title: Australian Aboriginal and Torres Strait Islander-focused primary healthcare social and emotional wellbeing research: a systematic review protocol

abstract: Research with a focus on Aboriginal and Torres Strait Islander Australian’s (hereafter referred to as Indigenous1) needs is crucial to ensure culturally appropriate evidence-based strategies are developed to improve health. However, concerns surrounding this research exist, arising from some previous research lacking community consultation, resulting in little community benefit or infringing on important cultural values. Values and Ethics: Guidelines for Ethical conduct in Aboriginal and Torres Strait Islander Health Research (hereafter referred to as Values and Ethics), developed by The National Health and Medical Research Council of Australia in 2003, is the ethical standard for Indigenous-focused health research. Researchers must address its Values in research design and conduct. However, its impact on research processes is unclear. Local Protocols should also be considered. This review aims to systematically examine practices related to Values and Ethics, Local Protocols and the processes of conducting Indigenous-focused primary healthcare research in collaboration with external researchers. The following electronic databases and grey literature will be searched (2003 to current): MEDLINE, EMBASE, CINAHL, Informit and HealthInfoNet—an Indigenous-specific research and program website. Indigenous-focused research will be included. Research must be conducted in one or more primary healthcare services, in collaboration with external researchers and with a focus on social and emotional well being. One reviewer will review titles and abstracts to remove obviously irrelevant research articles. Full-text research articles will be retrieved and independently examined by two reviewers. Data and quality assessment will be completed by one reviewer and verified by a second reviewer. Quality will be assessed using modified versions of established quality assessment tools. This review will provide information on research processes and the impact of Values and Ethics on Indigenous-focused primary healthcare research, informing communities and primary healthcare staff around research practices, and researchers and policy makers of strengths and weaknesses of practice. PROSPERO CRD42015024994


title: A humanoid robot drawing human portraits

abstract: This paper presents the creation of a robot capable of drawing artistic portraits. The application is purely entertaining and based on existing tools for face detection and image reconstruction, as well as classical tools for trajectory planning of a 4 DOFs robot arm. The innovation of the application lies in the care we took to make the whole process as human-like as possible. The robot's motions and its drawings follow a style characteristic to humans. The portraits conserve the esthetics features of the original images. The whole process is interactive, using speech recognition and speech synthesis to conduct the scenario


title: Generalized Interactions Using Virtual Tools within the Spring Framework: Cutting

abstract: We present schemes for real-time generalized mesh cutting. Starting with the a basic example, we describe the details of implementing cutting on single and multiple surface objects as well as hybrid and volumetric meshes using virtual tools with single and multiple cutting surfaces. These methods have been implemented in a robust surgical simulation environment allowing us to model procedures ranging from animal dissection to cleft lip correction.


title: Homomorphic encryption from codes

abstract: We propose a new homomorphic encryption scheme based on the hardness of decoding under independent random noise from certain affine families of codes. Unlike in previous lattice-based homomorphic encryption schemes, where the message is hidden in the noisy part of the ciphertext, our scheme carries the message in the affine part of the transformation and applies noise only to achieve security. Our scheme can tolerate noise of arbitrary magnitude, as long as the noise vector has sufficiently small hamming weight (and its entries are independent). ::: Our design achieves "proto-homomorphic" properties in an elementary manner: message addition and multiplication are emulated by pointwise addition and multiplication of the ciphertext vectors. Moreover, the extremely simple nature of our decryption makes the scheme easily amenable to bootstrapping. However, some complications are caused by the inherent presence of noticeable encryption error. Our main technical contribution is the development of two new techniques for handling this error in the homomorphic evaluation process. ::: We also provide a definitional framework for homomorphic encryption that may be useful elsewhere.


title: Lane-Level Integrity Provision for Navigation and Map Matching With GNSS, Dead Reckoning, and Enhanced Maps

abstract: Lane-level positioning and map matching are some of the biggest challenges for navigation systems. Additionally, in safety applications or in those with critical performance requirements (such as satellite-based electronic fee collection), integrity becomes a key word for the navigation community. In this scenario, it is clear that a navigation system that can operate at the lane level while providing integrity parameters that are capable of monitoring the quality of the solution can bring important benefits to these applications. This paper presents a pioneering novel solution to the problem of combined positioning and map matching with integrity provision at the lane level. The system under consideration hybridizes measurements from a global navigation satellite system (GNSS) receiver, an odometer, and a gyroscope, along with the road information stored in enhanced digital maps, by means of a multiple-hypothesis particle-filter-based algorithm. A set of experiments in real environments in France and Germany shows the very good results obtained in terms of positioning, map matching, and integrity consistency, proving the feasibility of our proposal.


Reading Strategies of Romanian Readers with Dyslexia in Upper Primary Grades


title: Evaluation of the diagnostic efficacy of enzyme colorimetric assay compared to tandem mass spectrometer in neonatal screening for phenylketonuria

abstract: The present study aims at evaluating the diagnostic efficacy of an enzyme-based colorimetric method (Enzolve’s kit) for quantitative determination of phenylalanine in comparison to the gold standard tandem mass spectrometer (MS/MS). This study was conducted on 155 neonates aged 3–7 days attending the neurometabolic clinic of Cairo University Children Hospital as a part of the neonatal screening program. Samples of patients were selected according to their phenylalanine levels obtained by MS/MS. Group 1 included 49 normal neonates with phenylalanine level 150 μmol/L selected to cover the analytical range of the commercial kit. Blood samples on filter paper cards were reassayed by an enzyme-based colorimetric test kit. On correlating the phenylalanine level assayed using enzyme-based colorimetric technique using Enzolve kit and MS/MS technique, r = 0.83 and P < 0.01 were found. Also it yielded a regression equation; phenylalanine level assayed using Enzolve kit = (30.6 + 0.98 × phenylalanine level assayed using MS/MS). Upon detecting positive PKU cases using Enzolve’s kit at a phenylalanine cutoff level of 127 μmol/L and by MS/MS technique at a cutoff level of 150 μmol/L, a statistically significantly association (P = 0.000) and an agreement with kappa of 0.64 (P = 0.000) were produced. On constructing ROC curve for assay of phenylalanine using Enzolve’s kit, an area under the curve of 0.963 was found. The Enzolve kit is a simple ,cost-effective, rapid with an accepted overall accuracy test that can be used preliminary to the use of the more sophisticated and expensive MS/MS in screening as well as monitoring patients with PKU.


title: Continual curiosity-driven skill acquisition from high-dimensional video inputs for humanoid robots

abstract: Abstract In the absence of external guidance, how can a robot learn to map the many raw pixels of high-dimensional visual inputs to useful action sequences? We propose here Continual Curiosity driven Skill Acquisition (CCSA). CCSA makes robots intrinsically motivated to acquire, store and reuse skills. Previous curiosity-based agents acquired skills by associating intrinsic rewards with world model improvements, and used reinforcement learning to learn how to get these intrinsic rewards. CCSA also does this, but unlike previous implementations, the world model is a set of compact low-dimensional representations of the streams of high-dimensional visual information, which are learned through incremental slow feature analysis. These representations augment the robot's state space with new information about the environment. We show how this information can have a higher-level (compared to pixels) and useful interpretation, for example, if the robot has grasped a cup in its field of view or not. After learning a representation, large intrinsic rewards are given to the robot for performing actions that greatly change the feature output, which has the tendency otherwise to change slowly in time. We show empirically what these actions are (e.g., grasping the cup) and how they can be useful as skills. An acquired skill includes both the learned actions and the learned slow feature representation. Skills are stored and reused to generate new observations, enabling continual acquisition of complex skills. We present results of experiments with an iCub humanoid robot that uses CCSA to incrementally acquire skills to topple, grasp and pick-place a cup, driven by its intrinsic motivation from raw pixel vision.


title: Contextualized Sarcasm Detection on Twitter

abstract: Sarcasm requires some shared knowledge between speaker and audience; it is a profoundly contextual phenomenon. Most computational approaches to sarcasm detection, however, treat it as a purely linguistic matter, using information such as lexical cues and their corresponding sentiment as predictive features. We show that by including extra-linguistic information from the context of an utterance on Twitter — such as properties of the author, the audience and the immediate communicative environment — we are able to achieve gains in accuracy compared to purely linguistic features in the detection of this complex phenomenon, while also shedding light on features of interpersonal interaction that enable sarcasm in conversation.


Architectural freeform structures from single curved panels


title: POETS: process-oriented, event-driven transaction systems

abstract: Abstract We present a high-level enterprise system architecture that closely models the domain ontology of resource and information flows in enterprises. It is: Process-oriented formal, user-definable specifications for the expected exchange of resources (money, goods, and services), notably contracts, are represented explicitly in the system state to reflect expectations on future events; Event-driven events denote relevant information about real-world transactions, specifically the transfer of resources and information between economic agents, to which the system reacts by matching against its portfolio of running processes/contracts in real time; Declarative user defined reporting functions can be formulated as declarative functions on the system state, including the representations of residual contractual obligations. We introduce the architecture and demonstrate how analyses of the standard reporting requirements for companies—the income statement and the balance sheet—can be used to drive the design of events that need registering for such reporting purposes. We then illustrate how the multi-party obligations in trade contracts (sale, purchase), including pricing and VAT payments, can be represented as formal contract expressions that can be subjected to analysis. To the best of our knowledge this is the first architecture for enterprise resource accounting that demonstrably maps high-level process and information requirements directly to executable specifications.


title: How effective is the IEEE 802.11 RTS/CTS handshake in ad hoc networks

abstract: IEEE 802.11 MAC mainly relies on two techniques to combat interference: physical carrier sensing and RTS/CTS handshake (also known as "virtual carrier sensing"). Ideally, the RTS/CTS handshake can eliminate most interference. However, the effectiveness of RTS/CTS handshake is based on the assumption that hidden nodes are within transmission range of receivers. In this paper, we prove using analytic models that in ad hoc networks, such an assumption cannot hold due to the fact that power needed for interrupting a packet reception is much lower than that of delivering a packet successfully. Thus, the "virtual carrier sensing" implemented by RTS/CTS handshake cannot prevent all interference. Physical carrier sensing can complement this in some degree. However, since interference happens at receivers, while physical carrier sensing is detecting transmitters (the same problem causing the hidden terminal situation), physical carrier sensing cannot help much, unless a very large carrier sensing range is adopted, which is limited by the antenna sensitivity. We investigate how effective is the RTS/CTS handshake in terms of reducing interference. We show that in some situations, the interference range is much larger than transmission range, where RTS/CTS cannot function well. Then, a simple MAC layer scheme is proposed to solve this problem. Simulation results verify that our scheme can help IEEE 802.11 resolve most interference caused by large interference range.


title: A kernel-based approach to document retrieval

abstract: In this paper we tackle the problem of document image retrieval by combining a similarity measure between documents and the probability that a given document belongs to a certain class. The membership probability to a specific class is computed using Support Vector Machines in conjunction with similarity measure based kernel applied to structural document representations. In the presented experiments, we use different document representations, both visual and structural, and we apply them to a database of historical documents. We show how our method based on similarity kernels outperforms the usual distance-based retrieval.


title: Detecting Ambiguities in Requirements Documents Using Inspections

abstract: Natural language is the most used representation for stating requirements on computer-based systems in industry. However, natural language is inherently ambiguous. Customers and software developers can disagree on the interpretation of a requirement without being aware of this fact. This disagreement can result in disastrous software failures. We argue that ambiguity is a more complex phenomenon than is often recognized in the literature. While inconsistencies and some types of incompleteness can be mechanically detected in formal specifications, ambiguities in informal specicifications often result in incorrect formal specifications. These misinterpretations can be detected only by execution or inspection of the formal specification. We suggest an inspection technique for detecting ambiguities in informal requirement documents before formal specifications are produced in order to avoid misinterpretations, rather than searching for them afterwards. We show how this technique can be tailored to different project contexts with the help of metamodels to increase its effectiveness. Finally, we report on experimental validation of the technique.


title: Where do mirror neurons come from ?

abstract: Debates about the evolution of the ‘mirror neuron system’ imply that it is an adaptation for action understanding. Alternatively, mirror neurons may be a byproduct of associative learning. Here I argue that the adaptation and associative hypotheses both offer plausible accounts of the origin of mirror neurons, but the associative hypothesis has three advantages. First, it provides a straightforward, testable explanation for the differences between monkeys and humans that have led some researchers to question the existence of a mirror neuron system. Second, it is consistent with emerging evidence that mirror neurons contribute to a range of social cognitive functions, but do not play a dominant, specialised role in action understanding. Finally, the associative hypothesis is supported by recent data showing that, even in adulthood, the mirror neuron system can be transformed by sensorimotor learning. The associative account implies that mirror neurons come from sensorimotor experience, and that much of this experience is obtained through interaction with others. Therefore, if the associative account is correct, the mirror neuron system is a product, as well as a process, of social interaction.


title: The Expansions of the Pectoral Girdle Muscles onto the Brachial Fascia: Morphological Aspects and Spatial Disposition

abstract: BACKGROUND/AIMS ::: The aim of this study was to analyse the relationships between the expansions of the pectoral girdle muscles, i.e. pectoralis major, latissimus dorsi and deltoid, and the brachial fascia. ::: ::: ::: METHODS ::: Thirty shoulder specimens from 15 unembalmed adult cadavers were studied by dissection and in vivo radiological studies were performed in 20 patients using magnetic resonance (MR) imaging. ::: ::: ::: RESULTS ::: The clavicular part of the pectoralis major muscle sent a fibrous expansion onto the anterior portion of the brachial fascia, its costal part onto the medial portion and medial intermuscular septum. The latissimus dorsi muscle showed a triangular fibrous expansion onto the posterior portion of the brachial fascia. The posterior part of the deltoid muscle inserted muscular fibres directly onto the posterior portion of the brachial fascia, its lateral part onto the lateral portion and the lateral intermuscular septum. In MR images, the brachial fascia appeared as a low-signal-intensity sinuous line of connective tissue, sharply delineated in T(1)-weighted sequences. ::: ::: ::: CONCLUSION ::: The expansions of the pectoral girdle muscles onto the brachial fascia were present in all the subjects and showed a quite constant course with a specific spatial organization. During the various movements of the arm, these expansions stretch selective portions of the brachial fascia, with possible activation of specific patterns of fascial proprioceptors.


title: Above the Clouds: A Berkeley View of Cloud Computing

abstract: Cloud Computing, the long-held dream of computing as a utility, has the potential to transform a large part of the IT industry, making software even more attractive as a service and shaping the way IT hardware is designed and purchased. Developers with innovative ideas for new Internet services no longer require the large capital outlays in hardware to deploy their service or the human expense to operate it. They need not be concerned about overprovisioning for a service whose popularity does not meet their predictions, thus wasting costly resources, or underprovisioning for one that becomes wildly popular, thus missing potential customers and revenue. Moreover, companies with large batch-oriented tasks can get results as quickly as their programs can scale, since using 1000 servers for one hour costs no more than using one server for 1000 hours. This elasticity of resources, without paying a premium for large scale, is unprecedented in the history of IT. Cloud Computing refers to both the applications delivered as services over the Internet and the hardware and systems software in the datacenters that provide those services. The services themselves have long been referred to as Software as a Service (SaaS). The datacenter hardware and software is what we will call a Cloud. When a Cloud is made available in a pay-as-you-go manner to the general public, we call it a Public Cloud; the service being sold is Utility Computing. We use the term Private Cloud to refer to internal datacenters of a business or other organization, not made available to the general public. Thus, Cloud Computing is the sum of SaaS and Utility Computing, but does not include Private Clouds. People can be users or providers of SaaS, or users or providers of Utility Computing. We focus on SaaS Providers (Cloud Users) and Cloud Providers, which have received less attention than SaaS Users. From a hardware point of view, three aspects are new in Cloud Computing.


title: Briggs et al. / TRUST IN ONLINE ADVICE Trust in Online Advice

abstract: Many people are now influenced by the information and advice they find on the Internet, much of it of dubious quality. This article describes two studies concerned with those factors capable of influencing people's response to online advice. The first study is a qualitative account of a group of house-hunters attempting to find worthwhile information online. The second study describes a survey of more than 2,500 people who had actively sought advice over the Internet. A framework for understanding trust in online advice is proposed in which first impressions are distinguished from more detailed evaluations. Good web design can influence the first process, but three key factors--source credibility, personalization, and predictability--are shown to predict whether people actually follow the advice given.


Use of Graph Database for the Integration of Heterogeneous Biological Data


title: Managing Emergency Situations in the Smart City: The Smart Signal

abstract: In a city there are numerous items, many of them unnoticed but essential; this is the case of the signals. Signals are considered objects with reduced technological interest, but in this paper we prove that making them smart and integrating in the IoT (Internet of Things) could be a relevant contribution to the Smart City. This paper presents the concept of Smart Signal, as a device conscious of its context, with communication skills, able to offer the best message to the user, and as a ubiquitous element that contributes with information to the city. We present the design considerations and a real implementation and validation of the system in one of the most challenging environments that may exist in a city: a tunnel. The main advantages of the Smart Signal are the improvement of the actual functionality of the signal providing new interaction capabilities with users and a new sensory mechanism of the Smart City.


title: Analysis of service satisfaction in web auction logistics service using a combination of Fruit fly optimization algorithm and general regression neural network

abstract: When constructing classification and prediction models, most researchers used genetic algorithm, particle swarm optimization algorithm, or ant colony optimization algorithm to optimize parameters of artificial neural network models in their previous studies. In this paper, a brand new approach using Fruit fly optimization algorithm (FOA) is adopted to optimize artificial neural network model. First, we carried out principal component regression on the results data of a questionnaire survey on logistics quality and service satisfaction of online auction sellers to construct our logistics quality and service satisfaction detection model. Relevant principal components in the principal component regression analysis results were selected for independent variables, and overall satisfaction level toward auction sellers’ logistics service as indicated in the questionnaire survey was selected as a dependent variable for sample data of this study. In the end, FOA-optimized general regression neural network (FOAGRNN), PSO-optimized general regression neural network (PSOGRNN), and other data mining techniques for ordinary general regression neural network were used to construct a logistics quality and service satisfaction detection model. In the study, 4–6 principal components in principal component regression analysis were selected as independent variables of the model. Analysis results of the study show that of the four data mining techniques, FOA-optimized GRNN model has the best detection capacity.


title: Through-polymer-via for 3D heterogeneous integration and packaging

abstract: Microelectronics industry is moving towards three dimensional stacking, packaging and integration of chips to answer to the need for increased functionality, miniaturization and cost reduction of smart systems. Their increasing complexity require novel and more robust approaches for the fabrication of vertical-interconnections (vias) to connect chips, devices, interconnection layers and wafers in out of plane direction. However, due to significant technology difficulties and high production costs, existing 3D technologies have limited usages. This paper describes the development of a new and robust fabrication method for dense high-aspect-ratio conductive through-polymer-vias for three-dimensional stacking, packaging and heterogeneous integration of semiconductor dies and wafers. Our approach relies on patterning micro-pillars in a 350 μm thick layer of photo resist on a carrier wafer or substrate. The pillars are conformal coated with a metal film and subsequently encapsulated inside an epoxy molding compound, resulting in vertical through-polymer interconnect vias (TPV). For subsequent interconnect processing, a clean and free of epoxy top surface of the pillars is crucial which was achieved by using foil assisted transfer molding technology. Even for very thin pillars with extreme aspect ratios of >15 clean connection surfaces are obtained. Advantages of this technology are: 1) pillars are more easily exposed and wetted by the plating solution 2) faster metallization than bottom-up plating 3) no voiding nor trapping of plating chemicals 4) suitable for parallel fabrication 5) lithographically defined, enabling layout variations and extremely accurate positioning of the vias. The technology is promising for low-cost, large-scale parallel fabrication of micro-vias for 3D heterogeneous integration and packaging.


title: Lexically Constrained Decoding for Sequence Generation Using Grid Beam Search

abstract: We present Grid Beam Search (GBS), an algorithm which extends beam search to allow the inclusion of pre-specified lexical constraints. The algorithm can be used with any model that generates a sequence $ \mathbf{\hat{y}} = \{y_{0}\ldots y_{T}\} $, by maximizing $ p(\mathbf{y} | \mathbf{x}) = \prod\limits_{t}p(y_{t} | \mathbf{x}; \{y_{0} \ldots y_{t-1}\}) $. Lexical constraints take the form of phrases or words that must be present in the output sequence. This is a very general way to incorporate additional knowledge into a model's output without requiring any modification of the model parameters or training data. We demonstrate the feasibility and flexibility of Lexically Constrained Decoding by conducting experiments on Neural Interactive-Predictive Translation, as well as Domain Adaptation for Neural Machine Translation. Experiments show that GBS can provide large improvements in translation quality in interactive scenarios, and that, even without any user input, GBS can be used to achieve significant gains in performance in domain adaptation scenarios.


title: Adaptive random testing: an illusion of effectiveness?

abstract: Adaptive Random Testing (ART) has been proposed as an enhancement to random testing, based on assumptions on how failing test cases are distributed in the input domain. The main assumption is that failing test cases are usually grouped into contiguous regions. Many papers have been published in which ART has been described as an effective alternative to random testing when using the average number of test case executions needed to find a failure (F-measure). But all the work in the literature is based either on simulations or case studies with unreasonably high failure rates. In this paper, we report on the largest empirical analysis of ART in the literature, in which 3727 mutated programs and nearly ten trillion test cases were used. Results show that ART is highly inefficient even on trivial problems when accounting for distance calculations among test cases, to an extent that probably prevents its practical use in most situations. For example, on the infamous Triangle Classification program, random testing finds failures in few milliseconds whereas ART execution time is prohibitive. Even when assuming a small, fixed size test set and looking at the probability of failure (P-measure), ART only fares slightly better than random testing, which is not sufficient to make it applicable in realistic conditions. We provide precise explanations of this phenomenon based on rigorous empirical analyses. For the simpler case of single-dimension input domains, we also perform formal analyses to support our claim that ART is of little use in most situations, unless drastic enhancements are developed. Such analyses help us explain some of the empirical results and identify the components of ART that need to be improved to make it a viable option in practice.


title: Temporally-aware algorithms for document classification

abstract: Automatic Document Classification (ADC) is still one of the major information retrieval problems. It usually employs a supervised learning strategy, where we first build a classification model using pre-classified documents and then use this model to classify unseen documents. The majority of supervised algorithms consider that all documents provide equally important information. However, in practice, a document may be considered more or less important to build the classification model according to several factors, such as its timeliness, the venue where it was published in, its authors, among others. In this paper, we are particularly concerned with the impact that temporal effects may have on ADC and how to minimize such impact. In order to deal with these effects, we introduce a temporal weighting function (TWF) and propose a methodology to determine it for document collections. We applied the proposed methodology to ACM-DL and Medline and found that the TWF of both follows a lognormal. We then extend three ADC algorithms (namely kNN, Rocchio and Naive Bayes) to incorporate the TWF. Experiments showed that the temporally-aware classifiers achieved significant gains, outperforming (or at least matching) state-of-the-art algorithms.


title: Content-based image retrieval by clustering

abstract: In a typical content-based image retrieval (CBIR) system, query results are a set of images sorted by feature similarities with respect to the query. However, images with high feature similarities to the query may be very different from the query in terms of semantics. This is known as the semantic gap. We introduce a novel image retrieval scheme, CLUster-based rEtrieval of images by unsupervised learning (CLUE), which tackles the semantic gap problem based on a hypothesis: semantically similar images tend to be clustered in some feature space. CLUE attempts to capture semantic concepts by learning the way that images of the same semantics are similar and retrieving image clusters instead of a set of ordered images. Clustering in CLUE is dynamic. In particular, clusters formed depend on which images are retrieved in response to the query. Therefore, the clusters give the algorithm as well as the users semantic relevant clues as to where to navigate. CLUE is a general approach that can be combined with any real-valued symmetric similarity measure (metric or nonmetric). Thus it may be embedded in many current CBIR systems. Experimental results based on a database of about 60, 000 images from COREL demonstrate improved performance.


title: Enriching a protein drink with leucine augments muscle protein synthesis after resistance exercise in young and older men.

abstract: Maximizing anabolic responses to feeding and exercise is crucial for muscle maintenance and adaptation to exercise training. We hypothesized that enriching a protein drink with leucine would improve anabolic responses to resistance exercise (RE: 6 × 8 knee-extension repetitions at 75% of 1-RM) in both young and older adults. Groups (n = 9) of young (24 ± 6 y, BMI 23 ± 2 kg m-2) and older men (70 ± 5 y, BMI 25 ± 2 kg m-2) were randomized to either: (i) RE followed by Slim-Fast Optima (SFO 10 g PRO; 24 g CHO) with 4.2 g of leucine (LEU) or, (ii) RE + SFO with 4.2 g of alanine (ALA; isonitrogenous control). Muscle biopsies were taken before, immediately after, and 1, 2 and 4 h after RE and feeding. Muscle protein synthesis (MPS) was measured by incorporation of [1, 2-13C2] leucine into myofibrillar proteins and the phosphorylation of p70S6K1 by immunoblotting. In young men, both area under the curve (AUC; FSR 0-4 h P < 0.05) and peak FSR (0.11 vs. 0.08%.h.-1; P < 0.05) were greater in the SFO + LEU than in the SFO + ALA group, after RE. Similarly, in older men, AUC analysis revealed that post-exercise anabolic responses were greater in the SFO + LEU than SFO + ALA group, after RE (AUC; FSR 0-4 h P < 0.05). Irrespective of age, increases in p70S6K1 phosphorylation were evident in response to both SFO + LEU and SFO + ALA, although greater with leucine supplementation than alanine (fold-change 2.2 vs. 3.2; P < 0.05), specifically in the older men. We conclude that addition of Leucine to a sub-maximal PRO bolus improves anabolic responses to RE in young and older men.


title: An implantable low-power ultrasonic platform for the Internet of Medical Things

abstract: Wirelessly networked systems of implantable medical devices endowed with sensors and actuators will be the basis of many innovative, sometimes revolutionary therapies. The biggest obstacle in realizing this vision of networked implantable devices is posed by the dielectric nature of the human body, which strongly attenuates radio-frequency (RF) electromagnetic waves. In this paper we present the first hardware and software architecture of an Internet of Medical Things (IoMT) platform with ultrasonic connectivity for intra-body communications that can be used as a basis for building future IoT-ready medical implantable and wearable devices. We show that ultrasonic waves can be efficiently generated and received with low-power and mm-sized components, and that despite the conversion loss introduced by ultrasonic transducers the gap in attenuation between 2.4GHz RF and ultrasonic waves is still substantial, e.g., ultrasounds offer 70dB less attenuation over 10cm. We show that the proposed IoMT platform requires much lower transmission power compared to 2.4 GHz RF with equal reliability in tissues, e.g., 35 dBm lower over 12 cm for 10−3 Bit Error Rate (BEr) leading to lower energy per bit and longer device lifetime. Finally, we show experimentally that 2.4 GHz RF links are not functional at all above 12 cm, while ultrasonic links achieve a reliability of 10−6 up to 20 cm with less than 0 dBm transmission power.


title: Brain development during childhood and adolescence: a longitudinal MRI study

abstract: Pediatric neuroimaging studies1,2,3,4,5, up to now exclusively cross sectional, identify linear decreases in cortical gray matter and increases in white matter across ages 4 to 20. In this large-scale longitudinal pediatric neuroimaging study, we confirmed linear increases in white matter, but demonstrated nonlinear changes in cortical gray matter, with a preadolescent increase followed by a postadolescent decrease. These changes in cortical gray matter were regionally specific, with developmental curves for the frontal and parietal lobe peaking at about age 12 and for the temporal lobe at about age 16, whereas cortical gray matter continued to increase in the occipital lobe through age 20.


title: Detecting Code Smells in Python Programs

abstract: As a traditional dynamic language, Python is increasingly used in various software engineering tasks. However, due to its flexibility and dynamism, Python is a particularly challenging language to write code in and maintain. Consequently, Python programs contain code smells which indicate potential comprehension and maintenance problems. With the aim of supporting refactoring strategies to enhance maintainability, this paper describes how to detect code smells in Python programs. We introduce 11 Python smells and describe the detection strategy. We also implement a smell detection tool named Pysmell and use it to identify code smells in five real world Python systems. The results show that Pysmell can detect 285 code smell instances in total with the average precision of 97.7%. It reveals that Large Class and Large Method are most prevalent. Our experiment also implies Python programs may be suffering code smells further.


DUTIR at the BioCreative V CDR Task : Disease Named Entity Recognition and Normalization and the Chemical-Disease Relation Extraction from Biomedical Text


Oblique Lateral Lumbar Interbody Fusion (OLLIF): Technical Notes and Early Results of a Single Surgeon Comparative Study


title: DeepArt: Learning Joint Representations of Visual Arts

abstract: This paper aims to generate a better representation of visual arts, which plays a key role in visual arts analysis works. Museums and galleries have a large number of artworks in the database, hiring art experts to do analysis works (e.g., classification, annotation) is time consuming and expensive and the analytic results are not stable because the results highly depend on the experiences of art experts. The problem of generating better representation of visual arts is of great interests to us because of its application potentials and interesting research challenges---both content information and each unique style information within one artwork should be summarized and learned when generating the representation. For example, by studying a vast number of artworks, art experts summary and enhance the knowledge of unique characteristics of each visual arts to do visual arts analytic works, it is non-trivial for computer. In this paper, we present a unified framework, called DeepArt, to learn joint representations that can simultaneously capture contents and style of visual arts. This framework learns unique characteristics of visual arts directly from a large-scale visual arts dataset, it is more flexible and accurate than traditional handcraft approaches. We also introduce Art500k, a large-scale visual arts dataset containing over 500,000 artworks, which are annotated with detailed labels of artist, art movement, genre, etc. Extensive empirical studies and evaluations are reported based on our framework and Art500k and all those reports demonstrate the superiority of our framework and usefulness of Art500k. A practical system for visual arts retrieval and annotation is implemented based on our framework and dataset. Code, data and system are publicly available at http://deepart.ece.ust.hk.


Classification Of E-Customer Sessions Based On Support Vector Machine.


title: A sparse-response deep belief network based on rate distortion theory

abstract: Abstract Deep belief networks (DBNs) are currently the dominant technique for modeling the architectural depth of brain, and can be trained efficiently in a greedy layer-wise unsupervised learning manner. However, DBNs without a narrow hidden bottleneck typically produce redundant, continuous-valued codes and unstructured weight patterns. Taking inspiration from rate distortion (RD) theory, which encodes original data using as few bits as possible, we introduce in this paper a variant of DBN, referred to as sparse-response DBN (SR-DBN). In this approach, Kullback–Leibler divergence between the distribution of data and the equilibrium distribution defined by the building block of DBN is considered as a distortion function, and the sparse response regularization induced by L 1 -norm of codes is used to achieve a small code rate. Several experiments by extracting features from different scale image datasets show that our approach SR-DBN learns codes with small rate, extracts features at multiple levels of abstraction mimicking computations in the cortical hierarchy, and obtains more discriminative representation than PCA and several basic algorithms of DBNs.


title: Short-term memory for serial order: A recurrent neural network model

abstract: Despite a century of research, the mechanisms underlying short-term or working memory for serial order remain uncertain. Recent theoretical models have converged on a particular account, based on transient associations between independent item and context representations. In the present article, the authors present an alternative model, according to which sequence information is encoded through sustained patterns of activation within a recurrent neural network architecture. As demonstrated through a series of computer simulations, the model provides a parsimonious account for numerous benchmark characteristics of immediate serial recall, including data that have been considered to preclude the application of recurrent neural networks in this domain. Unlike most competing accounts, the model deals naturally with findings concerning the role of background knowledge in serial recall and makes contact with relevant neuroscientific data. Furthermore, the model gives rise to numerous testable predictions that differentiate it from competing theories. Taken together, the results presented indicate that recurrent neural networks may offer a useful framework for understanding short-term memory for serial order.


title: Detecting Objects in Scene Point Cloud: A Combinational Approach

abstract: Object detection is a fundamental task in computer vision. As the 3D scanning techniques become popular, directly detecting objects through 3D point cloud of a scene becomes an immediate need. We propose an object detection framework combining learning-Based classification, local descriptor, a new variance of RANSAC imposing rigid-body constraint and an iterative process for multi-object detection in continuous point clouds. The framework not only takes global and local information into account, but also benefits from both learning and empirical methods. The experiments performed on the challenging ground Lidar dataset show the effectiveness of our method.


Explaining Recommendations by Means of User Reviews


title: Development and validation of a novel dementia of Alzheimer's type (DAT) score based on metabolism FDG-PET imaging

abstract: Fluorodeoxyglucose positron emission tomography (FDG-PET) imaging based 3D topographic brain glucose metabolism patterns from normal controls (NC) and individuals with dementia of Alzheimer's type (DAT) are used to train a novel multi-scale ensemble classification model. This ensemble model outputs a FDG-PET DAT score (FPDS) between 0 and 1 denoting the probability of a subject to be clinically diagnosed with DAT based on their metabolism profile. A novel 7 group image stratification scheme is devised that groups images not only based on their associated clinical diagnosis but also on past and future trajectories of the clinical diagnoses, yielding a more continuous representation of the different stages of DAT spectrum that mimics a real-world clinical setting. The potential for using FPDS as a DAT biomarker was validated on a large number of FDG-PET images (N=2984) obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database taken across the proposed stratification, and a good classification AUC (area under the curve) of 0.78 was achieved in distinguishing between images belonging to subjects on a DAT trajectory and those images taken from subjects not progressing to a DAT diagnosis. Further, the FPDS biomarker achieved state-of-the-art performance on the mild cognitive impairment (MCI) to DAT conversion prediction task with an AUC of 0.81, 0.80, 0.77 for the 2, 3, 5 years to conversion windows respectively.


title: Toward a better understanding of the influences on physical activity: the role of determinants, correlates, causal variables, mediators, moderators, and confounders.

abstract: Background: For research on physical activity interventions to progress systematically, the mechanisms of action must be studied. In doing so, the research methods and their associated concepts and terminology become more complex. It is particularly important to clearly distinguish among determinants, correlates, mediators, moderators, and confounder variables used in physical activity research. This article examines the factors that are correlated with and that may have a causal relationship to physical activity. Methods and Results: We propose that the term “correlate” be used, instead of “determinant,” to describe statistical associations or correlations between measured variables and physical activity. Studies of the correlates of physical activity are reviewed. The findings of these studies can help to critique existing theories of health behavior change and can provide hypotheses to be tested in intervention studies from which it is possible to draw causal inferences. Mediator, moderator, and confounder variables can act to influence measured changes in physical activity. Intervening causal variables that are necessary to complete a cause– effect pathway between an intervention and physical activity are termed “mediators.” The relationship between an intervention and physical activity behaviors may vary for different groups; the strata by which they vary are levels of “moderators” of the relationship. Other factors may distort or affect the observed relationships between program exposure and physical activity, and are known as “confounders.”


title: SeMFIS: A flexible engineering platform for semantic annotations of conceptual models

abstract: In this paper, we present SeMFIS - a flexible engineering platform for semantic annotations of conceptual models. Conceptual models have in the past been used for many purposes in the context of information systems' engineering. These purposes include for example the elicitation of requirements, the simulation of the behavior of future information systems, the generation of code or the interaction with information systems through models at runtime. Semantic annotations of conceptual models constitute a recently established approach for dynamically extending the semantic representation and semantic analysis scope of conceptual modeling languages. Thereby, elements in conceptual models are linked to concepts in ontologies via anno- tations. Thus, additional knowledge aspects can be represented without modifications of the modeling languages. These aspects can then be analyzed using queries, specifically designed algorithms or external tools and services. At its core, SeMFIS provides a set of meta models for visually representing ontologies and semantic annotations as models. In addition, the tool contains an analysis component, a web service interface, and an import/export component to query and exchange model information. SeM- FIS has been implemented using the freely available ADOxx meta modeling platform. It can thus be easily added to the large variety of other modeling methods based on this platform or used as an additional service for other tools. We present the main features of SeMFIS and briefly discuss use cases where it has been applied. SeMFIS is freely available via the OMiLAB.org website at http://www.omilab.org/web/semfis.


title: Design considerations on low voltage synchronous power MOSFETs with monolithically integrated gate voltage pull-down circuitry

abstract: In this paper, a monolithically integrated gate voltage pull-down circuitry is presented to avoid the unintentional C·dV/dt induced turn-on. The concept of a low threshold voltage MOSFET with this integrated gate voltage pull-down circuitry is introduced as a contributing factor to the next generation high frequency DC-DC converter efficiency improvement. Design considerations on this new device and influences of critical design parameters on device/circuit performance will be fully discussed. In synchronous buck application, this integrated power module achieves more than 2% efficiency improvement over reference solution at high operation frequency (1MHz) under 19V input and 1.3V output condition.


title: Human gut microbiome viewed across age and geography

abstract: Gut microbial communities represent one source of human genetic and metabolic diversity. To examine how gut microbiomes differ among human populations, here we characterize bacterial species in fecal samples from 531 individuals, plus the gene content of 110 of them. The cohort encompassed healthy children and adults from the Amazonas of Venezuela, rural Malawi and US metropolitan areas and included mono- and dizygotic twins. Shared features of the functional maturation of the gut microbiome were identified during the first three years of life in all three populations, including age-associated changes in the genes involved in vitamin biosynthesis and metabolism. Pronounced differences in bacterial assemblages and functional gene repertoires were noted between US residents and those in the other two countries. These distinctive features are evident in early infancy as well as adulthood. Our findings underscore the need to consider the microbiome when evaluating human development, nutritional needs, physiological variations and the impact of westernization.


title: Simplified Interval Type-2 Fuzzy Neural Networks

abstract: This paper describes a self-evolving interval type-2 fuzzy neural network (FNN) for various applications. As type-1 fuzzy systems cannot effectively handle uncertainties in information within the knowledge base, we propose a simple interval type-2 FNN, which uses interval type-2 fuzzy sets in the premise and the Takagi-Sugeno-Kang (TSK) type in the consequent of the fuzzy rule. The TSK-type consequent of fuzzy rule is a linear combination of exogenous input variables. Given an initially empty the rule-base, all rules are generated with on-line type-2 fuzzy clustering. Instead of the time-consuming K-M iterative procedure, the design factors ql and qr are learned to adaptively adjust the upper and lower positions on the left and right limit outputs, using the parameter update rule based on a gradient descent algorithm. Simulation results demonstrate that our approach yields fewer test errors and less computational complexity than other type-2 FNNs.


Running Head: AN EXPLORATORY FACTOR ANALYSIS OF THE GLOBAL PERSPECTIVE INVENTORY 1 An Exploratory Factor Analysis of the Global Perspective Inventory


title: Understanding semantic change of words over centuries

abstract: In this paper, we propose to model and analyze changes that occur to an entity in terms of changes in the words that co-occur with the entity over time. We propose to do an in-depth analysis of how this co-occurrence changes over time, how the change influences the state (semantic, role) of the entity, and how the change may correspond to events occurring in the same period of time. We propose to identify clusters of topics surrounding the entity over time using Topics-Over-Time (TOT) and k-means clustering. We conduct this analysis on Google Books Ngram dataset. We show how clustering words that co-occur with an entity of interest in 5-grams can shed some lights to the nature of change that occurs to the entity and identify the period for which the change occurs. We find that the period identified by our model precisely coincides with events in the same period that correspond to the change that occurs.


A Library based compiler to execute MATLAB Programs on a Heterogeneous Platform


title: Relations Among Positive Parenting, Children's Effortful Control, and Externalizing Problems: A Three-Wave Longitudinal Study

abstract: In a 3-wave longitudinal study (with assessments 2 years apart) involving 186 early adolescents (M ages of approximately 9.3, 11.4, and 13.4), the hypothesis that parental warmth/positive expressivity predicts children's effortful control (EC) (a temperamental characteristic contributing to emotion regulation) 2 years later, which in turn predicts low levels of externalizing problems another 2 years later, was examined. The hypothesis that children's EC predicts parenting over time was also examined. Parents were observed interacting with their children; parents and teachers reported children's EC and externalizing problems; and children's persistence was assessed behaviorally. Children's EC mediated the relation between positive parenting and low levels of externalizing problems (whereas there was no evidence that children's EC predicted parenting).


An Improved Decision-DNNF Compiler


title: The effect of government satisfaction on collective action intention—Panel data analysis(2007–2010)

abstract: This article examined the effect of government satisfaction on collective action intention under Chinese background by panel data analysis (2007–2010). Results demonstrate that among the variables work-life satisfaction, economic situation of country/region, economic development anticipation, social justice have negatively significant effect on collective action intention (p<.05). The social stress and income level do not (p>.05). The government satisfaction has moderate effect between the antecedents and collective action. The fit index of the model is good.


title: The Face–Time Continuum Lifespan Changes in Facial Width-to-Height Ratio Impact Aging-Associated Perceptions

abstract: Aging influences how a person is perceived on multiple dimensions (e.g., physical power). Here we examined how facial structure informs these evolving social perceptions. Recent work examining young adults’ faces has revealed the impact of the facial width-to-height ratio (fWHR) on perceived traits, such that individuals with taller, thinner faces are perceived to be less aggressive, less physically powerful, and friendlier. These perceptions are similar to those stereotypically associated with older adults. Examining whether fWHR might contribute to these changing perceptions over the life span, we found that age provides a shifting context through which fWHR differentially impacts aging-related social perceptions (Study 1). In addition, archival analyses (Study 2) established that fWHR decreases across age, and a subsequent study found that fWHR mediated the relationship between target age and multiple aging-related perceptions (Study 3). The findings provide evidence that fWHR decreases across age and ...


RATIONAL DECISION-MAKING IN BUSINESS


title: A non-monotonic method for large-scale nonnegative least squares

abstract: We present a new algorithm for solving the non-negative least-squares NNLS problem. Our algorithm extends the unconstrained quadratic optimization algorithm of Barzilai and Borwein BB [J. Barzilai and J. M. Borwein; Two-Point Step Size Gradient Methods. IMA J. Numer. Anal. 1988.] to handle nonnegativity constraints. Our extension differs from other constrained BB variants in simple but crucial aspects, the most notable being our modification to the BB stepsize itself. Our stepsize computation takes into account the nonnegativity constraints, and is further refined by a stepsize scaling strategy. These changes, in combination with orthogonal projections onto the nonnegative orthant, yield an effective NNLS algorithm. We compare our algorithm with several competing approaches, including established bound-constrained solvers, popular BB-based methods, and also a specialised NNLS algorithm. On several synthetic and real-world datasets our method displays highly competitive empirical performance.


title: On estimating depressive tendencies of Twitter users utilizing their tweet data

abstract: In this paper, we investigate the effectiveness of the records of user's activities in Twitter, which is a popular microblogging site, for estimating his/her depressive tendency. We construct multiple regression model to estimate user's depressive tendency from the frequencies of words used by the user. We perform experiments to estimate participants' depressive tendencies using the constructed regression model. Our experimental results show that there exists medium positive correlation (correlation coefficient r ≃ 0.45) between the Zung's Self-rating Depression Scale, which is a popular measure for estimating depressive tendency, and estimated score obtained from the regression model.


title: Fast Global Registration

abstract: We present an algorithm for fast global registration of partially overlapping 3D surfaces. The algorithm operates on candidate matches that cover the surfaces. A single objective is optimized to align the surfaces and disable false matches. The objective is defined densely over the surfaces and the optimization achieves tight alignment with no initialization. No correspondence updates or closest-point queries are performed in the inner loop. An extension of the algorithm can perform joint global registration of many partially overlapping surfaces. Extensive experiments demonstrate that the presented approach matches or exceeds the accuracy of state-of-the-art global registration pipelines, while being at least an order of magnitude faster. Remarkably, the presented approach is also faster than local refinement algorithms such as ICP. It provides the accuracy achieved by well-initialized local refinement algorithms, without requiring an initialization and at lower computational cost.


title: Business Process Intelligence

abstract: Business Process Management Systems (BPMSs) are software platforms that support the definition, execution, and tracking of business processes. BPMSs have the ability of logging information about the business processes they support. Proper analysis of BPMS execution logs can yield important knowledge and help organizations improve the quality of their business processes and services to their business partners. This paper presents a set of integrated tools that supports business and IT users in managing process execution quality by providing several features, such as analysis, prediction, monitoring, control, and optimization. We refer to this set of tools as the Business Process Intelligence (BPI) tool suite. Experimental results presented in this paper are very encouraging. We plan to investigate further enhancements on the BPI tools suite, including automated exception prevention, and refinement of process data preparation stage, as well as integrating other data mining techniques.


title: Gender differences in consumers' perception of online consumer reviews

abstract: Since the early days of the Internet, gender gap has existed in using the Internet, and it is particularly evident for online shopping. Females perceive higher level of risk for online shopping, and as a result, they tend to hesitate to make purchase online. Online consumer reviews can effectively mitigate such perceived risk by females and thereby attract them to buy online. This study investigates the effect of online consumer reviews on consumer's purchase intention. In particular, we examine whether there are gender differences in responding to online consumer reviews. The results show that the effect of online consumer reviews on purchase intention is stronger for females than males. The negativity effect, that consumers are influenced by a negative review more than by a positive review, is also found to be more evident for females. These findings have practical implications for online sellers to guide them to effectively use online consumer reviews to engage females in online shopping.


title: Vascular complications of hyaluronic acid fillers and the role of hyaluronidase in management.

abstract: Skin necrosis following the inadvertent arterial injection of hyaluronic acid (HA) is a serious complication. It is not clear whether or not subcutaneous injections of hyaluronidase decrease skin necrosis in HA-induced vascular complications. We had four cases of HA-induced vascular complications, two of which were treated with hyaluronidase the next day. All of the patients had skin necrosis and scarring. We performed an animal study with rabbit ears in which HA filler was injected into the auricular arteries of both ears. Five rabbits each received a subcutaneous injection of 750 IU of hyaluronidase 4 and 24 h after the filler injection. The hyaluronidase-treated ears in the 4-h intervention group had significantly smaller necrotic areas (p<0.05), while the 24-h intervention group had no differences in the area of necrosis. Hyaluronidase reduced the vascular complications of HA fillers when used early, but there was no benefit to hyaluronidase injection after 24 h.


title: Somatic PHF6 mutations in 1760 cases with various myeloid neoplasms

abstract: Abstract Leukemia accepted article preview online, 01 August 2016. doi:10.1038/leu.2016.212.


Research on social media feeds – A GIScience perspective


title: The vitamin B6 paradox: Supplementation with high concentrations of pyridoxine leads to decreased vitamin B6 function.

abstract: Vitamin B6 is a water-soluble vitamin that functions as a coenzyme in many reactions involved in amino acid, carbohydrates and lipid metabolism. Since 2014, >50 cases of sensory neuronal pain due to vitamin B6 supplementation were reported. Up to now, the mechanism of this toxicity is enigmatic and the contribution of the various B6 vitamers to this toxicity is largely unknown. In the present study, the neurotoxicity of the different forms of vitamin B6 is tested on SHSY5Y and CaCo-2 cells. Cells were exposed to pyridoxine, pyridoxamine, pyridoxal, pyridoxal-5-phosphate or pyridoxamine-5-phosphate for 24h, after which cell viability was measured using the MTT assay. The expression of Bax and caspase-8 was tested after the 24h exposure. The effect of the vitamers on two pyridoxal-5-phosphate dependent enzymes was also tested. Pyridoxine induced cell death in a concentration-dependent way in SHSY5Y cells. The other vitamers did not affect cell viability. Pyridoxine significantly increased the expression of Bax and caspase-8. Moreover, both pyridoxal-5-phosphate dependent enzymes were inhibited by pyridoxine. In conclusion, the present study indicates that the neuropathy observed after taking a relatively high dose of vitamin B6 supplements is due to pyridoxine. The inactive form pyridoxine competitively inhibits the active pyridoxal-5'-phosphate. Consequently, symptoms of vitamin B6 supplementation are similar to those of vitamin B6 deficiency.


title: Convolutional Neural Networks for Multi-topic Dialog State Tracking

abstract: The main task of the fourth Dialog State Tracking Challenge (DSTC4) is to track the dialog state by filling in various slots, each of which represents a major subject discussed in the dialog. In this article we focus on the ‘INFO’ slot that tracks the general information provided in a sub-dialog segment, and propose an approach for this slot-filling using convolutional neural networks (CNNs). Our CNN model is adapted to multi-topic dialog by including a convolutional layer with general and topic-specific filters. The evaluation on DSTC4 common test data shows that our approach outperforms all other submitted entries in terms of overall accuracy of the ‘INFO’ slot.


title: Vetting browser extensions for security vulnerabilities with VEX

abstract: The browser has become the de facto platform for everyday computation and a popular target for attackers of computer systems. Among the many potential attacks that target or exploit browsers, vulnerabilities in browser extensions have received relatively little attention. Currently, extensions are vetted by manual inspection, which is time consuming and subject to human error. In this paper, we present VEX, a framework for applying static information flow analysis to JavaScript code to identify security vulnerabilities in browser extensions. We describe several patterns of flows that can lead to privilege escalations in Firefox extensions. VEX analyzes Firefox extensions for such flow patterns using high-precision, context-sensitive, flow-sensitive static analysis. We subject 2460 browser extensions to the analysis, and VEX finds 5 of the 18 previously known vulnerabilities and 7 previously unknown vulnerabilities.


title: Internal and External Visual Cue Preferences for Visualizations in Presentations

abstract: Presenters, such as analysts briefing to an executive committee, often use visualizations to convey information. In these cases, providing clear visual guidance is important to communicate key concepts without confusion. This paper explores visual cues that guide attention to a particular area of a visualization. We developed a visual cue taxonomy distinguishing internal from external cues, designed a web tool based on the taxonomy, and conducted a user study with 24 participants to understand user preferences in choosing visual cues. Participants perceived internal cues e.g., transparency, brightness, and magnification as the most useful visual cues and often combined them with other internal or external cues to emphasize areas of focus for their audience. Interviews also revealed that the choice of visual cues depends on not only the chart type, but also the presentation setting, the audience, and the function cues are serving. Considering the complexity of choosing visual cues, we provide design implications for improving the organization, consistency, and integration of visual cues within existing workflows.


title: Radiation pattern measurement system for millimeter-wave antenna fed by contact probe

abstract: This paper presents a radiation pattern measurement system in order to evaluate mm-wave antenna fed by contact probe. It reduces the transmission loss by using a waveguide as a revolving arm. Three cut patterns can be measured by changing the orientation of the revolving arm. A differential feeding dipole antenna with a reflection plate was evaluated by using this measurement system. The measurement result of half-power beam width agrees well with the simulation result and the dynamic range is more than 20dB. By covering the possible scatterers in the anechoic chamber by planar electromagnetic absorber, the ripple in the half-power beam width is suppressed below ±0.5dB.


[A new method of treating interstitial plasma cell pneumonia in premature infant with 5-valent antimony & aromatic diamidines].


title: Analysis and design of a broadband dual-polarized planar antenna for 2G/3G/4G base stations

abstract: A broadband dual-polarized dual-dipole (DPDD) planar antenna is analyzed and designed for 2G/3G/4G base stations. The DPDD antenna consists of two perpendicularly crossed dual-dipole elements. Each dual-dipole element is comprised of two identical side-by-side dipoles which are connected through a coplanar strip line that also acts as an impedance transformer. Two dual-dipole elements are printed respectively on two sides of a thin substrate and are excited directly by two coaxial cables, making the antenna configuration planar, compact, and simple. Due to the mutual coupling between two crossed dual-dipole elements, a broad bandwidth can be obtained. It is shown that the DPDD antenna achieves a bandwidth of 51% (1.61–2.71 GHz) for return loss >15 dB with an isolation of higher than 30 dB. The DPDD antenna also features a high gain of about 9 dBi a half-power beamwidth (HPBW) of 65±5° over a broad bandwidth.


title: Chainlets: A New Descriptor for Detection and Recognition

abstract: Detecting and recognizing objects in images is one of the most challenging tasks in computer vision, as it seeks to detect subtle objects while ignoring massive numbers of negatives. While deep networks have led to advances in many problems, new representations and approaches are needed for applications without millions of training samples or where explanations are required. This paper focuses on a new representation that can be used for detection/recognition in many applications of computer vision, and demonstrates it on two very different applications: pedestrian detection and ear recognition. This paper proposes the use of Chainlets, ordered oriented data computed from deep contourbased edge detection, as a novel object descriptor. Chainlets address the problem with Histograms of Oriented Gradients, in that HOG does not model edge connectedness. We extend HOG using Histograms of Chain Codes, which improve object descriptiveness and can even provide orientation invariance. These descriptors significantly outperform existing feature sets, including both existing hand-crafted and deep features for human ear recognition, and are near state of the art on pedestrian detection. Results from our Chainlets algorithm underwent independent testing as part of the new Unconstrained Ear Recognition Challenge dataset, where the competition's evaluation showed Chainlets yielded a significant improvement over other state of the art approaches. To show further generality, we performed an evaluation on the INRIA person detection dataset with results that are near state-of-the-art deep network and boosted classifier results. Overall, the experimental results show that the novel Chainlets representation is competitive with, or better than, state-of-the-art algorithms on both pedestrian detection and ear recognition applications.


title: Underwater Image Super-Resolution by Descattering and Fusion

abstract: Underwater images are degraded due to scatters and absorption, resulting in low contrast and color distortion. In this paper, a novel self-similarity-based method for descattering and super resolution (SR) of underwater images is proposed. The traditional approach of preprocessing the image using a descattering algorithm, followed by application of an SR method, has the limitation that most of the high-frequency information is lost during descattering. Consequently, we propose a novel high turbidity underwater image SR algorithm. We first obtain a high resolution (HR) image of scattered and descattered images by using a self-similarity-based SR algorithm. Next, we apply a convex fusion rule for recovering the final HR image. The super-resolved images have a reasonable noise level after descattering and demonstrate visually more pleasing results than conventional approaches. Furthermore, numerical metrics demonstrate that the proposed algorithm shows a consistent improvement and that edges are significantly enhanced.


title: Steerable CNNs

abstract: It has long been recognized that the invariance and equivariance properties of a representation are critically important for success in many vision tasks. In this paper we present Steerable Convolutional Neural Networks, an efficient and flexible class of equivariant convolutional networks. We show that steerable CNNs achieve state of the art results on the CIFAR image classification benchmark. The mathematical theory of steerable representations reveals a type system in which any steerable representation is a composition of elementary feature types, each one associated with a particular kind of symmetry. We show how the parameter cost of a steerable filter bank depends on the types of the input and output features, and show how to use this knowledge to construct CNNs that utilize parameters effectively.


title: An event-based conceptual model for context-aware movement analysis

abstract: Current tracking technologies enable collection of data, describing movements of various kinds of objects, including people, animals, icebergs, vehicles, containers with goods and so on. Analysis of movement data is now a hot research topic. However, most of the suggested analysis methods deal with movement data alone. Little has been done to support the analysis of movement in its spatio-temporal context, which includes various spatial and temporal objects as well as diverse properties associated with spatial locations and time moments. Comprehensive analysis of movement requires detection and analysis of relations that occur between moving objects and elements of the context in the process of the movement. We suggest a conceptual model in which movement is considered as a combination of spatial events of diverse types and extents in space and time. Spatial and temporal relations occur between movement events and elements of the spatial and temporal contexts. The model gives a ground to a generic approach based on extraction of interesting events from trajectories and treating the events as independent objects. By means of a prototype implementation, we tested the approach on complex real data about movement of wild animals. The testing showed the validity of the approach.


Evolution of Artificial Soundscape in a Natural Environment


WINDKESSEL MODEL ANALYSIS IN MATLAB


title: SeqSLAM: Visual route-based navigation for sunny summer days and stormy winter nights

abstract: Learning and then recognizing a route, whether travelled during the day or at night, in clear or inclement weather, and in summer or winter is a challenging task for state of the art algorithms in computer vision and robotics. In this paper, we present a new approach to visual navigation under changing conditions dubbed SeqSLAM. Instead of calculating the single location most likely given a current image, our approach calculates the best candidate matching location within every local navigation sequence. Localization is then achieved by recognizing coherent sequences of these “local best matches”. This approach removes the need for global matching performance by the vision front-end - instead it must only pick the best match within any short sequence of images. The approach is applicable over environment changes that render traditional feature-based techniques ineffective. Using two car-mounted camera datasets we demonstrate the effectiveness of the algorithm and compare it to one of the most successful feature-based SLAM algorithms, FAB-MAP. The perceptual change in the datasets is extreme; repeated traverses through environments during the day and then in the middle of the night, at times separated by months or years and in opposite seasons, and in clear weather and extremely heavy rain. While the feature-based method fails, the sequence-based algorithm is able to match trajectory segments at 100% precision with recall rates of up to 60%.


title: Graph mining approach to suspicious transaction detection

abstract: Suspicious transaction detection is used to report banking transactions that may be connected with criminal activities. Obviously, perpetrators of criminal acts strive to make the transactions as innocent-looking as possible. Because activities such as money laundering may involve complex organizational schemes, machine learning techniques based on individual transactions analysis may perform poorly when applied to suspicious transaction detection. In this paper, we propose a new machine learning method for mining transaction graphs. The method proposed in this paper builds a model of subgraphs that may contain suspicious transactions. The model used in our method is parametrized using fuzzy numbers which represent parameters of transactions and of the transaction subgraphs to be detected. Because money laundering may involve transferring money through a variable number of accounts the model representing transaction subgraphs is also parametrized with respect to some structural features. In contrast to some other graph mining methods in which graph isomorphisms are used to match data to the model, in our method we perform a fuzzy matching of graph structures.


title: Handwriting recognition research: Twenty years of achievement... and beyond

abstract: 240,505. Schieferstein, G. H. April 28, 1924. Machinery, utilizing inertia to obtain movements in. - An elastically-supported mass excited gradually to movement in a circle or other closed curve by rotary driving means acting thereon through an extremely elastic coupling, constitutes a mechanical system having pronounced natural frequency and stated to be applicable for regulating rotating mechanisms such as motors, generators, &c., electric &c. clocks &c. with continuously-moving hands, phonographs, telescope-movers &c. Various embodiments of the invention are described. In Fig. 1, the mass a constrained by springs b , b , b , b is gradually excited to movement in a circle by a crank g acting through an extremely flexible coupling k. In Fig. 2, the mass is constrained by rectangularly arranged springs b , b and the flexible coupling comprises a crank g formed at the end of a wire coil k. Such coupling means may extend on both sides of the mass a. In Fig. 4, an annular mass a, supported peripherally by a number of radial springs b , b , is driven through a spring k by a concentric crank g, an initial displacement being produced either artificially or by the sag of the springs. In a numbers of forms the mass is supported in the manner of a circular pendulum at the end of a spring b, Fig. 5, or of a rod connected to the spring, Fig. 8, the upper end of the spring being fixed to a support, and the coupling k being in the form of a conical spring fixed to the mass with its end engaging a hole or slot in a crank disc g. One or more arms c may be carried by the pendulum rod as shown in Fig. 8, enabling linear motion to be transmitted to or from the device through an elastic coupling k . In another form, the crank disc g is at the upper end of the pendulum, being connected through a spring, such as k, to a ring secured to the pendulum rod and enclosing the fixed support. In Fig. 9, a pair of weights a , a is supported by a membrane or elastic plate b and executes a tumbling movement. In another form, a pendular mass is supported by a number of spring bars or coiled springs. Slight modifications of the spring coupling means k, shown in Figs. 1 and 2, are described. An elliptical movement may be obtained by an asymmetric arrangement of the springs &c. Specifications 7252/86 and 1644/78, [Class 103, Railway &c. vehicles], are referred to.


An Efficient Data Hiding Scheme Using Steganography and Cryptography Technique


title: Dermoscopy basics and melanocytic lesions (Part 2 of 2)

abstract: In this second part of the review, the basic variations of parallel pattern in acral melanocytic lesions will be discussed. The relation between parallel pattern and dermatopathology will be elaborated. Finally, the second step of the 2-step procedure will be presented to illustrate how to judge whether the lesion is malignant or benign based on the distribution of colours and structures.


A comparison of alternative systems for measuring smoking-attributable deaths in Oregon, USA


title: A hypothesis for basal ganglia-dependent reinforcement learning in the songbird

abstract: Abstract Most of our motor skills are not innately programmed, but are learned by a combination of motor exploration and performance evaluation, suggesting that they proceed through a reinforcement learning (RL) mechanism. Songbirds have emerged as a model system to study how a complex behavioral sequence can be learned through an RL-like strategy. Interestingly, like motor sequence learning in mammals, song learning in birds requires a basal ganglia (BG)-thalamocortical loop, suggesting common neural mechanisms. Here, we outline a specific working hypothesis for how BG-forebrain circuits could utilize an internally computed reinforcement signal to direct song learning. Our model includes a number of general concepts borrowed from the mammalian BG literature, including a dopaminergic reward prediction error and dopamine-mediated plasticity at corticostriatal synapses. We also invoke a number of conceptual advances arising from recent observations in the songbird. Specifically, there is evidence for a specialized cortical circuit that adds trial-to-trial variability to stereotyped cortical motor programs, and a role for the BG in “biasing” this variability to improve behavioral performance. This BG-dependent “premotor bias” may in turn guide plasticity in downstream cortical synapses to consolidate recently learned song changes. Given the similarity between mammalian and songbird BG-thalamocortical circuits, our model for the role of the BG in this process may have broader relevance to mammalian BG function. This article is part of a Special Issue entitled: Function and Dysfunction of the Basal Ganglia.


title: Game Theory for Networks: A tutorial on game-theoretic tools for emerging signal processing applications

abstract: The aim of this tutorial is to provide an overview, although necessarily incomplete, of game theory (GT) for signal processing (SP) in networks. One of the main features of this contribution is to gather in a single article some fundamental game-theoretic notions and tools that, over the past few years, have become widespread in the SP literature. In particular, both strategic-form and coalition-form games are described in detail, and the key connections and differences between them are outlined. Moreover, particular attention is also devoted to clarifying the connections between strategic-form games and distributed optimization and learning algorithms. Beyond an introduction to the basic concepts and main solution approaches, several carefully designed examples are provided to allow a better understanding of how to apply the described tools.


title: Automated feature generation from structured knowledge

abstract: The prediction accuracy of any learning algorithm highly depends on the quality of the selected features; but often, the task of feature construction and selection is tedious and nonscalable. In recent years, however, there have been numerous projects with the goal of constructing general-purpose or domain-specific knowledge bases with entity-relationship-entity triples extracted from various Web sources or collected from user communities, e.g. YAGO, DBpedia, Freebase, UMLS, etc. This paper advocates the simple and yet far-reaching idea that the structured knowledge contained in such knowledge bases can be exploited to automatically extract features for general learning tasks. We introduce an expressive graph-based language for extracting features from such knowledge bases and a theoretical framework for constructing feature vectors from the extracted features. Our experimental evaluation on different learning scenarios provides evidence that the features derived through our framework can considerably improve the prediction accuracy, especially when the labeled data at hand is sparse.


title: Microplastic transport in soil by earthworms

abstract: Despite great general benefits derived from plastic use, accumulation of plastic material in ecosystems, and especially microplastic, is becoming an increasing environmental concern. Microplastic has been extensively studied in aquatic environments, with very few studies focusing on soils. We here tested the idea that microplastic particles (polyethylene beads) could be transported from the soil surface down the soil profile via earthworms. We used Lumbricus terrestris L., an anecic earthworm species, in a factorial greenhouse experiment with four different microplastic sizes. Presence of earthworms greatly increased the presence of microplastic particles at depth (we examined 3 soil layers, each 3.5 cm deep), with smaller PE microbeads having been transported downward to a greater extent. Our study clearly shows that earthworms can be significant transport agents of microplastics in soils, incorporating this material into soil, likely via casts, burrows (affecting soil hydraulics), egestion and adherence to the earthworm exterior. This movement has potential consequences for exposure of other soil biota to microplastics, for the residence times of microplastic at greater depth, and for the possible eventual arrival of microplastics in the groundwater.


title: Energy intake and other determinants of relative weight13

abstract: The relationships of relative weight to energy intake and to physical activity were studied among 141 females aged 34-59 y. As observed in previous studies Quetelet index (wt/ht2) was inversely related to energy intake (r = -0.11). However, obese women tended to be older (r = 0.16), exercise less (r = -0.30), and drink less alcohol (r = -0.16) than nonobese women. Older women had lower energy intake (r = -0.23) and exercised less (r = -0.12) than younger women. Energy intake and physical activity were positively related (r = 0.23). After adjustment for age, physical activity, alcohol, and smoking, the inverse correlation between relative weight and energy intake was significantly reduced (p = 0.04) from r = -0.11 to r = -0.02. Obese women reported higher intakes of total fat, and relative weight was significantly correlated with intakes of total fat (r = 0.20) and saturated fatty acids (r = 0.16). These data highlight the importance of considering factors that may confound the relationship between energy intake and obesity, and they suggest that fat intake may play a role in obesity that is independent of total energy intake.


title: Social, proximal and conditioned threat

abstract: Abstract Responding to threats in the environment is crucial for survival. Certain types of threat produce defensive responses without necessitating previous experience and are considered innate, whereas other threats are learned by experiencing aversive consequences. Two important innate threats are whether an encountered stimulus is a member of the same species (social threat) and whether a stimulus suddenly appears proximal to the body (proximal threat). These threats are manifested early in human development and robustly elicit defensive responses. Learned threat, on the other hand, enables adaptation to threats in the environment throughout the life span. A well-studied form of learned threat is fear conditioning, during which a neutral stimulus acquires the ability to eliciting defensive responses through pairings with an aversive stimulus. If innate threats can facilitate fear conditioning, and whether different types of innate threats can enhance each other, is largely unknown. We developed an immersive virtual reality paradigm to test how innate social and proximal threats are related to each other and how they influence conditioned fear. Skin conductance responses were used to index the autonomic component of the defensive response. We found that social threat modulates proximal threat, but that neither proximal nor social threat modulates conditioned fear. Our results suggest that distinct processes regulate autonomic activity in response to proximal and social threat on the one hand, and conditioned fear on the other.


title: The influence of personality on Facebook usage, wall postings, and regret

abstract: The Five Factor Model of personality has been used extensively in the management and psychology fields to predict attitudes and behaviors. Only recently have researchers begun to examine the role of psychological factors in influencing an individual's use of technology platforms, such as Facebook. This study uses both a survey of Facebook users and actual Facebook data to uncover why some individuals are more involved in Facebook than others. 219 undergraduate students participated in a survey that assessed their personality and their reported usage of Facebook. Of these, 143 voluntarily befriended the investigator, which gave her access to their actual Facebook sites and objective data on their number of friends, photos, and wall postings. Results showed personality to explain significant amounts of variance over and above gender and Facebook experience in terms of actual number of Facebook friends, the nature of their wall postings and on their level of regret for inappropriate Facebook content.


title: Communicating Context to the Crowd for Complex Writing Tasks

abstract: Crowd work is typically limited to simple, context-free tasks because they are easy to describe and understand. In contrast, complex tasks require communication between the requester and workers to achieve mutual understanding, which can be more work than it is worth. This paper explores the notion of structured communication: using structured microtasks to support communication in the domain of complex writing. Our studies compare a variety of communication mechanisms with respect to the costs to the requester in providing information and the value of that information to workers while performing the task. We find that different mechanisms are effective at different stages of writing. For early drafts, asking the requester to state the biggest problem in the current write-up is valuable and low cost, while later it is more useful for the worker if the requester highlights the text that needs to be improved. These findings can be used to enable richer, more interactive crowd work than what currently seems possible. We incorporate the findings in a workflow for crowdsourcing written content using appropriately timed mechanisms for communicating with the crowd.


A Survey on Sentiment Analysis Algorithms for Opinion Mining


title: Towards Contactless, Low-Cost and Accurate 3D Fingerprint Identification

abstract: Human identification using fingerprint impressions has been widely studied and employed for more than 2000 years. Despite new advancements in the 3D imaging technologies, widely accepted representation of 3D fingerprint features and matching methodology is yet to emerge. This paper investigates 3D representation of widely employed 2D minutiae features by recovering and incorporating (i) minutiae height z and (ii) its 3D orientation φ information and illustrates an effective matching strategy for matching popular minutiae features extended in 3D space. One of the obstacles of the emerging 3D fingerprint identification systems to replace the conventional 2D fingerprint system lies in their bulk and high cost, which is mainly contributed from the usage of structured lighting system or multiple cameras. This paper attempts to addresses such key limitations of the current 3D fingerprint technologies bydeveloping the single camera-based 3D fingerprint identification system. We develop a generalized 3D minutiae matching model and recover extended 3D fingerprint features from the reconstructed 3D fingerprints. 2D fingerprint images acquired for the 3D fingerprint reconstruction can themselves be employed for the performance improvement and have been illustrated in the work detailed in this paper. This paper also attempts to answer one of the most fundamental questions on the availability of inherent discriminable information from 3D fingerprints. The experimental results are presented on a database of 240 clients 3D fingerprints, which is made publicly available to further research efforts in this area, and illustrate the discriminant power of 3D minutiae representation and matching to achieve performance improvement.


title: A Case for Hardware Protection of Guest VMs from Compromised Hypervisors in Cloud Computing

abstract: Cloud computing, enabled by virtualization technologies, is becoming a mainstream computing model. Many companies are starting to utilize the infrastructure-as-a-service (IaaS) cloud computing model, leasing guest virtual machines (VMs) from the infrastructure providers for economic reasons: to reduce their operating costs and to increase the flexibility of their own infrastructures. Yet, many companies may be hesitant to move to cloud computing due to security concerns. An integral part of any virtualization technology is the all-powerful hyper visor. A hyper visor is a system management software layer which can access all resources of the platform. Much research has been done on using hyper visors to monitor guest VMs for malicious code and on hardening hyper visors to make them more secure. There is, however, another threat which has not been addressed by researchers -- that of compromised or malicious hyper visors that can extract sensitive or confidential data from guest VMs. Consequently, we propose that a new research direction needs to be undertaken to tackle this threat. We further propose that new hardware mechanisms in the multi core microprocessors are a viable way of providing protections for the guest VMs from the hyper visor, while still allowing the hyper visor to flexibly manage the resources of the physical platform.


title: Interactive knowledge-based kernel PCA

abstract: Data understanding is an iterative process in which domain experts combine their knowledge with the data at hand to explore and confirm hypotheses. One important set of tools for exploring hypotheses about data are visualizations. Often, however, traditional, unsupervised dimensionality reduction algorithms are used for visualization. These tools allow for interaction, i.e., exploring different visualizations, only by means of manipulating some technical parameters of the algorithm. Therefore, instead of being able to intuitively interact with the visualization, domain experts have to learn and argue about these technical parameters. In this paper we propose a knowledge-based kernel PCA approach that allows for intuitive interaction with data visualizations. Each embedding direction is given by a non-convex quadratic optimization problem over an ellipsoid and has a globally optimal solution in the kernel feature space. A solution can be found in polynomial time using the algorithm presented in this paper. To facilitate direct feedback, i.e., updating the whole embedding with a sufficiently high frame-rate during interaction, we reduce the computational complexity further by incremental up- and down-dating. Our empirical evaluation demonstrates the flexibility and utility of this approach.


title: Sensor and vision based autonomous AGRIBOT for sowing seeds

abstract: Autonomous agriculture robot is one of the promising solutions for precision agriculture. This paper presents the proposed sensor and vision based agricultural robot for sowing seeds. This prototype can navigate it on any agricultural land and perform seed sowing operation simultaneously. The onboard sensors along with vision system and vision based approaches achieves the navigation and localization tasks. Self-awareness of the robot's position is determined by the global and local maps generated from Global Positioning System (GPS) and on-board vision system paired with a personal computer. This paper also presents the proposed sensor based precision seed metering and sowing mechanism. The proposed robot is a micro planter whose primary task would be to sow seeds at prefixed seeding intervals in the field. The dimensions of the proposed robot are 26.5 × 18.5 × 19.65 mm as L × B × H respectively. A suspension system has been used to maintain the stability of the vehicle and prevent it from toppling in motion.


title: An Empirical Evaluation of Deep Learning for Network Anomaly Detection

abstract: Deep learning has been given a great deal of attention with its success story in many areas such as image analysis and speech recognition. In particular, deep learning is good at dealing with high-dimensional data exhibiting non-linearity. Our preliminary study reveals a very high degree of non-linearity from network traffic data, which explains why it is hard to improve the detection accuracy by using conventional machine learning techniques (e.g., SVM, Random Forest, Ad-aboosting). In this study, we empirically evaluate deep learning to see its feasibility for network anomaly detection. We examine a set of deep learning models constructed based on the Fully Connected Network (FCN), Variational AutoEncoder (VAE), and Long Short-Term Memory with Sequence to Sequence (LSTM Seq2Seq) structures, with two public traffic data sets that have distinctive properties with respect to the distribution of normal and attack populations. Our experimental results confirm the potential of deep learning models for network anomaly detection, and the model based on the LSTM Seq2Seq structure shows a highly promising performance, yielding 99% of binary classification accuracy on the public data sets.


Binarized LSTM Language Model


title: Sparse component separation from Poisson measurements

abstract: Blind source separation (BSS) aims at recovering signals from mixtures. This problem has been extensively studied in cases where the mixtures are contaminated with additive Gaussian noise. However, it is not well suited to describe data that are corrupted with Poisson measurements such as in low photon count optics or in high-energy astronomical imaging (e.g. observations from the Chandra or Fermi telescopes). To that purpose, we propose a novel BSS algorithm coined pGMCA that specifically tackles the blind separation of sparse sources from Poisson measurements.


title: Subgroup analysis , covariate adjustment and baseline comparisons in clinical trial reporting : current practice and problems

abstract: Clinical trial investigators often record a great deal of baseline data on each patient at randomization. When reporting the trial's findings such baseline data can be used for (i) subgroup analyses which explore whether there is evidence that the treatment difference depends on certain patient characteristics, (ii) covariate-adjusted analyses which aim to refine the analysis of the overall treatment difference by taking account of the fact that some baseline characteristics are related to outcome and may be unbalanced between treatment groups, and (iii) baseline comparisons which compare the baseline characteristics of patients in each treatment group for any possible (unlucky) differences. This paper examines how these issues are currently tackled in the medical journals, based on a recent survey of 50 trial reports in four major journals. The statistical ramifications are explored, major problems are highlighted and recommendations for future practice are proposed. Key issues include: the overuse and overinterpretation of subgroup analyses; the underuse of appropriate statistical tests for interaction; inconsistencies in the use of covariate-adjustment; the lack of clear guidelines on covariate selection; the overuse of baseline comparisons in some studies; the misuses of significance tests for baseline comparability, and the need for trials to have a predefined statistical analysis plan for all these uses of baseline data.