title: Mental rotation in human infants: A sex difference

abstract: A sex difference on mental-rotation tasks has been demonstrated repeatedly, but not in children less than 4 years of age. To demonstrate mental rotation in human infants, we habituated 5-month-old infants to an object revolving through a 240° angle. In successive test trials, infants saw the habituation object or its mirror image revolving through a previously unseen 120° angle. Only the male infants appeared to recognize the familiar object from the new perspective, a feat requiring mental rotation. These data provide evidence for a sex difference in mental rotation of an object through three-dimensional space, consistently seen in adult populations.


title: Modeling vacuum bellows soft pneumatic actuators with optimal mechanical performance

abstract: This paper presents the concept and model of “Vacuum Bellows,” a cylindrical membrane-reinforced contractile vacuum soft pneumatic actuator (V-SPAs). These actuators consist of a tubular membrane connected to a series of interior rigid rings periodically spaced along its length. Our model shows how the rings can be spaced to achieve a desired actuator force profile. For example, the contraction ratio can be maximized by spacing the rings one diameter apart inside the tube. The work output of the actuator can be concentrated in the initial portion of the stroke by increasing the ring spacing. And, usefully, an approximately constant force-to-pressure relationship can be created by spacing the rings a fraction of a diameter apart. Our experimental results highlight the utility of the model and some practical considerations for actuator fabrication and use. The experimental results demonstrate how the ring spacing can be used to achieve high peak forces per unit pressure (three times greater than an equivalent-diameter piston achieved experimentally) or large contractions (achieved contraction to 30 % of the extended length). Our model suggests that this performance can be improved with improved fabrication techniques.


title: Kathará: A container-based framework for implementing network function virtualization and software defined networks

abstract: Network Function Virtualization (NFV) and Software-Defined Networking (SDN) are deeply changing the networking field by introducing software at any level, aiming at decoupling the logic from the hardware. Together, they bring several benefits, mostly in terms of scalability and flexibility. Up to now, SDN has been used to support NFV from the routing and the architectural point of view. In this paper we present Kathara, a framework based on containers, that allows network operators to deploy Virtual Network Functions (VNFs) through the adoption of emerging data-plane programmable capabilities, such as P4-compliant switches. It also supports the coexistence of SDN and traditional routing protocols in order to set up arbitrarily complex networks. As a side effect, thanks to Kathara, we demonstrate that implementing NFV by means of specific-purpose equipment is feasible and it provides a gain in performance while preserving the benefits of NFV. We measure the resource consumption of Kathara and we show that it performs better than frameworks that implement virtual networks using virtual machines by several orders of magnitude.


title: Requirements for smart cities: Results from a systematic review of literature

abstract: Smart cities are gaining increasingly more importance in both research and business circles. Much effort is spent to defining what a smart city is and how it could be realized with today's or future technologies. However, from requirements engineering perspective, our knowledge of smart cities is fragmented; little is known about the requirements for smart cities as complex systems, or as systems of systems, in specific application domains. In this paper, we elicit requirements for smart city systems by carrying out a systematic review of scientific literature focused on the so-called “hard” domains of smart cities. Based on 32 selected publications, we gathered and classified requirements in respect to three types of smart city systems (instrumented, interconnected, and intelligent systems) and four classes of requirements: end-to-end experience, architectural, security, and infrastructure requirements. Our most important findings are that: (1) most authors took a bottom-up approach to defining requirements for smart cities; their efforts focused mainly on requirements important for designing an architecture that could scale up to any size and include any device or system; and (2) very little is mentioned on the newly emerging security and privacy challenges that are critical to gain the citizens' acceptance of smart city apps.


title: Analyzing Opinion Spammers’ Network Behavior in Online Review Systems

abstract: Review systems are indisputably important resource for users when making various decisions on products or services. Consequently, they become increas- ingly targeted by attackers who deliberately inject biased reviews, so called opinion spams, aiming to influence normal users’ decisions for financial gain. In this paper, we perform an empirical analysis of opinion spammers on one of the famous online review systems, Yelp. Specifically, we analyze two different types of networks: implicit and explicit networks of opinion spammers and those of non- spammers. Through analyzing the network characteristics in different networks, we show similarities and differences between opinion spammers and non-spammers in terms of statistical characteristics and network properties. More specifically, through extensive analysis on Yelp dataset, we show that (i) the explicit network of non-spammers exhibits typical “small-world” properties of social networks. (ii) the implicit network of non-spammers is close to random networks. (iii) in both explicit and implicit networks, opin- ion spammers form near-isolated communities with dense inner connections among themselves, while exhibiting the lower level of “small-world” properties.


A dataset for multimodal question answering in the cultural heritage domain


title: Real time car parking system: A novel taxonomy for integrated vehicular computing

abstract: Automation of real time car parking system (RTCPS) using mobile cloud computing (MCC) and vehicular networking (VN) has given rise to a novel concept of integrated communication-computing platforms (ICCP). The aim of ICCP is to evolve an effective means of addressing challenges such as improper parking management scheme, traffic congestion in parking lots, insecurity of vehicles (safety applications), and other Infrastructure-to-Vehicle (I2V) services for providing data dissemination and content delivery services to connected Vehicular Clients (VCs). Edge (parking lot based) Fog computing (EFC) through road side sensor based monitoring is proposed to achieve ICCP. A real-time cloud to vehicular clients (VCs) in the context of smart car parking system (SCPS) which satisfies deterministic and non-deterministic constraints is introduced. Vehicular cloud computing (VCC) and intra-Edge-Fog node architecture is presented for ICCP. This is targeted at distributed mini-sized self-energized Fog nodes/data centers, placed between distributed remote cloud and VCs. The architecture processes data-disseminated real-time services to the connected VCs. The work built a prototype testbed comprising a black box PSU, Arduino IoT Duo, GH-311RT ultrasonic distance sensor and SHARP 2Y0A21 passive infrared sensor for vehicle detection; LinkSprite 2MP UART JPEG camera module, SD card module, RFID card reader, RDS3115 metal gear servo motors, FPM384 fingerprint scanner, GSM Module and a VCC web portal. The testbed functions at the edge of the vehicular network and is connected to the served VCs through Infrastructure-to-Vehicular (I2V) TCP/IP-based single-hop mobile links. This research seeks to facilitate urban renewal strategies and highlight the significance of ICCP prototype testbed. Open challenges and future research directions are discussed for an efficient VCC model which runs on networked fog centers (NetFCs).


title: Embryonic Amygdalar Transplants in Adult Rats with Motor Cortex Lesions: A Molecular and Electrophysiological Analysis

abstract: Transplants of embryonic nervous tissue ameliorate motor deficits induced by motor cortex lesions in adult animals. Restoration of lost brain functions has been recently shown in grafts of homotopic cortical origin, to be associated with a functional integration of the transplant after development of reciprocal host-graft connections. Nevertheless little is known about physiological properties or gene expression profiles of cortical implants with functional restorative capacity but no cortical origin. In this study, we show molecular and electrophysiological evidence supporting the functional development and integration of heterotopic transplants of embryonic amygdalar tissue placed into pre lesioned motor cortex of adult rats. Grafts were analyzed 3 months post-transplantation. Using reverse transcriptase quantitative polymerase chain reaction (RT-qPCR), we found that key glutamatergic, GABAergic and muscarinic receptors transcripts were expressed at different quantitative levels both in grafted and host tissues, but were all continuously present in the graft. Parallel sharp electrode recordings of grafted neurons in brain slices showed a regular firing pattern of transplanted neurons similar to host amygdalar pyramidal neurons. Synaptic connections from the adjacent host cortex on grafted neurons were electrophysiologically investigated and confirmed our molecular results. Taken together, our findings indicate that grafted neurons from a non-cortical, non-motor-related, but ontogenetical similar source, not only received functionally effective contacts from the adjacent motor cortex, but also developed electrophysiological and gene expression patterns comparable to host pyramidal neurons; suggesting an interesting tool for the field of neural repair and donor tissue in adults.


title: Simple Hybrid ARQ Schemes Based on Systematic Polar Codes for IoT Applications

abstract: Hybrid ARQ (H-ARQ) schemes enable wireless reliability with high transmission efficiency, which is demanded for battery-operated low-complexity applications, such as Internet of Things. We propose two schemes to enable type-II H-ARQ based on systematic polar codes. We introduce code inversion for polar codes to recover the message from the redundant bits only. The first scheme we propose is an H-ARQ based on half-rate polar codes and code inversion. The second scheme develops a more general H-ARQ technique assuming an arbitrary code rate. We provide a theoretical framework for calculation of the frame error rate in polar codes with incomplete codeword elements. Simulations show that the proposed schemes can achieve a significant performance gain up to 4 dB in terms of throughput efficiency compared with the state-of-the-art H-ARQ scheme used in the high-quality-of-service mode of the IEEE 802.15.6-2012 standard for wireless body area networks.


title: The Contagious Leader: Impact of the Leader's Mood on the Mood of Group Members, Group Affective Tone, and Group Processes.

abstract: The present study examined the effects of leaders' mood on (a) the mood of individual group members, (b) the affective tone of groups, and (c) 3 group processes: coordination, effort expenditure, and task strategy. On the basis of a mood contagion model, the authors found that when leaders were in a positive mood, in comparison to a negative mood, (a) individual group members experienced more positive and less negative mood, and (b) groups had a more positive and a less negative affective tone. The authors also found that groups with leaders in a positive mood exhibited more coordination and expended less effort than did groups with leaders in a negative mood. Applied implications of the results are discussed.


title: Subinhibitory concentrations of the penicillin antibiotics induce quorum-dependent violacein synthesis in Chromobacterium violaceum

abstract: The goal of the work was to analyze the ability of natural and semisynthetic penicillins to regulate the quorum sensing system in bacteria. The test strain was C. violaceum NCTC 13274 (CV 026) bearing mini-Tn5 insertion in the cviI gene that led to irreversible loss of N-acyl-L-homoserine lactone (AHL) biosynthesis and reversible suppression of violacein biosynthesis. Within the temperature range suboptimal for C. violaceum growth (17–22°C), subinhibitory concentrations of piperacillin, azlocillin, ticarcillin, carbenicillin, and oxacillin showed a quorum-dependent induction of violacein production, probably acting as AHL-mimetics. Cleavage of these antibiotics with beta-lactamase I and II led to the loss of both antibacterial and quorum-inducing activities. Inhibition of C. violaceum beta-lactamase by sulbactam resulted in manifestation of combined antibacterial and quorum-inducing activity of ampicillin and amoxicillin. These results improve our understanding of the role of beta-lactams and beta-lactamases in the processes of interspecific antagonism and in microbial intercellular communication.


title: Applying visual domain style transfer and texture synthesis techniques to audio: insights and challenges

abstract: Style transfer is a technique for combining two images based on the activations and feature statistics in a deep learning neural network architecture. This paper studies the analogous task in the audio domain and takes a critical look at the problems that arise when adapting the original vision-based framework to handle spectrogram representations. We conclude that CNN architectures with features based on 2D representations and convolutions are better suited for visual images than for time–frequency representations of audio. Despite the awkward fit, experiments show that the Gram matrix determined “style” for audio is more closely aligned with timbral signatures without temporal structure, whereas network layer activity determining audio “content” seems to capture more of the pitch and rhythmic structures. We shed insight on several reasons for the domain differences with illustrative examples. We motivate the use of several types of one-dimensional CNNs that generate results that are better aligned with intuitive notions of audio texture than those based on existing architectures built for images. These ideas also prompt an exploration of audio texture synthesis with architectural variants for extensions to infinite textures, multi-textures, parametric control of receptive fields and the constant-Q transform as an alternative frequency scaling for the spectrogram.


title: Linearized Alternating Direction Method with Gaussian Back Substitution for Separable Convex Programming

abstract: Recently, we have proposed to combine the alternating direction method (ADM) with a Gaussian back substitution procedure for solving the convex minimization model with linear constraints and a general separable objective function, i.e., the objective function is the sum of many functions without coupled variables. In this paper, we further study this topic and show that the decomposed subproblems in the ADM procedure can be substantially alleviated by linearizing the involved quadratic terms arising from the augmented Lagrangian penalty on the model's linear constraints. When the resolvent operators of the separable functions in the objective have closed- form representations, embedding the linearization into the ADM subproblems becomes necessary to yield easy subproblems with closed-form solutions. We thus show theoretically that the blend of ADM, Gaussian back substitution and linearization works effectively for the separable convex minimization model under consideration.


Distributionally Robust Deep Learning as a Generalization of Adversarial Training


title: Alcohol Drinking Cessation and the Risk of Laryngeal and Pharyngeal Cancers: A Systematic Review and Meta-Analysis

abstract: To evaluate the effect of alcohol cessation on the risk of developing laryngeal and pharyngeal cancers, combining available evidence in the scientific literature in a meta-analysis.


title: Process performance model for predicting Delivered Defect Density in a software scrum project

abstract: Agile scrum projects are gaining popularity for software development and delivery, due to its inherent attributes of flexibility in execution and quicker time to market. However, the critics of Agile have been raising questions on the predictability of success of Agile execution as the emphasis given to quantitative management using predictive techniques for an Agile project may not be to the extent of large mission critical projects which uses in-process leading indicators to predict outcomes. One possible solution to this challenge is to use the best practices from industry adopted models like the Capability Maturity Model Integration® for planning and managing Agile software delivery. In this paper, we present a study were a prediction model was developed for use in Agile projects for systematic in-process monitoring and decision making. The purpose of this paper is to propose a predictive model as a driver for continuous improvement of Delivered Defect Density in the context of a Scrum project.Our findings show that using the prediction model, the success of Agile project could be forecasted and controlled during its planning and execution phases as against waiting for the final release to The paper starts with defining the background and context of the study, research methodology and study of existing literature in this area. A brief description of the major knowledge areas considered in the study namely Agile, CMMI and High Maturity practices in CMMI is then presented. An introduction on measures, metrics and prediction models is also provided. The development of the Agile Prediction Model and the application of the model Agile SCRUM projects is addressed as the main theme of the paper which concludes with the advantages, shortcomings and limitations of the model developed. 1. Background of the study In organizations where quality of software is a primary goal for the software projects, it is useful to measure and observe Delivered Defect Density as a lagging indicator signifying the quality of developed software. Though this is a good metric to indicate the goodness of the delivered software, the metric lacks the ability to be controlled, as it is an outcome of the software development process and not an in-process metric. In this context, the need for in-process or leading indicators which will help practitioners get visibility on the outcome is important. In this paper, the processes and sub-processes leading to Delivered Defect Density is


title: The Bag-of-Opinions Method for Review Rating Prediction from Sparse Text Patterns

abstract: The problem addressed in this paper is to predict a user's numeric rating in a product review from the text of the review. Unigram and n-gram representations of text are common choices in opinion mining. However, unigrams cannot capture important expressions like "could have been better", which are essential for prediction models of ratings. N-grams of words, on the other hand, capture such phrases, but typically occur too sparsely in the training set and thus fail to yield robust predictors. This paper overcomes the limitations of these two models, by introducing a novel kind of bag-of-opinions representation, where an opinion, within a review, consists of three components: a root word, a set of modifier words from the same sentence, and one or more negation words. Each opinion is assigned a numeric score which is learned, by ridge regression, from a large, domain-independent corpus of reviews. For the actual test case of a domain-dependent review, the review's rating is predicted by aggregating the scores of all opinions in the review and combining it with a domain-dependent unigram model. The paper presents a constrained ridge regression algorithm for learning opinion scores. Experiments show that the bag-of-opinions method outperforms prior state-of-the-art techniques for review rating prediction.


title: Revise Saturated Activation Functions

abstract: In this paper, we revise two commonly used saturated functions, the logistic sigmoid and the hyperbolic tangent (tanh). ::: We point out that, besides the well-known non-zero centered property, slope of the activation function near the origin is another possible reason making training deep networks with the logistic function difficult to train. We demonstrate that, with proper rescaling, the logistic sigmoid achieves comparable results with tanh. ::: Then following the same argument, we improve tahn by penalizing in the negative part. We show that "penalized tanh" is comparable and even outperforms the state-of-the-art non-saturated functions including ReLU and leaky ReLU on deep convolution neural networks. ::: Our results contradict to the conclusion of previous works that the saturation property causes the slow convergence. It suggests further investigation is necessary to better understand activation functions in deep architectures.


title: 3D Touchless Fingerprints: Compatibility with Legacy Rolled Images

abstract: Fingerprints are traditionally captured based on contact of the finger on paper or a platen surface. This often results in partial or degraded images due to improper finger placement, skin deformation, slippage and smearing, or sensor noise from wear and tear of surface coatings. A new generation of touchless live scan devices that generate 3D representation of finger-prints iS appearing in the market. This new sensing technology addresses manye of the problems stated above. However, 3D touchless fingerprint images need to be compatible with the legacy rolled images used in Automated Fingerprint Identification Systems (AFIS). In order to solve this interoperability issue, we propose an unwrapping algorithm that unfolds the 3D fingerprint in such a way that it resembles the effect of virtually rolling the 3D finger on a 2D plane Our preliminary experiments show promising results in obtaining touchless fingerprint images that are of high quality and at the same time compatible with legacy rolled fingerprint images.


title: A multiscale based approach for automatic shadow detection and removal in natural images

abstract: Shadow is a natural phenomenon observed in most natural images. It can reveal information about the objects shape as well as the illumination direction. In computer vision algorithms, shadow can affect negatively image segmentation results, feature extraction, or object tracking. For that, it is necessary to detect and eliminate shadow. Texture remains the best feature used to detect the shadow and photometric information can be used to eliminate it. However, in case of an image with a shadow projected on a complex texture, most of the proposed approaches in literature are useless. In this study, we propose an automatic and data-driven approach for shadow detection and elimination based on the Bidimensional Empirical Mode Decomposition (BEMD). The main idea is to decompose the shaded image into intrinsic components (IMF) that contains only texture and a residue with only objects shape. Then, shadow detection is performed on the IMFs by matching the pair of segmented regions using texture features, while elimination is carried out via a Gaussian approximation applied only on the residue. Finally, the shadow-free image is obtained by adding all the IMFs and the shadow-free residue. The proposed approach is evaluated in comparison with recent approaches on images with the different type of shadow.


title: A 50 Gb/s 190 mW Asymmetric 3-Tap FFE VCSEL Driver

abstract: This paper describes the design of an energy-efficient vertical-cavity surface-emitting laser (VCSEL) driver circuit implemented in a 130 nm SiGe BiCMOS technology. The driver features a 3-tap feed-forward equalizer where positive and negative peaks are added to the main signal to compensate for the low-pass characteristic of VCSELs. The circuit is also able to generate asymmetric pre-emphasis to counteract the VCSEL nonlinearity. Bonded to an 18 GHz VCSEL, the driver can reach an error-free (bit error rate < 10−12) optical data rate of 50 Gb/s with an horizontal eye opening better than 0.2 unit interval using a 22 GHz photoreceiver without equalization, retiming, and limiting amplifier at the receiver side. At 48 Gb/s, the horizontal eye opening is 0.5 unit interval. The circuit dissipates only 190 mW from a dual supply of 2.5 and 3.3 V, including the VCSEL power. To the best of the authors’ knowledge, this is the fastest common-cathode VCSEL driver with lowest power consumption for data rates higher than 35 Gb/s. Thanks to the active delay line and the application of vertical inductor, the driver is very compact with an active area of only 0.036 mm2 including the inductor.


title: Compression of convolutional neural networks: A short survey

abstract: Nowadays, convolutional neural networks (CNN) are considered as the state-of-the-art algorithms for various tasks, especially for image classification and recognition. Because of that, more and more attention is aimed towards implementation of CNNs on embedded systems. Main obstacles for implementing CNNs on embedded systems are their large model size and large number of operations needed for inference. In order to surpass these obstacles, algorithms for CNN compression tend to lower model size and number of operations needed for inference. In this paper we review the state-of-the-art in CNN compression. To this end we divided all approaches for CNN compression into three groups: precision reduction, network pruning and design of compact network architectures. After presenting the main approaches in each group we conclude that the future CNN compression algorithms should be co-designed with hardware which will process deep learning algorithms.


title: Analysis of ECDSA Authentication Processing in VANETs

abstract: Transport safety applications aim at avoiding vehicular accidents by using secure broadcast vehicle-to-vehicle (V2V) communications. However, any security mechanism used for authenticating broadcast V2V messages comes with overhead in terms of computation and communications. The IEEE1609.2 standard for vehicular ad hoc networks is based on the ECDSA algorithm for supporting the authentication mechanism. This paper provides an assessment of the processing overhead of ECDSA. We describe the impact of this mechanism on VANET performance. Then we focus on the impact of authentication processing on the braking distance.


title: The Humanitarian eXchange Language: Coordinating disaster response with semantic web technologies

abstract: The Humanitarian eXchange Language (HXL) is a project by the United Nations Office for the Coordination of Humanitarian Affairs that aims at refining data management and exchange for disaster response. Data exchange in this field, which often has to deal with chaotic environments heavily affected by an emergency such as a natural disaster or an armed conflict, still happens mostly manually. The goal of HXL is to contribute to the automatization of many of these processes, saving valuable time for staff in the field and improving the information flow for decision makers who have to allocate resources for response activities. This paper gives an overview of this initiative, which is set to significantly improve information exchange in the humanitarian domain. We introduce the HXL vocabulary, which provides a formal definition of the terminology used in this domain, and an initial set of tools and services that produce and consume HXL data. The HXL system infrastructure is introduced, along with its data management principles. The paper concludes with an outlook on the future of HXL and its role in the humanitarian ecosystem.


title: Efficient Lipschitz Extensions for High-Dimensional Graph Statistics and Node Private Degree Distributions

abstract: Lipschitz extensions were recently proposed as a tool for designing node differentially private algorithms. However, efficiently computable Lipschitz extensions were known only for 1-dimensional functions (that is, functions that output a single real value). In this paper, we study efficiently computable Lipschitz extensions for multi-dimensional (that is, vector-valued) functions on graphs. We show that, unlike for 1-dimensional functions, Lipschitz extensions of higher-dimensional functions on graphs do not always exist, even with a non-unit stretch. We design Lipschitz extensions with small stretch for the sorted degree list and for the degree distribution of a graph. Crucially, our extensions are efficiently computable. ::: We also develop new tools for employing Lipschitz extensions in the design of differentially private algorithms. Specifically, we generalize the exponential mechanism, a widely used tool in data privacy. The exponential mechanism is given a collection of score functions that map datasets to real values. It attempts to return the name of the function with nearly minimum value on the data set. Our generalized exponential mechanism provides better accuracy when the sensitivity of an optimal score function is much smaller than the maximum sensitivity of score functions. ::: We use our Lipschitz extension and the generalized exponential mechanism to design a node-differentially private algorithm for releasing an approximation to the degree distribution of a graph. Our algorithm is much more accurate than algorithms from previous work.


LAMB WAVE NON-DESTRUCTIVE INSPECTION OF REPAIRED COMPOSITE LAMINATED STRUCTURES


title: The exposure of honey bees (Apis mellifera; Hymenoptera: Apidae) to pesticides: Room for improvement in research.

abstract: Losses of honey bees have been repeatedly reported from many places worldwide. The widespread use of synthetic pesticides has led to concerns regarding their environmental fate and their effects on pollinators. Based on a standardised review, we report the use of a wide variety of honey bee matrices and sampling methods in the scientific papers studying pesticide exposure. Matrices such as beeswax and beebread were very little analysed despite their capacities for long-term pesticide storage. Moreover, bioavailability and transfer between in-hive matrices were poorly understood and explored. Many pesticides were studied but interactions between molecules or with other stressors were lacking. Sampling methods, targeted matrices and units of measure should have been, to some extent, standardised between publications to ease comparison and cross checking. Data on honey bee exposure to pesticides would have also benefit from the use of commercial formulations in experiments instead of active ingredients, with a special assessment of co-formulants (quantitative exposure and effects). Finally, the air matrix within the colony must be explored in order to complete current knowledge on honey bee pesticide exposure.


title: Deep learning driven blockwise moving object detection with binary scene modeling

abstract: As an important and challenging topic in computer vision, moving object detection is typically posed as a problem of scene analysis, which usually requires both robust feature description and effective statistical modeling. In general, the conventional detection methods make use of pre-defined hand-crafted features and sophisticated background models for scene analysis. Therefore, they usually have a low generalization capability of adapting to different scenes with diverse spatio-temporal motion information. In the face of high-definition video data, sophisticated statistical modeling often suffers from an expensive computation or memory cost because of its low efficiency in evaluation and parallelization. In order to address this issue, we propose a deep learning based block-wise scene analysis method equipped with a binary spatio-temporal scene model. Based on the stacked denoising autoencoder, the deep learning module of the proposed method aims to learn an effective deep image representation encoding the intrinsic scene information, which leads to the robustness of feature description. Furthermore, the proposed binary scene model captures the spatio-temporal scene distribution information in the Hamming space, which ensures the high efficiency of moving object detection. Experimental results on several datasets demonstrate the effectiveness and efficiency of the proposed method.


title: FPGA-based 6-DoF pose estimation with a monocular camera using non co-planer marker and application on micro quadcopter

abstract: Self-localization using visual markers is widely used for precise control of Unmanned aerial vehicles (UAVs). This paper presents a novel method of 6-DoF pose estimation using a monocular camera and non co-planar markers. We conducted numerical simulations comparing pose estimation using co-planar markers and using non co-planar markers, and designed a non co-planar marker feasible for robotics applications. Our method uses a single CPU core and a field programmable gate array (FPGA) and can conduct fast and precise 6-DoF pose estimation at the same frequency of the camera input (100 fps). In addition, to show the performance of our method, we applied our 6-DoF pose estimation algorithm to a palm-sized quadcopter and flew it autonomously using only an on-board system with a monocular camera and three-axis gyro sensor. To the authors' knowledge, this is the first study on fully on-board autonomous flight control of an MAV in 6-DoF space without an accelerometer.


title: The aphasic utterance: A significal perspective

abstract: Abstract On the opportunity to celebrate Welby's centenary of death, I would like to enhance the contribution of her significs – which fortunately reached us, mainly, through the extraordinary effort of Susan Petrilli – to the research developed in the field of neurolinguistics, especially on aphasia. The vital need for signifying underlies all forms of communication – even when language is severely impacted by neurological episodes, as in aphasia states. Traditional approaches usually focus their analyses on what is missing in the linguistic system (generally taken as a code), not only to evaluate language and classify the aphasic into stable categories, but also – and even worse – to base clinical work on those classifications. In the opposite direction, within “discursive neurolinguistics” (as we have been calling the field we develop), we are interested in all kinds of resources that still remain (verbal and/or non-verbal signs) and aim to help aphasics to develop alternative/creative ways to continue in the discursive flow of signification. The refined terminology postulated by Welby and, mainly, her approach to the concrete processes of signification/interpretation enlighten and ground our reflections, as I intend to illustrate by bringing to this text analyses of some aphasic utterances in the significal perspective.


title: © Science Publications 2006 Mathematical Attacks on RSA Cryptosystem

abstract: In this paper some of the most common attacks against Rivest, Shamir, and Adleman (RSA) cryptosystem are presented. We describe the integer factoring attacks, attacks on the underlying mathematical function, as well as attacks that exploit details in implementations of the algorithm. Algorithms for each type of attacks are developed and analyzed by their complexity, memory requirements and area of usage.


title: A physically-based approach to reflection separation

abstract: We propose a physically-based approach to separate reflection using multiple polarized images with a background scene captured behind glass. The input consists of three polarized images, each captured from the same view point but with a different polarizer angle separated by 45 degrees. The output is the high-quality separation of the reflection and background layers from each of the input images. A main technical challenge for this problem is that the mixing coefficient for the reflection and background layers depends on the angle of incidence and the orientation of the plane of incidence, which are spatially-varying over the pixels of an image. Exploiting physical properties of polarization for a double-surfaced glass medium, we propose an algorithm which automatically finds the optimal separation of the reflection and background layers. Thorough experiments, we demonstrate that our approach can generate superior results to those of previous methods.


title: Capability Models and Their Applications in Planning

abstract: One important challenge for a set of agents to achieve more efficient collaboration is for these agents to maintain proper models of each other. An important aspect of these models of other agents is that they are often not provided, and hence must be learned from plan execution traces. As a result, these models of other agents are inherently partial and incomplete. Most existing agent models are based on action modeling and do not naturally allow for incompleteness. In this paper, we introduce a new and inherently incomplete modeling approach based on the representation of capabilities, which has several unique advantages. First, we show that the structures of capability models can be learned or easily specified, and both model structure and parameter learning are robust to high degrees of incompleteness in plan traces (e.g., with only start and end states partially observed). Furthermore, parameter learning can be performed efficiently online via Bayesian learning. While high degrees of incompleteness in plan traces presents learning challenges for traditional (complete) models, capability models can still learn to extract useful information. As a result, capability models are useful in applications in which traditional models are difficult to obtain, or models must be learned from incomplete plan traces, e.g., robots learning human models from observations and interactions. Furthermore, we discuss using capability models for single agent planning, and then extend it to multi-agent planning (with each agent modeled separately by a capability model), in which the capability models of agents are used by a centralized planner. The limitation, however, is that the synthesized "plans" (called c-plans) are incomplete, i.e., there may or may not be a complete plan for a c-plan. This is, however, unavoidable for planning using partial and incomplete models (e.g., considering planning using action models learned from partial and noisy plan traces).


title: Generating Long and Diverse Responses with Neural Conversation Models

abstract: Building general-purpose conversation agents is a very challenging task, but necessary on the road toward intelligent agents that can interact with humans in natural language. Neural conversation models -- purely data-driven systems trained end-to-end on dialogue corpora -- have shown great promise recently, yet they often produce short and generic responses. This work presents new training and decoding methods that improve the quality, coherence, and diversity of long responses generated using sequence-to-sequence models. Our approach adds self-attention to the decoder to maintain coherence in longer responses, and we propose a practical approach, called the glimpse-model, for scaling to large datasets. We introduce a stochastic beam-search algorithm with segment-by-segment reranking which lets us inject diversity earlier in the generation process. We trained on a combined data set of over 2.3B conversation messages mined from the web. In human evaluation studies, our method produces longer responses overall, with a higher proportion rated as acceptable and excellent as length increases, compared to baseline sequence-to-sequence models with explicit length-promotion. A back-off strategy produces better responses overall, in the full spectrum of lengths.


A Fiber Tracking Method for Building Patient Specific Dynamic Musculoskeletal Models from Diffusion Tensor Data


title: Seeing Sounds and Hearing Colors: An Event-related Potential Study of Auditory–Visual Synesthesia

abstract: In auditory-visual synesthesia, sounds automatically elicit conscious and reliable visual experiences. It is presently unknown whether this reflects early or late processes in the brain. It is also unknown whether adult audiovisual synesthesia resembles auditory-induced visual illusions that can sometimes occur in the general population or whether it resembles the electrophysiological deflection over occipital sites that has been noted in infancy and has been likened to synesthesia. Electrical brain activity was recorded from adult synesthetes and control participants who were played brief tones and required to monitor for an infrequent auditory target. The synesthetes were instructed to attend either to the auditory or to the visual (i.e., synesthetic) dimension of the tone, whereas the controls attended to the auditory dimension alone. There were clear differences between synesthetes and controls that emerged early (100 msec after tone onset). These differences tended to lie in deflections of the auditory-evoked potential (e.g., the auditory N1, P2, and N2) rather than the presence of an additional posterior deflection. The differences occurred irrespective of what the synesthetes attended to (although attention had a late effect). The results suggest that differences between synesthetes and others occur early in time, and that synesthesia is qualitatively different from similar effects found in infants and certain auditory-induced visual illusions in adults. In addition, we report two novel cases of synesthesia in which colors elicit sounds, and vice versa.


title: Compact Ultra-Wideband Stepped-Impedance Resonator Filter With Vertical Coupling Structure

abstract: In this paper, a novel ultra wideband (UWB) bandpass filter is presented using stepped-impedance resonator and vertical coupling structure. This filter configuration is presented with bandwidth up to 135.8 percent covering the 2.2-11.5 GHz range. Excellent stopband performance is obtained up to 17.7 GHz. They show the insertion loss of 1.8 dB and return loss of 14 dB at the center frequency 6.85 GHz.


title: HIV testing and linkage to services for youth

abstract: Introduction : HIV testing is the portal to serostatus knowledge that can empower linkage to care for HIV treatment and HIV prevention. However, young people’s access to HIV testing is uneven worldwide. The objective of this paper is to review the context and concerns faced by youth around HIV testing in low- as well as high-income country settings. Discussion : HIV testing is a critical entry point for primary and secondary prevention as well as care and treatment for young people including key populations of vulnerable youth. We provide a framework for thinking about the role of testing in the continuum of prevention and care for young people. Brief case study examples from Kenya and the US illustrate some of the common barriers and issues involved for young people. Conclusions : Young people worldwide need more routine access to HIV testing services that effectively address the developmental, socio-political and other issues faced by young women and men. Keywords: HIV testing; HIV continuum of care; youth; adolescents; key populations; development. (Published: 26 February 2015) Citation: Kurth AE et al. Journal of the International AIDS Society 2015, 18(Suppl 1) :19433 http://www.jiasociety.org/index.php/jias/article/view/19433 | http://dx.doi.org/10.7448/IAS.18.2.19433


title: The Language Experience and Proficiency Questionnaire (LEAP-Q): Assessing Language Profiles in Bilinguals and Multilinguals

abstract: Purpose To develop a reliable and valid questionnaire of bilingual language status with predictable relationships between self-reported and behavioral measures. Method In Study 1, the internal vali...


title: An All-Silk-Derived Dual-Mode E-skin for Simultaneous Temperature–Pressure Detection

abstract: Flexible skin-mimicking electronics are highly desired for development of smart human–machine interfaces and wearable human-health monitors. Human skins are able to simultaneously detect different information, such as touch, friction, temperature, and humidity. However, due to the mutual interferences of sensors with different functions, it is still a big challenge to fabricate multifunctional electronic skins (E-skins). Herein, a combo temperature–pressure E-skin is reported through assembling a temperature sensor and a strain sensor in both of which flexible and transparent silk-nanofiber-derived carbon fiber membranes (SilkCFM) are used as the active material. The temperature sensor presents high temperature sensitivity of 0.81% per centigrade. The strain sensor shows an extremely high sensitivity with a gauge factor of ∼8350 at 50% strain, enabling the detection of subtle pressure stimuli that induce local strain. Importantly, the structure of the SilkCFM in each sensor is designed to be passive to ot...


title: Double-Sided Bow-Tie Antenna for 2G/3G/WiFi and Mobile WiMAX

abstract: A double-sided dual-band bow-tie antenna with two arms printed on top and bottom of dielectric substrate of epsivr=2.2 is proposed to operate at both the CDMA/GSM band ranging from 800 MHz to 960 MHz and the mobile 3G /WiFi/WiMAX band ranging from 1.7 GHz to 2.69 GHz. The two arms of the bow-tie are overlapped to tune the operation band and designed to be asymmetrical for beam tilt. To cover the potential mobile 2.6 GHz WiMAX band, the impedance matching structure is carefully improved and optimized by numerical simulation software. Quasi-omnidirectional radiation patterns, reasonable gain values and beam tilt as required in the GSM/CDMA and 3G/WiFi/WiMAX repeater are proposed.


Understanding the Antecedents of Mobile Game Addiction: The Roles of Perceived Visibility, Perceived Enjoyment and Flow.


title: When Weather Matters: IoT-Based Electrical Load Forecasting for Smart Grid

abstract: Electrical load forecasting is still a challenging open problem due to the complex and variable influences (e.g., weather and time). Although, with the recent development of IoT and smart meter technology, people have obtained the ability to record relevant information on a large scale, traditional methods struggle in analyzing such complicated relationships for their limited abilities in handling nonlinear data. In the article, we introduce an IoTbased deep learning system to automatically extract features from the captured data, and ultimately, give an accurate estimation of future load value. One significant advantage of our method is the specially designed two-step forecasting scheme, which significantly improves the forecasting precision. Also, the proposed method is able to quantitatively analyze the influences of some major factors, which is of great guiding significance to select attribute combination and deploy onboard sensors for smart grids with vast areas, variable climates, and social conventions. Simulations demonstrate that our method outperforms some existing approaches, and can be well applied in various situations.


title: Density-based semi-supervised clustering

abstract: Semi-supervised clustering methods guide the data partitioning and grouping process by exploiting background knowledge, among else in the form of constraints. In this study, we propose a semi-supervised density-based clustering method. Density-based algorithms are traditionally used in applications, where the anticipated groups are expected to assume non-spherical shapes and/or differ in cardinality or density. Many such applications, among else those on GIS, lend themselves to constraint-based clustering, because there is a priori knowledge on the group membership of some records. In fact, constraints might be the only way to prevent the formation of clusters that do not conform to the applications' semantics. For example, geographical objects, e.g. houses, separated by a borderline or a river may not be assigned to the same cluster, independently of their physical proximity. We first provide an overview of constraint-based clustering for different families of clustering algorithms. Then, we concentrate on the density-based algorithms' family and select the algorithm DBSCAN, which we enhance with Must-Link and Cannot-Link constraints. Our enhancement is seamless: we allow DBSCAN to build temporary clusters, which we then split or merge according to the constraints. Our experiments on synthetic and real datasets show that our approach improves the performance of the algorithm.


title: An effective personal mobile robot agent through symbiotic human-robot interaction

abstract: Several researchers, present authors included, envision personal mobile robot agents that can assist humans in their daily tasks. Despite many advances in robotics, such mobile robot agents still face many limitations in their perception, cognition, and action capabilities. In this work, we propose a symbiotic interaction between robot agents and humans to overcome the robot limitations while allowing robots to also help humans. We introduce a visitor's companion robot agent, as a natural task for such symbiotic interaction. The visitor lacks knowledge of the environment but can easily open a door or read a door label, while the mobile robot with no arms cannot open a door and may be confused about its exact location, but can plan paths well through the building and can provide useful relevant information to the visitor. We present this visitor companion task in detail with an enumeration and formalization of the actions of the robot agent in its interaction with the human. We briefly describe the wifi-based robot localization algorithm and show results of the different levels of human help to the robot during its navigation. We then test the value of robot help to the visitor during the task to understand the relationship tradeoffs. Our work has been fully implemented in a mobile robot agent, CoBot, which has successfully navigated for several hours and continues to navigate in our indoor environment.


Affected Agriculture/horticulture Produce Based on Combined Color and Texture Feature Extraction


title: Pose-and-illumination-invariant face representation via a triplet-loss trained deep reconstruction model

abstract: Face recognition under variable pose and illumination is a challenging problem in computer vision tasks. In this paper, we solve this problem by proposing a new residual based deep face reconstruction neural network to extract discriminative pose-and-illumination-invariant (PII) features. Our deep model can change arbitrary pose and illumination face images to the frontal view with standard illumination. We propose a new triplet-loss training method instead of Euclidean loss to optimize our model, which has two advantages: a) The training triplets can be easily augmented by freely choosing combinations of labeled face images, in this way, overfitting can be avoided; b) The triplet-loss training makes the PII features more discriminative even when training samples have similar appearance. By using our PII features, we achieve 83.8% average recognition accuracy on MultiPIE face dataset which is competitive to the state-of-the-art face recognition methods.


Bo Waggoner - Research Statement


title: Information-Theoretic Inference of Large Transcriptional Regulatory Networks

abstract: The paper presents MRNET, an original method for inferring genetic networks from microarray data. The method is based on maximum relevance/minimum redundancy (MRMR), an effective information-theoretic technique for feature selection in supervised learning. The MRMR principle consists in selecting among the least redundant variables the ones that have the highest mutual information with the target. MRNET extends this feature selection principle to networks in order to infer gene-dependence relationships from microarray data. The paper assesses MRNET by benchmarking it against RELNET, CLR, and ARACNE, three state-of-the-art information-theoretic methods for large (up to several thousands of genes) network inference. Experimental results on thirty synthetically generated microarray datasets show that MRNET is competitive with these methods.


Machine Learning algorithms: a study on noise


title: Design of the 1.0V bandgap reference on chip

abstract: This paper proposes the design method of the 1.0V bandgap reference circuit on chip based on the bandgap reference nucleus. The voltage reference which is lower than 1.25V can be obtained with zero temperature coefficients under normal temperature, in parallel with small resistances near the bipolar transistors. The paper analyzes the impact on the circuit performance by the feedback loop and offset voltage of the operational amplifier (OPA) two main factors and then discusses the key points of the circuit design, including the conditions must be followed in respect of the parameter selection of related devices in order to ensure the OPA negative feedback polarity, the measures taken in the structure of the OPA to reduce the offset voltage, and the start-up circuit added so as to avoid zero current balance, etc. The simulation results show that the performance of the integrated circuit is good, thus meets the design requirements of low temperature drift, high precision.


15-424: Foundations of Cyber-Physical Systems Lecture Notes on Differential & Temporal Proofs


Autonomous Aerial Water Sampling.


title: A digital receiver architecture for RFID readers

abstract: This paper presents a digital receiver architecture for an RFID reader. The main challenge in RFID reader design is the detection of the backscattered signals from the tags, which can be severely complicated due to the largely varying scale of possible receive powers. Furthermore noise, which power depends on the environment can degrade the detection performance. The detection of the signals of the tags is additionally impeded by the very strong self interference at the reader with the carrier it needs to send in order to supply the tags with energy. To fight these various disturbances, a new RFID receiver algorithm is proposed, that sets its decision threshold adaptively, depending on the strength of the input signal, the noise power at the receiver and the extent of the carrier interference. This is the first algorithm for signal detection in RFID, setting its threshold accordingly to the environmental conditions, and thus leading to near optimum performance. Details of the implementation of the digital receiver architecture on an FPGA are introduced. Bit error ratio measurements have been carried out to rate the receivers performance, which have never been shown before for RFID receivers. Presented measurement results substantiate the performance of the suggested algorithm.


title: Improved churn prediction in telecommunication industry using data mining techniques

abstract: We have employed Decision Tree, Artificial Neural Networks, K-Nearest Neighbors, and Support Vector Machine to improve churn prediction.Using the data of an Iranian mobile company these techniques were experienced and were compared to each other.We proposed a hybrid methodology which made considerable improvements to the value of some of evaluations metrics.Results showed that above 95% accuracy for Recall and Precision is easily achievable.A new methodology for extracting influential features is introduced and experienced. To survive in today's telecommunication business it is imperative to distinguish customers who are not reluctant to move toward a competitor. Therefore, customer churn prediction has become an essential issue in telecommunication business. In such competitive business a reliable customer predictor will be regarded priceless. This paper has employed data mining classification techniques including Decision Tree, Artificial Neural Networks, K-Nearest Neighbors, and Support Vector Machine so as to compare their performances. Using the data of an Iranian mobile company, not only were these techniques experienced and compared to one another, but also we have drawn a parallel between some different prominent data mining software. Analyzing the techniques' behavior and coming to know their specialties, we proposed a hybrid methodology which made considerable improvements to the value of some of the evaluations metrics. The proposed methodology results showed that above 95% accuracy for Recall and Precision is easily achievable. Apart from that a new methodology for extracting influential features in dataset was introduced and experienced.


Supervised and Unsupervised Intrusion Detection Based on CAN Message Frequencies for In-vehicle Network


title: Digital Hammurabi: design and development of a 3D scanner for cuneiform tablets

abstract: Cuneiform is an ancient form of writing in which wooden reeds were used to impress shapes upon moist clay tablets. Upon drying, the tablets preserved the written script with remarkable accuracy and durability. There are currently hundreds of thousands of cuneiform tablets spread throughout the world in both museums and private collections. The global scale of these artifacts presents several problems for scholars who wish to study them. It may be difficult or ::: impossible to obtain access to a given collection. In addition, photographic records of the tablets many times prove to be inadequate for proper examination. Photographs lack the ability to alter the lighting conditions and view direction. As a solution to these problems, we describe a 3D scanner capable of acquiring the shape, color, and reflectance of a tablet as a complete 3D object. This data set could then be stored in an online library and manipulated by suitable rendering software that would allow a user to specify any view direction and lighting condition. The scanner utilizes a camera and telecentric lens to acquire images of the tablet under varying controlled illumination conditions. Image data are processed using photometric stereo and structured light techniques to determine the tablet shape; color information is reconstructed from primary color monochrome image data. The scanned surface is sampled at 26.8 μm lateral spacing and the height information is calculated on a much smaller scale. Scans of adjacent tablet sides are registered together to form a 3D surface model.


title: Screen time and sleep among school-aged children and adolescents: A systematic literature review

abstract: Summary We systematically examined and updated the scientific literature on the association between screen time (e.g., television, computers, video games, and mobile devices) and sleep outcomes among school-aged children and adolescents. We reviewed 67 studies published from 1999 to early 2014. We found that screen time is adversely associated with sleep outcomes (primarily shortened duration and delayed timing) in 90% of studies. Some of the results varied by type of screen exposure, age of participant, gender, and day of the week. While the evidence regarding the association between screen time and sleep is consistent, we discuss limitations of the current studies: 1) causal association not confirmed; 2) measurement error (of both screen time exposure and sleep measures); 3) limited data on simultaneous use of multiple screens, characteristics and content of screens used. Youth should be advised to limit or reduce screen time exposure, especially before or during bedtime hours to minimize any harmful effects of screen time on sleep and well-being. Future research should better account for the methodological limitations of the extant studies, and seek to better understand the magnitude and mechanisms of the association. These steps will help the development and implementation of policies or interventions related to screen time among youth.


title: Primate Vocalization, Gesture, and the Evolution of Human Language

abstract: The performance of language is multimodal, not confined to speech. Review of monkey and ape communication demonstrates greater flexibility in the use of hands and body than for vocalization. Nonetheless, the gestural repertoire of any group of nonhuman primates is small compared with the vocabulary of any human language and thus, presumably, of the transitional form called protolanguage. We argue that it was the coupling of gestural communication with enhanced capacities for imitation that made possible the emergence of protosign to provide essential scaffolding for protospeech in the evolution of protolanguage. Similarly, we argue against a direct evolutionary path from nonhuman primate vocalization to human speech. The analysis refines aspects of the mirror system hypothesis on the role of the primate brain’s mirror system for manual action in evolution of the human language‐ready brain.


title: Glomerular function and microalbuminuria in children with insulin-dependent diabetes

abstract: Renal function has been evaluated in 45 diabetic children (age 12.5±4 years) with a mean diabetes duration of 4.9±3.5 years. Glomerular filtration rate (GFR; inulin and creatinine clearances), renal plasma flow (RPF; PAH clearance), resting urinary albumin excretion (UAE) were measured and compared with indexes of metabolic control: Hb A1C and blood glucose values (mean, post-prandial and maximal excursion) on the same day. GFR (inulin clearance) and RPF were significantly increased in the diabetic group (171±31 and 778±172 ml/min per 1.73 m2) compared with controls (124±18 and 631±128 ml/min per 1.73 m2). Both parameters were strongly correlated (r=0.73;P<0.001). Creatinine clearance was not correlated to inulin clearance. Hyperfiltration (inulin clearance above 160 ml/min per 1.73 m2) was noted in 61% of the patients and was independent of diabetes duration. Five diabetic children had a UAE level above 15 μg/min. No relationship could be established between UAE and any of the metabolic indexes; GFR was weakly correlated to HbA1C (r=0.35;P<0.05), to mean (r=0.35;P<0.05) and post-prandial blood glucose (r=0.37;P<0.05). In contrast, there was a strong correlation between GFR and the maximal blood excursion (r=0.62;P<0.001). The study shows that renal abnormalities can be detected with a high frequency in diabetic subjects characterized by both an early onset and a short duration of diabetes and suggests the need for a more systematic evaluation of renal parameters in this population.


Time series prediction and online learning.


title: Structured linux kernel projects for teaching operating systems concepts

abstract: Linux has emerged as a widely-used platform for enabling hands-on kernel programming experience to learn about operating system concepts. However, developing pedagogically-effective programming projects in the context of a complex, production operating system can be a challenge. We present a structured series of five Linux kernel programming projects suitable for a one semester introductory operating systems course to address this issue. Each assignment introduces students to a core topic and major component of an operating system while implicitly teaching them about various aspects of a real-world operating system. Projects are of modest coding complexity, but require students to understand and leverage core components of the Linux operating system. The learning benefits for students from this approach include learning from real-world operating system code examples by expert kernel designers and gaining software engineering experience managing production code complexity. We have successfully used these structured Linux kernel projects to teach over a thousand students in the introductory operating systems course at Columbia University.


title: Metrics for Community Analysis: A Survey

abstract: Detecting and analyzing dense groups or communities from social and information networks has attracted immense attention over last one decade due to its enormous applicability in different domains. Community detection is an ill-defined problem, as the nature of the communities is not known in advance. The problem has turned out to be even complicated due to the fact that communities emerge in the network in various forms - disjoint, overlapping, hierarchical etc. Various heuristics have been proposed depending upon the application in hand. All these heuristics have been materialized in the form of new metrics, which in most cases are used as optimization functions for detecting the community structure, or provide an indication of the goodness of detected communities during evaluation. There arises a need for an organized and detailed survey of the metrics proposed with respect to community detection and evaluation. In this survey, we present a comprehensive and structured overview of the start-of-the-art metrics used for the detection and the evaluation of community structure. We also conduct experiments on synthetic and real-world networks to present a comparative analysis of these metrics in measuring the goodness of the underlying community structure.


title: A 1.8-GHz CMOS Power Amplifier Using Stacked nMOS and pMOS Structures for High-Voltage Operation

abstract: A class-E power amplifier is proposed in this study. It uses both nMOS and pMOS as switching devices to reduce the voltage stress of each transistor. A voltage-combining scheme with nMOS and pMOS is proposed, and a transformer is designed using this scheme. The power amplifier is implemented in a 0.18-mum RF CMOS process. Measurements show a maximum output power of 30.2 dBm with 36.8% power-added efficiency at a 3.3-V supply voltage. The power amplifier sustains a supply voltage of up to 3.9 V.


title: IoT energy management platform for microgrid

abstract: IoT smart energy management is to maximize energy efficiency through energy information collection, energy DR management and energy sharing/trading by developing IoT-based smart energy platform technology. IoT smart energy management services provide energy efficiency enhancement, energy sharing and trading services through interconnection and integration of energy supply-transfer-utilization energy systems using the Internet of things. For this, this paper shows IoT energy management platform with energy big data system.


title: A monad for deterministic parallelism

abstract: We present a new programming model for deterministic parallel computation in a pure functional language. The model is monadic and has explicit granularity, but allows dynamic construction of dataflow networks that are scheduled at runtime, while remaining deterministic and pure. The implementation is based on monadic concurrency, which has until now only been used to simulate concurrency in functional languages, rather than to provide parallelism. We present the API with its semantics, and argue that parallel execution is deterministic. Furthermore, we present a complete work-stealing scheduler implemented as a Haskell library, and we show that it performs at least as well as the existing parallel programming models in Haskell.


title: Self-supervised CNN for Unconstrained 3D Facial Performance Capture from an RGB-D Camera

abstract: We present a novel method for real-time 3D facial performance capture with consumer-level RGB-D sensors. Our capturing system is targeted at robust and stable 3D face capturing in the wild, in which the RGB-D facial data contain noise, imperfection and occlusion, and often exhibit high variability in motion, pose, expression and lighting conditions, thus posing great challenges. The technical contribution is a self-supervised deep learning framework, which is trained directly from raw RGB-D data. The key novelties include: (1) learning both the core tensor and the parameters for refining our parametric face model; (2) using vertex displacement and UV map for learning surface detail; (3) designing the loss function by incorporating temporal coherence and same identity constraints based on pairs of RGB-D images and utilizing sparse norms, in addition to the conventional terms for photo-consistency, feature similarity, regularization as well as geometry consistency; and (4) augmenting the training data set in new ways. The method is demonstrated in a live setup that runs in real-time on a smartphone and an RGB-D sensor. Extensive experiments show that our method is robust to severe occlusion, fast motion, large rotation, exaggerated facial expressions and diverse lighting.


title: The influence of resilience factors in the treatment of substances addiction : A Multylayer Perceptron Predictive Model

abstract: Scientific literature gives more and more importance to the stress-coping model in the framework of drug addiction. Recently, studies have highlighted resilience as a crucial element of the stress-coping model. In this work stress-coping-resilience is presented as an elective model to treat addictions. Through the use of neural networks, a Multilayer Perceptron is simulated with the aim of understanding the importance that stress-copingresilience model variables have in influencing the seriousness of addiction. Results show the correctness of the model underlining the importance of resilience factors and coping strategies in the treatment of addiction.


title: AMC: AutoML for Model Compression and Acceleration on Mobile Devices

abstract: Model compression is a critical technique to efficiently deploy neural network models on mobile devices which have limited computation resources and tight power budgets. Conventional model compression techniques rely on hand-crafted heuristics and rule-based policies that require domain experts to explore the large design space trading off among model size, speed, and accuracy, which is usually sub-optimal and time-consuming. In this paper, we propose AutoML for Model Compression (AMC) which leverage reinforcement learning to provide the model compression policy. This learning-based compression policy outperforms conventional rule-based compression policy by having higher compression ratio, better preserving the accuracy and freeing human labor. Under 4x FLOPs reduction, we achieved 2.7% better accuracy than the hand- crafted model compression policy for VGG-16 on ImageNet. We applied this automated, push-the-button compression pipeline to MobileNet and achieved 1.81x speedup of measured inference latency on an Android phone and 1.43x speedup on the Titan XP GPU, with only 0.1% loss of ImageNet Top-1 accuracy.


title: VBTree: forward secure conjunctive queries over encrypted data for cloud computing

abstract: This paper concerns the fundamental problem of processing conjunctive keyword queries over an outsourced data table on untrusted public clouds in a privacy-preserving manner. The data table can be properly implemented with tree-based searchable symmetric encryption schemes, such as the known Keyword Red---Black tree and the Indistinguishable Bloom-filter Tree in ICDE'17. However, as for these trees, there still exist many limitations to support sub-linear time updates. One of the reasons is that their tree branches are directly exposed to the cloud. To achieve efficient conjunctive queries while supporting dynamic updates, we introduce a novel tree data structure called virtual binary tree (VBTree). Our key design is to organize indexing elements into the VBTree in a top-down fashion, without storing any tree branches and tree nodes. The tree only exists in a logical view, and all of the elements are actually stored in a hash table. To achieve forward privacy, which is discussed by Bost in CCS'16, we also propose a storage mechanism called version control repository (VCR), to record and control versions of keywords and queries. VCR has a smaller client-side storage compared to other forward-private schemes. With our proposed approach, data elements can be quickly searched while the index can be privately updated. The security of the VBTree is formally proved under the IND-CKA2 model. We test our scheme on a real e-mail dataset and a user location dataset. The testing results demonstrate its high efficiency and scalability in both searching and updating processes.


title: Automatic Facial Attractiveness Prediction by Deep Multi-Task Learning

abstract: Facial Attractiveness Prediction (FAP) is a useful yet challenging problem in the domain of computer vision. In this paper, we propose a deep learning based approach. Different from the existing deep methods, the proposed one models both the texture and shape clues within a multi-task learning framework consisting of attractiveness score prediction and fiducial landmark localization, thus highlighting both of their roles in assessing attractiveness of faces. Considering that the training data are not extensive, a lightweight CNN is designed to jointly learn the facial representation, landmark location, and facial attractiveness score. The proposed method is evaluated on the SCUT-FBP database, and a prediction correlation 0.92, is delivered, which shows the effectiveness of our method. Furthermore, two additional experiments in terms of comparison between facial images before and after make-up or beautification are conducted. The results also prove the advantage of the proposed method.


title: Pentuplet Loss for Simultaneous Shots and Critical Points Detection in a Video

abstract: Critical events in videos amount to the set of frames where the user attention is heightened. Such events are usually fine-grained activities and do not necessarily have defined shot boundaries. Traditional approaches to the task of Shot Boundary Detection (SBD) in videos perform frame-level classification to obtain shot boundaries and fail to identify the critical shots in the video. We model the problem of identifying critical frames and shot boundaries in a video as learning an image frame similarity metric where the distance relationships between different types of video frames are modeled. We propose a novel pentuplet loss to learn the frame image similarity metric through a pentuplet-based deep learning framework. We showcase the results of our proposed framework on soccer highlight videos against state-of-the-art baselines and significantly outperform them for the task of shot boundary detection. The proposed framework shows promising results for the task of critical frame detection against human annotations on soccer highlight videos.


Single Balanced Mixer Using Substrate Integrated Waveguide (SIW) 90° Coupler


title: Porous Hollow Carbon@Sulfur Composites for High-Power Lithium-Sulfur Batteries

abstract: C @ S nanocomposites based on mesoporous hollow carbon capsules were prepared by a template approach. Their excellent properties as a cathode material in a lithium secondary battery of S-sequestration of elemental sulfur in the carbon capsules, a restricted polysulfide shuttling and an improved electron transport on sulfur are attributed.


title: Towards Cooperative Driving: Involving the Driver in an Autonomous Vehicle's Decision Making

abstract: Although there are already fully autonomous vehicles on the roads for testing purposes, a rollout is far away. Autonomous vehicles are still not able to handle everyday driving and remain reliant on the driver when they reach their system limitations. One suggested approach to this problem is handing over the control entirely to the driver, which might become annoying when such situations occur frequently. In contrast, we suggest the usage of cooperative interfaces to avoid full handovers in situations in which the system needs the driver, for instance to approve or monitor a specific maneuver. A driving simulator study with 32 participants revealed that they felt comfortable choosing how the system should handle a situation. They reportedly assessed the situations first instead of relying blindly on the system and were able to handle every situation safely. We report lessons learned regarding cooperative interaction and interfaces, and their in-lab evaluation.


title: Semantic text similarity using corpus-based word similarity and string similarity

abstract: We present a method for measuring the semantic similarity of texts using a corpus-based measure of semantic word similarity and a normalized and modified version of the Longest Common Subsequence (LCS) string matching algorithm. Existing methods for computing text similarity have focused mainly on either large documents or individual words. We focus on computing the similarity between two sentences or two short paragraphs. The proposed method can be exploited in a variety of applications involving textual knowledge representation and knowledge discovery. Evaluation results on two different data sets show that our method outperforms several competing methods.


Fourier Transform Decoding of Non-Binary LDPC Codes


title: PTEN hamartoma tumor syndromes

abstract: The PTEN hamartoma tumor syndromes (PHTS) are a collection of rare clinical syndromes characterized by germline mutations of the tumor suppressor PTEN. These syndromes are driven by cellular overgrowth, leading to benign hamartomas in virtually any organ. Cowden syndrome (CS), the prototypic PHTS syndrome, is associated with increased susceptibility to breast, thyroid, and endometrial cancer. PTEN is located on chromosome 10q22–23 and negatively regulates the prosurvival PI3K/Akt/mTOR pathway through its lipid phosphatase activity. Loss of PTEN activates this pathway and leads to increased cellular growth, migration, proliferation, and survival. Clinical management of patients with PHTS, particularly those with CS, should include early and frequent screening, surveillance, and preventive care for associated malignancies. Concomitant with improved understanding of the biology of PTEN and the PI3K/Akt/mTOR pathway, inhibitors of this pathway are being developed as anticancer agents. These medications could have applications for patients with PHTS, for whom no medical options currently exist.


title: A heuristic asymptotic formula concerning the distribution of prime numbers

abstract: Suppose fi, f2, -*, fk are polynomials in one variable with all coefficients integral and leading coefficients positive, their degrees being hi, h2, **. , hk respectively. Suppose each of these polynomials is irreducible over the field of rational numbers and no two of them differ by a constant factor. Let Q(fi , f2, ... , fk ; N) denote the number of positive integers n between 1 and IV inclusive such that fi(n) , f2(n), , fk(n) are all primes. (We ignore the finitely many values of n for which some fi(n) is negative.) Then heuristically we would expect to have for N large


title: Validation of a New Instrument for Evaluating Low Back Pain in the Young Athlete

abstract: Objective: To validate a back-specific instrument for functional assessment in the young athlete for clinical and research purposes, the Micheli Functional Scale (MFS). Design: Prospective cohort study, Diagnostic Level II. Setting: Division of Sports Medicine Clinic, Children's Hospital Boston. Participants: Male and female patients aged 12 to 22 with and without low back pain. Interventions: Patients presenting with back pain were compared with a control group of patients presenting with complaints other than back pain. Both groups were given the modified Oswestry Low Back Pain Disability Questionnaire (ODQ) and MFS. Main Outcome Measures: Micheli Functional Scale and ODQ score correlations. Results: A total of 94 patients were enrolled (44 patients with low back pain and 50 patients without low back pain). Ages ranged from 12 to 22 for both groups, with no differences in age (P = 0.07) or gender (P = 0.50). Patients with back pain had significantly higher ODQ scores (median, 32 points) and MFS scores (median, 47 points) compared with controls (median, 0 points; P Conclusions: The MFS is a valid instrument for assessing pain and functional levels in the young athlete. This scale considers pain, athletic function, and athletic disability.


title: Hairstyle recommendation system for women

abstract: A perfect hairstyle enhanced anyone's self-confidence, especially women. However, in order to choose a good hairstyle, one was limited to rely on knowledge of a beauty expert. This paper presented a hairstyle recommendation system for women based on hairstyle experts' knowledge and a face shape classification scheme that the authors devised in a previous study. The system showed a user's face with a recommended or not recommended hairstyle on a monitor.


title: Developmental changes in the expression patterns of IGFs, type 1 IGF receptor and IGF-binding proteins-2 and -4 in perinatal rat lung.

abstract: The insulin-like growth factors (IGF-I and IGF-II), their receptors and binding proteins (IGFBPs) are endogenously expressed in a number of tissues including the lung during fetal and neonatal development. This endogenous autocrine/paracrine IGF 'system', together with endocrine sources, contributes to the regulation of lung cell proliferation. We investigated the expression of the mRNAs encoding IGF-I, IGF-II, the type 1 IGF receptor (IGF-T1R) and two IGF-binding proteins (IGFBP-2 and IGFBP-4) in rat lung during the perinatum. These were compared in lung with surfactant apoprotein A (Sp-A) mRNA levels. mRNA in extracts of fetal tissues collected between day 17 of gestation (17f) and day 9 after birth (9d) was estimated by Northern blot or RNase protection analysis. At day 20 of gestation IGF-I, IGF-T1R and IGFBP-4 mRNA levels were higher in lung than liver (all P < 0.01), whereas IGF-II and IGFBP-2 mRNA levels were higher in liver than lung (each P < 0.02). The expression of IGF-1, IGFBP-2 and IGFBP-4 in lung was high before birth (days 17-20f) but decreased to low levels at days 21f, 22f or at birth (1d) but increased in the neonatal lung. IGF-II expression in lung was high at 17f but decreased before birth and remained low after birth. The IGF-T1R was expressed at moderate levels before birth, decrease before birth but peaked at days 2-5 after birth. The decrease in expression of these growth regulators before birth expression of these growth regulators before birth was matched by an increased in Sp-A expression which was clearly seen at day 20f, peaked at 1d and then was clearly seen at day 20f, peaked at 1d and then was maintained at high levels after birth. Primary cell cultures of 18f lung epithelia express IGFBP-2 while fibroblasts from the same animals express only IGFBP-4. Cells grown from 22f lung tissue express IGFBP-2 and IGFBP-4 at lower levels, behaving in vitro as they do in vivo. The contrasting levels of expression of different components of the IGF system in the fetal lung and liver indicate organ-specific regulation. IGFBP-2 and IGFBP-4 expression in different cell types within lung but with similar temporal changes suggests cell-specific regulation, perhaps by a common agent. The patterns by a common agent. The patterns of expression of IGF-I, IGF-T1R, IGFBP-2 and IGFBP-4, but not IGF-II, in developing lung correspond to previously described phasic changes in lung cell proliferation rates. The nadir in expression of these four major components of the lung IGF system occurs in the saccular phase when the lung begins to differentiate, probably under the influence of certain endocrine agents.


title: Towards a model of privacy and security for smart homes

abstract: The domain of smart home environments is viewed as a key element of the future Internet, and many homes are becoming “smarter” by using Internet of Things (IoT) technology to improve home security, energy efficiency and comfort. At the same time, enforcing privacy in IoT environments has been identified as one of the main barriers for realizing the vision of the smart home. Based on the results of a risk analysis of a smart home automation system developed in collaboration with leading industrial actors, we outline the first steps towards a general model of privacy and security for smart homes. As such, it is envisioned as support for enforcing system security and enhancing user privacy, and it can thus help to further realize the potential in smart home environments.


title: Simultaneously learning actions and goals from demonstration

abstract: Our research aim is to develop interactions and algorithms for learning from naive human teachers through demonstration. We introduce a novel approach to leverage the goal-oriented nature of human teachers by learning an action model and a goal model simultaneously from the same set of demonstrations. We use robot motion data to learn an action model for executing the skill. We use a generic set of perceptual features to learn a goal model and use it to monitor the executed action model. We evaluate our approach with data from 8 naive teachers demonstrating two skills to the robot. We show that the goal models in the perceptual feature space are consistent across users and correctly recognize demonstrations in cross-validation tests. We additionally observe that a subset of users were not able to teach a successful action model whereas all of them were able to teach a mostly successful goal model. When the learned action models are executed on the robot, the success was on average 66.25 %. Whereas the goal models were on average 90 % correct at deciding on success/failure of the executed action, which we call monitoring.


title: State of art of power electronics in circuit breaker technology

abstract: Circuit breakers play an important role in an electrical system performance in terms of system safety, control, maintenance and cost. In some cases, the conventional mechanical circuit breakers (MCB) may be too slow for the electrical network to operate within the desired safety limits. Besides their other limitations, the MCB also forces the network elements to be rated much higher. The uses of power semiconductor devices to overcome some of their limitations are discussed in this paper. These usages are in terms of solid-state circuit breaker (SCB) and hybrid circuit breaker (HCB) concepts. This paper summarizes and reviews the various circuits of SCB and HCB available in the literature. The relevant simulation results for some example circuits have also been presented in this paper. This study will provide a useful framework and point of reference for the future development of HCB and SCB technologies.


title: Multi-Scale Difference Map Fusion for Tamper Localization Using Binary Ranking Hashing

abstract: The block-based analysis for tamper localization is a prevailing mechanism in hash-based forgery detection algorithm. One of the main problems with a block-based analysis is its rough localization stemming from the demand to use relatively large blocks to reduce hash length. While decreasing the block size can improve the localization resolution, the hash length tends to become too long to be practical. In this paper, we propose a binary ranking hashing approach that satisfies both the requirements of compact hash length and small block size, to obtain a binary map combined with spatial information. Meanwhile, we investigate a multiscale difference map fusion approach that fuses multiple candidate difference maps, resulting from the analysis of the subtraction between two binary maps with different sliding windows, to obtain a single, more reliable tampering map with better localization resolution. We use manifold ranking to model this multiscale difference map fusion problem and propose a two-stage scheme, namely, ranking with tampering queries and nontampering queries. Our results indicate that the proposed tamper detection method can improve the tamper localization resolution compared with state-of-the-art methods.


title: Haptic wearables as sensory replacement, sensory augmentation and trainer – a review

abstract: Sensory impairments decrease quality of life and can slow or hinder rehabilitation. Small, computationally powerful electronics have enabled the recent development of wearable systems aimed to improve function for individuals with sensory impairments. The purpose of this review is to synthesize current haptic wearable research for clinical applications involving sensory impairments. We define haptic wearables as untethered, ungrounded body worn devices that interact with skin directly or through clothing and can be used in natural environments outside a laboratory. Results of this review are categorized by degree of sensory impairment. Total impairment, such as in an amputee, blind, or deaf individual, involves haptics acting as sensory replacement; partial impairment, as is common in rehabilitation, involves haptics as sensory augmentation; and no impairment involves haptics as trainer. This review found that wearable haptic devices improved function for a variety of clinical applications including: rehabilitation, prosthetics, vestibular loss, osteoarthritis, vision loss and hearing loss. Future haptic wearables development should focus on clinical needs, intuitive and multimodal haptic displays, low energy demands, and biomechanical compliance for long-term usage.


title: Exploring Sparsity in Recurrent Neural Networks

abstract: Recurrent Neural Networks (RNN) are widely used to solve a variety of problems and as the quantity of data and the amount of available compute have increased, so have model sizes. The number of parameters in recent state-of-the-art networks makes them hard to deploy, especially on mobile phones and embedded devices. The challenge is due to both the size of the model and the time it takes to evaluate it. In order to deploy these RNNs efficiently, we propose a technique to reduce the parameters of a network by pruning weights during the initial training of the network. At the end of training, the parameters of the network are sparse while accuracy is still close to the original dense neural network. The network size is reduced by 8x and the time required to train the model remains constant. Additionally, we can prune a larger dense network to achieve better than baseline performance while still reducing the total number of parameters significantly. Pruning RNNs reduces the size of the model and can also help achieve significant inference time speed-up using sparse matrix multiply. Benchmarks show that using our technique model size can be reduced by 90% and speed-up is around 2x to 7x.


title: Human Skin Detection Using RGB, HSV and YCbCr Color Models

abstract: Human Skin detection deals with the recognition of skin-colored pixels and regions in a given image. Skin color is often used in human skin detection because it is invariant to orientation and size and is fast to process. A new human skin detection algorithm is proposed in this paper. The three main parameters for recognizing a skin pixel are RGB (Red, Green, Blue), HSV (Hue, Saturation, Value) and YCbCr (Luminance, Chrominance) color models. The objective of proposed algorithm is to improve the recognition of skin pixels in given images. The algorithm not only considers individual ranges of the three color parameters but also takes into ac- count combinational ranges which provide greater accuracy in recognizing the skin area in a given image.


title: Corticolous lichen diversity, a potential indicator for monitoring air pollution in tropics

abstract: As information on lichen diversity and air quality in tropical ecosystems are limited, attempts were made in this study to correlate the distribution and diversity of corticolous (tree bark colonizing) lichens in the Western Province of Sri Lanka. The major source of air pollution in the study area was vehicular emissions. Thirty one sites located on six transects diverging from the Colombo City were selected for this purpose. Eight trees representing three species ( Cocos nucifera , Mangifera indica and Artocarpus heterophyllus ) within each site (1 km 2 ) were chosen for the study. The coverage and frequency of corticolous lichens found on the selected trees were recorded by using 250 cm 2 grids. After studying their morphology, anatomy, reproductive structures and chemistry, the lichens were identiÞed using taxonomic keys. In addition, land use pattern, trafÞc density, pH of the substrates and exposure levels of the bark of these trees to light were also recorded. Ambient SO 2 and NO 2 levels at each site were determined using passive samplers having Þlter pads coated with the absorbing reagents ethylene glycol and acetone for SO 2 , and NaI, NaOH and ethylene glycol for NO 2 . Data were analyzed statistically using the mean comparison, correlation coefÞcient and principal component analysis to investigate the relationship between the diversity of lichens and environmental parameters. Lichen diversity data collected on selected trees were used to formulate the index of atmospheric purity (IAP). A negative correlation was found to exist between the lichen diversity expressed as IAP and the concentrations of SO 2 and NO 2 levels in the study sites. These results revealed that corticolous lichens have the potential to be used as indicators of air quality monitoring in a tropical environment as well.


title: Joint Connectivity-Coverage Temperature-Aware Algorithms for Wireless Sensor Networks

abstract: Temperature variations have a significant effect on low power wireless sensor networks as wireless communication links drastically deteriorate when temperature increases. A reliable deployment should take temperature into account to avoid network connectivity problems resulting from poor wireless links when temperature increases. A good deployment needs also to adapt its operation and save resources when temperature decreases and wireless links improve. Taking into account the probabilistic nature of the wireless communication channel, we develop a mathematical model that provides the most energy efficient deployment in function of temperature without compromising the correct operation of the network by preserving both connectivity and coverage. We use our model to design three temperature-aware algorithms that seek to save energy (i) by putting some nodes in hibernate mode as in the SO (Stop-Operate) algorithm, or (ii) by using transmission power control as in PC (Power-Control), or (iii) by doing both techniques as in SOPC (Stop-Operate Power-Control). All proposed algorithms are fully distributed and solely rely on temperature readings without any information exchange between neighbors, which makes them low overhead and robust. Our results identify the optimal operation of each algorithm and show that a significant amount of energy can be saved by taking temperature into account.


title: Fast 3D reconstruction of the rib cage from biplanar radiographs

abstract: The stereoradiographic reconstruction method allows obtaining the three-dimensional (3D) rib cage geometry, which is essential for clinical evaluation or biomechanical studies. However, reconstruction time is still high (about 20 min considering operator time). The purpose of this study is to propose a 3D reconstruction of the rib cage from biplanar radiographs, based on the deformation of a prepersonalized object. Validation in comparison with computed tomography (CT-scan) acquisitions was performed. Local parameters (rib length, cord length, maximum width, area, and rib orientations) were computed from reconstructions. Parameters’ reproducibility was assessed with two observers and two measurements for 15 subjects. Regarding validation of the parameters, the mean difference with the CT-scan was between 6.3 and 1.3%. Observer variability was maximal for rib area (6.2%) and was lower than 4.2% for others parameters. The proposed reconstruction method reduced time (less than three minutes for operator time) to obtain a 3D reconstruction of the rib cage.


title: Preconception maternal nutrition: a multi-site randomized controlled trial

abstract: Background: Research directed to optimizing maternal nutrition commencing prior to conception remains very limited, despite suggestive evidence of its importance in addition to ensuring an optimal nutrition environment in the periconceptional period and throughout the first trimester of pregnancy. Methods/Study design: This is an individually randomized controlled trial of the impact on birth length (primary outcome) of the time at which a maternal nutrition intervention is commenced: Arm 1: ≥ 3 mo preconception vs. Arm 2: 12-14 wk gestation vs. Arm 3: none. 192 (derived from 480) randomized mothers and living offspring in each arm in each of four research sites (Guatemala, India, Pakistan, Democratic Republic of the Congo). The intervention is a daily 20 g lipid-based (118 kcal) multi-micronutient (MMN) supplement. Women randomized to receive this intervention with body mass index (BMI) <20 or whose gestational weight gain is low will receive an additional 300 kcal/d as a balanced energy-protein supplement. Researchers will visit homes biweekly to deliver intervention and monitor compliance, pregnancy status and morbidity; ensure prenatal and delivery care; and promote breast feeding. The primary outcome is birth length. Secondary outcomes include: fetal length at 12 and 34 wk; incidence of low birth weight (LBW); neonatal/ infant anthropometry 0-6 mo of age; infectious disease morbidity; maternal, fetal, newborn, and infant epigenetics; maternal and infant nutritional status; maternal and infant microbiome; gut inflammatory biomarkers and bioactive and nutritive compounds in breast mil k. The primary analysis will compare birth Length-for-Age Z-score (LAZ) among trial arms (independently for each site, estimated effect size: 0.35). Additional statistical analyses will examine the secondary outcomes and a pooled analysis of data from all sites. Discussion: Positive results of this trial will support a paradigm shift in attention to nutrition of all females of child-bearing age.


title: Experience with inspection in ultralarge-scale development

abstract: The use of code inspection to detect errors in ultralarge software projects is discussed. In particular, quantitative results based on a 1988 study of inspection of 2.5 million lines of high-level code at Bell-Northern Research are presented. The data represent one of the largest published studies in the industry and confirm that code inspection is still one of the most efficient ways to remove software defects. How to introduce inspections in large-scale production environments successfully is described. >