct appropriate studies. About two thirds of its budget is provided by the US National Institute on Aging (NIA), and about one third comes from pharmaceutical companies; private foundations have also contributed.


Current concepts on the management of tooth wear: part 4. An overview of the restorative techniques and dental materials commonly applied for the management of tooth wear


title: A new wideband modified biquad antenna at VHF for communication systems

abstract: A new wideband antenna operating at VHF band for communication systems is designed and implemented in this paper. The antenna at first consists of two quad antenna (Biquad). Gain of around 5 dB of this section are demonstrated; narrower beam widths and a further increase of gain is achieved using a reflector. For further increase of gain and beam width in azimuth plane, some passive elements are added. Simulated results show that 10-dB impedance bandwidth ranges from 125 to 210 MHz (bandwidth of more than 40%), with the voltage standing wave ratio (VSWR) less than 1.5 and a good radiation pattern with 11.5-dBi gain is earned. To validate the design, a prototype of antenna is fabricated and tested. Measured results of a full-scale prototype fabricated with aluminum wipe are in good agreement with simulated ones, which demonstrates the antenna's potential applications in lots of application in VHF Band.


title: Face and Image Representation in Deep CNN Features

abstract: Face recognition algorithms based on deep convolutional neural networks (DCNNs) have made progress on the task of recognizing faces in unconstrained viewing conditions. These networks operate with compact feature-based face representations derived from learning a very large number of face images. Although the learned feature sets produced by DCNNs can be highly robust to changes in viewpoint, illumination, and appearance, little is known about the nature of the face code that emerges at the top level of these networks. We analyzed the DCNN features produced by two recent face recognition algorithms. In the first set of experiments, we used the top-level features from the DCNNs as input into linear classifiers aimed at predicting metadata about the images. The results showed that the DCNN features contained surprisingly accurate information about the yaw and pitch of a face, and about whether the input face came from a still image or a video frame. In the second set of experiments, we measured the extent to which individual DCNN features operated in a view-dependent or view-invariant manner for different identities. We found that view-dependent coding was a characteristic of the identities rather than the DCNN features– with some identities coded consistently in a view-dependent way and others in a view-independent way. In our third analysis, we visualized the DCNN feature space for 24,000+ images of 500 identities. Images in the center of the space were uniformly of low quality (e.g., extreme views, face occlusion, poor contrast, low resolution). Image quality increased monotonically as a function of distance from the origin. This result suggests that image quality information is available in the DCNN features, such that consistently average feature values reflect coding failures that reliably indicate poor or unusable images. Combined, the results offer insight into the coding mechanisms that support robust representation of faces in DCNNs.


title: An integrated conceptual model for information system security risk management supported by enterprise architecture management

abstract: Risk management is today a major steering tool for any organisation wanting to deal with information system (IS) security. However, IS security risk management (ISSRM) remains a difficult process to establish and maintain, mainly in a context of multi-regulations with complex and inter-connected IS. We claim that a connection with enterprise architecture management (EAM) contributes to deal with these issues. A first step towards a better integration of both domains is to define an integrated EAM-ISSRM conceptual model. This paper is about the elaboration and validation of this model. To do so, we improve an existing ISSRM domain model, i.e. a conceptual model depicting the domain of ISSRM, with the concepts of EAM. The validation of the EAM-ISSRM integrated model is then performed with the help of a validation group assessing the utility and usability of the model.


title: Understanding and Comparing Deep Neural Networks for Age and Gender Classification

abstract: Recently, deep neural networks have demonstrated excellent performances in recognizing the age and gender on human face images. However, these models were applied in a black-box manner with no information provided about which facial features are actually used for prediction and how these features depend on image preprocessing, model initialization and architecture choice. We present a study investigating these different effects. ::: In detail, our work compares four popular neural network architectures, studies the effect of pretraining, evaluates the robustness of the considered alignment preprocessings via cross-method test set swapping and intuitively visualizes the model's prediction strategies in given preprocessing conditions using the recent Layer-wise Relevance Propagation (LRP) algorithm. Our evaluations on the challenging Adience benchmark show that suitable parameter initialization leads to a holistic perception of the input, compensating artefactual data representations. With a combination of simple preprocessing steps, we reach state of the art performance in gender recognition.


title: Novel Approach to Fabricate Carbon Molecular-Sieve Membranes Based on Consideration of Interpenetrating Networks

abstract: Carbon molecular-sieve membranes (CMSMs) have shown great potential for gas separation. They exhibit high selectivity by permitting effective size- and shape-separation between gas molecules of similar molecular dimensions. Hence, the control of their pore size is very important. While previous studies have focused on the conditions of pyrolysis and its effect on CMSM properties, a novel approach is reported here whereby the precursor polymer is chemically modified pnor to pyrolysis and the resultant CMSM was investigated for its gas separation properties. Pyrolysis of chemically crosslinked and uncrosslinked Matrimid® resulted in a change in d-spacing from 5.6 to 3.6 and 3.7 A, respectively. The crosslinked CMSM also exhibited greater ordering in its packing. The Matrimid-derived CMSMs exhibited excellent separation properties for CO 2 /CH 4 .


title: A dynamic single actuator vertical climbing robot

abstract: A climbing robot mechanism is introduced, which uses dynamic movements to climb between two parallel vertical walls. This robot relies on its own internal dynamic motions to gain height, unlike previous mechanisms which are quasi- static. One benefit of dynamics is that it allows climbing with only a single actuated degree of freedom. We show with analysis, simulations and experiments that this dynamic robot is capable of climbing vertically between parallel walls. We introduce simplifications that enable us to obtain closed form approximations of the robot motion. Furthermore, this provides us with some design considerations and insights into the mechanism's ability to climb.


title: SIW Rotman Lens Antenna With Ridged Delay Lines and Reduced Footprint

abstract: A dual-layer Rotman lens with reduced footprint and wide field of view is introduced here in substrate integrated waveguide (SIW) technology in the 24-GHz band. Both layers are connected by a high-efficiency transition made by an SIW integrated reflector and several coupling vias. The delay lines of the Rotman lens are implemented in ridged waveguides for compactness and broadband operation over a wide angular range, and the radiating part is an SIW slotted waveguide array made of 15 resonant waveguides. Design guidelines and performance are provided for the transition and the ridged delay lines. Experimental results show very good scanning performance over an angular sector of ±48°.


Iterative Network and Channel Decoding on a Tanner


title: Suicidal Thoughts and Behaviors Among First-Year College Students: Results From the WMH-ICS Project

abstract: Objective College entrance may be a strategically well-placed “point of capture” for detecting late adolescents with suicidal thoughts and behaviors (STB). However, a clear epidemiological picture of STB among incoming college students is lacking. We present the first cross-national data on prevalence as well as socio-demographic and college-related correlates for STB among first-year college students. Method Web-based self-report surveys were obtained from 13,984 first-year students (response rate 45.5%) across 19 colleges in 8 countries (Australia, Belgium, Germany, Mexico, Northern Ireland, South Africa, Spain, and the United States). Results Lifetime prevalence of suicidal ideation, plans, and attempts was 32.7%, 17.5%, and 4.3%, respectively. The 12-month prevalence was 17.2%, 8.8%, and 1.0%, respectively. About three-fourths of STB cases had onset before the age of 16 years (Q3 = 15.8), with persistence figures in the range of 41% to 53%. About one-half (53.4%) of lifetime ideators transitioned to a suicide plan; 22.1% of lifetime planners transitioned to an attempt. Attempts among lifetime ideators without plan were less frequent (3.1%). Significant correlates of lifetime STB were cross-nationally consistent and generally modest in effect size (median adjusted odds ratio [aOR] = 1.7). Nonheterosexual orientation (aOR range 3.3−7.9) and heterosexual orientation with some same-sex attraction (aOR range 1.9−2.3) were the strongest correlates of STB, and of transitioning from ideation to plans and/or attempts (aOR range 1.6−6.1). Conclusion The distribution of STB in first-year students is widespread, and relatively independent of socio-demographic risk profile. Multivariate risk algorithms based on a high number of risk factors are indicated to efficiently link high-risk status with effective preventive interventions.


title: ELEGANT: Exchanging Latent Encodings with GAN for Transferring Multiple Face Attributes

abstract: Recent studies on face attribute transfer have achieved great success. A lot of models are able to transfer face attributes with an input image. However, they suffer from three limitations: (1) incapability of generating image by exemplars; (2) being unable to transfer multiple face attributes simultaneously; (3) low quality of generated images, such as low-resolution or artifacts. To address these limitations, we propose a novel model which receives two images of opposite attributes as inputs. Our model can transfer exactly the same type of attributes from one image to another by exchanging certain part of their encodings. All the attributes are encoded in a disentangled manner in the latent space, which enables us to manipulate several attributes simultaneously. Besides, our model learns the residual images so as to facilitate training on higher resolution images. With the help of multi-scale discriminators for adversarial training, it can even generate high-quality images with finer details and less artifacts. We demonstrate the effectiveness of our model on overcoming the above three limitations by comparing with other methods on the CelebA face database. A pytorch implementation is available at https://github.com/Prinsphield/ELEGANT.


title: COCO-Stuff: Thing and Stuff Classes in Context

abstract: Semantic classes can be either things (objects with a well-defined shape, e.g. car, person) or stuff (amorphous background regions, e.g. grass, sky). While lots of classification and detection works focus on thing classes, less attention has been given to stuff classes. Nonetheless, stuff classes are important as they allow to explain important aspects of an image, including (1) scene type; (2) which thing classes are likely to be present and their location (through contextual reasoning); (3) physical attributes, material types and geometric properties of the scene. To understand stuff and things in context we introduce COCO-Stuff1, which augments all 164K images of the COCO 2017 dataset with pixel-wise annotations for 91 stuff classes. We introduce an efficient stuff annotation protocol based on superpixels, which leverages the original thing annotations. We quantify the speed versus quality trade-off of our protocol and explore the relation between annotation time and boundary complexity. Furthermore, we use COCO-Stuff to analyze: (a) the importance of stuff and thing classes in terms of their surface cover and how frequently they are mentioned in image captions; (b) the spatial relations between stuff and things, highlighting the rich contextual relations that make our dataset unique; (c) the performance of a modern semantic segmentation method on stuff and thing classes, and whether stuff is easier to segment than things.


title: Voltage Doubler Rectified Boost-Integrated Half Bridge (VDRBHB) Converter for Digital Car Audio Amplifiers

abstract: A new voltage doubler rectified boost-integrated half bridge converter for digital car audio amplifiers is proposed. The proposed converter shows low conduction loss due to low voltage stress of the secondary diodes, no dc magnetizing current for the transformer, and a lack of stored energy in the transformer. Moreover, since the primary MOSFETs are turned-on under zero voltage switching conditions and the secondary diodes are turned-off under zero current switching conditions, the proposed converter has minimized switching losses. In addition, the input filter can be minimized due to a continuous input current, and an output filter does not need an inductor in the proposed converter. Therefore, the proposed converter has the desired features, high efficiency and low profile, for a viable power supplies for digital car audio amplifiers. A 60-W industrial sample of the proposed converter has been implemented for digital car audio amplifiers with a measured efficiency of 88.3% at nominal input voltage.


title: Reliability of the Dutch Pediatric Evaluation of Disability Inventory (PEDI)

abstract: Objective: To evaluate the reliability of the Dutch version of the Pediatric Evaluation of Disability Inventory (PEDI), an instrument for measuring functional status (capability and performance in self-care, mobility and social function) of young children using parent interviews.Design: Inter-interviewer reliability was studied after scoring audiotaped interviews by a second researcher. For test–retest reliability the same parent was interviewed twice within three weeks; in inter-respondent reliability both parents of a child were interviewed independently within a few days. On item level, percentage identical scores were computed, and on scale level intraclass correlation coefficients (ICC) and Cronbach's alphas were calculated.Subjects: Parents of 63 nondisabled and 53 disabled (various diagnosis) children aged between 7 and 88 months were interviewed.Results: On scale level, all ICCs were above 0.90 and Cronbach's alpha was 0.89 for the self-care domain, 0.74 for the mobility domain and 0.87 for the so...


title: Circular Pulley Versus Variable Radius Pulley: Optimal Design Methodologies and Dynamic Characteristics Analysis

abstract: Human-centered robotics has received growing interest in low-impedance actuations. In particular, pneumatic artificial muscles (PAMs) provide compliance and high force-to-weight ratio, which allow for safe actuation. However, several performance drawbacks prevent PAMs from being more pervasive. Although many approaches have been proposed to overcome the low control bandwidth of PAMs, some limitations of PAMs, such as restricted workspace and torque capacity, remain to be addressed. This paper analyzes the characteristics and limitations of PAMs-driven joints and subsequently provides an optimization strategy for circular pulleys (CPs) in order to improve joint torque capacity over a large workspace. In addition to CPs, this paper proposes a design methodology to synthesize a pair of variable radius pulleys (VRPs) for further improvement. Simulation and experimental results show that newly synthesized VRPs significantly improve torque capacity in the enlarged workspace without loss of dynamic performance. Finally, the characteristics of CPs and VRPs are discussed in terms of physical human-robot interaction.


title: Analysis of Envelope-Tracking Power Amplifier Using Mathematical Modeling

abstract: This paper describes analysis of an envelope-tracking power amplifier (ET PA) to show its operational behavior. The RF PA is modeled by sweeping the input power and supply voltage. The RF PA model is composed of three 2-D lookup tables including the amplitude-to-amplitude modulation, amplitude-to-phase modulation, and amplitude-to-efficiency modulation. The hybrid switching supply modulator is also modeled using an ideal op-amp, ideal switches, and other assisting blocks. Based on the mathematical models of the RF PA and supply modulator, the ET PA can be analyzed with a fast calculation speed and a good accuracy to find the optimum ET operation point. A power control strategy is presented for the optimal ET operation over a broad output power range. The effect of the delay mismatch on the characteristics of the ET PA is also described to assist the time alignment algorithm. For a 10-MHz long-term evolution signal with a 7.44-dB PAPR, the implemented ET PA at 1.71 GHz delivers a PAE of 44.3%, a gain of 29 dB, an evolved universal terrestrial radio access adjacent channel leakage ratio of ${-}{\hbox{35.1 }}$ dBc, and an error vector magnitude of 2.91% at an average output power of 28 dBm.


title: Volume upscaling with convolutional neural networks

abstract: Volume upscaling generates high-resolution volumes from low-resolution volumes to make data exploration more effective. Traditional methods, such as the simple trilinear or cubic-spline interpolation, may blur boundaries of features and lead to jagged artifacts. Inspired by recent progress in image super-resolution with Convolutional Neural Networks (CNN), we propose a CNN-based volume upscaling method. Our CNN contains three hidden layers: block extraction and representation, non-linear mapping, and reconstruction. It directly learns an end-to-end mapping from low-resolution blocks to high-resolution volume. Compared to previous methods, our CNN can preserve better structures and details of features, and provide a better volume quality in both the visualization and evaluation metrics.


title: Towards applying machine learning to adaptive transactional memory

abstract: There is tremendous diversity among the published algorithms for implementing Transactional Memory (TM). Each of these algorithms appears to be well suited to certain workloads and architectures. However, for programs that operate in distinct phases, exhibit input-dependent behavior, or must run on many different classes of machine, the best algorithm cannot be selected before the program actually runs. We introduce a mechanism for dynamic profiling of a running transactional program, and show how the profile can be used with machine learning techniques to select a TM implementation at run-time. Our preliminary results on the STAMP benchmark suite show good performance, providing a baseline for future research into adaptivity mechanisms for TM.


title: Bounding the Partition Function using Holder's Inequality

abstract: We describe an algorithm for approximate inference in graphical models based on Holder's inequality that provides upper and lower bounds on common summation problems such as computing the partition function or probability of evidence in a graphical model. Our algorithm unifies and extends several existing approaches, including variable elimination techniques such as mini-bucket elimination and variational methods such as tree reweighted belief propagation and conditional entropy decomposition. We show that our method inherits benefits from each approach to provide significantly better bounds on sum-product tasks.


title: Measuring with Murray: Touchscreen technology and preschoolers' STEM learning

abstract: American students rank well below international peers in the disciplines of science, technology, engineering, and mathematics (STEM). Early exposure to STEM-related concepts is critical to later academic achievement. Given the rise of tablet-computer use in early childhood education settings, interactive technology might be one particularly fruitful way of supplementing early STEM education. Using a between-subjects experimental design, we sought to determine whether preschoolers could learn a fundamental math concept (i.e., measurement with non-standard units) from educational technology, and whether interactivity is a crucial component of learning from that technology. Participants who either played an interactive tablet-based game or viewed a non-interactive video demonstrated greater transfer of knowledge than those assigned to a control condition. Interestingly, interactivity contributed to better performance on near transfer tasks, while participants in the non-interactive condition performed better on far transfer tasks. Our findings suggest that, while preschool-aged children can learn early STEM skills from educational technology, interactivity may only further support learning in certain contexts.


title: $\gamma$-soft $^{146}$Ba and the role of non-axial shapes at N ~ 90

abstract: Low-spin states in the neutron-rich, N = 90 nuclide $^{146}$Ba were populated following $\beta$-decay of $^{146}$Cs, with the goal of clarifying the development of deformation in Ba isotopes through delineation of their non-yrast structures. Fission fragments of $^{146}$Cs were extracted from a 1.7-Ci $^{252}$Cf source and mass-selected using the CARIBU facility. Low-energy ions were deposited at the center of a box of thin $\beta$ detectors, surrounded by a high-efficiency HPGe array. The new $^{146}$Ba decay scheme now contains 31 excited levels extending up to ~2.5 MeV excitation energy, double what was previously known. These data are compared to predictions from the Interacting Boson Approximation (IBA) model. It appears that the abrupt shape change found at N = 90 in Sm and Gd is much more gradual in Ba and Ce, due to an enhanced role of the $\gamma$ degree of freedom.


title: A new small-signal modeling approach applied to GaN devices

abstract: A new small-signal modeling approach applied to GaN-based devices is presented. In this approach, a new method for extracting the parasitic elements of the GaN device is developed. This method is based on two steps, which are: 1) using cold S-parameter measurements, high-quality starting values for the extrinsic parameters that would place the extraction close to the global minimum of the objective function for the distributed equivalent circuit model are generated and 2) the optimal model parameter values are searched through optimization using the starting values already obtained. The bias-dependent intrinsic parameter extraction procedure is improved for optimal extraction. The validity of the developed modeling approach and the proposed small-signal model is verified by comparing the simulated wide-band small-signal S-parameter, over a wide bias range, with measured data of a 0.5-/spl mu/m GaN high electron-mobility transistor with a 2/spl times/50 /spl mu/m gatewidth.


title: Application for Text Processing of Cardiology Medical Records

abstract: This paper approaches the problem of processing medical records with the aim of data analysis from a larger set of domain specific medical data. We solve this problem in cooperation with doctors from Department of Cardiology of the East Slovak Institute of Cardiovascular Disease. For this purpose, we designed, implemented and tested first prototype of a software system able to read and process medical records about cardiological patients and provide anonymized data for further analysis.


title: RecResNet: A Recurrent Residual CNN Architecture for Disparity Map Enhancement

abstract: We present a neural network architecture applied to the problem of refining a dense disparity map generated by a stereo algorithm to which we have no access. Our approach is able to learn which disparity values should be modified and how, from a training set of images, estimated disparity maps and the corresponding ground truth. Its only input at test time is a disparity map and the reference image. Two design characteristics are critical for the success of our network: (i) it is formulated as a recurrent neural network, and (ii) it estimates the output refined disparity map as a combination of residuals computed at multiple scales, that is at different up-sampling and down-sampling rates. The first property allows the network, which we named RecResNet, to progressively improve the disparity map, while the second property allows the corrections to come from different scales of analysis, addressing different types of errors in the current disparity map. We present competitive quantitative and qualitative results on the KITTI 2012 and 2015 benchmarks that surpass the accuracy of previous disparity refinement methods.


title: Exploiting the Reactive Power in Deep Neural Models for Non-Intrusive Load Monitoring

abstract: Non-intrusive load monitoring (NILM) is defined as the task of retrieving the active power consumption of two or more appliances from information gathered at a single metering point. In this work, the use of the reactive aggregate power as an additional feature to the commonly used active power for deep neural models is proposed. The NILM problem is formulated as a denoising problem, and denoising autoencoder (dAE) neural architectures are used to estimate the appliances individual active power consumption. The proposed approach is evaluated on two public datasets: the Almanac of Minutely Power dataset (AMPds) and the UK Domestic Appliance-Level Electricity (UK-DALE) dataset. In order to better evaluate the generalization capabilities of the algorithm, different testing conditions are considered for the UK-DALE dataset, namely a seen and an unseen scenario. The results show that introducing the reactive power can indeed bring and overall performance increase in all scenarios, ranging from +4.9% to +8.4% of the energy-based F1 score.


title: Value Iteration Networks

abstract: We introduce the value iteration network (VIN): a fully differentiable neural network with a `planning module' embedded within. VINs can learn to plan, and are suitable for predicting outcomes that involve planning-based reasoning, such as policies for reinforcement learning. Key to our approach is a novel differentiable approximation of the value-iteration algorithm, which can be represented as a convolutional neural network, and trained end-to-end using standard backpropagation. We evaluate VIN based policies on discrete and continuous path-planning domains, and on a natural-language based search task. We show that by learning an explicit planning computation, VIN policies generalize better to new, unseen domains.


title: Exploiting User Consuming Behavior for Effective Item Tagging

abstract: Automatic tagging techniques are important for many applications such as searching and recommendation, which has attracted many researchers' attention in recent years. Existing methods mainly rely on users' tagging behavior or items' content information for tagging, yet users' consuming behavior is ignored. In this paper, we propose to leverage such information and introduce a probabilistic model called joint-tagging LDA to improve tagging accuracy. An effective algorithm based on Zero-Order Collapsed Variational Bayes is developed. Experiments conducted on a real dataset demonstrate that joint-tagging LDA outperforms existing competing methods.


Effective Word Representation for Named Entity Recognition


The Impact of Internal Social Media Usage on Organizational Socialization and Commitment


title: Characterization of the Immediate Effect of a Training Session on a Manual Wheelchair Simulator With Haptic Biofeedback: Towards More Effective Propulsion

abstract: Eighteen manual wheelchair users (MWUs) with spinal cord injury participated in a training session on a new manual wheelchair simulator with haptic biofeedback (HB). The training aimed to modify participants' mechanical effective force (MEF) along the push phase to achieve a target MEF pattern slightly more effective than their pre-training pattern. More HB was provided if the participants' achieved MEF pattern deviated from the target. Otherwise, less HB was provided. The deviation between the participants' achieved MEF and the target, as well as the mean achieved MEF, were computed before, during and after the training session. During the training, participants generally exceeded the target pattern at the beginning of the push cycle and achieved it towards the end. On average, participants also increased their mean MEF by up to 15.7% on the right side and 12.4% on the left side between the pre-training and training periods. Finally, eight participants could modify their MEF pattern towards the target in post-training. The simulator tested in this study represents a valuable tool for developing new wheelchair propulsion training programs. Haptic biofeedback also provides interesting potential for training MWUs to improve propulsion effectiveness.


title: Capsule Networks for Hyperspectral Image Classification

abstract: Convolutional neural networks (CNNs) have recently exhibited an excellent performance in hyperspectral image classification tasks. However, the straightforward CNN-based network architecture still finds obstacles when effectively exploiting the relationships between hyperspectral imaging (HSI) features in the spectral–spatial domain, which is a key factor to deal with the high level of complexity present in remotely sensed HSI data. Despite the fact that deeper architectures try to mitigate these limitations, they also find challenges with the convergence of the network parameters, which eventually limit the classification performance under highly demanding scenarios. In this paper, we propose a new CNN architecture based on spectral–spatial capsule networks in order to achieve a highly accurate classification of HSIs while significantly reducing the network design complexity. Specifically, based on Hinton’s capsule networks, we develop a CNN model extension that redefines the concept of capsule units to become spectral–spatial units specialized in classifying remotely sensed HSI data. The proposed model is composed by several building blocks, called spectral–spatial capsules, which are able to learn HSI spectral–spatial features considering their corresponding spatial positions in the scene, their associated spectral signatures, and also their possible transformations. Our experiments, conducted using five well-known HSI data sets and several state-of-the-art classification methods, reveal that our HSI classification approach based on spectral–spatial capsules is able to provide competitive advantages in terms of both classification accuracy and computational time.


title: Directed assembly in epitaxial zinc oxide films on focused ion beam modified sapphire substrates

abstract: A new method for directed self-assembly using focused ion beam (FIB) and physical vapor deposition is presented. The high resolution and site-specific patterning capabilities of FIB are coupled with the self-assembly process in heteroepitaxial thin film growth. An efficient FIB-induced damage mechanism is exploited to pattern amorphous regions in sapphire substrates which direct the subsequent assembly of a sputter-deposited zinc oxide film. This novel approach allows for the fabrication of in-plane nano- to microscale heterostructures comprising epitaxial regions with high strain and defect density that are separated by regions of randomly oriented (in-plane) grains with much lower strain and defect density.


title: Fast recommendation on latent collaborative relations

abstract: Devise a recommendation algorithm based on latent factor model.Combines latent factors and ź2 norm to formulate the recommendation problem as a k-nearest-neighbor problem, in which we further use locality sensitive hashing (LSH) to reduce search time complexity.Speedup the retrieval by 5X -313X on three data sets used in the experiments. One important property of collaborative filtering recommender systems is that popular items are recommended disproportionately often because they provide extensive usage data and, thus, can be recommended to more users. Compared to popular products, the niches can be as economically attractive as mainstream fare for online retailers. The online retailers can stock virtually everything, and the number of available niche products exceeds the hits by several orders of magnitude. This work addresses accuracy, coverage and prediction time issues to propose a novel latent factor model called latent collaborative relations (LCR), which transforms the recommendation problem into a nearest neighbor search problem by using the proposed scoring function. We project users and items to the latent space, and calculate their similarities based on Euclidean metric. Additionally, the proposed model provides an elegant way to incorporate with locality sensitive hashing (LSH) to provide a fast recommendation while retaining recommendation accuracy and coverage. The experimental results indicate that the speedup is significant, especially when one is confronted with large-scale data sets. As for recommendation accuracy and coverage, the proposed method is competitive on three data sets.


title: A fully packaged D-band MIMO transmitter using high-density flip-chip interconnects on LCP substrate

abstract: In this paper, a fully packaged D-band MIMO phased-array transmitter using system-on-package (SoP) technology is presented. The transmitter chip is implemented in a 32nm CMOS SOI process and assembled on the liquid crystal polymer (LCP) substrate by flip-chip interconnects. The MIMO operation is enabled by an on-chip transformer based 4×4 Butler Matrix. The package fabrication realizes a consistent minimum feature size of 20µm on a 2-mil flexible LCP substrate. Moreover, the CMOS chip with a total of 72 RF and DC pads is well aligned with and successfully bonded onto the corresponding patterns on the LCP package. The capability of package fabrication with fine features and high-density chip-to-package interconnections enables the implementation of cost-effective and high-performance hybrid complex systems at mm-wave. Measurement results based on probing demonstrate the functionalities of the entire system.


title: AI Safety Gridworlds

abstract: We present a suite of reinforcement learning environments illustrating various safety properties of intelligent agents. These problems include safe interruptibility, avoiding side effects, absent supervisor, reward gaming, safe exploration, as well as robustness to self-modification, distributional shift, and adversaries. To measure compliance with the intended safe behavior, we equip each environment with a performance function that is hidden from the agent. This allows us to categorize AI safety problems into robustness and specification problems, depending on whether the performance function corresponds to the observed reward function. We evaluate A2C and Rainbow, two recent deep reinforcement learning agents, on our environments and show that they are not able to solve them satisfactorily.


title: Compact half-wave balun using microstrip and lumped-element artificial transmission lines

abstract: In this paper, a compact two-section half-wave balun using artificial transmission lines is presented. The developed balun comprises four sections of artificial transmission lines, including two sections realized with lumped chip capacitors and via holes. The theoretical performance of the proposed balun is verified through circuit simulations and measurements of a fabricated prototype at 0.9 GHz. The proposed balun exhibits excellent return loss, insertion losses and phase characteristics over the operational frequency band. The measured return loss of input port remain above 11dB over a bandwidth from 0.8 GHz to 1 GHz, while in this frequency range the amplitude imbalance varies from −0.48 dB to 0.37 dB, and the phase difference of 177° to 180°. Measurements show a good agreement with the corresponding simulations.


title: Angiotensin II-induced negative inotropy in rat ventricular myocytes: role of reactive oxygen species and p38 MAPK

abstract: The octapeptide angiotensin II (ANG II) can modulate cardiac contractility and is increased in heart failure, where contractile function is impaired. In rat cardiac myocytes, 1 μM of ANG II produces a negative inotropic effect (NIE) (24.6 ± 5% reduction). However, the subcellular signaling involved in this effect remains elusive. We examined the mechanisms and signaling events involved in the reduction in contractile function induced by the peptide in indo-1-loaded rat cardiomyocytes. The results showed that the NIE of ANG II was not associated with a parallel decrease in the intracellular Ca2+ transient, indicating that a decrease in myofilament responsiveness to Ca2+ underlies the reduction in contractility. We assessed the role of PKC, tyrosine kinases, reactive oxygen species (ROS), and mitogen-activated protein kinases (MAPKs) in the NIE of the peptide. Pretreatment of cells with the NAD(P)H oxidase inhibitor diphenyleneiodonium chloride or with the superoxide scavenger 4,5-dihydroxy-1,3-benzene-disu...


title: Predicting at-risk novice Java programmers through the analysis of online protocols

abstract: In this study, we attempted to quantify indicators of novice programmer progress in the task of writing programs, and we evaluated the use of these indicators for identifying academically at-risk students. Over the course of nine weeks, students completed five different graded programming exercises in a computer lab. Using an instrumented version of BlueJ, an integrated development environment for Java, we collected novice compilations and explored the errors novices encountered, the locations of these errors, and the frequency with which novices compiled their programs. We identified which frequently encountered errors and which compilation behaviors were characteristic of at-risk students. Based on these findings, we developed linear regression models that allowed prediction of students' scores on a midterm exam. However, the models derived could not accurately predict the at-risk students. Although our goal of identifying at-risk students was not attained, we have gained insights regarding the compilation behavior of our students, which may help us identify students who are in need of intervention.


title: Influence of individual characteristics, perceived usefulness and ease of use on mobile entertainment adoption

abstract: This study aims at empirically investigating the influence of individual characteristics, Perceived Usefulness (PU) and Perceived Ease of Use (PEOU) on the Consumer Intention to Use (CIU) mobile entertainment (m-entertainment/ME) in Malaysia. This study employs the Technology Acceptance Model (TAM) in the context of m-entertainment (ME) by incorporating individual characteristics with TAM to provide better understanding and insights about the adoption of ME among Malaysians. The findings revealed that PU, PEOU, academic qualification and past adoption behaviour as factors that influence Malaysian ME adoption. This study contributed to better understanding and insights regarding factors that influence ME adoption. This study has successfully incorporated individual characteristics with the TAM. The enhanced model offers a greater understanding of user acceptance of ME in Malaysia.


title: Fractal Dimension Pattern Based Multiresolution Analysis for Rough Estimator of Person-Dependent Audio Emotion Recognition

abstract: As a general means of expression, audio analysis and recognition has attracted much attentions for its wide applications in real-life world. Audio emotion recognition (AER) attempts to understand emotional states of human with the given utterance signals, and has been studied abroad for its further development on friendly human-machine interfaces. Distinguish from other existing works, the person-dependent patterns of audio emotions are conducted, and fractal dimension features are calculated for acoustic feature extraction. Furthermore, it is able to efficiently learn intrinsic characteristics of auditory emotions, while the utterance features are learned from fractal dimensions of each sub-bands. Experimental results show the proposed method is able to provide comparative performance for audio emotion recognition.


title: The Modality Effect: A Result of Methodology?

abstract: Modality differences for short-term (STM) and long-term memory (LTM) were investigated. Sixty-eight subjects participated in one of four conditions: Auditory STM, Verbal STM, Auditory LTM, and Verbal LTM. Analysis of the number of words correctly recalled showed a significant interaction between modality and store. Contrary to both Engle and Mobley's (1976) and Penney's (1975, 1989a, 1989b) findings, further analysis showed that the auditory mode was superior in STM and that no modality difference existed in LTM. It was concluded that the LTM modality effect is less robust than that found in STM. Possible methodological reasons for the conflicting results of the past as well as areas of potential future. research were discussed.


title: Interleaved-Boost Converter With High Voltage Gain

abstract: This paper presents an interleaved-boost converter, magnetically coupled to a voltage-doubler circuit, which provides a voltage gain far higher than that of the conventional boost topology. Besides, this converter has low-voltage stress across the switches, natural-voltage balancing between output capacitors, low-input current ripple, and magnetic components operating with the double of switching frequency. These features make this converter suitable to applications where a large voltage step-up is demanded, such as grid-connected systems based on battery storage, renewable energies, and uninterruptible power system applications. Operation principle, main equations, theoretical waveforms, control strategy, dynamic modeling, and digital implementation are provided. Experimental results are also presented validating the proposed topology.


The role of solar energy for carbon neutrality in Helsinki Metropolitan area


title: Similarity evaluation on tree-structured data

abstract: Tree-structured data are becoming ubiquitous nowadays and manipulating them based on similarity is essential for many applications. The generally accepted similarity measure for trees is the edit distance. Although similarity search has been extensively studied, searching for similar trees is still an open problem due to the high complexity of computing the tree edit distance. In this paper, we propose to transform tree-structured data into an approximate numerical multidimensional vector which encodes the original structure information. We prove that the L 1 distance of the corresponding vectors, whose computational complexity is O(|T 1 | + |T 2 |), forms a lower bound for the edit distance between trees. Based on the theoretical analysis, we describe a novel algorithm which embeds the proposed distance into a filter-and-refine framework to process similarity search on tree-structured data. The experimental results show that our algorithm reduces dramatically the distance computation cost. Our method is especially suitable for accelerating similarity query processing on large trees in massive datasets.


title: Medial-based Bayesian tracking for vascular segmentation: Application to coronary arteries in 3D CT angiography

abstract: We propose a new Bayesian, stochastic tracking algorithm for the segmentation of blood vessels from 3D medical image data. Inspired by the recent developments in particle filtering, it relies on a constrained, medial-based geometric model and on an original sampling scheme for the selection of tracking hypotheses. A key property of this new sampling scheme is the ability to take into account a distribution of hypotheses broader than similar methods such as classical particle filters, while remaining computationally efficient. The proposed method was applied to the challenging and medically critical task of coronary artery segmentation from 3D cardiac computed tomography (CT) images. Prior knowledge, injected in the process, was learned from a manually segmented database of 19 cases. Qualitative and quantitative evaluation is presented on clinical data, including pathologies and local anomalies.


title: Extra-axial brain tumors

abstract: Meningiomas, schwannomas, metastases, maldevelopmental cysts, epidermoids, dermoids and bone tumors represent the vast majority of extra-axial brain tumors. The location of extra-axial brain tumors affects treatment planning and predicts their prognosis. Computed tomography and particularly magnetic resonance imaging are used for diagnosis and localization. In this article, the imaging findings of the extra-axial brain tumors are discussed.


title: An entropy based classification scheme for land applications of polarimetric SAR

abstract: The authors outline a new scheme for parameterizing polarimetric scattering problems, which has application in the quantitative analysis of polarimetric SAR data. The method relies on an eigenvalue analysis of the coherency matrix and employs a three-level Bernoulli statistical model to generate estimates of the average target scattering matrix parameters from the data. The scattering entropy is a key parameter is determining the randomness in this model and is seen as a fundamental parameter in assessing the importance of polarimetry in remote sensing problems. The authors show application of the method to some important classical random media scattering problems and apply it to POLSAR data from the NASA/JPL AIRSAR data base.


title: Beyond Adversarial: The Case for Game AI as Storytelling

abstract: As a field, artificial intelligence (AI) has been applied to games for more than 50 years, beginning with traditional two-player adversarial games like tic-tac-toe and chess and extending to modern strategy games, first-person shooters, and social simulations. AI practitioners have become adept at designing algorithms that enable computers to play games at or beyond human levels in many cases. In this paper, we argue that the traditional goal of AI in games—to win the game—is not the only, nor the most interesting goal. An alternative goal for game AI is to make the human player’s play experience “better.” AI systems in games should reason about how to deliver the best possible experience within the context of the game. The key insight of this paper is that approaching AI reasoning for games as storytelling reasoning makes this goal much more attainable. We present an overview of traditional game AI techniques as well as a few more recent AI storytelling techniques. We also provide a foundation for describing and reasoning about games as stories, citing a number of examples. We conclude by discussing the implications for future directions. Author Keywords Artificial intelligence, machine learning, story telling, narrative, drama management


Religion, Social Practice, and Contested Hegemonies


Tree vulnerability to climate change: improving exposure‐based assessments using traits as indicators of sensitivity


title: Benchmarking Amazon EC2 for hig-performance scientific computing

abstract: How effective are commercial cloud computers for high-performance scientific computing compared to currently avaible alternatives? I aim to answer a specific instance of this question by examining the performance of Amazon EC2 for high-performance scientific applications. I used macro and micro benchmarks to study the performance of a cluster composed of EC2 high-CPU compute nodes and compared this against the performance of a cluster composed of equivalent processors avaible to the open scientific research community. My results show a significant performance gap in the examined clusters that system builders, computational scientist, and commercial cloud computing vendors need to be aware of .


title: On the design of adaptive automation for complex systems

abstract: This article presents a constrained review of human factors issues relevant to adaptive automation (AA), including designing complex system interfaces to support AA, facilitating human–computer interaction and crew interactions in adaptive system operations, and considering workload associated with AA management in the design of human roles in adaptive systems. Unfortunately, these issues have received limited attention in earlier reviews of AA. This work is aimed at supporting a general theory of human-centered automation advocating humans as active information processors in complex system control loops to support situation awareness and effective performance. The review demonstrates the need for research into user-centered design of dynamic displays in adaptive systems. It also points to the need for discretion in designing transparent interfaces to facilitate human awareness of modes of automated systems. Finally, the review identifies the need to consider critical human–human interactions in designing adaptive systems. This work describes important branches of a developing framework of AA research and contributes to the general theory of human-centered automation.


title: Towards an optimal cnf encoding of boolean cardinality constraints

abstract: We consider the problem of encoding Boolean cardinality constraints in conjunctive normal form (CNF). Boolean cardinality constraints are formulae expressing that at most (resp. at least) k out of n propositional variables are true. We give two novel encodings that improve upon existing results, one which requires only 7n clauses and 2n auxiliary variables, and another one demanding O(n·k) clauses, but with the advantage that inconsistencies can be detected in linear time by unit propagation alone. Moreover, we prove a linear lower bound on the number of required clauses for any such encoding.


title: A Comparative Study between Two Three-DOF Parallel Kinematic Machines using Kinetostatic Criteria and Interval Analysis

abstract: This paper addresses the workspace analysis of two 3-DOF translational parallel mechanisms designed for machining applications. The two machines features three fixed linear joints. The joint axes of the first machine are orthogonal whereas these of the second are parallel. In both cases, the mobile platform moves in the Cartesian $x-y-z$ space with fixed orientation. The workspace analysis is conducted on the basis of prescribed kinetostatic performances. Interval analysis based methods are used to compute the dextrous workspace and the largest cube enclosed in this workspace.


title: RLV's re-entry trajectory optimization based on B-spline theory

abstract: A new computational optimization approach is proposed to generate Reusable Launch Vehicle (RLV)'s re-entry trajectory. With the application of the new method, all state variables are approximated by B-spline curves. After the trajectory optimization problem is transformed into a general nonlinear programming (NLP) problem, which parameters to be optimized are coefficients of the B-spline curves and control variables at every node and interpolation point, a nonlinear programming solver is selected to determine the parameters. Simulation results show that the optimized re-entry trajectories satisfy the path constraints, and can accomplish the desired terminal conditions. Compared with the Direct Collocation with Nonlinear Programming (DCNLP) method, the new approach is better in continuity and has fewer coefficients to be determined; moreover, the new approach can achieve better performance index.


title: From conceptual design to performance optimization of ETL workflows: current state of research and open problems

abstract: In this paper, we discuss the state of the art and current trends in designing and optimizing ETL workflows. We explain the existing techniques for: (1) constructing a conceptual and a logical model of an ETL workflow, (2) its corresponding physical implementation, and (3) its optimization, illustrated by examples. The discussed techniques are analyzed w.r.t. their advantages, disadvantages, and challenges in the context of metrics such as autonomous behavior, support for quality metrics, and support for ETL activities as user-defined functions. We draw conclusions on still open research and technological issues in the field of ETL. Finally, we propose a theoretical ETL framework for ETL optimization.


title: Combining NEAT and PSO for learning tactical human behavior

abstract: This article presents and discusses a machine learning algorithm called PIGEON used to build agents capable of displaying tactical behavior in various domains. Such tactical behavior can be relevant in military simulations and video games, as well as in everyday tasks in the physical world, such as driving an automobile. Furthermore, PIGEON displays good performance across two different approaches to learning (observational and experiential) and across multiple domains. PIGEON is a hybrid algorithm, combining NEAT and PSO in two different manners. The investigation described in this paper compares the performance of the two versions of PIGEON to each other as well as to NEAT and to PSO individually. These four machine learning algorithms are applied in two different approaches to learning--through observation of human performance and through experience, as well as in three distinct domain testbeds. The criteria used to compare them were high proficiency in task completion and rapid learning. Results indicate that overall, PIGEON worked best when NEAT and PSO are applied in an alternating manner. This combination was called PIGEON-Alternate, or simply Alternate. The two versions of the PIGEON algorithm, the tests conducted, the results obtained and the conclusions are described in detail.


title: Self-labeled techniques for semi-supervised learning: taxonomy, software and empirical study

abstract: Semi-supervised classification methods are suitable tools to tackle training sets with large amounts of unlabeled data and a small quantity of labeled data. This problem has been addressed by several approaches with different assumptions about the characteristics of the input data. Among them, self-labeled techniques follow an iterative procedure, aiming to obtain an enlarged labeled data set, in which they accept that their own predictions tend to be correct. In this paper, we provide a survey of self-labeled methods for semi-supervised classification. From a theoretical point of view, we propose a taxonomy based on the main characteristics presented in them. Empirically, we conduct an exhaustive study that involves a large number of data sets, with different ratios of labeled data, aiming to measure their performance in terms of transductive and inductive classification capabilities. The results are contrasted with nonparametric statistical tests. Note is then taken of which self-labeled models are the best-performing ones. Moreover, a semi-supervised learning module has been developed for the Knowledge Extraction based on Evolutionary Learning software, integrating analyzed methods and data sets.


title: Risk-Sensitive Inverse Reinforcement Learning via Gradient Methods

abstract: We address the problem of inverse reinforcement learning in Markov decision processes where the agent is risk-sensitive. In particular, we model risk-sensitivity in a reinforcement learning framework by making use of models of human decision-making having their origins in behavioral psychology, behavioral economics, and neuroscience. We propose a gradient-based inverse reinforcement learning algorithm that minimizes a loss function defined on the observed behavior. We demonstrate the performance of the proposed technique on two examples, the first of which is the canonical Grid World example and the second of which is a Markov decision process modeling passengers decisions regarding ride-sharing. In the latter, we use pricing and travel time data from a ride-sharing company to construct the transition probabilities and rewards of the Markov decision process.


title: CURRENT DIRECTIONS IN PSYCHOLOGICAL SCIENCE Increasing Retention Without Increasing Study Time

abstract: Because people forget much of what they learn, students could benefit from learning strategies that yield long-lasting knowledge. Yet surprisingly little is known about how long-term retention is most efficiently achieved.Hereweexaminehowretentionisaffectedbytwo variables: the duration of a study session and the temporal distribution of study time across multiple sessions. Our results suggest that a single session devoted to the study of some material should continue long enough to ensure that masteryisachievedbutthatimmediatefurtherstudyofthe same material is an inefficient use of time. Our data also showthatthebenefitofdistributingafixedamountofstudy


title: Molecular phylogenetics of the Pectinidae (Mollusca: Bivalvia) and effect of increased taxon sampling and outgroup selection on tree topology

abstract: Evolutionary relationships of the Pectinidae were examined using two mitochondrial genes (12S rRNA, 16S rRNA) and one nuclear gene (Histone H3) for 46 species. Outgroup taxa from Propeamussidae, Spondylidae and Limidae were also sequenced to examine the impact of outgroup choice on pectinid topologies. Our phylogenetic analyses resolved the Pectinidae as monophyletic, but many of the subfamilies and tribes within the family do not form monophyletic clades. The paraphyletic Aequipectinini group is the most basal member of the Pectinidae, with the Chlamydinae and Palliolinae representing the most recently derived pectinid groups. These results are in contrast with the current morphological hypothesis of Pectinidae evolution, which suggests the Chlamydinae and Pallioline are basal groups within the Pectinidae. Ingroup topology was found to be sensitive to outgroup choice and increasing taxon sampling within the Pectinidae resulted in more robust phylogenies.


Developments in Business Simulation & Experiential Exercises , Volume 11 , 1984 253 LEADERSHIP AND STRATEGIC BEHAVIOR


title: Structural and optical properties of molybdenum doped bismuth vanadate powders

abstract: Molybdenum doped BiVO4 powders were prepared by Sol-gel method. Monoclinic scheelite phase was confirmed from X-ray diffraction (XRD) patterns and micro-Raman vibrational bands. Substitution of molybdenum in crystal sites of BiVO 4 was evidenced from XRD by higher angle 2θ shift of the characteristic peak (−121) and from Raman showing lower frequency shift of dominant peak from 831 to 822 cm−1 which corresponds to V-O symmetric stretching mode. The morphological properties were analyzed by FE-SEM which confirmed the formation of homogeneous spherical shaped particles with size around 200–300nm. Optical properties were analyzed by Diffuse Reflectance Spectra which show higher absorption in the range of 550–850nm. Optical band gap energies were calculated by using Kubelka-Munk formula, i.e, 2.46 eV for 2 wt% Mo-BiVO 4 and 2.47eV for undoped BiVO 4 . This confirms that, Mo-BiVO 4 particles have the same band gap but induce higher absorption in visible light region compared to BiVO 4 .


abstract: A non-reward attractor theory of depression is proposed based on the operation of the lateral orbitofrontal cortex and supracallosal cingulate cortex. The orbitofrontal cortex contains error neurons that respond to non-reward for many seconds in an attractor state that maintains a memory of the non-reward. The human lateral orbitofrontal cortex is activated by non-reward during reward reversal, and by a signal to stop a response that is now incorrect. Damage to the human orbitofrontal cortex impairs reward reversal learning. Not receiving reward can produce depression. The theory proposed is that in depression, this lateral orbitofrontal cortex non-reward system is more easily triggered, and maintains its attractor-related firing for longer. This triggers negative cognitive states, which in turn have positive feedback top-down effects on the orbitofrontal cortex non-reward system. Treatments for depression, including ketamine, may act in part by quashing this attractor. The mania of bipolar disorder is hypothesized to be associated with oversensitivity and overactivity in the reciprocally related reward system in the medial orbitofrontal cortex and pregenual cingulate cortex.


title: A quantile regression analysis of growth and convergence in the EU: potential implications for Portugal

abstract: This paper applies a quantile regression approach to examine the growth and convergence process of fourteen EU member states over the period 1986-2009. From the results of the estimation of an accounting growth regression we conclude that an increase in the weight of the non-tradables sector and a loss of (price) competitiveness are especially harmful for growth for under-performing countries, while these benefit the most from physical capital accumulation and are less negatively affected by an increase in government consumption. Additionally, technological convergence is felt less strongly by low-growth member states. The variables retained are robustly related to growth at all quantiles, but the quantitative importance of the respective coefficients differs across quantiles in some cases. Given the changes in growth rhythms that Portugal recorded throughout the period under analysis, we derive some potential implications from these results for a better understanding of the Portuguese growth and convergence process after European integration. Our findings suggest that, given the growth deceleration that the Portuguese economy has been experiencing since the late 1990s, policies to enhance growth should pay more attention to promoting competitiveness and changing the specialization pattern away from the non-tradables sectors, as well as to increasing investment.


title: Energy-based Models for Video Anomaly Detection

abstract: Automated detection of abnormalities in data has been studied in research area in recent years because of its diverse applications in practice including video surveillance, industrial damage detection and network intrusion detection. However, building an effective anomaly detection system is a non-trivial task since it requires to tackle challenging issues of the shortage of annotated data, inability of defining anomaly objects explicitly and the expensive cost of feature engineering procedure. Unlike existing appoaches which only partially solve these problems, we develop a unique framework to cope the problems above simultaneously. Instead of hanlding with ambiguous definition of anomaly objects, we propose to work with regular patterns whose unlabeled data is abundant and usually easy to collect in practice. This allows our system to be trained completely in an unsupervised procedure and liberate us from the need for costly data annotation. By learning generative model that capture the normality distribution in data, we can isolate abnormal data points that result in low normality scores (high abnormality scores). Moreover, by leverage on the power of generative networks, i.e. energy-based models, we are also able to learn the feature representation automatically rather than replying on hand-crafted features that have been dominating anomaly detection research over many decades. We demonstrate our proposal on the specific application of video anomaly detection and the experimental results indicate that our method performs better than baselines and are comparable with state-of-the-art methods in many benchmark video anomaly detection datasets.


title: Extracting medication information from clinical text

abstract: The Third i2b2 Workshop on Natural Language Processing Challenges for Clinical Records focused on the identification of medications, their dosages, modes (routes) of administration, frequencies, durations, and reasons for administration in discharge summaries. This challenge is referred to as the medication challenge. For the medication challenge, i2b2 released detailed annotation guidelines along with a set of annotated discharge summaries. Twenty teams representing 23 organizations and nine countries participated in the medication challenge. The teams produced rule-based, machine learning, and hybrid systems targeted to the task. Although rule-based systems dominated the top 10, the best performing system was a hybrid. Of all medication-related fields, durations and reasons were the most difficult for all systems to detect. While medications themselves were identified with better than 0.75 F-measure by all of the top 10 systems, the best F-measure for durations and reasons were 0.525 and 0.459, respectively. State-of-the-art natural language processing systems go a long way toward extracting medication names, dosages, modes, and frequencies. However, they are limited in recognizing duration and reason fields and would benefit from future research.


title: A New Evidential Reasoning-Based Method for Online Safety Assessment of Complex Systems

abstract: It is vital to online assess the safety of a complex dynamic system by taking into account the current state, degradation trend, and historical records together. This paper proposes a new safety assessment model with an online algorithm based on the evidential reasoning (ER) approach. It does not only take into account the relative importance of each safety indicator, but also consider the reliability of each indicator. To obtain the integrated safety level, multiple safety indicators are fused at first and the “history,” “current,” and “future” safety states are then integrated. First, a forecasting model based on the third-order Volterra filter is proposed to predict the safety indicators’ information online. Second, an adaptive weighting model is developed to automatically adjust to various conditions and track the characteristics of the dynamic system, and the reliability of each indicator is considered to diminish the influence of inherent disturbance and/or noise. Finally, a safety assessment aggregation scheme based on the ER approach is presented to fuse the history, current, and future safety indicators to obtain the corresponding safety state, and the safety states are then fused synthetically to obtain a comprehensive safety assessment result of the complex dynamic system. A numerical study is examined to demonstrate the implementation and effectiveness. Moreover, a practical example of the inertial navigation system is studied to show the potential applications of the proposed ER-based safety assessment method.


title: Survey on Multi-Access Edge Computing for Internet of Things Realization

abstract: The Internet of Things (IoT) has recently advanced from an experimental technology to what will become the backbone of future customer value for both product and service sector businesses. This underscores the cardinal role of IoT on the journey towards the fifth generation (5G) of wireless communication systems. IoT technologies augmented with intelligent and big data analytics are expected to rapidly change the landscape of myriads of application domains ranging from health care to smart cities and industrial automations. The emergence of Multi-Access Edge Computing (MEC) technology aims at extending cloud computing capabilities to the edge of the radio access network, hence providing real-time, high-bandwidth, low-latency access to radio network resources. IoT is identified as a key use case of MEC, given MEC's ability to provide cloud platform and gateway services at the network edge. MEC will inspire the development of myriads of applications and services with demand for ultra low latency and high Quality of Service (QoS) due to its dense geographical distribution and wide support for mobility. MEC is therefore an important enabler of IoT applications and services which require real-time operations. In this survey, we provide a holistic overview on the exploitation of MEC technology for the realization of IoT applications and their synergies. We further discuss the technical aspects of enabling MEC in IoT and provide some insight into various other integration technologies therein.


Concepts, Applications, and Research of Virtual Reality Learning Environments