Query: Towards edge-caching for image recognition

Relevant passages:
Object Recognition from Local Scale-Invariant Features <=SEP=> Proc. of the International Conference on Computer Vision, Corfu (Sept. 1999) An object recognition system has been developed that uses a new class of local image features. The features are invariant to image scaling, translation, and rotation, and partially invariant to illumination changes and affine or 3D projection. These features share similar properties with neurons in inferior temporal cortex that are used for object recognition in primate vision. Features are efficiently detected through a staged filtering approach that identifies stable points in scale space. Image keys are created that allow for local geometric deformations by representing blurred image gradients in multiple orientation planes and at multiple scales. The keys are used as input to a nearest-neighbor indexing method that identifies candidate object matches. Final verification of each match is achieved by finding a low-residual least-squares solution for the unknown model parameters. Experimental results show that robust object recognition can be achieved in cluttered partially-occluded images with a computation time of under 2 seconds.

Optimised KD-trees for fast image descriptor matching <=SEP=> In this paper, we look at improving the KD-tree for a specific usage: indexing a large number of SIFT and other types of image descriptors. We have extended priority search, to priority search among multiple trees. By creating multiple KD-trees from the same data set and simultaneously searching among these trees, we have improved the KD-treepsilas search performance significantly.We have also exploited the structure in SIFT descriptors (or structure in any data set) to reduce the time spent in backtracking. By using Principal Component Analysis to align the principal axes of the data with the coordinate axes, we have further increased the KD-treepsilas search performance.

Computer Vision - Algorithms and Applications <=SEP=> As known, adventure and experience about lesson, entertainment, and knowledge can be gained by only reading a book. Even it is not directly done, you can know more about this life, about the world. We offer you this proper and easy way to gain those all. We offer many book collections from fictions to science at all. One of them is this computer vision algorithms and applications that can be your partner.

The Case for VM-Based Cloudlets in Mobile Computing <=SEP=> Mobile computing continuously evolve through the sustained effort of many researchers. It seamlessly augments users' cognitive abilities via compute-intensive capabilities such as speech recognition, natural language processing, etc. By thus empowering mobile users, we could transform many areas of human activity. This article discusses the technical obstacles to these transformations and proposes a new architecture for overcoming them. In this architecture, a mobile user exploits virtual machine (VM) technology to rapidly instantiate customized service software on a nearby cloudlet and then uses that service over a wireless LAN; the mobile device typically functions as a thin client with respect to the service. A cloudlet is a trusted, resource-rich computer or cluster of computers that's well-connected to the Internet and available for use by nearby mobile devices. Our strategy of leveraging transiently customized proximate infrastructure as a mobile device moves with its user through the physical world is called cloudlet-based, resource-rich, mobile computing. Crisp interactive response, which is essential for seamless augmentation of human cognition, is easily achieved in this architecture because of the cloudlet's physical proximity and one-hop network latency. Using a cloudlet also simplifies the challenge of meeting the peak bandwidth demand of multiple users interactively generating and receiving media such as high-definition video and high-resolution images. Rapid customization of infrastructure for diverse applications emerges as a critical requirement, and our results from a proof-of-concept prototype suggest that VM technology can indeed help meet this requirement.

Reading Digits in Natural Images with Unsupervised Feature Learning <=SEP=> Detecting and reading text from natural images is a hard computer vision task that is central to a variety of emerging applications. Related problems like document character recognition have been widely studied by computer vision and machine learning researchers and are virtually solved for practical applications like reading handwritten digits. Reliably recognizing characters in more complex scenes like photographs, however, is far more difficult: the best existing methods lag well behind human performance on the same tasks. In this paper we attack the problem of recognizing digits in a real application using unsupervised feature learning methods: reading house numbers from street level photos. To this end, we introduce a new benchmark dataset for research use containing over 600,000 labeled digits cropped from Street View images. We then demonstrate the difficulty of recognizing these digits when the problem is approached with hand-designed features. Finally, we employ variants of two recently proposed unsupervised feature learning methods and find that they are convincingly superior on our benchmarks.

Why do we trust new technology? A study of initial trust formation with organizational information systems <=SEP=> Recent trust research in the information systems (IS) field has described trust as a primary predictor of technology usage and a fundamental construct for understanding user perceptions of technology. Initial trust formation is particularly relevant in an IS context, as users must overcome perceptions of risk and uncertainty before using a novel technology. With initial trust in a more complex, organizational information system, there are a number of external determinants, trusting bases, that may explain trust formation and provide organizations with the needed levers to form or change individuals’ initial trust in technology. In this study, a research model of initial trust formation is developed and includes trusting bases, trusting beliefs, trusting attitude and subjective norm, and trusting intentions. Eight trusting base factors are assessed including personality, cognitive, calculative, and both technology and organizational factors of the institutional base. The model is empirically tested with 443 subjects in the context of initial trust in a national identity system (NID). The proposed model was supported and the results indicate that subjective norm and the cognitive–reputation, calculative, and organizational situational normality base factors significantly influence initial trusting beliefs and other downstream trust constructs. Factors from some of the more commonly investigated bases, personality and technology institutional, did not significantly affect trusting beliefs. The findings have strategic implications for agencies implementing e-government systems and organizational information systems in general. Published by Elsevier B.V.

A Vector Space for Distributional Semantics for Entailment <=SEP=> Distributional semantics creates vectorspace representations that capture many forms of semantic similarity, but their relation to semantic entailment has been less clear. We propose a vector-space model which provides a formal foundation for a distributional semantics of entailment. Using a mean-field approximation, we develop approximate inference procedures and entailment operators over vectors of probabilities of features being known (versus unknown). We use this framework to reinterpret an existing distributionalsemantic model (Word2Vec) as approximating an entailment-based model of the distributions of words in contexts, thereby predicting lexical entailment relations. In both unsupervised and semi-supervised experiments on hyponymy detection, we get substantial improvements over previous results.

Detecting Duplicate Posts in Programming QA Communities via Latent Semantics and Association Rules <=SEP=> Programming community-based question-answering (PCQA) websites such as Stack Overflow enable programmers to find working solutions to their questions. Despite detailed posting guidelines, duplicate questions that have been answered are frequently created. To tackle this problem, Stack Overflow provides a mechanism for reputable users to manually mark duplicate questions. This is a laborious effort, and leads to many duplicate questions remain undetected. Existing duplicate detection methodologies from traditional community based question-answering (CQA) websites are difficult to be adopted directly to PCQA, as PCQA posts often contain source code which is linguistically very different from natural languages. In this paper, we propose a methodology designed for the PCQA domain to detect duplicate questions. We model the detection as a classification problem over question pairs. To extract features for question pairs, our methodology leverages continuous word vectors from the deep learning literature, topic model features and phrases pairs that co-occur frequently in duplicate questions mined using machine translation systems. These features capture semantic similarities between questions and produce a strong performance for duplicate detection. Experiments on a range of real-world datasets demonstrate that our method works very well; in some cases over 30% improvement compared to state-of-the-art benchmarks. As a product of one of the proposed features, the association score feature, we have mined a set of associated phrases from duplicate questions on Stack Overflow and open the dataset to the public.

The tear trough and lid/cheek junction: anatomy and implications for surgical correction. <=SEP=> BACKGROUND
The tear trough and the lid/cheek junction become more visible with age. These landmarks are adjacent, forming in some patients a continuous indentation or groove below the infraorbital rim. Numerous, often conflicting procedures have been described to improve the appearance of the region. The purpose of this study was to evaluate the anatomy underlying the tear trough and the lid/cheek junction and to evaluate the procedures designed to correct them.


METHODS
Twelve fresh cadaver lower lid and midface dissections were performed (six heads). The orbital regions were dissected in layers, and medical photography was performed.


RESULTS
In the subcutaneous plane, the tear trough and lid/cheek junction overlie the junction of the palpebral and orbital portions of the orbicularis oculi muscle and the cephalic border of the malar fat pad. In the submuscular plane, these landmarks differ. Along the tear trough, the orbicularis muscle is attached directly to the bone. Along the lid/cheek junction, the attachment is ligamentous by means of the orbicularis retaining ligament.


CONCLUSIONS
The tear trough and lid/cheek junction are primarily explained by superficial (subcutaneous) anatomical features. Atrophy of skin and fat is the most likely explanation for age-related visibility of these landmarks. "Descent" of this region with age is unlikely (the structures are fixed to bone). Bulging orbital fat accentuates these landmarks. Interventions must extend significantly below the infraorbital rim. Fat or synthetic filler may be best placed in the intraorbicularis plane (tear trough) and in the suborbicularis plane (lid/cheek junction).

A Broadband Dual Circularly Polarized Patch Antenna With Wide Beamwidth <=SEP=> A broadband dual circularly polarized patch antenna with wide beamwidth is presented. The patch is excited by four cross slots via a microstrip line with multiple matching segments underneath the ground plane. The four cross slots and the multiple matching segments are optimized simultaneously to obtain the best performance. Measurements show that the antenna has 10-dB return-loss bandwidth of 24%, 10-dB isolation bandwidth of 19%, 3-dB axial-ratio bandwidth of 16%, and beamwidth of 110 °. Moreover, the single feed makes it a good candidate for array design.

Cloud-Based Big Data Management and Analytics for Scholarly Resources: Current Trends, Challenges and Scope for Future Research <=SEP=> With the shifting focus of organizations and governments towards digitization of academic and technical documents, there has been an increasing need to use this reserve of scholarly documents for developing applications that can facilitate and aid in better management of research. In addition to this, the evolving nature of research problems has made them essentially interdisciplinary. As a result, there is a growing need for scholarly applications like collaborator discovery, expert finding and research recommendation systems. This research paper reviews the current trends and identifies the challenges existing in the architecture, services and applications of big scholarly data platform with a specific focus on directions for future research.

Perceivable Light Fields: Matching the Requirements Between the Human Visual System and Autostereoscopic 3-D Displays <=SEP=> Recently, there has been a substantial increase in efforts to develop 3-D visualization technologies that can provide the viewers with a realistic 3-D visual experience. Various terms such as “reality communication” have been used to categorize these efforts. In order to provide the viewers with a complete and realistic visual sensation, the display or visualization system and the displayed content need to match the physiological 3-D information sensing capabilities of the human visual system which can be quite complex. These may include spatial and temporal resolutions, depth perception, dynamic range, spectral contents, nonlinear effects, and vergence accommodation effects. In this paper, first we present an overview of some of the 3-D display research efforts which have been extensively pursued in Asia, Europe, and North America among other areas. Based on the limitations and comfort-based requirements of the human visual system when viewing a nonnatural visual input from 3-D displays, we present an analytical framework that combines main perception and human visual requirements with analytical tools and principles used in related disciplines such as optics, computer graphics, computational imaging, and signal processing. Building on the widely used notion of light fields, we define a notion of perceivable light fields to account for the human visual system physiological requirements, and propagate it back to the display device to determine the display device specifications. This helps us clarify the fundamental and practical requirements of the 3-D display devices for reality viewing communication. In view of the proposed analytical framework, we overview various methods that can be applied to overcome the extensive information needed to be displayed in order to meet the requirements imposed by the human visual system.

Age Prediction in Blogs: A Study of Style, Content, and Online Behavior in Pre- and Post-Social Media Generations <=SEP=> Research Assistant May 2008 present Currently working on a project to influence power and rifts in unstructured text project in classifying the age of authors as well as identifying opinion and persuasion in blogs (funded by IARPA SCIL) Previously worked on a project maintaining a pipeline for Question Answering System and creating system tools using Natural Language Processing techniques (funded by DARPA GALE). Computer administrator of the NLP group.

Twitter sentiment analysis using deep learning methods <=SEP=> The social media has Immense and popularity among all the services today. Data from SNS (Social Network Service) can be used for a lot of objectives such as prediction or sentiment analysis. Twitter is a SNS that has a huge data with user posting, with this significant amount of data, it has the potential of research related to text mining and could be subjected to sentiment analysis. But handling such a huge amount of unstructured data is a difficult task, machine learning is needed for handling such huge of data. Deep learning is of the machine learning method that use the deep feed forward neural network with many hidden layers in the term of neural network with the result of the experiment about 75%.

Automated vehicle system architecture with performance assessment <=SEP=> This paper proposes a reference architecture to increase reliability and robustness of an automated vehicle. The architecture exploits the benefits arising from the in-terdependencies of the system and provides self awareness. Performance Assessment units attached to subsystems quantify the reliability of their operation and return performance values. The Environment Condition Assessment, which is another important novelty of the architecture, informs augmented sensors on current sensing conditions. Utilizing environment conditions and performance values for subsequent centralized integrity checks allow algorithms to adapt to current driving conditions and thereby to increase their robustness. We demonstrate the benefit of the approach with the example of false positive object detection and tracking, where the detection of a ghost object is resolved in centralized performance assessment using a Bayesian network.

Using Intermediate Representations to Solve Math Word Problems <=SEP=> To solve math word problems, previous statistical approaches attempt at learning a direct mapping from a problem description to its corresponding equation system. However, such mappings do not include the information of a few higher-order operations that cannot be explicitly represented in equations but are required to solve the problem. The gap between natural language and equations makes it difficult for a learned model to generalize from limited data. In this work we present an intermediate meaning representation scheme that tries to reduce this gap. We use a sequence-to-sequence model with a novel attention regularization term to generate the intermediate forms, then execute them to obtain the final answers. Since the intermediate forms are latent, we propose an iterative labeling framework for learning by leveraging supervision signals from both equations and answers. Our experiments show using intermediate forms outperforms directly predicting equations.

Graph-based mining of multiple object usage patterns <=SEP=> The interplay of multiple objects in object-oriented programming often follows specific protocols, for example certain orders of method calls and/or control structure constraints among them that are parts of the intended object usages. Unfortunately, the information is not always documented. That creates long learning curve, and importantly, leads to subtle problems due to the misuse of objects.
 In this paper, we propose GrouMiner, a novel graph-based approach for mining the usage patterns of one or multiple objects. GrouMiner approach includes a graph-based representation for multiple object usages, a pattern mining algorithm, and an anomaly detection technique that are efficient, accurate, and resilient to software changes. Our experiments on several real-world programs show that our prototype is able to find useful usage patterns with multiple objects and control structures, and to translate them into user-friendly code skeletons to assist developers in programming. It could also detect the usage anomalies that caused yet undiscovered defects and code smells in those programs.

Performance improvement of synchronous reluctance motors: A review <=SEP=> Synchronous Reluctance motors can replace the more commonly used, induction, switched reluctance and permanent magnet motors. They have been shown to outperform similarly dimensioned induction motors in efficiency, torque and power density. They are simple and cheap to construct and require simple control. Such a motor would be ideal for wide spread use in Africa, which currently faces a power deficit, considering that motors account for about 43% of global electric power consumption. The use of synchronous reluctance motors in Electric Vehicles may contribute to the production of more efficient, heavier load and longer range vehicles. This would accelerate adoption of Electric Vehicles and it would help wean the world off fossil fuels. The synchronous reluctance motor has disadvantages of high torque ripple and poor power factor. A radical redesign of the rotor geometry and simultaneous optimisation of the rotor and the stator may significantly improve its performance.

A Systematic Analysis of XSS Sanitization in Web Application Frameworks <=SEP=> While most research on XSS defense has focused on techniques for securing existing applications and re-architecting browser mechanisms, sanitization remains the industry-standard defense mechanism. By streamlining and automating XSS sanitization, web application frameworks stand in a good position to stop XSS but have received little research attention. In order to drive research on web frameworks, we systematically study the security of the XSS sanitization abstractions frameworks provide. We develop a novel model of the web browserions frameworks provide. We develop a novel model of the web browser and characterize the challenges of XSS sanitization. Based on the model, we systematically evaluate the XSS abstractions in 14 major commercially-used web frameworks. We find that frameworks often do not address critical parts of the XSS conundrum. We perform an empirical analysis of 8 large web applications to extract the requirements of sanitization primitives from the perspective of realworld applications. Our study shows that there is a wide gap between the abstractions provided by frameworks and the requirements of applications.

Vectorized total variation defined by weighted L infinity norm for utilizing inter channel dependency <=SEP=> Vectorized total variation (VTV) is very successful convex regularizer to solve various color image recovery problems. Despite the fact that color channels of natural color images are closely related, existing variants of VTV can not utilize this prior efficiently. We proposed L∞-VTV as a convex regularizer can penalize the violation of such inter-channel dependency by employing weighted L∞ (L-infty) norm. We also introduce an effective algorithm for an image denoising problem using L∞-VTV. Experimental results shows that our proposed method can outperform the conventional methods.

Crafting Integrated Multichannel Retailing Strategies <=SEP=> Multichannel retailing is the set of activities involved in selling merchandise or services to consumers through more than one channel. Multichannel retailers dominate today's retail landscape. While there are many benefits of operating multiple channels, these retailers also face many challenges. In this article, we discuss the key issues concerning multichannel retailing, including the motivations and constraints of going multichannel, the challenges of crafting multichannel retailing strategies and opportunities for creating synergies across channels, key retail mix decisions facing multichannel retailers, and the dynamics of multichannel retailing. We synthesize current knowledge drawn from the academic literature and industry practice, and discuss potential directions for future research. © 2010 Direct Marketing Educational Foundation, Inc. Published by Elsevier Inc. All rights reserved.

Wire Speed Name Lookup: A GPU-based Approach <=SEP=> This paper studies the name lookup issue with longest prefix matching, which is widely used in URL filtering, content routing/switching, etc. Recently Content-Centric Networking (CCN) has been proposed as a clean slate future Internet architecture to naturally fit the contentcentric property of today’s Internet usage: instead of addressing end hosts, the Internet should operate based on the identity/name of contents. A core challenge and enabling technique in implementing CCN is exactly to perform name lookup for packet forwarding at wire speed. In CCN, routing tables can be orders of magnitude larger than current IP routing tables, and content names are much longer and more complex than IP addresses. In pursuit of conquering this challenge, we conduct an implementation-based case study on wire speed name lookup, exploiting GPU’s massive parallel processing power. Extensive experiments demonstrate that our GPU-based name lookup engine can achieve 63.52M searches per second lookup throughput on large-scale name tables containing millions of name entries with a strict constraint of no more than the telecommunication level 100μs per-packet lookup latency. Our solution can be applied to contexts beyond CCN, such as search engines, content filtering, and intrusion prevention/detection. c ⃝Prof. Qunfeng Dong (qunfeng.dong@gmail.com) and Prof. Bin Liu (lmyujie@gmail.com), placed in alphabetic order, are the correspondence authors of the paper. Yi Wang and Yuan Zu, placed in alphabetic order, are the lead student authors of Tsinghua University and University of Science and Technology of China, respectively. This paper is supported by 863 project (2013AA013502), NSFC (61073171, 61073184), Tsinghua University Initiative Scientific Research Program(20121080068), the Specialized Research Fund for the Doctoral Program of Higher Education of China(20100002110051), the Ministry of Education (MOE) Program for New Century Excellent Talents (NCET) in University, the Science and Technological Fund of Anhui Province for Outstanding Youth (10040606Y05), by the Fundamental Research Funds for the Central Universities (WK0110000007, WK0110000019), and Jiangsu Provincial Science Foundation (BK2011360).

Enable IoT interoperability in ambient assisted living: Active and healthy aging scenarios <=SEP=> Ambient Assisted Living (AAL) represents one of the most promising Internet of Things (IoT) applications due to its influence on the quality of life and health of the elderly people. However, the interoperability is one of the major issues that needs to be addressed to promote the adoption of AAL solutions in real environments. In this paper, an IoT prototype system for AAL called AAL-IoTSys is proposed, which includes a Smart IoT Gateway as a key component to enable the interoperability from several heterogeneous devices over different communication protocols and technologies (e.g., WiFi, ZigBee, Bluetooth, IEEE 802.15.4, 6LowPAN). This system aims to monitor environmental (temperature, humidity, C02), health (heart rate) and location (GPS) parameters in the elderly people's houses in order to facilitate events early detection and automatic sending notifications to elderly care stakeholders in active and healthy aging scenarios (included health professionals, caregivers, family members and emergency services).

Intelligent Tutoring Systems with Multiple Representations and Self-Explanation Prompts Support Learning of Fractions <=SEP=> Although a solid understanding of fractions is foundational in mathematics, the concept of fractions remains a challenging one. Previous research suggests that multiple graphical representations (MGRs) may promote learning of fractions. Specifically, we hypothesized that providing students with MGRs of fractions, in addition to the conventional symbolic notation, leads to better learning outcomes as compared to instruction incorporating only one graphical representation. We anticipated, however, that MGRs would make the students’ task more challenging, since they must link the representations and distill from them a common concept or principle. Therefore, we hypothesized further that selfexplanation prompts would help students benefit from working with MGRs. To investigate these hypotheses, we conducted a classroom study in which 112 6grade students used intelligent tutors for fraction conversion and fraction addition. The results of the study show that students learned more with MGRs of fractions than with a single representation, but only when prompted to self-explain how the graphics relate to the symbolic fraction representations.

A simple hybrid 3-level buck-boost DC-DC converter with efficient PWM regulation scheme <=SEP=> This paper describes a 3-level buck-boost converter with an output voltage range up to double the input voltage. The proposed converter can be used for applications that require stepping up/down the input voltage depending on the operating conditions like solar power harvesting. The converter has a hybrid structure between inductor-based converters and capacitor-based converters by utilizing a one flying capacitor and an inductor in the charge transfer process. The converter features smaller inductor size and current ripples as compared to the traditional buck-boost inductor-based converter while offering a high efficiency as compared to SC converters making it suitable for efficient on-chip integration. A prototype circuit has been designed on a 65nm technology using a 5-nH parasitic bondwire inductor. Simulation results show that the proposed converter features up to 20% improvement in the efficiency over a conventional 1:2 SC converter for a wide range of output voltage values.

Boosted key-frame selection and correlated pyramidal motion-feature representation for human action recognition <=SEP=> In this paper we propose a novel method for human action recognition based on boosted key-frame selection and correlated pyramidal motion feature representations. Instead of using an unsupervised method to detect interest points, a Pyramidal Motion Feature (PMF), which combines optical flow with a biologically inspired feature, is extracted from each frame of a video sequence. The AdaBoost learning algorithm is then applied to select the most discriminative frames from a large feature pool. In this way, we obtain the top-ranked boosted frames of each video sequence as the key frames which carry the most representative motion information. Furthermore, we utilise the correlogram which focuses not only on probabilistic distributions within one frame but also on the temporal relationships of the action sequence. In the classification phase, a Support-Vector Machine (SVM) is adopted as the final classifier for human action recognition. To demonstrate generalizability, our method has been systematically tested on a variety of datasets and shown to be more effective and accurate for action recognition compared to the previous work. We obtain overall accuracies of: 95.5%, 93.7%, and 36.5% with our proposed method on the KTH, the multiview IXMAS and the challenging HMDB51 datasets, respectively. & 2012 Elsevier Ltd. All rights reserved.

Consumers ’ attitude towards online shopping : Factors influencing employees of crazy domains to shop online <=SEP=> E-commerce offers many online marketing opportunities to companies worldwide and along with high rapid growth of online shopping; it has impressed many retailers to sell products and services through online channel to expand their market. Online shopping or marketing is the use of technology (i.e., computer, internet) for better marketing performance. And retailers are mixing strategies to meet the demand of online shoppers; they are busy in studying consumer in the field of online shopping, to see the consumer attitudes towards online shopping and specifically studying the factors influencing consumers to shop online. In this study, the multiple regression analysis was employed to measure the relationship between 9 independent variables and receptivity to online shopping. The score of Beta weight presented that all 9 independent variables had positive statistical significant effect to Internet users to accept online shopping. Among the 9 factors, the strongest influencers from highest to lowest were Price, Refund, Convenience, Auction websites, Security, Brand, Search engines, Promotion and Online shopping malls. According to independent t-test analysis for gender, there was significant different means between males and females for online shopping malls and Auctions websites factors to receptivity on online shopping. The means of female significant higher than male for these two factors. This study might contribute not only to a better understanding on what and how strongly the factors are involved in online consumer purchasing decisions but also this study provides e-retailer’s standpoint such the effectively manage and recommendations. However, eretailers should keep in mind that consumer behavior might change in time to time especially in online market so the e-retailer should investigate the consumer behavior in time to time and adapt the products and services to serve as the customer requirements.

Big Data Mobile Services for New York City Taxi Riders and Drivers <=SEP=> In recent years, taxis in New York City have been equipped with GPS sensors to record the start and end locations of every trip, in conjunction with fare collection equipment recording trip time, fare, and distance. This paper analyzes this vast dataset with big data technologies. We use MapReduce and Hive in order to understand traffic and travel patterns, discover relationships among the data, and make predictions on the taxi network. We also propose a service model combining the results of our data analysis with smartphone applications to offer users various taxi-related services based on results of our big data analysis.

Monogenic polyarteritis: the lesson of ADA2 deficiency <=SEP=> The deficiency of Adenosine Deaminase 2 (DADA2) is a new autoinflammatory disease characterised by an early onset vasculopathy with livedoid skin rash associated with systemic manifestations, CNS involvement and mild immunodeficiency.This condition is secondary to autosomal recessive mutations of CECR1 (Cat Eye Syndrome Chromosome Region 1) gene, mapped to chromosome 22q11.1, that encodes for the enzymatic protein adenosine deaminase 2 (ADA2). By now 19 different mutations in CECR1 gene have been detected.The pathogenetic mechanism of DADA2 is still unclear. ADA2 in a secreted protein mainly expressed by cells of the myeloid lineage; its enzymatic activity is higher in conditions of hypoxia, inflammation and oncogenesis. Moreover ADA2 is able to induce macrophages proliferation and differentiation; it's deficiency is in fact associated with a reduction of anti-inflammatory macrophages (M2). The deficiency of ADA2 is also associated with an up-regulation of neutrophils-expressed genes and an increased secretion of pro-inflammatory cytokines. The mild immunodeficiency detected in many DADA2 patients suggests a role of this protein in the adaptive immune response; an increased mortality of B cells and a reduction in the number of memory B cells, terminally differentiated B cells and plasmacells has been described in many patients. The lack of the protein is associated with endothelium damage; however the function of this protein in the endothelial homeostasis is still unknown.From the clinical point of view, this disease is characterized by a wide spectrum of severity. Chronic or recurrent systemic inflammation with fever, elevation of acute phase reactants and skin manifestations (mainly represented by livedo reticularis) is the typical clinical picture. While in some patients the disease is mild and skin-limited, others present a severe, even lethal, disease with multi-organ involvement; the CNS involvement is rather common with ischemic or hemorrhagic strokes. In many patients not only the clinical picture but also the histopathologic features are undistinguishable from those of systemic polyarteritis nodosa (PAN). Of note, patients with an unusual phenotype, mainly dominated by clinical manifestations suggestive for an immune-disrective condition, have been described.Due to its rarity, the response to treatment of DADA2 is still anecdotal. While steroids can control the disease's manifestations at high dosage, none of the common immunosuppressive drugs turned out to be effective. Biologic drugs have been used only in few patients, without a clear effectiveness; anti-TNF drugs are those associated to a better clinical response. Hematopoietic stem cells transplantation was effective in patients with a severe phenotype.

Understanding and Modeling of WiFi Signal Based Human Activity Recognition <=SEP=> Some pioneer WiFi signal based human activity recognition systems have been proposed. Their key limitation lies in the lack of a model that can quantitatively correlate CSI dynamics and human activities. In this paper, we propose CARM, a CSI based human Activity Recognition and Monitoring system. CARM has two theoretical underpinnings: a CSI-speed model, which quantifies the correlation between CSI value dynamics and human movement speeds, and a CSI-activity model, which quantifies the correlation between the movement speeds of different human body parts and a specific human activity. By these two models, we quantitatively build the correlation between CSI value dynamics and a specific human activity. CARM uses this correlation as the profiling mechanism and recognizes a given activity by matching it to the best-fit profile. We implemented CARM using commercial WiFi devices and evaluated it in several different environments. Our results show that CARM achieves an average accuracy of greater than 96%.