title: Dynamics and trajectory optimization for a soft spatial fluidic elastomer manipulator

abstract: The goal of this work is to develop a soft robotic manipulation system that is capable of autonomous, dynamic, and safe interactions with humans and its environment. First, we develop a dynamic model for a multi-body fluidic elastomer manipulator that is composed entirely from soft rubber and subject to the self-loading effects of gravity. Then, we present a strategy for independently identifying all unknown components of the system: the soft manipulator, its distributed fluidic elastomer actuators, as well as drive cylinders that supply fluid energy. Next, using this model and trajectory optimization techniques we find locally optimal open-loop policies that allow the system to perform dynamic maneuvers we call grabs. In 37 experimental trials with a physical prototype, we successfully perform a grab 92% of the time. By studying such an extreme example of a soft robot, we can begin to solve hard problems inhibiting the mainstream use of soft machines.


title: Code Hot Spot: A tool for extraction and analysis of code change history

abstract: Commercial software development teams have limited time available to focus on improvements to their software. These teams need a way to quickly identify areas of the source code that would benefit from improvement, as well as quantifiable data to defend the selected improvements to management. Past research has shown that mining configuration management systems for change information can be useful in determining faulty areas of the code. We present a tool named Code Hot Spot, which mines change records out of Microsoft's TFS configuration management system and creates a report of hot spots. Hot spots are contiguous areas of the code that have higher values of metrics that are indicators of faulty code. We present a study where we use this tool to study projects at ABB to determine areas that need improvement. The resulting data have been used to prioritize areas for additional code reviews and unit testing, as well as identifying change prone areas in need of refactoring.


Feature Selection as Causal Inference: Experiments with Text Classification


title: Measurement and Analysis of Mobile Web Cache Performance

abstract: The Web browser is a killer app on mobile devices such as smartphones. However, the user experience of mobile Web browsing is undesirable because of the slow resource loading. To improve the performance of Web resource loading, caching has been adopted as a key mechanism. However, the existing passive measurement studies cannot comprehensively characterize the performance of mobile Web caching. For example, most of these studies mainly focus on client-side implementations but not server-side configurations, suffer from biased user behaviors, and fail to study "miscached" resources. To address these issues, in this paper, we present a proactive approach for a comprehensive measurement study on mobile Web cache performance. The key idea of our approach is to proactively crawl resources from hundreds of websites periodically with a fine-grained time interval. Thus, we are able to uncover the resource update history and cache configurations at the server side, and analyze the cache performance in various time granularities. Based on our collected data, we build a new cache analysis model and study the upper bound of how high percentage of resources could potentially be cached and how effective the caching works in practice. We report detailed analysis results of different websites and various types of Web resources, and identify the problems caused by unsatisfactory cache performance. In particular, we identify two major problems -- Redundant Transfer and Miscached Resource, which lead to unsatisfactory cache performance. We investigate three main root causes: Same Content, Heuristic Expiration, and Conservative Expiration Time, and discuss what mobile Web developers can do to mitigate those problems.


title: Stability Analysis of Grid-Interfacing Inverter Control in Distribution Systems With Multiple Photovoltaic-Based Distributed Generators

abstract: A stability-enhancement inverter controller is proposed and its impact on the small-signal stability of distribution systems with multiple photovoltaic-based distributed generators (PV-DGs) is investigated. In specific, the random variation of the power output from multiple PV-DGs is considered. Being different from a single grid-connected PV system, the spatial correlation between PV-DGs and the system stability sensitivity with respect to different PV-DGs among the feeder are also taken into account. The small-signal stability analysis of the distribution system is performed using a probabilistic approach. As the PV-DG power output is randomly variable, the distribution system is not operating in a fixed mode. Consequently, the critical eigenvalues of the system move randomly. Through the Gram–Charlier expansion method, the probability density function of the critical eigenvalues of the distribution system is approximated and the probabilities of the system stability and instability are calculated. Taking the probability of the system instability as the performance metric, the effects of different system parameter settings of the grid-interfacing inverter controller on the distribution system are analyzed. The verification of the analytical results is carried out through Monte-Carlo time-domain simulations of the distribution system. The results suggest several methods to reduce the probability of system instability, including installation of the proposed inverter controller, choosing a large substation capacitor and/or choosing large capacity load and substation transformers.


title: FlowCover: Low-cost Flow Monitoring Scheme in Software Defined Networks

abstract: Network monitoring and measurement are crucial in network management to facilitate quality of service routing and performance evaluation. Software Defined Networking (SDN) makes network management easier by separating the control plane and data plane. Network monitoring in SDN is lightweight as operators only need to install a monitoring module into the controller. Active monitoring techniques usually introduce too many overheads into the network. The state-of-the-art approaches utilize sampling method, aggregation flow statistics and passive measurement techniques to reduce overheads. However, little work in literature has focus on reducing the communication cost of network monitoring. Moreover, most of the existing approaches select the polling switch nodes by sub-optimal local heuristics. Inspired by the visibility and central control of SDN, we propose FlowCover, a low-cost high-accuracy monitoring scheme to support various network management tasks. We leverage the global view of the network topology and active flows to minimize the communication cost by formulating the problem as a weighted set cover, which is proved to be NP-hard. Heuristics are presented to obtain the polling scheme efficiently and handle flow changes practically. We build a simulator to evaluate the performance of FlowCover. Extensive experiment results show that FlowCover reduces roughly 50% communication cost without loss of accuracy in most cases.


title: Efficient Distance-Aware Influence Maximization in Geo-Social Networks

abstract: Given a social network $\mathcal {G}$ and a positive integer $k$ , the influence maximization problem aims to identify a set of $k$ nodes in $\mathcal {G}$ that can maximize the influence spread under a certain propagation model. As the proliferation of geo-social networks, location-aware promotion is becoming more necessary in real applications. In this paper, we study the distance-aware influence maximization (DAIM) problem, which advocates the importance of the distance between users and the promoted location. Unlike the traditional influence maximization problem, DAIM treats users differently based on their distances from the promoted location. In this situation, the $k$ nodes selected are different when the promoted location varies. In order to handle the large number of queries and meet the online requirement, we develop two novel index-based approaches, MIA-DA and RIS-DA, by utilizing the information over some pre-sampled query locations. MIA-DA is a heuristic method which adopts the maximum influence arborescence (MIA) model to approximate the influence calculation. In addition, different pruning strategies as well as a priority-based algorithm are proposed to significantly reduce the searching space. To improve the effectiveness, in RIS-DA, we extend the reverse influence sampling (RIS) model and come up with an unbiased estimator for the DAIM problem. Through carefully analyzing the sample size needed for indexing, RIS-DA is able to return a $1 - 1/e -\epsilon$ approximate solution with at least $1 - \delta$ probability for any given query. Finally, we demonstrate the efficiency and effectiveness of proposed methods over real geo-social networks.


title: A graph-based hyper-heuristic for educational timetabling problems

abstract: This paper presents an investigation of a simple generic hyper-heuristic approach upon a set of widely used constructive heuristics (graph coloring heuristics) in timetabling. Within the hyper-heuristic framework, a tabu search approach is employed to search for permutations of graph heuristics which are used for constructing timetables in exam and course timetabling problems. This underpins a multi-stage hyper-heuristic where the tabu search employs permutations upon a different number of graph heuristics in two stages. We study this graph-based hyper-heuristic approach within the context of exploring fundamental issues concerning the search space of the hyper-heuristic (the heuristic space) and the solution space. Such issues have not been addressed in other hyper-heuristic research. These approaches are tested on both exam and course benchmark timetabling problems and are compared with the fine-tuned bespoke state-of-the-art approaches. The results are within the range of the best results reported in the literature. The approach described here represents a significantly more generally applicable approach than the current state of the art in the literature. Future work will extend this hyper-heuristic framework by employing methodologies which are applicable on a wider range of timetabling and scheduling problems.


title: Design of an Educational Adventure Game to teach computer security in the working environment

abstract: One of the emerging requirements for learning in the enterprise is finding new ways to keep the learner engaged. The use of advanced learning technologies that exploit a gameful design should increase engagement and encourage students to make connections between the simulated environment and the real world. This article presents game design guidelines for the development of an Educational Adventure Game and how they have been applied during the development of SIRET Security Game, a game that teaches workers the importance of following computer security policies.


title: An Empirical Investigation of Outsourcing Decisions

abstract: SUMMARY ::: ::: Eighty-five make or buy decisions, each made in a different enterprise, were classified into three categories (items/services made, items/services bought, new items/services) according to the method of acquisition before the resolution of the make or buy issue. Information was collected on a number of variables and analyzed by statistical methods. Items/services made (subjected to make or buy investigations) are characterized by low product complexity and low commercial uncertainty. On the contrary, items/services bought and new items/services (subjected to make or buy investigations) are characterized by high product complexity and high commercial uncertainty. The involvement of functions and hierarchical levels in make or buy investigations for new items/services was found to be relatively low. This fact increases the risk of making erroneous decisions that have a serious impact on the competitive position and profitability of enterprises.


title: Switched Beam Antenna Based on RF MEMS SPDT Switch on Quartz Substrate

abstract: This letter demonstrates a 20-GHz radio frequency microelectromechanical system (RF MEMS)-based electrically switchable antenna on a quartz substrate. Two quasi-Yagi antenna elements are monolithically integrated with a single-pole double-throw (SPDT) MEMS switch router network on a 21 mm times 8 mm chip. Electrical beam steering between two opposite directions is achieved using capacitive MEMS SPDT switches in the router. Port impedance and radiation patterns are studied numerically and experimentally. Measured results show that the switched beam antenna features a 27% impedance bandwidth (S11 = -10 dB), a gain of 4.6 dBi, and a front-to-back ratio of 14 dB at 20 GHz when the control voltage is applied to one of the switch pairs of the SPDT switch.


title: Measurement Bias from Address Aliasing

abstract: In this paper, we identify address aliasing as an important underlying mechanism causing measurement bias on modern Intel microarchitectures. By analyzing hardware performance counters, we show how bias arises from two external factors:size of environment variables, and characteristics of dynamically linked heap allocators. We demonstrate ways to deal with this type of bias, through runtime detection and correction of aliasing conditions. For heap allocators, we show that common implementations tend to give worst case alignment by default, favoring page alignment for large allocations. The performance impact of aliased memory accesses can be significant, causing up to a 2x performancedegradation in already optimized code. Our results can enable new optimization strategies to account for this phenomenon.


title: Agent-based computational models and generative social science

abstract: Agent-based computational modeling is changing the face of social science. In Generative Social Science , Joshua Epstein argues that this powerful, novel technique permits the social sciences to meet a fundamentally new standard of explanation, in which one "grows" the phenomenon of interest in an artificial society of interacting agents: heterogeneous, boundedly rational actors, represented as mathematical or software objects. After elaborating this notion of generative explanation in a pair of overarching foundational chapters, Epstein illustrates it with examples chosen from such far-flung fields as archaeology, civil conflict, the evolution of norms, epidemiology, retirement economics, spatial games, and organizational adaptation. In elegant chapter preludes, he explains how these widely diverse modeling studies support his sweeping case for generative explanation. This book represents a powerful consolidation of Epstein's interdisciplinary research activities in the decade since the publication of his and Robert Axtell's landmark volume, Growing Artificial Societies . Beautifully illustrated, Generative Social Science includes a CD that contains animated movies of core model runs, and programs allowing users to easily change assumptions and explore models, making it an invaluable text for courses in modeling at all levels.


title: PA Thermal Management and Packaging: Wideband PA and Packaging, History, and Recent Advances: Part 2

abstract: Wide-band power amplifiers (PAs) are considered the most expensive and critical component for RF and microwave applications. New power systems, including those for radars, transmitters, electronic warfare (EW) jammers, and electromagnetic compatibility (EMC) testers, require the latest, state-of-the-art semiconductor technologies for power, linearity, and bandwidth [1], [2]. In addition, wide bandwidth is essential for high-speed communication and high resolution in radar, imagers, and sensors [2], [3]. In modern electronic vehicles, complexity is rapidly increasing and imposing strict EMC regulations, which often require very high 1-dB compression point (P1dB) power [to kilowatt, continuous wave (CW)] across an ultrawide bandwidth (decade) with high linearity, good reliability, and long life [4], [5].


title: Modeling Online Browsing and Path Analysis Using Clickstream Data

abstract: Clickstream data provide information about the sequence of pages or the path viewed by users as they navigate a website. We show how path information can be categorized and modeled using a dynamic multinomial probit model of Web browsing. We estimate this model using data from a major online bookseller. Our results show that the memory component of the model is crucial in accurately predicting a path. In comparison, traditional multinomial probit and first-order Markov models predict paths poorly. These results suggest that paths may reflect a user's goals, which could be helpful in predicting future movements at a website. One potential application of our model is to predict purchase conversion. We find that after only six viewings purchasers can be predicted with more than 40% accuracy, which is much better than the benchmark 7% purchase conversion prediction rate made without path information. This technique could be used to personalize Web designs and product offerings based upon a user's path.


title: Semantic-Based Keyword Recovery Function for Keyword Extraction System

abstract: The goal of implementing a keyword extraction system is to increase as near as 100% of precision and recall. These values are affected by the amount of extracted keywords. There are two groups of errors happened i.e. false-rejected and false-accepted keywords. To improve the performance of the system, false-rejected keywords should be recovered and the false-accepted keywords should be reduced. In this paper, we enhance the conventional keyword extraction systems by attaching the keyword recovery function. This function recovers the previously false-rejected keywords by comparing their semantic information with the contents of each relevant document. The function is automated in three processes i.e. Domain Identification, Knowledge Base Generation and Keyword Determination. Domain identification process identifies domain of interest by searching domains from domain knowledge base by using extracted keywords.The most general domains are selected and then used subsequently. To recover the false-rejected keywords, we match them with keywords in the identified domain within the domain knowledge base rely on their semantics by keyword determination process. To semantically recover keywords, definitions of false-reject keywords and domain knowledge base are previously represented in term of conceptual graph by knowledge base generator process. To evaluate the performance of the proposed function, EXTRACTOR, KEA and our keyword-database-mapping based keyword extractor are compared. The experiments were performed in two modes i.e. training and recovering. In training mode, we use four glossaries from the Internet and 60 articles from the summary sections of IEICE transaction. While in the recovering mode, 200 texts from three resources i.e. summary section of 15 chapters in a computer textbook and articles from IEICE and ACM transactions are used. The experimental results revealed that our proposed function improves the precision and recall rates of the conventional keyword extraction systems approximately 3-5% of precision and 6-10% of recall, respectively.


title: Designing Statistical Language Learners: Experiments on Noun Compounds

abstract: The goal of this thesis is to advance the exploration of the statistical language learning design space. In pursuit of that goal, the thesis makes two main theoretical contributions: (i) it identifies a new class of designs by specifying an architecture for natural language analysis in which probabilities are given to semantic forms rather than to more superficial linguistic elements; and (ii) it explores the development of a mathematical theory to predict the expected accuracy of statistical language learning systems in terms of the volume of data used to train them. ::: The theoretical work is illustrated by applying statistical language learning designs to the analysis of noun compounds. Both syntactic and semantic analysis of noun compounds are attempted using the proposed architecture. Empirical comparisons demonstrate that the proposed syntactic model is significantly better than those previously suggested, approaching the performance of human judges on the same task, and that the proposed semantic model, the first statistical approach to this problem, exhibits significantly better accuracy than the baseline strategy. These results suggest that the new class of designs identified is a promising one. The experiments also serve to highlight the need for a widely applicable theory of data requirements.


title: Learning Segmentation Masks with the Independence Prior

abstract: An instance with a bad mask might make a composite image that uses it look fake. This encourages us to learn segmentation by generating realistic composite images. To achieve this, we propose a novel framework that exploits a new proposed prior called the independence prior based on Generative Adversarial Networks (GANs). The generator produces an image with multiple category-specific instance providers, a layout module and a composition module. Firstly, each provider independently outputs a category-specific instance image with a soft mask. Then the provided instances’ poses are corrected by the layout module. Lastly, the composition module combines these instances into a final image. Training with adversarial loss and penalty for mask area, each provider learns a mask that is as small as possible but enough to cover a complete category-specific instance. Weakly supervised semantic segmentation methods widely use grouping cues modeling the association between image parts, which are either artificially designed or learned with costly segmentation labels or only modeled on local pairs. Unlike them, our method automatically models the dependence between any parts and learns instance segmentation. We apply our framework in two cases: (1) Foreground segmentation on category-specific images with box-level annotation. (2) Unsupervised learning of instance appearances and masks with only one image of homogeneous object cluster (HOC). We get appealing results in both tasks, which shows the independence prior is useful for instance segmentation and it is possible to unsupervisedly learn instance masks with only one image.


title: A 1GS/s 11b Time-Interleaved ADC in 0.13/spl mu/m CMOS

abstract: A time-interleaved ADC architecture that eliminates the need to correct timing offsets and is scalable to high sampling rates is presented. This 1GS/S 11b ADC has 55dB peak SNDR, consumes 250mW power, and occupies 3.5mm2 area


title: Shadow Detection with Conditional Generative Adversarial Networks

abstract: We introduce scGAN, a novel extension of conditional Generative Adversarial Networks (GAN) tailored for the challenging problem of shadow detection in images. Previous methods for shadow detection focus on learning the local appearance of shadow regions, while using limited local context reasoning in the form of pairwise potentials in a Conditional Random Field. In contrast, the proposed adversarial approach is able to model higher level relationships and global scene characteristics. We train a shadow detector that corresponds to the generator of a conditional GAN, and augment its shadow accuracy by combining the typical GAN loss with a data loss term. Due to the unbalanced distribution of the shadow labels, we use weighted cross entropy. With the standard GAN architecture, properly setting the weight for the cross entropy would require training multiple GANs, a computationally expensive grid procedure. In scGAN, we introduce an additional sensitivity parameter w to the generator. The proposed approach effectively parameterizes the loss of the trained detector. The resulting shadow detector is a single network that can generate shadow maps corresponding to different sensitivity levels, obviating the need for multiple models and a costly training procedure. We evaluate our method on the large-scale SBU and UCF shadow datasets, and observe up to 17% error reduction with respect to the previous state-of-the-art method.


title: Semi-supervised Training for Improving Data Efficiency in End-to-end Speech Synthesis

abstract: Although end-to-end text-to-speech (TTS) models such as Tacotron have shown excellent results, they typically require a sizable set of high-quality pairs for training, which are expensive to collect. In this paper, we propose a semi-supervised training framework to improve the data efficiency of Tacotron. The idea is to allow Tacotron to utilize textual and acoustic knowledge contained in large, publicly-available text and speech corpora. Importantly, these external data are unpaired and potentially noisy. Specifically, first we embed each word in the input text into word vectors and condition the Tacotron encoder on them. We then use an unpaired speech corpus to pre-train the Tacotron decoder in the acoustic domain. Finally, we fine-tune the model using available paired data. We demonstrate that the proposed framework enables Tacotron to generate intelligible speech using less than half an hour of paired training data.


title: Vision-Based Parking-Slot Detection: A DCNN-Based Approach and a Large-Scale Benchmark Dataset

abstract: In the automobile industry, recent years have witnessed a growing interest in developing self-parking systems. For such systems, how to accurately and efficiently detect and localize the parking slots defined by regular line segments near the vehicle is a key and still unresolved issue. In fact, kinds of unfavorable factors, such as the diversity of ground materials, changes in illumination conditions, and unpredictable shadows caused by nearby trees, make the vision-based parking-slot detection much harder than it looks. In this paper, we attempt to solve this issue to some extent and our contributions are twofold. First, we propose a novel deep convolutional neural network (DCNN)-based parking-slot detection approach, namely, DeepPS, which takes the surround-view image as the input. There are two key steps in DeepPS, identifying all the marking points on the input image and classifying local image patterns formed by pairs of marking points. We formulate both of them as learning problems, which can be solved naturally by modern DCNN models. Second, to facilitate the study of vision-based parking-slot detection, a large-scale labeled dataset is established. This dataset is the largest in this field, comprising 12 165 surround-view images collected from typical indoor and outdoor parking sites. For each image, the marking points and parking slots are carefully labeled. The efficacy and efficiency of DeepPS have been corroborated on our collected dataset. To make our results fully reproducible, all the relevant source codes and the dataset have been made publicly available at https://cslinzhang.github.io/deepps/ .


title: Mobile Rescue Robot for Human BodyDetection in Rescue Operation of Disaster

abstract: Many areas of world are getting affected due to natural calamity. Disasters are exceptional & unstoppable events that are either man made or natural, such as terrorist attacks, earthquakes, wildfires and floods etc. [1]. Disasters create emergency situations to provide basic services to the victims must be coordinated quickly. Many times we observe that many people dies by trapping in these disasters but the people also dies on large scale just because they didn’t get help at instant time or the help provided to them is late. This paper propose an mobile robot based on Wireless Sensor Network (WSN) which is designed for human existence & detection in an unmanned area can be done only by an automated system. This system proposed a monitoring system using sensors unit and camera module to recording, analyze conditions of human body and transmit data [3]. Mobile robots perform cooperative Simultaneous human body localization function and communicate over the WSN. The main objective of this Paper is to rescue more & more number of people from the adverse condition.


title: Mobile games with intelligence: A killer application?

abstract: Mobile gaming is an arena full of innovation, with developers exploring new kinds of games, with new kinds of interaction between the mobile device, players, and the connected world that they live in and move through. The mobile gaming world is a perfect playground for AI and CI, generating a maelstrom of data for games that use adaptation, learning and smart content creation. In this paper, we explore this potential killer application for mobile intelligence. We propose combining small, light-weight AI/CI libraries with AI/CI services in the cloud for the heavy lifting. To make our ideas more concrete, we describe a new mobile game that we built that shows how this can work.


00 PMC : A Requirement-Aware DRAM Controller for Multi-core Mixed Criticality Systems


title: Dielectric loaded tm01 to te11 mode converter for S-band applications

abstract: In this paper, a compact, high efficiency TM01 to TE11 mode converter is presented. In the proposed structure, dielectric material is loaded below a tapered centre conductor, in lower section of a circular waveguide. The input TM01 mode is initially transformed to TEM mode by the tapered centre conductor. The loaded dielectric material is designed to provide the desired phase shift for the mode conversion. Different dielectric materials are analyzed for loading to achieve high conversion efficiency and wide bandwidth. The proposed mode converter is fabricated with PTFE as dielectric for mode conversion and the results are verified through far field radiation pattern measurements. The mode conversion efficiency is more than 90% for 2.95-3.60 GHz band. Due to compactness and high conversion efficiency for wide bandwidth, it is suitable for S-band high power microwave (HPM) applications.


title: Probabilistic Similarity Logic

abstract: Many machine learning applications require the ability to learn from and reason about noisy multi-relational data. To address this, several effective representations have been developed that provide both a language for expressing the structural regularities of a domain, and principled support for probabilistic inference. In addition to these two aspects, however, many applications also involve a third aspect-the need to reason about similarities-which has not been directly supported in existing frameworks. This paper introduces probabilistic similarity logic (PSL), a general-purpose framework for joint reasoning about similarity in relational domains that incorporates probabilistic reasoning about similarities and relational structure in a principled way. PSL can integrate any existing domain-specific similarity measures and also supports reasoning about similarities between sets of entities. We provide efficient inference and learning techniques for PSL and demonstrate its effectiveness both in common relational tasks and in settings that require reasoning about similarity.


title: Mobile-Based Eye-Blink Detection Performance Analysis on Android Platform

abstract: In this paper, we develop a real-time mobile phone-based gaze tracking and eye-blink detection system on Android platform. Our eye-blink detection scheme is developed based on the time difference between two open eye states. We develop our system by finding the greatest circle – pupil of an eye. So we combine the both Haar classifier and Normalized Summation of Square of Difference template matching method. We define the eyeball area that is extracted from the eye-region as the region of interest (ROI). The ROI helps to differentiate between the open state and closed state of the eyes. The output waveform of the scheme is analogous to binary trend, which alludes the blink detection distinctly. We categorize short, medium and long blink, depending on the degree of closure and blink duration. Our analysis is operated on medium blink under 15frames/sec. This combined solution for gaze tracking and eye-blink detection system has high detection accuracy and low time-consumption. We obtain 98% accuracy at zero degree angles for blink detection from both eyes. The system is also extensively experimented with various environments and setups, including variations in illuminations, subjects, gender, angles, processing speed, RAM capacity, and distance. We found that the system performs satisfactorily under varied conditions in real-time for both single eye and two eyes detection. These concepts can be exploited in different applications, e.g., to detect drowsiness of a driver, or to operate the computer cursor to develop an eye-operated mouse for disabled people.


title: A survey of data mining and knowledge discovery process models and methodologies

abstract: Up to now, many data mining and knowledge discovery methodologies and process models have been developed, with varying degrees of success. In this paper, we describe the most used (in industrial and academic projects) and cited (in scientific literature) data mining and knowledge discovery methodologies and process models, providing an overview of its evolution along data mining and knowledge discovery history and setting down the state of the art in this topic. For every approach, we have provided a brief description of the proposed knowledge discovery in databases (KDD) process, discussing about special features, outstanding advantages and disadvantages of every approach. Apart from that, a global comparative of all presented data mining approaches is provided, focusing on the different steps and tasks in which every approach interprets the whole KDD process. As a result of the comparison, we propose a new data mining and knowledge discovery process named refined data mining process for developing any kind of data mining and knowledge discovery project. The refined data mining process is built on specific steps taken from analyzed approaches.


title: Cathepsin D expression as a possible predictor of lymph node metastasis in submucosal colorectal cancer

abstract: The aim of this study was to clarify the usefulness of cathepsin D expression as a predictor of lymph node metastasis in submucosal colorectal cancer (CRC). Cathepsin D expression was examined immunohistochemically in cancer and stromal cells located at the deepest portion of 254 invasive tumours that had been resected from patients with submucosal CRC. In cancer cells, the expression was classified according to differences in intracellular localisation: polarity positive, apical type (PA); polarity positive, basal type (PB); polarity negative (PN); or no expression (NE). Lesions with PN or NE expression showed a significantly higher incidence of lymph node metastasis than those with PA or PB expression. Alternatively, lesions with positive expression in stromal cells showed a significantly higher incidence of lymph node metastasis than that of those with negative expression. None of the lesions with PA or PB expression and negative expression in stromal cells had metastasised to the lymph node. In conclusion, analysis combining cathepsin D expression in cancer and stromal cells may be a quite useful predictor for lymph node metastasis and may broaden the indications for curative endoscopic treatment of submucosal CRC.


title: Algorithm 583: LSQR: Sparse Linear Equations and Least Squares Problems

abstract: Received 4 June 1980; revised 23 September 1981, accepted 28 February 1982 This work was supported by Natural Sciences and Engineering Research Council of Canada Grant A8652, by the New Zealand Department of Scientific and Industrial Research; and by U S. National Science Foundation Grants MCS-7926009 and ECS-8012974, the Department of Energy under Contract AM03-76SF00326, PA No. DE-AT03-76ER72018, the Office of Naval Research under Contract N00014-75-C-0267, and the Army Research Office under Contract DAA29-79-C-0U0, Authors' addresses: C. C. Paige, School of Computer Science, McGill University, Montreal, Quebec, Canada H3A 2K6; M. A Saundem, Department of Operations Research, Stanford University, Stanford, CA 94305. Permmsion to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the ACM copyright notice and the title of the publication and its date appear, and notme is given that copying is by permission of the Association for Computing Machinery. To copy otherwise, or to republish, requires a fee and/or specific permission. © 1982 ACM 0098-3500/82/0600-0[95 $00 75


title: Relevance feedback for Content-based Image Retrieval using deep learning

abstract: The “semantic gap” issue which exists the low-level image pixels captured by machines and high-level semantic concepts perceived by human has always been a key challenge for lots of applications, such as computer vision, pattern recognition, Content-based Image Retrieval (CBIR). The recent success of deep learning researches bring a hope for bridging the semantic gap. Many researchers have attempted to explore deep learning techniques with applying to CBIR tasks. However, it still has some restrictions for CBIR tasks, because the machine could not well comprehend the semantic concepts from low-level pixels to high-level semantic concepts. In this paper, we introduced the Relevance Feedback (RF) Model , which captures the users' feedback information. When the system returned the initial retrieval results to the user and the initial retrieval results meet the demand of the user, the retrieval process is called off. Generally, relevance feedback is usually added due to the poor results of retrieval. Our CBIR system asks the user to feedback the information related to query image. So the system obtains the information of semantically similar and dissimilar images for the positive and negative feedback samples. Then the initial retrieval results are resorted based the updated user's feedback information. When the resorted results meet the demand of the user, RF model is stop and return the final optimal results to users. the RF is performed iteratively until the user is satisfied with the refined results. Our proposed feedback model is optimized for each user's results. From our empirical studies, deep learning combining with RF model significantly outperforms only applying deep learning for CBIR tasks.


title: Microwave filter design by synthesis and optimization

abstract: In this article, a process that might be described as "synthesis and approximation" was outlined. Starting from an exact prototype, the desired physical configuration using equivalent circuits and corrections to the resonators due to the influence of the coupling networks was approximated. A process that might be described as "approximation and optimization" was also outlined. Starting from an approximate filter network, optimization can be used to find an exact equal ripple solution. The starting point can be generated using synthesis or narrow-band approximations when appropriate. Applying optimization in an intelligent way allows the designer to circumvent some of the limitations of the classic ladder synthesis method. This article explores some ways to mix approximate filter dimensioning (based on network synthesis) and optimization (based on EM modeling), allowing a fast and accurate design of microwave filters


title: Growth and morphological development of laboratory-reared larval and juvenile giant gourami Osphronemus goramy (Perciformes: Osphronemidae)

abstract: Morphological development in laboratory-reared larval and juvenile Osphronemus goramy is described. Body lengths were 4.8 ± 0.1 (mean ± SD) mm just after hatching, and reached 9.8 ± 0.8 mm by day 16 and 15.3 ± 0.6 mm by day 40. Aggregate fin ray numbers attained full complements by 11.9 mm BL on day 22. All larvae began feeding by day 5 (ca. 6.7 mm BL). Conical jaw teeth appeared on day 7 (ca. 7.0 mm BL). Notochord flexion began on day 3 (ca. 6.5 mm BL), the yolk being absorbed during days 14–15 (ca. 8.0–10.0 mm BL). Pale melanophores, initially visible on the upper yolksac surface during days 0 and 1, became more intense and numerous with growth, and covered almost the entire body in juveniles. Seven or eight vertical bars appeared on the body on day 13 (ca. 9.0 mm BL), thereafter becoming more discernible. Pre-anal length, body depth, and eye diameter proportions became relatively constant after yolk absorption, with those of the head/snout/upper jaw lengths subsequently increasing. The labyrinth organ began to develop in juveniles larger than ca. 14.5 mm BL (day 30), and air-breathing behavior was first observed on days 35–40.


title: Integrated Phased Array Systems in Silicon

abstract: Silicon offers a new set of possibilities and challenges for RF, microwave, and millimeter-wave applications. While the high cutoff frequencies of the SiGe heterojunction bipolar transistors and the ever-shrinking feature sizes of MOSFETs hold a lot of promise, new design techniques need to be devised to deal with the realities of these technologies, such as low breakdown voltages, lossy substrates, low-Q passives, long interconnect parasitics, and high-frequency coupling issues. As an example of complete system integration in silicon, this paper presents the first fully integrated 24-GHz eight-element phased array receiver in 0.18-/spl mu/m silicon-germanium and the first fully integrated 24-GHz four-element phased array transmitter with integrated power amplifiers in 0.18-/spl mu/m CMOS. The transmitter and receiver are capable of beam forming and can be used for communication, ranging, positioning, and sensing applications.


title: A DDDAMS-based UAV and UGV team formation approach for surveillance and crowd control

abstract: The goal of this paper is to study the team formation of multiple UAVs and UGVs for collaborative surveillance and crowd control under uncertain scenarios (e.g. crowd splitting). A comprehensive and coherent dynamic data driven adaptive multi-scale simulation (DDDAMS) framework is adopted, with the focus on simulation-based planning and control strategies related to the surveillance problem considered in this paper. To enable the team formation of multiple UAVs and UGVs, a two stage approach involving 1) crowd clustering and 2) UAV/UGV team assignment is proposed during the system operations by considering the geometry of the crowd clusters and solving a multi-objective optimization problem. For the experiment, an integrated testbed has been developed based on agent-based hardware-in-the-loop simulation involving seamless communications among simulated and real vehicles. Preliminary results indicate the effectiveness and efficiency of the proposed approach for the team formation of multiple UAVs and UGVs.


title: Learning with Side Information through Modality Hallucination

abstract: We present a modality hallucination architecture for training an RGB object detection model which incorporates depth side information at training time. Our convolutional hallucination network learns a new and complementary RGB image representation which is taught to mimic convolutional mid-level features from a depth network. At test time images are processed jointly through the RGB and hallucination networks to produce improved detection performance. Thus, our method transfers information commonly extracted from depth training data to a network which can extract that information from the RGB counterpart. We present results on the standard NYUDv2 dataset and report improvement on the RGB detection task.


title: Grape antioxidant dietary fibre prevents mitochondrial apoptotic pathways by enhancing Bcl-2 and Bcl-xL expression and minimising oxidative stress in rat distal colonic mucosa.

abstract: Grape antioxidant dietary fibre (GADF) is a grape product rich in dietary fibre and natural antioxidants. We reported previously that GADF intake reduced apoptosis and induced a pro-reducing shift in the glutathione (GSH) redox status of the rat proximal colonic mucosa. The aim of the study was to elucidate the molecular mechanisms responsible for the anti-apoptotic effect of GADF and their association with the oxidative environment of the distal colonic mucosa. The ability of GADF to modify colonic crypt cell proliferation was also investigated. Male Wistar rats (n 20) were fed with diets containing either cellulose (control group) or GADF (GADF group) as fibre for 4 weeks. GADF did not modify cell proliferation but induced a significant reduction of colonic apoptosis. The anti-apoptotic proteins Bcl-2 (B-cell lymphoma-2) and Bcl-xL (B-cell lymphoma extra large) were up-regulated in the mitochondria and down-regulated in the cytosol of the GADF mucosa, whereas the opposite was found for the pro-apoptotic protein Bax (Bcl-2-associated X protein), leading to an anti-apoptotic shift in the pattern of expression of the Bcl-2 family. Cytosolic cytochrome c and cleaved caspase-3 levels and caspase-3 activity were reduced by GADF. The modulation of the antioxidant enzyme system and the increase of the cytosolic GSH:glutathione disulfide (GSSG) ratio elicited by GADF helped to reduce oxidative damage. The cytosolic GSH:GSSG ratio was negatively related to apoptosis. These results indicate that GADF acts on the expression of the pro- and anti- apoptotic Bcl-2 proteins, attenuating the mitochondrial apoptotic pathway in the distal colonic mucosa. This effect appears to be associated with the antioxidant properties of GADF.


title: Comparing the rain effects on hybrid network using optical wireless and GHz links

abstract: Optical wireless link provides high bandwidth solution to the last mile access bottleneck. However, an appreciable high availability of the link is the basic requirement of any communication application. Free space optics (FSO) links are highly weather dependent that reduces the link availability. Hybrid networks consisting of Free Space Optics (FSO) link and back up link in the GHz frequency range renders high availability besides providing comparable data rate. The back up link should be nearly immune to weather attenuation for achieving carrier class availability. Among all the attenuation factors for GHz back up link, rain is the most significant. In this paper effects of rain on FSO and GHz frequency range links are studied and some measurement results are presented. This analysis can help in selection of frequencies with least rain attenuation as a back up link.


title: Geo-indistinguishability: differential privacy for location-based systems

abstract: The growing popularity of location-based systems, allowing unknown/untrusted servers to easily collect huge amounts of information regarding users' location, has recently started raising serious privacy concerns. In this paper we introduce geoind, a formal notion of privacy for location-based systems that protects the user's exact location, while allowing approximate information -- typically needed to obtain a certain desired service -- to be released. This privacy definition formalizes the intuitive notion of protecting the user's location within a radius $r$ with a level of privacy that depends on r, and corresponds to a generalized version of the well-known concept of differential privacy. Furthermore, we present a mechanism for achieving geoind by adding controlled random noise to the user's location. We describe how to use our mechanism to enhance LBS applications with geo-indistinguishability guarantees without compromising the quality of the application results. Finally, we compare state-of-the-art mechanisms from the literature with ours. It turns out that, among all mechanisms independent of the prior, our mechanism offers the best privacy guarantees.


title: Designing a Moral Compass for the Future of Computer Vision Using Speculative Analysis

abstract: In this paper we discuss and analyze possible futures for technologies in the field of computer vision (CV). Using a method we have coined speculative analysis we take a broad look at research trends in the field to categorize risks, analyze which ones are most threatening and likely, and ultimately summarize conclusions for how the field may attempt to stem future harms caused by CV technologies. We develop narrative case studies to provoke dialogue and deeply explore possible risk scenarios we found to be most probable and severe. We arrive at the position that there are serious potentials for CV to cause discriminatory harm and exacerbate cybersecurity issues.


title: Frequent itemset mining using two-fold cross-validation model

abstract: In this parer we present novel approach in frequent itemset mining. The method is based on cross validation procedure and Apriori algorithm.


Heart Disease Prediction System using Data Mining Techniques and Intelligent Fuzzy Approach: A Review


title: Detecting and characterising returns in a pulsed ladar system

abstract: A new multi-spectral laser radar (ladar) system based on the time-correlated single photon counting, time-of-flight technique has been designed to detect and characterise distributed targets at ranges of several kilometres. The system uses six separated laser channels in the visible and near infrared part of the electromagnetic spectrum. The authors present a method to detect the numbers, positions, heights and shape parameters of returns from this system, used for range profiling and target classification. The algorithm has two principal stages: non-parametric bump hunting based on an analysis of the smoothed derivatives of the photon count histogram in scale space, and maximum likelihood estimation using Poisson statistics. The approach is demonstrated on simulated and real data from a multi-spectral ladar system, showing that the return parameters can be estimated to a high degree of accuracy.


title: An unsupervised training method for non-intrusive appliance load monitoring

abstract: Abstract Non-intrusive appliance load monitoring is the process of disaggregating a household's total electricity consumption into its contributing appliances. In this paper we propose an unsupervised training method for non-intrusive monitoring which, unlike existing supervised approaches, does not require training data to be collected by sub-metering individual appliances, nor does it require appliances to be manually labelled for the households in which disaggregation is performed. Instead, we propose an approach which combines a one-off supervised learning process over existing labelled appliance data sets, with an unsupervised learning method over unlabelled household aggregate data. First, we propose an approach which uses the Tracebase data set to build probabilistic appliance models which generalise to previously unseen households, which we empirically evaluate through cross validation. Second, we use the Reference Energy Disaggregation Data set to evaluate the accuracy with which these general models can be tuned to the appliances within a specific household using only aggregate data. Our empirical evaluation demonstrates that general appliance models can be constructed using data from only a small number of appliances (typically 3–6 appliances), and furthermore that 28–99% of the remaining behaviour which is specific to a single household can be learned using only aggregate data from existing smart meters.


title: ChDuino: A real-time controller for Arduino

abstract: ChDuino is a cross-platform GUI based tool for managing Arduino boards and controlling them with connected USB cable or wirelessly through Bluetooth communication. The primary purpose of this software is to simplify the procedure of learning Arduino and embedded systems so that people from various areas and K-12 students can also learn the concepts of microcontrollers. This paper presents the design and mechanism of ChDuino for real-time controlling Arduino boards. ChDuino supports multiple ways to communicate with a board, through USB or Bluetooth. It detects Arduino boards automatically with which libcharduino allows users to program an Arduino board in Ch without tedious compilation and other low-level technical issues.


title: Communication motifs: a tool to characterize social communications

abstract: Social networks mediate not only the relations between entities, but also the patterns of information propagation among them and their communication behavior. In this paper, we extensively study the temporal annotations (e.g., time stamps and duration) of historical communications in social networks and propose two novel tools -- communication motifs and maximum-flow communication motifs -- for characterizations of the patterns of information propagation in social networks. Using these motifs, we verify the following hypothesis in social communication network: 1) the functional behavioral patterns of information propagation within both social networks are stable over time; 2) the patterns of information propagation in synchronous and asynchronous social networks are different and sensitive to the cost of communication; and 3) the speed and the amount of information that is propagated through a network are correlated and dependent on individual profiles.


title: Efficient Discovery of Statistically Significant Association Rules

abstract: Searching statistically significant association rules is an important but neglected problem. Traditional association rules do not capture the idea of statistical dependence and the resulting rules can be spurious, while the most significant rules may be missing. This leads to erroneous models and predictions which often become expensive.The problem is computationally very difficult, because the significance is not a monotonic property. However, in this paper we prove several other properties, which can be used for pruning the search space. The properties are implemented in the StatApriori algorithm, which searches statistically significant, non-redundant association rules. Based on both theoretical and empirical observations, the resulting rules are very accurate compared to traditional association rules. In addition, StatApriori can work with extremely low frequencies, thus finding new interesting rules.


title: Self-biased transconductance amplifier

abstract: A CMOS, self-biased transconductance amplifier has been designed to be associated and integrated with a silicon capacitive microphone. To meet requirements especially on gain sensitivity, power consumption, and minimization of parasite capacitance effect, we have proposed a cascode structure with the cascode transistor source used as signa input. Switched-capacitor techniques have been applied for realizing self-bias for the amplifier and ensuring its high- gain operation. The proposed amplifier has been designed and fabricated in a 0.8 micrometers CMOS process. It has a surface area of 210 micrometers by 170 micrometers . Experimental results obtained from measuring the fabricated chip show a high-gain sensitivity and a low power dissipation for the amplifier. Results of simulations and measurements have been discussed.© (1999) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.


title: Blockchain-Based IoT-Cloud Authorization and Delegation

abstract: In a Smart City scenario, the authors envisioned an IoT-Cloud framework for the management of boards and resources scattered over a geographic area. It can also become a tool to let device owners contribute freely to the infrastructure. In comparison to datacenter-oriented Cloud middleware, the administrator and the owner of the infrastructure are not one and the same. This translates into the requirement to support delegation-enabled authorization. In this paper, the authors investigate an authorization and delegation model for the IoT-Cloud based on blockchain technology. In particular, the scheme is implemented in the form of smart contracts over the Ethereum platform. Indeed, this approach represents an enhancement, over a function previously designed in a centralized fashion, by enabling the user to audit authorization operations and inspect how access control is actually performed, without blindly trusting the Cloud as a proxy for access to resources.


Classification of Moral Foundations in Microblog Political Discourse


RECONSTRUCTIVE MEMORY FOR ABSTRACT SELECTIVE RECALL


Additional Multi-Touch Attribution for Online Advertising.


title: X-band microstrip narrowband BPF composed of split ring resonator

abstract: The paper deals with the development of X-band microstrip narrowband bandpass filter (BPF) composed of split ring resonator (SRR). The proposed filter is designed on a grounded 0.508mm thick Roger RT/Duroid® 5880 dielectric substrate with the dimension of 25mm length and 20mm width. Some parametric studies to achieve an optimum design of microstrip narrowband BPF are investigated prior realization by varying the physical parameters of SRR including the width of rings and the gap between rings. From the experimental characterization, it shows that the realized microstrip narrowband BPF has a center frequency of 9.02 GHz with the values of return loss and insertion loss of 15.231 dB and 2.768 dB, respectively. The measured result is 20MHz lower than the simulated one which has center frequency of 9 GHz with the values of return loss and insertion loss of 15.025 dB and 2.242 dB, respectively.


title: Student-t Processes as Alternatives to Gaussian Processes

abstract: We investigate the Student-t process as an alternative to the Gaussian process as a nonparametric prior over functions. We derive closed form expressions for the marginal likelihood and predictive distribution of a Student-t process, by integrating away an inverse Wishart process prior over the covariance kernel of a Gaussian process model. We show surprising equivalences between different hierarchical Gaussian process models leading to Student-t processes, and derive a new sampling scheme for the inverse Wishart process, which helps elucidate these equivalences. Overall, we show that a Student-t process can retain the attractive properties of a Gaussian process -- a nonparametric representation, analytic marginal and predictive distributions, and easy model selection through covariance kernels -- but has enhanced flexibility, and predictive covariances that, unlike a Gaussian process, explicitly depend on the values of training observations. We verify empirically that a Student-t process is especially useful in situations where there are changes in covariance structure, or in applications like Bayesian optimization, where accurate predictive covariances are critical for good performance. These advantages come at no additional computational cost over Gaussian processes.


title: Road Structure Refined CNN for Road Extraction in Aerial Image

abstract: In this letter, we propose a road structure refined convolutional neural network (RSRCNN) approach for road extraction in aerial images. In order to obtain structured output of road extraction, both deconvolutional and fusion layers are designed in the architecture of RSRCNN. For training RSRCNN, a new loss function is proposed to incorporate the geometric information of road structure in cross-entropy loss, thus called road-structure-based loss function. Experimental results demonstrate that the trained RSRCNN model is able to advance the state-of-the-art road extraction for aerial images, in terms of precision, recall, F-score, and accuracy.


abstract: Poster: "ECR 2014 / C-1006 / Superficial benign skin and subcutaneous lesions presenting in the head and neck" by: "O. Pearce1, C. Bowles1, A. Spedding1, S. Keohane1, D. Gibson2; 1Cosham/UK, 2Portsmouth/UK"


title: Design and Manufacturing of a Mobile Rescue Robot

abstract: This paper presents design and manufacturing procedure of a tele-operative rescue robot. First, the general task to be performed by such a robot is defined, and variant kinematic mechanisms to form the basic structure of the robot will be discussed. Choosing an appropriate mechanism, geometric dimensions, and mass properties will be detailed to develop a dynamics model for the system. Next, the strength of each component is analyzed to finalize its shape. To complete the design procedure, Patran/Nastran was used to apply the finite element method for strength analysis of complicated parts. Also, ADAMS was used to model the mechanisms, where 3D sketch of each component of the robot was generated by means of Solidworks, and several sets of equations governing the dimensions of system were solved using Matlab. Finally, the components are fabricated and assembled together with controlling hardware. Two main processors are used within the control system of the robot. The operator's PC as the master processor and the laptop installed on the robot as the slave processor. The performance of the system was demonstrated in Rescue robot league of RoboCup 2005 in Osaka (Japan) and achieved the 2nd best design award.


title: Capacitance and Force Computation Due to Direct and Fringing Effects in MEMS/NEMS Arrays

abstract: An accurate computation of electrical force is significant in analyzing the performance of microelectromechanical systems and nanoelectromechanical systems. Many analytical and empirical models are available for computing the forces, especially, for a single set of parallel plates. In general, these forces are computed based on the direct electric field between the overlapping areas of the plates and the fringing field effects. Most of the models, which are based on direct electric field effect, consider only the trivial cases of the fringing field effects. In this paper, we propose different models which are obtained from the numerical simulations. It is found to be useful in computing capacitance as well force in simple and complex configurations consisting of an array of beams and electrodes. For the given configurations, the analytical models are compared with the available models and numerical results. While the percentage error of the proposed model is found to be under 6% with respect to the numerical results, the error associated with the analytical model without the fringing field effects is similar to 50%. The proposed model can be applied to the devices in which the fringing field effects are dominant.


title: Extension of trajectory planning in parameterized spaces to articulated vehicles

abstract: The main objective of this research is to study a novel method for safe maneuvering of articulated vehicles in warehouses. The presented method extends the concept of probabilistic planning on manifolds to articulated vehicles, which will be capable of driving, maneuvering and performing obstacle avoidance in any scenario. The proposed technique involves the extension of a parameterized space, developed for the reactive navigation of differential driven vehicles, to include an additional degree of freedom and use a probabilistic planner to calculate kinematically feasible trajectories. As a result, the algorithm is able to successfully generate maneuvers for an articulated truck and to navigate towards specific target points. The approach was validated using three problems representing different driving scenarios, demonstrating the possible utilization of the method in real-case scenarios. The solutions have been further benchmarked on multiple runs to evaluate success rate and to demonstrate the validity of the algorithm.


title: Adult zebrafish model for pneumococcal pathogenesis

abstract: Streptococcus pneumoniae (pneumococcus) is a leading cause of community acquired pneumonia, septicemia, and meningitis. Due to incomplete understanding of the host and bacterial factors contributing to these diseases optimal treatment and prevention methods are lacking. In the present study we examined whether the adult zebrafish (Danio rerio) can be used to investigate the pathophysiology of pneumococcal diseases. Here we show that both intraperitoneal and intramuscular injections of the pneumococcal strain TIGR4 cause a fulminant, dose-dependent infection in adult zebrafish, while isogenic mutant bacteria lacking the polysaccharide capsule, autolysin, or pneumolysin are attenuated in the model. Infection through the intraperitoneal route is characterized by rapid expansion of pneumococci in the bloodstream, followed by penetration of the blood–brain barrier and progression to meningitis. Using Rag1 mutant zebrafish, which are devoid of somatic recombination and thus lack adaptive immune responses, we show that clearance of pneumococci in adult zebrafish depends mainly on innate immune responses. In conclusion, this study provides evidence that the adult zebrafish can be used as a model for a pneumococcal infection, and that it can be used to study both host and bacterial factors involved in the pathogenesis. However, our results do not support the use of the zebrafish in studies on the role of adaptive immunity in pneumococcal disease or in the development of new pneumococcal vaccines.


title: Artificial Intelligence for Wireless Connectivity and Security of Cellular-Connected UAVs

abstract: Cellular-connected unmanned aerial vehicles (UAVs) will inevitably be integrated into future cellular networks as new aerial mobile users. Providing cellular connectivity to UAVs will enable a myriad of applications ranging from online video streaming to medical delivery. However, to enable a reliable wireless connectivity for the UAVs as well as a secure operation, various challenges need to be addressed such as interference management, mobility management and handover, cyber-physical attacks, and authentication. In this paper, the goal is to expose the wireless and security challenges that arise in the context of UAV-based delivery systems, UAV-based real-time multimedia streaming, and UAV-enabled intelligent transportation systems. To address such challenges, artificial neural network (ANN) based solution schemes are introduced. The introduced approaches enable the UAVs to adaptively exploit the wireless system resources while guaranteeing a secure operation, in real-time. Preliminary simulation results show the benefits of the introduced solutions for each of the aforementioned cellular-connected UAV application use case.


title: MSRC: Multimodal Spatial Regression with Semantic Context for Phrase Grounding

abstract: Given an image and a natural language query phrase, a grounding system localizes the mentioned objects in the image according to the query's specifications. State-of-the-art methods address the problem by ranking a set of proposal bounding boxes according to the query's semantics, which makes them dependent on the performance of proposal generation systems. Besides, query phrases in one sentence may be semantically related in one sentence and can provide useful cues to ground objects. We propose a novel Multimodal Spatial Regression with semantic Context (MSRC) system which not only predicts the location of ground truth based on proposal bounding boxes, but also refines prediction results by penalizing similarities of different queries coming from same sentences. The advantages of MSRC are twofold: first, it removes the limitation of performance from proposal generation algorithms by using a spatial regression network. Second, MSRC not only encodes the semantics of a query phrase, but also deals with its relation with other queries in the same sentence (i.e., context) by a context refinement network. Experiments show MSRC system provides a significant improvement in accuracy on two popular datasets: Flickr30K Entities and Refer-it Game, with 6.64% and 5.28% increase over the state-of-the-arts respectively.


title: Tarazu: optimizing MapReduce on heterogeneous clusters

abstract: Data center-scale clusters are evolving towards heterogeneous hardware for power, cost, differentiated price-performance, and other reasons. MapReduce is a well-known programming model to process large amount of data on data center-scale clusters. Most MapReduce implementations have been designed and optimized for homogeneous clusters. Unfortunately, these implementations perform poorly on heterogeneous clusters (e.g., on a 90-node cluster that contains 10 Xeon-based servers and 80 Atom-based servers, Hadoop performs worse than on 10-node Xeon-only or 80-node Atom-only homogeneous sub-clusters for many of our benchmarks). This poor performance remains despite previously proposed optimizations related to management of straggler tasks. In this paper, we address MapReduce's poor performance on heterogeneous clusters. Our first contribution is that the poor performance is due to two key factors: (1) the non-intuitive effect that MapReduce's built-in load balancing results in excessive and bursty network communication during the Map phase, and (2) the intuitive effect that the heterogeneity amplifies load imbalance in the Reduce computation. Our second contribution is Tarazu, a suite of optimizations to improve MapReduce performance on heterogeneous clusters. Tarazu consists of (1) Communication-Aware Load Balancing of Map computation (CALB) across the nodes, (2) Communication-Aware Scheduling of Map computation (CAS) to avoid bursty network traffic and (3) Predictive Load Balancing of Reduce computation (PLB) across the nodes. Using the above 90-node cluster, we show that Tarazu significantly improves performance over a baseline of Hadoop with straightforward tuning for hardware heterogeneity.


title: Risk for Low Weight in Community-Dwelling, Older Adults

abstract: PURPOSE ::: The purpose of this study was to describe nutritional risk and low weight in community-dwelling elderly. ::: ::: ::: METHOD ::: This cross-sectional exploratory study used in-depth interviews conducted on older adults with a body mass index <24 kg/m. Depression, mental state, nutrition, and demographic data were measured. ::: ::: ::: RESULTS ::: These elders (n = 130) were mostly female (55%, n = 71), married, white (84%, n = 109), and had a greater than a high school education. In a multivariate logistic regression analysis, 3 variables were statistically significantly associated with being severely underweight: those who self-reported having an illness or condition that changed the kind and/or amount of food eaten, unintentional weight loss of 10 lb in the last 6 months, and needing assistance with traveling outside the home. ::: ::: ::: CONCLUSION ::: Awareness of the high nutritional risk should prompt consideration of early, appropriate assessment and therapy to prevent malnutrition and a declining quality of life.


title: Electron and hole drift velocity measurements in silicon and their empirical relation to electric field and temperature

abstract: The drift velocity of electrons and holes in silicon has been measured in a large range of the electric fields (from 3 . 102to 6 . 104V/cm) at temperatures up to 430 K. The experimental data have been fitted with a simple formula for the temperatures of interest. The mean square deviation was in all cases less than 3.8 percent. A more general formula has also been derived which allows to obtain by extrapolation drift velocity data at any temperature and electric field.


title: Reliable 300 mm Wafer Level Hybrid Bonding for 3D Stacked CMOS Image Sensors

abstract: 3D Stacked Image sensor is the stacking of a Back-Side Illuminated (BSI) CMOS Image Sensor on a logic die. It enables compact size, higher performances and additional functionalities compared to standard BSI sensors. The highest footprint reduction is obtained with 3D hybrid bonding with metal interconnects between top and bottom tiers. Hybrid bonding process with oxide / copper direct bonding allows the highest scalability of interconnect pitch. In this study we present the morphological and electrical characterizations of a test vehicle. The hybrid bonding of wafers from two different technology nodes is performed using a dual damascene integration for the hybrid bonding level. The main parameters to assess the bonding interface quality are analyzed such as the influence of the pad design, the impact of reworkability and wafer -- to-wafer overlays. The process robustness is studied through reliability tests and electromigration measurements.


title: PERSONALITY AND SOCIAL PSYCHOLOGY BULLETIN Rudman et al. / IMPLICIT GENDER STEREOTYPES Implicit Self-Concept and Evaluative Implicit Gender Stereotypes: Self and Ingroup Share Desirable Traits

abstract: Experiment 1 unexpectedly found sex differences in evaluative gender stereotypes (only men associated male with potency and only women associated female with warmth). Experiment 2 dramatically reduced sex differences in gender-potency judgments when measures were redesigned to avoid implying that potency was positive (the concepts, strong and weak, were represented by evaluatively matched words; e.g., destroy vs. feeble, loud vs. quiet, and mighty vs. gentle). Experiment 3 tested the hypothesis that these sex differences were in the service of self-esteem but found no correlation between own-gender-favorable stereotyping and implicit self-esteem. Rather, participants showed a correlation between linking self to the favorable potency trait and linking own gender to that trait. Experiment 4 confirmed the correlation between implicit self-concept and gender stereotype using the contrast between potency and warmth for the implicit stereotype measure. In concert, results suggest that people possess implicit ge...


title: Chapter 1 SELF-ORGANIZED CONTROL OF IRREGULAR OR PERTURBED NETWORK TRAFFIC

abstract: We present a fluid-dynamic model for the simulation of urban traffic networks with road sections of different lengths and capacities. The model allows one to efficiently simulate the transitions between free and congested traffic, taking into account congestion-responsive traffic assignment and adaptive traffic control. We observe dynamic traffic patterns which significantly depend on the respective network topology. Synchronization is only one interesting example and implies the emergence of green waves. In this connection, we will discuss adaptive strategies of traffic light control which can considerably improve throughputs and travel times, using self-organization principles based on local interactions between vehicles and traffic lights. Similar adaptive control principles can be applied to other queueing networks such as production systems. In fact, we suggest to turn push operation of traffic systems into pull operation: By removing vehicles as fast as possible from the network, queuing effects can be most efficiently avoided. The proposed control concept can utilize the cheap sensor technologies available in the future and leads to reasonable operation modes. It is flexible, adaptive, robust, and decentralized rather than based on precalculated signal plans and a vulnerable traffic control center.


title: Recovering Dense Stereo Depth Maps Using a Single Gaussian Blurred Structured Light Pattern

abstract: In this paper, we present a new single shot structured light method to recover dense depth maps. Contrary to most temporal coding methods which require projecting a series of patterns, our method needs one color pattern only. Unlike most single shot spatial coding methods which establish correspondence along the edges of the captured images, our method produces a dense set of correspondences. Our method is built based on a new important observation that a Gaussian blurred De Bruijn pattern preserves the desirable windowed uniqueness property. A Gaussian blurred De Bruijn pattern is used so that the color of every illuminated pixel is used to its fullest advantage. The simulated experiments show that the proposed method establishes a correspondence set whose density and accuracy are close to that of using a temporal coding method. We also demonstrate the robustness of our approach by applying it to several real-world datasets.


title: RapidSmith: Do-It-Yourself CAD Tools for Xilinx FPGAs

abstract: Creating CAD tools for commercial FPGAs is a difficult task. Closed proprietary device databases and unsupported interfaces are largely to blame for the lack of CAD research found on commercial architectures versus hypothetical architectures. This paper formally introduces RapidSmith, a new set of tools and APIs that enable CAD tool creation for Xilinx FPGAs. Based on the Xilinx Design Language (XDL), RapidSmith provides a compact, yet, fast device database with hundreds of APIs that enable the creation of placers, routers and several other tools for Xilinx devices. RapidSmith alleviates several of the difficulties of using XDL and this work demonstrates the kinds of research facilitated by removing such challenges.


title: Verifiable Oblivious Storage

abstract: We formalize the notion of Verifiable Oblivious Storage VOS, where a client outsources the storage of data to a server while ensuring data confidentiality, access pattern privacy, and integrity and freshness of data accesses. VOS generalizes the notion of Oblivious RAM ORAM in that it allows the server to perform computation, and also explicitly considers data integrity and freshness. ::: ::: We show that allowing server-side computation enables us to construct asymptotically more efficient VOS schemes whose bandwidth overhead cannot be matched by any ORAM scheme, due to a known lower bound by Goldreich and Ostrovsky. Specifically, for large block sizes we can construct a VOS scheme with constant bandwidth per query; further, answering queries requires only poly-logarithmic server computation. We describe applications of VOS to Dynamic Proofs of Retrievability, and RAM-model secure multi-party computation.


title: On Criminal Identification in Color Skin Images Using Skin Marks (RPPVSM) and Fusion With Inferred Vein Patterns

abstract: Relatively Permanent Pigmented or Vascular Skin Marks (RPPVSM) were recently introduced as a biometric trait for identification in the cases in which the evidence images show only the nonfacial body parts of the criminals or victims, such as in child sexual abuse and riots. As manual RPPVSM identification is tiring and time-consuming, an automated RPPVSM identification system is proposed in this paper. The system comprises skin segmentation, RPPVSM detection, and RPPVSM matching algorithms. The system was evaluated on 1,200 back images collected from 283 Asian and Caucasian subjects in varying pose and viewpoint conditions. The system achieved rank-1 and rank-10 identification accuracies of 76.79% and 88.97%, respectively, higher than the identification accuracies given by existing skin mark detection methods previously proposed for face recognition systems. To handle identification with limited numbers of RPPVSM, a fusion scheme with inferred vein patterns is also proposed. The fusion was evaluated on 2,360 images of chests, forearms, and thighs collected mostly from Asian subjects, who tend to have fewer RPPVSM than Caucasian subjects. The results show that the fusion improves vein identification in all body parts with improvement rates varying between 2% and 5% depending on the number of RPPVSM detected. To the best of our knowledge, this is the first work on automated identification in color skin images based on nonfacial skin marks and fusion with inferred vein patterns in forensic settings.


MSDL-II: A Scenario Language with QoS Scheme in a Distributed Multimedia System


title: Deep Learning-Based Food Calorie Estimation Method in Dietary Assessment

abstract: Obesity treatment requires obese patients to record all food intakes per day. Computer vision has been introduced to estimate calories from food images. In order to increase accuracy of detection and reduce the error of volume estimation in food calorie estimation, we present our calorie estimation method in this paper. To estimate calorie of food, a top view and side view is needed. Faster R-CNN is used to detect the food and calibration object. GrabCut algorithm is used to get each food's contour. Then the volume is estimated with the food and corresponding object. Finally we estimate each food's calorie. And the experiment results show our estimation method is effective.


title: Bridging Cognitive Programs and Machine Learning

abstract: While great advances are made in pattern recognition and machine learning, the successes of such fields remain restricted to narrow applications and seem to break down when training data is scarce, a shift in domain occurs, or when intelligent reasoning is required for rapid adaptation to new environments. In this work, we list several of the shortcomings of modern machine-learning solutions, specifically in the contexts of computer vision and in reinforcement learning and suggest directions to explore in order to try to ameliorate these weaknesses.


title: Fingerprinting the datacenter: automated classification of performance crises

abstract: Contemporary datacenters comprise hundreds or thousands of machines running applications requiring high availability and responsiveness. Although a performance crisis is easily detected by monitoring key end-to-end performance indicators (KPIs) such as response latency or request throughput, the variety of conditions that can lead to KPI degradation makes it difficult to select appropriate recovery actions. We propose and evaluate a methodology for automatic classification and identification of crises, and in particular for detecting whether a given crisis has been seen before, so that a known solution may be immediately applied. Our approach is based on a new and efficient representation of the datacenter's state called a fingerprint, constructed by statistical selection and summarization of the hundreds of performance metrics typically collected on such systems. Our evaluation uses 4 months of trouble-ticket data from a production datacenter with hundreds of machines running a 24x7 enterprise-class user-facing application. In experiments in a realistic and rigorous operational setting, our approach provides operators the information necessary to initiate recovery actions with 80% correctness in an average of 10 minutes, which is 50 minutes earlier than the deadline provided to us by the operators. To the best of our knowledge this is the first rigorous evaluation of any such approach on a large-scale production installation.


title: Good Signal Detection Practices: Evidence from IMI PROTECT

abstract: Over a period of 5 years, the Innovative Medicines Initiative PROTECT (Pharmacoepidemiological Research on Outcomes of Therapeutics by a European ConsorTium) project has addressed key research questions relevant to the science of safety signal detection. The results of studies conducted into quantitative signal detection in spontaneous reporting, clinical trial and electronic health records databases are summarised and 39 recommendations have been formulated, many based on comparative analyses across a range of databases (e.g. regulatory, pharmaceutical company). The recommendations point to pragmatic steps that those working in the pharmacovigilance community can take to improve signal detection practices, whether in a national or international agency or in a pharmaceutical company setting. PROTECT has also pointed to areas of potentially fruitful future research and some areas where further effort is likely to yield less.


title: Encoding novel face‐name associations: A functional MRI study

abstract: The process of forming new associations between previously unrelated items of information, such as a name and a face, likely requires the integration of activity within multiple brain regions. The hippocampus and related structures in the medial temporal lobe are thought to be particularly critical in binding together items of information. We studied eight healthy young subjects with functional magnetic resonance imaging (fMRI) during the encoding of novel face-name associations compared to viewing repeated face-name pairs. A consistent pattern of activation was observed in the hippocampus, pulvinar nucleus of the thalamus, fusiform and dorsolateral prefrontal cortices across individual subjects. The location of the activation within the hippocampus was more anterior than previously reported in studies using similar novel vs. repeated paradigms with stimuli that did not specifically require relational processing among unrelated items. These data suggest that the process of forming new face-name associations is supported by a distributed network of brain regions, and provide additional evidence for the essential role of the hippocampus in associative memory processes. Hum. Brain Mapping 14:129–139, 2001. © 2001 Wiley-Liss, Inc.


title: Effects of dietary lysine levels on the concentrations of selected nutrient metabolites in blood plasma of late‐stage finishing pigs

abstract: Lysine is the first-limiting amino acid (AA) in typical swine diets and plays very important roles in promoting growth performance of pigs. This research was conducted to study the effects of dietary lysine on blood plasma concentrations of protein, carbohydrate, and lipid metabolites of pigs. Eighteen crossbred finishing pigs (nine barrows and nine gilts; initial BW 92.3 ± 6.9 kg) were individually penned in an environment controlled barn. Pigs were assigned to three dietary treatments according to a randomized complete block design with gender as block and pig as experimental unit (6 pigs/treatment). Three corn and soybean meal-based diets were formulated to contain total lysine at 0.43%, 0.71%, and 0.98% (as-fed basis) for Diets I (lysine deficient), II (lysine adequate), and III (lysine excess) respectively. After 4 weeks on trial, jugular vein blood was collected and plasma was separated. The plasma concentrations of total protein, albumin, urea nitrogen (UN), triglyceride, total cholesterol, and glucose were determined using an ACE Clinical Chemistry System (Alfa Wassermann, Inc., West Caldwell, NJ, USA). Data were analysed using the GLM Procedure with PDIFF (adjust = T) option of SAS. No differences (p > 0.10) were found between barrows and gilts for any of the metabolites measured. While there were no differences (p > 0.10) between pigs fed Diets II and III in plasma concentrations of UN, albumin, and total cholesterol, the concentration of albumin in these pigs was higher (p < .05) than that of pigs fed Diet I, and the concentrations of UN and total cholesterol in these pigs were lower (p < .05) than that of pigs fed Diet I. There were no differences (p > 0.10) among the three dietary treatments in plasma concentrations of total protein, triglycerides, and glucose. These findings indicated that the plasma metabolite profile can be affected by changing dietary lysine content only. Thorough understanding how the plasma metabolite profile is alternated by dietary lysine will facilitate nutrient management for more sustainable swine production.


title: Accountable internet protocol (aip)

abstract: This paper presents AIP (Accountable Internet Protocol), a network architecture that provides accountability as a first-order property. AIP uses a hierarchy of self-certifying addresses, in which each component is derived from the public key of the corresponding entity. We discuss how AIP enables simple solutions to source spoofing, denial-of-service, route hijacking, and route forgery. We also discuss how AIP's design meets the challenges of scaling, key management, and traffic engineering.


title: Wristband-Type Driver Vigilance Monitoring System Using Smartwatch

abstract: Studies have presented that the driver vigilance level has serious implication in the causation of road accidents. This paper focuses on integrating both the vehicle-based control behavior and physiological state to predict the driver vigilance index which is evaluated by using a smartwatch. The vehicle control behavior can be observed from the steering wheel movement. Our study utilized the smartwatch motion sensors to study the steering wheel behavior. Meanwhile, physiological state of driver reflects the driver capability of safety alert driving which is estimated by photoplethysmogram (PPG) and respiration signals in this paper. The PPG sensor is integrated in a sport wristband with a Bluetooth low energy module, transmitted the PPG signals to smartwatch in real time. The steering angle is derived by the reading from smartwatch built-in accelerometer and gyroscope sensors. On the other hand, the respiration is derived using the PPG peak baseline method. In order to utterly investigate the sleepiness-induced factors, the time, spectral, and phase space domain features are calculated. Considering the smartwatch processing capability, mutual-information technique is applied to designate the ten most descriptive features. Then, the extracted descriptive features are serve as parameters to a classifier to determine the driver aptitude status. The features are analyzed for their correlation with the subjective Koralinska sleepiness scale and through recorded video observations. The experimental results reveal that our system is capable of estimating driver hypervigilance at average of 96.5% accuracy rate by evaluating on both driving behavior and driver physiological state, provided a novel and low-cost implementation.


title: An Open Source Grammar Development Environment and Broad-coverage English Grammar Using HPSG

abstract: The LinGO (Linguistic Grammars Online) project’s English Resource Grammar and the LKB grammar development environment are language resources which are freely available for download for any purpose, including commercial use (see http://lingo.stanford.edu). Executable programs and source code are both included. In this paper, we give an outline of the LinGO English grammar and LKB system, and discuss the ways in which they are currently being used. The grammar and processing system can be used independently or combined to give a central component which can be exploited in a variety of ways. Our intention in writing this paper is to encourage more people to use the technology, which supports collaborative development on many levels.


title: Code Design for Short Blocks: A Survey

abstract: The design of block codes for short information blocks (e.g., a thousand or less information bits) is an open research problem which is gaining relevance thanks to emerging applications in wireless communication networks. In this work, we review some of the most recent code constructions targeting the short block regime, and we compare then with both finite-length performance bounds and classical error correction coding schemes. We will see how it is possible to effectively approach the theoretical bounds, with different performance vs. decoding complexity trade-offs.


title: Qualitative research: standards, challenges, and guidelines

abstract: Summary Qualitative research methods could help us to improve our understanding of medicine. Rather than thinking of qualitative and quantitative strategies as incompatible, they should be seen as complementary. Although procedures for textual interpretation differ from those of statistical analysis, because of the different type of data used and questions to be answered, the underlying principles are much the same. In this article I propose relevance, validity, and reflexivity as overall standards for qualitative inquiry. I will discuss the specific challenges in relation to reflexivity, transferability, and shared assumptions of interpretation, which are met by medical researchers who do this type of research, and I will propose guidelines for qualitative inquiry.


title: Ultrastructure of the male reproductive system and spermatophore formation in Labidocera aestiva (Crust.: Copepoda)

abstract: The male reproductive system of Labidocera aestiva produces a flask-shaped spermatophore connected to a chitin-like coupling apparatus. As immature spermatozoa leave the anterior region of the testis, they pass through the lumen of a long, sinuous duct composed of a ductus deferens and seminal vesicle. Ultrastructural examination of the ductus deferens reveals a highly glandular, columnar epithelium. The cells contain arrays of rough endoplasmic reticulum and abundant, well-developed Golgi complexes. This region produces and releases into the lumen, a flocculent substance and two granular secretions that constitute the seminal fluid. In its terminal part, the ductus deferens synthesizes another secretion that forms the spermatophore wall enclosing the spermatozoa and seminal fluid. Final synthesis of the spermatophore wall occurs within the thin-walled seminal vesicle, although this region functions primarily as a storage organ. Contiguous to the seminal vesicle is an elongate, highly glandular spermatophore sac. The chitin-like coupling apparatus, which functions to attach the spermatophore to the female, is formed in the anterior region of the sac by secretions from eight cell types. The posterior region of the sac stores the flask-shaped spermatophore and produces secretions that aid ejaculation of the entire spermatophore complex.


title: Nanoelectrode arrays based on low site density aligned carbon nanotubes

abstract: Nanoelectrode arrays (NEAs) were fabricated from the low site density aligned carbon nanotubes (CNTs). The CNTs were grown by plasma enhanced chemical vapor deposition (PECVD) on Ni nanoparticles made by the electrochemical deposition.


title: An Improved Systolic Architecture for LU Decomposition

abstract: LU-Decomposition is a classic problem for which many systolic array implementations have been proposed, the best of which takes 3n-3 time on n2/2 PEs, for a dense, n?n matrix. In this paper, we first give a proof that if only nearest neighbor communication is allowed, this time is a lower bound. We then generalize it to 2n + n/k - 3 time if one allows k-bounded broadcasts (i.e., if it takes m/k time steps to broadcast a value to m destination nodes). We also present a new architecture with this improved execution time, which uses n2/2 PEs, each one consisting of two multiplier-subtractor units, but active only on alternate cycles. This leads to a speedup and efficiency of kn2/(6k+3) and 2k/(6k + 3) respectively. For k = 1, our proposed architecture achieves the performance of the best previously known systolic array implementation. Special cases of our results include similar improvements to algorithms for solving (upper and lower) triangular linear systems by (forward and backward) substitution.


title: 2007) “A Lender-Based Theory of Collateral

abstract: We consider an imperfectly competitive loan market in which a local relationship lender has an information advantage vis-a-vis distant transaction lenders. Competitive pressure from the transaction lenders prevents the local lender from extracting the full surplus from projects, so that she inefficiently rejects marginally profitable projects. Collateral mitigates the inefficiency by increasing the local lender’s payoff from precisely those marginal projects that she inefficiently rejects. The model predicts that, controlling for observable borrower risk, collateralized loans are more likely to default ex post, which is consistent with the empirical evidence. The model also predicts that borrowers for whom local lenders have a relatively smaller information advantage face higher collateral requirements, and that technological innovations that narrow the information advantage of local lenders, such as small business credit scoring, lead to a greater use of collateral in lending relationships.


title: Balanced to Unbalanced Power Divider With Arbitrary Power Ratio

abstract: In this letter, Balanced to Unbalanced (BTU) Gysel type Power divider (PD) with arbitrary power ratio is proposed. The proposed power divider shows out-of-phase characteristic at two unbalanced ports. Standard $ {4\times 4}$ scattering matrix of BTU PD with arbitrary power ratio is obtained from mixed mode scattering matrix. The analysis of the four port network is done by converting it into a two port network while other ports are matched terminated. Considering realizable impedance range of $ {20-120\;\Omega }$ for microstrip lines, the maximum theoretical power division ratio is 1:4.76. Measurements are performed on a fabricated prototype (power dividing ratio 1:2) to verify the analytical results.


title: Rapidly-exploring Random Belief Trees for motion planning under uncertainty

abstract: In this paper we address the problem of motion planning in the presence of state uncertainty, also known as planning in belief space. The work is motivated by planning domains involving nontrivial dynamics, spatially varying measurement properties, and obstacle constraints. To make the problem tractable, we restrict the motion plan to a nominal trajectory stabilized with a linear estimator and controller. This allows us to predict distributions over future states given a candidate nominal trajectory. Using these distributions to ensure a bounded probability of collision, the algorithm incrementally constructs a graph of trajectories through state space, while efficiently searching over candidate paths through the graph at each iteration. This process results in a search tree in belief space that provably converges to the optimal path. We analyze the algorithm theoretically and also provide simulation results demonstrating its utility for balancing information gathering to reduce uncertainty and finding low cost paths.


Theory-Based User Modeling for Personalized interactive Information Retrieval