title: Lower bounds by probabilistic arguments

abstract: The purpose of this paper is to resolve several open problems in the current literature on Boolean circuits, communication complexity, and hashing functions. These lower bound results share the common feature that their proofs utilize probabilistic arguments in an essential way. Specifically, we prove that, to compute the majority function of n Boolean variables, the size of any depth-3 monotone circuit must be greater than 2ne, and the size of any width-2 branching program must have super-polynomial growth. We also show that, for the problem of deciding whether i ≤ j for two n-bit integers i and j, the probabilistic e-error one-way communication complexity is of order θ(n), while the two-way e-error complexity is O((log n)2). We will also prove that, to compute i ? j mod p for an n-bit prime p, the probabilistic e-error two-way communication complexity is of order θ(n). Finally, we prove a conjecture of Ullman that uniform hashing is asymptotically optimal in its expected retrieval cost among open address hashing schemes.


title: An ensemble outlier detection method for multiclass classification problem in data mining

abstract: We proposed to develop a heterogeneous ensemble method that boost the performance of random forest classifier. The proposed method utilized the boosting capability of adaboost algorithm and the feature selection and bagging capability of random subspace algorithm. Both algorithms used random forest as the base classifier and were combined using voting methodology. We preprocessed the dataset by removing both redundant features and the detected outliers associated with dataset features. We addressed the multiclass problem of the dataset by decomposing the dataset into binary classes using the technique of 1 against 1 enhanced by pairwise coupling. Since supervised algorithms are designed to be biased with majority class, we overcome that challenge by generating synthetic instances using synthetic minority over sampling technique. The proposed SMOTE_Voted outlier ensemble method outperformed Random Forest, KNN, NaiveBayes, C4.5 and Support Vector machine outlier detection methods. We conclude that ensemble technique improves performance of outlier detection for multiclass problem.


title: OWL ontology to Ecore metamodel transformation for designing a domain specific language to develop aviation scenarios

abstract: Ontology-driven software development has gained significant interest in domain-specific application development. Ontologies are treated as formal models representing knowledge, which can be used for automatic code generation. Among existing technologies, Eclipse Modeling Framework (EMF) has been widely used to generate domain-specific metamodels and Java code using Ecore. Such robust technologies suggest mapping ontologies to metamodels through series of transformations. In this paper we suggest transforming an ontology model to EMF Ecore metamodel for designing a Domain-Specific Language (DSL). We present the ontology developed for Aviation Scenario Definition Language (ASDL) to capture simulation scenarios in the aviation domain. We demonstrate key terminologies and their definitions in order to generate flight simulation scenarios. The use of Web Ontology Language (OWL) in developing ASDL ontology is discussed and its relation to ASDL Ecore metamodel is presented. This paper finally elaborates the challenges and shortfalls in mapping OWL ontology to Ecore metamodel in EMF.


title: Getting Started in Text Mining

abstract: Text mining is the use of automated methods for exploiting the enormous amount of knowledge available in the biomedical literature. There are at least as many motivations for doing text mining work as there are types of bioscientists. Model organism database curators have been heavy participants in the development of the field due to their need to process large numbers of publications in order to populate the many data fields for every gene in their species of interest. Bench scientists have built biomedical text mining applications to aid in the development of tools for interpreting the output of high-throughput assays and to improve searches of sequence databases (see [1] for a review). Bioscientists of every stripe have built applications to deal with the dual issues of the double-exponential growth in the scientific literature over the past few years and of the unique issues in searching PubMed/MEDLINE for genomics-related publications. A surprising phenomenon can be noted in the recent history of biomedical text mining: although several systems have been built and deployed in the past few years—Chilibot, Textpresso, and PreBIND (see Text S1 for these and most other citations), for example—the ones that are seeing high usage rates and are making productive contributions to the working lives of bioscientists have been built not by text mining specialists, but by bioscientists. We speculate on why this might be so below. ::: ::: Three basic types of approaches to text mining have been prevalent in the biomedical domain. Co-occurrence–based methods do no more than look for concepts that occur in the same unit of text—typically a sentence, but sometimes as large as an abstract—and posit a relationship between them. (See [2] for an early co-occurrence–based system.) For example, if such a system saw that BRCA1 and breast cancer occurred in the same sentence, it might assume a relationship between breast cancer and the BRCA1 gene. Some early biomedical text mining systems were co-occurrence–based, but such systems are highly error prone, and are not commonly built today. In fact, many text mining practitioners would not consider them to be text mining systems at all. Co-occurrence of concepts in a text is sometimes used as a simple baseline when evaluating more sophisticated systems; as such, they are nontrivial, since even a co-occurrence–based system must deal with variability in the ways that concepts are expressed in human-produced texts. For example, BRCA1 could be referred to by any of its alternate symbols—IRIS, PSCP, BRCAI, BRCC1, or RNF53 (or by any of their many spelling variants, which include BRCA1, BRCA-1, and BRCA 1)—or by any of the variants of its full name, viz. breast cancer 1, early onset (its official name per Entrez Gene and the Human Gene Nomenclature Committee), as breast cancer susceptibility gene 1, or as the latter's variant breast cancer susceptibility gene-1. Similarly, breast cancer could be referred to as breast cancer, carcinoma of the breast, or mammary neoplasm. These variability issues challenge more sophisticated systems, as well; we discuss ways of coping with them in Text S1. ::: ::: Two more common (and more sophisticated) approaches to text mining exist: rule-based or knowledge-based approaches, and statistical or machine-learning-based approaches. The variety of types of rule-based systems is quite wide. In general, rule-based systems make use of some sort of knowledge. This might take the form of general knowledge about how language is structured, specific knowledge about how biologically relevant facts are stated in the biomedical literature, knowledge about the sets of things that bioscientists talk about and the kinds of relationships that they can have with one another, and the variant forms by which they might be mentioned in the literature, or any subset or combination of these. (See [3] for an early rule-based system, and [4] for a discussion of rule-based approaches to various biomedical text mining tasks.) At one end of the spectrum, a simple rule-based system might use hard-coded patterns—for example, plays a role in or is associated with —to find explicit statements about the classes of things in which the researcher is interested. At the other end of the spectrum, a rule-based system might use sophisticated linguistic and semantic analyses to recognize a wide range of possible ways of making assertions about those classes of things. It is worth noting that useful systems have been built using technologies at both ends of the spectrum, and at many points in between. In contrast, statistical or machine-learning–based systems operate by building classifiers that may operate on any level, from labelling part of speech to choosing syntactic parse trees to classifying full sentences or documents. (See [5] for an early learning-based system, and [4] for a discussion of learning-based approaches to various biomedical text mining tasks.) ::: ::: Rule-based and statistical systems each have their advantages and disadvantages. For example, rule systems are often assumed (not necessarily correctly) to take a significant amount of time to develop. Statistical systems typically require large amounts of expensive-to-get labelled training data. In practice, statistical and rule-based systems can be fruitfully combined. For example, a statistical system that classifies documents as to whether or not they are relevant to the subject of genetic variation in mouse genes might use the output of a rule-based mutation recognizer as one of its feature extractors. Many systems also employ an initial statistical processing step, followed by rule-based post-processing. ::: ::: A primary problem that either type of system must deal with is the issue of ambiguity: the existence of multiple relationships between language and meanings or categories. Ambiguity exists at every level of linguistic structure, from the part of speech of words to subtle issues in pragmatics. A common example of ambiguity in genomics text is related to gene names and symbols. Consider the string fat: is it an adjective, or a noun? Either part of speech is entirely plausible in biomedical texts, and PubMed returns almost 112 K hits for that single-word query (and more than 13 K even if we try to restrict the query to genomics by including the disjunction (gene OR genetic OR genetics). This ambiguity is relatively easy to resolve, but fat also turns out to be the name or symbol of a number of different genes—humans, mice, rats, Drosophila, zebrafish, chickens, M. mulatta, and two Lactobacilli have at least one gene whose name, official symbol, or alias is fat. Even if the species whose gene is being referred to can be determined, the ambiguity may still not be resolved—in humans, fat is the official symbol of Entrez Gene entry 2195 and an alternate symbol for Entrez Gene entry 948. The distinction is not trivial. The former is a cadhedrin, and is associated with tumor suppression and with bipolar disorder, while the latter is a thrombospondin receptor associated with atherosclerosis, platelet glycoprotein deficiency, hyperlipidemia, and insulin resistance, to name just a few phenotypes. These ambiguities are not trivial: if your analysis is wrong, you miss or erroneously extract information on relations between molecular biology and human disease.


A Matlab-based Implementation of the Livewire Interactive Segmentation Tool


title: It doesn't matter what you say: FMRI correlates of voice learning and recognition independent of speech content

abstract: Abstract Listeners can recognize newly learned voices from previously unheard utterances, suggesting the acquisition of high-level speech-invariant voice representations during learning. Using functional magnetic resonance imaging (fMRI) we investigated the anatomical basis underlying the acquisition of voice representations for unfamiliar speakers independent of speech, and their subsequent recognition among novel voices. Specifically, listeners studied voices of unfamiliar speakers uttering short sentences and subsequently classified studied and novel voices as “old” or “new” in a recognition test. To investigate “pure” voice learning, i.e., independent of sentence meaning, we presented German sentence stimuli to non-German speaking listeners. To disentangle stimulus-invariant and stimulus-dependent learning, during the test phase we contrasted a “same sentence” condition in which listeners heard speakers repeating the sentences from the preceding study phase, with a “different sentence” condition. Voice recognition performance was above chance in both conditions although, as expected, performance was higher for same than for different sentences. During study phases activity in the left inferior frontal gyrus (IFG) was related to subsequent voice recognition performance and same versus different sentence condition, suggesting an involvement of the left IFG in the interactive processing of speaker and speech information during learning. Importantly, at test reduced activation for voices correctly classified as “old” compared to “new” emerged in a network of brain areas including temporal voice areas (TVAs) of the right posterior superior temporal gyrus (pSTG), as well as the right inferior/middle frontal gyrus (IFG/MFG), the right medial frontal gyrus, and the left caudate. This effect of voice novelty did not interact with sentence condition, suggesting a role of temporal voice-selective areas and extra-temporal areas in the explicit recognition of learned voice identity, independent of speech content.


title: Artificial Lipid Membranes: Past, Present, and Future

abstract: The multifaceted role of biological membranes prompted early the development of artificial lipid-based models with a primary view of reconstituting the natural functions in vitro so as to study and exploit chemoreception for sensor engineering. Over the years, a fair amount of knowledge on the artificial lipid membranes, as both, suspended or supported lipid films and liposomes, has been disseminated and has helped to diversify and expand initial scopes. Artificial lipid membranes can be constructed by several methods, stabilized by various means, functionalized in a variety of ways, experimented upon intensively, and broadly utilized in sensor development, drug testing, drug discovery or as molecular tools and research probes for elucidating the mechanics and the mechanisms of biological membranes. This paper reviews the state-of-the-art, discusses the diversity of applications, and presents future perspectives. The newly-introduced field of artificial cells further broadens the applicability of artificial membranes in studying the evolution of life.


The effectiveness of a homemade IMSI catcher build with YateBTS and a BladeRF


title: Removable partial dentures--treatment now and for the future.

abstract: The use of a removable partial denture (RPD) in clinical practice remains a viable treatment modality. Various advancements have improved the quality of a RPD, subsequently improving the quality of life for the individuals that use them. This article describes four removable partial denture treatment modalities that provide valuable treatment for the partially edentulous patient. These modalities include: the implant supported RPD, attachment use in RPDs, rotational path RPDs, and Titanium and CAD/CAM RPDs. Data on future needs for RPDs indicate that while there is a decline in tooth loss in the U.S., the need for RPDs will actually increase as the population increases and ages. With the growth in the geriatric population, which includes a high percentage of partially edentulous patients, the use of RPDs in clinical treatment will continue to be predictable treatment option in clinical dentistry.


title: The problem of task allocation in the Internet of Things and the consensus-based approach

abstract: The realization of the Internet of Things (IoT) paradigm relies on the implementation of systems of cooperative intelligent objects with key interoperability capabilities. One of these interoperability features concerns the cooperation among nodes towards a collaborative deployment of applications taking into account the available resources, such as electrical energy, memory, processing, and object capability to perform a given task, which are often limited.In this paper, firstly, we define the issue related to resource allocation for the deployment of distributed applications in the IoT, and we describe the architecture and functionalities of a relevant middleware that represents a possible solution to this issue. Secondly, we propose a consensus protocol for the cooperation among network objects in performing the target application, which aims to distribute the burden of the application execution, so that resources are adequately shared. We demonstrate that, using the proposed protocol, the network converges to a solution where resources are homogeneously allocated among nodes. Performance evaluation of experiments in simulation mode and in real scenarios show that the algorithm converges with a percentage error of about 5% with respect to the optimal allocation obtainable with a centralized approach.


title: Adaptive cubature Kalman filter for ultra-tightly coupled BDS/INS integration

abstract: This paper presents an adaptive cubature Kalman filter to improve the adaptivity of ultra-tightly coupled(UTC) BDS/INS (BeiDou Navigation Satellite System/Inertial Navigation System) integrated system against uncertainty involved in process noise statistic. This method adopts the hypothesis testing method to identify the uncertain process noise statistic and introduces a defined adaptive factor into the prediction covariance to decrease the weight of the prior knowledge on filtering solution. It corrects the state estimation in the occurrence of uncertain process noise statistics and avoids the loss of precision for the state estimation without process noise uncertainty. The effectiveness of the proposed method is verified via the simulation of ultra-tightly coupled BDS/INS integrated system.


title: Mobile phone adoption: do existing models adequately capture the actual usage of older adults?

abstract: This paper investigates mobile phone adoption by older adults to better understand this user group's current level of adoption and to assess how well existing adoption models capture the acceptance levels of elderly users. The rationale is that mobile technology is continually improving and despite efforts to address their needs, this section of the population is known to be a largely untapped market. Data was captured on mobile phone usage of a sample of 46 users aged 60 to 87 through observation, a questionnaire and informal interviews. Using mostly qualitative and some quantitative data analysis techniques, the participants' levels of acceptance of the technology were determined and related to their age, how they acquired the phone, how long they have owned it, how often they use it and which functions they use. Our findings indicate that most participants in the sample have not reached full adoption but have not rejected the technology either. We found that a significant number of users stagnate in a phase of limited use with no inclination towards full adoption or complete rejection. This is contrary to the adoption presented by existing models that assume a user will reach a point of either rejection or acceptance of the technology. Therefore we conclude that existing technology and mobile phone adoption models do not adequately capture the mobile phone adoption patterns of the elderly. We have thus established the need to reconsider mobile phone adoption models. Furthermore, we have identified the specific age, the length of ownership, and the manner in which the phone was acquired as good predictors of where a user will end on the acceptance-rejection scale.


title: Moving tag detection via physical layer analysis for large-scale RFID systems

abstract: In a number of RFID-based applications such as logistics monitoring, the RFID systems are deployed to monitor a large number of RFID tags. They are usually required to track the movement of all tags in a real-time approach, since the tagged-goods are moved in and out in a rather frequent approach. However, a typical cycle of tag inventory in COTS RFID system usually takes tens of seconds to interrogate hundreds of RFID tags. This hinders the system to track the movement of all tags in time. One critical issue in such type of tag monitoring is to efficiently distinguish the motion status of all tags, i.e., stationary or moving. According to the motion status of different tags, the state-of-art localization schemes can further track those moving tags, instead of tracking all tags. In this paper, we propose a real-time approach to detect the moving tags in the monitoring area, which is a fundamental premise to support tracking the movement of all tags. We achieve the time efficiency by decoding collisions from the physical layer. Instead of using the EPC ID, which cannot be decoded in collision slots, we are able to extract two kinds of physical-layer features of RFID tags, i.e., the phase profile and the backscatter link frequency, to distinguish among different tags in different positions. By resolving the two physical-layer features from the tag collisions, we are able to derive the motion status of multiple tags simultaneously, and greatly improve the time-efficiency. Experiment result shows that our solution can accurately detect the moving tags while reducing 80% of inventory time compared with the state-of-art solutions.


title: The Art of Scenarios and Strategic Planning - Tools and Pitfalls

abstract: Abstract The term strategy has been misused and even abused. Worse, the word scenario is often confused with strategy to the point that clarification is needed if we are to understand one another. As a prolongation of the work done by the Rand Corporation in the 1960s, strategic planning, management and prospective approaches have been developed to help organizations master change. Over the past 25 years, we have contributed by creating or further developing various methodologies and procedures such as the Mactor and MICMAC methods for use in scenario building. These tools are doubly powerful in that they stimulate the imagination, reduce collective biases, and promote appropriation. One of the main functions of the strategic futures exercise is to eliminate two errors that we usually describe as the “hammer's risk” and the “nail's dream.” In other words, we forget what a hammer's function is when staring at a nail (the nail's dream) or we know how to use a hammer and imagine that every problem is like a nail (the hammer's risk). In our case, we strive to give simple tools that may be appropriated. However, these simple tools are inspired by intellectual rigor that enables one to ask the right questions. Of course, these tools do not come with a guarantee. The natural talent, common sense, and intuition of the futurist also count!


title: Long-term effects of a very low-carbohydrate diet and a low-fat diet on mood and cognitive function.

abstract: BACKGROUND ::: Very low-carbohydrate (LC) diets are often used to promote weight loss, but the long-term effects on psychological function remain unknown. ::: ::: ::: METHODS ::: A total of 106 overweight and obese participants (mean [SE] age, 50.0 [0.8] years; mean [SE] body mass index [calculated as weight in kilograms divided by height in meters squared], 33.7 [0.4]) were randomly assigned either to an energy-restricted (approximately 1433-1672 kcal [to convert to kilojoules, multiply by 4.186]), planned isocaloric, very low-carbohydrate, high-fat (LC) diet or to a high-carbohydrate, low-fat (LF) diet for 1 year. Changes in body weight, psychological mood and well-being (Profile of Mood States, Beck Depression Inventory, and Spielberger State Anxiety Inventory scores), and cognitive functioning (working memory and speed of processing) were assessed. ::: ::: ::: RESULTS ::: By 1 year, the overall mean (SE) weight loss was 13.7 (1.8) kg, with no significant difference between groups (P = .26). Over the course of the study, there were significant time x diet interactions for Spielberger State Anxiety Inventory, Beck Depression Inventory, and Profile of Mood States scores for total mood disturbance, anger-hostility, confusion-bewilderment, and depression-dejection (P < .05) as a result of greater improvements in these psychological mood states for the LF diet compared with the LC diet. Working memory improved by 1 year (P < .001 for time), but speed of processing remained largely unchanged, with no effect of diet composition on either cognitive domain. ::: ::: ::: CONCLUSIONS ::: Over 1 year, there was a favorable effect of an energy-restricted LF diet compared with an isocaloric LC diet on mood state and affect in overweight and obese individuals. Both diets had similar effects on working memory and speed of processing. Trial Registration anzctr.org.au Identifier: 12606000203550.


title: Heterogeneity-entropy based unsupervised feature learning for personality prediction with cross-media data

abstract: Personality prediction has broad prospects of application in real life. It can be accomplished by analyzing massive and variant data in social networks, which conveys one's personal traits through user generated contents, user's social relationships and behaviors. However, it is difficult to design an effective feature representation from such complex data to predict user's personality as well as high-level and abstract psychological concepts. In this paper, we propose a novel unsupervised cross-modal feature learning algorithm, named Heterogeneity Entropy Neural Network (HENN), to extract the common information between modalities and map it to the user's personality. HENN is constructed hierarchically on Deep Belief Networks (DBNs) and Auto-encoder (AE) with a modified loss function, in which an additional term named Heterogeneity Entropy (HE) is added to measure common information among different modalities. Experiments on a cross-media dataset collected from two famous Chinese social network platforms, i.e., Renren and SinaMicroblog, demonstrate the superiority of our method over several existing algorithms.


title: Deep Reinforcement Learning for Vision-Based Robotic Grasping: A Simulated Comparative Evaluation of Off-Policy Methods

abstract: In this paper, we explore deep reinforcement learning algorithms for vision-based robotic grasping. Model-free deep reinforcement learning (RL) has been successfully applied to a range of challenging environments, but the proliferation of algorithms makes it difficult to discern which particular approach would be best suited for a rich, diverse task like grasping. To answer this question, we propose a simulated benchmark for robotic grasping that emphasizes off-policy learning and generalization to unseen objects. Off-policy learning enables utilization of grasping data over a wide variety of objects, and diversity is important to enable the method to generalize to new objects that were not seen during training. We evaluate the benchmark tasks against a variety of Q-function estimation methods, a method previously proposed for robotic grasping with deep neural network models, and a novel approach based on a combination of Monte Carlo return estimation and an off-policy correction. Our results indicate that several simple methods provide a surprisingly strong competitor to popular algorithms such as double Q-learning, and our analysis of stability sheds light on the relative tradeoffs between the algorithms.


title: Synaptic electronics: materials, devices and applications

abstract: In this paper, the recent progress of synaptic electronics is reviewed. The basics of biological synaptic plasticity and learning are described. The material properties and electrical switching characteristics of a variety of synaptic devices are discussed, with a focus on the use of synaptic devices for neuromorphic or brain-inspired computing. Performance metrics desirable for large-scale implementations of synaptic devices are illustrated. A review of recent work on targeted computing applications with synaptic devices is presented.


title: Detecting and Tracking The Real-time Hot Topics: A Study on Computational Neuroscience

abstract: In this study, following the idea of our previous paper (Wang, et al., 2013a), we improve the method to detect and track hot topics in a specific field by using the real-time article usage data. With the "usage count" data provided by Web of Science, we take the field of computational neuroscience as an example to make analysis. About 10 thousand articles in the field of Computational Neuroscience are queried in Web of Science, when the records, including the usage count data of each paper, have been harvested and updated weekly from October 19, 2015 to March 21, 2016. The hot topics are defined by the most frequently used keywords aggregated from the articles. The analysis reveals that hot topics in Computational Neuroscience are related to the key technologies, like "fmri", "eeg", "erp", etc. Furthermore, using the weekly updated data, we track the dynamical changes of the topics. The characteristic of immediacy of usage data makes it possible to track the "heat" of hot topics timely and dynamically.


title: Deep Feature Factorization For Concept Discovery

abstract: We propose Deep Feature Factorization (DFF), a method capable of localizing similar semantic concepts within an image or a set of images. We use DFF to gain insight into a deep convolutional neural network's learned features, where we detect hierarchical cluster structures in feature space. This is visualized as heat maps, which highlight semantically matching regions across a set of images, revealing what the network `perceives' as similar. DFF can also be used to perform co-segmentation and co-localization, and we report state-of-the-art results on these tasks.


title: Recent Developments in Home Sleep-Monitoring Devices

abstract: Improving our understanding of sleep physiology and pathophysiology is an important goal for both medical and general wellness reasons. Although the gold standard for assessing sleep remains the laboratory polysomnogram, there is an increasing interest in portable monitoring devices that provide the opportunity for assessing sleep in real-world environments such as the home. Portable devices allow repeated measurements, evaluation of temporal patterns, and self-experimentation. We review recent developments in devices designed to monitor sleep-wake activity, as well as monitors designed for other purposes that could in principle be applied in the field of sleep (such as cardiac or respiratory sensing). As the body of supporting validation data grows, these devices hold promise for a variety of health and wellness goals. From a clinical and research standpoint, the capacity to obtain longitudinal sleep-wake data may improve disease phenotyping, individualized treatment decisions, and individualized health optimization. From a wellness standpoint, commercially available devices may allow individuals to track their own sleep with the goal of finding patterns and correlations with modifiable behaviors such as exercise, diet, and sleep aids.


title: Organizational psychology in sport: an introduction

abstract: This ground-breaking book is the first to provide a comprehensive overview of how organizational psychology can be used to understand and improve performance in elite sport. Using recent theoretical advances from this burgeoning area of research, each chapter offers key conceptual issues and practical insights across a range of topics. The book is structured into four constituent parts, • Attitudes and emotions in sports organizations • Stress and well-being in sports organizations • Behaviors in sports organizations • Environments in sports organizations Covering key areas such as attitudes to employment, conflict and change management, leadership, and relationships with the mass media, the book shines a spotlight on how organizational issues play a fundamental role in the experience of individuals and teams. In an era of ever-increasing professionalism in sport, the book provides an invaluable new perspective on performance at the elite level. Including contributions from an international range of academics and practitioners, it will be essential reading for any student or practitioners within sport and exercise psychology.


Swarm Intelligence 1 Swarm Intelligence


title: Laparoscopic Toupet fundoplication with duodenojejunostomy for the management of superior mesenteric artery syndrome with reflux symptoms

abstract: Rationale: The patient had symptoms of GERD and the reflux even caused the symptom of cough. Gaining weight is a risk factor for the treatment of reflux as it could exacerbated symptoms of reflux and the drug treatment is not effective. Surgical intervention becomes necessary when there is failure following conservative medical therapy or the patient. Patient concerns: The patient was not satisfied with the drug treatment. Diagnoses: Superior mesenteric artery syndrome, gastroesophageal reflux disease. Interventions: Laparoscopic Toupet fundoplication with duodenojejunostomy. Outcomes: The patient discharged from hospital 10 days after surgery without any postoperative complication. The patient achieved complete relief of symptoms and discontinuation of drug. Lessons subsections: Superior mesenteric artery (SMA) syndrome may manifest the symptoms of GERD such as heartburn, acid reflux and cough. It is necessary to complete examination to exclude superior mesenteric artery syndrome for these patients. Laparoscopic fundoplication with duodenojejunostomy provided an effective treatment for patients who failed drug treatment.


title: A Comprehensive Study of the Stability of Cocaine and Its Metabolites

abstract: The stability of cocaine (COC) in blood bank blood, postmortem human whole blood, and buffers was evaluated with consideration for the presence of the degradation products, benzoylecgonine (BE) and ecgonine methyl ester (EME). At COC concentrations commonly seen, the rate of COC hydrolysis was independent of concentration. COC was stable in blood for at least 150 days if the blood was adjusted to pH 5 and preserved with 2% NaF or organophosphates and maintained at 4 degrees C or lower. Without preservation, most COC hydrolyzed to EME. The addition of a pseudocholinesterase (PChE) inhibitor without a reduction of pH caused COC to hydrolyze to BE. COC also hydrolyzed to BE in phosphate buffer. The rate of COC hydrolysis in all studies increased with increasing pH and temperature. COC was more stable in unpreserved postmortem blood than blood bank blood due to the lower pH of the former. The incubation of COC in enzyme solutions provided further evidence of the generally accepted hypothesis that COC is hydrolyzed to EME by PChE and to BE by chemical hydrolysis. In unpreserved blood, BE was more stable than EME at room temperature. There was little loss of BE or EME at refrigerated temperature over a period of 35 days and no evidence that EME or BE could be hydrolyzed enzymatically.


title: Scalability of Knowledge Transfer in Complex Systems of Emergent "living" Communities

abstract: Communities are emergent, holistic living systems. Understanding the impact of social complex systems through spatial interactions via the lens of scalability requires the development of new methodological behavioural approaches. The evolution of social complex systems of cities and their regions can be investigated through the evolution of spatial structures. The clustering of entities within cities, regions and beyond presents behavioural elements for which methodological approaches need to be considered. The emergent aspect of complex entities by their very nature requires an understanding that can embrace unpredictability through emergence. Qualitative methodological approaches can be holistic with the ability to embrace bottom up and top down methods for analysis. Social complex systems develop structures by connecting "like minded" behaviour through scalability. How "mobile" these interactions are, is a concept that can be understood via "inter-organizational" and "inter- structural" comparative approaches. How do we indeed convey this adequately or appropriately? Just as a geographical area may contain characteristics that can help to support the formation of an emergent industry cluster, similar behaviours occur through emergent characteristics of complex systems that underpin the sustainability of an organization. The idea that complex systems have tacit structures, capable of displaying emergent behaviour, is not a common concept. These tacit structures can in turn, impact the structural sustainability of physical entities. More often than not, there is a focus on how these concepts of complex systems work, but the "why" questions depends upon scalability. Until recently, social complex adaptive systems were largely over looked due to the tacit nature of these network structures.


title: A Probabilistic Framework for Deep Learning

abstract: We develop a probabilistic framework for deep learning based on the Deep Rendering Mixture Model (DRMM), a new generative probabilistic model that explicitly capture variations in data due to latent task nuisance variables. We demonstrate that max-sum inference in the DRMM yields an algorithm that exactly reproduces the operations in deep convolutional neural networks (DCNs), providing a first principles derivation. Our framework provides new insights into the successes and shortcomings of DCNs as well as a principled route to their improvement. DRMM training via the Expectation-Maximization (EM) algorithm is a powerful alternative to DCN back-propagation, and initial training results are promising. Classification based on the DRMM and other variants outperforms DCNs in supervised digit classification, training 2-3 x faster while achieving similar accuracy. Moreover, the DRMM is applicable to semi-supervised and unsupervised learning tasks, achieving results that are state-of-the-art in several categories on the MNIST benchmark and comparable to state of the art on the CIFAR10 benchmark.


title: Multidimensional Waveform Encoding: A New Digital Beamforming Technique for Synthetic Aperture Radar Remote Sensing

abstract: This paper introduces the innovative concept of multidimensional waveform encoding for spaceborne synthetic aperture radar (SAR). The combination of this technique with digital beamforming on receive enables a new generation of SAR systems with improved performance and flexible imaging capabilities. Examples are high-resolution wide-swath radar imaging with compact antennas, enhanced sensitivity for applications like alongtrack interferometry and moving object indication, and the implementation of hybrid SAR imaging modes that are well suited to satisfy hitherto incompatible user requirements. Implementation-specific issues are discussed and performance examples demonstrate the potential of the new technique for different remote sensing applications.


title: Accuracy of magnetic resonance imaging, magnetic resonance arthrography and computed tomography for the detection of chondral lesions of the knee

abstract: PURPOSE ::: To assess the diagnostic test accuracy of magnetic resonance imaging (MRI), magnetic resonance arthrography (MRA) and computed tomography arthrography (CTA) for the detection of chondral lesions of the patellofemoral and tibiofemoral joints. ::: ::: ::: METHODS ::: A review of published and unpublished literature sources was conducted on 22nd September 2011. All studies assessing the diagnostic test accuracy (sensitivity/specificity) of MRI or MRA or CTA for the assessment of adults with chondral (cartilage) lesions of the knee (tibiofemoral/patellofemoral joints) with surgical comparison (arthroscopic or open) as the reference test were included. Data were analysed through meta-analysis. ::: ::: ::: RESULTS ::: Twenty-seven studies assessing 2,592 knees from 2,509 patients were included. The findings indicated that whilst presenting a high specificity (0.95-0.99), the sensitivity of MRA, MRI and CTA ranged from 0.70 to 0.80. MRA was superior to MRI and CTA for the detection of patellofemoral joint chondral lesions and that higher field-strength MRI scanner and grade four lesions were more accurately detected compared with lower field-strength and grade one lesions. There appeared no substantial difference in diagnostic accuracy between the interpretation from musculoskeletal and general radiologists when undertaking an MRI review of tibiofemoral and patellofemoral chondral lesions. ::: ::: ::: CONCLUSIONS ::: Specialist radiological imaging is specific for cartilage disease in the knee but has poorer sensitivity to determine the therapeutic options in this population. Due to this limitation, there remains little indication to replace the 'gold-standard' arthroscopic investigation with MRI, MRA or CTA for the assessment of adults with chondral lesions of the knee. ::: ::: ::: LEVEL OF EVIDENCE ::: II.


title: A comparative analysis of common YouTube comment spam filtering techniques

abstract: Ever since its development in 2005, YouTube has been providing a vital social media platform for video sharing. Unfortunately, YouTube users may have malicious intentions, such as disseminating malware and profanity. One way to do so is using the comment field for this purpose. Although YouTube provides a built-in tool for spam control, yet it is insufficient for combating malicious and spam contents within the comments. In this paper, a comparative study of the common filtering techniques used for YouTube comment spam is conducted. The study deploys datasets extracted from YouTube using its Data API. According to the obtained results, high filtering accuracy (more than 98%) can be achieved with low-complexity algorithms, implying the possibility of developing a suitable browser extension to alleviate comment spam on YouTube in future.


title: A Map-Reduce Framework for Clustering Metagenomes

abstract: The past few years has seen an explosion in the use of sequence technologies for met genomics i.e., determination of the collective genome of microorganisms co-existing within several environments. In parallel, there has been rapid development of computational tools for the quantification of abundance, diversity and functionality of different species within these communities. Several clustering algorithms (also called binning algorithms) have been developed to categorize similar met genome sequence reads for efficient post-processing and analysis. In this paper we present a distributed algorithm for clustering met genome sequence reads. The algorithm is implemented within the Map-Reduce based Hadoop platform, and approximates the computation of pair wise sequence similarity with a minwise hashing approach. The algorithm is capable of performing agglomerative hierarchical clustering or a greedy clustering approach and is referred to as MrMC-MinH. The key advantage of MrMC-MinH is it's ability to handle large volumes of sequence reads obtained from targeted 16S metagenomic or whole metagenomic data. We evaluate the performance of our algorithm on several real and simulated metagenome benchmarks and demonstrate that our approach is computationally efficient, and produces accurate clustering results when evaluated using external ground truth. The source code for MrMC-MinH will be made available at the supplementary website.


title: DA-RNN: Semantic Mapping with Data Associated Recurrent Neural Networks

abstract: 3D scene understanding is important for robots to interact with the 3D world in a meaningful way. Most previous works on 3D scene understanding focus on recognizing geometrical or semantic properties of the scene independently. In this work, we introduce Data Associated Recurrent Neural Networks (DA-RNNs), a novel framework for joint 3D scene mapping and semantic labeling. DA-RNNs use a new recurrent neural network architecture for semantic labeling on RGB-D videos. The output of the network is integrated with mapping techniques such as KinectFusion in order to inject semantic information into the reconstructed 3D scene. Experiments conducted on a real world dataset and a synthetic dataset with RGB-D videos demonstrate the ability of our method in semantic 3D scene mapping.


title: Improving HybrID: How to best combine indirect and direct encoding in evolutionary algorithms

abstract: Many challenging engineering problems are regular, meaning solutions to one part of a problem can be reused to solve other parts. Evolutionary algorithms with indirect encoding perform better on regular problems because they reuse genomic information to create regular phenotypes. However, on problems that are mostly regular, but contain some irregularities, which describes most real-world problems, indirect encodings struggle to handle the irregularities, hurting performance. Direct encodings are better at producing irregular phenotypes, but cannot exploit regularity. An algorithm called HybrID combines the best of both: it first evolves with indirect encoding to exploit problem regularity, then switches to direct encoding to handle problem irregularity. While HybrID has been shown to outperform both indirect and direct encoding, its initial implementation required the manual specification of when to switch from indirect to direct encoding. In this paper, we test two new methods to improve HybrID by eliminating the need to manually specify this parameter. Auto-Switch-HybrID automatically switches from indirect to direct encoding when fitness stagnates. Offset-HybrID simultaneously evolves an indirect encoding with directly encoded offsets, eliminating the need to switch. We compare the original HybrID to these alternatives on three different problems with adjustable regularity. The results show that both Auto-Switch-HybrID and Offset-HybrID outperform the original HybrID on different types of problems, and thus offer more tools for researchers to solve challenging problems. The Offset-HybrID algorithm is particularly interesting because it suggests a path forward for automatically and simultaneously combining the best traits of indirect and direct encoding.


title: Attack plan recognition and prediction using causal networks

abstract: Correlating and analyzing security alerts is a critical and challenging task in security management. Recently, some techniques have been proposed for security alert correlation. However, these approaches focus more on basic or low-level alert correlation. In this paper, we study how to conduct probabilistic inference to correlate and analyze attack scenarios. Specifically, we propose an approach to solving the following problems: 1) How to correlate isolated attack scenarios resulted from low-level alert correlation? 2) How to identify attacker's high-level strategies and intentions? 3) How to predict the potential attacks based on observed attack activities? We evaluate our approaches using DARPA's grand challenge problem (GCP) data set. The results demonstrate the capability of our approach in correlating isolated attack scenarios, identifying attack strategies and predicting future attacks.


title: Distributed Incremental Pattern Matching on Streaming Graphs

abstract: Big data has shifted the computing paradigm of data analysis. While some of the data can be treated as simple texts or independent data records, many other applications have data with structural patterns which are modeled as a graph, such as social media, road network traffic and smart grid, etc. However, there is still limited amount of work has been done to address the velocity problem of graph processing. In this work, we aim to develop a distributed processing system for solving pattern matching queries on streaming graphs where graphs evolve over time upon the arrives of streaming graph update events. To achieve the goal, we proposed an incremental pattern matching algorithm and implemented it on GPS, a vertex centric distributed graph computing framework. We also extended the GPS framework to support streaming graph, and adapted a subgraphcentric data model to further reduce communication overhead and system performance. Our evaluation using real wiki trace shows that our approach achieves a 3x -- 10x speedup over the batch algorithm, and significantly reduces network and memory usage.


title: Index Finger of a Human-Like Robotic Hand Using Thin Soft Muscles

abstract: This letter presents an index finger of human-like robotics hand which intends to closely replicate the human finger in terms of bones, ligaments, muscles, extensor mechanism, tendon, and its pulley system. We fabricated the muscles of the index finger using thin multifilament McKibben muscles that consist of three intrinsic and three extrinsic muscles with diameters of 1.3 mm and 4.0 mm, respectively. We present the fabrication method of the index finger and model it based on the Landsmeer Model I, II, and III. We validated the properties of our developed finger with the Landsmeer model for extension and flexion motion. Finally, we demonstrated the capabilities of the finger using motion capture, Tracker , to compare sweeps of the HR-hand finger with a cadaver finger motion for normal and finger deformity condition. Having McKibben muscles as the actuator that mimics the human muscle, one can better understand the human finger function and may use it for training and for modeling the human finger disorders.


title: Deep reverse tone mapping

abstract: Inferring a high dynamic range (HDR) image from a single low dynamic range (LDR) input is an ill-posed problem where we must compensate lost data caused by under-/over-exposure and color quantization. To tackle this, we propose the first deep-learning-based approach for fully automatic inference using convolutional neural networks. Because a naive way of directly inferring a 32-bit HDR image from an 8-bit LDR image is intractable due to the difficulty of training, we take an indirect approach; the key idea of our method is to synthesize LDR images taken with different exposures (i.e., bracketed images) based on supervised learning, and then reconstruct an HDR image by merging them. By learning the relative changes of pixel values due to increased/decreased exposures using 3D deconvolutional networks, our method can reproduce not only natural tones without introducing visible noise but also the colors of saturated pixels. We demonstrate the effectiveness of our method by comparing our results not only with those of conventional methods but also with ground-truth HDR images.


title: A classification and temporal filtering based system for online extrinsic camera calibration

abstract: This paper proposes an online extrinsic camera calibration system able to determine the pitch and roll angle of a moving, forward-looking camera relative to the road surface. Some road surfaces do not show enough texture to allow reliable optical flow calculation and accurate per-frame pitch and roll estimations, respectively. If inaccurate per-frame estimations are regarded in the final calibration, no exact and stable determination of calibration is possible. Therefore, the main contribution of this paper is a classifier system which enables us to distinguish between accurate and inaccurate per-frame angle estimations. To achieve a final calibration only the accurate (classified as positive) per-frame estimations are incorporated in a temporal filter. In our research we found that a fixed amount of positive classified per-frame estimations (a fixed temporal filter length) can be specified which guarantees an exact final calibration. With that, our system is able to autonomously signalizes the moment at which an accurate calibration is achieved while driving. With our developed system, road surfaces with no texture only delay the moment at which a final calibration is achieved but do not affect the exactness of calibration.


title: Formation of Essential Ultrastructural Interface between Cultured Hippocampal Cells and Gold Mushroom-Shaped MEA- Toward “IN-CELL” Recordings from Vertebrate Neurons

abstract: Using cultured Aplysia neurons we recently reported on the development of a novel approach in which an extracellular, non-invasive multi-electrode-array system provides multisite, attenuated, intracellular recordings of subthreshold synaptic potentials, and action potentials (APs), the so called "IN-CELL" recording configuration (to differentiate it from intracellular recordings). Because of its non-invasive nature, the configuration can be used for long term semi intracellular electrophysiological monitoring of APs and synaptic potentials. Three principals converge to generate the IN-CELL configuration: (a) engulfment of approximately 1 μm size gold mushroom-shaped microelectrodes (gMμE) by the neurons, (b) formation of high seal resistance between the cell's plasma membrane and the engulfed gMμE, and (c), autonomous localized increased conductance of the membrane patch facing the gMμE. Using dissociated rat hippocampal cultures we report here that the necessary morphological and ultrastructural relationships to generate the IN-CELL recording configuration are formed between hippocampal cells and the gMμEs. Interestingly, even <1 μm thin branches expand and engulf the gMμE structures. Recordings of spontaneous electrical activity revealed fast ∼2 ms, 0.04-0.75 mV positive monophasic APs (FPMP). We propose that the FPMP are attenuated APs generated by neurons that engulf gMμEs. Computer simulations of analog electrical circuits depicting the cell-gMμE configuration point out the parameters that should be altered to improve the neuron-gMμE electrical coupling.


title: Security analysis of the Diebold AccuVote-TS voting machine

abstract: This paper presents a fully independent security study of a Diebold AccuVote-TS voting machine, including its hardware and software. We obtained the machine from a private party. Analysis of the machine, in light of real election procedures, shows that it is vulnerable to extremely serious attacks. For example, an attacker who gets physical access to a machine or its removable memory card for as little as one minute could install malicious code; malicious code on a machine could steal votes undetectably, modifying all records, logs, and counters to be consistent with the fraudulent vote count it creates. An attacker could also create malicious code that spreads automatically and silently from machine to machine during normal election activities--a voting-machine virus. We have constructed working demonstrations of these attacks in our lab. Mitigating these threats will require changes to the voting machine's hardware and software and the adoption of more rigorous election procedures.


title: Summarizing large text collection using topic modeling and clustering based on MapReduce framework

abstract: Document summarization provides an instrument for faster understanding the collection of text documents and has a number of real life applications. Semantic similarity and clustering can be utilized efficiently for generating effective summary of large text collections. Summarizing large volume of text is a challenging and time consuming problem particularly while considering the semantic similarity computation in summarization process. Summarization of text collection involves intensive text processing and computations to generate the summary. MapReduce is proven state of art technology for handling Big Data. In this paper, a novel framework based on MapReduce technology is proposed for summarizing large text collection. The proposed technique is designed using semantic similarity based clustering and topic modeling using Latent Dirichlet Allocation (LDA) for summarizing the large text collection over MapReduce framework. The summarization task is performed in four stages and provides a modular implementation of multiple documents summarization. The presented technique is evaluated in terms of scalability and various text summarization parameters namely, compression ratio, retention ratio, ROUGE and Pyramid score are also measured. The advantages of MapReduce framework are clearly visible from the experiments and it is also demonstrated that MapReduce provides a faster implementation of summarizing large text collections and is a powerful tool in Big Text Data analysis.


title: A type-theoretical approach to Universal Grammar

abstract: The idea of Universal Grammar (UG) as the hypothetical linguistic structure shared by all human languages harkens back at least to the 13th century. The best known modern elaborations of the idea are due to Chomsky. Following a devastating critique from theoretical, typological and field linguistics, these elaborations, the idea of UG itself and the more general idea of language universals stand untenable and are largely abandoned. The proposal tackles the hypothetical contents of UG using dependent and polymorphic type theory in a framework very different from the Chomskyan ones. We introduce a type logic for a precise, universal and parsimonious representation of natural language morphosyntax and compositional semantics. The logic handles grammatical ambiguity (with polymorphic types), selectional restrictions and diverse kinds of anaphora (with dependent types), and features a partly universal set of morphosyntactic types (by the Curry-Howard isomorphism).


title: Bioavailability enhancers of herbal origin: An overview

abstract: Recently, the use of herbal medicines has been increased all over the world due to their therapeutic effects and fewer adverse effects as compared to the modern medicines. However, many herbal drugs and herbal extracts despite of their impressive in-vitro findings demonstrates less or negligible in-vivo activity due to their poor lipid solubility or improper molecular size, resulting in poor absorption and hence poor bioavailability. Nowadays with the advancement in the technology, novel drug delivery systems open the door towards the development of enhancing bioavailability of herbal drug delivery systems. For last one decade many novel carriers such as liposomes, microspheres, nanoparticles, transferosomes, ethosomes, lipid based systems etc. have been reported for successful modified delivery of various herbal drugs. Many herbal compounds including quercetin, genistein, naringin, sinomenine, piperine, glycyrrhizin and nitrile glycoside have demonstrated capability to enhance the bioavailability. The objective of this review is to summarize various available novel drug delivery technologies which have been developed for delivery of drugs (herbal), and to achieve better therapeutic response. An attempt has also been made to compile a profile on bioavailability enhancers of herbal origin with the mechanism of action (wherever reported) and studies on improvement in drug bioavailability, exhibited particularly by natural compounds.


title: Mind Wandering Behind the Wheel : Performance and Oculomotor Correlates

abstract: Objective: An experiment studied the frequency and correlates of driver mind wandering.Background: Driver mind wandering is associated with risk for crash involvement. The present experiment examined the performance and attentional changes by which this effect might occur.Method: Participants performed a car-following task in a high-fidelity driving simulator and were asked to report any time they caught themselves mind wandering. Vehicle control and eye movement data were recorded.Results: As compared with their attentive performance, participants showed few deficits in vehicle control while mind wandering but tended to focus visual attention narrowly on the road ahead.Conclusion: Data suggest that mind wandering can engender a failure to monitor the environment while driving.Application: Results identify behavioral correlates and potential risks of mind wandering that might enable efforts to detect and mitigate driver inattention.


title: Wireless Network Virtualization With SDN and C-RAN for 5G Networks: Requirements, Opportunities, and Challenges

abstract: Wireless network virtualization (WNV) has drawn attention from the researchers ranging from academia to industry as one of the significant technologies in the cellular network communication. It is considered as a pioneer to achieve effective resource utilization with decreased operating expenses and capital expenses by decoupling the networks functionalities of coexisting virtual networks. It facilitates fast deployment of new services and novel technologies. WNV paradigm is in the early stages, and there is a large room for the research community to develop new architectures, systems, and applications. The availability of software-defined networking (SDN) and cloud/centralized radio access network (C-RAN) steers up the hope for the WNV realization. This paper surveys WNV along with the recent developments in SDN and C-RAN technologies. Based on these technologies and WNV concepts, we identify the requirements and opportunities of future cellular networks. We then propose a general architectural framework for the WNV based on SDN. In-depth discussion of challenges and research issues as well as promising approaches for future networks communication improvements are also proposed. Finally, we give several promising candidates of future network services for residential customers and business customers.


title: Health care applications: a solution based on the internet of things

abstract: This paper proposes the Internet of Things communication framework as the main enabler for distributed worldwide health care applications. Starting from the recent availability of wireless medical sensor prototypes and the growing diffusion of electronic health care record databases, we analyze the requirements of a unified communication framework. Our investigation takes the move by decomposing the storyline of a day in Robert's life, our unlucky character in the not so far future, into simple processes and their interactions. Subsequently, we devise the main communication requirements for those processes and for their integration in the Internet as web services. Finally, we present the Internet of Things protocol stack and the advantages it brings to health care scenarios in terms of the identified requirements.


title: VM-centric snapshot deduplication for cloud data backup

abstract: Data deduplication is important for snapshot backup of virtual machines (VMs) because of excessive redundant content. Fingerprint search for source-side duplicate detection is resource intensive when the backup service for VMs is co-located with other cloud services. This paper presents the design and analysis of a fast VM-centric backup service with a tradeoff for a competitive deduplication efficiency while using small computing resources, suitable for running on a converged cloud architecture that cohosts many other services. The design consideration includes VM-centric file system block management for the increased VM snapshot availability. This paper describes an evaluation of this VM-centric scheme to assess its deduplication efficiency, resource usage, and fault tolerance.


title: A Memristor Crossbar Based Computing Engine Optimized for High Speed and Accuracy

abstract: Matrix-vector multiplication, as a key computing operation, has been largely adopted in applications and hence greatly affects the execution efficiency. A common technique to enhance the performance of matrix-vector multiplication is increasing execution parallelism, which results in higher design cost. In recent years, new devices and structures have been widely investigated as alternative solutions. Among them, memristor crossbar demonstrates a great potential for its intrinsic support of matrix-vector multiplication, high integration density, and built-in parallel execution. However, the computation accuracy and speed of such designs are limited and constrained by the features of crossbar array and peripheral circuitry. In this work, we propose a new memristor crossbar based computing engine design by leveraging a current sensing scheme. High operation parallelism and therefore fast computation can be achieved by simultaneously supplying analog voltages into a memristor crossbar and directly detecting weighted currents through current amplifiers. The performance and effectiveness of the proposed design were examined through the implementation of a neural network for pattern recognition based on MNIST database. Compared to a prior reported design, ours increases the recognition accuracy 8.1% (to 94.6%).


title: Differentially-Fed Millimeter-Wave Yagi-Uda Antennas With Folded Dipole Feed

abstract: Differentially-driven Yagi-Uda antennas have been developed for millimeter-wave applications. The antennas are built on both sides of a Teflon substrate (?r = 2.2). A key design aspect is the use of a folded dipole feed which increases the input impedance from 18 ? (standard dipole case) to 150 ? and is compatible with coplanar strip lines. A 7-element design results in a gain of 8-10 dB at 22-26 GHz and a cross-polarization level of 90% at 22-26 GHz) and is suitable for mm-wave point-to-point links communication systems.


title: Design of a CMOS Potentiostat Circuit for Electrochemical Detector Arrays

abstract: High-throughput electrode arrays are required for advancing devices for testing the effect of drugs on cellular function. In this paper, we present design criteria for a potentiostat circuit that is capable of measuring transient amperometric oxidation currents at the surface of an electrode with submillisecond time resolution and picoampere current resolution. The potentiostat is a regulated cascode stage in which a high-gain amplifier maintains the electrode voltage through a negative feedback loop. The potentiostat uses a new shared amplifier structure in which all of the amplifiers in a given row of detectors share a common half circuit permitting us to use fewer transistors per detector. We also present measurements from a test chip that was fabricated in a 0.5-mum, 5-V CMOS process through MOSIS. Each detector occupied a layout area of 35 mumtimes15 mum and contained eight transistors and a 50-fF integrating capacitor. The rms current noise at 2-kHz bandwidth is ap110 fA. The maximum charge storage capacity at 2 kHz is 1.26times106 electrons


title: Strategy as a Portfolio of Real Options

abstract: In financial terms, a business strategy is much more like a series of options than like a single projected cash flow. Executing a strategy almost always involves making a sequence of major decisions. Some actions are taken immediately while others are deliberately deferred so that managers can optimize their choices as circumstances evolve. While executives readily grasp the analogy between strategy and real options, until recently the mechanics of option pricing was so complex that few companies found it practical to use when formulating strategy. But advances in both computing power and our understanding of option pricing over the last 20 years now make it feasible to apply real-options thinking to strategic decision making. To analyze a strategy as a portfolio of related real options, this article exploits a framework presented by the author in "Investment Opportunities as Real Options: Getting Started on the Numbers" (HBR July-August 1998). That article explained how to get from discounted-cash-flow value to option value for a typical project; in other words, it was about reaching a number. This article extends that framework, exploring how, once you've worked out the numbers, you can use option pricing to improve decision making about the sequence and timing of a portfolio of strategic investments. Timothy Luehrman shows executives how to plot their strategies in two-dimensional "option space," giving them a way to "draw" a strategy in terms that are neither wholly strategic nor wholly financial, but some of both. Such pictures inject financial discipline and new insight into how a company's future opportunities can be actively cultivated and harvested.


title: Using Cohesion and Coupling for Software Remodularization: Is It Enough?

abstract: Refactoring and, in particular, remodularization operations can be performed to repair the design of a software system and remove the erosion caused by software evolution. Various approaches have been proposed to support developers during the remodularization of a software system. Most of these approaches are based on the underlying assumption that developers pursue an optimal balance between cohesion and coupling when modularizing the classes of their systems. Thus, a remodularization recommender proposes a solution that implicitly provides a (near) optimal balance between such quality attributes. However, there is still no empirical evidence that such a balance is the desideratum by developers. This article aims at analyzing both objectively and subjectively the aforementioned phenomenon. Specifically, we present the results of (1) a large study analyzing the modularization quality, in terms of package cohesion and coupling, of 100 open-source systems, and (2) a survey conducted with 29 developers aimed at understanding the driving factors they consider when performing modularization tasks. The results achieved have been used to distill a set of lessons learned that might be considered to design more effective remodularization recommenders.


title: Integrating Unstructured Data into Relational Databases

abstract: In this paper we present a system for automatically integrating unstructured text into a multi-relational database using state-of-the-art statistical models for structure extraction and matching. We show how to extend current highperforming models, Conditional Random Fields and their semi-markov counterparts, to effectively exploit a variety of recognition clues available in a database of entities, thereby significantly reducing the dependence on manually labeled training data. Our system is designed to load unstructured records into columns spread across multiple tables in the database while resolving the relationship of the extracted text with existing column values, and preserving the cardinality and link constraints of the database. We show how to combine the inference algorithms of statistical models with the database imposed constraints for optimal data integration.


Semantic Annotation of Finance Regulatory Text using Multilabel Classification


title: Dirichlet Processes for Joint Learning of Morphology and PoS Tags

abstract: This paper presents a joint model for learning morphology and part-of-speech (PoS) tags simultaneously. The proposed method adopts a finite mixture model that groups words having similar contextual features thereby assigning the same PoS tag to those words. While learning PoS tags, words are analysed morphologically by exploiting similar morphological features of the learned PoS tags. The results show that morphology and PoS tags can be learned jointly in a fully unsupervised setting.


Affective Objects


title: Diversifying trending topic discovery via Semidefinite Programming

abstract: Discovering trending topics from the Web has attracted much attention in recent years, due to users' increasing need for time-sensitive information. A large body of the existing research focuses on detecting trends by examining search traffic fluctuations, and the queries with large traffic increments would be detected as trends. The weakness of this approach is that the trends are dominated by popular fields such as celebrities, as those related queries have large search traffic. Consequently, other topics such as travel and shopping are rarely regarded as trends. In this paper, we present a scalable diversified trending topic discovery system with a MapReduce implementation. The trending topics are discovered based on three criteria: diversity, representativeness, and popularity. We explicitly model these three factors in our objective function and propose an efficient Semidefinite Programming algorithm to solve the corresponding optimization problem. To the best of our knowledge, no prior work in the literature tackles trending topic diversification. We conduct a comprehensive set of experiments with case studies to demonstrate the effectiveness of our approach. The proposed system has been successfully tested in the real-world operational environment, yielding significant improvement in traffic over the existing production system.


title: Fabricating articulated characters from skinned meshes

abstract: Articulated deformable characters are widespread in computer animation. Unfortunately, we lack methods for their automatic fabrication using modern additive manufacturing (AM) technologies. We propose a method that takes a skinned mesh as input, then estimates a fabricatable single-material model that approximates the 3D kinematics of the corresponding virtual articulated character in a piecewise linear manner. We first extract a set of potential joint locations. From this set, together with optional, user-specified range constraints, we then estimate mechanical friction joints that satisfy inter-joint non-penetration and other fabrication constraints. To avoid brittle joint designs, we place joint centers on an approximate medial axis representation of the input geometry, and maximize each joint's minimal cross-sectional area. We provide several demonstrations, manufactured as single, assembled pieces using 3D printers.


title: Extended Kaos to Support Variability for Goal Oriented Requirements Reuse

abstract: This work is done as part of the Tacos project1 whose aim is to define a component-based approach to specify trustworthy systems from the requirements phase to the specification phase, in the Cycab transportation domain. This paper mainly deals with the improvement of requirements elicitation in the context of Cycab domain. For that purpose, we propose to extend the Kaos goal oriented metamodel in order to enable explicit representation of variability at the early-phase of requirements engineering. This extension allows specifying a requirements family model which integrates both reusable assets and a variability model. The latter expresses the relevant domain facets along with different variants to realize them. The facets allow the structuration and organization of domain knowledge for reusability. The variability model then enables designers to explicitly state strategic decisions for requirements model development and then choose more accurately the relevant options of the system to-be. We also propose a software prototype that validates this work.


title: Bluetooth Low Energy (BLE) based mobile electrocardiogram monitoring system

abstract: A wireless electrocardiogram (ECG) monitoring system is developed which integrates Bluetooth Low Energy (BLE) technology. This BLE-based system is comprised of a single-chip ECG signal acquisition module, a Bluetooth module and a smart-phone. Apple's iPhone 4S is selected as the mobile device platform, which embedded with Bluetooth v4.0, Wi-Fi and iOS. In this paper, the monitoring system is able to acquire ECG signals through 2-lead electrocardiogram (ECG) sensor, transmit the ECG data via the Bluetooth wireless link, process and display the ECG waveform in a smart-phone. The results show that implementation of Bluetooth Low Energy (BLE) technology in the existing ECG monitoring system not only eliminates the physical constraints imposed by hard-wired link but also highly reduces the power consumption of the long-term monitoring system.


An Edge Deletion Semantics for Belief Propagation


title: Green supply chain management implications for "closing the loop"

abstract: In this paper we report on results from a cross-sectional survey with manufacturers in four typical Chinese industries, i.e., power generating, chemical/petroleum, electrical/electronic and automobile, to evaluate their perceived green supply chain management (GSCM) practices and relate them to closing the supply chain loop. Our findings provide insights into the capabilities of Chinese organizations on the adoption of GSCM practices in different industrial contexts and that these practices are not considered equitably across the four industries. Academic and managerial implications of our findings are discussed.


title: Big-data-generated traffic flow prediction using deep learning and dempster-shafer theory

abstract: This work addresses short-term traffic flow prediction by proposing a big-data-based framework. The proposed framework uses data fusion to deal with heterogeneous data generated from various sources. The data are categorized into two types: streams of data and event-based data. In this work, Deep Belief Networks (DBNs) are used to independently predict traffic flow using streams of data, i.e., historical traffic flow and weather data, and event-based data, i.e., tweets. Furthermore, Dempster's conditional rule for updating belief is used to fuse evidence coming from streams of data and event-based data modules to achieve enhanced prediction. The experimental results using real-world data show the merit of the proposed framework compared to the state-of-the-art ones.


title: UV Sentry: A Collaborative Approach to Creating a Collaborative System

abstract: Abstract : This paper will describe the processes and results of a pilot project conducted during FY11 as part of the Office of Naval Research's Center for Innovation in Naval Technology (CINT). The project goals were to assist in developing a networked, cross-organizational approach to establishing future requirements for multi-unmanned vehicle autonomy; and to identify technology gaps, challenges, and potential innovative approaches for technology needs. The UV Sentry concept, envisions a system of systems of heterogeneous vehicles, sensors and weapons operating semi-autonomously to provide force protection capability. This project focused on the application of the UV Sentry concept to the counter piracy mission off east Africa. A basic Concept of Operations (CONOPS) was defined and focused on the protection of assets navigating the international shipping channel through the Gulf of Aden. A list of desired system attributes was established including adaptability, scalability, persistence, ability to operate with fewer people, reduced bandwidth and communications requirements, sparse supervisory control, an improved human-machine interface, more rapid response times, and over the horizon communications. This paper will discuss the concept, previous work done, and how the CINT team approached the problem.


title: Auto-painter: Cartoon Image Generation from Sketch by Using Conditional Generative Adversarial Networks

abstract: Recently, realistic image generation using deep neural networks has become a hot topic in machine learning and computer vision. Images can be generated at the pixel level by learning from a large collection of images. Learning to generate colorful cartoon images from black-and-white sketches is not only an interesting research problem, but also a potential application in digital entertainment. In this paper, we investigate the sketch-to-image synthesis problem by using conditional generative adversarial networks (cGAN). We propose the auto-painter model which can automatically generate compatible colors for a sketch. The new model is not only capable of painting hand-draw sketch with proper colors, but also allowing users to indicate preferred colors. Experimental results on two sketch datasets show that the auto-painter performs better that existing image-to-image methods.


title: Ranking Domain-Specific Highlights by Analyzing Edited Videos

abstract: We present a fully automatic system for ranking domain-specific highlights in unconstrained personal videos by analyzing online edited videos. A novel latent linear ranking model is proposed to handle noisy training data harvested online. Specifically, given a search query (domain) such as “surfing”, our system mines the Youtube database to find pairs of raw and corresponding edited videos. Leveraging the assumption that edited video is more likely to contain highlights than the trimmed parts of the raw video, we obtain pair-wise ranking constraints to train our model. The learning task is challenging due to the amount of noise and variation in the mined data. Hence, a latent loss function is incorporated to robustly deal with the noise. We efficiently learn the latent model on a large number of videos (about 700 minutes in all) using a novel EM-like self-paced model selection procedure. Our latent ranking model outperforms its classification counterpart, a motion analysis baseline [15], and a fully-supervised ranking system that requires labels from Amazon Mechanical Turk. Finally, we show that impressive highlights can be retrieved without additional human supervision for domains like skating, surfing, skiing, gymnastics, parkour, and dog activity in unconstrained personal videos.


1 DESIGN & EMOTION : THE KANSEI ENGINEERING METHODOLOGY


title: Heterogeneous Computing: Here to Stay

abstract: Hardware and Software Perspectives


title: Medical Image Compression with Lossless Regions of Interest

abstract: Many classes of images contain some spatial regions which are more important than other regions. Compression methods which are capable of delivering higher reconstruction quality for the important parts are attractive in this situation. For medical images, only a small portion of the image might be diagnostically useful, but the cost of a wrong interpretation is high. Algorithms which deliver lossless compression within the regions of interest, and lossy compression elsewhere in the image, might be the key to providing efficient and accurate image coding to the medical community. We present and compare several new algorithms for lossless region-of-interest (ROI) compression. One is based on lossless coding with the S-transform, and two are based on lossy wavelet zerotree coding together with either pixel-domain or transform-domain coding of the regional residual. We survey previous methods for region-based coding of medical images.


title: Stereotype threat effects on Black and White athletic performance

abstract: Two experiments showed that framing an athletic task as diagnostic of negative racial stereotypes about Black or White athletes can impede their performance in sports. In Experiment 1, Black participants performed significantly worse than did control participants when performance on a golf task was framed as diagnostic of "sports intelligence." In comparison, White participants performed worse than did control participants when the golf task was framed as diagnostic of "natural athletic ability." Experiment 2 observed the effect of stereotype threat on the athletic performance of White participants for whom performance in sports represented a significant measure of their self-worth. The implications of the findings for the theory of stereotype threat (C. M. Steele, 1997) and for participation in sports are discussed.


title: Uncertainty in the Variational Information Bottleneck

abstract: We present a simple case study, demonstrating that Variational Information Bottleneck (VIB) can improve a network's classification calibration as well as its ability to detect out-of-distribution data. Without explicitly being designed to do so, VIB gives two natural metrics for handling and quantifying uncertainty.


Adaptive Parser-Centric Text Normalization


title: Immunocytochemical identification of Leishmania and Trypanosoma cruzi amastigotes in situ with homologous and heterologous polyclonal antibodies.

abstract: The unlabelled antibody peroxidase-antiperoxidase method was used to study the immunocytochemical properties of Leishmania and Trypanosoma cruzi amastigotes in situ after tissues had been submitted to different fixation procedures. Antisera were obtained from rabbits chronically infected with different strains of T. cruzi or immunized with L. mexicana amazonensis and L. braziliensis guyanensis, and were applied on 5 microns thick sections. T. cruzi antigens were well stained by the three anti-T. cruzi sera and the two anti-Leishmania sera at optimum dilution between 1:1,000 and 1:2,000, regardless the parasite strain. Differently, the leishmanial antigens were revealed by anti-Leishmania sera only at low dilutions (between 1:60-1:160), whereas the anti-T. cruzi sera, at these low dilutions, gave rather weak stainings. Although there is no clear explanation for this immunocytochemical "reverse-monodirectional" cross-reactivity between Leishmania and T. cruzi, the present results show that polyclonal antibodies agains Leishmania species, when used for immunocytochemical detection of these parasites in situ, react more strongly with T. cruzi amastigotes than with the homologous amastigotes.


title: Flexible Clustered Multi-Task Learning by Learning Representative Tasks

abstract: Multi-task learning (MTL) methods have shown promising performance by learning multiple relevant tasks simultaneously, which exploits to share useful information across relevant tasks. Among various MTL methods, clustered multi-task learning (CMTL) assumes that all tasks can be clustered into groups and attempts to learn the underlying cluster structure from the training data. In this paper, we present a new approach for CMTL, called flexible clustered multi-task (FCMTL), in which the cluster structure is learned by identifying representative tasks. The new approach allows an arbitrary task to be described by multiple representative tasks, effectively soft-assigning a task to multiple clusters with different weights. Unlike existing counterpart, the proposed approach is more flexible in that (a) it does not require clusters to be disjoint, (b) tasks within one particular cluster do not have to share information to the same extent, and (c) the number of clusters is automatically inferred from data. Computationally, the proposed approach is formulated as a row-sparsity pursuit problem. We validate the proposed FCMTL on both synthetic and real-world data sets, and empirical results demonstrate that it outperforms many existing MTL methods.


title: Automated Evaluation of Out-of-Context Errors

abstract: We present a new approach to evaluate computational models for the task of text understanding by the means of out-of-context error detection. Through the novel design of our automated modification process, existing large-scale data sources can be adopted for a vast number of text understanding tasks. The data is thereby altered on a semantic level, allowing models to be tested against a challenging set of modified text passages that require to comprise a broader narrative discourse. Our newly introduced task targets actual real-world problems of transcription and translation systems by inserting authentic out-of-context errors. The automated modification process is applied to the 2016 TEDTalk corpus. Entirely automating the process allows the adoption of complete datasets at low cost, facilitating supervised learning procedures and deeper networks to be trained and tested. To evaluate the quality of the modification algorithm a language model and a supervised binary classification model are trained and tested on the altered dataset. A human baseline evaluation is examined to compare the results with human performance. The outcome of the evaluation task indicates the difficulty to detect semantic errors for machine-learning algorithms and humans, showing that the errors cannot be identified when limited to a single sentence.


abstract: PURPOSE ::: This project describes a morphology-based subaxial cervical spine traumatic injury classification system. Using the same approach as the thoracolumbar system, the goal was to develop a comprehensive yet simple classification system with high intra- and interobserver reliability to be used for clinical and research purposes. ::: ::: ::: METHODS ::: A subaxial cervical spine injury classification system was developed using a consensus process among clinical experts. All investigators were required to successfully grade 10 cases to demonstrate comprehension of the system before grading 30 additional cases on two occasions, 1 month apart. Kappa coefficients (κ) were calculated for intraobserver and interobserver reliability. ::: ::: ::: RESULTS ::: The classification system is based on three injury morphology types similar to the TL system: compression injuries (A), tension band injuries (B), and translational injuries (C), with additional descriptions for facet injuries, as well as patient-specific modifiers and neurologic status. Intraobserver and interobserver reliability was substantial for all injury subtypes (κ = 0.75 and 0.64, respectively). ::: ::: ::: CONCLUSIONS ::: The AOSpine subaxial cervical spine injury classification system demonstrated substantial reliability in this initial assessment, and could be a valuable tool for communication, patient care and for research purposes.


title: Learning Real-World Stimuli in a Neural Network with Spike-Driven Synaptic Dynamics

abstract: We present a model of spike-driven synaptic plasticity inspired by experimental observations and motivated by the desire to build an electronic hardware device that can learn to classify complex stimuli in a semisupervised fashion. During training, patterns of activity are sequentially imposed on the input neurons, and an additional instructor signal drives the output neurons toward the desired activity. The network is made of integrate-and-fire neurons with constant leak and a floor. The synapses are bistable, and they are modified by the arrival of presynaptic spikes. The sign of the change is determined by both the depolarization and the state of a variable that integrates the postsynaptic action potentials. Following the training phase, the instructor signal is removed, and the output neurons are driven purely by the activity of the input neurons weighted by the plastic synapses. In the absence of stimulation, the synapses preserve their internal state indefinitely. Memories are also very robust to the disruptive action of spontaneous activity. A network of 2000 input neurons is shown to be able to classify correctly a large number (thousands) of highly overlapping patterns (300 classes of preprocessed Latex characters, 30 patterns per class, and a subset of the NIST characters data set) and to generalize with performances that are better than or comparable to those of artificial neural networks. Finally we show that the synaptic dynamics is compatible with many of the experimental observations on the induction of long-term modifications (spike-timing-dependent plasticity and its dependence on both the postsynaptic depolarization and the frequency of pre- and postsynaptic neurons).


title: Dependency Tree-based Sentiment Classification using CRFs with Hidden Variables

abstract: In this paper, we present a dependency tree-based method for sentiment classification of Japanese and English subjective sentences using conditional random fields with hidden variables. Subjective sentences often contain words which reverse the sentiment polarities of other words. Therefore, interactions between words need to be considered in sentiment classification, which is difficult to be handled with simple bag-of-words approaches, and the syntactic dependency structures of subjective sentences are exploited in our method. In the method, the sentiment polarity of each dependency subtree in a sentence, which is not observable in training data, is represented by a hidden variable. The polarity of the whole sentence is calculated in consideration of interactions between the hidden variables. Sum-product belief propagation is used for inference. Experimental results of sentiment classification for Japanese and English subjective sentences showed that the method performs better than other methods based on bag-of-features.


title: Why read if you can skim: towards enabling faster screen reading

abstract: Skimming broadly refers to different speed-reading methods that aim to enhance the rate of reading without unduly compromising on comprehension and retention of information. Skimming of content could be particularly useful for people with vision impairments, who frequently experience information overload when listening to reams of digital content online. Support for usable and useful skimming in modern screen readers remains very poor. This paper explores the user requirements for a usable non-visual skimming interface, informed by a large-scale human-subject experiment with blind individuals. Specifically, the study has: (1) helped identify the type of skimming that can be useful in screen reading main content in web pages; (2) led to the development of a usable interface for accessible online skimming; (3) demonstrated the utility of the accessible skimming interface in two realistic use scenarios; (4) identified automatic summarization techniques that could "closely" approximate skimming methods used by sighted people.


title: Throwable tetrahedral robot with transformation capability

abstract: In this paper, a tetrahedral mobile robot with central axis for transformation to the flat vehicle is presented. The throwable robot with the function of going into narrow spaces when its in the flat-vehicle mode is explained in detail. A prototype has been developed to illustrate the concept. Motion experiments confirm the novel properties of this mechanism: mode changing function and omnidirectional motion. Basic Motion experiments, with a test vehicle are also presented.


title: Cloud Computing: Data Storage Security Analysis and its Challenges

abstract: Cloud computing is emerging as a new technology in the field of computing which effects many aspects of computing. The users are enabled to move their data and application software to the network and access the services on-demand. Moving data outside the organizational boundaries and access them through internet makes complete loss of control from the owner’s side. The data over the cloud servers is easily accessed by both the computing providers and law enforcement agencies easier in comparison to data stored on your local disk. Cloud brings many different security issues and among them data security acts as one of the major challenge in cloud computing. Data security becomes an important issue for securing outsourced data and to maintain a level of trust among data owners. In this paper we will analyze the security requirements and the various approaches for data security. We also highlight the new emerging research challenges in data security and privacy.


title: Investigation of Adherence Degree of Agile Requirements Engineering Practices in Non-Agile Software Development Organizations

abstract: Requirements are critical for the success of software projects. Requirements are practically difficult to produce, as the hardest stage of building a software system is to decide what the system should do. Moreover, requirements errors are expensive to fix in the later phases of the software development life cycle. The rapidly changing business environment is highly challenging traditional Requirements Engineering (RE) practices. Most of the software development organizations are working in such dynamic environment, as a result, either by or without their awareness agile methodologies are adopted in various phases of their software development cycles. The aim of this paper is to investigate the adherence degree of agile RE practices in various software development organizations that are classifying themselves as adopting traditional (i.e. non-agile) software development methodologies. An approach is proposed for achieving this aim and it is applied on five different projects from four different organizations. The result shows that even the non-agile software development organizations are applying agile RE practices by different adherence degrees.


title: Wilkinson Power Divider With Complex Isolation Component and Its Miniaturization

abstract: By adding a complex isolation component between two 90 ° transmission lines at an arbitrary phase angle from the input terminal, a small Wilkinson power divider provides physical separation and electrical isolation between two output ports. General design equations for the complex isolation component are derived from even- and odd-mode analysis. Parallel and series RLC circuits are chosen to realize complex isolation components, respectively. Considering the bandwidths of S22, S33, and S32, inductors in both parallel and series RLC components are omitted to get the widest bandwidths; mathematical proof and design examples are also presented in this paper. A coupled line section with a compensating capacitor is introduced to reduce the circuit size, where characteristic impedances between even and odd mode are different in the coupled line section, and their electrical lengths are also different in inhomogeneous medium. Mathematical equations and simulation examples prove that the compensating capacitor compensates the characteristic impedance difference in the homogeneous medium and the electrical length difference in the inhomogeneous medium. Finally, an experimental circuit shows good agreement with the theoretical simulation.


title: Subregional hippocampal atrophy predicts Alzheimer’s dementia in the cognitively normal

abstract: Atrophic changes of the hippocampus are typically regarded as an early sign of Alzheimer's dementia (AD). Using the radial distance atrophy mapping approach, we compared the longitudinal MRI data of 10 cognitively normal elderly subjects who remained normal at 3-year and 6-year follow-up (NL-NL) and 7 cognitively normal elderly subjects who were diagnosed with mild cognitive impairment (MCI) 2.8 (range 2.0–3.9) and with AD 6.8 years (range 6.1–8.2) after baseline (NL-MCIAD). 3D statistical maps revealed greater hippocampal atrophy in the NL-MCIAD relative to the NL-NL group at baseline (left p = 0.05; right p = 0.06) corresponding to 10–15% CA1, and 10–25% subicular atrophy, and bilateral differences at 3-year follow-up (left p = 0.001, right p < 0.02) corresponding to 10–30% subicular, 10–20% CA1, and 10–20% newly developed CA2-3 atrophy. This preliminary study suggests that excess CA1 and subicular atrophy is present in cognitively normal individuals predestined to decline to amnestic MCI, while progressive involvement of the CA1 and subiculum, and atrophy spreading to the CA2-3 subfield in amnestic MCI, suggests future diagnosis of AD.