title: Don’t just say no: differential pathways and pharmacological responses to diverse nitric oxide donors

abstract: Abstract Nitric oxide (NO ) is a gaseous free radical molecule with a short half-life (∼1 s), which can gain or lose an electron into three interchangeable redox-dependent forms, the radical (NO ), the nitrosonium cation (NO+), and nitroxyl anion (HNO). NO acts as an intra and extracellular signaling molecule regulating a wide range of functions in the cardiovascular, immune, and nervous system. NO donors are collectively known by their ability to release NO in vitro and in vivo, being proposed as therapeutic pharmacological tools for the treatment of several pathologies, such as cardiovascular disease. The highly reactive NO molecule is easily oxidized under physiological conditions to N-oxides, nitrate/nitrite and nitrogen dioxide. Different cellular responses are triggered depending on: 1) NO concentration [e.g., nanomolar for heme coordination in the allosteric site of guanylate cyclase (sGC) enzyme]; 2) the type of chemical bound to the nitrosated group (i.e., bound to nitrogen, N-nitro, or bound to sulphur atom, S-nitro) determining post-translational cysteine nitrosation; 3) the time-dependent availability of molecular targets. Classic NO donors are: organic nitrates (e.g., nitroglycerin, or glyceryl trinitrate, GTN; isosorbide mononitrate, ISMN), diazeniumdiolates having a diolate group [or NONOates, e.g., 2-(N,N-diethylamino)-diazenolate-2-oxide], S-nitrosothiols (e.g., S-nitroso glutathione, GSNO; S-nitroso-N-acetylpenicillamine, SNAP) or the organic salt sodium nitroprusside (SNP). In addition, nitroxyl (HNO) donors such as Piloty’s acid and Angeli’s salt can also be considered. The specific NO form released, as well as its differential reactivity to thiols, could act on different molecular targets and should be discussed in the context of: a) the type and amount of NO species determining the sensitivity of molecular targets (e.g., heme coordination, or S-nitrosation); b) the cellular redox state that could gate different effects. Experimental designs should take special care when choosing which NO donors to use, since different outcomes are to be expected. This article will comment recent findings regarding physiological responses involving NO species and their pharmacological modulation with donor drugs, especially in the context of the photic transduction pathways at the hypothalamic circadian clock.


title: Rewilding ecological communities and rewiring ecological networks

abstract: Abstract Rewilding encompasses management actions such as reintroductions and translocations with the purpose of restoring ecological processes and ecosystem functions that were lost when species were locally extirpated. The success of a species introduction is conditioned by multiple factors, in particular, ecological interactions. To predict the fate of the introduced population and the community-level outcomes of the introduction, species interaction patterns need to be considered. Here I propose that ecological network models can help in rewilding projects in at least three ways. First, combining ecological information and probabilistic models it is possible to infer the most likely ways whereby the introduced species will integrate the community and which will be its role in the topology of the food web. Second, by determining the species more likely to interact directly or indirectly with the introduced species, it is possible to identify those species that may affect the success of the introduction and those that are more likely to be affected. Third, by constructing potential interaction networks representing the rewilding scenario, one can infer the possible ways by which the overall structure of the network will change and thus devise more efficient plans to monitor the community. Network models can be an important asset in rewilding, helping in feasibility and risk assessment as well as in monitoring the consequences after species release.


title: Experiences of System-Level Model-Based GUI Testing of an Android Application

abstract: This paper presents experiences in model-based graphical user interface testing of Android applications. We present how model-based testing and test automation was implemented with Android, including how applications were modeled, how tests were designed and executed, and what kind of problems were found in the tested application during the whole process. The main focus is on a case study that was performed with an Android application, the BBC News Widget. Our goal is to present actual data on the experiences and to discuss if advantages can be gained using model-based testing when compared with traditional graphical user interface testing. Another contribution of this paper is a description of a keyword-based test automation tool that was implemented for the Android emulator during the case study. All the models and the tools created or used in this case study are available as open source.


title: JPEG Error Analysis and Its Applications to Digital Image Forensics

abstract: JPEG is one of the most extensively used image formats. Understanding the inherent characteristics of JPEG may play a useful role in digital image forensics. In this paper, we introduce JPEG error analysis to the study of image forensics. The main errors of JPEG include quantization, rounding, and truncation errors. Through theoretically analyzing the effects of these errors on single and double JPEG compression, we have developed three novel schemes for image forensics including identifying whether a bitmap image has previously been JPEG compressed, estimating the quantization steps of a JPEG image, and detecting the quantization table of a JPEG image. Extensive experimental results show that our new methods significantly outperform existing techniques especially for the images of small sizes. We also show that the new method can reliably detect JPEG image blocks which are as small as 8 × 8 pixels and compressed with quality factors as high as 98. This performance is important for analyzing and locating small tampered regions within a composite image.


title: Risks of nutrigenomics and nutrigenetics? What the scientists say

abstract: Nutrigenomics and nutrigenetics (hereafter NGx) have stimulated expectations for beneficial applications in public health and individuals. Yet, the potential achievability of such promise is not without socioethical considerations that challenge NGx implementation. This paper focuses on the opinions of NGx researchers about potential risks raised by NGx. The results of an online survey show that these researchers (n = 126) are fairly confident about the potential benefits of NGx, and that most downplay its potential risks. Researchers in this field do not believe that NGx will reconfigure foods as medication or transform the conception of eating into a health hazard. The majority think that NGx will produce no added burden on individuals to get tested or to remain compliant with NGx recommendations, nor that NGx will threaten individual autonomy in daily food choice. The majority of researchers do not think that NGx will lead to discrimination against and/or stigmatization of people who do not comply with NGx dietary recommendations. Despite this optimism among NGx researchers, we suggest that key risk factors raised by the socioethical context in which NGx applications will be implemented need to be considered.


title: Arabic Phonetic Dictionaries for Speech Recognition

abstract: Phonetic dictionaries are essential components of large-vocabulary speaker-independent speech recognition systems. This paper presents a rule-based technique to generate phonetic dictionaries for a large vocabulary Arabic speech recognition system. The system used conventional Arabic pronunciation rules, common pronunciation rules of Modern Standard Arabic, as well as some common dialectal cases. The paper gives in detail an explanation of these rules as well as their formal mathematical presentation. The rules were used to generate a dictionary for a 5.4 hour corpus of broadcast news. The rules and the phone set were tested and evaluated on an Arabic speech recognition system. The system was trained on 4.3 hours of the 5.4 hours of Arabic broadcast news corpus and tested on the remaining 1.1 hours. The phonetic dictionary contains 23,841 definitions corresponding to about 14232 words. The language model contains both bi-grams and tri-grams. The Word Error Rate (WER) came to 9.0%.


title: Learning travel recommendations from user-generated GPS traces

abstract: The advance of GPS-enabled devices allows people to record their location histories with GPS traces, which imply human behaviors and preferences related to travel. In this article, we perform two types of travel recommendations by mining multiple users' GPS traces. The first is a generic one that recommends a user with top interesting locations and travel sequences in a given geospatial region. The second is a personalized recommendation that provides an individual with locations matching her travel preferences. To achieve the first recommendation, we model multiple users' location histories with a tree-based hierarchical graph (TBHG). Based on the TBHG, we propose a HITS (Hypertext Induced Topic Search)-based model to infer the interest level of a location and a user's travel experience (knowledge). In the personalized recommendation, we first understand the correlation between locations, and then incorporate this correlation into a collaborative filtering (CF)-based model, which predicts a user's interests in an unvisited location based on her locations histories and that of others. We evaluated our system based on a real-world GPS trace dataset collected by 107 users over a period of one year. As a result, our HITS-based inference model outperformed baseline approaches like rank-by-count and rank-by-frequency. Meanwhile, we achieved a better performance in recommending travel sequences beyond baselines like rank-by-count. Regarding the personalized recommendation, our approach is more effective than the weighted Slope One algorithm with a slightly additional computation, and is more efficient than the Pearson correlation-based CF model with the similar effectiveness.


title: Effect of Annealing on Microstructure and Tensile Properties of 5052/AZ31/5052 Clad Sheets

abstract: Three-layered 5052Al/AZ31Mg/5052Al (5052/AZ31/5052) clad sheets were fabricated by four-pass rolling and annealed under different conditions. Under the optimal annealing condition, homogeneous and equiaxial grains with an average AZ31 grain size of 5.24 µm were obtained and the maximum values of ultimate tensile strength and elongation of the clad sheet reached 230 MPa and 18%, respectively. Electron backscatter diffraction analysis showed that the AZ31 layer had a typical rolling texture with its c-axis parallel to the normal direction. The fraction of low-angle grain boundaries in the 5052 layer was nearly four times more than that in the AZ31 layer because of different deformation extent and recrystallization driving forces. The textures of Al3Mg2 and Mg17Al12 were similar to that of 5052 because of the deformation coordination during the rolling and recrystallization process. The orientation relationship between Mg17Al12 and AZ31 seemed to be (110) Mg17Al12//(10−11) AZ31.


title: Towards a Non-2PC Transaction Management in Distributed Database Systems

abstract: Shared-nothing architecture has been widely used in distributed databases to achieve good scalability. While it offers superior performance for local transactions, the overhead of processing distributed transactions can degrade the system performance significantly. The key contributor to the degradation is the expensive two-phase commit (2PC) protocol used to ensure atomic commitment of distributed transactions. In this paper, we propose a transaction management scheme called LEAP to avoid the 2PC protocol within distributed transaction processing. Instead of processing a distributed transaction across multiple nodes, LEAP converts the distributed transaction into a local transaction. This benefits the processing locality and facilitates adaptive data repartitioning when there is a change in data access pattern. Based on LEAP, we develop an online transaction processing (OLTP) system, L-Store, and compare it with the state-of-the-art distributed in-memory OLTP system, H-Store, which relies on the 2PC protocol for distributed transaction processing, and H^L-Store, a H-Store that has been modified to make use of LEAP. Results of an extensive experimental evaluation show that our LEAP-based engines are superior over H-Store by a wide margin, especially for workloads that exhibit locality-based data accesses.


title: Flight data recorder: monitoring persistent-state interactions to improve systems management

abstract: Mismanagement of the persistent state of a system---all the executable files, configuration settings and other data that govern how a system functions---causes reliability problems, security vulnerabilities, and drives up operation costs. Recent research traces persistent state interactions---how state is read, modified, etc.---to help troubleshooting, change management and malware mitigation, but has been limited by the difficulty of collecting, storing, and analyzing the 10s to 100s of millions of daily events that occur on a single machine, much less the 1000s or more machines in many computing environments. We present the Flight Data Recorder (FDR) that enables always-on tracing, storage and analysis of persistent state interactions. FDR uses a domain-specific log format, tailored to observed file system workloads and common systems management queries. Our lossless log format compresses logs to only 0.5--0.9 bytes per interaction. In this log format, 1000 machine-days of logs---over 25 billion events---can be analyzed in less than 30 minutes. We report on our deployment of FDR to 207 production machines at MSN, and show that a single centralized collection machine can potentially scale to collecting and analyzing the complete records of persistent state interactions from 4000+ machines. Furthermore, our tracing technology is shipping as part of the Windows Vista OS.


title: Academic procrastination of undergraduates: Low self-efficacy to self-regulate predicts higher levels of procrastination

abstract: Abstract This article reports two studies exploring the academic procrastination of 456 undergraduates. Study 1 explores the relationships among academic procrastination, self-regulation, academic self-efficacy, self-esteem, and self-efficacy for self-regulation. Results reveal that although other self-variables are related to procrastination, self-efficacy for self-regulation is most predictive of procrastination tendencies. Study 2 examines academic and motivation characteristics of “negative procrastinators,” the undergraduates who are most adversely influenced by procrastination. The 25% of 195 participants in Study 2 who were classified as negative procrastinators had significantly lower GPAs, higher levels of daily and task procrastination, lower predicted and actual class grades, and lower self-efficacy for self-regulation. After controlling for GPA, daily procrastination and self-efficacy for self-regulation significantly predicted the negative impact of procrastination. The article concludes with a discussion of the importance that self-efficacy for self-regulation holds for procrastination research, and with suggestions for practitioners who work with students who are adversely affected by procrastination.


title: The View-Update Problem for Indefinite Databases

abstract: This paper introduces and studies a declarative framework for updating views over indefinite databases. An indefinite database is a database with null values that are represented, following the standard database approach, by a single null constant. The paper formalizes views over such databases as indefinite deductive databases, and defines for them several classes of database repairs that realize view-update requests. Most notable is the class of constrained repairs. Constrained repairs change the database "minimally" and avoid making arbitrary commitments. They narrow down the space of alternative ways to fulfill the view-update request to those that are grounded, in a certain strong sense, in the database, the view and the view-update request.


title: Art of Generator Synchronizing

abstract: This paper presents a review of power-system synchronization, proper sync-check coordination, and fast synchronization methods. When paralleling two sources, it is crucial that the interconnecting circuit breaker be closed only when both sources are in voltage, frequency, and phase coincidence. An operator can synchronize manually or use one of the latest, state-of-the-art automatic synchronizers (ANSI/IEEE device 25A) and sync-check relays (ANSI/IEEE device 25) to automate closing. Generator and bus synchronization share most principles—with some important differences—for each type of synchronization. For generation plants, the method of generator synchronization selected depends greatly on the plant configuration and operating mode. For bus-line applications, the synchronizing method selected depends on the power-system “stiffness,” motor loads, and whether a wye-delta transformer is located between the line and bus.


title: Biologically inspired design: process and products

abstract: Biologically inspired engineering design uses analogies to biological systems to develop solutions for engineering problems. We conducted a study of biologically inspired design in the context of an interdisciplinary introductory course on biologically inspired engineering design in Fall of 2006. The goals of this study were to understand the process of biologically inspired engineering design and to provide insight into biologically inspired design as a type of design activity. This paper provides a descriptive account of biologically inspired design processes and products, and summarizes our main observations: 1) designers use two distinct starting points for biologically inspired design; 2) regular patterns of practice emerge in biologically inspired design; and 3) certain errors occur regularly in the design process.


title: Failure Mode and Effect Analysis Under Uncertainty: An Integrated Multiple Criteria Decision Making Approach

abstract: Nowadays, failure mode and effect analysis (FMEA) is a widely used reliability analysis technique for products, processes, and services because of its relative simplicity. The conventional risk priority number method, however, has been criticized as having many limitations, such as in the assessment of failure modes, the weighting of risk factors, and the prioritization of failure modes. In this paper, we aim to develop a new risk priority model for FMEA by integrating hesitant 2-tuple linguistic term sets and an extended QUALIFLEX approach. The concept of hesitant 2-tuple linguistic term sets is first presented to express various uncertainties in the assessment information of FMEA team members. Borrowing the idea of grey relational analysis, a multiple objective optimization model is constructed to determine the relative weights of risk factors with incomplete weight information. The extended QUALIFLEX approach with an inclusion comparison method is then suggested to determine the risk ranking of failure modes identified in FMEA. Finally, the practicality and effectiveness of the proposed FMEA are demonstrated through a case study, and the results show that the new risk priority approach is useful and flexible for handling complicated FMEA problems and can yield a reasonable and credible priority ranking of failure modes.


title: A Multiscale Wavelet Solver with O(n) Complexity

abstract: In this paper, we use the biorthogonal wavelets recently constructed by Dahlke and Weinreich to implement a highly efficient procedure for solving a certain class of one-dimensional problems, (?21/?x21)u = f,I ? Z, I > 0. For these problems, the discrete biorthogonal wavelet transform allows us to set up a system of wavelet-Galerkin equations in which the scales are uncoupled, so that a true multiscale solution procedure may be formulated. We prove that the resulting stiffness matrix is in fact an almost perfectly diagonal matrix (the original aim of the construction was to achieve a block diagonal structure) and we show that this leads to an algorithm whose cost is O(n). We also present numerical results which demonstrate that the multiscale biorthogonal wavelet algorithm is superior to the more conventional single scale orthogonal wavelet approach both in terms of speed and in terms of convergence.


title: An introduction to toothpaste - its purpose, history and ingredients

abstract: Toothpaste is a paste or gel to be used with a toothbrush to maintain and improve oral health and aesthetics. Since their introduction several thousand years ago, toothpaste formulations have evolved considerably - from suspensions of crushed egg shells or ashes to complex formulations with often more than 20 ingredients. Among these can be compounds to combat dental caries, gum disease, malodor, calculus, erosion and dentin hypersensitivity. Furthermore, toothpastes contain abrasives to clean and whiten teeth, flavors for the purpose of breath freshening and dyes for better visual appeal. Effective toothpastes are those that are formulated for maximum bioavailability of their actives. This, however, can be challenging as compromises will have to be made when several different actives are formulated in one phase. Toothpaste development is by no means complete as many challenges and especially the poor oral substantivity of most active ingredients are yet to overcome.


title: Illumination invariant face recognition based on improved Local Binary Pattern

abstract: Local Binary Pattern (LBP) is a kind of discriminative texture descriptor for characterization of face patterns. However, the value of LBP operator is greatly changed under non-monotonic intensity transformations. As a result, the recognition performance of LBP descriptor for face images with significant illumination variations is severely dropped. In this paper, a novel illumination-invariant face recognition algorithm that applies LBP descriptor is proposed to overcome the performance degradation of LBP descriptor caused by varying illumination conditions. In our proposed algorithm, illumination variation is first compensated by the so called Dynamic Morphological Quotient Image (DMQI) which generates quotient image after morphological filtering. Then, powerful LBP operator is applied to the DMQI to derive a distinctive and robust representation for face patterns in images. We compared the recognition accuracy of the proposed algorithm with that of traditional PCA-based, LDA-based and raw LBP-based method on Yale face dataset B which contains face images with severe lighting variations. Evaluation result demonstrates that our proposed algorithm outperforms the PCA-based, LDA-based and raw LBP-based method by 22.5%, 17.4%, and 5%, respectively, in terms of the recognition accuracy on the first rank. Another advantage of our algorithm is its computational simplicity. It only takes 0.48 seconds on a Pentium IV 3.0G CPU, so it is very suitable for real-time manipulation.


title: What's up with software metrics? - A preliminary mapping study

abstract: Background: Many papers are published on the topic of software metrics but it is difficult to assess the current status of metrics research. Aim: This paper aims to identify trends in influential software metrics papers and assess the possibility of using secondary studies to integrate research results. Method: Search facilities in the SCOPUS tool were used to identify the most cited papers in the years 2000-2005 inclusive. Less cited papers were also selected from 2005. The selected papers were classified according factors such as to main topic, goal and type (empirical or theoretical or mixed). Papers classified as ''Evaluation studies'' were assessed to investigate the extent to which results could be synthesized. Results: Compared with less cited papers, the most cited papers were more frequently journal papers, and empirical validation or data analysis studies. However, there were problems with some empirical validation studies. For example, they sometimes attempted to evaluate theoretically invalid metrics and fail to appreciate the importance of the context in which data are collected. Conclusions: This paper, together with other similar papers, confirms that there is a large body of research related to software metrics. However, software metrics researchers may need to refine their empirical methodology before they can answer useful empirical questions.


title: The Hero's Farewell: What Happens When CEOs Retire

abstract: How a business replaces its chief executive is a vital issue for the firm's future. Unless the transfer of power is managed effectively, the business may be thrown into turmoil--with profound implications not only for the CEO, but also for the other employees, the shareholders, and sometimes even the industry as a whole. Yet not enough is known about this crucial moment in the history of a company. ___Filled with inside stories from corporate boardrooms and fresh conceptual perspectives, The Hero's Farewell describes in rich detail the factors that affect executive succession, focusing for the first time on the critical role a CEO's departure style plays in helping or hindering the transfer of power. Sonnenfeld has obtained candid interviews with fifty prominent retired chief executives from corporations such as ATT Generals, who leave reluctantly and spend their retirement planning a comeback; Ambassadors, who retain close ties with their former firms; and Governors, who willingly serve a limited term and leave to pursue new interests. Understanding how these leadership styles affect the transition process can enable both CEOs and their firms to better prepare for the changes to come. ___Executive successions are rarely easy, especially today when, as the author points out, many of our business leaders have become heroes. These heroes are often unwilling to surrender control of a firm to which they have dedicated their lives. Retirement deprives them of a clear sense of purpose and indeed, for many, it is equated with abdication of responsibility and even death. Yet some retiring leaders exit constructively, starting new lives for themselves and leaving behind a firm that is capably managed. ___Capturing the human drama of these departures and succession battles, The Hero's Farewell will fascinate anyone intrigued by power struggles in large corporations. More important, in outlining the ways to smooth out the inevitable transfers of power that corporations must face, it provides essential information for all top executives and especially for CEOs.


title: Ontology-Aware Token Embeddings for Prepositional Phrase Attachment

abstract: Type-level word embeddings use the same set of parameters to represent all instances of a word regardless of its context, ignoring the inherent lexical ambiguity in language. Instead, we embed semantic concepts (or synsets) as defined in WordNet and represent a word token in a particular context by estimating a distribution over relevant semantic concepts. We use the new, context-sensitive embeddings in a model for predicting prepositional phrase(PP) attachments and jointly learn the concept embeddings and model parameters. We show that using context-sensitive embeddings improves the accuracy of the PP attachment model by 5.4% absolute points, which amounts to a 34.4% relative reduction in errors.


title: Permanent scatterers in SAR interferometry

abstract: Temporal and geometrical decorrelation often prevents SAR interferometry from being an operational tool for surface deformation monitoring and topographic profile reconstruction. Moreover, atmospheric disturbances can strongly compromise the accuracy of the results. The authors present a complete procedure for the identification and exploitation of stable natural reflectors or permanent scatterers (PSs) starting from long temporal series of interferometric SAR images. When, as it often happens, the dimension of the PS is smaller than the resolution cell, the coherence is good even for interferograms with baselines larger than the decorrelation one, and all the available images of the ESA ERS data set can be successfully exploited. On these pixels, submeter DEM accuracy and millimetric terrain motion detection can be achieved, since atmospheric phase screen (APS) contributions can be estimated and removed. Examples are then shown of small motion measurements, DEM refinement, and APS estimation and removal in the case of a sliding area in Ancona, Italy. ERS data have been used.


Gender and language-variety Identification with MicroTC


title: MHz GaN-based interleaved CRM bi-directional buck/boost converter with coupled inductor

abstract: This paper presents a high frequency high efficiency GaN device based interleaved critical current mode (CRM) bi-directional buck/boost converter with a coupled inductor. The switching frequency is continually driven to MHz range with GaN devices due to their small switching loss and driving loss, which greatly reduce the size of the passive components. The coupled inductor further reduces the core volume due to certain DC flux reductions. The equivalent inductance and the impact of the coupled inductor on the CRM buck-boost converter are analyzed in detail. The resonant period at CRM is less with a coupled inductor than with a non-coupled inductor, which is beneficial for high frequency operation. The soft-switching range and the circulating energy are both improved using a coupled inductor at CRM. Experimental results validate the theoretical analysis, and the coupled inductor prototype efficiency is 98.5% at 1MHz, which is 0.3% higher than a prototype with a non-coupled inductor.


title: Class representative autoencoder for low resolution multi-spectral gender classification

abstract: Gender is one of the most common attributes used to describe an individual. It is used in multiple domains such as human computer interaction, marketing, security, and demographic reports. Research has been performed to automate the task of gender recognition in constrained environment using face images, however, limited attention has been given to gender classification in unconstrained scenarios. This work attempts to address the challenging problem of gender classification in multi-spectral low resolution face images. We propose a robust Class Representative Autoencoder model, termed as AutoGen for the same. The proposed model aims to minimize the intra-class variations while maximizing the inter-class variations for the learned feature representations. Results on visible as well as near infrared spectrum data for different resolutions and multiple databases depict the efficacy of the proposed model. Comparative results with existing approaches and two commercial off-the-shelf systems further motivate the use of class representative features for classification.


title: Magnetic fields: how is plant growth and development impacted?

abstract: This review provides detailed insight on the effects of magnetic fields on germination, growth, development, and yield of plants focusing on ex vitro growth and development and discussing the possible physiological and biochemical responses. The MFs considered in this review range from the nanoTesla (nT) to geomagnetic levels, up to very strong MFs greater than 15 Tesla (T) and also super-weak MFs (near 0 T). The theoretical bases of the action of MFs on plant growth, which are complex, are not discussed here and thus far, there is limited mathematical background about the action of MFs on plant growth. MFs can positively influence the morphogenesis of several plants which allows them to be used in practical situations. MFs have thus far been shown to modify seed germination and affect seedling growth and development in a wide range of plants, including field, fodder, and industrial crops; cereals and pseudo-cereals; grasses; herbs and medicinal plants; horticultural crops (vegetables, fruits, ornamentals); trees; and model crops. This is important since MFs may constitute a non-residual and non-toxic stimulus. In addition to presenting and summarizing the effects of MFs on plant growth and development, we also provide possible physiological and biochemical explanations for these responses including stress-related responses of plants, explanations based on dia-, para-, and ferromagnetism, oriented movements of substances, and cellular and molecular changes.


title: Smartphone Addiction Inventory (SPAI): Psychometric properties and confirmatory factor analysis

abstract: The Smartphone Addiction Inventory (SPAI) was developed to assess smartphone addiction in Taiwanese university students. The purpose of the current research is to evaluate the factor structure and psychometric properties of the SPAI in Italian university students. A total of 485 university students (29.3% boys) completed the SPAI and the Young's Internet Addiction Test (IAT).The proposed four-factor model of the SPAI-I was tested by computing CFA, and the results of the goodness of fit indices indicated an ambiguous solution. An exploratory-confirmatory cross validation strategy was applied for a better trimming of SPAI-I factorial structure. Results suggested a five-factor solution that explained 53% of the total variance (Time Spent, Compulsivity, Daily Life Interference, Craving, and Sleep Interference). Excellent internal consistency and five factor correlations were reported for the SPAI-I. This five-factor model showed good convergent validity with the Internet Addiction Disorder Test. Considering that evidence shows that overuse of a smartphone can be associated with adverse and problematic effects, the SPAI-I should be useful for determining its potential for the use in future research in Europe and for comparing the evaluation of smartphone overuse of young European people with other international Asiatic studies that have incorporated the SPAI. The SPAI was tested using factorial analysis procedures in a large sample.The exploratory factor analysis revealed five factors rather than the original four.The SPAI-I showed satisfactory psychometric properties.The SPAI-I is suitable for measuring smartphone addiction.The SPAI-I may help to promote unified research in the smartphone addiction field.


title: An approach for systems identification using artificial inteligence

abstract: In this paper we presented an approach for systems identification using artificial intelligence. To demonstrate its practical applicability, we tested it in the medical field in the interpretation of electrophoresis tests, but this application can be implemented in different areas of interest e.g. in business, management, advertising, etc. Extensive research has been performed exploiting the robust properties of genetic algorithms and demonstrating their capabilities across a large range of problems. We can use this application in many activities areas (medical, business, etc.) The resulting recommendations may constitute the basis of future work in the use of genetic algorithms in business area.


Deep-space Optical Terminals ( DOT ) Ground Laser Transmitter ( GLT ) Trades and Conceptual Point Design


title: A Proposal to add Explicit Congestion Notification ( ECN ) to IPv 6 and to TCP

abstract: This note describes a proposed addition of ECN (Explicit Congestion Notification) to IP. TCP is currently the dominant transport protocol used in the Internet. We begin by describing TCP's use of packet drops as an indication of congestion. Next we argue that with the addition of active queue management (e.g., RED) to the Internet infrastructure, where routers detect congestion before the queue overflows, routers are no longer limited to packet drops as an indication of congestion. Routers could instead set a Congestion Experienced (CE) bit in the packet header of packets from ECN-capable transport protocols. We describe when the CE bit would be set in the routers, and describe what modifications would be needed to TCP to make it ECN-capable. Modifications to other transport protocols (e.g., unreliable unicast or multicast, reliable multicast, other reliable unicast transport protocols) could be considered as those protocols are developed and advance through the standards process.


Self-Adaptive Double Bootstrapped DDPG


title: SySeVR: A Framework for Using Deep Learning to Detect Software Vulnerabilities

abstract: The detection of software vulnerabilities (or vulnerabilities for short) is an important problem that has yet to be tackled, as manifested by many vulnerabilities reported on a daily basis. This calls for machine learning methods to automate vulnerability detection. Deep learning is attractive for this purpose because it does not require human experts to manually define features. Despite the tremendous success of deep learning in other domains, its applicability to vulnerability detection is not systematically understood. In order to fill this void, we propose the first systematic framework for using deep learning to detect vulnerabilities. The framework, dubbed Syntax-based, Semantics-based, and Vector Representations (SySeVR), focuses on obtaining program representations that can accommodate syntax and semantic information pertinent to vulnerabilities. Our experiments with 4 software products demonstrate the usefulness of the framework: we detect 15 vulnerabilities that are not reported in the National Vulnerability Database. Among these 15 vulnerabilities, 7 are unknown and have been reported to the vendors, and the other 8 have been"silently"patched by the vendors when releasing newer versions of the products.


title: Robot Learning in Homes: Improving Generalization and Reducing Dataset Bias

abstract: Data-driven approaches to solving robotic tasks have gained a lot of traction in recent years. However, most existing policies are trained on large-scale datasets collected in curated lab settings. If we aim to deploy these models in unstructured visual environments like people's homes, they will be unable to cope with the mismatch in data distribution. In such light, we present the first systematic effort in collecting a large dataset for robotic grasping in homes. First, to scale and parallelize data collection, we built a low cost mobile manipulator assembled for under 3K USD. Second, data collected using low cost robots suffer from noisy labels due to imperfect execution and calibration errors. To handle this, we develop a framework which factors out the noise as a latent variable. Our model is trained on 28K grasps collected in several houses under an array of different environmental conditions. We evaluate our models by physically executing grasps on a collection of novel objects in multiple unseen homes. The models trained with our home dataset showed a marked improvement of 43.7% over a baseline model trained with data collected in lab. Our architecture which explicitly models the latent noise in the dataset also performed 10% better than one that did not factor out the noise. We hope this effort inspires the robotics community to look outside the lab and embrace learning based approaches to handle inaccurate cheap robots.


Allergic contact dermatitis caused by panthenol: a rare but relevant sensitizer


title: A haplotype map of genomic variations and genome-wide association studies of agronomic traits in foxtail millet (Setaria italica)

abstract: Foxtail millet (Setaria italica) is an important grain crop that is grown in arid regions. Here we sequenced 916 diverse foxtail millet varieties, identified 2.58 million SNPs and used 0.8 million common SNPs to construct a haplotype map of the foxtail millet genome. We classified the foxtail millet varieties into two divergent groups that are strongly correlated with early and late flowering times. We phenotyped the 916 varieties under five different environments and identified 512 loci associated with 47 agronomic traits by genome-wide association studies. We performed a de novo assembly of deeply sequenced genomes of a Setaria viridis accession (the wild progenitor of S. italica) and an S. italica variety and identified complex interspecies and intraspecies variants. We also identified 36 selective sweeps that seem to have occurred during modern breeding. This study provides fundamental resources for genetics research and genetic improvement in foxtail millet.


title: FDA Approves First 3-D Mammography Imaging System

abstract: Breast imaging has gone 3-D. The FDA announced today the approval of Hologic Inc.’s Selenia Dimensions System, the first X-ray mammography device that provides 3-D images for breast cancer screening and diagnosis.


title: Small-signal analysis and control design of asymmetrical half-bridge DC-DC converters

abstract: This paper presents the small-signal modeling, dynamic analysis, and control design of the asymmetrical half-bridge dc-dc converter that employs a clamp capacitor and a magnetizing inductor to accommodate pulsewidth-modulated operation with asymmetrical duty ratios. The circuit averaging technique is applied to extract the small-signal dynamics of the power stage, and a graphical loop-gain method is used to design the feedback compensation and analyze the closed-loop performance of the converter. The distinctive power-stage dynamics of the converter are addressed and design guidelines for voltage feedback compensation are established. The results of the control design and closed-loop analysis are substantiated by experiments using an experimental converter.


title: Learning from small sample sets by combining unsupervised meta-training with CNNs

abstract: This work explores CNNs for the recognition of novel categories from few examples. Inspired by the transferability properties of CNNs, we introduce an additional unsupervised meta-training stage that exposes multiple top layer units to a large amount of unlabeled real-world images. By encouraging these units to learn diverse sets of low-density separators across the unlabeled data, we capture a more generic, richer description of the visual world, which decouples these units from ties to a specific set of categories. We propose an unsupervised margin maximization that jointly estimates compact high-density regions and infers low-density separators. The low-density separator (LDS) modules can be plugged into any or all of the top layers of a standard CNN architecture. The resulting CNNs significantly improve the performance in scene classification, fine-grained recognition, and action recognition with small training samples.


title: How and why consciousness arises: Some considerations from physics and physiology

abstract: We offer a scientific approach to the philosophical ‘hard problem’ of consciousness, as formulated by David Chalmers in this journal. Our treatment is based upon two recent insights concerning (1) the endogenous nature of consciousness and (2) the minimal thermodynamic conditions for being alive. We suggest that a combination of these insights specifies sufficient conditions for attributing feeling to being.


title: Design of omnidirectional mobile robots with ACROBAT wheel mechanisms

abstract: In this paper, we study the design of omnidirectional mobile robots with Active-Caster RObotic drive with BAll Transmission (ACROBAT). ACROBAT system has been developed by the authors group which realizes mechanical coordination of wheel and steering motions for creating caster behaviors without computer calculations. A motion in the specific direction relative to a robot body is fully depends on the motion of a specific motor. This feature gives a robot designer to build an omnidirectional mobile robot propelled by active-casters with no redundant actuation with a simple control. A controller of the robot becomes as simple as that for omni-wheeled robotic bases. Namely 3DOF of the omnidirectional robot is controlled by three motors using a simple and constant kinematics. ACROBAT includes a unique dual-ball transmission to transmit traction power to rotate and orient a drive wheel with distributing velocity components to wheel and steering axes in an appropriate ratio. Therefore a sensor for measuring a wheel orientation and calculations for velocity distributions are totally removed from a conventional control system. To build an omnidirectional vehicle by ACROBAT, the significant feature is some multiple drive shafts can be driven by a common motor which realizes non-redundant actuation of the robotic platform. A kinematic model of the proposed robot with ACROBAT is analyzed and a mechanical condition for realizing a non-redundant actuation is derived. Based on the kinematic model and the mechanical condition, computer simulations of the mechanism are performed. A prototype two-wheeled robot with two ACROBATs is designed and built to verify the availability of the proposed system. In the experiments, the prototype robot shows successful omnidirectional motions with a simple and constant kinematics based control.


title: DETECTING PERSONS USING HOUGH CIRCLE TRANSFORM IN SURVEILLANCE VIDEO

abstract: Robust person detection in real-world images is interesting and important for a variety of applications, such as visual surveillance. We address the task of detecting persons in elevator surveillance scenes in this paper. To get more passengers in the lift car, the camera usually installed at the corner of ceiling. However, the high and space of lift car are limited, which makes person occluded by each other or some parts of body invisible in captured images. In this paper, we propose a novel approach to detect head contours, which includes three main steps: pre-processing, head contour detection and post-processing. Hough circle transform is adopted in the second stage, which is robust to discontinuous boundaries in circle detection. Proposed pre-processing and post-processing methods are efficient to remove false alarms on background or body part. Experimental results show our proposed approach is time saving and has better person detection results than some other methods.


title: Does Print Size Matter for Reading? A Review of Findings from Vision Science and Typography

abstract: The size and shape of printed symbols determine the legibility of text. In this paper we focus on print size because of its crucial role in understanding reading performance and its significance in the history and contemporary practice of typography. We present evidence supporting the hypothesis that the distribution of print sizes in historical and contemporary publications falls within the psychophysically defined range of fluent print size — the range over which text can be read at maximum speed. The fluent range extends over a factor of 10 in angular print size (x-height) from approximately 0.2° to 2°. Assuming a standard reading distance of 40 cm (16 inches), the corresponding physical x-heights are 1.4 mm (4 points) and 14 mm (40 points). We provide new data on the distributions of print sizes in published books and newspapers and in type founders' specimens, and consider factors influencing these distributions. We discuss theoretical concepts from vision science concerning visual size coding that help inform our understanding of historical and modern typographical practices. While economic, social, technological and artistic factors influence type design and selection, we conclude that properties of human visual processing play a dominant role in constraining the distribution of print sizes in common use.


title: Language to Code: Learning Semantic Parsers for If-This-Then-That Recipes

abstract: Using natural language to write programs is a touchstone problem for computational linguistics. We present an approach that learns to map natural-language descriptions of simple “if-then” rules to executable code. By training and testing on a large corpus of naturally-occurring programs (called “recipes”) and their natural language descriptions, we demonstrate the ability to effectively map language to code. We compare a number of semantic parsing approaches on the highly noisy training data collected from ordinary users, and find that loosely synchronous systems perform best.


title: Effective Straggler Mitigation: Attack of the Clones

abstract: Small jobs, that are typically run for interactive data analyses in datacenters, continue to be plagued by disproportionately long-running tasks called stragglers. In the production clusters at Facebook and Microsoft Bing, even after applying state-of-the-art straggler mitigation techniques, these latency sensitive jobs have stragglers that are on average 8 times slower than themedian task in that job. Such stragglers increase the average job duration by 47%. This is because current mitigation techniques all involve an element of waiting and speculation. We instead propose full cloning of small jobs, avoiding waiting and speculation altogether. Cloning of small jobs only marginally increases utilization because workloads show that while the majority of jobs are small, they only consume a small fraction of the resources. The main challenge of cloning is, however, that extra clones can cause contention for intermediate data. We use a technique, delay assignment, which efficiently avoids such contention. Evaluation of our system, Dolly, using production workloads shows that the small jobs speedup by 34% to 46% after state-of-the-artmitigation techniques have been applied, using just 5% extra resources for cloning.


title: Intelligent traffic management system

abstract: This paper presents an intelligent traffic management system using RFID technology. The system is capable of providing practically important traffic data which would aid in reducing the travel time for the users. Also, it can be used for other purposes like tracing of stolen cars, vehicles that evade traffic signals/tickets, toll collection or vehicle taxes etc. The system consists of a passive tag, an RFID reader, a microcontroller, a GPRS module, a high-speed server with a database system and a user module. Using RFID technology, this system collects the required data and calculates average speed of vehicles on each road of a city under consideration. It then transmits the acquired data i.e., average speed calculated at various junctions to the central computation server which calculates the time taken by a vehicle to travel in a particular road. Through Dijkstra's algorithm, the central server computes the fastest route to all the nodes (junctions) considering each node as the initial point in the city. Therefore, the system creates a map of shortest time paths of the whole city. This data is accessed by users through an interface module placed in their vehicles.


title: Topographical relationship of the greater palatine artery and the palatal spine. Significance for periodontal surgery.

abstract: AIM ::: The aims of this study were to (1) identify the branching pattern and course of the greater palatine artery (GPA), (2) carry out a morphological analysis of the palatal bony prominence that divides the medial and lateral grooves and (3) characterize the topographical relationships between these two structures. ::: ::: ::: METHODS ::: Thirty-six hemimaxillae were studied with the aid of a surgical microscope to elucidate the GPA. A further 25 dry skulls were examined to establish the morphology of the palatal spine. ::: ::: ::: RESULTS ::: The most common GPA branching pattern was type I (41.7%, 15 sides), which gave off the medial and canine branches after the bony prominence. The distances from the CEJ to the lateral branch of the GPA were 9.04 ± 2.93 mm (canine), 11.12 ± 1.89 mm (first premolar), 13.51 ± 2.08 mm (second premolar), 13.76 ± 2.86 mm (first molar) and 13.91 ± 2.20 mm (second molar). The palatal spine was frequently observed as the bony prominence (66.3%, 57 sides), and was located at 6.49 ± 1.76 mm from the greater palatine foramen, with a length of 10.42 ± 2.45 mm. There was no a correlation between the bony prominence shape and the GPA branching pattern. ::: ::: ::: CONCLUSIONS ::: These results could provide the reference data regarding the topography of the GPA for periodontal surgery.


title: Face Detection with the Faster R-CNN

abstract: The Faster R-CNN has recently demonstrated impressive results on various object detection benchmarks. By training a Faster R-CNN model on the large scale WIDER face dataset, we report state-of-the-art results on two widely used face detection benchmarks, FDDB and the recently released IJB-A.


title: Learning Robust Hash Codes for Multiple Instance Image Retrieval

abstract: In this paper, for the first time, we introduce a multiple instance (MI) deep hashing technique for learning discriminative hash codes with weak bag-level supervision suited for large-scale retrieval. We learn such hash codes by aggregating deeply learnt hierarchical representations across bag members through a dedicated MI pool layer. For better trainability and retrieval quality, we propose a two-pronged approach that includes robust optimization and training with an auxiliary single instance hashing arm which is down-regulated gradually. We pose retrieval for tumor assessment as an MI problem because tumors often coexist with benign masses and could exhibit complementary signatures when scanned from different anatomical views. Experimental validations on benchmark mammography and histology datasets demonstrate improved retrieval performance over the state-of-the-art methods.


title: Identification and features extraction of systolic and diastolic murmurs

abstract: This paper presents an automatic heart murmur detection and delineation algorithm. Multiple cardiac cycles with murmurs are processed in two steps. In the first step, the first and second heart sounds are detected and identified to label cardiac cycles as the first heart sound (S 1 ), followed by a systole period, the second heart sound (S 2 ), a diastole period, and then repeated with S 1 and the rest. In the second step, potential murmur occurs within systole or diastole are identified and delineated with generated features such as timing, duration, shape configuration and pitch frequency, which are useful measures to assist clinical diagnosis. The performance of the proposed algorithm was examined and confirmed with six different types of diastolic and systolic murmurs of clinical data.


title: Diagnosis of Chronic Kidney Disease using effective classification and feature selection technique

abstract: The massive amount of data collected by healthcare sector can be effective for analysis, diagnosis and decision making if it is mined properly. Hidden information extracted from the voluminous data can provide help and remedy to handle critical healthcare situations. Chronic kidney disease is a fatal illness of kidney which can be prevented with early correct predictions and proper precautions. Data mining of the information collected from previously diagnosed patients opened up a new phase of medical advancement. However, specific techniques must be executed to accomplish better consequence. In this manuscript the capability of the classification of Support Vector Machine, Decision tree, Naive Bayes and K-Nearest Neighbor algorithm, in analyzing the Chronic Kidney Disease dataset collected from UCI repository, was investigated to predict the presence of kidney disease. Data set has been analyzed in terms of accuracy, Root Mean Squared Error, Mean Absolute Error and Receiver Operating Characteristic curve. In the present study, Decision tree shows promising results when implemented through WEKA data mining tool. Ranking algorithm provides vital improvements in classifications with proper number attributes. 15 proves to be the magic number for selecting attributes for the given dataset resulting highest percent of improvement in accuracy.


title: A default Bayesian hypothesis test for mediation

abstract: In order to quantify the relationship between multiple variables, researchers often carry out a mediation analysis. In such an analysis, a mediator (e.g., knowledge of a healthy diet) transmits the effect from an independent variable (e.g., classroom instruction on a healthy diet) to a dependent variable (e.g., consumption of fruits and vegetables). Almost all mediation analyses in psychology use frequentist estimation and hypothesis-testing techniques. A recent exception is Yuan and MacKinnon (Psychological Methods, 14, 301–322, 2009), who outlined a Bayesian parameter estimation procedure for mediation analysis. Here we complete the Bayesian alternative to frequentist mediation analysis by specifying a default Bayesian hypothesis test based on the Jeffreys–Zellner–Siow approach. We further extend this default Bayesian test by allowing a comparison to directional or one-sided alternatives, using Markov chain Monte Carlo techniques implemented in JAGS. All Bayesian tests are implemented in the R package BayesMed (Nuijten, Wetzels, Matzke, Dolan, & Wagenmakers, 2014).


title: In vitro investigation of the cellular toxicity of boron nitride nanotubes.

abstract: Nanotubes present one of the most promising opportunities in nanotechnology with a plethora of applications in nanoelectronics, mechanical engineering, as well as in biomedical technology. Due to their structure and some physical properties, boron nitride (BN) nanotubes (BNNTs) possess several advantages over carbon nanotubes (CNTs), and they are now commercially produced and used on a large scale. The human and environmental exposure to BN nanomaterials is expected to increase in the near future, and their biological responses need to be examined. Using complementary assays, we have extensively investigated the effects of BNNTs on the viability and metabolic status of different cell types: on the one hand, the effects on cells present in the lung alveoli, and on the other hand, on human embryonic kidney (HEK) cells. Our results indicate that BNNTs are cytotoxic for all cell types studied and, in most cases, are more cytotoxic than CNTs in their pristine (p-CNT) and functionalized (f-CNT) form. However, t...


title: Measuring safety climate: identifying the common features

abstract: Abstract In UK industry, particularly in the energy sector, there has been a movement away from ‘lagging’ measures of safety based on retrospective data, such as lost time accidents and incidents, towards ‘leading’ or predictive assessments of the safety climate of the organisation or worksite. A number of different instruments have been developed by industrial psychologists for this purpose, resulting in a proliferation of scales with distinct developmental histories. Reviewing the methods and results from a sample of industrial surveys, the thematic basis of 18 scales used to assess safety climate is examined. This suggests that the most typically assessed dimensions relate to management (72% of studies), the safety system (67%), and risk (67%), in addition themes relating to work pressure and competence appear in a third of the studies.


title: A measurement study of google play

abstract: Although millions of users download and use third-party Android applications from the Google Play store, little information is known on an aggregated level about these applications. We have built PlayDrone, the first scalable Google Play store crawler, and used it to index and analyze over 1,100,000 applications in the Google Play store on a daily basis, the largest such index of Android applications. PlayDrone leverages various hacking techniques to circumvent Google's roadblocks for indexing Google Play store content, and makes proprietary application sources available, including source code for over 880,000 free applications. We demonstrate the usefulness of PlayDrone in decompiling and analyzing application content by exploring four previously unaddressed issues: the characterization of Google Play application content at large scale and its evolution over time, library usage in applications and its impact on application portability, duplicative application content in Google Play, and the ineffectiveness of OAuth and related service authentication mechanisms resulting in malicious users being able to easily gain unauthorized access to user data and resources on Amazon Web Services and Facebook.


title: Approaches for refining heterologous protein production in filamentous fungi

abstract: Fungi combine the advantages of a microbial system such as a simple fermentability with the capability of secreting proteins that are modified according to a general eukaryotic scheme. Filamentous fungi such as Aspergillus niger efficiently secrete genuine proteins but the secretion of recombinant proteins turned out be a difficult task. Aspergillus niger is an attractive organism because of its high secretion capacity and is frequently used as a model organism. Whereas high production yields can be obtained when homologous proteins are expressed, much lower amounts are obtained with the production of heterologous proteins. To fully exploit the potential of filamentous fungi, understanding of the molecular genetics, their physiology, and the glycosylation metabolism has to be investigated and clarified in more detail. This review summarizes recent developments in heterologous protein production by filamentous fungi and also generalizes the possibilities of improving the protein production by various genetic and bioprocessing approaches, thereby easing recognition of filamentous fungi as a relevant and reliable expression platform.


title: An overview of vertical handover decision strategies in heterogeneous wireless networks

abstract: In the next generation of wireless networks, mobile users can move between heterogeneous networks, using terminals with multiple access interfaces and non-real-time or real-time services. The most important issue in such environment is the Always Best Connected (ABC) concept allowing the best connectivity to applications anywhere at anytime. To answer ABC requirement, various vertical handover decision strategies have been proposed in the literature recently, using advanced tools and proven concepts. In this paper, we give an overview of the most interesting and recent strategies. We classify it into five categories for which we present their main characteristics. We also compare each one with the others in order to introduce our vertical handover decision approach.


Electromagnetic brain mapping - IEEE Signal Processing Magazine


title: AN IMPROVED ROUND ROBIN CPU SCHEDULING ALGORITHM WITH VARYING TIME QUANTUM

abstract: Process management is one of the important tasks performed by the operating system. The performance of the system depends on theCPU schedulingalgorithms. The main aim of the CPU scheduling algorithms is to minimize waiting time, turnaround time, response time and context switching and maximizing CPU utilization. First-Come-First-Served (FCFS) Round Robin (RR), Shortest Job First (SJF) and, Priority Scheduling are some popular CPU scheduling algorithms . In time shared systems, Round Robin CPU scheduling is the preferred choice. In Round Robin CPU scheduling,performance of the system depends on the choice of the optimal time quantum. This paper presents an improved Round Robin CPU scheduling algorithm coined enhancing CPU per formance using the features of Shortest Job First and Round Robin scheduling with varying time quantum. The proposed algorithm is experimentally proven better than conventional RR.The simulation results showthat the waiting time and turnaround time havebeen reduced in the proposed algorithm compared to traditional RR.


title: PLoRa: a passive long-range data network from ambient LoRa transmissions

abstract: This paper presents PLoRa, an ambient backscatter design that enables long-range wireless connectivity for batteryless IoT devices. PLoRa takes ambient LoRa transmissions as the excitation signals, conveys data by modulating an excitation signal into a new standard LoRa "chirp" signal, and shifts this new signal to a different LoRa channel to be received at a gateway faraway. PLoRa achieves this by a holistic RF front-end hardware and software design, including a low-power packet detection circuit, a blind chirp modulation algorithm and a low-power energy management circuit. To form a complete ambient LoRa backscatter network, we integrate a light-weight backscatter signal decoding algorithm with a MAC-layer protocol that work together to make coexistence of PLoRa tags and active LoRa nodes possible in the network. We prototype PLoRa on a four-layer printed circuit board, and test it in various outdoor and indoor environments. Our experimental results demonstrate that our prototype PCB PLoRa tag can backscatter an ambient LoRa transmission sent from a nearby LoRa node (20 cm away) to a gateway up to 1.1 km away, and deliver 284 bytes data every 24 minutes indoors, or every 17 minutes outdoors. We also simulate a 28-nm low-power FPGA based prototype whose digital baseband processor achieves 220 μW power consumption.


title: Discussions on control loop design in average current mode control [PWM DC/DC power convertors]

abstract: Large control output ripple in ACMC (average current mode control) PWM DC/DC converters exposes challenges in accurate modeling and design. In this paper, superposition models are utilized to perform control design analysis for ACMC PWM DC/DC converters. The ACMC DC/DC PWM converter system is converted to a cascade control structure and a step-by-step control design algorithm is then proposed. The control design algorithms differ from existing, conventional algorithms because they carefully compensate for newly discovered feed forward influence in the model.


title: A Fundamental Frequency 120-GHz SiGe BiCMOS Distance Sensor With Integrated Antenna

abstract: This paper describes the first fundamental frequency single-chip transceiver operating at -band. The low-IF monostatic transceiver integrates on a single chip two 120-GHz voltage-controlled oscillators (VCOs), a 120-GHz divide-by-64 chain, two in-phase/quadrature (IQ) receivers with phase-calibration circuitry, a variable gain transmit amplifier, an antenna directional coupler, a patch antenna, bias circuitry, a transmit power detector, and a temperature sensor. A quartz antenna resonator with 6-dBi gain and simulated 50% efficiency is placed directly above the on-chip patch to transmit and receive the 120-GHz signals. The circuit with the above-integrated-circuit antenna occupies an area of 2.2 mm 2.6 mm, consumes 900 mW from 1.2- and 1.8-V supplies, and was wire-bonded in an open-lid 7 mm 7 mm quad-flat no-leads package. Some transceiver performance parameters were characterized on the packaged chip, mounted on an evaluation board, while others, such as receiver noise figure and VCO phase noise at the 120-GHz output were measured on circuit breakouts. The AMOS-varactor VCOs have a typical phase noise of at 1-MHz offset and a tuning range of 115.2-123.9 GHz. The receiver gain and the transmitter output power are each adjustable over a range of 15 dB with a maximum transmitter output power of 3.6 dBm. The receiver IQ phase difference, measured at the IF outputs of the packaged transceiver, is adjustable from 70° to 110°, while the amplitude imbalance remains less than 1 dB. The receiver breakout gain and double-sideband noise figure are 10.5-13 and 10.5-11.5 dB, respectively, with an input compression point of . Several experiments were conducted through the air over distances of up to 2.1 m with a focusing lens placed above the packaged chip.


title: A Patent Analysis for Sustainable Technology Management

abstract: Technology analysis (TA) is an important issue in the management of technology. Most R&D (Research & Development) policies have depended on diverse TA results. Traditional TA results have been obtained through qualitative approaches such as the Delphi expert survey, scenario analysis, or technology road mapping. Although they are representative methods for TA, they are not stable because their results are dependent on the experts’ knowledge and subjective experience. To solve this problem, recently many studies on TA have been focused on quantitative approaches, such as patent analysis. A patent document has diverse information of developed technologies, and thus, patent is one form of objective data for TA. In addition, sustainable technology has been a big issue in the TA fields, because most companies have their technological competitiveness through the sustainable technology. Sustainable technology is a technology keeping the technological superiority of a company. So a country as well as a company should consider sustainable technology for technological competition and continuous economic growth. Also it is important to manage sustainable technology in a given technology domain. In this paper, we propose a new patent analysis approach based on statistical analysis for the management of sustainable technology (MOST). Our proposed methodology for the MOST is to extract a technological structure and relationship for knowing the sustainable technology. To do this, we develop a hierarchical diagram of technology for finding the causal relationships among technological keywords of a given domain. The aim of the paper is to select the sustainable technology and to create the hierarchical technology paths to sustainable technology for the MOST. This contributes to planning R&D strategy for the sustainability of a company. To show how the methodology can be applied to real problem, we perform a case study using retrieved patent documents related to telematics technology.


title: Computerized processing of EEG – EOG – EMG artifacts for multi-centric studies in EEG oscillations and event-related potentials

abstract: The aim of this study is to present a package including standard software for the electroencephalographic (EEG), electro-oculographic (EOG) and electromyographic (EMG) preliminary data analysis, which may be suitable to standardize the results of many EEG research centers studies (i.e. multi-centric studies ) especially focused on event- related potentials. In particular, our software package includes (semi)automatic procedures for (i) EOG artifact detection and correction, (ii) EMG analysis, (iii) EEG artifact analysis, (iv) optimization of the ratio between artifact- free EEG channels and trials to be rejected. The performances of the software package on EOG-EEG-EMG data related to cognitive-motor tasks were evaluated with respect to the preliminary data analysis performed by two expert electroencephalographists (gold standard). Due to its extreme importance for multi-centric EEG studies, we compared the performances of two representative 'regression' methods for the EOG correction in time and frequency domains. The aim was the selection of the most suitable method in the perspective of a multi-centric EEG study. The results showed an acceptable agreement of approximately 95% between the human and software behaviors, for the detection of vertical and horizontal EOG artifacts, the measurement of hand EMG responses for a cognitive-motor paradigm, the detection of involuntary mirror movements, and the detection of EEG artifacts. Furthermore, our results indicated a particular reliability of a 'regression' EOG correction method operating in time domain(i.e. ordinary least squares). These results suggest that such a software package could be used for multi-centric EEG studies. 2002 Elsevier Science B.V. All rights reserved.


title: Vietnamese food recognition using convolutional neural networks

abstract: In this paper, we investigate Vietnamese food recognition problem and compare different methods by using traditional hand-crafted features and convolutional neural networks (CNNs). To this aim, we first introduce one dataset including five popular Vietnamese dishes with more than 2000 images. We then conduct various experiments on that dataset and analyze the performance of each approach. The experiments show that CNNs outperform the hand-crafted techniques by a significant margin.


title: Ladder Variational Autoencoders

abstract: Variational Autoencoders are powerful models for unsupervised learning. However deep models with several layers of dependent stochastic variables are difficult to train which limits the improvements obtained using these highly expressive models. We propose a new inference model, the Ladder Variational Autoencoder, that recursively corrects the generative distribution by a data dependent approximate likelihood in a process resembling the recently proposed Ladder Network. We show that this model provides state of the art predictive log-likelihood and tighter log-likelihood lower bound compared to the purely bottom-up inference in layered Variational Autoencoders and other generative models. We provide a detailed analysis of the learned hierarchical latent representation and show that our new inference model is qualitatively different and utilizes a deeper more distributed hierarchy of latent variables. Finally, we observe that batch normalization and deterministic warm-up (gradually turning on the KL-term) are crucial for training variational models with many stochastic layers.


title: Growth and morphological development of laboratory-reared larval and juvenile climbing perch Anabas testudineus

abstract: Morphological development, including fin and labyrinth organ, body proportions and pigmentation, in laboratory-reared larval and juvenile climbing perch Anabas testudineus was described and behavioral features under rearing condition were observed. Body lengths (BL) of larvae and juveniles were 1.9 ± 0.1 (mean ± SD) mm just after hatching (day-0), 8.7 ± 1.3 mm on day-19, reaching 18.4 ± 2.1 mm on day-35 after hatching. Aggregate fin ray numbers attained full complements in juveniles larger than 8.3 mm BL. Preflexion larvae started feeding on day-2 following formation of the upper and lower jaws, the yolk being completely absorbed by day-7 after hatching. Teeth appeared in flexion larvae larger than 5 mm BL on day-6, with cannibalism starting shortly after and continuing with further growth. Melanophores on the body increased with growth, a large dark spot developing on the lateral midline around caudal margin of the body in the postflexion and juvenile stages. The labyrinth organ differentiated in postflexion larvae larger than 7.2 mm BL on day-16, with air-breathing starting at the same time. Body proportions attained constant in postflexion larvae larger than 7.0 mm BL, and habitat of fish shifted from bottom to mid-layer. With the exception of fin ray numbers, the above morphological developments corresponded to behavioral shifts that occurred in the postflexion stage (ca. 7 mm BL), their subsequent continuity illustrating that the species possessed most juvenile-equivalent functions from ca. 7 mm BL.


title: Analysis of Efficient CNN Design Techniques for Semantic Segmentation

abstract: Majority of CNN architecture design is aimed at achieving high accuracy in public benchmarks by increasing the complexity. Typically, they are over-specified by a large margin and can be optimized by a factor of 10-100x with only a small reduction in accuracy. In spite of the increase in computational power of embedded systems, these networks are still not suitable for embedded deployment. There is a large need to optimize for hardware and reduce the size of the network by orders of magnitude for computer vision applications. This has led to a growing community which is focused on designing efficient networks. However, CNN architectures are evolving rapidly and efficient architectures seem to lag behind. There is also a gap in understanding the hardware architecture details and incorporating it into the network design. The motivation of this paper is to systematically summarize efficient design techniques and provide guidelines for an application developer. We also perform a case study by benchmarking various semantic segmentation algorithms for autonomous driving.


title: On Distributionally Robust Chance Constrained Programs with Wasserstein Distance

abstract: This paper studies a distributionally robust chance constrained program (DRCCP) with Wasserstein ambiguity set, where the uncertain constraints should be satisfied with a probability at least a given threshold for all the probability distributions of the uncertain parameters within a chosen Wasserstein distance from an empirical distribution. In this work, we investigate equivalent reformulations and approximations of such problems. We first show that a DRCCP can be reformulated as a conditional value-at-risk constrained optimization problem, and thus admits tight inner and outer approximations. We also show that a DRCCP of bounded feasible region is mixed integer representable by introducing big-M coefficients and additional binary variables. For a DRCCP with pure binary decision variables, by exploring the submodular structure, we show that it admits a big-M free formulation, which can be solved by a branch and cut algorithm. Finally, we present a numerical study to illustrate the effectiveness of the proposed formulations.


title: Agility literature review: Classifications, training and testing

abstract: Abstract At present, no agreement on a precise definition of agility within the sports science community exists. The term is applied to a broad range of sport contexts, but with such great inconsistency, it further complicates our understanding of what trainable components may enhance agility. A new definition of agility is proposed: “a rapid whole-body movement with change of velocity or direction in response to a stimulus”. Agility has relationships with trainable physical qualities such as strength, power and technique, as well as cognitive components such as visual-scanning techniques, visual-scanning speed and anticipation. Agility testing is generally confined to tests of physical components such as change of direction speed, or cognitive components such as anticipation and pattern recognition. New tests of agility that combine physical and cognitive measures are encouraged.


title: The impact of head movements on EEG and contact impedance: An adaptive filtering solution for motion artifact reduction

abstract: Designing and developing a comfortable and convenient EEG system for daily usage that can provide reliable and robust EEG signal, encompasses a number of challenges. Among them, the most ambitious is the reduction of artifacts due to body movements. This paper studies the effect of head movement artifacts on the EEG signal and on the dry electrode-tissue impedance (ETI), monitored continuously using the imec's wireless EEG headset. We have shown that motion artifacts have huge impact on the EEG spectral content in the frequency range lower than 20 Hz. Coherence and spectral analysis revealed that ETI is not capable of describing disturbances at very low frequencies (below 2 Hz). Therefore, we devised a motion artifact reduction (MAR) method that uses a combination of a band-pass filtering and multi-channel adaptive filtering (AF), suitable for real-time MAR. This method was capable of substantially reducing artifacts produced by head movements.


Verbal and Nonverbal Discourse Planning


title: [Pseudo-exfoliation syndrome as a risk factor for vitreous loss in extra-capsular cataract extraction. The Erlangen Eye Information Group].

abstract: A prospective study for evaluation of the pseudoexfoliation syndrome (PES) as a risk factor in extracapsular surgery was undertaken from February 1986 to June 1988. The study included 2042 planned extracapsular cataract extractions performed by 12 surgeons. Seventy-two eyes revealed PES (3.5%). Frequency of PES increased with age (p less than 0.005). The male-female ratio of PES was 1:1.6, and eyes of average axial length showed PES more frequently than myopic or hyperopic eyes, although these differences were not significant statistically. The incidence of vitreous loss was 11.1% in eyes with PES and 1.6% in eyes without PES, a sevenfold increase that was statistically significant (p less than 0.0001). Rupture of the posterior lens capsule was observed in 4.2% of PES eyes compared to 2.8% of eyes without PES (no significance). The pseudoexfoliation syndrome is not a harmless anomaly, but a potentially disastrous disease of the anterior segment of the eye. As precautionary measures we recommend thorough biomicroscopic examination preoperatively for signs of PES, iridotomy or sector iridectomy in cases with poorly dilating pupils, very gentle manipulation intraoperatively, and posterior chamber lens sulcus implantation in eyes with zonular defects or phacodonesis.


title: Adversarial Patch

abstract: We present a method to create universal, robust, targeted adversarial image patches in the real world. The patches are universal because they can be used to attack any scene, robust because they work under a wide variety of transformations, and targeted because they can cause a classifier to output any target class. These adversarial patches can be printed, added to any scene, photographed, and presented to image classifiers; even when the patches are small, they cause the classifiers to ignore the other items in the scene and report a chosen target class.


title: A Novel High-Speed and Energy Efficient 10-Transistor Full Adder Design

abstract: In this paper, we propose a novel full adder design using as few as ten transistors per bit. Compared with other low-gate-count full adder designs using pass transistor logic, the proposed design features lower operating voltage, higher computing speed and lower energy (power delay product) operation. The design adopts inverter buffered xor/xnor designs to alleviate the threshold voltage loss problem commonly encountered in pass transistor logic design. This problem usually prevents the full adder design from operating in low supply voltage or cascading directly without extra buffering. The proposed design successfully embeds the buffering circuit in the full adder design and the transistor count is minimized. The improved buffering helps the design operate under lower supply voltage compared with existing works. It also enhances the speed performance of the cascaded operation significantly while maintaining the performance edge in energy consumption. For performance comparison, both dc andperformances of the proposed design against various full adder designs are evaluated via extensive HSPICE simulations. The simulation results, based on TSMC 2P4M 0.35-mum process models, indicate that the proposed design has the lowest working Vdd and highest working frequency among all designs using ten transistors. It also features the lowest energy consumption per addition among these designs. In addition, the performance edge of the proposed design in both speed and energy consumption becomes even more significant as the word length of the adder increases


title: Vertically aligned carbon nanotube-ruthenium dioxide core-shell cathode for non-aqueous lithium-oxygen batteries

abstract: Abstract Exploitation of hierarchical porous carbons is increasingly attractive for high-capacity lithium (Li)-oxygen (O 2 ) battery cathodes. However, their practical applications in non-aqueous electrolytes are limited by poor rechargeability, primarily due to the decomposition of carbon electrode and electrolyte. In this work, we report a vertically aligned carbon nanotube (VACNT)-ruthenium dioxide (RuO 2 ) core-shell (VACNT@RuO 2 ) cathode for non-aqueous Li-O 2 batteries. The cathode is fabricated with VACNT as the core material and hydrous RuO 2 as the shell material, which eliminates the direct contact between the carbon and nucleophilic reactive intermediate species in the electrolyte. In comparison with the VACNT cathode, the VACNT@RuO 2 cathode presents a superior rate capability (3.3-fold less reduction in capacity) and cycling stability (sustainable for 100 cycles), with a maximum capacity as large as 13.2 mAh cm −2 (6600 mAh g electrode −1 ) at 1.0 mA cm −2 . The proposed cathode exhibiting a binder-free and hierarchical core-shell structure is a promising candidate for rechargeable non-aqueous Li-O 2 batteries.


title: Passive radar from history to future

abstract: The history of passive radar dates back to the early days of radar in 1935 when the Daventry experiment was conducted in the UK. It continues in WW II with the German Klein Heidelberg passive radar and receives new interest today, as passive covert radar (PCR) systems like Silent Sentry and Homeland Alerter 100 are ready for operation. The future of PCR will strongly depend on the availability of transmitters of opportunity such as FM-radio and digital broadcast networks.


title: Parallel-plate-waveguide luneburg lens through a holey plate metasurface

abstract: This paper presents a parallel plate waveguide (PPW) Luneburg lens designed using an array of subwavelength circular holes etched on a metallic plate printed on a dielectric substrate. Variation of the hole radius modulates the effective permittivity. The lens operates in the Ka band of frequencies in a quasi-TEM mode. Easy to implement and low cost, the lens could be a good candidate as a lens-like beamformer for low loss multiple beam applications.


title: Learning Object Affordances: From Sensory--Motor Coordination to Imitation

abstract: Affordances encode relationships between actions, objects, and effects. They play an important role on basic cognitive capabilities such as prediction and planning. We address the problem of learning affordances through the interaction of a robot with the environment, a key step to understand the world properties and develop social skills. We present a general model for learning object affordances using Bayesian networks integrated within a general developmental architecture for social robots. Since learning is based on a probabilistic model, the approach is able to deal with uncertainty, redundancy, and irrelevant information. We demonstrate successful learning in the real world by having an humanoid robot interacting with objects. We illustrate the benefits of the acquired knowledge in imitation games.


title: Closest-point problems

abstract: A number of seemingly unrelated problems involving the proximity of N points in the plane are studied, such as finding a Euclidean minimum spanning tree, the smallest circle enclosing the set, k nearest and farthest neighbors, the two closest points, and a proper straight-line triangulation. For most of the problems considered a lower bound of O(N log N) is shown. For all of them the best currently-known upper bound is O(N2) or worse. The purpose of this paper is to introduce a single geometric structure, called the Voronoi diagram, which can be constructed rapidly and contains all of the relevant proximity information in only linear space. The Voronoi diagram is used to obtain O(N log N) algorithms for all of the problems.


title: Eurographics/ IEEE-VGTC Symposium on Visualization (2006)

abstract: We describe a terrain rendering algorithm for spherical terrains based on clipmaps. It leverages the high geometry throughput of current GPU to render large static triangle sets. The vertices are displaced by a height map texture. Our main contribution is mapping of texture coordinates to calculate the height map sample position based on the static vertex offset and the variable view position.


title: Query understanding through knowledge-based conceptualization

abstract: The goal of query conceptualization is to map instances in a query to concepts defined in a certain ontology or knowledge base. Queries usually do not observe the syntax of a written language, nor do they contain enough signals for statistical inference. However, the available context, i.e., the verbs related to the instances, the adjectives and attributes of the instances, do provide valuable clues to understand instances. In this paper, we first mine a variety of relations among terms from a large web corpus and map them to related concepts using a probabilistic knowledge base. Then, for a given query, we conceptualize terms in the query using a random walk based iterative algorithm. Finally, we examine our method on real data and compare it to representative previous methods. The experimental results show that our method achieves higher accuracy and efficiency in query conceptualization.


title: Measuring Urban Social Diversity Using Interconnected Geo-Social Networks

abstract: Large metropolitan cities bring together diverse individuals, creating opportunities for cultural and intellectual exchanges, which can ultimately lead to social and economic enrichment. In this work, we present a novel network perspective on the interconnected nature of people and places, allowing us to capture the social diversity of urban locations through the social network and mobility patterns of their visitors. We use a dataset of approximately 37K users and 42K venues in London to build a network of Foursquare places and the parallel Twitter social network of visitors through check-ins. We define four metrics of the social diversity of places which relate to their social brokerage role, their entropy, the homogeneity of their visitors and the amount of serendipitous encounters they are able to induce. This allows us to distinguish between places that bring together strangers versus those which tend to bring together friends, as well as places that attract diverse individuals as opposed to those which attract regulars. We correlate these properties with wellbeing indicators for London neighbourhoods and discover signals of gentrification in deprived areas with high entropy and brokerage, where an influx of more affluent and diverse visitors points to an overall improvement of their rank according to the UK Index of Multiple Deprivation for the area over the five-year census period. Our analysis sheds light on the relationship between the prosperity of people and places, distinguishing between different categories and urban geographies of consequence to the development of urban policy and the next generation of socially-aware location-based applications.


title: Fault tree analysis for the communication of a fleet formation flight of UAVs

abstract: Fleet flight of drones has been an interesting topic in the last years due to the advantages of the cooperative fleet of UAVs in both civilians and military applications. Fault Tree Analysis (FTA) is a well-known technique used to identify the causes that lead to the failure of a top event. In this paper, we aim to analyze the reliability of communications between drones or between drones and ground station control using FTA. Improving the reliability of the communication system would guarantee the success of the mission. Data for basic events has been gathered from the Nonelectronic Parts Reliability Data Publication (NPRD). The probability of occurrence of communication failure between the drones is calculated taking into consideration the presence or absence of a crash.


title: Electronic structure of (Zn,Cd)(S,Se)-based polytype superlattices.

abstract: The local empirical pseudopotential theory in the S-matrix implementation was used to study the structure of (Zn,Cd)(S,Se) polytypes considered as natural heterocrystalline superlattices. Hexagonal polytype superlattices of (Zn,Cd)(S,Se) are predicted to be characterized by direct band gaps and have minibands derived from evanescent bulk states, which collapse into the heterocrystalline interface bound states or resonances above the valence-band edge. There is a linear relationship between various band gaps and hexagonality h. The superlattice wave function is strongly localized in the cubic portions of superlattice unit cell.


Accuracy and Fairness for Juvenile Justice Risk Assessments


A comprehensive grasp taxonomy


title: New Approaches to Encrypt and Decrypt Data in Image using Cryptography and Steganography Algorithm

abstract: Nowadays, network has important roles for transferring data accurately and fast from source to a destination. The data is not secure enough to transfer highly confidential. The security of information has become one of the principle challenges of resource sharing with data communication over computer network. Cryptography and Steganography are two methods for protecting data from intruders while transferring over an open channel network. Cryptography is a method to encrypt data and steganography is the art and science of hiding secret message in a cover image. In this paper a Hash Least Significant Bit (H-LSB) with Affine cipher algorithm has been proposed for providing more security to data in a network environment. First we encrypt the data with the new cryptography algorithm and then embed in the image. Eight bits of the secret message are divided into 3, 3, 2 and embedding into the RGB pixels values of the cover image respectively. A hash function is used to select the particular position of insertion in LSB bits. This system allows a message sender to select keys to encrypt the secret message before embedding into the image and a receiver is used the keys to decrypt the message. Receiver can be decrypted the encrypt message with incorrect the keys but to a different form from the original message. This system has the ability to provide better security while transferring the secret message from one end to the other end in network environment.


Using eTesting methods in CS education


title: A solar-powered hand-launchable UAV for low-altitude multi-day continuous flight

abstract: This paper presents the conceptual design, detailed development and flight testing of AtlantikSolar, a 5.6m-wingspan solar-powered Low-Altitude Long-Endurance (LALE) Unmanned Aerial Vehicle (UAV) designed and built at ETH Zurich. The UAV is required to provide perpetual endurance at a geographic latitude of 45°N in a 4-month window centered around June 21st. An improved conceptual design method is presented and applied to maximize the perpetual flight robustness with respect to local meteorological disturbances such as clouds or winds. Airframe, avionics hardware, state estimation and control method development for autonomous flight operations are described. Flight test results include a 12-hour flight relying solely on batteries to replicate night-flight conditions. In addition, we present flight results from Search-And-Rescue field trials where a camera and processing pod were mounted on the aircraft to create high-fidelity 3D-maps of a simulated disaster area.


title: HIN2Vec: Explore Meta-paths in Heterogeneous Information Networks for Representation Learning

abstract: In this paper, we propose a novel representation learning framework, namely HIN2Vec, for heterogeneous information networks (HINs). The core of the proposed framework is a neural network model, also called HIN2Vec, designed to capture the rich semantics embedded in HINs by exploiting different types of relationships among nodes. Given a set of relationships specified in forms of meta-paths in an HIN, HIN2Vec carries out multiple prediction training tasks jointly based on a target set of relationships to learn latent vectors of nodes and meta-paths in the HIN. In addition to model design, several issues unique to HIN2Vec, including regularization of meta-path vectors, node type selection in negative sampling, and cycles in random walks, are examined. To validate our ideas, we learn latent vectors of nodes using four large-scale real HIN datasets, including Blogcatalog, Yelp, DBLP and U.S. Patents, and use them as features for multi-label node classification and link prediction applications on those networks. Empirical results show that HIN2Vec soundly outperforms the state-of-the-art representation learning models for network data, including DeepWalk, LINE, node2vec, PTE, HINE and ESim, by 6.6% to 23.8% of $micro$-$f_1$ in multi-label node classification and 5% to 70.8% of $MAP$ in link prediction.


title: Integrated Method for Personal Thermal Comfort Assessment and Optimization through Users’ Feedback, IoT and Machine Learning: A Case Study †

abstract: Thermal comfort has become a topic issue in building performance assessment as well as energy efficiency. Three methods are mainly recognized for its assessment. Two of them based on standardized methodologies, face the problem by considering the indoor environment in steady-state conditions (PMV and PPD) and users as active subjects whose thermal perception is influenced by outdoor climatic conditions (adaptive approach). The latter method is the starting point to investigate thermal comfort from an overall perspective by considering endogenous variables besides the traditional physical and environmental ones. Following this perspective, the paper describes the results of an in-field investigation of thermal conditions through the use of nearable and wearable solutions, parametric models and machine learning techniques. The aim of the research is the exploration of the reliability of IoT-based solutions combined with advanced algorithms, in order to create a replicable framework for the assessment and improvement of user thermal satisfaction. For this purpose, an experimental test in real offices was carried out involving eight workers. Parametric models are applied for the assessment of thermal comfort; IoT solutions are used to monitor the environmental variables and the users' parameters; the machine learning CART method allows to predict the users' profile and the thermal comfort perception respect to the indoor environment.


title: Parallel Pencil Drawing Stylization via Structure-Aware Optimization

abstract: This paper presents a non-photorealistic rendering technique for stylizing a photograph in the pencil drawing style, which can well preserve the fine structure of the original image. We first construct a structure map from the anti-color image of the original image to model the detailed underlying fine structure of the original image, and generate a coarse pencil drawing image with line integral convolution. Then we refine the coarse pencil drawing image with the structure map for enrich the structure information. The presented algorithm is highly parallel allowing a real-time performance with GPU implementation. Experimental results show that our approach can produce more attractive and impressive pencil drawing effects with a variety of photographs.