title: Optical Flow Constraints on Deformable Models with Applications to Face Tracking

abstract: Optical flow provides a constraint on the motion of a deformable model. We derive and solve a dynamic system incorporating flow as a hard constraint, producing a model-based least-squares optical flow solution. Our solution also ensures the constraint remains satisfied when combined with edge information, which helps combat tracking error accumulation. Constraint enforcement can be relaxed using a Kalman filter, which permits controlled constraint violations based on the noise present in the optical flow information, and enables optical flow and edge information to be combined more robustly and efficiently. We apply this framework to the estimation of face shape and motion using a 3D deformable face model. This model uses a small number of parameters to describe a rich variety of face shapes and facial expressions. We present experiments in extracting the shape and motion of a face from image sequences which validate the accuracy of the method. They also demonstrate that our treatment of optical flow as a hard constraint, as well as our use of a Kalman filter to reconcile these constraints with the uncertainty in the optical flow, are vital for improving the performance of our system.


title: Near optimal placement of virtual network functions

abstract: Network Function Virtualization (NFV) is a new networking paradigm where network functions are executed on commodity servers located in small cloud nodes distributed across the network, and where software defined mechanisms are used to control the network flows. This paradigm is a major turning point in the evolution of networking, as it introduces high expectations for enhanced economical network services, as well as major technical challenges. In this paper, we address one of the main technical challenges in this domain: the actual placement of the virtual functions within the physical network. This placement has a critical impact on the performance of the network, as well as on its reliability and operation cost. We perform a thorough study of the NFV location problem, show that it introduces a new type of optimization problems, and provide near optimal approximation algorithms guaranteeing a placement with theoretically proven performance. The performance of the solution is evaluated with respect to two measures: the distance cost between the clients and the virtual functions by which they are served, as well as the setup costs of these functions. We provide bi-criteria solutions reaching constant approximation factors with respect to the overall performance, and adhering to the capacity constraints of the networking infrastructure by a constant factor as well. Finally, using extensive simulations, we show that the proposed algorithms perform well in many realistic scenarios.


title: Identifying Important Nodes in Weighted Covert Networks Using Generalized Centrality Measures

abstract: For investigators working on criminal covert networks, identification of key actor(s) in the network is a major objective. Taking out key nodes will decrease the ability of the criminal network to function normally. Traditionally, the node centrality measurements have relied solely on the number of edges incident to nodes but not on the weights of those edges. However, in some generalizations for centrality measures for weighted networks, the focus shifts solely to the weights of the links and they don't account for the number of ties which was the central idea in the original centrality measures. Hence, answering which nodes are most central in a network with weighted relations depends on what imporantce is given the weights of the incident edges in comparison to the number of those edges. Opsahl et al. propose a generalized method for controlling the relative importance between the number of incident ties (nodal degree) versus the total weight of those ties (nodal strength). Research in TNA has largely focused on un-weighted ties, whereas richer and more sophisticated models of covert networks are needed to give precise and more realistic knowledge about such networks. Moreover, the existing implementation of node centrality algorithms in TNA tools don't support networks having weighted/values relations among nodes. New implementations of node centrality algorithms for weighted networks based on the generalized approach have been developed in Crime Fighter Assistant tool and are evaluated with known network dataset of the 9/11 incident.


title: Is Crowdsourcing a Source of Worker Empowerment or Exploitation? Understanding Crowd Workers’ Perceptions of Crowdsourcing Career

abstract: Crowdsourcing offers new forms of work arrangements enabled and facilitated by the advancements in Internet technologies and growing popularity of social media. However, do these new forms of work empower workers to craft their own careers or do they create a sweatshop where workers complete fragmented tasks to earn minimal pay? We posit that career theories and job crafting approaches collectively provide valuable theoretical perspectives for examining this question. By assessing the degree to which these platforms afford or constrain the workers to exert their personal agencies (i.e., affords career and job crafting preferences), we argue, will partially determine whether these new forms of work are a harbinger of worker empowerment or exploitation. Preliminary findings of this exploratory research-in-progress, conducted using two types of workers on Amazon-Mechanical-Turk, reveal that these new forms of work arrangements have a potential for both empowerment and exploitation of workers.


title: A Hierarchical Deep Architecture and Mini-batch Selection Method for Joint Traffic Sign and Light Detection

abstract: Traffic light and sign detectors on autonomous cars are integral for road scene perception. The literature is abundant with deep learning networks that detect either lights or signs, not both, which makes them unsuitable for real-life deployment due to the limited graphics processing unit (GPU) memory and power available on embedded systems. The root cause of this issue is that no public dataset contains both traffic light and sign labels, which leads to difficulties in developing a joint detection framework. We present a deep hierarchical architecture in conjunction with a mini-batch proposal selection mechanism that allows a network to detect both traffic lights and signs from training on separate traffic light and sign datasets. Our method solves the overlapping issue where instances from one dataset are not labelled in the other dataset. We are the first to present a network that performs joint detection on traffic lights and signs. We measure our network on the Tsinghua-Tencent 100K benchmark for traffic sign detection and the Bosch Small Traffic Lights benchmark for traffic light detection and show it outperforms the existing Bosch Small Traffic light state-of-the-art method. We focus on autonomous car deployment and show our network is more suitable than others because of its low memory footprint and real-time image processing time. Qualitative results can be viewed at https://youtu.be/ YmogPzBXOw.


title: MATLAB-graphical user interface (GUI) to study PV module characteristics

abstract: Solar radiation and temperature have direct effect on the characteristics of Solar photovoltaic (PV) modules. This paper presents a MATLAB - Graphical User Interface (GUI) for studying the PV module characteristics, i.e., (I, V) and (P, V) under different values of radiation and temperature. In addition, the GUI can provide solar PV module information including maximum power, voltage and current at maximum power point, efficiency, fill factor (FF) of the PV module, when parameters such as, module temperature, solar radiation, short circuit current, open circuit voltage, and module area are provided.


title: PAIRWISE LINKAGE FOR POINT CLOUD SEGMENTATION

abstract: Abstract. In this paper, we first present a novel hierarchical clustering algorithm named Pairwise Linkage (P-Linkage), which can be used for clustering any dimensional data, and then effectively apply it on 3D unstructured point cloud segmentation. The P-Linkage clustering algorithm first calculates a feature value for each data point, for example, the density for 2D data points and the flatness for 3D point clouds. Then for each data point a pairwise linkage is created between itself and its closest neighboring point with a greater feature value than its own. The initial clusters can further be discovered by searching along the linkages in a simple way. After that, a cluster merging procedure is applied to obtain the finally refined clustering result, which can be designed for specialized applications. Based on the P-Linkage clustering, we develop an efficient segmentation algorithm for 3D unstructured point clouds, in which the flatness of the estimated surface of a 3D point is used as its feature value. For each initial cluster a slice is created, then a novel and robust slicemerging method is proposed to get the final segmentation result. The proposed P-Linkage clustering and 3D point cloud segmentation algorithms require only one input parameter in advance. Experimental results on different dimensional synthetic data from 2D to 4D sufficiently demonstrate the efficiency and robustness of the proposed P-Linkage clustering algorithm and a large amount of experimental results on the Vehicle-Mounted, Aerial and Stationary Laser Scanner point clouds illustrate the robustness and efficiency of our proposed 3D point cloud segmentation algorithm.


title: Asymptotic Minimax Regret for Data Compression, Gambling, and Prediction

abstract: For problems of data compression, gambling, and prediction of individual sequences x/sub 1/, /spl middot//spl middot//spl middot/, x/sub n/ the following questions arise. Given a target family of probability mass functions p(x/sub 1/, /spl middot//spl middot//spl middot/, x/sub n/|/spl theta/), how do we choose a probability mass function q(x/sub 1/, /spl middot//spl middot//spl middot/, x/sub n/) so that it approximately minimizes the maximum regret/belowdisplayskip10ptminus6pt max (log1/q(x/sub 1/, /spl middot//spl middot//spl middot/, x/sub n/)-log1/p(x/sub 1/, /spl middot//spl middot//spl middot/, x/sub n/|/spl theta//spl circ/)) and so that it achieves the best constant C in the asymptotics of the minimax regret, which is of the form (d/2)log(n/2/spl pi/)+C+o(1), where d is the parameter dimension? Are there easily implementable strategies q that achieve those asymptotics? And how does the solution to the worst case sequence problem relate to the solution to the corresponding expectation version min/sub q/ max/sub 0/ E/sub 0/(log1/q(x/sub 1/, /spl middot//spl middot//spl middot/, x/sub n/)-log1/p(x/sub 1/, /spl middot//spl middot//spl middot/, x/sub n/|/spl theta/))? In the discrete memoryless case, with a given alphabet of size m, the Bayes procedure with the Dirichlet(1/2, /spl middot//spl middot//spl middot/, 1/2) prior is asymptotically maximin. Simple modifications of it are shown to be asymptotically minimax. The best constant is C/sub m/=log(/spl Gamma/(1/2)/sup m//(/spl Gamma/(m/2)) which agrees with the logarithm of the integral of the square root of the determinant of the Fisher information. Moreover, our asymptotically optimal strategies for the worst case problem are also asymptotically optimal for the expectation version. Analogous conclusions are given for the case of prediction, gambling, and compression when, for each observation, one has access to side information from an alphabet of size k. In this setting the minimax regret is shown to be k(m-1)/2logn/2/spl pi/k+kC/sub m/+o(1).


title: BubbleView: an alternative to eye-tracking for crowdsourcing image importance

abstract: We present BubbleView, a methodology to replace eye-tracking with mouse clicks. Participants are presented with a series of blurred images and click to reveal "bubbles" - small, circular areas of the image at original resolution, similar to having a confined area of focus like the eye fovea. We evaluated BubbleView on a variety of image types: information visualizations, natural images, static webpages, and graphic designs, and compared the clicks to eye fixations collected with eye-trackers in controlled lab settings. We found that BubbleView can be used to successfully approximate eye fixations on different images, and that the regions where people click using BubbleView can also be used to rank image and design elements by importance. BubbleView is designed to measure which information people consciously choose to examine, and works best for defined tasks such as describing the content of an information visualization or measuring image importance. Compared to related methodologies based on a moving-window approach, BubbleView provides more reliable and less noisy data.


title: Joint SFM and detection cues for monocular 3D localization in road scenes

abstract: We present a system for fast and highly accurate 3D localization of objects like cars in autonomous driving applications, using a single camera. Our localization framework jointly uses information from complementary modalities such as structure from motion (SFM) and object detection to achieve high localization accuracy in both near and far fields. This is in contrast to prior works that rely purely on detector outputs, or motion segmentation based on sparse feature tracks. Rather than completely commit to tracklets generated by a 2D tracker, we make novel use of raw detection scores to allow our 3D bounding boxes to adapt to better quality 3D cues. To extract SFM cues, we demonstrate the advantages of dense tracking over sparse mechanisms in autonomous driving scenarios. In contrast to complex scene understanding, our formulation for 3D localization is efficient and can be regarded as an extension of sparse bundle adjustment to incorporate object detection cues. Experiments on the KITTI dataset show the efficacy of our cues, as well as the accuracy and robustness of our 3D object localization relative to ground truth and prior works.


title: Efficient computation of adaptive threshold surfaces for image binarization

abstract: The problem of binarization of gray level images acquired under nonuniform illumination is reconsidered. Yanowitz and Bruckstein (1989) proposed to use an adaptive threshold surface, determined by interpolation of the image gray levels at points where the image gradient is high. The rationale is that a high image gradient indicates probable object edges, and there the image values are between the object and background gray levels. The threshold surface was determined by successive overrelaxation as the solution of the Laplace equation. This work proposes a different method to determine an adaptive threshold surface. In this new method, inspired by multiresolution approximation, the threshold surface is constructed with considerably lower computational complexity and is smooth, yielding faster image binarizations and better visual performance.


Ascaris lumbricoides in the oral cavity: a case report


title: Enabling efficient access control with dynamic policy updating for big data in the cloud

abstract: Due to the high volume and velocity of big data, it is an effective option to store big data in the cloud, because the cloud has capabilities of storing big data and processing high volume of user access requests. Attribute-Based Encryption (ABE) is a promising technique to ensure the end-to-end security of big data in the cloud. However, the policy updating has always been a challenging issue when ABE is used to construct access control schemes. A trivial implementation is to let data owners retrieve the data and re-encrypt it under the new access policy, and then send it back to the cloud. This method incurs a high communication overhead and heavy computation burden on data owners. In this paper, we propose a novel scheme that enabling efficient access control with dynamic policy updating for big data in the cloud. We focus on developing an outsourced policy updating method for ABE systems. Our method can avoid the transmission of encrypted data and minimize the computation work of data owners, by making use of the previously encrypted data with old access policies. Moreover, we also design policy updating algorithms for different types of access policies. The analysis show that our scheme is correct, complete, secure and efficient.


Design and Implementation of the POWER 5 TM Microprocessor


Using the Kalman Filter to track Human Interactive Motion -- Modelling and Initialization of the Kalman Filter for Translational Motion


title: The generation of textual entailment with NLML in an intelligent dialogue system for language learning CSIEC

abstract: This paper introduces the generation of textual entailment within the project CSIEC (computer simulation in educational communication), an interactive Web-based human-computer dialogue system with natural language for English instruction. The generation of textual entailment (GTE) is critical to the further improvement of CSIEC project and other natural language generation program. Up to now we have found few literatures on the general algorithm for GTE. Simulating the process that a human being learns English as a foreign language, we explore our naive approach to tackle the GTE problem and its algorithm within the framework of CSIEC, i.e. rule annotation in NLML, pattern recognition and entailment transformation. The time and space complexity of our algorithm is tested with some entailment examples. An interactive command line textual entailment editor is created to generalize an entailment rule from a case pair of text and entailment. The test version of this innovative GTE approach can be accessed in the CSIEC website. Further works include the rules annotation based on the English textbooks and a GUI interface for normal users to edit the entailment rules.


title: Background modeling from a free-moving camera by Multi-Layer Homography Algorithm

abstract: This paper proposes a novel multi-layer homography algorithm for background modeling from a free-moving camera. Background is composed of many planes. Different planes satisfy with different homographies which can be found by our algorithm. Each pixel except for the moving pixel definitely belongs to some plane. Rectified by the corresponding homography, each static pixel in the shared view can find its match in the previous frame. Thus, frames can be rectified to a specific viewpoint for background modeling. Experiment shows it is effective. Our approach can be used in motion detection from a free-moving camera.


title: Ontology based classification for multi-label image annotation

abstract: Image annotation has been an important task for visual information retrieval. It usually involves a multi-class multi-label classification problem. To solve this problem, many researches have been conducted during last two decades, although most of the proposed methods rely on the training data with the ground truth. To prepare such a ground truth is an expensive and laborious task that cannot be easily scaled, and “semantic gaps” between low-level visual features and high-level semantics still remain. In this paper, we propose a novel approach, ontology based supervised learning for multi-label image annotation, where classifiers' training is conducted using easily gathered Web data. Moreover, it takes advantage of both low-level visual features and high-level semantic information of given images. Experimental results using 0.507 million Web images database show effectiveness of the proposed framework over existing method.


title: Measurement Coding for Compressive Sensing of Color Images

abstract: From the perspective of reducing the sampling cost of color images at high resolution, block-based compressive sensing (CS) has attracted considerable attention as a promising alternative to conventional Nyquist/Shannon sampling. On the other hand, for storing/transmitting applications, CS requires a very efficient way of representing the measurement data in terms of data volume. This paper addresses this problem by developing a measurement-coding method with the proposed customized Huffman coding. In addition, by noting the difference in visual importance between the luma and chroma channels, this paper proposes measurement coding in YCbCr space rather than in conventional RGB color space for better rate allocation. Furthermore, as the proper use of the image property in pursuing smoothness improves the CS recovery, this paper proposes the integration of a low pass filter to the CS recovery of color images, which is the block-based l 20 -norm minimization. The proposed coding scheme shows considerable gain compared to conventional measurement coding.


title: Assay of Chlortetracycline in Animal Feeds by Liquid Chromatography with Fluorescence Detection

abstract: A liquid chromatographic (LC) method is described to assay chlortetracycline (CTC) at gram-per-ton and gram-per-pound levels. Feeds and premixes are extracted with acid-acetone following AOAC Method 957.23B(h). Extracts are filtered and quantitated by LC using a C18 column and fluorescence detection at 390 nm excitation and 512 nm emission. The mobile phase (1.5 mL/min) uses a methanol gradient with an initial hold time of 5 min at 35% methanol followed by an increase to 60% methanol over 8 min. The aqueous component contains CaCl2, disodium EDTA, and sodium acetate buffered to pH 6.5. The method provides significant improvement in selectivity and limit of quantitation compared to LC with UV detection. Recovery of spiked samples averaged 98.1% and assay of samples correlated closely with the results obtained with AOAC microbiological Method 967.39. CTC and epi-CTC eluted at approximately 12.5 and 4.0 min, respectively. No interference was observed from several drugs and other antibiotics often used in combination with CTC in feeds.


title: Compound Memory Networks for Few-Shot Video Classification

abstract: In this paper, we propose a new memory network structure for few-shot video classification by making the following contributions. First, we propose a compound memory network (CMN) structure under the key-value memory network paradigm, in which each key memory involves multiple constituent keys. These constituent keys work collaboratively for training, which enables the CMN to obtain an optimal video representation in a larger space. Second, we introduce a multi-saliency embedding algorithm which encodes a variable-length video sequence into a fixed-size matrix representation by discovering multiple saliencies of interest. For example, given a video of car auction, some people are interested in the car, while others are interested in the auction activities. Third, we design an abstract memory on top of the constituent keys. The abstract memory and constituent keys form a layered structure, which makes the CMN more efficient and capable of being scaled, while also retaining the representation capability of the multiple keys. We compare CMN with several state-of-the-art baselines on a new few-shot video classification dataset and show the effectiveness of our approach.


title: Improving Neural Text Simplification Model with Simplified Corpora

abstract: Text simplification (TS) can be viewed as monolingual translation task, translating between text variations within a single language. Recent neural TS models draw on insights from neural machine translation to learn lexical simplification and content reduction using encoder-decoder model. But different from neural machine translation, we cannot obtain enough ordinary and simplified sentence pairs for TS, which are expensive and time-consuming to build. Target-side simplified sentences plays an important role in boosting fluency for statistical TS, and we investigate the use of simplified sentences to train, with no changes to the network architecture. We propose to pair simple training sentence with a synthetic ordinary sentence via back-translation, and treating this synthetic data as additional training data. We train encoder-decoder model using synthetic sentence pairs and original sentence pairs, which can obtain substantial improvements on the available WikiLarge data and WikiSmall data compared with the state-of-the-art methods.


title: Robust sparse canonical correlation analysis

abstract: Canonical correlation analysis (CCA) is a multivariate statistical method which describes the associations between two sets of variables. The objective is to find linear combinations of the variables in each data set having maximal correlation. In genomics, CCA has become increasingly important to estimate the associations between gene expression data and DNA copy number change data. The identification of such associations might help to increase our understanding of the development of diseases such as cancer. However, these data sets are typically high-dimensional, containing a lot of variables relative to the number of objects. Moreover, the data sets might contain atypical observations since it is likely that objects react differently to treatments. We discuss a method for Robust Sparse CCA, thereby providing a solution to both issues. Sparse estimation produces canonical vectors with some of their elements estimated as exactly zero. As such, their interpretability is improved. Robust methods can cope with atypical observations in the data. We illustrate the good performance of the Robust Sparse CCA method by several simulation studies and three biometric examples. Robust Sparse CCA considerably outperforms its main alternatives in (1) correctly detecting the main associations between the data sets, in (2) accurately estimating these associations, and in (3) detecting outliers. Robust Sparse CCA delivers interpretable canonical vectors, while at the same time coping with outlying observations. The proposed method is able to describe the associations between high-dimensional data sets, which are nowadays commonplace in genomics. Furthermore, the Robust Sparse CCA method allows to characterize outliers.


title: Differential Stepped-Slot UWB Antenna With Common-Mode Suppression and Dual Sharp-Selectivity Notched Bands

abstract: A differential stepped-slot UWB antenna with common-mode suppression and dual sharp-selectivity notched bands is proposed in this letter. The antenna adopts stepped slot as radiator to achieve ultrawideband. Under differential-mode operation, the stepped slot can be effectively excited by feeding microstrip, and differential signals can be radiated. However, under common-mode operation, the stepped slot would not be excited so that the common-mode suppression can be achieved. In order to achieve dual sharp-selectivity notch-band characteristic, quarter–wavelength slits are etched on the ground, and half-wavelength stubs are introduced beside the microstrip, respectively. As a result, dual second-order notched bands with sharp selectivity are obtained. The measured results show that the proposed antenna obtains good common-mode suppression characteristic and dual sharp-selectivity notched bands from 5.1 to 6.0 GHz and 7.83 to 8.47 GHz. Good agreement is achieved between the measured results and the simulated results.


title: Proposal Flow

abstract: Finding image correspondences remains a challenging problem in the presence of intra-class variations and large changes in scene layout. Semantic flow methods are designed to handle images depicting different instances of the same object or scene category. We introduce a novel approach to semantic flow, dubbed proposal flow, that establishes reliable correspondences using object proposals. Unlike prevailing semantic flow approaches that operate on pixels or regularly sampled local regions, proposal flow benefits from the characteristics of modern object proposals, that exhibit high repeatability at multiple scales, and can take advantage of both local and geometric consistency constraints among proposals. We also show that proposal flow can effectively be transformed into a conventional dense flow field. We introduce a new dataset that can be used to evaluate both general semantic flow techniques and region-based approaches such as proposal flow. We use this benchmark to compare different matching algorithms, object proposals, and region features within proposal flow, to the state of the art in semantic flow. This comparison, along with experiments on standard datasets, demonstrates that proposal flow significantly outperforms existing semantic flow methods in various settings.


title: Systematic literature studies: database searches vs. backward snowballing

abstract: Systematic studies of the literature can be done in different ways. In particular, different guidelines propose different first steps in their recommendations, e.g. start with search strings in different databases or start with the reference lists of a starting set of papers. In software engineering, the main recommended first step is using search strings in a number of databases, while in information systems, snowballing has been recommended as the first step. This paper compares the two different search approaches for conducting literature review studies. The comparison is conducted by searching for articles addressing "Agile practices in global software engineering". The focus of the paper is on evaluating the two different search approaches. Despite the differences in the included papers, the conclusions and the patterns found in both studies are quite similar. The strengths and weaknesses of each first step are discussed separately and in comparison with each other. It is concluded that none of the first steps is outperforming the other, and the choice of guideline to follow, and hence the first step, may be context-specific, i.e. depending on the area of study.


title: A Human Computation Framework for Boosting Combinatorial Solvers

abstract: We propose a general framework for boosting combinatorial solvers through human computation. Our framework combines insights from human workers with the power of combinatorial optimization. The combinatorial solver is also used to guide requests for the workers, and thereby obtain the most useful human feedback quickly. Our approach also incorporates a problem decomposition approach with a general strategy for discarding incorrect human input. We apply this framework in the domain of materials discovery, and demonstrate a speedup of over an order of magnitude.


title: Domain Classification of Textual Conversation Using Machine Learning Approach

abstract: This paper presents an approach for classification of textual conversation into multiple domain categories using support vector classifier. The feature reduction is done through Principal Component Analysis (PCA) to extract the important features from the feature vector. These features are passed to different configurations of SVM and the best one is chosen for the final process of classification. The domain's categories are defined on real life situations and conversation to train the system like education & research, personal, patriotism, terrorism, medical, religious, sports, and business. The experiment results show that the proposed method works effectively with more than 75% accuracy.


title: IoT Based Automatic Electricity Monitoring and Remote Load Control System Using PIC18F4550

abstract: With the development in modern communication technology, every physical device is now connecting with the internet. IoT is getting emerging technology for connecting physical devices with the user. In this paper we combined existing energy meter with the IoT technology. By implementation of IoT in the case of meter reading for electricity can give customer relief in using electrical energy. In this work a digital energy meter is connected with cloud server via IoT device. It sends the amount of consumed energy of connected customer to webserver. There is a feature for disconnection in the case of unauthorized and unpaid consumption and also have option for renew the connection by paying bill online. We tried to build up a consumer and business friendly system.


title: HOMI JEHANGIR BHABHA: HIS COLLABORATORS, CITATION IDENTITY, AND HIS CITATION IMAGE MAKERS

abstract: In this paper, we studied synchronous references (references in the same papers) and diachronous citations (citations to the papers) of the renowned theoretical physicist, Homi Jehangir Bhabha. We utilized the Science Citation Index (SCI) on CD from 1982 – 2006. We identified his coauthors, his most cited works, citation identity (authors whom he had cited) and citation image makers (authors citing him). Bhabha published his first paper in 1933 at the age of 24 and the last paper in 1966, the year of his demise. His 66 papers could be categorized into nine fields: Cosmic ray physics (18 papers); Elementary particle physics, and Field theory (14 papers each); Quantum electrodynamics (6 papers); Nuclear physics (4 papers); General, and Interaction of radiation with matter (3 papers each); and Mathematical physics and General physics (2 papers each). He collaborated with 18 authors in 18 papers; the remaining 48 papers were single-authored. His citation identity consisted of 212 different authors, of whom six were Nobel laureates. He received 328 citations to his works, his overall citation rate being 10.6 per cited paper and the highest citation rate was 21.2 to his papers in the field of Elementary particle physics. His most often cited paper was in Quantum electrodynamics with 54 citations. His image makers comprised 537 different authors. Bhabha has the distinction of being cited by at least two Nobel laureates, P.M.S. Blackett and H. Yukawa in their respective Nobel lectures.


title: Revisiting The American Voter on Twitter

abstract: The American Voter - a seminal work in political science - uncovered the multifaceted nature of voting behavior which has been corroborated in electoral research for decades since. In this paper, we leverage The American Voter as an analysis framework in the realm of computational political science, employing the factors of party, personality, and policy to structure the analysis of public discourse on online social media during the 2016 U.S. presidential primaries. Our analysis of 50 million tweets reveals the continuing importance of these three factors; our understanding is also enriched by the application of sentiment analysis techniques. The overwhelmingly negative sentiment of conversations surrounding 10 major presidential candidates reveals more "crosstalk" from Democratic leaning users towards Republican candidates, and less vice-versa. We uncover the lack of moderation as the most discussed personality dimension during this campaign season, as the political field becomes more extreme - Clinton and Rubio are perceived as moderate, while Trump, Sanders, and Cruz are not. While the most discussed issues are foreign policy and immigration, Republicans tweet more about abortion than Democrats who tweet more about gay rights than Republicans. Finally, we illustrate the importance of multifaceted political discourse analysis by applying regression to quantify the impact of party, personality, and policy on national polls.


title: Handbook of membrane channels : molecular and cellular physiology

abstract: Part 1 Potassium channels: conservation of K+ channel properties in gene subfamilies, N.V. Shen and P.J. Pfaffinger the permeation properties of cloned K+ channels, T. Begenisich gating currents of cloned Shaker K+ channels, E. Stefani et al Shaw-related K+ channels in mammals, E.Vega-Saenz de Miera et al the molecular workings of large conductance (maxi) CA 2+ activated K+ channels, R. Latorre. Part 2 Sodium channels: ionic channels of myelinated axons, P. Shrager and J.V. Wu cardiac Na channel expression in Xynopus oocytes, D. Krafte molecular analysis of sodium channel inactivation, A.L. Goldin unitary properties of batrachotoxin-trapped state of voltage-sensitive sodium channels.


Personality and work engagement in a financial institution


title: Qualitative Research: A Grounded Theory Example and Evaluation Criteria

abstract: The qualitative research paradigm, although occasionally applied, is not widely discussed in agribusiness and agricultural economics literature. The primary goals of this paper are (a) to present insights into qualitative research approaches and processes by outlining grounded theory as an example of a systematic and rigorous qualitative approach, and (b) to discuss criteria for scientific rigor applicable to qualitative research. In addition, assessing qualitative research is demonstrated by using a published example.


title: Parameter investigation in brain storm optimization

abstract: Human being is the most intelligent organism in the world and the brainstorming process popularly used by them has been demonstrated to be a significant and promising way to create great ideas for problem solving. Brain storm optimization (BSO) is a new kind of swarm intelligence algorithm inspired by human being creative problem solving process. BSO transplants the brainstorming process in human being into optimization algorithm design and gains successes. BSO generally uses the grouping, replacing, and creating operators to produce ideas as many as possible to approach the problem solution generation by generation. In these operators, BSO involves mainly three control parameters named: (1) p_replce to control the replacing operator; (2) p_one to control the creating operator to create new ideas between one cluster and two clusters; and (3) p_center (p_one_center and p_two_center) to control using cluster center or random idea to create new idea. In this paper, we make investigations on these parameters to see how they affect the performance of BSO. More importantly, a new BSO variant designed according to the investigation results is proposed and its performance is evaluated.


title: Algorithms for Mining Distance-Based Outliers in Large Datasets

abstract: This paper deals with finding outliers (exceptions) in large, multidimensional datasets. The identification of outliers can lead to the discovery of truly unexpected knowledge in areas such as electronic commerce, credit card fraud, and even the analysis of performance statistics of professional athletes. Existing methods that we have seen for finding outliers in large datasets can only deal efficiently with two dimensions/attributes of a dataset. Here, we study the notion of DB- (DistanceBased) outliers. While we provide formal and empirical evidence showing the usefulness of DB-outliers, we focus on the development of algorithms for computing such outliers. First, we present two simple algorithms, both having a complexity of O(k N’), k being the dimensionality and N being the number of objects in the dataset. These algorithms readily support datasets with many more than two attributes. Second, we present an optimized cell-based algorithm that has a complexity that is linear wrt N, but exponential wrt k. Third, for datasets that are mainly disk-resident, we present another version of the cell-based algorithm that guarantees at most 3 passes over a dataset. We provide


title: Metamorphic Malware Detection Using Statistical Analysis

abstract: Typically, computer viruses and other malware are detected by searching for a string of bits found in the virus or malware. Such a string can be viewed as a "fingerprint" of the virus identified as the signature of the virus. The technique of detecting viruses using signatures is known as signature based detection. Today, virus writers often camouflage their viruses by using code obfuscation techniques in an effort to defeat signature-based detection schemes. So-called metamorphic viruses transform their code as they propagate, thus evading detection by static signature-based virus scanners, while keeping their functionality but differing in internal structure. Many dynamic analysis based detection have been proposed to detect metamorphic viruses but dynamic analysis technique have limitations like difficult to learn normal behavior, high run time overhead and high false positive rate compare to static detection technique. A similarity measure method has been successfully applied in the field of document classification problem. We want to apply similarity measures methods on static feature, API calls of executable to classify it as malware or benign. In this paper we present limitations of signature based detection for detecting metamorphic viruses. We focus on statically analyzing an executable to extract API calls and count the frequency this API calls to generate the feature set. These feature set is used to classify unknown executable as malware or benign by applying various similarity function.


title: A declarative approach for flexible business processes management

abstract: Management of dynamic processes in an important issue in rapidly changing organizations. Workflow management systems are systems that use detailed process models to drive the business processes. Current business process modelling languages and models are of imperative nature – they strictly prescribe how to work. Systems that allow users to maneuver within the process model or even change the model while working are considered to be the most suitable for dynamic processes management. However, in many companies it is not realistic to expect that end-users are able to change their processes. Moreover, the imperative nature of these languages forces designer to over-specify processes, which results in frequent changes. We propose a fundamental paradigm shift for flexible process management and propose a more declarative approach. Declarative models specify what should be done without specifying how it should be done. We propose the ConDec language for modelling and enacting dynamic business processes. ConDec is based on temporal logic rather than some imperative process modelling language.


title: Redox Dependant Processes in the Transition from Oxic to Anoxic Conditions: An in Situ Study Concerning Remineralization, Nutrient Release and Heavy Metal Solubilization

abstract: Whenever the rate of supply of oxygen in natural waters cannot cope with its consumption rate in organic matter oxidation, redox gradients develop and reducing conditions occur. In the marine environment the most widespread systems exhibiting redox gradients are the upper layers of shallow water and continental shelf sediments. Under certain conditions of morphology, density stratification and organic matter supply the redoxcline may cross over into the overlying water column leading there to a more or less transient anoxic milieu (see for a review: Deuser, 1975, Grashoff, 1975). Fjords being connected to the open ocean by a shallow sill, often meet these preconditions.


title: Information and communications technologies for elderly ubiquitous healthcare in a smart home

abstract: Over the past century, in most countries, there has been a continual increase in life expectancy primarily due to improvements in public health, nutrition, personal hygiene and medicine. However, these improvements are now coupled with aging population demographics and falling birth rates, which, when combined, are expected to significantly burden the socioeconomic well-being of many of these countries. In fact, never before in human history have we been confronted with such a large aging population, nor have we developed solid, cost-effective solutions for the well-being, healthcare and social needs of the elderly. One efficient and cost-effective solution to the problem of elderly/patient care is remote healthcare monitoring so they can continue to live at home rather than in nursing homes or hospitals that are very expensive and with limited spaces. These remote monitoring systems will allow medical personnel to keep track of important physiological signs with reduced human resources, at less cost and in real time. This paper introduces several low-cost, noninvasive, user-friendly sensing and actuating systems using information and communication technologies. Such systems can be used to create engineering solutions to some of the pressing healthcare problems in our society, especially as it pertains to the elderly. One example is the integration of sensors, wireless communications, low-power electronics and intelligent computing to determine health-related information using signals from walking patterns. Such a sensing system will be suitable for prolonged use in a home environment. It will be wearable, noninvasive and non-intrusive, similar to smart socks, smart wrist-bands or smart belts. Other examples such as a smart joint monitor and a smart sleeping environment will be discussed, and future perspectives and research challenges in smart home technologies will be described.


title: Generating Natural Language Explanations for Visual Question Answering using Scene Graphs and Visual Attention

abstract: In this paper, we present a novel approach for the task of eXplainable Question Answering (XQA), i.e., generating natural language (NL) explanations for the Visual Question Answering (VQA) problem. We generate NL explanations comprising of the evidence to support the answer to a question asked to an image using two sources of information: (a) annotations of entities in an image (e.g., object labels, region descriptions, relation phrases) generated from the scene graph of the image, and (b) the attention map generated by a VQA model when answering the question. We show how combining the visual attention map with the NL representation of relevant scene graph entities, carefully selected using a language model, can give reasonable textual explanations without the need of any additional collected data (explanation captions, etc). We run our algorithms on the Visual Genome (VG) dataset and conduct internal user-studies to demonstrate the efficacy of our approach over a strong baseline. We have also released a live web demo showcasing our VQA and textual explanation generation using scene graphs and visual attention.


title: Model-based and model-free Pavlovian reward learning: Revaluation, revision, and revelation

abstract: Evidence supports at least two methods for learning about reward and punishment and making predictions for guiding actions. One method, called model-free, progressively acquires cached estimates of the long-run values of circumstances and actions from retrospective experience. The other method, called model-based, uses representations of the environment, expectations, and prospective calculations to make cognitive predictions of future value. Extensive attention has been paid to both methods in computational analyses of instrumental learning. By contrast, although a full computational analysis has been lacking, Pavlovian learning and prediction has typically been presumed to be solely model-free. Here, we revise that presumption and review compelling evidence from Pavlovian revaluation experiments showing that Pavlovian predictions can involve their own form of model-based evaluation. In model-based Pavlovian evaluation, prevailing states of the body and brain influence value computations, and thereby produce powerful incentive motivations that can sometimes be quite new. We consider the consequences of this revised Pavlovian view for the computational landscape of prediction, response, and choice. We also revisit differences between Pavlovian and instrumental learning in the control of incentive motivation.


title: A Variational U-Net for Conditional Appearance and Shape Generation

abstract: Deep generative models have demonstrated great performance in image synthesis. However, results deteriorate in case of spatial deformations, since they generate images of objects directly, rather than modeling the intricate interplay of their inherent shape and appearance. We present a conditional U-Net for shape-guided image generation, conditioned on the output of a variational autoencoder for appearance. The approach is trained end-to-end on images, without requiring samples of the same object with varying pose or appearance. Experiments show that the model enables conditional image generation and transfer. Therefore, either shape or appearance can be retained from a query image, while freely altering the other. Moreover, appearance can be sampled due to its stochastic latent representation, while preserving shape. In quantitative and qualitative experiments on COCO, DeepFashion, shoes, Market-1501 and handbags, the approach demonstrates significant improvements over the state-of-the-art.


title: Data fusion for 3D thermal imaging using depth and stereo camera for robust self-localization

abstract: Infrared thermography is a common method of measuring surface temperature distributions. New approaches to the three-dimensional (3D) recording of these measurements have been developed in the last decade. A given 3D real-time imaging system [1] consisting of an infrared (IR) camera and a near-infrared (NIR) depth sensor is the object of investigation. The system uses an improved KinectFusion processing algorithm and its Iterative Closest Point (ICP) method for self-localization. The aim of this work is to reduce the impact of correspondence problems when scanning objects with few geometric features on 3D modeling. In order to achieve robust self-localization, a stereo camera is integrated into the system and its localization data is fused with the existing system data. By means of indoor and outdoor test scenarios the system shows its ability to bridge sections of ICP localization problems.


title: Challenges in Measuring the Impact of Interruption on Patient Safety and Workflow Outcomes

abstract: Objective: To examine the problem of studying interruption in healthcare. Methods: Review of the interruption literature from psychology, human-computer interaction; experimental studies of electronic prescribing and error behaviour; observational studies in emergency and intensive care. Results: Primary task and interruption variables which contribute to the outcomes of an interruption include the type of task (primary and interrupting task); point of interruption; duration of interruption; similarity of interruptive task to primary task; modality of interruption; environmental cues; and interruption handling strategy. Effects of interruption on task performance can be examined by measuring errors, the time on task, interruption lag and resumption lag. Conclusions: Interruptions are a complex phenomenon where multiple variables including the characteristics of primary tasks, the interruptions themselves, and the environment may influence patient safety and workflow outcomes. Observational studies present significant challenges for recording many of the process variables that influence the effects of interruptions. Controlled experiments provide an opportunity to examine the specific effects of variables on errors and efficiency. Computational models can be used to identify the situations in which interruptions to clinical tasks could be disruptive and to investigate the aggregate effects of interruptions.


title: Compact X-band Synthetic Aperture Radar for 100kg Class Satellite

abstract: We are developing a small synthetic aperture radar (SAR) that is compatible with 100kg class satellite. A constellation of such small SAR satellites is promising for shot revisit time of earth observations. The SAR antenna is a deployable honeycomb panel antenna with slot array, that can be stowed compactly. RF instruments are on a satellite structure and feed through waveguides. RF signal feeds through a choke flange at a hinge. The current status of the hardware development will be presented.


title: Incubation, insight, and creative problem solving: a unified theory and a connectionist model

abstract: This article proposes a unified framework for understanding creative problem solving, namely, the explicit–implicit interaction theory. This new theory of creative problem solving constitutes an attempt at providing a more unified explanation of relevant phenomena (in part by reinterpreting/integrating various fragmentary existing theories of incubation and insight). The explicit–implicit interaction theory relies mainly on 5 basic principles, namely, (a) the coexistence of and the difference between explicit and implicit knowledge, (b) the simultaneous involvement of implicit and explicit processes in most tasks, (c) the redundant representation of explicit and implicit knowledge, (d) the integration of the results of explicit and implicit processing, and (e) the iterative (and possibly bidirectional) processing. A computational implementation of the theory is developed based on the CLARION cognitive architecture and applied to the simulation of relevant human data. This work represents an initial step in the development of process-based theories of creativity encompassing incubation, insight, and various other related phenomena.


title: The neural basis of error detection: conflict monitoring and the error-related negativity

abstract: According to a recent theory, anterior cingulate cortex is sensitive to response conflict, the coactivation of mutually incompatible responses. The present research develops this theory to provide a new account of the error-related negativity (ERN), a scalp potential observed following errors. Connectionist simulations of response conflict in an attentional task demonstrated that the ERN—its timing and sensitivity to task parameters— can be explained in terms of the conflict theory. A new experiment confirmed predictions of this theory regarding the ERN and a second scalp potential, the N2, that is proposed to reflect conflict monitoring on correct response trials. Further analysis of the simulation data indicated that errors can be detected reliably on the basis of post-error conflict. It is concluded that the ERN can be explained in terms of response conflict and that monitoring for conflict may provide a simple mechanism for detecting errors. Errors are an important source of information in the regulation of cognitive processes. The mechanism by which people detect and correct their errors has been the object of study for many years, but research interest has increased in recent years following the discovery of neural correlates of performance monitoring. In particular, studies of event-related brain potentials (ERPs) have revealed a neural response following errors that has been labeled the errorrelated negativity (ERN or Ne; Falkenstein, Hohnsbein, Hoorman, & Blanke, 1990, 1991; Gehring, Goss, Coles, Meyer, & Donchin, 1993). The most likely neural generator of the ERN is anterior cingulate cortex (ACC), an area that in recent years has been implicated in another function related to the evaluation of performance, monitoring for competition (or conflict) during information processing. The present research attempts to provide an integrative account of error- and conflict-related activity observed in anterior cingulate cortex. Specifically, we propose a new account of the ERN and error processing in terms of the conflict monitoring theory of anterior cingulate function. Background


Primary Care Groups and Trusts


title: A Metric for Sentence Ordering Assessment Based on Topic-Comment Structure

abstract: Sentence ordering (SO) is a key component of verbal ability. It is also crucial for automatic text generation. While numerous researchers developed various methods to automatically evaluate the informativeness of the produced contents, the evaluation of readability is usually performed manually. In contrast to that, we present a self-sufficient metric for SO assessment based on text topic-comment structure. We show that this metric has high accuracy.


Automatic Opinion Question Generation


title: Integrating Big Data: A Semantic Extract-Transform-Load Framework

abstract: Current tools that facilitate the extract-transform-load (ETL) process focus on ETL workflow, not on generating meaningful semantic relationships to integrate data from multiple, heterogeneous sources. A proposed semantic ETL framework applies semantics to various data fields and so allows richer data integration.


title: HyperLink: Virtual Machine Introspection and Memory Forensic Analysis without Kernel Source Code

abstract: Virtual Machine Introspection (VMI) is an approach to inspecting and analyzing the software running inside a virtual machine from the hypervisor. Similarly, memory forensics analyzes the memory snapshots or dumps to understand the runtime state of a physical or virtual machine. The existing VMI and memory forensic tools rely on up-to-date kernel information of the target operating system (OS) to work properly, which often requires the availability of the kernel source code. This requirement prevents these tools from being widely deployed in real cloud environments. In this paper, we present a VMI tool called HyperLink that partially retrieves running process information from a guest virtual machine without its source code. While current introspection and memory forensic solutions support only one or a limited number of kernel versions of the target OS, HyperLink is a one-for-many introspection and forensic tool, i.e., it supports most, if not all, popular OSes regardless of their versions. We implement both online and offline versions of HyperLink. We validate the efficacy of HyperLink under different versions of Linux, Windows, FreeBSD, and Mac OS X. For all the OSes we tested, HyperLink can successfully retrieve the process information in one minute or several seconds. Through online and offline analyses, we demonstrate that HyperLink can help users detect real-world kernel rootkits and play an important role in intrusion detection. Due to its version-agnostic property, HyperLink could become the first introspection and forensic tool that works well in autonomic cloud computing environments.


title: Soft robotics: a bioinspired evolution in robotics.

abstract: Animals exploit soft structures to move effectively in complex natural environments. These capabilities have inspired robotic engineers to incorporate soft technologies into their designs. The goal is to endow robots with new, bioinspired capabilities that permit adaptive, flexible interactions with unpredictable environments. Here, we review emerging soft-bodied robotic systems, and in particular recent developments inspired by soft-bodied animals. Incorporating soft technologies can potentially reduce the mechanical and algorithmic complexity involved in robot design. Incorporating soft technologies will also expedite the evolution of robots that can safely interact with humans and natural environments. Finally, soft robotics technology can be combined with tissue engineering to create hybrid systems for medical applications.


title: Mind your SMSes : Mitigating Social Engineering in Second Factor Authentication

abstract: SMS-based second factor authentication is a cornerstone for many service providers, ranging from email service providers and social networks to financial institutions and online marketplaces. Attackers have not been slow to capitalize on the vulnerabilities of this mechanism by using social engineering techniques to coerce users to forward authentication codes. We demonstrate one social engineering attack for which we experimentally obtained a 50% success rate against Google's SMS-based authentication. At the heart of the problem is the messaging associated with the authentication code, and how this must not have been developed with security against social engineering in mind. Pursuing a top-down methodology, we generate alternative messages and experimentally test these against an array of social engineering attempts. Our most robust messaging approach reduces the success of the most effective social engineering attack to 8%, or a sixth of its success against Google's standard second factor verification code messages.


title: Distributed shared persistent memory

abstract: Next-generation non-volatile memories (NVMs) will provide byte addressability, persistence, high density, and DRAM-like performance. They have the potential to benefit many datacenter applications. However, most previous research on NVMs has focused on using them in a single machine environment. It is still unclear how to best utilize them in distributed, datacenter environments. We introduce Distributed Shared Persistent Memory (DSPM), a new framework for using persistent memories in distributed data-center environments. DSPM provides a new abstraction that allows applications to both perform traditional memory load and store instructions and to name, share, and persist their data. We built Hotpot, a kernel-level DSPM system that provides low-latency, transparent memory accesses, data persistence, data reliability, and high availability. The key ideas of Hotpot are to integrate distributed memory caching and data replication techniques and to exploit application hints. We implemented Hotpot in the Linux kernel and demonstrated its benefits by building a distributed graph engine on Hotpot and porting a NoSQL database to Hotpot. Our evaluation shows that Hotpot outperforms a recent distributed shared memory system by 1.3× to 3.2× and a recent distributed PM-based file system by 1.5× to 3.0×.


title: A Survey on Banknote Recognition Methods by Various Sensors

abstract: Despite a decrease in the use of currency due to the recent growth in the use of electronic financial transactions, real money transactions remain very important in the global market. While performing transactions with real money, touching and counting notes by hand, is still a common practice in daily life, various types of automated machines, such as ATMs and banknote counters, are essential for large-scale and safe transactions. This paper presents studies that have been conducted in four major areas of research (banknote recognition, counterfeit banknote detection, serial number recognition, and fitness classification) in the accurate banknote recognition field by various sensors in such automated machines, and describes the advantages and drawbacks of the methods presented in those studies. While to a limited extent some surveys have been presented in previous studies in the areas of banknote recognition or counterfeit banknote recognition, this paper is the first of its kind to review all four areas. Techniques used in each of the four areas recognize banknote information (denomination, serial number, authenticity, and physical condition) based on image or sensor data, and are actually applied to banknote processing machines across the world. This study also describes the technological challenges faced by such banknote recognition techniques and presents future directions of research to overcome them.


title: Convolutional Neural Networks over Control Flow Graphs for Software Defect Prediction

abstract: Existing defects in software components is unavoidable and leads to not only a waste of time and money but also many serious consequences. To build predictive models, previous studies focus on manually extracting features or using tree representations of programs, and exploiting different machine learning algorithms. However, the performance of the models is not high since the existing features and tree structures often fail to capture the semantics of programs. To explore deeply programs' semantics, this paper proposes to leverage precise graphs representing program execution flows, and deep neural networks for automatically learning defect features. Firstly, control flow graphs are constructed from the assembly instructions obtained by compiling source code; we thereafter apply multi-view multi-layer directed graph-based convolutional neural networks (DGCNNs) to learn semantic features. The experiments on four real-world datasets show that our method significantly outperforms the baselines including several other deep learning approaches.


title: Privacy by Design - Principles of Privacy-Aware Ubiquitous Systems

abstract: This paper tries to serve as an introductory reading to privacy issues in the field of ubiquitous computing. It develops six principles for guiding system design, based on a set of fair information practices common in most privacy legislation in use today: notice, choice and consent, proximity and locality, anonymity and pseudonymity, security, and access and recourse. A brief look at the history of privacy protection, its legal status, and its expected utility is provided as a background.


title: A VoIP emergency services architecture and prototype

abstract: Providing emergency services in VoIP networks are vital to the success of VoIP. It not only presents design and implementation challenges, but also gives an opportunity to enhance the existing emergency call handling infrastructure. We propose an architecture to deliver emergency services in SIP-based VoIP networks, which can accommodate PSTN calls through PSTN to SIP gateways. Our architecture addresses the issues of identifying emergency calls, determining callers' locations, routing emergency calls to appropriate public safety answering points (PSAPs), and presenting required information to emergency call takers. We have developed a prototype implementation to prove our architecture's feasibility and scalability. We expect to undertake a pilot project at a working PSAP with our implementation once it is thoroughly tested.


title: Online data deduplication for in-memory big-data analytic systems

abstract: Given a set of files that show a certain degree of similarity, we consider a novel problem of performing data redundancy elimination across a set of distributed worker nodes in a shared-nothing in-memory big data analytic system. The redundancy elimination scheme is designed in a manner that is: (i) space-efficient: the total space needed to store the files is minimized and, (ii) access-isolation: data shuffling among server is also minimized. In this paper, we first show that finding an access-efficient and space optimal solution is an NP-Hard problem. Following this, we present the file partitioning algorithms that locate access-efficient solutions in an incremental manner with minimal algorithm time complexity (polynomial time). Our experimental verification on multiple data sets confirms that the proposed file partitioning solution is able to achieve compression ratio close to the optimal compression performance achieved by a centralized solution.


title: Log-Periodic patch antenna with tunable frequency

abstract: This paper describes the design and analysis of a Log-Periodic Microstrip Antenna Array operating between 3.3 Gigahertz (GHz) and 4.5 GHz. A five square patches fed by inset feed line technique are connected with a single transmission line by a log-periodic array formation. By applying five PIN Diodes at the transmission line with a quarter-wave length radial stub biasing, four different sub-band frequencies are configured by switching ON and OFF the PIN Diode. Simulation as well as measurement results with antenna design is presented and it shows that a good agreement in term of return loss. The simulated radiation pattern and realized gain for every sub bands also presented and discussed.


Affordances in Grounded Language Learning


title: Effects of Mycotoxins on Neuropsychiatric Symptoms and Immune Processes.

abstract: PURPOSE ::: The effects of air pollutants have been receiving increased attention both clinically and in the media. One such pollutant is mold, fungal growth in the form of multicellular filaments known as hyphae. The growth of molds is omnipresent not only in outdoor settings but also in indoor environments containing excessive amounts of moisture. ::: ::: ::: METHODS ::: PubMed was searched for relevant articles using terms such as mold, mycotoxins, fungi, immunity, inflammation, neurodevelopment, cognition, Alzheimer's, and autism. ::: ::: ::: FINDINGS ::: Exposure to molds is most commonly associated with allergies and asthma. However, it is now thought to be associated with many complex health problems, since some molds, especially Trichoderma, Fusarium and Stachybotrys spp, produce mycotoxins that are absorbed from the skin, airways, and intestinal lining. People exposed to molds and mycotoxins present with symptoms affecting multiple organs, including the lungs, musculoskeletal system, as well as the central and peripheral nervous systems. Furthermore, evidence has recently implicated exposure to mycotoxins in the pathogenesis of autism spectrum disorder. The effects of mycotoxins can be mediated via different pathways that include the secretion of pro-inflammatory cytokines, especially from mast cells. ::: ::: ::: IMPLICATIONS ::: The information reviewed indicates that exposure to mold and mycotoxins can affect the nervous system, directly or through immune cell activation, thus contributing to neurodevelopmental disorders such as autism spectrum disorder.


title: Efficiently Combining Water Reuse and Desalination through Forward Osmosis—Reverse Osmosis (FO-RO) Hybrids: A Critical Review

abstract: Forward osmosis (FO) is a promising membrane technology to combine seawater desalination and water reuse. More specifically, in a FO-reverse osmosis (RO) hybrid process, high quality water recovered from the wastewater stream is used to dilute seawater before RO treatment. As such, lower desalination energy needs and/or water augmentation can be obtained while delivering safe water for direct potable reuse thanks to the double dense membrane barrier protection. Typically, FO-RO hybrid can be a credible alternative to new desalination facilities or to implementation of stand-alone water reuse schemes. However, apart from the societal (public perception of water reuse for potable application) and water management challenges (proximity of wastewater and desalination plants), FO-RO hybrid has to overcome technical limitation such as low FO permeation flux to become economically attractive. Recent developments (i.e., improved FO membranes, use of pressure assisted osmosis, PAO) demonstrated significant improvement in water flux. However, flux improvement is associated with drawbacks, such as increased fouling behaviour, lower rejection of trace organic compounds (TrOCs) in PAO operation, and limitation in FO membrane mechanical resistance, which need to be better considered. To support successful implementation of FO-RO hybrid in the industry, further work is required regarding up-scaling to apprehend full-scale challenges in term of mass transfer limitation, pressure drop, fouling and cleaning strategies on a module scale. In addition, refined economics assessment is expected to integrate fouling and other maintenance costs/savings of the FO/PAO-RO hybrid systems, as well as cost savings from any treatment step avoided in the water recycling.


title: The use of mobile technology for online shopping and entertainment among older adults in Finland

abstract: Older adults use mobile devices for online shopping as often as younger adults.Mobile-based entertainment use is most typical for younger male adults.Older female adults use mobile-based entertainment least frequently.Age, household structure and high education predict mobile-based online shopping.Mobile-based entertainment use is best predicted by age and gender. Older adults are becoming an important market segment for all internet-based services, but few studies to date have considered older adults as online shoppers and users of entertainment media. Utilising the concept of life course, this article investigates the use of mobile technologies for online shopping and entertainment among consumers aged 55 to 74. The data were collected with a web-based survey completed by a panel of respondents representing Finnish television viewers (N=322). The results reveal that consumers aged 55 to 74 use a smartphone or tablet to purchase products or services online as often as younger consumers. In contrast, listening to internet radio and watching videos or programmes online with a smartphone or tablet are most typical for younger male consumers. The results demonstrate that mobile-based online shopping is best predicted by age, higher education, and household type (children living at home), and use of entertainment media by age and gender.


title: Modeling trajectories with recurrent neural networks

abstract: Modeling trajectory data is a building block for many smart-mobility initiatives. Existing approaches apply shallow models such as Markov chain and inverse reinforcement learning to model trajectories, which cannot capture the long-term dependencies. On the other hand, deep models such as Recurrent Neural Network (RNN) have demonstrated their strength of modeling variable length sequences. However, directly adopting RNN to model trajectories is not appropriate because of the unique topological constraints faced by trajectories. Motivated by these findings, we design two RNN-based models which can make full advantage of the strength of RNN to capture variable length sequence and meanwhile to address the constraints of topological structure on trajectory modeling. Our experimental study based on real taxi trajectory datasets shows that both of our approaches largely outperform the existing approaches.


title: Human-robot physical interaction and collaboration using an industrial robot with a closed control architecture

abstract: In physical Human-Robot Interaction, the basic problem of fast detection and safe robot reaction to unexpected collisions has been addressed successfully on advanced research robots that are torque controlled, possibly equipped with joint torque sensors, and for which an accurate dynamic model is available. In this paper, an end-user approach to collision detection and reaction is presented for an industrial manipulator having a closed control architecture and no additional sensors. The proposed detection and reaction schemes have minimal requirements: only the outer joint velocity reference to the robot manufacturer's controller is used, together with the available measurements of motor currents and joint positions. No a priori information on the robot dynamic model and existing low-level joint controllers is strictly needed. A suitable on-line processing of the motor currents allows to distinguish between accidental collisions and intended human-robot contacts, so as to switch the robot to a collaboration mode when needed. Two examples of reaction schemes for collaboration are presented, with the user pushing/pulling the robot at any point of its structure (e.g., for manual guidance) or with a compliant-like robot behavior in response to forces applied by the human. The actual performance of the methods is illustrated through experiments on a KUKA KR5 manipulator.


title: Galvanic skin response (GSR) as an index of cognitive load

abstract: Multimodal user interfaces (MMUI) allow users to control computers using speech and gesture, and have the potential to minimise users. experienced cognitive load, especially when performing complex tasks. In this paper, we describe our attempt to use a physiological measure, namely Galvanic Skin Response (GSR), to objectively evaluate users. stress and arousal levels while using unimodal and multimodal versions of the same interface. Preliminary results show that users. GSR readings significantly increase when task cognitive load level increases. Moreover, users. GSR readings are found to be lower when using a multimodal interface, instead of a unimodal interface. Cross-examination of GSR data with multimodal data annotation showed promising results in explaining the peaks in the GSR data, which are found to correlate with sub-task user events. This interesting result verifies that GSR can be used to serve as an objective indicator of user cognitive load level in real time, with a very fine granularity.


title: Design and Comparison of Vernier Permanent Magnet Machines

abstract: Vernier permanent magnet (VPM) machines can be utilized for direct drive applications by virtue of their high torque density and high efficiency. The purpose of this paper is to develop a general design guideline for split-slot low-speed VPM machines, generalize the operation principle, and illustrate the relationship among the numbers of the stator slots, coil poles, permanent magnet (PM) pole pairs, thereby laying a solid foundation for the design of various kinds of VPM machines. Depending on the PM locations, three newly designed VPM machines are reported in this paper and they are referred to as 1) rotor-PM Vernier machine, 2) stator-tooth-PM Vernier machine, and 3) stator-yoke-PM Vernier machine. The back-electromotive force (back-EMF) waveforms, static torque, and air-gap field distribution are predicted using time-stepping finite element method (TS-FEM). The performances of the proposed VPM machines are compared and reported.


title: SSR Mitigation With a New Control of PV Solar Farm as STATCOM (PV-STATCOM)

abstract: This paper presents a novel control of a large-scale PV solar farm as STATCOM, termed PV-STATCOM, for alleviation of subsynchronous resonance (SSR) in a steam turbine driven synchronous generator connected to a series compensated transmission line. During nighttime, the PV solar farm can operate as a STATCOM with its entire inverter capacity for SSR mitigation. During daytime, if a system fault triggers SSR, the solar farm autonomously discontinues its normal active power generation and releases its entire inverter capacity to operate as PV-STATCOM for SSR prevention. Once the subsynchronous resonances are damped, the solar farm returns to its normal real power production. Electromagnetic transients studies using EMTDC/PSCAD are performed to demonstrate that a solar farm connected at the terminals of synchronous generator in the IEEE First SSR Benchmark system can damp all the four torsional modes at all the four critical levels of series compensation, and return to normal PV power production in less than half a minute. This proposed PV-STATCOM technology can either obviate or reduce the need of an expensive flexible ac transmission system device to accomplish the same objective. Furthermore, this technology is more than an order of magnitude cheaper than a conventional static var compensators or STATCOM of similar size.


title: Sparse Recovery of Hyperspectral Signal from Natural RGB Images

abstract: Hyperspectral imaging is an important visual modality with growing interest and range of applications. The latter, however, is hindered by the fact that existing devices are limited in either spatial, spectral, and/or temporal resolution, while yet being both complicated and expensive. We present a low cost and fast method to recover high quality hyperspectral images directly from RGB. Our approach first leverages hyperspectral prior in order to create a sparse dictionary of hyperspectral signatures and their corresponding RGB projections. Describing novel RGB images via the latter then facilitates reconstruction of the hyperspectral image via the former. A novel, larger-than-ever database of hyperspectral images serves as a hyperspectral prior. This database further allows for evaluation of our methodology at an unprecedented scale, and is provided for the benefit of the research community. Our approach is fast, accurate, and provides high resolution hyperspectral cubes despite using RGB-only input.


title: A simple totally ordered broadcast protocol

abstract: This is a short overview of a totally ordered broadcast protocol used by ZooKeeper, called Zab. It is conceptually easy to understand, is easy to implement, and gives high performance. In this paper we present the requirements ZooKeeper makes on Zab, we show how the protocol is used, and we give an overview of how the protocol works.


title: Systematics of the trilobite families Lichidae Hawle & Corda, 1847 and Lichakephalidae Tripp, 1957: The application of Bayesian inference to morphological data

abstract: Synopsis The first cladistic and Bayesian analyses of the trilobite families Lichidae Hawle & Corda, 1847 and Lichakepalidae Tripp, 1957 are presented. Thirty‐one lichid genera and five lichakephalid outgroup taxa were coded for 48 characters using published descriptions. Two methods of phylogenetic inference were adopted: (1) a maximum‐parsimony (MP) approach and, for the first time in a palaeontological context, (2) a Bayesian inference (BI) approach. The consensus trees from the MP and BI analyses were topologically similar, but differed principally in the deeper branches (i.e. the relationships between major clades). The Lichidae is monophyletic with respect to the Lichakephalidae in both analyses. The Trochurinae (Thomas & Holloway, 1988) is well supported by both analyses. Other groups are also supported (i.e. Tetralichinae, Echinolichinae and Platylichinae); two, however, are not (i.e. Homolichinae and Lichinae). A classification is proposed that represents the main branching pattern of the MP tree...


title: E-SATISFACTION AND E-LOYALTY OF CONSUMERS SHOPPING ONLINE

abstract: The objective of this research is to study the impact of emotional state and perceived risk of remote purchase on e-satisfaction during the Internet shopping. As well, it aims to study the influence of e-satisfaction on e-loyalty. The data gathering was carried out by a laboratory experiment followed by a questionnaire. The results show that three dimensions of the emotional state during Internet shopping (the pleasure, stimulation and dominance) have a significant positive impact on e-satisfaction. Dimensions of the perceived risk of remote purchase, (the total risk, the financial risk, the social risk, the psychological risk, the functional risk, and the physical risk) don’t have a significant impact on e-satisfaction, except the risk of loss of time has a negative impact. Finally esatisfaction influences positively and significantly the e-loyalty of the cyber consumers.


title: Sentiment analysis on social media using morphological sentence pattern model

abstract: Social media became popular than ever as people are willing to share their emotions and opinions or to participate in social networking. Accordingly, the understanding of social media usage became important. The sentiment analysis is emerged as one of useful methods to analyze emotional stats expressed in textual data including social media data. However, this method still presents some limitations, particularly with an accuracy issue. For example, our previous sentiment analysis used a probability model and needed to adopt human-coded train-sets to maintain an acceptable accuracy level (89%). To overcome and improve this weakness, we propose an automated sentiment analysis in this paper by using the morphological sentence pattern model. We found that this new approach presented in this paper allowed us to achieve a higher level of accuracy (91.2%). The movie reviews were used for this analysis from IMDb, Rotten Tomatoes, Metacritic, YouTube and Twitter.


title: Design of a time-based capacitance-to-digital converter using current starved inverters

abstract: This paper presents a new design methodology of time-based capacitance-to-digital converters (T-CDCs). The sensor capacitance is the load capacitance of a voltage-to-time converter (VTC) circuit based on current starved inverters. The VTC circuit used has improved sensitivity, linearity and dynamic range. The time delay provided by the VTC circuit is buffered providing a time pulse proportional to the sensor capacitance (Cs). Thus, the VTC circuit becomes a capacitance-to-time converter (CTC) circuit. The time pulse is then digitized using a time-to-digital converter (TDC) circuit providing an output digital code proportional to the sensor capacitance. The output digital code is PVT calibrated using a reference capacitor (C ref ). The proposed T-CDC system is a time-based ADC, but with the sensor capacitance as the load capacitance of the used VTC circuit and the input voltages of the current starved inverters in the VTC circuit are fixed and used for biasing. The prototype of the proposed T-CDC is implemented in UMC 130 nm CMOS technology. The T-CDC system is clocked with a 1-MHz clock signal to the VTC circuit. Experimental results show that for a 1 ns counting cycle of the TDC circuit, the system achieves a 8.5-bit resolution and consumes 20 μW with an energy efficiency figure-of-merit (FoM) of 55 fJ/step and a conversion time of 1 μs.


title: S1 guideline for diagnostic evaluation in androgenetic alopecia in men, women and adolescents

abstract: Androgenetic alopecia (AGA) is the most common hair loss disorder, affecting both men and women. Due to the frequency and the often significant impairment of life perceived by the affected patients, competent advice, diagnosis and treatment is particularly important. As evidence-based guidelines on hair disorders are rare, a European consensus group was constituted to develop guidelines for the diagnostic evaluation and treatment of AGA. This S1 guideline for diagnostic evaluation of AGA in men, women and adolescents reviews the definition of AGA and presents expert opinion-based recommendations for sex-dependent steps in the diagnostic procedure.


title: Software intelligence: the future of mining software engineering data

abstract: Mining software engineering data has emerged as a successful research direction over the past decade. In this position paper, we advocate Software Intelligence (SI) as the future of mining software engineering data, within modern software engineering research, practice, and education. We coin the name SI as an inspiration from the Business Intelligence (BI) field, which offers concepts and techniques to improve business decision making by using fact-based support systems. Similarly, SI offers software practitioners (not just developers) up-to-date and pertinent information to support their daily decision-making processes. SI should support decision-making processes throughout the lifetime of a software system not just during its development phase. The vision of SI has yet to become a reality that would enable software engineering research to have a strong impact on modern software practice. Nevertheless, recent advances in the Mining Software Repositories (MSR) field show great promise and provide strong support for realizing SI in the near future. This position paper summarizes the state of practice and research of SI, and lays out future research directions for mining software engineering data to enable SI.


title: Malaria prevalence in asymptomatic and symptomatic children in Kiwangwa, Bagamoyo district, Tanzania

abstract: Malaria prevalence continues to decline across sub-Saharan Africa as a result of various intervention strategies. However, the diseases still poses a public health concern in the region. While symptomatic malaria is recognized and treated, asymptomatic infections become increasingly important for interrupting transmission. A cross-sectional survey was conducted to assess malaria prevalence in symptomatic and asymptomatic children in Kiwangwa ward in Bagamoyo District in Tanzania. Four hundred school-aged children in Kiwanga ward were recruited in the study; 200 from Kiwangwa dispensary and 200 from nearby schools. Primary health parameters were examined and blood samples collected and examined for Plasmodium falciparum prevalence using rapid diagnostic test (RDT), light microscopy (LM) and reverse transcription quantitative PCR (RT-qPCR) targeting transcripts of A-type 18s rRNA of P. falciparum. Gametocytes were detected by LM and RT-qPCR targeting transcripts of gametocyte specific marker, Pfs25. Overall P. falciparum prevalence was 73.3, 40.8 and 36.3% by RT-qPCR, RDT and LM in the study area, respectively (P < 0.001). As expected symptomatic children had a significantly higher prevalence of 89, 67.5 and 64.5% by qPCR, RDT and LM, compared to 57.5, 14 and 8% in the asymptomatic group, respectively. However, gametocyte prevalence in asymptomatic individuals was higher by both LM (2%) and qPCR (14%) than in symptomatic individuals LM (0.5%) and qPCR (3%). A substantial difference in prevalence of symptomatic and asymptomatic infections observed in Kiwangwa ward underpins the use of molecular tools in malaria surveillance aiming at estimating prevalence and transmission. Notably, the higher gametocytaemia observed in asymptomatic children indicates the reservoir infections and points to the need for detection and treatment of both asymptomatic and symptomatic malaria.


title: Theories of the syllogism: A meta-analysis

abstract: Syllogisms are arguments about the properties of entities. They consist of 2 premises and a conclusion, which can each be in 1 of 4 “moods”: All A are B, Some A are B, No A are B, and Some A are not B. Their logical analysis began with Aristotle, and their psychological investigation began over 100 years ago. This article outlines the logic of inferences about syllogisms, which includes the evaluation of the consistency of sets of assertions. It also describes the main phenomena of reasoning about properties. There are 12 extant theories of such inferences, and the article outlines each of them and describes their strengths and weaknesses. The theories are of 3 main sorts: heuristic theories that capture principles that could underlie intuitive responses, theories of deliberative reasoning based on formal rules of inference akin to those of logic, and theories of deliberative reasoning based on set-theoretic diagrams or models. The article presents a meta-analysis of these extant theories of syllogisms using data from 6 studies. None of the 12 theories provides an adequate account, and so the article concludes with a guide— based on its qualitative and quantitative analyses— of how best to make progress toward a satisfactory theory.


title: Unsupervised Image-to-Image Translation with Generative Adversarial Networks

abstract: It's useful to automatically transform an image from its original form to some synthetic form (style, partial contents, etc.), while keeping the original structure or semantics. We define this requirement as the "image-to-image translation" problem, and propose a general approach to achieve it, based on deep convolutional and conditional generative adversarial networks (GANs), which has gained a phenomenal success to learn mapping images from noise input since 2014. In this work, we develop a two step (unsupervised) learning method to translate images between different domains by using unlabeled images without specifying any correspondence between them, so that to avoid the cost of acquiring labeled data. Compared with prior works, we demonstrated the capacity of generality in our model, by which variance of translations can be conduct by a single type of model. Such capability is desirable in applications like bidirectional translation


title: PPAR-γ2 Pro12Ala Variant Is Associated with Greater Insulin Sensitivity in Childhood Obesity

abstract: Several genetic variants of peroxisome proliferator-activated receptor-gamma2 (PPAR-gamma2), a molecule known to be involved in transcription of target genes, have been identified. Pro12Ala, a missense mutation in exon 2 of the gene, is highly prevalent in Caucasian populations. Conflicting conclusions about the association between this mutation and complex traits such as obesity, insulin sensitivity, and T2DM have been reported. We have investigated the association of PPAR-gamma2 Pro12Ala polymorphism with measures of insulin sensitivity in a population of Italian obese children (n = 200; mean age, 10.38 +/- 2.8 y) in whom clinical and biochemical analyses were performed. To estimate the insulin sensitivity status, the homeostasis model assessment of insulin resistance (HOMA-IR) was calculated in all subjects. The effect of the Pro12Ala polymorphism on quantitative variables was tested using multiple linear regression analysis. The frequency of Ala carriers was 17%, similar to that reported in other adult Caucasian populations. The X12Ala (either Pro12Ala or Ala12Ala) genotype was associated with significantly lower fasting insulin levels compared with Pro/Pro (p = 0.008). Consistent with this finding, significantly lower HOMA-IR was observed in X12Ala carriers (p = 0.023). In conclusion, our observations demonstrate that the X12Ala variant is significantly associated with greater insulin sensitivity in childhood obesity. Because obesity is one of the most important risk factors for cardiovascular diseases and type 2 diabetes, obese children, who are presumably at a higher risk, may be protected from these diseases by the phenotypic effect of the Ala 12 allele on insulin resistance.


title: Updating the NCTUns-6.0 tool to simulate parallel optical burst switching of all-optical ultra-dense WDM systems

abstract: Optical burst switching (OBS) is proposed as suitable switching architectures for directly transporting traffic over a bufferless wavelength division multiplexing (WDM) networks. Parallel optical burst switching (POBS) is a variant of the OBS model that takes this concept further by transmitting data bursts wavelength and time dimensions. However, there is a lack of simulator that simulates POBS networks. This paper presents an update to the conventional OBS model in the NCTUns-6.0 simulator ($$\hbox {NCTU}_{\mathrm{ns}\text {-}\mathrm{POBS}}$$NCTUns-POBS). The $$\hbox {NCTU}_{\mathrm{ns}\text {-}\mathrm{POBS}}$$NCTUns-POBS tool is capable of simulating POBS networks for ultra-dense WDM. It analyzes the features of POBS networks, enables to adjust the parameters of POBS networks and enhances their switching technology. To test and validate the performance of the tool, the proposed random wavelength assignment technique (RWAT) is compared with the existing sequential wavelength assignment technique (SWAT) of the POBS model and the conventional OBS model. The results of the simulation show that, the $$\hbox {NCTU}_{\mathrm{ns}\text {-}\mathrm{POBS}}$$NCTUns-POBS successfully simulates the POBS networks in which the proposed RWAT enables the POBS to yield higher throughput compared to the existing SWAT and the OBS conventional technique.


title: A de-identifier for medical discharge summaries

abstract: Objective: Clinical records contain significant medical information that can be useful to researchers in various disciplines. However, these records also contain personal health information (PHI) whose presence limits the use of the records outside of hospitals. The goal of de-identification is to remove all PHI from clinical records. This is a challenging task because many records contain foreign and misspelled PHI; they also contain PHI that are ambiguous with non-PHI. These complications are compounded by the linguistic characteristics of clinical records. For example, medical discharge summaries, which are studied in this paper, are characterized by fragmented, incomplete utterances and domain-specific language; they cannot be fully processed by tools designed for lay language. Methods and results: In this paper, we show that we can de-identify medical discharge summaries using a de-identifier, Stat De-id, based on support vector machines and local context (F-measure=97% on PHI). Our representation of local context aids de-identification even when PHI include out-of-vocabulary words and even when PHI are ambiguous with non-PHI within the same corpus. Comparison of Stat De-id with a rule-based approach shows that local context contributes more to de-identification than dictionaries combined with hand-tailored heuristics (F-measure=85%). Comparison with two well-known named entity recognition (NER) systems, SNoW (F-measure=94%) and IdentiFinder (F-measure=36%), on five representative corpora show that when the language of documents is fragmented, a system with a relatively thorough representation of local context can be a more effective de-identifier than systems that combine (relatively simpler) local context with global context. Comparison with a Conditional Random Field De-identifier (CRFD), which utilizes global context in addition to the local context of Stat De-id, confirms this finding (F-measure=88%) and establishes that strengthening the representation of local context may be more beneficial for de-identification than complementing local with global context.


An Obstacle Detection and Guidance System for Mobility of Visually Impaired in Unfamiliar Indoor Environments


title: Local Excision for the Treatment of Penile Verrucous Carcinoma

abstract: Penile verrucous carcinoma is known for its favorable biologic behavior and lack of metastatic potential. For preservation of function, treatment has been focused on partial penectomy. Despite partial penectomy for preservation of minimal functional and aesthetic aspects, patients have experienced psychosexual problems. A 73-year-old man had a cauliflower-like verrucous carcinoma on the penile glans and coronary sulcus diagnosed by using excisional biopsy. He underwent degloving excision to save the penile shaft and glans penis. Surgical margin was 3 mm. He had been tumor-free at the 2-year follow-up. For maximum preservation of the functional and aesthetic aspects, we recommend degloving excision.


title: Expertise for cars and birds recruits brain areas involved in face recognition

abstract: Expertise with unfamiliar objects (‘greebles’) recruits face-selective areas in the fusiform gyrus (FFA) and occipital lobe (OFA). Here we extend this finding to other homogeneous categories. Bird and car experts were tested with functional magnetic resonance imaging during tasks with faces, familiar objects, cars and birds. Homogeneous categories activated the FFA more than familiar objects. Moreover, the right FFA and OFA showed significant expertise effects. An independent behavioral test of expertise predicted relative activation in the right FFA for birds versus cars within each group. The results suggest that level of categorization and expertise, rather than superficial properties of objects, determine the specialization of the FFA.