title: Federated Meta-Learning for Recommendation.

abstract: Recommender systems have been widely studied from the machine learning perspective, where it is crucial to share information among users while preserving user privacy. In this work, we present a federated meta-learning framework for recommendation in which user information is shared at the level of algorithm, instead of model or data adopted in previous approaches. In this framework, user-specific recommendation models are locally trained by a shared parameterized algorithm, which preserves user privacy and at the same time utilizes information from other users to help model training. Interestingly, the model thus trained exhibits a high capacity at a small scale, which is energy- and communication-efficient. Experimental results show that recommendation models trained by meta-learning algorithms in the proposed framework outperform the state-of-the-art in accuracy and scale. For example, on a production dataset, a shared model under Google Federated Learning (McMahan et al., 2017) with 900,000 parameters has prediction accuracy 76.72%, while a shared algorithm under federated meta-learning with less than 30,000 parameters achieves accuracy of 86.23%.


title: Comparison of coconut/sesame/castor oils and their blends for transformer insulation

abstract: Different vegetable oils have been tested and proven as alternatives to the transformer mineral oil since recent years. This paper presents a comparison of physical, chemical and electrical properties of coconut, sesame and castor oils and their blends. The tested properties were color, acidity, viscosity, pour point, Frequency Domain Dielectric Spectroscopy (FDS) and breakdown voltage. First, commercially available vegetable oils were neutralized and de-moisturized, and were tested. Then the oils and their blends were exposed to corona ageing for 120 hours and were tested again. It was found that physical, chemical and electrical properties of oil blends change depending on the ratios of mixtures. Due to corona aging, dielectric losses and conductivity of the vegetable oils were increased.


title: Understanding and exploiting plant beneficial microbes

abstract: After a century of incremental research, technological advances, coupled with a need for sustainable crop yield increases, have reinvigorated the study of beneficial plant–microbe interactions with attention focused on how microbiomes alter plant phenotypes. We review recent advances in plant microbiome research, and describe potential applications for increasing crop productivity. The phylogenetic diversity of plant microbiomes is increasingly well characterized, and their functional diversity is becoming more accessible. Large culture collections are available for controlled experimentation, with more to come. Genetic resources are being brought to bear on questions of microbiome function. We expect that microbial amendments of varying complexities will expose rules governing beneficial plant–microbe interactions contributing to plant growth promotion and disease resistance, enabling more sustainable agriculture.


title: Aromatherapy: Does It Help to Relieve Pain, Depression, Anxiety, and Stress in Community-Dwelling Older Persons?

abstract: To examine the effectiveness of an aromatherapy programme for older persons with chronic pain. The community-dwelling elderly people who participated in this study underwent a four-week aromatherapy programme or were assigned to the control group, which did not receive any interventions. Their levels of pain, depression, anxiety, and stress were collected at the baseline and at the postintervention assessment after the conclusion of the four-week programme. Eighty-two participants took part in the study. Forty-four participants (37 females, 7 males) were in the intervention group and 38 participants (30 females, 8 males) were in the control group. The pain scores were 4.75 (SD 2.32) on a 10-point scale for the intervention group and 5.24 (SD 2.14) for the control group before the programme. There was a slight reduction in the pain score of the intervention group. No significant differences were found in the same-group and between-group comparisons for the baseline and postintervention assessments. The depression, anxiety, and stress scores for the intervention group before the programme were 11.18 (SD 6.18), 9.64 (SD 7.05), and 12.91 (SD 7.70), respectively. A significant reduction in negative emotions was found in the intervention group (P < 0.05). The aromatherapy programme can be an effective tool to reduce pain, depression, anxiety, and stress levels among community-dwelling older adults.


title: A Dual-Criticality Memory Controller (DCmc): Proposal and Evaluation of a Space Case Study

abstract: Multicore Dual-Criticality systems comprise two types of applications, each with a different criticality level. In the space domain these types are referred as payload and control applications, which have high-performance and real time requirements respectively. In order to control the interaction (contention) among payload and control applications in the access to the main memory, reaching the goals of high bandwidth for the former and guaranteed timing bounds for the latter, we propose a Dual-Criticality memory controller (DCmc). DCmc virtually divides memory banks into real-time and high-performance banks, deploying a different request scheduler policy to each bank type, which facilitates achieving both goals. Our evaluation with a multicore cycle-accurate simulator and a real space case study shows that DCmc enables deriving tight WCET estimates, regardless of the co-running payload applications, hence effectively isolating the effect of contention in the access to memory. DCmc also enables payload applications exploiting memory locality, which is needed for high performance.


title: Framing Public Opinion in Competitive Democracies

abstract: The past quarter century of scholarship on public opinion has shown that citizens’ attitudes can be influenced significantly by how elites frame their communications in the mass media. In the parlance of this research, a speaker “frames” an issue by encouraging readers or listeners to emphasize certain considerations above others when evaluating that issue. A framing “effect” occurs when individuals arrive at different positions on the issue, depending on the priority given to various considerations (Druckman and Nelson 2003: 730). For example, a newspaper editorial defending a hate group rally in terms of the group’s free speech rights may move readers to favor allowing the rally by causing them to weigh speech concerns more heavily when assessing the issue. Alternatively, an editorial challenging the rally as a threat to public safety may lead readers to give priority to maintaining social order and turn them against the rally (Nelson, Clawson, and Oxley 1997). Such studies raise questions about the capacity of citizens to provide autonomous input into the democratic process. If public preferences can be arbitrarily manipulated by how issues are framed, there can be no legitimate representation of public interests or meaningful discussion of government responsiveness (e.g., Bartels 2003; Entman 1993; Zaller 1992). Public opinion fails in these instances as a reliable guide to policy. Most of this research, however, has drawn its conclusions from observations of noncompetitive political contexts in which elite frames are conveyed without debate or opposition. There has been surprisingly


title: GITAM: A Model for the Adoption of Green IT

abstract: Over the last decade, an increasing number of businesses are undertaking initiatives to reduce their environmental footprint and improve their -green- credentials. As information technology (IT) has permeated most, if not all, business processes and supply chains, it offers an important means to tackle the climate problem. On the other hand, IT managers are pressed to reduce the total cost of IT operations and run energy efficient IT shops. Greening IT has therefore become one of the latest considerations to improve a business's environmental sustainability whilst reducing the cost of IT operations. A growing number of consulting reports speculate the benefits of greening IT for both IT vendors and mainstream businesses that use IT. While the opportunities and potentials of Green IT might be attractive, the extent of Green IT adoption and the actual realization of the benefits that Green IT aficionados allude to remain unknown. Indeed, unless business organisations incorporate Green IT into their operations, the potential benefits of Green IT remain potential and not reality. Currently, there does not appear to be a model to systematically investigate Green IT adoption. This paper proposes such a model called the Green IT Adoption Model (GITAM). The model defines Green IT from four distinct but interrelated perspectives. It posits that the technological, organisational and environmental contextual variables, dynamic Green IT readiness dimensions and strong order Green IT drivers can predict the intention and the breadth and depth of Green IT adoption.


abstract: Users nowadays are normally involved in multiple (usually more than two) online social networks simultaneously to enjoy more social network services. Some of the networks that users are involved in can share common structures either due to the analogous network construction purposes or because of the similar social network features. However, the social network datasets available in research are usually pre-anonymized and accounts of the shared users in different networks are mostly isolated without any known connections. In this paper, we want to identify such connections between the shared users' accounts in multiple social networks (i.e., the anchor links), which is formally defined as the M-NASA (Multiple Anonymized Social Networks Alignment) problem. M-NASA is very challenging to address due to (1) the lack of known anchor links to build models, (2) the studied networks are anonymized, where no users' personal profile or attribute information is available, and (3) the "transitivity law" and the "one-to-one property" based constraints on anchor links. To resolve these challenges, a novel two-phase network alignment framework UMA (Unsupervised Multi-network Alignment) is proposed in this paper. Extensive experiments conducted on multiple real-world partially aligned social networks demonstrate that UMA can perform very well in solving the M-NASA problem.


title: Physician Acceptance Behavior of the Electronic Medical Records Exchange: An Extended Decomposed Theory of Planned Behavior

abstract: In this study, we examined physician acceptance behavior of the electronic medical record (EMR) exchange. Although several prior studies have focused on factors that affect the adoption or use of EMRs, empirical study that captures the success factors that encourage physicians to adopt the EMR exchange is limited. Therefore, drawing on institutional trust integrated with the decomposed theory of planned behavior (TPB) model, we propose a theoretical model to examine physician intentions of using the EMR exchange. A field survey was conducted in Taiwan to collect data from physicians. Structural equation modeling (SEM) using the partial least squares (PLS) method was employed to test the research model. The results showed that the usage intention of physicians is significantly influenced by 4 factors (i.e., attitude, subjective norm, perceived behavior control, and institutional trust). These 4 factors were assessed by their perceived usefulness and compatibility, facilitating conditions and self-efficacy, situational normality, and structural assurance, respectively. The results also indicated that institutional trust integrated with the decomposed TPB model provides an improved method for predicting physician intentions to use the EMR exchange. Finally, the implications of this study are discussed.


title: Stub- and Capacitor-Loaded Folded Dipole Antenna for Digital Terrestrial TV Reception

abstract: A bandwidth enhancement method for a folded dipole antenna is proposed for digital terrestrial television (TV) reception. The antenna has a pair of stubs inside a folded dipole and a capacitor loaded on the folded dipole. A cylindrical wire model with a lumped element capacitor is analyzed by the commercial simulator ldquoFEKO.rdquo The analysis shows the effect of the stubs and capacitor on bandwidth enhancement, using current distributions and a comparison of dipoles without stubs and/or capacitor. A prototype antenna printed on polyethylene terephthalate (PET) film has dimensions of 15 mm by 280 mm with a line width of 1 mm. The capacitor is implemented with a slit on the line for ease of fabrication. The antenna in free space exhibits a voltage standing wave ratio (VSWR) of less than 2.6, and figure-of-eight radiation patterns with gain from 0.7 to 2.5 dBi in a frequency range from 470 to 950 MHz. The antenna attached to flat dielectric plates of materials that are typically used in automotive body parts, maintains a VSWR of less than 2.7 from 470 to 710 MHz for digital terrestrial TV reception.


Reconstruction and Visualization of Fiber and Sheet Structure with Regularized Tensor Diffusion MRI in the Human Heart


title: Sparse Directional Image Representations using the Discrete Shearlet Transform

abstract: Abstract In spite of their remarkable success in signal processing applications, it is now widely acknowledged that traditional wavelets are not very effective in dealing multidimensional signals containing distributed discontinuities such as edges. To overcome this limitation, one has to use basis elements with much higher directional sensitivity and of various shapes, to be able to capture the intrinsic geometrical features of multidimensional phenomena. This paper introduces a new discrete multiscale directional representation called the discrete shearlet transform. This approach, which is based on the shearlet transform, combines the power of multiscale methods with a unique ability to capture the geometry of multidimensional data and is optimally efficient in representing images containing edges. We describe two different methods of implementing the shearlet transform. The numerical experiments presented in this paper demonstrate that the discrete shearlet transform is very competitive in denoising applications both in terms of performance and computational efficiency.


title: Domain Adaptation With Structural Correspondence Learning

abstract: Discriminative learning methods are widely used in natural language processing. These methods work best when their training and test data are drawn from the same distribution. For many NLP tasks, however, we are confronted with new domains in which labeled data is scarce or non-existent. In such cases, we seek to adapt existing models from a resource-rich source domain to a resource-poor target domain. We introduce structural correspondence learning to automatically induce correspondences among features from different domains. We test our technique on part of speech tagging and show performance gains for varying amounts of source and target training data, as well as improvements in target domain parsing accuracy using our improved tagger.


Research in Big Data - An Overview


title: Towards autonomous neuroprosthetic control using Hebbian reinforcement learning

abstract: Objective. Our goal was to design an adaptive neuroprosthetic controller that could learn the mapping from neural states to prosthetic actions and automatically adjust adaptation using only a binary evaluative feedback as a measure of desirability/undesirability of performance. Approach. Hebbian reinforcement learning (HRL) in a connectionist network was used for the design of the adaptive controller. The method combines the efficiency of supervised learning with the generality of reinforcement learning. The convergence properties of this approach were studied using both closed-loop control simulations and open-loop simulations that used primate neural data from robot-assisted reaching tasks. Main results. The HRL controller was able to perform classification and regression tasks using its episodic and sequential learning modes, respectively. In our experiments, the HRL controller quickly achieved convergence to an effective control policy, followed by robust performance. The controller also automatically stopped adapting the parameters after converging to a satisfactory control policy. Additionally, when the input neural vector was reorganized, the controller resumed adaptation to maintain performance. Significance. By estimating an evaluative feedback directly from the user, the HRL control algorithm may provide an efficient method for autonomous adaptation of neuroprosthetic systems. This method may enable the user to teach the controller the desired behavior using only a simple feedback signal.


title: Beekeeping in the future — Smart apiary management

abstract: Future of the traditional beekeeping is to implement smart apiary management and start to use automatic and remote tools for bee colony monitoring together with beehive control mechanisms to improve bee colony productivity. Within the ERA-NET ICT-Agri project “ITAPIC” different bee colony monitoring and control systems together with its combinations were introduced and analysed. This paper presents authors vision for implementation of Precision Beekeeping together with the smart apiary concept. Different parameters of the bee colony can be monitored: temperature, humidity, gas content, sound, vibration etc. Continuous monitoring of some bee colony parameters is very challenging and not user friendly, allowing using it only for research purposes, not for practical implementation by the beekeepers. Precision beekeeping idea is to introduce tools that can be easily implemented into beekeeping practice. This paper describes developed systems and its combinations for successful smart apiary management. Developed systems are based on temperature, sound and video monitoring. Both data transmission types: wired and wireless are applied and compared. As well discussion of automatic beehive heating or/and cooling system implementation into practice is opened. As apiaries usually are placed outside in rural areas, important part of the smart beekeeping is usage of alternative energy for powering all the devices. Most suitable alternative power supply to this moment is usage of solar power with solar panels, which can be mounted on the hive. Together with hardware part it is needed to develop software part for data observation. Software part should be developed as a web system or/and mobile application. Cloud system with decision support functionality and with additional option for informing the beekeepers about changes in the state of the bee colonies could be considered as well.


title: How sensitive are online gamers to network quality?

abstract: Game-playing time is strongly related to network QoS, helping determine user satisfaction and deliver better service quality to online gamers.


title: High frequency ultrasound imaging of surface and subsurface structures of fingerprints

abstract: High frequency ultrasound is suitable for non-invasive evaluation of skin because it can obtain both morphological and biomechanical information. A specially developed acoustic microscope system with the central frequency of 100 MHz was developed. The system was capable of (1) conventional C-mode acoustic microscope imaging of thinly sliced tissue, (2) ultrasound impedance imaging of the surface of in vivo thick tissue and (3) 3D ultrasound imaging of inside of the in vivo tissue. In the present study, ultrasound impedance imaging and 3D ultrasound imaging of in vivo fingerprints were obtained. The impedance image showed pores of the sweat glands in the surface of fingerprint and 3D ultrasound imaging showed glands of the rear surface of fingerprint. Both findings were not visualized by normal optical imaging, thus the system can be applied to pathological diagnosis of skin lesions and assessment of aging of the skin in cosmetic point of view.


title: College students' uses and perceptions of social networking sites for health and wellness information

abstract: Introduction. This study explores college students' use of social networking sites for health and wellness information and their perceptions of this use. Method. Thirty-eight college students were interviewed. Analysis. The interview transcripts were analysed using the qualitative content analysis method. Results. Those who had experience using social networking sites for health information used the platform mainly to check health updates of a loved one, find lifestyle information and ask about treatments for mild conditions. Overall, participants were sceptical about the quality of information, concerned about the lack of medical knowledge of their friends or peers and wary about possible social risks and invasion of privacy. Based on the results, a model of users' acceptance of social networking sites for health and wellness information was proposed and implications for designing social platforms to better support health inquiries were discussed. Conclusions. Using social networking sites for health and wellness information is not a popular behaviour among college students in this study and social networking sites seem not to be a well-perceived platform for health and wellness information.


title: Time Series Forecasting Using Distribution Enhanced Linear Regression

abstract: Amongst the wealth of available machine learning algorithms for forecasting time series, linear regression has remained one of the most important and widely used methods, due to its simplicity and interpretability. A disadvantage, however, is that a linear regression model may often have higher error than models that are produced by more sophisticated techniques. In this paper, we investigate the use of a grouping based quadratic mean loss function for improving the performance of linear regression. In particular, we propose segmenting the input time series into groups and simultaneously optimizing both the average loss of each group and the variance of the loss between groups, over the entire series. This aims to produce a linear model that has low overall error, is less sensitive to distribution changes in the time series and is more robust to outliers. We experimentally investigate the performance of our method and find that it can build models which are different from those produced by standard linear regression, whilst achieving significant reductions in prediction errors.


title: TCP in wireless environments: problems and solutions

abstract: The Internet provides a platform for rapid and timely information exchange among a disparate array of clients and servers. TCP and IP are separately designed and closely tied protocols that define the rules of communication between end hosts, and are the most commonly used protocol suite for data transfer in the Internet. The combination of TCP/IP dominates today's communication in various networks from the wired backbone to the heterogeneous network due to its remarkable simplicity and reliability, TCP has become the de facto standard used in most applications ranging from interactive sessions such as Telnet and HTTP, to bulk data transfer like FTP. TCP was originally designed primarily for wired networks. In a wired network, random bit error rate, a characteristic usually more pronounced in the wireless network, is negligible, and congestion is the main cause of packet loss. The emerging wireless applications, especially high-speed multimedia services and the advent of wireless IP communications carried by the Internet, call for calibration and sophisticated enhancement or modifications of this protocol suite for improved performance. Based on the assumption that packet losses are signals of network congestion, the additive increase multiplicative decrease congestion control of the standard TCP protocol reaches the steady state, which reflects the protocol's efficiency in terms of throughput and link utilization. However, this assumption does not hold when the end-to-end path also includes wireless links. Factors such as high BER, unstable channel characteristics, and user mobility may all contribute to packet losses. Many studies have shown that the unmodified standard TCP performs poorly in a wireless environment due to its inability to distinguish packet losses caused by network congestion from those attributed to transmission errors. In this article, following a brief introduction to TCP, we analyze the problems TCP exhibits in the wireless IP communication environment, and illustrate viable solutions by detailed examples.


title: Achieving integrated convoys: cargo unmanned ground vehicle development and experimentation

abstract: The Cargo UGV project was initiated in 2010 with the aim of developing and experimenting with advanced autonomous ::: vehicles capable of being integrated unobtrusively into manned logistics convoys. The intent was to validate two ::: hypotheses in complex, operationally representative environments: first, that unmanned tactical wheeled vehicles ::: provide a force protection advantage by creating standoff distance to warfighters during ambushes or improvised ::: explosive device attacks; and second, that these UGVs serve as force multipliers by enabling a single operator to control ::: multiple unmanned assets. ::: To assess whether current state-of-the-art autonomous vehicle technology was sufficiently capable to permit resupply ::: missions to be executed with decreased risk and reduced manpower, and to assess the effect of UGVs on customary ::: convoy tactics, the Marine Corps Warfighting Laboratory and the Joint Ground Robotics Enterprise sponsored Oshkosh ::: Defense and the National Robotics Engineering Center to equip two standard Marine Corps cargo trucks for autonomous ::: operation. ::: This paper details the system architecture, hardware implementation, and software modules developed to meet the ::: vehicle control, perception, and planner requirements compelled by this application. Additionally, the design of a ::: custom human machine interface and an accompanying training program are described, as is the creation of a realistic ::: convoy simulation environment for rapid system development. ::: Finally, results are conveyed from a warfighter experiment in which the effectiveness of the training program for novice ::: operators was assessed, and the impact of the UGVs on convoy operations was observed in a variety of scenarios via ::: direct comparison to a fully manned convoy.


title: Optimizing the Topology of Tendon-Driven Fingers: Rationale, Predictions and Implementation

abstract: Tendon-driven mechanisms in general, and tendon-driven fingers in particular, are ubiquitous in nature, and are an important class of bio-inspired mechatronic systems. However, the mechanical complexity of tendon-driven systems has hindered our understanding of biological systems and the optimization of the design, performance, control, and construction of mechatronic systems. Here we apply our recently-developed analytical approach to tendon-driven systems [1] to describe a novel, systematic approach to analyze and optimize the routing of tendons for force-production capabilities of a reconfigurable 3D tendon-driven finger. Our results show that these capabilities could be increased by up to 277 % by rerouting tendons and up to 82 % by changing specific pulley sizes for specific routings. In addition, we validate these large gains in performance experimentally. The experimental results for 6 implemented tendon routings correlated very highly with theoretical predictions with an \( R^{2} \) value of 0.987, and the average effect of unmodeled friction decreased performance an average of 12 %. We not only show that, as expected, functional performance can be highly sensitive to tendon routing and pulley size, but also that informed design of fingers with fewer tendons can exceed the performance of some fingers with more tendons. This now enables the systematic simplification and/or optimization of the design and construction of novel robotic/prosthetic fingers. Lastly, this design and analysis approach can now be used to model complex biological systems such as the human hand to understand the synergistic nature of anatomical structure and neural control.


title: PU-Net: Point Cloud Upsampling Network

abstract: Learning and analyzing 3D point clouds with deep networks is challenging due to the sparseness and irregularity of the data. In this paper, we present a data-driven point cloud upsampling technique. The key idea is to learn multi-level features per point and expand the point set via a multi-branch convolution unit implicitly in feature space. The expanded feature is then split to a multitude of features, which are then reconstructed to an upsampled point set. Our network is applied at a patch-level, with a joint loss function that encourages the upsampled points to remain on the underlying surface with a uniform distribution. We conduct various experiments using synthesis and scan data to evaluate our method and demonstrate its superiority over some baseline methods and an optimization-based method. Results show that our upsampled points have better uniformity and are located closer to the underlying surfaces.


The Tale of e-Government: A Review of the Stories that Have Been Told So Far and What is Yet to Come


title: Garment-integrated wetness sensing for leak detection

abstract: Detecting fluid emissions (e.g. urination or leaks) that extend beyond containment systems (e.g. diapers or adult pads) is a cause of concern for users and developers of wearable fluid containment products. Immediate, automated detection would allow users to address the situation quickly, preventing medical conditions such as adverse skin effects and avoiding embarrassment. For product development, fluid emission detection systems would enable more accurate and efficient lab and field evaluation of absorbent products. This paper describes the development of a textile-based fluid-detection sensing method that uses a multi-layer "keypad matrix" sensing paradigm using stitched conductive threads. Bench characterization tests determined the effects of sensor spacing, spacer fabric property, and contact pressures on wetness detection for a 5mL minimum benchmark fluid volume. The sensing method and bench-determined requirements were then applied in a close-fitting torso garment for babies that fastens at the crotch (onesie) that is able to detect diaper leakage events. Mannequin testing of the resulting garment confirmed the ability of using wetness sensing timing to infer location of induced 5 mL leaks.


title: Another stemmer

abstract: In natural language processing, conflation is the process of merging or lumping together nonidentical words which refer to the same principal concept. This can relate both to words which are entirely different in form (e.g., "group" and "collection"), and to words which share some common root (e.g., "group", "grouping", "subgroups"). In the former case the words can only be mapped by referring to a dictionary or thesaurus, but in the latter case use can be made of the orthographic similarities between the forms. One popular approach is to remove affixes from the input words, thus reducing them to a stem; if this could be done correctly, all the variant forms of a word would be converted to the same standard form. Since the process is aimed at mapping for retrieval purposes, the stem need not be a linguistically correct lemma or root (see also Frakes 1982).


title: Bounding the average sensitivity and noise sensitivity of polynomial threshold functions

abstract: We give the first non-trivial upper bounds on the average sensitivity and noise sensitivity of degree-d polynomial threshold functions (PTFs). These bounds hold both for PTFs over the Boolean hypercube {-1,1}n and for PTFs over Rn under the standard n-dimensional Gaussian distribution N(0,In). Our bound on the Boolean average sensitivity of PTFs represents progress towards the resolution of a conjecture of Gotsman and Linial [17], which states that the symmetric function slicing the middle d layers of the Boolean hypercube has the highest average sensitivity of all degree-d PTFs. Via the L1 polynomial regression algorithm of Kalai et al. [22], our bounds on Gaussian and Boolean noise sensitivity yield polynomial-time agnostic learning algorithms for the broad class of constant-degree PTFs under these input distributions. ::: The main ingredients used to obtain our bounds on both average and noise sensitivity of PTFs in the Gaussian setting are tail bounds and anti-concentration bounds on low-degree polynomials in Gaussian random variables [20, 7]. To obtain our bound on the Boolean average sensitivity of PTFs, we generalize the "critical-index" machinery of [37] (which in that work applies to halfspaces, i.e. degree-1 PTFs) to general PTFs. Together with the "invariance principle" of [30], this lets us extend our techniques from the Gaussian setting to the Boolean setting. Our bound on Boolean noise sensitivity is achieved via a simple reduction from upper bounds on average sensitivity of Boolean PTFs to corresponding bounds on noise sensitivity.


Lexicalized Meaning and the Internal Temporal Structure of Events


title: On the existence and multiplicity of solutions for a fourth order discrete BVP

abstract: We investigate the existence and multiplicity of solutions for fourth order discrete boundary value problems via critical point theory.


title: Theoretical weaknesses: Fundamental flaws in cognitive-behavioral theories are more than a problem of probability

abstract: Corrigan (this issue) has responded to my recent article (Lee, 1989) with a defence of the status quo, which raises some apparently strong arguments but, in my opinion, misses the fundamental point that I am endeavoring to make. The basic argument raised by Lee (1989) is that many modern theories of psychology suffer from an insurmountable flaw: they rely on undefined and unobservable variables in order to explain behavior. These variables are hypothesized to interact in a complex manner to produce everyday human behavior. The undefined nature of the variables, the lack of consensus concerning subtle differences between differently named variables, and the unspecified and unobservable nature of their interactions, however, in combination with the difficulties of reliance on self-report, produce fundamental problems. These problems mean that the models are useless in understanding or predicting behavior, although they may be seductive ways of talking about that behavior afterwards. Corrigan (this issue) appears to be arguing that this problem is surmountable. It is a problem, he says, of research design. rather than a fundamental weakness in the models themselves. The use of appropriate statistics, he argues, should allow us to make guesses about the relationships of these variables. Armed with these preliminary hypotheses, we could then collect further information and test specific models. In this way the models would be strengthened and we would eventually develop better theories. They would need to be probabilistic in nature, rather than completely deterministic, because (I think this is the argument) they are. after all, dealing with selfreports. which are imperfect indicators of what is really going on. Research attempting to refine the nature of the relationships between cognitive variables has, of course, been going on for some time. Much of this research is extremely rigorously conducted and makes use of large samples and complex statistical techniques such as the ones Corrigan mentions (e.g., Feltz & Mugno, 1983; Godding & Glasgow, 1985). I have even conducted some of it myself (e.g., Lee, 1984). But a relatively large proportion of the variance in behavior is left unexplained by cognitive variables (e.g., Baer, Holt, & Lichtenstein, 1986). while observable variables such as behavior are frequently found to be more accurate predictors of outcomes than are sophisticated assessments of self-report (e.g., Feltz & Mugno, 1983; Lee, 1983). Models with a basis in behavior theory and empirical evidence are already providing alternatives to their more baroque cognitive competitors (e.g., Biglan, 1987; Rachlin, Logue, Gibbon, & Frankel, 1986; Tryon, 1982), although they do not have the same popularity. Baer, Wolf, and Risley (1987) argue that psychologists generally find non-cognitive explanations of


title: Familial Acanthosis Nigricans Due to K650T FGFR3 Mutation

abstract: Background Acanthosis nigricans is a feature of several syndromes caused by activating mutations of the fibroblast growth factor receptor 3 gene ( FGFR3 ), including Crouzon syndrome with acanthosis nigricans, thanatophoric dysplasia, and severe achondroplasia with developmental delay and acanthosis nigricans (SADDAN syndrome). Observations We describe a healthy 4-year-old African American girl with generalized acanthosis nigricans since infancy. Her father had a history of acanthosis nigricans since childhood, in addition to Crohn disease, obesity, and adult-onset diabetes mellitus. A pedigree with numerous affected family members was constructed. Other than slightly short stature, no associated anomalies were found, including dysmorphic features or skeletal or neurologic defects. Genetic testing revealed a previously undescribed, heterozygous lysine to threonine mutation at codon 650 of the FGFR3 gene in the 4 affected family members who were tested. Conclusion Extensive acanthosis nigricans in early childhood, especially with a family history of acanthosis nigricans, may warrant testing for FGFR3 mutations.


title: Hormonal and hemodynamic effects of aliskiren and valsartan and their combination in sodium-replete normotensive individuals.

abstract: BACKGROUND AND OBJECTIVES ::: An AT1 receptor antagonist induces a counterregulatory renin release whose intensity and duration reflect the magnitude of the renin-angiotensin blockade. We investigated whether a renin inhibitor may neutralize this counterregulation and amplify the effects of AT1 receptor antagonists. ::: ::: ::: DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS ::: In 12 normotensive male individuals who were on a high-sodium diet, a double-blind, placebo-controlled, randomized, crossover design was used to study the hormonal and BP effects of single oral administrations of 300 mg of the renin inhibitor aliskiren, 320 mg of valsartan, and a combination of these two drugs, each at half dosage (150 mg of aliskiren and 160 mg of valsartan). ::: ::: ::: RESULTS ::: Valsartan (320 mg) increased plasma renin activity and angiotensin I and angiotensin II levels, but 300 mg of aliskiren decreased them for 48 h. Aliskiren (300 mg) stimulated immunoreactive renin release more strongly than 320 mg of valsartan, decreased urinary aldosterone excretion for longer than 320 mg of valsartan, and had a similar BP-lowering effect as 320 mg of valsartan. In combination, 150 mg of aliskiren neutralized the valsartan (160 mg)-induced increase in plasma angiotensins for 48 h. The renin and aldosterone effects of the combination of 150 mg of aliskiren and 160 mg of valsartan were similar to those of 300 mg of aliskiren and greater than those of 320 mg of valsartan. When plasma drug concentrations were taken into account, the combination of 150 mg of aliskiren and 160 mg of valsartan had a synergistic effect on renin release. The BP-lowering effect of 150 mg of aliskiren and 160 mg of valsartan was similar to that of 300 mg of aliskiren and 320 mg of valsartan at peak but was more prolonged. ::: ::: ::: CONCLUSION ::: The stronger and longer lasting effects on plasma active renin and urinary aldosterone of aliskiren, alone or in combination, demonstrate a more effective blockade of the renin-angiotensin system than that obtained with 320 mg of valsartan alone.


title: Reducing Specific Energy to Shrink the Carbon Footprint in a Copper Electrowinning Facility

abstract: Various technologies and strategies to reduce specific energy consumption are examined. Results include 16 harvest cycles for 30 cells totalling 62,400 cathodes with a weight of 2,500 Tons. The industrial site was Zaldivar, a Barrick mining operation located at 175 km Southeast from Antofagasta, Chile. The production averages 140,000 Ton/year of copper cathodes. With the technologies tested in this work, 14 thousand Megawatt-hour and corresponding 14 thousand tons of CO2 will be saved per year.


title: The ripple of fear, sympathy and solidarity during the Boston bombings

abstract: The Boston Marathon bombing presents a rare opportunity to study how a disruptive event can trigger inter-communal emotions and expressions - where members of one community express feelings about and support for members of a distant community. In this work, we use over 180 million geocoded tweets over an entire month to study how Twitter users from different cities expressed three different emotions: fear, sympathy and solidarity, in reaction to the bombings. We capture spikes in fear in different cities by using sentiment and time-series analyses, and track expressions of sympathy and solidarity based on the emergent use of two hashtags, #prayforboston and #bostonstrong, widely adopted after the bombings. We find first that the extent to which communities outside Boston express these emotions is correlated with their geographic proximity, social network connections, and residents’ direct, physical experiences with Boston (captured by the number of citizens who had visited Boston recently). This general effect shows interesting differences across the different kinds of emotions, however. Analyses show that the extent to which residents of a city visit Boston is the best predictor of fear and solidarity expression, as well as a strong predictor of the expression of sympathy. The expression of fear is also directly related to the expression of solidarity. Our study has theoretical implications regarding the diffusion of information and emotional contagion as well as practical implications for understanding how important information and social support can be effectively collected and distributed to populations in need.


Teaching health science students foundation motivational interviewing skills: use of motivational interviewing treatment integrity and self-reflection to approach transformative learning


title: Analysis and Tests of a Dual Three-Phase 12-Slot 10-Pole Permanent-Magnet Motor

abstract: In order to increase the fault tolerance of a motor drive, multiphase systems are adopted. Since custom solutions are expensive, machines with dual three-phase windings supplied by two parallel converters seem to be more convenient. In the event of fault, one of the two three-phase windings (the faulty winding) is disconnected and the motor is operated by means of the healthy winding only. A fractional-slot permanent magnet motor with 12-slot and 10-pole is considered with two different rotor topologies : the interior permanent magnet (IPM) rotor and the surface mounted permanent magnet (SPM) rotor. Various winding configurations of dual three-phase windings are taken into account, comparing average torque, torque ripple, mutual coupling among phases, overload capability and short-circuit behavior. Considerations are given regarding the winding arrangements so as to avoid excessive torque ripple and unbalanced radial forces in faulty operating conditions. An IPM motor prototype has been built and experimental results are carried out in order to verify the numerical predictions.


Factors Affecting Accuracy in Image Translation based on Generative Adversarial Network


title: KLOTSKI: reprioritizing web content to improve user experience on mobile devices

abstract: Despite web access on mobile devices becoming commonplace, users continue to experience poor web performance on these devices. Traditional approaches for improving web performance (e.g., compression, SPDY, faster browsers) face an uphill battle due to the fundamentally conflicting trends in user expectations of lower load times and richer web content. Embracing the reality that page load times will continue to be higher than user tolerance limits for the foreseeable future, we ask: How can we deliver the best possible user experience? ::: ::: To this end, we present KLOTSKI, a system that prioritizes the content most relevant to a user's preferences. In designing KLOTSKI, we address several challenges in: (1) accounting for inter-resource dependencies on a page; (2) enabling fast selection and load time estimation for the subset of resources to be prioritized; and (3) developing a practical implementation that requires no changes to websites. Across a range of user preference criteria, KLOTSKI can significantly improve the user experience relative to native websites.


The Ethics of Germline Gene Editing


title: Levelwise Search and Borders of Theories in Knowledge Discovery

abstract: One of the basic problems in knowledge discovery in databases (KDD) is the following: given a data set r, a class L of sentences for defining subgroups of r, and a selection predicate, find all sentences of L deemed interesting by the selection predicate. We analyze the simple levelwise algorithm for finding all such descriptions. We give bounds for the number of database accesses that the algorithm makes. For this, we introduce the concept of the border of a theory, a notion that turns out to be surprisingly powerful in analyzing the algorithm. We also consider the verification problem of a KDD process: given r and a set of sentences S ⊆ L determine whether S is exactly the set of interesting statements about r. We show strong connections between the verification problem and the hypergraph transversal problem. The verification problem arises in a natural way when using sampling to speed up the pattern discovery step in KDD.


Quantifying condition of coral reef communities in the Kahana and Honokahua Priority Watersheds, West Maui Project Information


title: Ranking the technical dimensions of e-banking service quality evaluation models using Analytical Hierarchy Process

abstract: With the expansion of internet, delivering e-services are rapidly growing in the bank industry. Therefore improving e-service quality for delivering better services is one of the most important areas of activity for banks. One of the most important aspects of e-service quality is the technical aspect, so the aim of this paper is to evaluate these dimensions of e-service quality for identifying the most important dimensions. Therefore we first reviewed e-banking service quality models to identify technical aspects then ranked them with AHP method. Base on the results, we identified 8 major dimensions which the most important ones are security with a score of 0.226, technical sufficiency with a score of 0.131, and reliability with a score of 0.128. Regarding to the findings the least important dimension is outcome quality with a score of 0.089.


title: Identification of MDP (muramyl dipeptide)-binding key domains in NOD2 (nucleotide-binding and oligomerization domain-2) receptor of Labeo rohita

abstract: In lower eukaryotes-like fish, innate immunity contributed by various pattern recognition receptor (PRR) plays an essential role in protection against diseases. Nucleotide-binding and oligomerization domain (NOD)-2 is a cytoplasmic PRR that recognizes MDP (muramyl dipeptide) of the Gram positive and Gram negative bacteria as ligand and activates signalling to induce innate immunity. Hypothesizing a similar NOD2 signalling pathway of higher eukaryotes, the peripheral blood leucocytes (PBLs) of rohu (Labeo rohita) was stimulated with MDP. The data of quantitative real-time PCR (qRT-PCR) revealed MDP-mediated inductive expression of NOD2 and its down-stream molecule RICK/RIP2 (receptor-interacting serine-threonine protein kinase-2). This observation suggested the existence of MDP-binding sites in rohu NOD2 (rNOD2). To investigate it, 3D model of ligand-binding leucine-rich repeat (LRR) region of rNOD2 (rNOD2-LRR) was constructed following ab initio and threading approaches in I-TASSER web server. Structural refinement of the model was performed by energy minimization, and MD (molecular dynamics) simulation was performed in GROMACS (Groningen Machine for Chemical Simulations). The refined model of rNOD2-LRR was validated through SAVES, ProSA, ProQ, WHAT IF and MolProbity servers, and molecular docking with MDP was carried out in GOLD 4.1. The result of docking identified LRR3-7 comprising Lys820, Phe821, Asn822, Arg847, Gly849, Trp877, Trp901 and Trp931 as MDP-binding critical amino acids in rNOD2. This is the first study in fish to provide an insight into the 3D structure of NOD2-LRR region and its important motifs that are expected to be engaged in MDP binding and innate immunity.


title: THE NEUROSCIENCES AND MUSIC III—DISORDERS AND PLASTICITY A Neuroscientific Perspective on Music Therapy

abstract: During the last years, a number of studies demonstrated that music listening (and even more so music production) activates a multitude of brain structures involved in cognitive, sensorimotor, and emotional processing. For example, music engages sensory processes, attention, memory-related processes, perception-action mediation (“mirror neuron system” activity), multisensory integration, activity changes in core areas of emotional processing, processing of musical syntax and musical meaning, and social cognition. It is likely that the engagement of these processes by music can have beneficial effects on the psychological and physiological health of individuals, although the mechanisms underlying such effects are currently not well understood. This article gives a brief overview of factors contributing to the effects of music-therapeutic work. Then,neuroscientificstudiesusingmusictoinvestigateemotion,perception-actionmediation (“mirror function”), and social cognition are reviewed, including illustrations of the relevance of these domains for music therapy.


title: KnowLife: a versatile approach for constructing a large knowledge graph for biomedical sciences

abstract: Background ::: Biomedical knowledge bases (KB’s) have become important assets in life sciences. Prior work on KB construction has three major limitations. First, most biomedical KBs are manually built and curated, and cannot keep up with the rate at which new findings are published. Second, for automatic information extraction (IE), the text genre of choice has been scientific publications, neglecting sources like health portals and online communities. Third, most prior work on IE has focused on the molecular level or chemogenomics only, like protein-protein interactions or gene-drug relationships, or solely address highly specific topics such as drug effects.


title: Machine learning techniques for classification of diabetes and cardiovascular diseases

abstract: This paper presents the overview of machine learning techniques in classification of diabetes and cardiovascular diseases (CVD) using Artificial Neural Networks (ANNs) and Bayesian Networks (BNs). The comparative analysis was performed on selected papers that are published in the period from 2008 to 2017. The most commonly used type of ANN in selected papers is multilayer feedforward neural network with Levenberg-Marquardt learning algorithm. On the other hand, the most commonly used type of BN is Na'ive Bayesian network which shown the highest accuracy values for classification of diabetes and CVD, 99.51% and 97.92% retrospectively. Moreover, the calculation of mean accuracy of observed networks has shown better results using ANN, which indicates that higher possibility to obtain more accurate results in diabetes and/or CVD classification is when it is applied to ANN.


title: Registration of Laser Scanning Point Clouds: A Review

abstract: The integration of multi-platform, multi-angle, and multi-temporal LiDAR data has become important for geospatial data applications. This paper presents a comprehensive review of LiDAR data registration in the fields of photogrammetry and remote sensing. At present, a coarse-to-fine registration strategy is commonly used for LiDAR point clouds registration. The coarse registration method is first used to achieve a good initial position, based on which registration is then refined utilizing the fine registration method. According to the coarse-to-fine framework, this paper reviews current registration methods and their methodologies, and identifies important differences between them. The lack of standard data and unified evaluation systems is identified as a factor limiting objective comparison of different methods. The paper also describes the most commonly-used point cloud registration error analysis methods. Finally, avenues for future work on LiDAR data registration in terms of applications, data, and technology are discussed. In particular, there is a need to address registration of multi-angle and multi-scale data from various newly available types of LiDAR hardware, which will play an important role in diverse applications such as forest resource surveys, urban energy use, cultural heritage protection, and unmanned vehicles.


title: Logistic regression prediction model identify type 2 diabetes mellitus as a prognostic factor for human papillomavirus-16 associated head and neck squamous cell carcinoma

abstract: BACKGROUND ::: HPV-16-positive HNSCC and HPV-16-negative HNSCC have different clinical factors, representing distinct forms of cancers. The study aimed to identify patient-specific factors for HPV-16-positive HNSCC based on baseline clinical data. ::: ::: ::: METHOD ::: Factors associated with HPV-16-positive HNSCC were identified using the data from 210 patients diagnosed with HNSCC at University College of London Hospital between January 1, 2003, and April 30, 2015, inclusive. A series of models were developed using logistic regression methods, and the overall model fit was compared using Akaike Information Criterion. Survival analysis was carried with Cox proportional hazards model for survival-time outcomes. The survival time for individual patients was defined as the time from diagnosis of HNSCC to the date of death from any cause. For patients who did not die, they were censored at the end of study on April 30, 2015. ::: ::: ::: RESULTS ::: Of the 210 patients, 151 (72%) were found to have HPV-16-positive HNSCC. The logistic regression model showed that the prevalence of developing HPV-16-positive HNSCC was 3.79 times higher in patients with Type 2 Diabetes Mellitus (T2DM) (odd ratio [OR], 3.79; 95% CI, 1.70-8.44) than in those without T2DM, and 8.84 times higher in patients with history of primary HNSCC (OR, 8.84; 95% CI, 2.30-33.88) than in those without a history of primary HNSCC. HPV-16-positive HNSCC was also observed more in tonsils (OR, 4.02; 95% CL, 1.56-10.36) and less in non-alcohol drinker's oral cavity (OR, 0.14; 95% CI, 0.03-0.56). Furthermore, individual patients were followed-up for 1 to 13 years (median of 1 year). Patients with HPV-positive HNSCC had a median survival of 5 years (95% CI, 2.6-7.3 years). Among HPV-16-positive HNSCC cohort, T2DM was a risk for poorer prognosis (hazard ratio, 2.57; 95% Cl, 1.09-6.07), and had lower median survival of 3 years (95% CI, 1.8-4.1 years), as compared to 6 years (95% CI, 2.8-9.1 years) in non-T2DM. ::: ::: ::: CONCLUSIONS ::: Patient-specific factors for HPV-positive HNSCC are T2DM, history of primary HNSCC and tonsillar site. T2DM is associated with poorer prognosis. These findings suggest that it might be beneficial if routine HPV-16 screening is carried out in T2DM patients which can provide better therapeutic and management strategies.


title: ISB recommendation on definitions of joint coordinate system of various joints for the reporting of human joint motion--part I: ankle, hip, and spine. International Society of Biomechanics.

abstract: The Standardization and Terminology Committee (STC) of the International Society of Biomechanics (ISB) proposes a general reporting standard for joint kinematics based on the Joint Coordinate System (JCS), first proposed by Grood and Suntay for the knee joint in 1983 (J. Biomech. Eng. 105 (1983) 136). There is currently a lack of standard for reporting joint motion in the field of biomechanics for human movement, and the JCS as proposed by Grood and Suntay has the advantage of reporting joint motions in clinically relevant terms. In this communication, the STC proposes definitions of JCS for the ankle, hip, and spine. Definitions for other joints (such as shoulder, elbow, hand and wrist, temporomandibular joint (TMJ), and whole body) will be reported in later parts of the series. The STC is publishing these recommendations so as to encourage their use, to stimulate feedback and discussion, and to facilitate further revisions. For each joint, a standard for the local axis system in each articulating bone is generated. These axes then standardize the JCS. Adopting these standards will lead to better communication among researchers and clinicians.


title: Exploring possible applications of augmented reality in education

abstract: In recent years, use of Augmented Reality (AR) has been explored in a wide range of applications in various fields. Among these, one of the most inquired is the field of education. Many of these studies have shown that the use of AR in any learning environment promotes critical thinking, better understanding and motivates the learner for further studies. This is due to the real-time experience that AR brings with it to the learners. This paper reports some of the recent studies that list positive and negative impact of AR in an educational setting and how beneficial the employment of this technology is. The paper also gives an overview of some of the possible and promising applications of AR in the fields of science, social science, mathematics and language. Furthermore, the paper discusses trends and the vision towards future opportunities for possible research in augmented reality for education.


title: Unsupervised Diverse Colorization via Generative Adversarial Networks

abstract: Colorization of grayscale images is a hot topic in computer vision. Previous research mainly focuses on producing a color image to recover the original one in a supervised learning fashion. However, since many colors share the same gray value, an input grayscale image could be diversely colorized while maintaining its reality. In this paper, we design a novel solution for unsupervised diverse colorization. Specifically, we leverage conditional generative adversarial networks to model the distribution of real-world item colors, in which we develop a fully convolutional generator with multi-layer noise to enhance diversity, with multi-layer condition concatenation to maintain reality, and with stride 1 to keep spatial information. With such a novel network architecture, the model yields highly competitive performance on the open LSUN bedroom dataset. The Turing test on 80 humans further indicates our generated color schemes are highly convincible.


Zero-Knowledge twenty years after its invention


title: Prioritizing Research to Reduce Youth Suicide and Suicidal Behavior

abstract: The goal of the National Action Alliance for Suicide Prevention is to reduce suicide and suicide attempts in the U.S. by 40% in the next decade. In this paper, a public health approach is applied to suicide prevention to illustrate how reductions in youth suicide and suicidal behavior might be achieved by prioritizing research in two areas: (1) increasing access to primary care–based behavioral health interventions for depressed youth and (2) improving continuity of care for youth who present to emergency departments after a suicide attempt. Finally, some scientific, clinical, and methodologic breakthroughs needed to achieve rapid, substantial, and sustained reductions in youth suicide and suicidal behavior are discussed.


title: Dermoscopic Findings of Pilomatricomas

abstract: Background: To the best of our knowledge, no specific dermoscopic criteria have been described in the medical literature for the diagnosis of pilomatricomas. Objectives: To evaluate the morphological findings of pilomatricomas under dermoscopic observation. Material and Methods: Dermoscopic examination (using the Dermlite Foto; 3Gen, LLC, Dana Point, Calif., USA) of 10 patients with pilomatricomas was performed to evaluate specific dermoscopic criteria and patterns. Results: The most frequently occurring dermoscopic features were found to be: irregular white structures (80%) and white streaks (70%); vascular structures in all lesions (reddish homogeneous area in 90%, hairpin vessels in 70%, linear-irregular vessels in 70% and dotted vessels in 40%); ulceration (60%), and structureless gray-blue areas in 20% of cases. The results of our study reveal that the absence of specific dermoscopic criteria for other skin tumors and the presence of irregular white structures and polymorphous/atypical vessels is the most frequent dermoscopic pattern in pilomatricomas. Conclusions: Dermoscopy may be a useful tool for improving the recognition of pilomatricomas. However, dermoscopy does not assure 100% diagnostic accuracy and pilomatricomas may represent in some cases, above all in elderly patients, a dermoscopic pitfall, being difficult to differentiate dermoscopically from other lesions, such as melanoma or basal cell carcinoma.


title: Design of an economical SCARA robot for industrial applications

abstract: This paper presents mechanical design process of an industrial and economical SCARA robot, called FUM SCARA, designed by a team of students at the Ferdowsi University of Mashhad, in Iran. SCARA robots are among the most widely used robots in industry due to their inherent rigidity and high accuracy. The design process included, joint design, link design, controller design as well as selection of mechanical and electrical components. The challenge was to use readily available components in Iran with an eye on keeping the costs down. The FUM SCARA robot offers impressive performance such as ~±0.01 mm repeatability, maximum linear velocity of 8.5 m/s in xy plane, 0.5 seconds pick and place cycle time and a flexible control system. These specifications are in line with existing industrial robots. However, unlike the existing commercial robots, the control architecture in FUM SCARA is designed to allow for simple implementation of new control algorithms. Finally, using a PID controller, a critical trajectory in robot's workspace is traced. Results indicate low error during the fast trajectory.


title: Why students drop out CS1 course?

abstract: This study focuses on CS minor students' decisions to drop out from the CS1 course. The high level of drop out percentage has been a problem at Helsinki University of Technology for many years. This course has yearly enrolment of 500-600 students and the drop out percentage has varied from 30-50 percents.Since we did not have clear picture of drop out reasons we conducted a qualitative interview research in which 18 dropouts from the CS1 course were interviewed. The reasons of drop out were categorized and, in addition, each case was investigated individually. This procedure enabled us to both list the reasons and to reveal the cumulative nature of drop out reasons.The results indicate that several reasons affect students' decision to quit the CS1 course. The most frequent reasons were the lack of time and the lack of motivation. However, both of these reasons were in turn affected by factors, such as the perceived difficulty of the course, general difficulties with time managing and planning studies, or the decision to prefer something else. Furthermore, low comfort level and plagiarism played a role in drop out. In addition, drop out reasons cumulated.This study shows that the complexity and large variety of factors involved in students' decision to drop the course. This indicates that simple actions to improve teaching or organization on a CS1 course to reduce drop out may be ineffective. Efficient intervention to the problem apparently requires a combination of many different actions that take into consideration the versatile nature of reasons involved in drop out.


title: End-to-End Detection of Caller ID Spoofing Attacks

abstract: Caller ID (caller identification) is a service provided by telephone operators where the phone number and/or the name of the caller is transmitted to inform the callee who is calling. Today, most people trust the caller ID information and some banks even use Caller ID to authenticate customers. However, with the proliferation of smartphones and VoIP, it is easy to spoof caller ID information by installing a particular application on the smartphone or by using service providers that offer Caller ID spoofing. As the phone network is fragmented between countries and companies and upgrades of old hardware is costly, no mechanism is available today to let end-users easily detect Caller ID spoofing attacks. In this article, we propose a new approach of using end-to-end caller ID verification schemes that leverage features of the existing phone network infrastructure (CallerDec). We design an SMS-based and a timing-based version of CallerDec that works with existing combinations of landlines, cellular and VoIP networks and can be deployed at the liberty of the users. We implemented both CallerDec schemes as an App for Android-based phones and validated their effectiveness in detecting spoofing attacks in various scenarios.


Cloud Computing Benchmarking : A Survey


F-interference law generation and its feature recognition


title: Comparisons of SiC MOSFET and Si IGBT Based Motor Drive Systems

abstract: With the rapid development of silicon carbide (SiC) material quality, SiC power devices are gaining tremendous attentions in power electronics. In this paper, a SiC device based motor drive system is performed to provide a quantitative estimate of the system improvement. Two 60 kW motor drive systems based on SiC MOSFET/Schottky diode and Si IGBTs are designed. The power losses of the two inverters with sinusoidal pulse width modulation (SPWM) control are calculated analytically. By comparing the efficiencies, sizes and temperatures of the two designed systems, SiC device shows the superior advantages of smaller loss, better efficiency and smaller size in the same motor drive application.


title: Serum Lectin-Like Oxidized-Low Density Lipoprotein Receptor-1 and Adiponectin Levels Are Associated With Coronary Artery Disease Accompanied With Metabolic Syndrome

abstract: Coronary artery disease (CAD) is a major public health problem for developed and developing countries and is the single leading cause of death worldwide.There is very few evidence regarding changes of both serum Lectin-like oxidized-low density lipoprotein receptor-1 (LOX-1) and adiponectin in patients with CAD accompanied with metabolic syndrome (MS). Here we aimed to evaluate serum levels of LOX-1 and adiponectin in patients with CAD accompanied with MS.Thirty patients with coronary artery disease without metabolic syndrome, 30 patients with coronary artery disease and metabolic syndrome, 30 ones with metabolic syndrome and 30 healthy subjects were enrolled. For all subjects, a questionnaire was filled to collect data, and peripheral blood samples were collected aseptically from the antecubital vein to measure serum Lectin-like oxidized-low density lipoprotein receptor-1 and adiponectin levels by enzyme-linked immunosorbent assay.Serum LOX-1 level was highest in CAD + MS group; the difference between control and disease groups was statistically significant (P < 0.001). Adiponectin level had the lowest value in CAD + MS group; the difference between control and disease groups was statistically significant (P < 0.05). No significant differences were observed in serum Lectin-like oxidized-low density lipoprotein receptor-1and adiponectin in patients with different ages and gender. Serum LOX-1 level was changed negatively and linearly (R2 = 0.721) correlated with adiponectin level in different groups.Patient with CAD and MS had higher risk than those with only CAD because of lipid and glucose metabolism abnormalities. Combination measurements of serum LOX-1 and adiponectin levels may be helpful to evaluate the severity of CAD together with MS.


Crime in developing countries: the contribution of crime science


title: Dynamic route planning with real-time traffic predictions

abstract: Situation aware route planning gathers increasing interest as cities become crowded and jammed. We present a system for individual trip planning that incorporates future traffic hazards in routing. Future traffic conditions are computed by a Spatio-Temporal Random Field based on a stream of sensor readings. In addition, our approach estimates traffic flow in areas with low sensor coverage using a Gaussian Process Regression. The conditioning of spatial regression on intermediate predictions of a discrete probabilistic graphical model allows us to incorporate historical data, streamed online data and a rich dependency structure at the same time. We demonstrate the system with a real-world use-case from Dublin city, Ireland. HighlightsDynamic traffic cost prediction.Situation dependent trip planner.Prediction-as-a-service with TUD streams framework.


title: Advances in wildlife road-crossing early-alert system: New architecture and experimental validation

abstract: In this paper, the experimental validation of Doppler radar sensors integrated in a wireless sensor network for the wildlife road crossing detection is presented. The collision between wildlife and vehicles represents a sensitive issue in Alps region and a valuable as well as inexpensive solution has not been identified yet. The proposed system aims at alerting the approaching driver through light signals activated only if a dangerous situation is recognized. The detection process is implemented on the wireless nodes installed along the road sides and equipped with low cost Doppler radars for the real-time detection and identification of wildlife movements. The warning messages are then transmitted towards a centralized control unit through multihop wireless connections for further data processing and statistical analysis about road-crossing risk zones. The performance of the investigated radar modules and their integration in a small and low power prototype are here discussed in order to assess the potentialities and the limitations of the system and its future adoption in large deployments.


title: A study of Poisson query generation model for information retrieval

abstract: Many variants of language models have been proposed for information retrieval. Most existing models are based on multinomial distribution and would score documents based on query likelihood computed based on a query generation probabilistic model. In this paper, we propose and study a new family of query generation models based on Poisson distribution. We show that while in their simplest forms, the new family of models and the existing multinomial models are equivalent. However, based on different smoothing methods, the two families of models behave differently. We show that the Poisson model has several advantages, including naturally accommodating per-term smoothing and modeling accurate background more efficiently. We present several variants of the new model corresponding to different smoothing methods, and evaluate them on four representative TREC test collections. The results show that while their basic models perform comparably, the Poisson model can out perform multinomial model with per-term smoothing. The performance can be further improved with two-stage smoothing.


title: Minimum Loss Network Reconfiguration Using Mixed-Integer Convex Programming

abstract: This paper proposes a mixed-integer conic programming formulation for the minimum loss distribution network reconfiguration problem. This formulation has two features: first, it employs a convex representation of the network model which is based on the conic quadratic format of the power flow equations and second, it optimizes the exact value of the network losses. The use of a convex model in terms of the continuous variables is particularly important because it ensures that an optimal solution obtained by a branch-and-cut algorithm for mixed-integer conic programming is global. In addition, good quality solutions with a relaxed optimality gap can be very efficiently obtained. A polyhedral approximation which is amenable to solution via more widely available mixed-integer linear programming software is also presented. Numerical results on practical test networks including distributed generation show that mixed-integer convex optimization is an effective tool for network reconfiguration.


Modeling of GSM-Based Energy Recharge Scheme for Prepaid Meter


title: The impact of physical activity and fitness on academic achievement and cognitive performance in children

abstract: The potential for physical activity and fitness to improve cognitive function, learning and academic achievement in children has received attention by researchers and policy makers. This paper reports a systematic approach to identification, analysis and review of published studies up to early 2009. A three-step search method was adopted to identify studies that used measures of physical activity or fitness to assess either degree of association with or effect on a) academic achievement and b) cognitive performance. A total of 18 studies including one randomised control trial, six quasi-experimental and 11 correlational studies were included for data extraction. No studies meeting criteria that examined the links between physical activity and cognitive function were found. Weak positive associations were found between both physical activity and fitness and academic achievement and fitness and elements of cognitive function, but this was not supported by intervention studies. There is insufficient evidence...


title: Knowledge Graph Embedding for Link Prediction and Triplet Classification

abstract: The link prediction (LP) and triplet classification (TC) are important tasks in the field of knowledge graph mining. However, the traditional link prediction methods of social networks cannot directly apply to knowledge graph data which contains multiple relations. In this paper, we apply the knowledge graph embedding method to solve the specific tasks with Chinese knowledge base Zhishi.me. The proposed method has been successfully used in the evaluation task of CCKS2016. Hopefully, it can achieve excellent performance.


title: Protecting Internet users from becoming victimized attackers of click-fraud

abstract: Internet users are often victimized by malicious attackers. Some attackers infect and use innocent users' machines to launch large-scale attacks without the users' knowledge. One of such attacks is the click-fraud attack. Click-fraud happens in pay-per-click ad networks where the ad network charges advertisers for every click on their ads. Click-fraud has been proved to be a serious problem for the online advertisement industry. In a click-fraud attack, a user or an automated software clicks on an ad with a malicious intent and advertisers need to pay for those valueless clicks. Among many forms of click-fraud, botnets with the automated clickers are the most severe ones. In this study, we present a method for detecting automated clickers from the user side. The proposed method to fight click-fraud, FCFraud, can be integrated into the desktop and smart device operating systems. Since most modern operating systems already provide some kind of antimalware service, our proposed method can be implemented as a part of the service. We believe that an effective protection at the operating system level can save billions of dollars of the advertisers. Experiments show that FCFraud is 99.6% (98.2% in mobile ad library–generated traffic) accurate in classifying ad requests from all user processes and it is 100% successful in detecting clickbots in both desktop and mobile devices. We implement a cloud backend for the FCFraud service to save battery power in mobile devices. The overhead of executing FCFraud is also analyzed and we show that it is reasonable for both the platforms.


title: Computational experiment in era of HPC

abstract: In this note we discuss the impact of development of architecture and technology of parallel computing on the typical life-cycle of the computational experiment. In particular, it is argued that development and installation of high-performance computing systems is indeed important itself regardless of specific scientific tasks, since the presence of cutting-age HPC systems within an academic infrastructure gives wide possibilities and stimulates new researches.


Complex COTS-based software systems: practical steps for their maintenance


title: Measuring performance of middleware technologies for medical systems: Ice vs AMQP

abstract: After decades of design, development and usage of distributed application technologies, there are numerous communication middleware architectures and implementations in the market that have reached a considerable maturity level. A large number of them are open source initiatives that have shown efficiency and good performance in a broad range of domains, from banking to gaming. These are low cost solutions, easily programmable and of high interest to be explored in areas such as cyber-physical medical systems that have special requirements for safety, availability, communication latency, real-time operation, and fault tolerance. This paper analyzes the suitability of two open source communication middleware technologies, Ice (Internet Communication Engine) and AMQP (Advanced Message Queuing Protocol), as software elements suitable for developing audio transmission and reception systems for low cost medical applications. The paper simulates an audio application with both technologies, made of a server (nurse central) that receives and processes audio media from several clients (patients); communication can be triggered concurrent from multiple patients and in both directions. Stress tests with high load conditions are simulated in the experiments to show the behavior of both technologies mainly with respect to their stability and overhead.


title: General general game AI

abstract: Arguably the grand goal of artificial intelligence research is to produce machines with general intelligence: the capacity to solve multiple problems, not just one. Artificial intelligence (AI) has investigated the general intelligence capacity of machines within the domain of games more than any other domain given the ideal properties of games for that purpose: controlled yet interesting and computationally hard problems. This line of research, however, has so far focused solely on one specific way of which intelligence can be applied to games: playing them. In this paper, we build on the general game-playing paradigm and expand it to cater for all core AI tasks within a game design process. That includes general player experience and behavior modeling, general non-player character behavior, general AI-assisted tools, general level generation and complete game generation. The new scope for general general game AI beyond game-playing broadens the applicability and capacity of AI algorithms and our understanding of intelligence as tested in a creative domain that interweaves problem solving, art, and engineering.


title: Characterization of social media response to natural disasters

abstract: Online social networking websites such as Twitter and Facebook often serve a breaking-news role for natural disasters: these websites are among the first ones to mention the news, and because they are visited by millions of users regularly the websites also help communicate the news to a large mass of people. In this paper, we examine how news about these disasters spreads on the social network. In addition to this, we also examine the countries of the Tweeting users. We examine Twitter logs from the 2010 Philippines typhoon, the 2011 Brazil flood and the 2011 Japan earthquake. We find that although news about the disaster may be initiated in multiple places in the social network, it quickly finds a core community that is interested in the disaster, and has little chance to escape the community via social network links alone. We also find evidence that the world at large expresses concern about such largescale disasters, and not just countries geographically proximate to the epicenter of the disaster. Our analysis has implications for the design of fund raising campaigns through social networking websites.


title: WiFi RSS fingerprinting indoor localization for mobile devices

abstract: While WiFi-based indoor localization is attractive, the need for a significant degree of pre-deployment effort is a key challenge. In this paper, indoor localization with no pre-deployment effort in an indoor space, such as an office building corridor, with WiFi coverage but no a priori knowledge of the placement of the access points(APs) is implemented for mobile devices. WiFi Received Signal Strength(RSS) in the considered environment is used to build radio maps using WiFi fingerprinting approach. An offline RSS fingerprint of the environment is compared with an online RSS measurement to estimate the location of a user. Two architectures are developed based on this localization algorithm. The first one involves a client-server approach where the localization algorithm runs on the server whereas the second one is a standalone architecture and the algorithm runs on the SD card of the mobile device. Experimental results in the considered environment validate the approach for both architectures.


Towards a Data Warehouse Architecture for Managing Big Data Evolution


title: Instant Texture Synthesis by Numbers

abstract: Appearance Space Texture Synthesis (ASTS) provides fast texture synthesis by example. Unfortunately, speed is only achieved at the cost of a long pre-processing step. This is tedious for artists and limits the input resolution. In addition, the aggressively pruned search space results in strong artifacts when synthesizing multiple textures under user guidance (Texture-By-Numbers, TBN). In this thesis, we replace the k-coherence search step used by most modern synthesis algorithms with a new parallel, coherent random walk. We show that this drastically improves the synthesis quality with TBN, while maintaining the parallel nature, speed, and flexibility of the original ASTS runtime. Since it removes the expensive pre-computation of k-coherent candidates, we are able to use larger inputs, and start synthesis much faster. This is essential for artists designing high-quality exemplars.


title: Problems and Review of Line Segmentation of Handwritten Text Document

abstract: Optical character recognition (OCR) is a very popular research area since 1950's. Many people has done a lot of work on various scripts. Line segmentation is a very important step in OCR as the accuracy of the recognition algorithm highly depends on the correct line segmentation. Incorrect line segmentation not only decreases the accuracy but also may lead to some other errors. The objective of this paper is to provide some information of problems that usually comes during line segmentation and also to provide a survey of existing methods of line segmentation on handwritten Indian scripts.


title: Real-Time and Accurate Segmentation of 3-D Point Clouds Based on Gaussian Process Regression

abstract: In LIght Detection And Ranging (LIDAR)-based object detection, accurate object segmentation is of great importance, since segmentation is an essential preprocessing step for other perception tasks, such as classification and tracking. For segmenting objects, most of the previous methods have tried to eliminate the ground first, which typically incurs considerable overhead in computation and inaccuracy in object detection with point clouds gathered by using 3-D LIDARs. However, in many real-time applications, such as automated driving, segmentation should be performed within a specified time, because even a small delay in computation could result in vehicle collisions. In this paper, we propose a real-time and accurate object segmentation algorithm for 3-D point clouds, which does not carry out ground extraction as a first step. In the proposed algorithm, we generate candidate points of objects and find their borders based on the integrated structure of a 2-D grid and an undirected graph, which enables fast processing and yields an accurate segmentation result independent of ground extraction error. In order to enhance segmentation accuracy, we employ Gaussian process, which reduces over-segmentation that separates an object into multiple portions. We apply two types of Gaussian process models to alternately provide cues for merging adjacent over-segmented objects. Experimental results demonstrate that this paper achieves a real-time processing speed and higher segmentation accuracy than previous works in most evaluation metrics. With the application to tracking, we show that the enhanced segmentation accuracy increases the tracking accuracy by 11.4% even in the worst case.


title: A proactive intelligent decision support system for predicting the popularity of online news

abstract: Due to the Web expansion, the prediction of online news popularity is becoming a trendy research topic. In this paper, we propose a novel and proactive Intelligent Decision Support System (IDSS) that analyzes articles prior to their publication. Using a broad set of extracted features (e.g., keywords, digital media content, earlier popularity of news referenced in the article) the IDSS first predicts if an article will become popular. Then, it optimizes a subset of the articles features that can more easily be changed by authors, searching for an enhancement of the predicted popularity probability. Using a large and recently collected dataset, with 39,000 articles from the Mashable website, we performed a robust rolling windows evaluation of five state of the art models. The best result was provided by a Random Forest with a discrimination power of 73%. Moreover, several stochastic hill climbing local searches were explored. When optimizing 1000 articles, the best optimization method obtained a mean gain improvement of 15 percentage points in terms of the estimated popularity probability. These results attest the proposed IDSS as a valuable tool for online news authors.


title: Linguistic Regularities in Continuous Space Word Representations

abstract: Continuous space language models have recently demonstrated outstanding results across a variety of tasks. In this paper, we examine the vector-space word representations that are implicitly learned by the input-layer weights. We find that these representations are surprisingly good at capturing syntactic and semantic regularities in language, and that each relationship is characterized by a relation-specific vector offset. This allows vector-oriented reasoning based on the offsets between words. For example, the male/female relationship is automatically learned, and with the induced vector representations, “King Man + Woman” results in a vector very close to “Queen.” We demonstrate that the word vectors capture syntactic regularities by means of syntactic analogy questions (provided with this paper), and are able to correctly answer almost 40% of the questions. We demonstrate that the word vectors capture semantic regularities by using the vector offset method to answer SemEval-2012 Task 2 questions. Remarkably, this method outperforms the best previous systems.


title: Three-dimensional Facial Anatomy: Structure and Function as It Relates to Injectable Neuromodulators and Soft Tissue Fillers

abstract: BACKGROUND ::: The advent of new neuromodulators and soft-tissue fillers continues to expand the nonsurgical repertoire of the core cosmetic physician. ::: ::: ::: METHODS ::: The authors review relevant facial anatomy as it relates to facial aging and the resultant structural changes that occur. These changes are important for the cosmetic physician to identify and understand in order to perform both safe and effective placement of neuromodulators and soft-tissue fillers. In this anatomic review, the authors review key structures that serve as landmarks for successful treatment using both neuromodulators and fillers. ::: ::: ::: CONCLUSIONS ::: Knowledge of key facial anatomical structures allows the cosmetic physician to perform injection of neuromodulators and soft-tissue fillers in a reproducible manner to optimizing patient safety and results.


title: Cosmetic Clitoridectomy in a 33-Year-Old Woman

abstract: The Female Genital Mutilation Act (2003) in England allows for mental health exceptions for cosmetic surgery resulting from perceived abnormality. Similar legislation exists in other countries. There are no reported cases of clitoridectomy for cosmetic reasons or any discussion in the literature of mental health exceptions to the Act. This is a single case report on a 33-year-old married, heterosexual woman who had already had a cosmetic labiaplasty and was seeking a clitoridectomy for aesthetic reasons. At assessment, there were no psychiatric contra-indications or unrealistic expectations and the patient proceeded with a clitoridectomy. At 9 and 22 months follow-up, she was reassessed and was very pleased with the outcome. There were improvements in the satisfaction with her genital appearance, sexual satisfaction, and quality of life related to body image. Assessments for cosmetic clitoridectomy will continue to be rare, but this case may provide some guidance for practitioners who are confronted with such requests for body modification. However there remains only limited understanding of the motivation for such a request.


title: A Circuit for Energy Harvesting Using On-Chip Solar Cells

abstract: This paper addresses on-chip solar energy harvesting and proposes a circuit that can be employed to generate high voltages from integrated photodiodes. The proposed circuit uses a switched-inductor approach to avoid stacking photodiodes to generate high voltages. The effect of parasitic photodiodes present in integrated circuits (ICs) is addressed and a solution to minimize their impact is presented. The proposed circuit employs two switch transistors and two off-chip components: an inductor and a capacitor. A theoretical analysis of a switched-inductor dc-dc converter is carried out and a mathematical model of the energy harvester is developed. Measurements taken from a fabricated IC are presented and shown to be in good agreement with hardware measurements. Measurement results show that voltages of up to 2.81 V (depending on illumination and loading conditions) can be generated from a single integrated photodiode. The energy harvester circuit achieves a maximum conversion efficiency of 59%.


title: Exploiting Multiple Sources for Open-Domain Hypernym Discovery

abstract: Hypernym discovery aims to extract such noun pairs that one noun is a hypernym of the other. Most previous methods are based on lexical patterns but perform badly on opendomain data. Other work extracts hypernym relations from encyclopedias but has limited coverage. This paper proposes a simple yet effective distant supervision framework for Chinese open-domain hypernym discovery. Given an entity name, we try to discover its hypernyms by leveraging knowledge from multiple sources, i.e., search engine results, encyclopedias, and morphology of the entity name. First, we extract candidate hypernyms from the above sources. Then, we apply a statistical ranking model to select correct hypernyms. A set of novel features is proposed for the ranking model. We also present a heuristic strategy to build a large-scale noisy training data for the model without human annotation. Experimental results demonstrate that our approach outperforms the state-of-the-art methods on a manually labeled test dataset.


title: Dithered Images using GLCM, Wavelet Decomposition Techniques

abstract: When changing the format of an image from simple RGB to HSV, YIQ and Dithered image, the characteristics of image also change. In this paper, the similar images in the above formats are retrieved using statistical and structural retrieving techniques i.e. GLCM (Gray Level Co-occurrence Matrix) and Wavelet Decomposition techniques. The best results are coming for dithered, HSV images by using GLCM technique for feature extraction and by using Wavelet decomposition; HSV images are giving the best results. While other formats also give the correct retrieval but from the accuracy point of view, HSV images are retrieved with better accuracy using both techniques.


title: Semantic trajectory mining for location prediction

abstract: Research on predicting movements of mobile users has attracted a lot of attentions in recent years. Many of those prediction techniques are developed based only on geographic features of mobile users' trajectories. In this paper, we propose a novel approach for predicting the next location of a user's movement based on both the geographic and semantic features of users' trajectories. The core idea of our prediction model is based on a novel cluster-based prediction strategy which evaluates the next location of a mobile user based on the frequent behaviors of similar users in the same cluster determined by analyzing users' common behavior in semantic trajectories. Through a comprehensive evaluation by experiments, our proposal is shown to deliver excellent performance.


title: Stress, Coping, and Social Support Processes: Where Are We? What Next?

abstract: I review existing knowledge, unanswered questions, and new directions in research on stress, coping resource, coping strategies, and social support processes. New directions in research on stressors include examining the differing impacts of stress across a range of physical and mental health outcomes, the carry-overs of stress from one role domain or stage of life into another, the benefits derived from negative experiences, and the determinants of the meaning of stressors. Although a sense of personal control and perceived social support influence health and mental health both directly and as stress buffers, the theoretical mechanisms through which they do so still require elaboration and testing. New work suggests that coping flexibility and structural constraints on individuals' coping efforts may be important to pursue. Promising new directions in social support research include studies of the negative effects of social relationships and of support giving, mutual coping and support-giving dynamics, optimal matches between individuals' needs and support received, and properties of groups which can provide a sense of social support. Qualitative comparative analysis, optimal matching analysis, and event-structure analysis are new techniques which may help advance research in these broad topic areas. To enhance the effectiveness of coping and social support interventions, intervening mechanisms need to be better understood. Nevertheless, the policy implications of stress research are clear and are important given current interest in health care reform in the United States.


title: The Effect of Aggressive and Prosocial Video Games on Aggressive and Prosocial Behavior

abstract: The relationship between video games and aggressive and prosocial behavior has been a topic of special interest for psychologists, as it may have important implications for society. Research has suggested that violent video games increase aggressive behavior, and prosocial video games increase prosocial behavior (Adachi, Good and Willoughby, 2012; Brauer, Greitemeyer and Osswald, 2010). However, the literature on aggressive behavior is somewhat conflicting and the research on prosocial behavior is relatively recent. Therefore, the current study aimed to fill in some of the gaps in the current literature by examining the effect of prosocial and aggressive video games on prosocial and aggressive behavior. Participants were given the Buss-Perry Aggression Questionnaire and Self-Report Altruism Scale to measure individual characteristics. They were then randomly assigned to one of the following three games: Monster Shooter 2: Back to Earth (aggressive), Ants: Mission of Salvation (prosocial) or Monkey Ball 2 (neutral) and, afterwards, engaged in a Prisoner’s Dilemma task in order to determine the game’s influence on their aggressive or prosocial behaviors. It was hypothesized that students assigned to playMonster Shooter 2:Back to Earth would display more aggressive behavior in the Prisoner’s Dilemma task and students assigned to playAnts: Mission of Salvation would exhibit more prosocial behavior in the Prisoner’s Dilemma task. Results indicated that there was no significant relationship between game type and behavior. The implications of this research for the field of psychology are


Markovian Models for Sequential Data


title: Training Quantized Nets: A Deeper Understanding

abstract: Currently, deep neural networks are deployed on low-power portable devices by first training a full-precision model using powerful hardware, and then deriving a corresponding low-precision model for efficient inference on such systems. However, training models directly with coarsely quantized weights is a key step towards learning on embedded platforms that have limited computing resources, memory capacity, and power consumption. Numerous recent publications have studied methods for training quantized networks, but these studies have mostly been empirical. In this work, we investigate training methods for quantized neural networks from a theoretical viewpoint. We first explore accuracy guarantees for training methods under convexity assumptions. We then look at the behavior of these algorithms for non-convex problems, and show that training algorithms that exploit high-precision representations have an important greedy search phase that purely quantized training methods lack, which explains the difficulty of training using low-precision arithmetic.