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With the high demand and sub-nanometer design for integrated circuits, surface defect complexity and frequency for semiconductor wafers have increased; subsequently emphasizing the need for highly accurate fault detection and root-cause analysis systems as manual defect diagnosis is more time-intensive, and expensive. As such, machine learning and deep learning methods have been integrated to automated inspection systems for wafer map defect recognition and classification to enhance performance, overall yield, and cost-efficiency. Concurrent with algorithm and hardware advances, in particular the onset of neural networks like the convolutional neural network, the literature for wafer map defect detection exploded with new developments to address the limitations of data preprocessing, feature representation and extraction, and model learning strategies. This article aims to provide a comprehensive review on the advancement of machine learning and deep learning applications for wafer map defect recognition and classification. The defect recognition and classification methods are introduced and analyzed for discussion on their respective advantages, limitations, and scalability. The future challenges and trends of wafer map detection research are also presented. [ABSTRACT FROM AUTHOR]

Recent studies have shown promising results on joint learning of local feature detectors and descriptors. To address the lack of ground-truth keypoint supervision, previous methods mainly inject appropriate knowledge about keypoint attributes into the network to facilitate model learning. In this paper, inspired by traditional corner detectors, we develop an end-to-end deep network, named Deep Corner, which adds a local similarity-based keypoint measure into a plain convolutional network. Deep Corner enables finding reliable keypoints and thus benefits the learning of the distinctive descriptors. Moreover, to improve keypoint localization, we first study previous multi-level keypoint detection strategies and then develop a multi-level U-Net architecture, where the similarity of features at multiple levels can be exploited effectively. Finally, to improve the invariance of descriptors, we propose a feature self-transformation operation, which transforms the learned features adaptively according to the specific local information. The experimental results on several tasks and comprehensive ablation studies demonstrate the effectiveness of our method and the involved components. [ABSTRACT FROM AUTHOR]

As digital transformation progresses across industries, digital twins have emerged as an important technology. In healthcare, digital twins are created by digitizing patient parameters, medical records, and treatment plans to enable personalized care, assist diagnosis, and improve planning. Data is core to digital twins, originating from physical and virtual entities as well as services. Once processed and integrated, data drives various components. Medical records are critical healthcare data but present unique challenges for digital twins. However, directly storing or encrypting medical records has issues. Plaintext risks privacy leaks while encryption hinders retrieval. To address this, we present a cloud-based solution combining post-quantum searchable encryption. Our system includes key generation using Physical Unable Functions (PUF). It encrypts medical records in cloud storage, verifies records using blockchain, and retrieves records via cloud. By integrating cloud encryption, blockchain verification and cloud retrieval, we propose a secure and efficient cloud-based medical records system for digital twins. Our implementation demonstrates the system provides users efficient and secure medical record services, compared to related designs. This highlights digital twins' potential to transform healthcare through secure data-driven personalized care, diagnosis and planning. [ABSTRACT FROM AUTHOR]

Microscopic examination of urinary sediments is a common laboratory procedure. Automated image-based classification of urinary sediments can reduce analysis time and costs. Inspired by cryptographic mixing protocols and computer vision, we developed an image classification model that combines a novel Arnold Cat Map (ACM)- and fixed-size patch-based mixer algorithm with transfer learning for deep feature extraction. Our study dataset comprised 6,687 urinary sediment images belonging to seven classes: Cast, Crystal, Epithelia, Epithelial nuclei, Erythrocyte, Leukocyte, and Mycete. The developed model consists of four layers: (1) an ACM-based mixer to generate mixed images from resized 224 × 224 input images using fixed-size 16 × 16 patches; (2) DenseNet201 pre-trained on ImageNet1K to extract 1,920 features from each raw input image, and its six corresponding mixed images were concatenated to form a final feature vector of length 13,440; (3) iterative neighborhood component analysis to select the most discriminative feature vector of optimal length 342, determined using a k-nearest neighbor (kNN)-based loss function calculator; and (4) shallow kNN-based classification with ten-fold cross-validation. Our model achieved 98.52% overall accuracy for seven-class classification, outperforming published models for urinary cell and sediment analysis. We demonstrated the feasibility and accuracy of deep feature engineering using an ACM-based mixer algorithm for image preprocessing combined with pre-trained DenseNet201 for feature extraction. The classification model was both demonstrably accurate and computationally lightweight, making it ready for implementation in real-world image-based urine sediment analysis applications. [ABSTRACT FROM AUTHOR]

The considerable improvement of technology produced for various applications has resulted in a growth in data sizes, such as healthcare data, which is renowned for having a large number of variables and data samples. Artificial neural networks (ANN) have demonstrated adaptability and effectiveness in classification, regression, and function approximation tasks. ANN is used extensively in function approximation, prediction, and classification. Irrespective of the task, ANN learns from the data by adjusting the edge weights to minimize the error between the actual and predicted values. Back Propagation is the most frequent learning technique that is used to learn the weights of ANN. However, this approach is prone to the problem of sluggish convergence, which is especially problematic in the case of Big Data. In this paper, we propose a Distributed Genetic Algorithm based ANN Learning Algorithm for addressing challenges associated with ANN learning for Big data. Genetic Algorithm is one of the well-utilized bio-inspired combinatorial optimization methods. Also, it is possible to parallelize it at multiple stages, and this may be done in an extremely effective manner for the distributed learning process. The proposed model is tested with various datasets to evaluate its realizability and efficiency. The results obtained from the experiments show that after a specific volume of data, the proposed learning method outperformed the traditional methods in terms of convergence time and accuracy. The proposed model outperformed the traditional model by almost 80% improvement in computational time. [ABSTRACT FROM AUTHOR]

In this paper, we introduce MARVEL, a system designed to simplify the teaching of MapReduce, a popular distributed programming paradigm, through software visualization. At its core, it allows a teacher to describe and recreate a MapReduce application by interactively requesting, through a graphical interface, the execution of a sequence of MapReduce transformations that target an input dataset. Then, the execution of each operation is illustrated on the screen by playing an appropriate graphical animation stage, highlighting aspects related to its distributed nature. The sequence of all animation stages, played back one after the other in a sequential order, results in a visualization of the whole algorithm. The content of the resulting visualization is not simulated or fictitious, but reflects the real behavior of the requested operations, thanks to the adoption of an architecture based on a real instance of a distributed system running on Apache Spark. On the teacher's side, it is expected that by using MARVEL he/she will spend less time preparing materials and will be able to design a more interactive lesson than with electronic slides or a whiteboard. To test the effectiveness of the proposed approach on the learner side, we also conducted a small scientific experiment with a class of volunteer students who formed a control group. The results are encouraging, showing that the use of software visualization guarantees students a learning experience at least equivalent to that of conventional approaches. [ABSTRACT FROM AUTHOR]

Infection is the most common cause of mortality early in life, yet the broad potential of immunization is not fully realized in this vulnerable population. Most vaccines are administered during infancy and childhood, but in some cases the full benefit of vaccination is not realized in-part. New adjuvants are cardinal to further optimize current immunization approaches for early life. However, only a few classes of adjuvants are presently incorporated in vaccines approved for human use. Recent advances in the discovery and delivery of Toll-like receptor (TLR) agonist adjuvants have provided a new toolbox for vaccinologists. Prominent among these candidate adjuvants are synthetic small molecule TLR7/8 agonists. The development of an effective infant Bordetella pertussis vaccine is urgently required because of the resurgence of pertussis in many countries, contemporaneous to the switch from whole cell to acellular vaccines. In this context, TLR7/8 adjuvant based vaccine formulation strategies may be a promising tool to enhance and accelerate early life immunity by acellular B. pertussis vaccines. In the present study, we optimized (a) the formulation delivery system, (b) structure, and (c) immunologic activity of novel small molecule imidazoquinoline TLR7/8 adjuvants towards human infant leukocytes, including dendritic cells. Upon immunization of neonatal mice, this TLR7/8 adjuvant overcame neonatal hyporesponsiveness to acellular pertussis vaccination by driving a T helper (Th)1/Th17 biased T cell- and IgG2c-skewed humoral response to a licensed acellular vaccine (DTaP). This potent immunization strategy may represent a new paradigm for effective immunization against pertussis and other pathogens in early life. [ABSTRACT FROM AUTHOR]

The wealth of properties in functional materials at the nanoscale has attracted tremendous interest over the last decades, spurring the development of ever more precise and ingenious characterization techniques. In ferroelectrics, for instance, scanning probe microscopy based techniques have been used in conjunction with advanced optical methods to probe the structure and properties of nanoscale domain walls, revealing complex behaviours such as chirality, electronic conduction or localised modulation of mechanical response. However, due to the different nature of the characterization methods, only limited and indirect correlation has been achieved between them, even when the same spatial areas were probed. Here, we propose a fast and unbiased analysis method for heterogeneous spatial data sets, enabling quantitative correlative multi-technique studies of functional materials. The method, based on a combination of data stacking, distortion correction, and machine learning, enables a precise mesoscale analysis. When applied to a data set containing scanning probe microscopy piezoresponse and second harmonic generation polarimetry measurements, our workflow reveals behaviours that could not be seen by usual manual analysis, and the origin of which is only explainable by using the quantitative correlation between the two data sets. [ABSTRACT FROM AUTHOR]

We propose a simple model for distributed query processing based on the concept of a distributed array. Such an array has fields of some data type whose values can be stored on different machines. It offers operations to manipulate all fields in parallel within the distributed algebra. The arrays considered are one-dimensional and just serve to model a partitioned and distributed data set. Distributed arrays rest on a given set of data types and operations called the basic algebra implemented by some piece of software called the basic engine. It provides a complete environment for query processing on a single machine. We assume this environment is extensible by types and operations. Operations on distributed arrays are implemented by one basic engine called the master which controls a set of basic engines called the workers. It maps operations on distributed arrays to the respective operations on their fields executed by workers. The distributed algebra is completely generic: any type or operation added in the extensible basic engine will be immediately available for distributed query processing. To demonstrate the use of the distributed algebra as a language for distributed query processing, we describe a fairly complex algorithm for distributed density-based similarity clustering. The algorithm is a novel contribution by itself. Its complete implementation is shown in terms of the distributed algebra and the basic algebra. As a basic engine the Secondo system is used, a rich environment for extensible query processing, providing useful tools such as main memory M-trees, graphs, or a DBScan implementation. [ABSTRACT FROM AUTHOR]

Numerous changes in algorithms have been observed by object detection to enhance both speed and accuracy. In this research, we present a method to improve the behavioral clone of self-driving cars. Thus, we first create a collection of videos and information required for safe driving on different routes and conditions. The detection of obstacles is done with the proposed algorithm called "YOLO non-maximum suppression fuzzy algorithm, which performs the driver reaction to obstacles with greater accuracy and more speed than the obstacles detection algorithms using the designed framework. The network is trained by the driver's performance, and hence, the output used to control the vehicle is obtained. The non-maximum suppression algorithm plays an essential role in object detection and tracking. An effective hybrid method of fuzzy and NMS algorithms is provided in this paper to improve the problem mentioned. The proposed method improves the average accuracy of the detection network. The performance of the designed algorithm was examined using two different types of KITTI data and the data collected using the personal vehicle and the data we gathered. The proposed algorithm was assessed with evaluation accuracy criteria, which revealed that the method has a higher speed (above 64.41%), a lower FPR (below 6.89%), and a lower FNR (below 3.95%) compared with the baseline YOLOv3 model. According to the loss function, the accuracy rate of the network performance is 95%, implying that we have achieved good results. [ABSTRACT FROM AUTHOR]

Correlators in symmetric orbifold CFTs are given by a finite sum of admissible branched covers of the 2d spacetime. We consider a Gross-Mende like limit where all operators have large twist, and show that the corresponding branched covers can be described via a Penner-like matrix model. The limiting branched covers are given in terms of the spectral curve for this matrix model, which remarkably turns out to be directly related to the Strebel quadratic differential on the covering space. Interpreting the covering space as the world-sheet of the dual string theory, the spacetime CFT correlator thus has the form of an integral over the entire world-sheet moduli space weighted with a Nambu-Goto-like action. Quite strikingly, at leading order this action can also be written as the absolute value of the Schwarzian of the covering map. Given the equivalence of the symmetric product CFT to tensionless string theory on AdS3, this provides an explicit realisation of the underlying mechanism of gauge-string duality originally proposed in [1] and further refined in [2]. [ABSTRACT FROM AUTHOR]

In recent years, as one of the new advanced manufacturing modes, cloud manufacturing has been received wide attentions around the world. The technology of cloud manufacturing intergrades the services-oriented techniques as well as manufacturing processes based on cloud computing. With the aid of the cloud computing platforms, the manufacturing services are provided in manufacturing clouds. However, one of the key challenges of cloud manufacturing is the security and safety of information transmission. Traditional network security architectures are based on RSA and elliptic curve cryptographic systems, which is claimed to be broken on quantum computers. We exploit the countermeasures of post-quantum algorithms to protect cloud manufacturing against quantum computer attacks. We propose a post-quantum secure scheme for cloud manufacturing. First, in order to retain confidentiality in cloud manufacturing, we propose a post-quantum asymmetric-key encryption scheme to encrypt the message with the generated session key. Second, in order to retain authentication security in cloud manufacturing, we propose a post-quantum public-key signature generation scheme. Third, based on the encryption scheme and signature generation scheme, we propose a post-quantum secure communication system for cloud manufacturing. We implement our design on cloud-based environment and the comparison with related designs show that our design is suitable for protecting communication in cloud manufacturing. Besides, the post-quantum secure communication system can be extended to other applications of intelligent manufacturing. [ABSTRACT FROM AUTHOR]

The estimation of geolocation for aerial images is significant for tasks like map creating, or automatic navigation for unmanned aerial vehicles (UAVs). We propose a novel geolocalization method for the UAVs using only aerial images and reference road map. The corresponding road maps of the aerial images are firstly merged into a whole mosaic image using our newly-designed aerial image mosaicking algorithm, where the relative homography transformations between road images are firstly estimated using keypoints tracking in RGB aerial images, and then further refined with registration between detected roads. The geolocalization of the aerial mosaic image is then taken as the problem of registering observed roads in the aerial images to the reference road map under the homography transformation. The registration problem is solved with our fast search algorithm based on a novel projective-invariant feature, which consists of two road intersections augmented with their tangents. Experiments demonstrate that the proposed method can localize the aerial image sequence over an area larger than 1000 km 2 within a few seconds. [ABSTRACT FROM AUTHOR]

Collecting and curating large medical-image datasets for deep neural network (DNN) algorithm development is typically difficult and resource-intensive. While transfer learning (TL) decreases reliance on large data collections, current TL implementations are tailored to two-dimensional (2D) datasets, limiting applicability to volumetric imaging (e.g., computed tomography). Targeting performance enhancement of a DNN algorithm based on a small image dataset, we assessed incremental impact of 3D-to-2D projection methods, one supporting novel data augmentation (DA); photometric grayscale-to-color conversion (GCC); and/or TL on training of an algorithm from a small coronary computed tomography angiography (CCTA) dataset (200 examinations, 50% with atherosclerosis and 50% atherosclerosis-free) producing 245 diseased and 1127 normal coronary arteries/branches. Volumetric CCTA data was converted to a 2D format creating both an Aggregate Projection View (APV) and a Mosaic Projection View (MPV), supporting DA per vessel; both grayscale and color-mapped versions of each view were also obtained. Training was performed both without and with TL, and algorithm performance of all permutations was compared using area under the receiver operating characteristics curve. Without TL, APV performance was 0.74 and 0.87 on grayscale and color images, respectively, compared to 0.90 and 0.87 for MPV. With TL, APV performance was 0.78 and 0.88 on grayscale and color images, respectively, compared with 0.93 and 0.91 for MPV. In conclusion, TL enhances performance of a DNN algorithm from a small volumetric dataset after proposed 3D-to-2D reformatting, but additive gain is achieved with application of either GCC to APV or the proposed novel MPV technique for DA. [ABSTRACT FROM AUTHOR]

In this paper we consider the problem of motion planning for perception of a target position. A robot has to move to a position from where it can sense the target, while minimizing both motion and perception costs. The problem of finding paths for robots executing perception tasks can be solved optimally using informed search. In perception path planning, the solution when considering a straight line without obstacles is used as heuristic. In this work, we propose a heuristic that can improve the search efficiency. In order to reduce the node expansion using a more informed search, we use the robot Approximate Visibility Map (A-VM), which is used as a representation of the observability capability of a robot in a given environment. We show how the critical points used in A-VM provide information on the geometry of the environment, which can be used to improve the heuristic, increasing the search efficiency. The critical points allow a better estimation of the minimum motion and perception cost for targets in non-traversable regions that can only be sensed from further away. Finally, we show the contributed heuristic with improvements dominates the base PA* heuristic built on the euclidean distance, and then present the results of the performance increase in terms of node expansion and computation time. [ABSTRACT FROM AUTHOR]

We introduce a predictive modeling solution that provides high quality predictive analytics over aggregation queries in Big Data environments. Our predictive methodology is generally applicable in environments in which large-scale data owners may or may not restrict access to their data and allow only aggregation operators like COUNT to be executed over their data. In this context, our methodology is based on historical queries and their answers to accurately predict ad-hoc queries’ answers. We focus on the widely used set-cardinality, i.e., COUNT, aggregation query, as COUNT is a fundamental operator for both internal data system optimizations and for aggregation-oriented data exploration and predictive analytics. We contribute a novel, query-driven Machine Learning (ML) model whose goals are to: (i) learn the query-answer space from past issued queries, (ii) associate the query space with local linear regression & associative function estimators, (iii) define query similarity, and (iv) predict the cardinality of the answer set of unseen incoming queries, referred to the Set Cardinality Prediction (SCP) problem. Our ML model incorporates incremental ML algorithms for ensuring high quality prediction results. The significance of contribution lies in that it (i) is the only query-driven solution applicable over general Big Data environments, which include restricted-access data, (ii) offers incremental learning adjusted for arriving ad-hoc queries, which is well suited for query-driven data exploration, and (iii) offers a performance (in terms of scalability, SCP accuracy, processing time, and memory requirements) that is superior to data-centric approaches. We provide a comprehensive performance evaluation of our model evaluating its sensitivity, scalability and efficiency for quality predictive analytics. In addition, we report on the development and incorporation of our ML model in Spark showing its superior performance compared to the Spark’s COUNT method. [ABSTRACT FROM AUTHOR]

We propose a formalism for specifying event stream abstractions for use in spacecraft telemetry processing. Our work is motivated by the need to quickly process streams with millions of events generated e.g. by the Curiosity rover on Mars. The approach builds a hierarchy of event abstractions for telemetry visualization and querying to aid human comprehension. Such abstractions can also be used as input to other runtime verification tools. Our notation is inspired by Allen’s Temporal Logic, and provides a rule-based declarative way to express event abstractions. We present an algorithm for applying specifications to an event stream and explore modifications to improve the algorithm’s asymptotic complexity. The system is implemented in both Scala and C, with the specification language implemented as internal as well as external DSLs. We illustrate the solution with several examples, a performance evaluation, and a real telemetry analysis scenario. [ABSTRACT FROM AUTHOR]

There is an increasing demand for computing the relevant structures, equilibria, and long-timescale kinetics of biomolecular processes, such as protein-drug binding, from highthroughput molecular dynamics simulations. Current methods employ transformation of simulated coordinates into structural features, dimension reduction, clustering the dimension-reduced data, and estimation of a Markov state model or related model of the interconversion rates between molecular structures. This handcrafted approach demands a substantial amount of modeling expertise, as poor decisions at any step will lead to large modeling errors. Here we employ the variational approach for Markov processes (VAMP) to develop a deep learning framework for molecular kinetics using neural networks, dubbed VAMPnets. A VAMPnet encodes the entire mapping from molecular coordinates to Markov states, thus combining the whole data processing pipeline in a single end-to-end framework. Our method performs equally or better than state-of-the-art Markov modeling methods and provides easily interpretable few-state kinetic models. [ABSTRACT FROM AUTHOR]

We explore quantum signatures of classical chaos by studying the rate of information gain in quantum tomography. The tomographic record consists of a time series of expectation values of a Hermitian operator evolving under the application of the Floquet operator of a quantum map that possesses (or lacks) time-reversal symmetry. We find that the rate of information gain, and hence the fidelity of quantum state reconstruction, depends on the symmetry class of the quantum map involved. Moreover, we find an increase in information gain and hence higher reconstruction fidelities when the Floquet maps employed increase in chaoticity. We make predictions for the information gain and show that these results are well described by random matrix theory in the fully chaotic regime. We derive analytical expressions for bounds on information gain using random matrix theory for different classes of maps and show that these bounds are realized by fully chaotic quantum systems. [ABSTRACT FROM AUTHOR]

Data is key resource in the modern world. Big data has become a popular term which is used to describe the exponential growth and availability of data. In practice, the growing demand for large-scale data processing and data analysis applications spurred the development of novel solutions from both the industry and academia. For a decade, the MapReduce framework, and its open source realization, Hadoop, has emerged as a highly successful framework that has created a lot of momentum in both the research and industrial communities such that it has become the defacto standard of big data processing platforms. However, in recent years, academia and industry have started to recognize the limitations of the Hadoop framework in several application domains and big data processing scenarios such as large scale processing of structured data, graph data and streaming data. Thus, we have witnessed an unprecedented interest to tackle these challenges with new solutions which constituted a new wave of mostly domain-specific, optimized big data processing platforms. In this article, we refer to this new wave of systems as Big Data 2.0 processing systems. To better understand the latest ongoing developments in the world of big data processing systems, we provide a taxonomy and detailed analysis of the state-of-the-art in this domain. In addition, we identify a set of the current open research challenges and discuss some promising directions for future research. [ABSTRACT FROM AUTHOR]

Feature vectors can be anything from simple surface normals to more complex feature descriptors. Feature extraction is important to solve various computer vision problems: e.g. registration, object recognition and scene understanding. Most of these techniques cannot be computed online due to their complexity and the context where they are applied. Therefore, computing these features in real-time for many points in the scene is impossible. In this work, a hardware-based implementation of 3D feature extraction and 3D object recognition is proposed to accelerate these methods and therefore the entire pipeline of RGBD based computer vision systems where such features are typically used. The use of a GPU as a general purpose processor can achieve considerable speed-ups compared with a CPU implementation. In this work, advantageous results are obtained using the GPU to accelerate the computation of a 3D descriptor based on the calculation of 3D semi-local surface patches of partial views. This allows descriptor computation at several points of a scene in real-time. Benefits of the accelerated descriptor have been demonstrated in object recognition tasks. Source code will be made publicly available as contribution to the Open Source Point Cloud Library. [ABSTRACT FROM AUTHOR]

General purpose (GP)GPU programming demands to couple highly parallel computing units with classic CPUs to obtain a high performance. Heterogenous systems lead to complex designs combining multiple paradigms and programming languages to manage each hardware architecture. In this paper, we present tools to harness GPGPU programming through the high-level OCaml programming language. We describe the SPOC library that allows to handle GPGPU subprograms (kernels) and data transfers between devices. We then present how SPOC expresses GPGPU kernel: through interoperability with common low-level extensions (from Cuda and OpenCL frameworks) but also via an embedded DSL for OCaml. Using simple benchmarks as well as a real world HPC software, we show that SPOC can offer a high performance while efficiently easing development. To allow better abstractions over tasks and data, we introduce some parallel skeletons built upon SPOC as well as composition constructs over those skeletons. [ABSTRACT FROM AUTHOR]

Process planers typically utilize different datum features than designers use when specifying tolerances. Datum features used in process plans are chosen to simplify setups and achieve desired geometric accuracy. Meanwhile, proper machining tolerances are required to be reassigned to satisfy design requirements. However, existing methods to transfer geometric tolerances directly and accurately are still missing due to incompatible mathematical models of tolerances. Also, the affection of material conditions on datum and partial constraint situations have not been deeply considered yet. Since cylindrical features are often used as datum features, this paper describes the use of tolerance maps (T-Maps) (patent no. 6963824) and manufacturing maps (M-maps) to establish analytical relationship among all relevant design and machining tolerances for transfer of cylindrical datum. Firstly, a parametric model of datum transfer is proposed to describe factors involving the process. Next, based on spatial and geometric parameters, as well as tolerances information, variation analysis among features is implemented to formulate transformed T-Maps, sum of which constructs M-Map. Then, distinct bounding boxes of cross-sections in M-Map are extracted through computing vertex coordinates of their boundaries due to complete and partial constraint scenarios. Thereafter, by virtue of bounding boxes, relationship among design and machining tolerances are obtained through fitting M-Maps into T-Maps. Finally, an example is introduced to verify feasibility of the proposed model and method. [ABSTRACT FROM AUTHOR]

Cloud computing is ideal for image storage and processing because it provides enormously scalable storage and processing resources at low cost. One of the major drawbacks of cloud computing, however, is the lack of robust mechanisms for the users to control the privacy of the data they farm out to the clouds. In this paper, we develop an image encoding scheme that enhances the privacy of image data that is outsourced to the clouds for processing. Unlike previously proposed image encryption schemes, our encoding scheme allows different forms of pixel-level image processing to take place in the clouds while the actual image is not revealed to the cloud provider. Our encoding scheme uses a chaotic map to transform the image after it is masked with an arbitrarily chosen ambient image. We use template matching as a common image processing task to demonstrate the ability of our scheme to perform computations on privacy enhanced images. A simplified prototype of the image processing system was implemented and the experimental results are presented in this paper. [ABSTRACT FROM AUTHOR]

We suggest a holographic energy model in which the energy coming from spatial curvature, matter and radiation can be obtained by using the particle horizon for the infrared cut-off. We show the consistency between the holographic dark-energy model and the holographic energy model proposed in this paper. Then, we give a holographic description of the universe. [ABSTRACT FROM AUTHOR]

Transporter proteins from the MATE (multidrug and toxic compound extrusion) family are vital in metabolite transport in plants, directly affecting crop yields worldwide. MATE transporters also mediate multiple-drug resistance (MDR) in bacteria and mammals, modulating the efficacy of many pharmaceutical drugs used in the treatment of a variety of diseases. MATE transporters couple substrate transport to electrochemical gradients and are the only remaining class of MDR transporters whose structure has not been determined. Here we report the X-ray structure of the MATE transporter NorM from Vibrio cholerae determined to 3.65?Å, revealing an outward-facing conformation with two portals open to the outer leaflet of the membrane and a unique topology of the predicted 12 transmembrane helices distinct from any other known MDR transporter. We also report a cation-binding site in close proximity to residues previously deemed critical for transport. This conformation probably represents a stage of the transport cycle with high affinity for monovalent cations and low affinity for substrates. [ABSTRACT FROM AUTHOR]

In the model-based development context, metamodel-based languages are increasingly being defined and adopted either for general purposes or for specific domains of interest. However, meta-languages such as the MOF (Meta Object Facility)-combined with the OCL (Object Constraint Language) for expressing constraints-used to specify metamodels focus on structural and static semantics but have no built-in support for specifying behavioral semantics. This paper introduces a formal semantic framework for the definition of the semantics of metamodel-based languages. Using metamodelling principles, we propose several techniques, some based on the translational approach while others based on the weaving approach, all showing how the Abstract State Machine formal method can be integrated with current metamodel engineering environments to endow language metamodels with precise and executable semantics. We exemplify the use of our semantic framework by applying the proposed techniques to the OMG metamodelling framework for the behaviour specification of the Finite State Machines provided in terms of a metamodel. [ABSTRACT FROM AUTHOR]

Recent growth of geospatial information online has made it possible to access various maps and orthoimagery. Conflating these maps and imagery can create images that combine the visual appeal of imagery with the attribution information from maps. The existing systems require human intervention to conflate maps with imagery. We present a novel approach that utilizes vector datasets as “glue” to automatically conflate street maps with imagery. First, our approach extracts road intersections from imagery and maps as control points. Then, it aligns the two point sets by computing the matched point pattern. Finally, it aligns maps with imagery based on the matched pattern. The experiments show that our approach can conflate various maps with imagery, such that in our experiments on TIGER-maps covering part of St. Louis county, MO, 85.2% of the conflated map roads are within 10.8 m from the actual roads compared to 51.7% for the original and georeferenced TIGER-map roads. [ABSTRACT FROM AUTHOR]

Urban travel geometry is a generalization of patterns of movement in cities and regions where route configuration and prevailing traffic speeds constrain or direct movement in distinctive and repeatable patterns. In this paper we use these properties to construct time surfaces on which distance equates to the time of travel in the urban plane. Such surfaces can be two- or three-dimensional and are useful in the study of urban structure, locational analysis, transport planning and traffic management. A particular niche addressed in this paper is non-conformal time surface transformations in which speed or the cost of travel is constrained according to co-ordinate directions. It is argued that such models may be more suited to gridded and orbital-radial cities than previously used conformal transformations. After explaining the rationale behind the approach, a mathematical basis is developed and several calibrated examples are provided based on regions in the UK. The paper concludes with some examples of potential applications, and an annex provides a detailed mathematical framework. [ABSTRACT FROM AUTHOR]

In order to commission a large hydroelectric power plant in 1992, the Republic of Slovakia diverted the Danube River with a dam at a common section between Hungary and Slovakia. The dam is located at Gabcikovo in what now is Slovakian territory. The diversion, known as the Gabcikovo Barrage System (GBS), subsequently impacted one of the most ecologically important and unique alluvial floodplains of the Danube Basin. This, in turn, affected the hydrological regime of the Danube downstream and so, potentially, water supplies and water quality for millions of people. The potential environmental impacts of the diversion to the floodplain and downstream were not thoroughly studied prior to construction of the dam. The project was originally started jointly between Hungary and Slovakia in 1977 and conflicts arose between the two countries resulting in a case before The International Court of Justice (IJC) in 1993. In 1997, the IJC rendered a decision that a compromise solution had to be worked out accommodating the needs of both Hungary and Slovakia. The IJC said, in essence, that the dam would remain in place, but must be modified so as to minimize environmental impact. This paper reviews the history of the project and describes some impacts of the river diversion that may be attributed to changes in the water regime. In order to assess environmental impacts to the region due to diversion of water from the natural channel of the Danube, this study assessed, using satellite imagery, land cover change between 1988 and 1997. This study also correlated the satellite-derived data with reports from the Hungarian Ministries of Agriculture and the Environment and the North-Transdanubian Environmental Inspectorate. The analysis determined that, although land cover change occurred in the region during this period, not all of the changes could, necessarily, be related to the hydroelectric facility. The results of the analysis show that: (i) there were land cover changes in the study period within the study area; (ii) more time is needed in order to establish a link between the hydroelectric facility and environmental changes; (iii) satellite imagery could provide useful information in studies of this type, but the imagery must be used in conjunction with ground observations. This paper represents the views and opinions of the authors and not necessarily those of either the National Science Foundation or the Hungarian Academy of Sciences. [ABSTRACT FROM AUTHOR]

Examines issues related to the unification of disparate maps. Difficulties with the management of spatial data based on spatial unit, usually rectangular lattices; Interactive method proposed by the author for unifying spatial data.

In this paper we present a complete approach for the conflation of attributed vector digital mapping data such as the Vector Product Format (VPF) datasets produced and disseminated by the National Imagery and Mapping Agency (NIMA). While other work in the field of conflation has traditionally used statistical techniques based on proximity of features, the approach presented here utilizes all information associated with data, including attribute information such as feature codes from a standardized set, associated data quality information of varying levels, and topology, as well as more traditional measures of geometry and proximity. In particular, we address the issues associated with the problem of matching features and maintaining accuracy requirements. A hierarchical rule-based approach augmented with capabilities for reasoning under uncertainty is presented for feature matching as well as for the determination of attribute sets and values for the resulting merged features. Additionally, an in-depth analysis of horizontal accuracy considerations with respect to point features is given. An implementation of the attribute and geometrical matching phases within the scope of an expert system has proven the efficacy of the approach and is discussed within the context of the VPF data. [ABSTRACT FROM AUTHOR]

A number of technologies enabled by Internet of Thing (IoT) have been used for the prevention of various chronic diseases, continuous and real-time tracking system is a particularly important one. Wearable medical devices with sensor, health cloud and mobile applications have continuously generating a huge amount of data which is often called as streaming big data. Due to the higher speed of the data generation, it is difficult to collect, process and analyze such massive data in real-time in order to perform real-time actions in case of emergencies and extracting hidden value. using traditional methods which are limited and time-consuming. Therefore, there is a significant need to real-time big data stream processing to ensure an effective and scalable solution. In order to overcome this issue, this work proposes a new architecture for real-time health status prediction and analytics system using big data technologies. The system focus on applying distributed machine learning model on streaming health data events ingested to Spark streaming through Kafka topics. Firstly, we transform the standard decision tree (DT) (C4.5) algorithm into a parallel, distributed, scalable and fast DT using Spark instead of Hadoop MapReduce which becomes limited for real-time computing. Secondly, this model is applied to streaming data coming from distributed sources of various diseases to predict health status. Based on several input attributes, the system predicts health status, send an alert message to care providers and store the details in a distributed database to perform health data analytics and stream reporting. We measure the performance of Spark DT against traditional machine learning tools including Weka. Finally, performance evaluation parameters such as throughput and execution time are calculated to show the effectiveness of the proposed architecture. The experimental results show that the proposed system is able to effectively process and predict real-time and massive amount of medical data enabled by IoT from distributed and various diseases. [ABSTRACT FROM AUTHOR]

Different plant cultivars of the same genus and species can exhibit vastly different genetic transformation efficiencies. However, the genetic factors underlying these differences in transformation rate remain largely unknown. In barley, 'Golden Promise' is one of a few cultivars reliable for Agrobacterium-mediated transformation. By contrast, cultivar 'Haruna Nijo' is recalcitrant to genetic transformation. We identified genomic regions of barley important for successful transformation with Agrobacterium, utilizing the 'Haruna Nijo' × 'Golden Promise' F2 generation and genotyping by 124 genome-wide SNP markers. We observed significant segregation distortions of these markers from the expected 1:2:1 ratio toward the 'Golden Promise'-type in regions of chromosomes 2H and 3H, indicating that the alleles of 'Golden Promise' in these regions might contribute to transformation efficiency. The same regions, which we termed Transformation Amenability (TFA) regions, were also conserved in transgenic F2 plants generated from a 'Morex' × 'Golden Promise' cross. The genomic regions identified herein likely include necessary factors for Agrobacterium-mediated transformation in barley. The potential to introduce these loci into any haplotype of barley opens the door to increasing the efficiency of transformation for target alleles into any haplotype of barley by the TFA-based methods proposed in this report. [ABSTRACT FROM AUTHOR]