Query: Autonomous Vehicle Navigation by Building 3 D Map and by Detecting Human Trajectory using LIDAR

Relevant passages:
Autonomous Driving in Urban Environments: Boss and the Urban Challenge <=SEP=> Chris Urmson, Joshua Anhalt, Drew Bagnell, Christopher Baker, Robert Bittner, M. N. Clark, John Dolan, Dave Duggins, Tugrul Galatali, Chris Geyer, Michele Gittleman, Sam Harbaugh, Martial Hebert, Thomas M. Howard, Sascha Kolski, Alonzo Kelly, Maxim Likhachev, Matt McNaughton, Nick Miller, Kevin Peterson, Brian Pilnick, Raj Rajkumar, Paul Rybski, Bryan Salesky, Young-Woo Seo, Sanjiv Singh, Jarrod Snider, Anthony Stentz, William “Red” Whittaker, Ziv Wolkowicki, and Jason Ziglar Carnegie Mellon University Pittsburgh, Pennsylvania 15213 e-mail: curmson@ri.cmu.edu

Winning the DARPA Grand Challenge with an AI Robot <=SEP=> This paper describes the software architecture of Stanley, an autonomous land vehicle developed for high-speed desert driving without human intervention. The vehicle recently won the DARPA Grand Challenge, a major robotics competition. The article describes the software architecture of the robot, which relied pervasively on state-of-the-art AI technologies, such as machine learning and probabilistic reasoning.

Junior: The Stanford entry in the Urban Challenge <=SEP=> 

Classification and tracking of dynamic objects with multiple sensors for autonomous driving in urban environments <=SEP=> Future driver assistance systems are likely to use a multisensor approach with heterogeneous sensors for tracking dynamic objects around the vehicle. The quality and type of data available for a data fusion algorithm depends heavily on the sensors detecting an object. This article presents a general framework which allows the use sensor specific advantages while abstracting the specific details of a sensor. Different tracking models are used depending on the current set of sensors detecting the object. A sensor independent algorithm for classifying objects regarding their current and past movement state is presented. The described architecture and algorithms have been successfully implemented in Tartan racingpsilas autonomous vehicle for the urban grand challenge. Results are presented and discussed.

Robust Real-time Object Detection <=SEP=> This paper describes a visual object detection framework that is capable of processing images extremely rapidly while achieving high detection rates. There are three key contributions. The first is the introduction of a new image representation called the “Integral Image” which allows the features used by our detector to be computed very quickly. The second is a learning algorithm, based on AdaBoost, which selects a small number of critical visual features and yields extremely efficient classifiers [6]. The third contribution is a method for combining classifiers in a “cascade” which allows background regions of the image to be quickly discarded while spending more computation on promising object-like regions. A set of experiments in the domain of face detection are presented. The system yields face detection performace comparable to the best previous systems [18, 13, 16, 12, 1]. Implemented on a conventional desktop, face detection proceeds at 15 frames per second.

A 3-$\mu\hbox{W}$  CMOS Glucose Sensor for Wireless Contact-Lens Tear Glucose Monitoring <=SEP=> This paper presents a noninvasive wireless sensor platform for continuous health monitoring. The sensor system integrates a loop antenna, wireless sensor interface chip, and glucose sensor on a polymer substrate. The IC consists of power management, readout circuitry, wireless communication interface, LED driver, and energy storage capacitors in a 0.36-mm2 CMOS chip with no external components. The sensitivity of our glucose sensor is 0.18 μA·mm-2·mM-1. The system is wirelessly powered and achieves a measured glucose range of 0.05-1 mM with a sensitivity of 400 Hz/mM while consuming 3 μW from a regulated 1.2-V supply.

A Review and Characterization of Progressive Visual Analytics <=SEP=> Progressive Visual Analytics (PVA) has gained increasing attention over the past years. It brings the user into the loop during otherwise long-running and non-transparent computations by producing intermediate partial results. These partial results can be shown to the user for early and continuous interaction with the emerging end result even while it is still being computed. Yet as clear-cut as this fundamental idea seems, the existing body of literature puts forth various interpretations and instantiations that have created a research domain of competing terms, various definitions, as well as long lists of practical requirements and design guidelines spread across different scientific communities. This makes it more and more difficult to get a succinct understanding of PVA’s principal concepts, let alone an overview of this increasingly diverging field. The review and discussion of PVA presented in this paper address these issues and provide (1) a literature collection on this topic, (2) a conceptual characterization of PVA, as well as (3) a consolidated set of practical recommendations for implementing and using PVA-based visual analytics solutions.

A closer look at recognition-based graphical passwords on mobile devices <=SEP=> Graphical password systems based on the recognition of photographs are candidates to alleviate current over-reliance on alphanumeric passwords and PINs. However, despite being based on a simple concept -- and user evaluations consistently reporting impressive memory retention -- only one commercial example exists and overall take-up is low. Barriers to uptake include a perceived vulnerability to observation attacks; issues regarding deployability; and the impact of innocuous design decisions on security not being formalized. Our contribution is to dissect each of these issues in the context of mobile devices -- a particularly suitable application domain due to their increasing significance, and high potential to attract unauthorized access. This produces: 1) A novel yet simple solution to the intersection attack that permits greater variability in login challenges; 2) Detailed analysis of the shoulder surfing threat that considers both simulated and human testing; 3) A first look at image processing techniques to contribute towards automated photograph filtering. We operationalize our observations and gather data in a field context where decentralized mechanisms of varying entropy were installed on the personal devices of participants. Across two working weeks success rates collected from users of a high entropy version were similar to those of a low entropy version at 77%, and login durations decreased significantly across the study.

Robust Inference via Generative Classifiers for Handling Noisy Labels <=SEP=> Large-scale datasets may contain significant proportions of noisy (incorrect) class labels, and it is well-known that modern deep neural networks (DNNs) poorly generalize from such noisy training datasets. To mitigate the issue, we propose a novel inference method, termed Robust Generative classifier (RoG), applicable to any discriminative (e.g., softmax) neural classifier pre-trained on noisy datasets. In particular, we induce a generative classifier on top of hidden feature spaces of the pre-trained DNNs, for obtaining a more robust decision boundary. By estimating the parameters of generative classifier using the minimum covariance determinant estimator, we significantly improve the classification accuracy with neither re-training of the deep model nor changing its architectures. With the assumption of Gaussian distribution for features, we prove that RoG generalizes better than baselines under noisy labels. Finally, we propose the ensemble version of RoG to improve its performance by investigating the layer-wise characteristics of DNNs. Our extensive experimental results demonstrate the superiority of RoG given different learning models optimized by several training techniques to handle diverse scenarios of noisy labels.

Domain Specific Sentence Level Mood Extraction from Malayalam Text <=SEP=> Natural Language Processing (NLP) is a field which studies the interactions between computers and natural languages. NLP is used to enable computers to attain the capability of manipulating natural languages with a level of expertise equivalent to humans. There exists a wide range of applications for NLP, of which sentiment analysis(SA) plays a major role. In sentimental analysis, the emotional polarity of a given text is analysed, and classified as positive, negative or neutral. A more difficult task is to refine the classification into different moods such as happy, sad, angry etc. Analysing a natural language for mood extraction is not at all an easy task for a computer. Even after achieving capabilities of massive amount of computation within a matter of seconds, understanding the sentiments embodied in phrases and sentences of textual information remains one of the toughest tasks for a computer till date. This paper focuses on tagging the appropriate mood in Malayalam text. Tagging is used to specify whether a sentence indicates a sad, happy or angry mood of the person involved or if the sentence contains just facts, devoid of emotions. This research work is heavily dependent on the language since the structures vary from language to language. Mood extraction and tagging has been successfully implemented for English and other European languages. For the south Indian language Malayalam, no significant work has yet been done on mood extraction. We will be focusing on domain-specific sentence-level mood extraction from Malayalam text. The extraction process involves parts-of-speech tagging of the input sentence, extracting the patterns from the input sentence which will contribute to the mood of the sentence, such as the adjective, adverb etc., and finally scoring the sentence on an emotive scale by calculating the semantic orientation of the sentence using the extracted patterns. Sentiment classification is becoming a promising topic with the rise of social media such as blogs, social networking sites, where people express their views on various topics. Mood extraction enables computers to automate the activities performed by human for making decisions based on the moods of opinions expressed in Malayalam text.

A Reinforcement Learning Approach to Interactive-Predictive Neural Machine Translation <=SEP=> We present an approach to interactivepredictive neural machine translation that attempts to reduce human effort from three directions: Firstly, instead of requiring humans to select, correct, or delete segments, we employ the idea of learning from human reinforcements in form of judgments on the quality of partial translations. Secondly, human effort is further reduced by using the entropy of word predictions as uncertainty criterion to trigger feedback requests. Lastly, online updates of the model parameters after every interaction allow the model to adapt quickly. We show in simulation experiments that reward signals on partial translations significantly improve character F-score and BLEU compared to feedback on full translations only, while human effort can be reduced to an average number of 5 feedback requests for every input.

A Critical Evaluation of User Participation Research: Gaps and Future Directions <=SEP=> In this theoretical synthesis, we juxtapose three traditions of prior research on user participation and involvement: the survey and experimental literature on the relationship between user participation and IS success, the normative literature on alternative development approaches, and qualitative studies that examine user participation from a variety of theoretical perspectives. We also assess progress made in the three bodies of literature, and identify gaps and directions of future research for improving user participation.

Epidermoid cyst of the clitoris: a case report. <=SEP=> We report a rare case of spontaneous clitoral epidermal cyst without any declared previous female genital mutilation. This patient was successfully and surgically resected with good local and cosmetic results.

Multi-threshold CMOS design for low power digital circuits <=SEP=> Multi-threshold CMOS (MTCMOS) power gating is a design technique in which a power gating transistor is connected between the logic transistors and either power or ground, thus creating a virtual supply rail or virtual ground rail, respectively. Power gating transistor sizing, transition (sleep mode to active mode) current, short circuit current and transition time are design issues for power gating design. The use of power gating design results in the delay overhead in the active mode. If both nMOS and pMOS sleep transistor are used in power gating, delay overhead will increase. This paper proposes the design methodology for reducing the delay of the logic circuits during active mode. This methodology limits the maximum value of transition current to a specified value and eliminates short circuit current. Experiment results show 16.83% reduction in the delay.

A 0.4 V 63 $\mu$W 76.1 dB SNDR 20 kHz Bandwidth Delta-Sigma Modulator Using a Hybrid Switching Integrator <=SEP=> This paper presents a delta-sigma modulator operating at a supply voltage of 0.4 V. The designed delta-sigma modulator uses a proposed hybrid switching integrator and operates at a low supply voltage without clock boosting or bootstrapped switches. The proposed integrator consists of both switched-resistor and switched-capacitor operations and significantly reduces distortion at a low supply voltage. Variation in the turn-on resistance, which is the main source of distortion, is avoided by placing the switches at the virtual ground node of the amplifier. The proposed low-voltage design scheme can replace commonly-used clock boosting techniques, which rely on internal high-voltage generation circuits. A fabricated modulator achieves a 76.1 dB signal-to-noise-plus-distortion ratio (SNDR) and an 82 dB dynamic range at a 20 kHz bandwidth. The measured total power consumption is 63 μW from a 0.4 V supply voltage. The measured results show robust SNDR performance, even at ±10% supply voltage variations. The measured results also show stable performance over a wide temperature range.

PyDial: A Multi-domain Statistical Dialogue System Toolkit <=SEP=> Statistical Spoken Dialogue Systems have been around for many years. However, access to these systems has always been difficult as there is still no publicly available end-to-end system implementation. To alleviate this, we present PyDial, an opensource end-to-end statistical spoken dialogue system toolkit which provides implementations of statistical approaches for all dialogue system modules. Moreover, it has been extended to provide multidomain conversational functionality. It offers easy configuration, easy extensibility, and domain-independent implementations of the respective dialogue system modules. The toolkit is available for download under the Apache 2.0 license.

A study of injection locking and pulling in oscillators <=SEP=> Injection locking characteristics of oscillators are derived and a graphical analysis is presented that describes injection pulling in time and frequency domains. An identity obtained from phase and envelope equations is used to express the requisite oscillator nonlinearity and interpret phase noise reduction. The behavior of phase-locked oscillators under injection pulling is also formulated.

Quantum generalisation of feedforward neural networks <=SEP=> We propose a quantum generalisation of a classical neural network. The classical neurons are firstly rendered reversible by adding ancillary bits. Then they are generalised to being quantum reversible, i.e. unitary. (The classical networks we generalise are called feedforward, and have step-function activation functions.) The quantum network can be trained efficiently using gradient descent on a cost function to perform quantum generalisations of classical tasks. We demonstrate numerically that it can: (i) compress quantum states onto a minimal number of qubits, creating a quantum autoencoder, and (ii) discover quantum communication protocols such as teleportation. Our general recipe is theoretical and implementation-independent. The quantum neuron module can naturally be implemented photonically.

Life Cycle of Smart Contracts in Blockchain Ecosystems <=SEP=> This paper discusses the life cycle of decentralized smart contracts, i.e. digital and executable representations of rights and obligations in a multi-party environment. The life cycle relies on blockchain technology, i.e. a distributed digital ledger, to ensure proper implementation and integrity of the smart contracts. The life cycle consists of four subsequent phases: Creation, freezing, execution, and finalization. For each phase actors and technological services are identified and explained in detail. With the life cycle at hand, risks and limitations of smart contracts and the underlying blockchain technology are briefly discussed.

Depth Selective Camera: A Direct, On-Chip, Programmable Technique for Depth Selectivity in Photography <=SEP=> Time of flight (ToF) cameras use a temporally modulated light source and measure correlation between the reflected light and a sensor modulation pattern, in order to infer scene depth. In this paper, we show that such correlational sensors can also be used to selectively accept or reject light rays from certain scene depths. The basic idea is to carefully select illumination and sensor modulation patterns such that the correlation is non-zero only in the selected depth range - thus light reflected from objects outside this depth range do not affect the correlational measurements. We demonstrate a prototype depth-selective camera and highlight two potential applications: imaging through scattering media and virtual blue screening. This depth-selectivity can be used to reject back-scattering and reflection from media in front of the subjects of interest, thereby significantly enhancing the ability to image through scattering media-critical for applications such as car navigation in fog and rain. Similarly, such depth selectivity can also be utilized as a virtual blue-screen in cinematography by rejecting light reflecting from background, while selectively retaining light contributions from the foreground subject.

Power factor improvement of a transverse flux machine with high torque density <=SEP=> Design of a permanent magnet transverse flux machine optimized for high torque density and high power factor is presented. The optimization process focuses on performance enhancement through improvement of power factor without sacrificing torque density. Simple magnetostatic simulation based method is proposed for the optimization process. Magnetic couplings among axially separated phases are also discussed in the article. Experimental results demonstrating the correlation of measured inductances with that of simulation results are provided.

A probabilistic model for item-based recommender systems <=SEP=> Recommender systems estimate the conditional probability P(χj|χi) of item χj being bought, given that a customer has already purchased item χi. While there are different ways of approximating this conditional probability, the expression is generally taken to refer to the frequency of co-occurrence of items in the same basket, or other user-specific item lists, rather than being seen as the co-occurrence of χj with χi as a proportion of all other items bought alongside χi. This paper proposes a probabilistic calculus for the calculation of conditionals based on item rather than basket counts. The proposed method has the consequence that items bough together as part of small baskets are more predictive of each other than if they co-occur in large baskets. Empirical results suggests that this may result in better take-up of personalised recommendations.

Efficacy of music therapy treatment based on cycles of sessions: a randomised controlled trial. <=SEP=> We undertook a randomised controlled trial to assess whether a music therapy (MT) scheme of administration, including three working cycles of one month spaced out by one month of no treatment, is effective to reduce behavioural disturbances in severely demented patients. Sixty persons with severe dementia (30 in the experimental and 30 in the control group) were enrolled. Baseline multidimensional assessment included demographics, Mini Mental State Examination (MMSE), Barthel Index and Neuropsychiatry Inventory (NPI) for all patients. All the patients of the experimental and control groups received standard care (educational and entertainment activities). In addition, the experimental group received three cycles of 12 active MT sessions each, three times a week. Each 30-min session included a group of three patients. Every cycle of treatment was followed by one month of wash-out. At the end of this study, MT treatment resulted to be more effective than standard care to reduce behavioural disorders. We observed a significant reduction over time in the NPI global scores in both groups (F(7,357) = 9.06, p < 0.001) and a significant difference between groups (F(1,51) = 4.84, p < 0.05) due to a higher reduction of behavioural disturbances in the experimental group at the end of the treatment (Cohen's d = 0.63). The analysis of single NPI items shows that delusions, agitation and apathy significantly improved in the experimental, but not in the control group. This study suggests the effectiveness of MT approach with working cycles in reducing behavioural disorders of severely demented patients.

Uninterruptible power supply systems provide protection <=SEP=> Nowadays, uninterruptible power supply (UPS) systems are in use throughout the world, helping to supply a wide variety of critical loads, in situations of power outage or anomalies of the mains. This article describes the most common line problems and the relationship between these and the different existing kinds of UPS, showing their operation modes as well as the existent energy storage systems. It also addresses an overview of the control schemes applied to different distributed UPS configurations. Finally, it points out the applicability of such systems in distributed generation, microgrids, and renewable energy systems.

The examination timetabling problem at Universiti Malaysia Pahang: Comparison of a constructive heuristic with an existing software solution <=SEP=> This paper presents a real-world, capacitated examination timetabling problem from Universiti Malaysia Pahang (UMP), Malaysia. The problem has constraints which have not been modelled before, these being the distance between examination rooms and splitting exams across several rooms. These constraints provide additional challenges in defining a suitable model and in developing a constructive heuristic. One of the contributions of this paper is to formally define this real-world problem. A further contribution is the constructive heuristic that is able to produce good quality solutions for the problem, which are superior to the solutions that are produced using the university’s current software. Moreover, our method adheres to all hard constraints which the current systems fails to do. 2010 Elsevier B.V. All rights reserved.

No-Reference Image Quality Assessment in the Spatial Domain <=SEP=> We propose a natural scene statistic-based distortion-generic blind/no-reference (NR) image quality assessment (IQA) model that operates in the spatial domain. The new model, dubbed blind/referenceless image spatial quality evaluator (BRISQUE) does not compute distortion-specific features, such as ringing, blur, or blocking, but instead uses scene statistics of locally normalized luminance coefficients to quantify possible losses of “naturalness” in the image due to the presence of distortions, thereby leading to a holistic measure of quality. The underlying features used derive from the empirical distribution of locally normalized luminances and products of locally normalized luminances under a spatial natural scene statistic model. No transformation to another coordinate frame (DCT, wavelet, etc.) is required, distinguishing it from prior NR IQA approaches. Despite its simplicity, we are able to show that BRISQUE is statistically better than the full-reference peak signal-to-noise ratio and the structural similarity index, and is highly competitive with respect to all present-day distortion-generic NR IQA algorithms. BRISQUE has very low computational complexity, making it well suited for real time applications. BRISQUE features may be used for distortion-identification as well. To illustrate a new practical application of BRISQUE, we describe how a nonblind image denoising algorithm can be augmented with BRISQUE in order to perform blind image denoising. Results show that BRISQUE augmentation leads to performance improvements over state-of-the-art methods. A software release of BRISQUE is available online: http://live.ece.utexas.edu/research/quality/BRISQUE_release.zip for public use and evaluation.

Forward Thinking: Building Deep Random Forests <=SEP=> The success of deep neural networks has inspired many to wonder whether other learners could benefit from deep, layered architectures. We present a general framework called forward thinking for deep learning that generalizes the architectural flexibility and sophistication of deep neural networks while also allowing for (i) different types of learning functions in the network, other than neurons, and (ii) the ability to adaptively deepen the network as needed to improve results. This is done by training one layer at a time, and once a layer is trained, the input data are mapped forward through the layer to create a new learning problem. The process is then repeated, transforming the data through multiple layers, one at a time, rendering a new dataset, which is expected to be better behaved, and on which a final output layer can achieve good performance. In the case where the neurons of deep neural nets are replaced with decision trees, we call the result a Forward Thinking Deep Random Forest (FTDRF). We demonstrate a proof of concept by applying FTDRF on the MNIST dataset. We also provide a general mathematical formulation, called Forward Thinking that allows for other types of deep learning problems to be considered.

Exploiting Shallow Linguistic Information for Relation Extraction from Biomedical Literature <=SEP=> We propose an approach for extracting relations between entities from biomedical literature based solely on shallow linguistic information. We use a combination of kernel functions to integrate two different information sources: (i) the whole sentence where the relation appears, and (ii) the local contexts around the interacting entities. We performed experiments on extracting gene and protein interactions from two different data sets. The results show that our approach outperforms most of the previous methods based on syntactic and semantic information.

Oxidants, antioxidants, and the degenerative diseases of aging. <=SEP=> Metabolism, like other aspects of life, involves tradeoffs. Oxidant by-products of normal metabolism cause extensive damage to DNA, protein, and lipid. We argue that this damage (the same as that produced by radiation) is a major contributor to aging and to degenerative diseases of aging such as cancer, cardiovascular disease, immune-system decline, brain dysfunction, and cataracts. Antioxidant defenses against this damage include ascorbate, tocopherol, and carotenoids. Dietary fruits and vegetables are the principal source of ascorbate and carotenoids and are one source of tocopherol. Low dietary intake of fruits and vegetables doubles the risk of most types of cancer as compared to high intake and also markedly increases the risk of heart disease and cataracts. Since only 9% of Americans eat the recommended five servings of fruits and vegetables per day, the opportunity for improving health by improving diet is great.

A theoretical framework of a BIM-based multi-disciplinary collaboration platform <=SEP=> a r t i c l e i n f o Keywords: BIM BIM-server Multidisciplinary collaboration Operational technical requirements Support technical requirements Most complex projects in the Architecture, Engineering, and Construction (AEC) industries involve multidisciplinary collaboration and the exchange of large building data set. Traditionally, the collaboration efforts across the disciplines have been based on the frequent exchange of 2D drawings and documents. However, during the past decade, the widespread adoption of object-oriented Computer-aided Design (CAD) tools has generated more interests in Building Information Modelling (BIM). A number of BIM-compliant applications such as analysis tools, model checkers and facility management applications are being developed. This paper develops a theoretical framework of technical requirements for using BIM-server as a multidisciplinary collaboration platform. The methodologies that are used to develop the framework include focus group interviews (FGIs) with representatives from the diverse AEC disciplines, a case study of an Architectural project using a state-of-the-art BIM-server, and a critical review and analysis of current collaboration platforms that are available to the AEC industries. This paper concludes that greater emphasis should be placed on supporting technical requirements to facilitate technology management and implementation across disciplines. Their implications for user-centric technology development in design and construction industry are also discussed. Traditionally, the inter-disciplinary collaboration in the Architecture, Engineering, and Construction (AEC) industries has revolved around the exchange of 2D drawings and documents. Even though the separate design disciplines have been using 3D models and applications for visualization and design development, the collaboration practices have remained more or less 2D-based until recently. The widespread use and proliferation of object-oriented Computer-Aided Design (CAD) packages and increased constructability and level of automation in construction processes provide encouraging motives for the exchange of 3D data in the collaboration process. Building Information Modelling (BIM) is envisaged to play a significant role in this transformation. BIM is an advanced approach to object-oriented CAD, which extends the capability of traditional CAD approach by defining and applying intelligent relationships between the elements in the building model. BIM models include both geometric and non-geometric data such as object attributes and specifications. The built-in intelligence allows automated extraction of 2D drawings, documentation and other building information directly from the BIM model. This built-in intelligence also provides constraints that can reduce modelling errors and prevent technical flaws in the design, based on the rules encoded in the software [11,16,21]. Most recent CAD packages such as ArchiCAD …