query: Foreground Segmentation for Anomaly Detection in Surveillance Videos Using Deep Residual Networks

positive: DeepAnomaly: Combining Background Subtraction and Deep Learning for Detecting Obstacles and Anomalies in an Agricultural Field Convolutional neural network (CNN)-based systems are increasingly used in autonomous vehicles for detecting obstacles. CNN-based object detection and per-pixel classification (semantic segmentation) algorithms are trained for detecting and classifying a predefined set of object types. These algorithms have difficulties in detecting distant and heavily occluded objects and are, by definition, not capable of detecting unknown object types or unusual scenarios. The visual characteristics of an agriculture field is homogeneous, and obstacles, like people, animals and other obstacles, occur rarely and are of distinct appearance compared to the field. This paper introduces DeepAnomaly, an algorithm combining deep learning and anomaly detection to exploit the homogenous characteristics of a field to perform anomaly detection. We demonstrate DeepAnomaly as a fast state-of-the-art detector for obstacles that are distant, heavily occluded and unknown. DeepAnomaly is compared to state-of-the-art obstacle detectors including "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks" (RCNN). In a human detector test case, we demonstrate that DeepAnomaly detects humans at longer ranges (45-90 m) than RCNN. RCNN has a similar performance at a short range (0-30 m). However, DeepAnomaly has much fewer model parameters and (182 ms/25 ms =) a 7.28-times faster processing time per image. Unlike most CNN-based methods, the high accuracy, the low computation time and the low memory footprint make it suitable for a real-time system running on a embedded GPU (Graphics Processing Unit).

positive: Identity Mappings in Deep Residual Networks Deep residual networks [1] have emerged as a family of extremely deep architectures showing compelling accuracy and nice convergence behaviors. In this paper, we analyze the propagation formulations behind the residual building blocks, which suggest that the forward and backward signals can be directly propagated from one block to any other block, when using identity mappings as the skip connections and after-addition activation. A series of ablation experiments support the importance of these identity mappings. This motivates us to propose a new residual unit, which makes training easier and improves generalization. We report improved results using a 1001-layer ResNet on CIFAR-10 (4.62% error) and CIFAR-100, and a 200-layer ResNet on ImageNet. Code is available at: https://github.com/KaimingHe/ resnet-1k-layers.

positive: SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation We present a novel and practical deep fully convolutional neural network architecture for semantic pixel-wise segmentation termed SegNet. This core trainable segmentation engine consists of an encoder network, a corresponding decoder network followed by a pixel-wise classification layer. The architecture of the encoder network is topologically identical to the 13 convolutional layers in the VGG16 network [1] . The role of the decoder network is to map the low resolution encoder feature maps to full input resolution feature maps for pixel-wise classification. The novelty of SegNet lies is in the manner in which the decoder upsamples its lower resolution input feature map(s). Specifically, the decoder uses pooling indices computed in the max-pooling step of the corresponding encoder to perform non-linear upsampling. This eliminates the need for learning to upsample. The upsampled maps are sparse and are then convolved with trainable filters to produce dense feature maps. We compare our proposed architecture with the widely adopted FCN [2] and also with the well known DeepLab-LargeFOV [3] , DeconvNet [4] architectures. This comparison reveals the memory versus accuracy trade-off involved in achieving good segmentation performance. SegNet was primarily motivated by scene understanding applications. Hence, it is designed to be efficient both in terms of memory and computational time during inference. It is also significantly smaller in the number of trainable parameters than other competing architectures and can be trained end-to-end using stochastic gradient descent. We also performed a controlled benchmark of SegNet and other architectures on both road scenes and SUN RGB-D indoor scene segmentation tasks. These quantitative assessments show that SegNet provides good performance with competitive inference time and most efficient inference memory-wise as compared to other architectures. We also provide a Caffe implementation of SegNet and a web demo at http://mi.eng.cam.ac.uk/projects/segnet/.

positive: CDnet 2014: An Expanded Change Detection Benchmark Dataset Change detection is one of the most important lowlevel tasks in video analytics. In 2012, we introduced the changedetection.net (CDnet) benchmark, a video dataset devoted to the evalaution of change and motion detection approaches. Here, we present the latest release of the CDnet dataset, which includes 22 additional videos (70; 000 pixel-wise annotated frames) spanning 5 new categories that incorporate challenges encountered in many surveillance settings. We describe these categories in detail and provide an overview of the results of more than a dozen methods submitted to the IEEE Change DetectionWorkshop 2014. We highlight strengths and weaknesses of these methods and identify remaining issues in change detection.

positive: Microsoft COCO: Common Objects in Context We present a new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding. This is achieved by gathering images of complex everyday scenes containing common objects in their natural context. Objects are labeled using per-instance segmentations to aid in precise object localization. Our dataset contains photos of 91 objects types that would be easily recognizable by a 4 year old. With a total of 2.5 million labeled instances in 328k images, the creation of our dataset drew upon extensive crowd worker involvement via novel user interfaces for category detection, instance spotting and instance segmentation. We present a detailed statistical analysis of the dataset in comparison to PASCAL, ImageNet, and SUN. Finally, we provide baseline performance analysis for bounding box and segmentation detection results using a Deformable Parts Model.

negative: Does Online Technology Make Us More or Less Sociable? A Preliminary Review and Call for Research. How does online technology affect sociability? Emerging evidence-much of it inconclusive-suggests a nuanced relationship between use of online technology (the Internet, social media, and virtual reality) and sociability (emotion recognition, empathy, perspective taking, and emotional intelligence). Although online technology can facilitate purely positive behavior (e.g., charitable giving) or purely negative behavior (e.g., cyberbullying), it appears to affect sociability in three ways, depending on whether it allows a deeper understanding of people's thoughts and feelings: (a) It benefits sociability when it complements already-deep offline engagement with others, (b) it impairs sociability when it supplants deeper offline engagement for superficial online engagement, and (c) it enhances sociability when deep offline engagement is otherwise difficult to attain. We suggest potential implications and moderators of technology's effects on sociability and call for additional causal research.

negative: A Rule-Based Approach For Effective Sentiment Analysis The success of Web 2.0 applications has made online social media websites tremendous assets for supporting critical business intelligence applications. The knowledge gained from social media can potentially lead to the development of novel services that are better tailored to users’ needs and at the same time meet the objectives of businesses offering them. Online consumer reviews are one of the critical social media contents. Proper analysis of consumer reviews not only provides valuable information to facilitate the purchase decisions of customers but also helps merchants or product manufacturers better understand general responses of customers on their products for marketing campaign improvement. This study aims at designing an approach for supporting the effective analysis of the huge volume of online consumer reviews and, at the same time, settling the major limitations of existing approaches. Specifically, the proposed rule-based sentiment analysis (R-SA) technique employs the class association rule mining algorithm to automatically discover interesting and effective rules capable of extracting product features or opinion sentences for a specific product feature interested. According to our preliminary evaluation results, the R-SA technique performs well in comparison with its benchmark technique.

negative:
Making Sense of Entities and Quantities in Web Tables HTML tables and spreadsheets on the Internet or in enterprise intranets often contain valuable information, but are created ad-hoc. As a result, they usually lack systematic names for column headers and clear vocabulary for cell values. This limits the re-use of such tables and creates a huge heterogeneity problem when comparing or aggregating multiple tables.
 This paper aims to overcome this problem by automatically canonicalizing header names and cell values onto concepts, classes, entities and uniquely represented quantities registered in a knowledge base.
 To this end, we devise a probabilistic graphical model that captures coherence dependencies between cells in tables and candidate items in the space of concepts, entities and quantities. We give specific consideration to quantities which are mapped into a "measure, value, unit" triple over a taxonomy of physical (e.g. power consumption), monetary (e.g. revenue), temporal (e.g. date) and dimensionless (e.g. counts) measures.
 Our experiments with Web tables from diverse domains demonstrate the viability of our method and its benefits over baselines.

negative: A Laplacian Framework for Option Discovery in Reinforcement Learning Representation learning and option discovery are two of the biggest challenges in reinforcement learning (RL). Proto-RL is a well known approach for representation learning in MDPs. The representations learned with this framework are called proto-value functions (PVFs). In this paper we address the option discovery problem by showing how PVFs implicitly define options. We do it by introducing eigenpurposes, intrinsic reward functions derived from the learned representations. The options discovered from eigenpurposes traverse the principal directions of the state space. They are useful for multiple tasks because they are independent of the agents’ intentions. Moreover, by capturing the diffusion process of a random walk, different options act at different time scales, making them helpful for exploration strategies. We demonstrate features of eigenpurposes in traditional tabular domains as well as in Atari 2600 games.

negative: Accessible Crowdwork?: Understanding the Value in and Challenge of Microtask Employment for People with Disabilities We present the first formal study of crowdworkers who have disabilities via in-depth open-ended interviews of 17 people (disabled crowdworkers and job coaches for people with disabilities) and a survey of 631 adults with disabilities. Our findings establish that people with a variety of disabilities currently participate in the crowd labor marketplace, despite challenges such as crowdsourcing workflow designs that inadvertently prohibit participation by, and may negatively affect the worker reputations of, people with disabilities. Despite such challenges, we find that crowdwork potentially offers different opportunities for people with disabilities relative to the normative office environment, such as job flexibility and lack of a need to rely on public transit. We close by identifying several ways in which crowd labor platform operators and/or individual task requestors could improve the accessibility of this increasingly important form of employment.

negative: Micro-Doppler effect in radar: phenomenon, model, and simulation study When, in addition to the constant Doppler frequency shift induced by the bulk motion of a radar target, the target or any structure on the target undergoes micro-motion dynamics, such as mechanical vibrations or rotations, the micro-motion dynamics induce Doppler modulations on the returned signal, referred to as the micro-Doppler effect. We introduce the micro-Doppler phenomenon in radar, develop a model of Doppler modulations, derive formulas of micro-Doppler induced by targets with vibration, rotation, tumbling and coning motions, and verify them by simulation studies, analyze time-varying micro-Doppler features using high-resolution time-frequency transforms, and demonstrate the micro-Doppler effect observed in real radar data.

negative: Breakfast reduces declines in attention and memory over the morning in schoolchildren Twenty-nine schoolchildren were tested throughout the morning on 4 successive days, having a different breakfast each day (either of the cereals Cheerios or Shreddies, glucose drink or No breakfast). A series of computerised tests of attention, working memory and episodic secondary memory was conducted prior to breakfast and again 30, 90, 150 and 210 min later. The glucose drink and No breakfast conditions were followed by declines in attention and memory, but the declines were significantly reduced in the two cereal conditions. This study provides objective evidence that a typical breakfast of cereal rich in complex carbohydrates can help maintain mental performance over the morning.

negative: Deep Neural Architecture for Multi-Modal Retrieval based on Joint Embedding Space for Text and Images Recent advances in deep learning and distributed representations of images and text have resulted in the emergence of several neural architectures for cross-modal retrieval tasks, such as searching collections of images in response to textual queries and assigning textual descriptions to images. However, the multi-modal retrieval scenario, when a query can be either a text or an image and the goal is to retrieve both a textual fragment and an image, which should be considered as an atomic unit, has been significantly less studied. In this paper, we propose a gated neural architecture to project image and keyword queries as well as multi-modal retrieval units into the same low-dimensional embedding space and perform semantic matching in this space. The proposed architecture is trained to minimize structured hinge loss and can be applied to both cross- and multi-modal retrieval. Experimental results for six different cross- and multi-modal retrieval tasks obtained on publicly available datasets indicate superior retrieval accuracy of the proposed architecture in comparison to the state-of-art baselines.

negative: Customer relationship management capabilities Measurement , antecedents and consequences Purpose – This study seeks to extend the resource-based view to the context of customer relationship management. It is intended to develop a measurement model of customer relationship management (CRM) capabilities, and to explore the key antecedents and performance consequences of CRM capabilities. Design/methodology/approach – Questionnaire survey was used to collect data. In order to develop a reliable and valid measurement model of CRM capabilities, several rounds of questionnaire survey were conducted, and hypotheses were tested by utilizing the technique of structural equation modeling. Findings – A three-factor (customer interaction management capability, customer relationship upgrading capability and customer win-back capability) measurement model of CRM capabilities is developed and tested. Furthermore, results support the hypothesized influences of customer orientation, customer-centric organizational system and CRM technology on CRM capabilities, as well as the influence of CRM capabilities on organizational performance. Practical implications – This study provides a useful measurement mode of CRM capabilities that managers can use to evaluate the status in quo of CRM capabilities of their firms. Managers may also improve their CRM programs more effectively and efficiently by deploying such strategic resources of firms as customer orientation, customer-centric organizational system and CRM technology to build and strengthen their CRM capabilities. Originality/value – The paper addresses significant gaps in the current literature by taking a capability view of CRM, developing a valid measurement model of CRM capabilities, and examining how possession of important CRM resources influences the effective deployment of CRM capabilities.

negative: Reduced interference in working memory following mindfulness training is associated with increases in hippocampal volume Proactive interference occurs when previously relevant information interferes with retaining newer material. Overcoming proactive interference has been linked to the hippocampus and deemed critical for cognitive functioning. However, little is known about whether and how this ability can be improved or about the neural correlates of such improvement. Mindfulness training emphasizes focusing on the present moment and minimizing distraction from competing thoughts and memories. It improves working memory and increases hippocampal density. The current study examined whether mindfulness training reduces proactive interference in working memory and whether such improvements are associated with changes in hippocampal volume. 79 participants were randomized to a 4-week web-based mindfulness training program or a similarly structured creative writing active control program. The mindfulness group exhibited lower proactive interference error rates compared to the active control group following training. No group differences were found in hippocampal volume, yet proactive interference improvements following mindfulness training were significantly associated with volume increases in the left hippocampus. These results provide the first evidence to suggest that (1) mindfulness training can protect against proactive interference, and (2) that these benefits are related to hippocampal volumetric increases. Clinical implications regarding the application of mindfulness training in conditions characterized by impairments to working memory and reduced hippocampal volume such as aging, depression, PTSD, and childhood adversity are discussed.

negative: Real-Time Top-View People Counting Based on a Kinect and NVIDIA Jetson TK1 Integrated Platform In this paper, we describe how to establish an embedded framework for real-time top-view people counting. The development of our system consists of two parts, i.e. establishing an embedded signal processing platform and designing a people counting algorithm for the embedded system. For the hardware platform construction, we use Kinect as the camera and exploit NVIDIA Jetson TK1 board as the embedded processing platform. We describe how to build a channel to make Kinect for windows version 2.0 communicate with Jetson TK1. Based on the embedded system, we adapt a water filling based scheme for top-view people counting, which integrates head detection based on water drop, people tracking and counting. Gaussian Mixture Model is used to construct and update the background model. The moving people in each video frame are extracted using background subtraction method. Additionally, the water filling algorithm is used to segment head area as Region Of Interest(ROI). Tracking and counting people are performed by calculating the distance of ROI center point before and after the frame. The whole framework is flexible and practical for real-time application.

negative: Mobile technology and the digitization of healthcare. The convergence of science and technology in our dynamic digital era has resulted in the development of innovative digital health devices that allow easy and accurate characterization in health and disease. Technological advancements and the miniaturization of diagnostic instruments to modern smartphone-connected and mobile health (mHealth) devices such as the iECG, handheld ultrasound, and lab-on-a-chip technologies have led to increasing enthusiasm for patient care with promises to decrease healthcare costs and to improve outcomes. This 'hype' for mHealth has recently intersected with the 'real world' and is providing important insights into how patients and practitioners are utilizing digital health technologies. It is also raising important questions regarding the evidence supporting widespread device use. In this state-of-the-art review, we assess the current literature of mHealth and aim to provide a framework for the advances in mHealth by understanding the various device, patient, and clinical factors as they relate to digital health from device designs and patient engagement, to clinical workflow and device regulation. We also outline new strategies for generation and analysis of mHealth data at the individual and population-based levels.

negative: Experimental implementation of underactuated potential energy shaping on a powered ankle-foot orthosis Traditional control methodologies of rehabilitation orthoses/exoskeletons aim at replicating normal kinematics and thus fall into the category of kinematic control. This control paradigm depends on pre-defined reference trajectories, which can be difficult to adjust between different locomotor tasks and human subjects. An alternative control category, kinetic control, enforces kinetic goals (e.g., torques or energy) instead of kinematic trajectories, which could provide a flexible learning environment for the user while freeing up therapists to make corrections. We propose that the theory of underactuated potential energy shaping, which falls into the category of kinetic control, could be used to generate virtual body-weight support for stroke gait rehabilitation. After deriving the nonlinear control law and simulating it on a human-like biped model, we implemented this controller on a powered ankle-foot orthosis that was designed specifically for testing torque control strategies. Experimental results with an able-bodied human subject demonstrate the feasibility of the control approach for both positive and negative virtual body-weight augmentation.

negative: Distance estimation with 2 . 5 D anchors and its application to robot navigation Estimating the distance of a target object from a single image is a challenging task since a large variation in the object appearance makes the regression of the distance difficult. In this paper, to tackle such the challenge, we propose 2.5D anchors which provide the candidate of distances based on a perspective camera model. This candidate is expected to relax the difficulty of the regression model since only the residual from the candidate distance needs to be taken into account. We show the effectiveness of the regression with our proposed anchors, by comparing with ordinary regression methods and state-of-the-art 3D object detection methods, through Pascal 3D+ TV monitor and KITTI car experiments. In addition, we also show an example of practical uses of our proposed method in a real-time system, robot navigation, by integrating with ROS-based simultaneous localization and mapping.

negative: Machine morality: bottom-up and top-down approaches for modelling human moral faculties The implementation of moral decision making abilities in artificial intelligence (AI) is a natural and necessary extension to the social mechanisms of autonomous software agents and robots. Engineers exploring design strategies for systems sensitive to moral considerations in their choices and actions will need to determine what role ethical theory should play in defining control architectures for such systems. The architectures for morally intelligent agents fall within two broad approaches: the top-down imposition of ethical theories, and the bottom-up building of systems that aim at goals or standards which may or may not be specified in explicitly theoretical terms. In this paper we wish to provide some direction for continued research by outlining the value and limitations inherent in each of these approaches.

negative:
A systematic review of implementation strategies for assessment, prevention, and management of ICU delirium and their effect on clinical outcomes INTRODUCTION
Despite recommendations from professional societies and patient safety organizations, the majority of ICU patients worldwide are not routinely monitored for delirium, thus preventing timely prevention and management. The purpose of this systematic review is to summarize what types of implementation strategies have been tested to improve ICU clinicians' ability to effectively assess, prevent and treat delirium and to evaluate the effect of these strategies on clinical outcomes.

METHOD
We searched PubMed, Embase, PsychINFO, Cochrane and CINAHL (January 2000 and April 2014) for studies on implementation strategies that included delirium-oriented interventions in adult ICU patients. Studies were suitable for inclusion if implementation strategies' efficacy, in terms of a clinical outcome, or process outcome was described.

RESULTS
We included 21 studies, all including process measures, while 9 reported both process measures and clinical outcomes. Some individual strategies such as "audit and feedback" and "tailored interventions" may be important to establish clinical outcome improvements, but otherwise robust data on effectiveness of specific implementation strategies were scarce. Successful implementation interventions were frequently reported to change process measures, such as improvements in adherence to delirium screening with up to 92%, but relating process measures to outcome changes was generally not possible. In meta-analyses, reduced mortality and ICU length of stay reduction were statistically more likely with implementation programs that employed more (six or more) rather than less implementation strategies and when a framework was used that either integrated current evidence on pain, agitation and delirium management (PAD) or when a strategy of early awakening, breathing, delirium screening and early exercise (ABCDE bundle) was employed. Using implementation strategies aimed at organizational change, next to behavioral change, was also associated with reduced mortality.

CONCLUSION
Our findings may indicate that multi-component implementation programs with a higher number of strategies targeting ICU delirium assessment, prevention and treatment and integrated within PAD or ABCDE bundle have the potential to improve clinical outcomes. However, prospective confirmation of these findings is needed to inform the most effective implementation practice with regard to integrated delirium management and such research should clearly delineate effective practice change from improvements in clinical outcomes.

negative: Development of DC to Single-Phase AC Voltage Source Inverter With Active Power Decoupling Based on Flying Capacitor DC/DC Converter In the present, a power decoupling method without additional component is proposed for a dc to single-phase ac converter, which consists of a flying capacitor dc/dc converter (FCC) and the voltage source inverter (VSI). In particular, a small flying capacitor in the FCC is used for both a boost operation and a double-line-frequency power ripple reduction. Thus, the dc-link capacitor value can be minimized in order to avoid the use of a large electrolytic capacitor. In addition, component design, of, e.g., the boost inductor and the flying capacitor, is clarified when the proposed control is applied. Experiments were carried out using a 1.5-kW prototype in order to verify the validity of the proposed control. The experimental results revealed that the use of the proposed control reduced the dc-link voltage ripple by 74.5%, and the total harmonic distortion (THD) of the inverter output current was less than 5%. Moreover, a maximum system efficiency of 95.4% was achieved at a load of 1.1 kW. Finally, the high power density design is evaluated by the Pareto front optimization. The power densities of three power decoupling topologies, such as a boost topology, a buck topology, and the proposed topology are compared. As a result, the proposed topology achieves the highest power density (5.3 kW/dm3) among the topologies considered herein.

negative: Ontology and Information Systems Ontology as a branch of philosophy is the science of what is, of the kinds and structures of objects, properties, events, processes and relations in every area of reality. ‘Ontology’ is often used by philosophers as a synonym for ‘metaphysics’ (literally: ‘what comes after the Physics’), a term which was used by early students of Aristotle to refer to what Aristotle himself called ‘first philosophy’. The term ‘ontology’ (or ontologia) was itself coined in 1613, independently, by two philosophers, Rudolf Göckel (Goclenius), in his Lexicon philosophicum and Jacob Lorhard (Lorhardus), in his Theatrum philosophicum. The first occurrence in English recorded by the OED appears in Bailey’s dictionary of 1721, which defines ontology as ‘an Account of being in the Abstract’.

negative: Deep convolutional acoustic word embeddings using word-pair side information Recent studies have been revisiting whole words as the basic modelling unit in speech recognition and query applications, instead of phonetic units. Such whole-word segmental systems rely on a function that maps a variable-length speech segment to a vector in a fixed-dimensional space; the resulting acoustic word embeddings need to allow for accurate discrimination between different word types, directly in the embedding space. We compare several old and new approaches in a word discrimination task. Our best approach uses side information in the form of known word pairs to train a Siamese convolutional neural network (CNN): a pair of tied networks that take two speech segments as input and produce their embeddings, trained with a hinge loss that separates same-word pairs and different-word pairs by some margin. A word classifier CNN performs similarly, but requires much stronger supervision. Both types of CNNs yield large improvements over the best previously published results on the word discrimination task.

negative: Verification of Periodically Controlled Hybrid Systems: Application to an Autonomous Vehicle This article introduces Periodically Controlled Hybrid Automata (PCHA) for modular specification of embedded control systems. In a PCHA, control actions that change the control input to the plant occur roughly periodically, while other actions that update the state of the controller may occur in the interim. Such actions could model, for example, sensor updates and information received from higher-level planning modules that change the set point of the controller. Based on periodicity and subtangential conditions, a new sufficient condition for verifying invariant properties of PCHAs is presented. For PCHAs with polynomial continuous vector fields, it is possible to check these conditions automatically using, for example, quantifier elimination or sum of squares decomposition. We examine the feasibility of this automatic approach on a small example. The proposed technique is also used to manually verify safety and progress properties of a fairly complex planner-controller subsystem of an autonomous ground vehicle. Geometric properties of planner-generated paths are derived which guarantee that such paths can be safely followed by the controller.

negative: Water-walking devices We report recent efforts in the design and construction of water-walking machines inspired by insects and spiders. The fundamental physical constraints on the size, proportion and dynamics of natural water-walkers are enumerated and used as design criteria for analogous mechanical devices. We report devices capable of rowing along the surface, leaping off the surface and climbing menisci by deforming the free surface. The most critical design constraint is that the devices be lightweight and non-wetting. Microscale manufacturing techniques and new man-made materials such as hydrophobic coatings and thermally actuated wires are implemented. Using highspeed cinematography and flow visualization, we compare the functionality and dynamics of our devices with those of their natural counterparts.

negative: Genome engineering in Saccharomyces cerevisiae using CRISPR-Cas systems Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) and CRISPR-associated (Cas) systems in bacteria and archaea use RNA-guided nuclease activity to provide adaptive immunity against invading foreign nucleic acids. Here, we report the use of type II bacterial CRISPR-Cas system in Saccharomyces cerevisiae for genome engineering. The CRISPR-Cas components, Cas9 gene and a designer genome targeting CRISPR guide RNA (gRNA), show robust and specific RNA-guided endonuclease activity at targeted endogenous genomic loci in yeast. Using constitutive Cas9 expression and a transient gRNA cassette, we show that targeted double-strand breaks can increase homologous recombination rates of single- and double-stranded oligonucleotide donors by 5-fold and 130-fold, respectively. In addition, co-transformation of a gRNA plasmid and a donor DNA in cells constitutively expressing Cas9 resulted in near 100% donor DNA recombination frequency. Our approach provides foundations for a simple and powerful genome engineering tool for site-specific mutagenesis and allelic replacement in yeast.

negative: Cognitive programs: software for attention's executive What are the computational tasks that an executive controller for visual attention must solve? This question is posed in the context of the Selective Tuning model of attention. The range of required computations go beyond top-down bias signals or region-of-interest determinations, and must deal with overt and covert fixations, process timing and synchronization, information routing, memory, matching control to task, spatial localization, priming, and coordination of bottom-up with top-down information. During task execution, results must be monitored to ensure the expected results. This description includes the kinds of elements that are common in the control of any kind of complex machine or system. We seek a mechanistic integration of the above, in other words, algorithms that accomplish control. Such algorithms operate on representations, transforming a representation of one kind into another, which then forms the input to yet another algorithm. Cognitive Programs (CPs) are hypothesized to capture exactly such representational transformations via stepwise sequences of operations. CPs, an updated and modernized offspring of Ullman's Visual Routines, impose an algorithmic structure to the set of attentional functions and play a role in the overall shaping of attentional modulation of the visual system so that it provides its best performance. This requires that we consider the visual system as a dynamic, yet general-purpose processor tuned to the task and input of the moment. This differs dramatically from the almost universal cognitive and computational views, which regard vision as a passively observing module to which simple questions about percepts can be posed, regardless of task. Differing from Visual Routines, CPs explicitly involve the critical elements of Visual Task Executive (vTE), Visual Attention Executive (vAE), and Visual Working Memory (vWM). Cognitive Programs provide the software that directs the actions of the Selective Tuning model of visual attention.

negative: A line feature based SLAM with low grade range sensors using geometric constraints and active exploration for mobile robot This paper describes a geometrically constrained Extended Kalman Filter (EKF) framework for a line feature based SLAM, which is applicable to a rectangular indoor environment. Its focus is on how to handle sparse and noisy sensor data, such as PSD infrared sensors with limited range and limited number, in order to develop a low-cost navigation system. It has been applied to a vacuum cleaning robot in our research. In order to meet the real-time objective with low computing power, we develop an efficient line feature extraction algorithm based upon an iterative end point fit (IEPF) technique assisted by our constrained version of the Hough transform. It uses a geometric constraint that every line is orthogonal or parallel to each other because in a general indoor setting, most furniture and walls satisfy this constraint. By adding this constraint to the measurement model of EKF, we build a geometrically constrained EKF framework which can estimate line feature positions more accurately as well as allow their covariance matrices to converge more rapidly when compared to the case of an unconstrained EKF. The experimental results demonstrate the accuracy and robustness to the presence of sensor noise and errors in an actual indoor environment.

negative: Ontology based Semantic Search Engine We live in a connected age when every entity is linked to each other through numerous relationships thus forming complex networks, information networks etc., In this meshy scenario, storing analyzing, managing extracting information is going to be highly challenging task. Information retrieval happened to be an activity that only a few people engaged in. due to the advent of search engines and communication facilities, millions of people are engaged in information retrieval and extraction process. The information retrieval deals with finding a set of documents relevant to the user query. Commercial search engines like Google deals with key word search which is based on Boolean logical queries. The major disadvantage of this kind of keyword search is that it returns a lot of irrelevant information to the users which results in low precision. Nowadays the field of information retrieval is moving towards semantic level from syntactic level. These semantic search engines are based on the concept of ontology which gives a Meta data representation of concepts. In this paper we focus on the retrieval mechanisms related to it. This includes various phases in which the ontology is designed first and then the indexing and retrieval phases work. Key wordsOntology, semantic web, Information retrieval, Query builder I.INTRODUCTION In this digital age each and every concept is linked to each other through various relationships forming the complex information web. By passing through the web we face some sort of challenges in storing and extracting the information from them. We therefore rely on search engines to overcome those difficulties. Any way these search engines facilitate only keyword search which does not returns accurate results to the external user. Aiming to solve the limitations of keyword based models, the idea of semantic search, understood as searching by meanings rather than by literals has been the focus of a wide body of research in the information retrieval and the semantic web communities Semantic search has been present in the information retrieval since the early eighties. Some of these approaches are based on the statistical methods that study the co-occurrence of terms that capture and exploit tough and fuzzy conceptualizations. Other Information retrieval approaches apply linguistic algorithms modeled on human language processing structures and taxonomies, where the level of conceptualization is often shallow and sparse, especially the level of relations, which are commonly at the core of expressing user needs and finding the answers. Ontologies are used to represent knowledge in a conceptual manner that can be distributed among various applications. The ontology has its application in information retrieval where it exploits the knowledge bases enhancing the semantic search, steering in one hand the use of fully fledged ontologies in the semantic based perspective, and on the other the consideration of unstructured content as target search space. In other words, this work explores the use of semantic information to support more expensive queries and more accurate results, while the retrieval problem is formulated in a way that is consistent with the Information retrieval field, thus drawing benefit from the state of art in this area and enabling more realistic and applicable approaches. II. RELEATED WORK A. Semantic web Current World Wide Web (WWW) is a huge library of interlinked documents that are transferred by computers and presented to people. It has grown from hypertext systems, but the difference is that anyone can contribute to it. This also means that the quality of information or even the persistence of documents cannot be generally guaranteed. Current WWW contains a lot of information and knowledge, but machines usually serve only to deliver and present the content of documents describing the knowledge. People have to connect all the sources of relevant information and interpret them themselves Semantic web is an effort to enhance current web so that computers can process the information presented on WWW, interpret and connect it, to help humans to find required knowledge. In the same way as WWW is a huge distributed hypertext system, semantic web is intended to form a huge distributed knowledge based system. The focus of semantic web is to share data instead of documents. In other words, it is a project that should provide a common framework that allows data to be shared and reused across application, enterprise, and community boundaries. It is a collaborative effort led by World Wide Web Consortium (W3C). The semantic web has got its peak in recent years by the use of explicit metadata representation of information in the web. The metadata representation is embedded in web page using RDF to enhance the visualization of results. Semantic Web can be seen as a huge engineering solution... but it is more than that. We will find that as it becomes easier to publish data in a repurposable form, so more people will want to publish data, and there will be a knock-on or domino effect. N.Vanjulavalli et al IJCSET |August 2012 | Vol 2, Issue 8, 1349-1353 ISSN:2231-0711 Available online at www.ijcset.net 1349 We may find that a large number of Semantic Web applications can be used for a variety of different tasks, increasing the modularity of applications on the Web. But enough subjective reasoning. onto how this will be accomplished. The Semantic Web is generally built on syntaxes which use URIs to represent data, usually in triples based structures: i.e. many triples of URI data that can be held in databases, or interchanged on the world Wide Web using a set of particular syntaxes developed especially for the task. These syntaxes are called "Resource Description Framework" syntaxes. Once information is in RDF form, it becomes easy to process it, since RDF is a generic format, which already has many parsers. XML RDF is quite a verbose specification, and it can take some getting used to (for example, to learn XML RDF properly, you need to understand a little about XML and namespaces beforehand...), but let's take a quick look at an example of XML RDF right now:<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdfsyntax-ns#" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:foaf="http://xmlns.com/0.1/foaf/" > <rdf:Description rdf:about=""> <dc:creator rdf:parseType="Resource"> <foaf:name>Sean B. Palmer</foaf:name> </dc:creator> <dc:title>The Semantic Web: An Introduction</dc:title>

negative: Automated camera dysfunctions detection Surveillance systems depend greatly on the robustness and availability of the video streams. The cameras must deliver reliable streams from an angle corresponding to the correct viewpoint. In other words, the field of view and video quality must remain unchanged after the initial installation of a surveillance camera. The paper proposes an approach to detect changes (such as displacement, out of focus, obstruction) automatically in a difficult environment with illumination variations and dynamic background and foreground objects.

negative: Deep Impression: Audiovisual Deep Residual Networks for Multimodal Apparent Personality Trait Recognition Here, we develop an audiovisual deep residual network for multimodal apparent personality trait recognition. The network is trained end-to-end for predicting the Big Five personality traits of people from their videos. That is, the network does not require any feature engineering or visual analysis such as face detection, face landmark alignment or facial expression recognition. Recently, the network won the third place in the ChaLearn First Impressions Challenge with a test accuracy of 0.9109.

negative: Detecting and discriminating behavioural anomalies This paper aims to address the problem of anomaly detection and discrimination in complex behaviours, where anomalies are subtle and difficult to detect owing to the complex temporal dynamics and correlations among multiple objects’ behaviours. Specifically, we decompose a complex behaviour pattern according to its temporal characteristics or spatial-temporal visual contexts. The decomposed behaviour is then modelled using a cascade of Dynamic Bayesian Networks (CasDBNs). In contrast to existing standalone models, the proposed behaviour decomposition and cascade modelling offers distinct advantage in simplicity for complex behaviour modelling. Importantly, the decomposition and cascade structure map naturally to the structure of complex behaviour, allowing for a more effective detection of subtle anomalies in surveillance videos. Comparative experiments using both indoor and outdoor data are carried out to demonstrate that, in addition to the novel capability of discriminating different types of anomalies, the proposed framework outperforms existing methods in detecting durational anomalies in complex behaviours and subtle anomalies that are difficult to detect when objects are viewed in isolation. & 2010 Elsevier Ltd. All rights reserved.

negative: Texture Segmentation Based Video Compression Using Convolutional Neural Networks There has been a growing interest in using different approaches to improve the coding efficiency of modern video codec in recent years as demand for web-based video consumption increases. In this paper, we propose a model-based approach that uses texture analysis/synthesis to reconstruct blocks in texture regions of a video to achieve potential coding gains using the AV1 codec developed by the Alliance for Open Media (AOM). The proposed method uses convolutional neural networks to extract texture regions in a frame, which are then reconstructed using a global motion model. Our preliminary results show an increase in coding efficiency while maintaining satisfactory visual quality. Introduction With the increasing amount of videos being created and consumed, better video compression tools are needed to provide fast transmission and high visual quality. Modern video coding standards utilize spatial and temporal redundancy in the videos to achieve high coding efficiency and high visual quality with motion compensation techniques and 2-D orthogonal transforms. However, efficient exploitation of statistical dependencies measured by a mean squared error (MSE) does not always produce the best psychovisual result, and may require higher data rate to preserve detail information in the video. Recent advancement in GPU computing has enabled the analysis of large scale data using deep learning method. Deep learning techniques have shown promising performance in many applications such as object detection, natural language process, and synthetic images generation [1, 2, 3, 4]. Several methods have been developed for video applications to improve coding efficiency using deep learning. In [5], sample adaptive offset (SAO) is replaced by a CNN-based in-loop filter (IFCNN) to improve the coding efficiency in HEVC. By learning the predicted residue between the quantized reconstructed frames obtained after deblocking filter (DF) and the original frames, IFCNN is able to reconstruct video frames with higher quality without requiring any bit transmission during coding process. Similar to [5], [6] proposes a Variable-filter-size Residue-learning CNN (VRCNN) to improving coding efficiency by replacing DF and SAO in HEVC. VRCNN is based on the concept of ARCNN [7] which is originally designed for JPEG applications. Instead of only using spatial information to train a CNN to reduce the coding artifacts in HEVC, [8] proposed a spatial temporal residue network (STResNet) as an additional in-loop filter after SAO. A rate-distortion optimization strategy is used to control the on/off switch of the proposed in-loop filter. There are also some works that have been done in the decoder of HEVC to improve the coding efficiency. In [9], a deep CNN-based auto decoder (DCAD) is implemented in the decoder of HEVC to improve the video quality of decoded video. DCAD is trained to learn the predict residue between decoded video frames and original video frames. By adding the predicted residual generated from DCAD to the compressed video frames, this method enhances the compressed video frames to higher quality. In summary, the above methods improve the coding efficiency by enhancing the quality of reconstructed video frames. However, they require different trained models for video reconstruction at different quantization levels. We are interested in developing deep learning approaches to only encode visually relevant information and use a different coding method for “perceptually insignificant” regions in a frame, which can lead to substantial data rate reductions while maintaining visual quality. In particular, we have developed a model based approach that can be used to improve the coding efficiency by identifying texture areas in a video frame that contain detail irrelevant information, which the viewer does not perceive specific details and can be skipped or encoded at a much lower data rate. The task is then to divide a frame into “perceptually insignificant” texture region and then use a texture model for the pixels in that region. In 1959, Schreiber and colleagues proposed a coding method that divides an image into textures and edges and used it in image coding [10]. This work was later extended by using the human visual system and statistical model to determine the texture region [11, 12, 13]. More recently, several groups have focused on adapting perceptual based approaches to the video coding framework [14]. In our previous work [15], we introduced a texture analyzer before encoding the input sequences to identify detail irrelevant regions in the frame which are classified into different texture classes. At the encoder, no inter-frame prediction is performed for these regions. Instead, displacement of the entire texture region is modeled by just one set of motion parameters. Therefore, only the model parameters are transmitted to the decoder for reconstructing the texture regions using a texture synthesizer. Non-texture regions in the frame are coded conventionally. Since this method uses feature extraction based on texture segmentation technique, a proper set of parameters are required to achieve accurate texture segmentation for different videos. Deep learning methods usually do not require such parameter tuning for inference. As a result, deep learning techniques can be developed to perform texture segmentation and classification for the proposed model-based video coding. A Fisher vector convolution neural networks (FVCNN) that can produce segmentation labels for different texture classes was proposed in [16]. One of the advantage of FV-CNN is that the image input size is flexible and is not limited by the network architecture. Instead of doing pixel-wise classification on texture regions, a texture classification CNN network was described in [17]. To reduce computational expenses, [17] uses a small classification network to classify image patches with size of 227 × 227. A smaller network is needed to classify smaller image patches in our case. In this paper, we propose a block-based texture segmentation method to extract texture region in a video frame using convolutional neural networks. The block-based segmentation network classifies each 16 × 16 block in a frame as texture or non-texture. The identified texture region is then synthesized using the temporal correlations among the frames. Our method was implemented using the AOM/AV1 codec. Preliminary results show significant bitrate savings while maintaining a satisfactory visual quality.

negative: Deep Residual Learning for Image Recognition Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers - 8× deeper than VGG nets [40] but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.

negative: Moving object detection, tracking and classification for smart video surveillance MOVING OBJECT DETECTION, TRACKING AND CLASSIFICATION FOR SMART VIDEO SURVEILLANCE Yiğithan Dedeoğlu M.S. in Computer Engineering Supervisor: Assist. Prof. Dr. Uğur Güdükbay August, 2004 Video surveillance has long been in use to monitor security sensitive areas such as banks, department stores, highways, crowded public places and borders. The advance in computing power, availability of large-capacity storage devices and high speed network infrastructure paved the way for cheaper, multi sensor video surveillance systems. Traditionally, the video outputs are processed online by human operators and are usually saved to tapes for later use only after a forensic event. The increase in the number of cameras in ordinary surveillance systems overloaded both the human operators and the storage devices with high volumes of data and made it infeasible to ensure proper monitoring of sensitive areas for long times. In order to filter out redundant information generated by an array of cameras, and increase the response time to forensic events, assisting the human operators with identification of important events in video by the use of “smart” video surveillance systems has become a critical requirement. The making of video surveillance systems “smart” requires fast, reliable and robust algorithms for moving object detection, classification, tracking and activity analysis. In this thesis, a smart visual surveillance system with real-time moving object detection, classification and tracking capabilities is presented. The system operates on both color and gray scale video imagery from a stationary camera. It can handle object detection in indoor and outdoor environments and under changing illumination conditions. The classification algorithm makes use of the shape of the detected objects and temporal tracking results to successfully categorize objects into pre-defined classes like human, human group and vehicle. The system is also able to detect the natural phenomenon fire in various scenes reliably. The proposed tracking algorithm successfully tracks video objects even in full occlusion cases. In addition to these, some important needs of a robust iii

negative: Breast Cancer Classification from Histopathological Images with Inception Recurrent Residual Convolutional Neural Network The Deep Convolutional Neural Network (DCNN) is one of the most powerful and successful deep learning approaches. DCNNs have already provided superior performance in different modalities of medical imaging including breast cancer classification, segmentation, and detection. Breast cancer is one of the most common and dangerous cancers impacting women worldwide. In this paper, we have proposed a method for breast cancer classification with the Inception Recurrent Residual Convolutional Neural Network (IRRCNN) model. The IRRCNN is a powerful DCNN model that combines the strength of the Inception Network (Inception-v4), the Residual Network (ResNet), and the Recurrent Convolutional Neural Network (RCNN). The IRRCNN shows superior performance against equivalent Inception Networks, Residual Networks, and RCNNs for object recognition tasks. In this paper, the IRRCNN approach is applied for breast cancer classification on two publicly available datasets including BreakHis and Breast Cancer (BC) classification challenge 2015. The experimental results are compared against the existing machine learning and deep learning–based approaches with respect to image-based, patch-based, image-level, and patient-level classification. The IRRCNN model provides superior classification performance in terms of sensitivity, area under the curve (AUC), the ROC curve, and global accuracy compared to existing approaches for both datasets.