Query: From Depth Data to Head Pose Estimation: a Siamese approach

Relevant passages:
Real-Time Head Orientation from a Monocular Camera Using Deep Neural Network <=SEP=> [1] J. Xiao, S. Baker, I. Matthews, T. Kanade, “Real-Time Combined 2D + 3D Active Appearance Models,” CVPR, pp.535-542, 2004. [2] N. Gourier, D. Hall, J. L. Crowley, “Estimating Face Orientation from Robust Detection of Salient Facial Features”, Proceedings of Pointing 2004, ICPR, International Workshop on Visual Observation of Deictic Gestures, 2004. [3] G. Fanelli, M. Dantone, J. Gall, A. Jssati, L. Van Gool, “Random Forests for Real Time 3D Face Analysis,” IJCV, pp.437-458, 2013. [4] Y. LeCun, R. Bottou, Y. Bengio, P. Haffner, “Gradient-based Learning Applied to Document Recognition,” Proc. IEEE, vol. 86, no. 11, pp.2278-2324, 1998. [5] A. Krizhevsky, I. Sutskever, G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” NIPS, pp.1106-1114, 2012. This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIP) (No. 2010-0028680). Proposed Method  Conventional Head Pose Estimation Approaches

Robust Real-Time Face Detection <=SEP=> 

Driver head pose estimation using efficient descriptor fusion <=SEP=> A great interest is focused on driver assistance systems using the head pose as an indicator of the visual focus of attention and the mental state. In fact, the head pose estimation is a technique allowing to deduce head orientation relatively to a view of camera and could be performed by model-based or appearance-based approaches. Modelbased approaches use a face geometrical model usually obtained from facial features, whereas appearance-based techniques use the whole face image characterized by a descriptor and generally consider the pose estimation as a classification problem. Appearance-based methods are faster andmore adapted to discrete pose estimation. However, their performance depends strongly on the head descriptor, which should be well chosen in order to reduce the information about identity and lighting contained in the face appearance. In this paper, we propose an appearancebased discrete head pose estimation aiming to determine the driver attention level from monocular visible spectrum images, even if the facial features are not visible. Explicitly, we first propose a novel descriptor resulting from the fusion of four most relevant orientation-based head descriptors, namely the steerable filters, the histogram of oriented gradients (HOG), the Haar features, and an adapted version of speeded up robust feature (SURF) descriptor. Second, in order to derive a compact, relevant, and consistent subset of descriptor’s features, a comparative study is conducted on some well-known feature selection algorithms. Finally, the obtained subset is subject to the classification process, performed by the support vector machine (SVM), to learn head pose variations. As we show in experiments with the public database (Pointing’04) as well as with our real-world sequence, our approach describes the head with a high accuracy and provides robust estimation of the head pose, compared to state-of-the-art methods.

Real time head pose estimation with random regression forests <=SEP=> Fast and reliable algorithms for estimating the head pose are essential for many applications and higher-level face analysis tasks. We address the problem of head pose estimation from depth data, which can be captured using the ever more affordable 3D sensing technologies available today. To achieve robustness, we formulate pose estimation as a regression problem. While detecting specific face parts like the nose is sensitive to occlusions, learning the regression on rather generic surface patches requires enormous amount of training data in order to achieve accurate estimates. We propose to use random regression forests for the task at hand, given their capability to handle large training datasets. Moreover, we synthesize a great amount of annotated training data using a statistical model of the human face. In our experiments, we show that our approach can handle real data presenting large pose changes, partial occlusions, and facial expressions, even though it is trained only on synthetic neutral face data. We have thoroughly evaluated our system on a publicly available database on which we achieve state-of-the-art performance without having to resort to the graphics card.

3D head pose estimation with convolutional neural network trained on synthetic images <=SEP=> In this paper, we propose a method to estimate head pose with convolutional neural network, which is trained on synthetic head images. We formulate head pose estimation as a regression problem. A convolutional neural network is trained to learn head features and solve the regression problem. To provide annotated head poses in the training process, we generate a realistic head pose dataset by rendering techniques, in which we consider the variation of gender, age, race and expression. Our dataset includes 74000 head poses rendered from 37 head models. For each head pose, RGB image and annotated pose parameters are given. We evaluate our method on both synthetic and real data. The experiments show that our method improves the accuracy of head pose estimation.

Multilingual Speech Recognition with a Single End-to-End Model <=SEP=> Training a conventional automatic speech recognition (ASR) system to support multiple languages is challenging because the sub-word unit, lexicon and word inventories are typically language specific. In contrast, sequence-to-sequence models are well suited for multilingual ASR because they encapsulate an acoustic, pronunciation and language model jointly in a single network. In this work we present a single sequence-to-sequence ASR model trained on 9 different Indian languages, which have very little overlap in their scripts. Specifically, we take a union of language-specific grapheme sets and train a grapheme-based sequence-to-sequence model jointly on data from all languages. We find that this model, which is not explicitly given any information about language identity, improves recognition performance by 21% relative compared to analogous sequence-to-sequence models trained on each language individually. By modifying the model to accept a language identifier as an additional input feature, we further improve performance by an additional 7% relative and eliminate confusion between different languages.

The coevolution of cultural groups and ingroup favoritism. <=SEP=> Cultural boundaries have often been the basis for discrimination, nationalism, religious wars, and genocide. Little is known, however, about how cultural groups form or the evolutionary forces behind group affiliation and ingroup favoritism. Hence, we examine these forces experimentally and show that arbitrary symbolic markers, though initially meaningless, evolve to play a key role in cultural group formation and ingroup favoritism because they enable a population of heterogeneous individuals to solve important coordination problems. This process requires that individuals differ in some critical but unobservable way and that their markers be freely and flexibly chosen. If these conditions are met, markers become accurate predictors of behavior. The resulting social environment includes strong incentives to bias interactions toward others with the same marker, and subjects accordingly show strong ingroup favoritism. When markers do not acquire meaning as accurate predictors of behavior, players show a markedly reduced taste for ingroup favoritism. Our results support the prominent evolutionary hypothesis that cultural processes can reshape the selective pressures facing individuals and so favor the evolution of behavioral traits not previously advantaged.

Feature hashing for large scale multitask learning <=SEP=> Empirical evidence suggests that hashing is an effective strategy for dimensionality reduction and practical nonparametric estimation. In this paper we provide exponential tail bounds for feature hashing and show that the interaction between random subspaces is negligible with high probability. We demonstrate the feasibility of this approach with experimental results for a new use case --- multitask learning with hundreds of thousands of tasks.

Efficiency-Oriented Design of ZVS Half-Bridge Series Resonant Inverter With Variable Frequency Duty Cycle Control <=SEP=> The efficiency of zero voltage switching half-bridge series resonant inverter can be decreased under certain load conditions due to the high switching frequencies required. The proposed variable frequency duty cycle (VFDC) control is intended to improve the efficiency in the medium and low output power levels because of the decreased switching frequencies. The study performed in this letter includes, in a first step, a theoretical analysis of power balance as a function of control parameters. In addition, restrictions due to snubber capacitors and deadtime, and variability of the loads have been considered. Afterward, an efficiency analysis has been carried out to determine the optimum operation point. Switching and conduction losses have been calculated to examine the overall efficiency improvement. VFDC strategy efficiency improvement is achieved by means of a switching-frequency reduction, mainly at low-medium power range, and with low-quality factor loads. Domestic induction heating application is suitable for the use of VFDC strategy due to its special load characteristics. For this reason, the simulation results have been validated using an induction heating inverter with a specially designed load.

Implementation of RFID on Computer Based Test (RF-CBT) system <=SEP=> The paper presents an improvement of assessment model using RFID (Radio Frequency Identification) technology which implemented on Computer Based Test (CBT). The RFID worked at frequency 13.56 MHz. The RF-CBT system is constructed with User-PC and Server-PC. A RFID reader module (Mifare RC522, Microcontroller ATMEGA328) and LabVIEW program are installed on User-PC. An RFID tag information from User-PC was sent into Server-PC which is installed with MySQL database and PHP. The registered RFID tag on Server-PC could log in the CBT automatically. Our system is proposed not only for auto login to CBT but also for developing a random question and scoring test result on CBT. The system could reduce the opportunity to cheat in test as anyone cannot login to the test except have the authentic RFID tag. This system is feasible to measure students' cognitive abilities at once because it uses multiple choices test type.

Social influence on the use of Clinical Decision Support Systems: Revisiting the Unified Theory of Acceptance and Use of Technology by the fuzzy DEMATEL technique <=SEP=> The aim of study is to examine whether social influence affects medical professionals’ behavioral intention to use while introducing a new Clinical Decision Support System (CDSS). The series of Technology Acceptance Models (TAMs) have been widely applied to examine new technology acceptance by scholars; nevertheless, these models omit system diversity and the user’s profession. On the other hand, causal analysis greatly affects the efficiency of decision-making, and it is usually analyzed by Structural Equation Modeling (SEM); however, the method is often misapplied. This research applies the Decision-Making Trial and Evaluation Laboratory (DEMATEL) technique to explore the causal relationship between the significant Unified Theory of Acceptance and Use of Technology (UTAUT) variables. Fuzzy concept is applied to illustrate human vague judgment. It is significant that, in contrary with UTAUT, this study found that social influence does not matter in the behavioral intention to use the CDSS for medical professionals. 2011 Elsevier Ltd. All rights reserved.

An in-pipe robot with underactuated parallelogram crawler modules <=SEP=> In this paper, we present a new in-pipe robot with independent underactuated parallelogram crawler modules, which can automatically overcome inner obstacles in the pipes. The parallelogram crawler modules are adopted to maintain the anterior-posterior symmetry of forward and backward movements, and a simple differential mechanism based on a pair of spur gears is installed to provide underactuated mechanisms. A central base unit connects each crawler module through foldable pantograph mechanisms. To verify the basic behavior of this robot, primary experiments in pipes with different diameters and at partial steps were conducted.

Evaluating the influence of cultural competence education on students' transcultural self-efficacy perceptions. <=SEP=> Guided by the cultural competence and confidence (CCC) model, the Transcultural Self-Efficacy Tool (TSET) was used to evaluate the influence of cultural competence education on the transcultural self-efficacy (TSE) perceptions of undergraduate nursing students following an integrated approach to cultural competence education. Results continue to support that TSE is influenced by formalized education and other learning experiences. As hypothesized, compared with novice students, advanced students' scores were higher for all subscales in both cross-sectional (n = 147) and longitudinal (n = 36) study designs. Results from analysis of variance and covariance demonstrated that none of the demographic variables predicted change; semester was the sole predictor, lending additional support that (a) the educational interventions throughout the four semesters influenced TSE changes and (b) all students regardless of background benefit (and require) formalized cultural competence education. Implications for nurse educators conclude the article.

Feature extraction approaches based on matrix pattern: MatPCA and MatFLDA <=SEP=> Feature Extraction Approaches Based On Matrix Pattern: MatPCA and MatFLDA Songcan Chen 1* Yulian Zhu 1 Daoqiang Zhang 1 Jing-Yu Yang 2 Dept. of Computer Science and Engineering, Nanjing University of Aeronautics & Astronautics, Nanjing 210016, People’s Republic of China. Dept. of Computer Science, Nanjing University of Science & Technology, Nanjing 210094, People’s Republic of China Abstract Principle component analysis (PCA) and Fisher linear discriminant analysis (FLDA), as two popular feature extraction approaches in Pattern recognition and data analysis, extract so-needed features directly based on vector patterns, i.e., before applying them, any non-vector pattern such as an image is first vectorized into a vector pattern by some technique like concatenation. However, such a vectorization has been proved not to be beneficial for image recognition due to consequences of both the algebraic feature extraction approach and 2DPCA. In this paper, inspired by the above two approaches, we try an opposite direction to extract features for any vector pattern by first matrixizing it into a matrix pattern and then applying the matrixized versions of PCA and FLDA, MatPCA and MatFLDA, to the pattern. MatFLDA uses, in essence, the same principle as the algebraic feature extraction approach and is constructed in terms of similar objective function to FLDA while MatPCA uses a minimization of the reconstructed error for the training samples like PCA to obtain a set of projection vectors, which is somewhat different derivation from 2DPCA despite of equivalence. Finally experiments on 10 publicly obtainable datasets show that both MatPCA and MatFLDA gain performance improvement in different degrees respectively on 7 and 5

Performance analysis and security dependence of on paper digital signature using random and critical content <=SEP=> The content of physical paper documents is a major concern, after the development of modern scanning and printing technologies. The tendency for the document fraud has been growing and the paper-based documents require authenticating the sender identity and ensuring the content integrity. This paper presents a reliable and simple method for authenticating the content of a paper document by implementing an on paper digital signature. This method randomly selects the words at random positions of the document along with critical content and creates on paper digital signature. This method employs an online document verification system and the unauthorized access is denied by the user authentication mechanism. Performance analysis and security dependence on the system is also studied in this paper.

Population-Level Prediction of Type 2 Diabetes From Claims Data and Analysis of Risk Factors. <=SEP=> We present a new approach to population health, in which data-driven predictive models are learned for outcomes such as type 2 diabetes. Our approach enables risk assessment from readily available electronic claims data on large populations, without additional screening cost. Proposed model uncovers early and late-stage risk factors. Using administrative claims, pharmacy records, healthcare utilization, and laboratory results of 4.1 million individuals between 2005 and 2009, an initial set of 42,000 variables were derived that together describe the full health status and history of every individual. Machine learning was then used to methodically enhance predictive variable set and fit models predicting onset of type 2 diabetes in 2009-2011, 2010-2012, and 2011-2013. We compared the enhanced model with a parsimonious model consisting of known diabetes risk factors in a real-world environment, where missing values are common and prevalent. Furthermore, we analyzed novel and known risk factors emerging from the model at different age groups at different stages before the onset. Parsimonious model using 21 classic diabetes risk factors resulted in area under ROC curve (AUC) of 0.75 for diabetes prediction within a 2-year window following the baseline. The enhanced model increased the AUC to 0.80, with about 900 variables selected as predictive (p < 0.0001 for differences between AUCs). Similar improvements were observed for models predicting diabetes onset 1-3 years and 2-4 years after baseline. The enhanced model improved positive predictive value by at least 50% and identified novel surrogate risk factors for type 2 diabetes, such as chronic liver disease (odds ratio [OR] 3.71), high alanine aminotransferase (OR 2.26), esophageal reflux (OR 1.85), and history of acute bronchitis (OR 1.45). Liver risk factors emerge later in the process of diabetes development compared with obesity-related factors such as hypertension and high hemoglobin A1c. In conclusion, population-level risk prediction for type 2 diabetes using readily available administrative data is feasible and has better prediction performance than classical diabetes risk prediction algorithms on very large populations with missing data. The new model enables intervention allocation at national scale quickly and accurately and recovers potentially novel risk factors at different stages before the disease onset.

Deep learning approach for Network Intrusion Detection in Software Defined Networking <=SEP=> Software Defined Networking (SDN) has recently emerged to become one of the promising solutions for the future Internet. With the logical centralization of controllers and a global network overview, SDN brings us a chance to strengthen our network security. However, SDN also brings us a dangerous increase in potential threats. In this paper, we apply a deep learning approach for flow-based anomaly detection in an SDN environment. We build a Deep Neural Network (DNN) model for an intrusion detection system and train the model with the NSL-KDD Dataset. In this work, we just use six basic features (that can be easily obtained in an SDN environment) taken from the forty-one features of NSL-KDD Dataset. Through experiments, we confirm that the deep learning approach shows strong potential to be used for flow-based anomaly detection in SDN environments.

Shot boundary detection from videos using entropy and local descriptor <=SEP=> Video shot segmentation is an important step in key frame selection, video copy detection, video summarization, and video indexing for retrieval. Although some types of video data, e.g., live sports coverage, have abrupt shot boundaries that are easy to identify using simple heuristics, it is much more difficult to identify shot boundaries in other types such as cinematic movies. We propose an algorithm for shot boundary detection able to accurately identify not only abrupt shot boundaries, but also the fade-in and fade-out boundaries typical of cinematic movies. The algorithm is based on analysis of changes in the entropy of the gray scale intensity over consecutive frames and analysis of correspondences between SURF features over consecutive frames. In an experimental evaluation on the TRECVID-2007 shot boundary test set, the algorithm achieves substantial improvements over state of the art methods, with a precision of 97.8% and a recall of 99.3%.

SIGNet: Semantic Instance Aided Unsupervised 3D Geometry Perception <=SEP=> Unsupervised learning for visual perception of 3D geometry is of great interest to autonomous systems. Recent works on unsupervised learning have made considerable progress on geometry perception; however, they perform poorly on dynamic objects and scenarios with dark and noisy environments. In contrast, supervised learning algorithms, which are robust, require large labeled geometric dataset. This paper introduces SIGNet, a novel framework that provides robust geometry perception without requiring geometrically informative labels. Specifically, SIGNet integrates semantic information to make unsupervised robust geometric predictions for dynamic objects in low lighting and noisy environments. SIGNet is shown to improve upon the state-of-the-art unsupervised learning for geometry perception by 30% (in squared relative error for depth prediction). In particular, SIGNet improves the dynamic object class performance by 39% in depth prediction and 29% in flow prediction.

Analysis of hidden units in a layered network trained to classify sonar targets <=SEP=> -A neural network learning procedure has been applied to the classification ~/sonar returns [kom two undersea targets, a metal cylinder and a similarly shaped rock. Networks with an intermediate layer ~/ hidden processing units achieved a classification accuracy as high as 100% on a training set of l04 returns. These net~orks correctly classified up to 90.4% of 104 test returns not contained in the training set. This perfi~rmance was better than that of a nearest neighbor classifier, which was 82.7%. and was close to that of an optimal Bayes classifie~ Specific signal features extracted by hidden units in a trained network were identified and related to coding schemes in the pattern of connection strengths between the input and the hidden units. Network perlbrmance and class[/~cation strategy was comparable to that of trained human listeners. Keywords--Learning algorithms, Hidden units. Multilayered neural network, Sonar, Signal processing.

Pushover Analysis of 4 Storey ’ s Reinforced Concrete Building <=SEP=> The earthquakes in the Indian subcontinent have led to an increase in the seismic zoning factor over many parts of the country. Also, ductility has become an issue for all building that was designed and detailed using earlier versions of the codes. Under such circumstances, seismic qualification of building has become extremely important. The structural engineering profession has been using the nonlinear static procedure (NSP) or pushover analysis. Modeling for such analysis requires the determination of the nonlinear properties of each component in the structure, quantified by strength and deformation capacities, which depend on the modeling assumptions. Pushover analysis is carried out for either user-defined nonlinear hinge properties or default-hinge properties, available in some programs based on the FEMA-356 and ATC-40 guidelines. This paper aims to evaluate the zone –IV selected reinforced concrete building to conduct the non-linear static analysis (Pushover Analysis). The pushover analysis shows the pushover curves, capacity spectrum, plastic hinges and performance level of the building. The non-linear static analysis gives better understanding and more accurate seismic performance of buildings of the damage or failure element.

A Low-Profile Wideband Substrate-Integrated Waveguide Cavity-Backed E-Shaped Patch Antenna for the Q-LINKPAN Applications <=SEP=> A low-profile substrate-integrated waveguide cavity-backed E-shaped patch antenna is proposed for the Q-LINKPAN application in this paper. In order to expand the operating bandwidth, a co-planar waveguide (CPW) is used to feed the proposed antenna with a metallized via for generating one more resonant mode. In addition, a substrate-integrated cavity is employed to suppress the surface wave and improve the radiation efficiency. A differential feeding network is used in the design to improve the symmetry of the E-plane radiation pattern and the H-plane cross polarization. A  $2 \times 2$  prototype is designed, fabricated, and measured for a demonstration. The measured results show that the prototype has a 10 dB impedance bandwidth of 34.4%, a gain of around 12.5 dBi with a narrow E-plane radiation beam within 37.5–46 GHz for long distance applications, and a gain of around 8 dBi with a broad E-plane radiation beam within 47–53 GHz for short distance applications. The proposed technique can be used to develop compact planar antenna for meeting both the short- and long-rang communication requirements of the emerging Q-LINKPAN wireless system.

Knowledge Collaboration in Online Communities <=SEP=> O communities (OCs) are a virtual organizational form in which knowledge collaboration can occur in unparalleled scale and scope, in ways not heretofore theorized. For example, collaboration can occur among people not known to each other, who share different interests and without dialogue. An exploration of this organizational form can fundamentally change how we theorize about knowledge collaboration among members of organizations. We argue that a fundamental characteristic of OCs that affords collaboration is their fluidity. This fluidity engenders a dynamic flow of resources in and out of the community—resources such as passion, time, identity, social disembodiment of ideas, socially ambiguous identities, and temporary convergence. With each resource comes both a negative and positive consequence, creating a tension that fluctuates with changes in the resource. We argue that the fluctuations in tensions can provide an opportunity for knowledge collaboration when the community responds to these tensions in ways that encourage interactions to be generative rather than constrained. After offering numerous examples of such generative responses, we suggest that this form of theorizing—induced by online communities—has implications for theorizing about the more general case of knowledge collaboration in organizations.

IP Lookup using Two-level Indexing and B-Trees <=SEP=> Networks are expanding very fast and the number of clients is increasing dramatically, this causes the router forwarding table to become very large and present more demand on faster router operations. In this paper, we address the problem of packet forwarding in the routers aiming to increase the speed of address lookup and minimize the memory required for storing the forwarding table. We propose a new algorithm that makes use of two-level indexing and Btrees. We test the approach and compare it to other famous IP lookup approaches. The preliminary simulations show 20% less memory requirements and the lookup speed scaling linearly with increasing table size.

mTOR Signaling in Growth Control and Disease <=SEP=> The mechanistic target of rapamycin (mTOR) signaling pathway senses and integrates a variety of environmental cues to regulate organismal growth and homeostasis. The pathway regulates many major cellular processes and is implicated in an increasing number of pathological conditions, including cancer, obesity, type 2 diabetes, and neurodegeneration. Here, we review recent advances in our understanding of the mTOR pathway and its role in health, disease, and aging. We further discuss pharmacological approaches to treat human pathologies linked to mTOR deregulation.

Counterintuitive Behavior of Social Systems <=SEP=> This paper addresses several social concerns: population trends; quality of urban life; policies for urban growth; and the unexpected, ineffective, or detrimental results often generated by government programs. Society becomes frustrated as repeated attacks on deficiencies in social systems lead only to worse symptoms. Legislation is debated and passed with great hope, but many programs prove to be ineffective. Results are often far short of expectations. Because dynamic behavior of social systems is not understood, government programs often cause exactly the reverse of desired results. The field of system dynamics now can explain how such contrary results happen. Fundamental reasons cause people to misjudge behavior of social systems. Orderly processes in creating human judgment and intuition lead people to wrong decisions when faced with complex and highly interacting systems. Until we reach a much better public understanding of social systems, attempts to develop corrective programs for social troubles will continue to be disappointing. This paper cautions against continuing to depend on the same past approaches that have led to present feelings of frustration. New methods developed over the last 30 years will lead to a better understanding of social systems and thereby to more effective policies for guiding the future. 1 Updated March, 1995. This paper was first copyrighted © 1971 by Jay W. Forrester. It is based on testimony for the Subcommittee on Urban Growth of the Committee on Banking and Currency, U.S. House of Representatives, on October 7, 1970. The original text appeared in the January, 1971, issue of the Technology Review published by the Alumni Association of the Massachusetts Institute of Technology. All figures are taken from World Dynamics by Jay W. Forrester, Productivity Press, Portland, Oregon. 2 Germeshausen Professor Emeritus and Senior Lecturer, Massachusetts Institute of Technology, Cambridge, MA, USA Copyright © 1995 Jay W. Forrester

Overview of narrowband IoT in LTE Rel-13 <=SEP=> In 3GPP Rel-13, a narrowband system, named Narrowband Internet of Things (NB-IoT), has been introduced to provide low-cost, low-power, wide-area cellular connectivity for the Internet of Things. This system, based on Long Term Evolution (LTE) technology, supports most LTE functionalities albeit with essential simplifications to reduce device complexity. Further optimizations to increase coverage, reduce overhead and reduce power consumption while increasing capacity have been introduced as well. The design objectives of NB-IoT include low-complexity devices, high coverage, long device battery life, and massive capacity. Latency is relaxed although a delay budget of 10 seconds is the target for exception reports. This paper provides an overview of NB-IoT design, including salient features from the physical and higher layers. Illustrative results with respect to performance objectives are also provided. Finally, NB-IoT enhancements in LTE Rel-14 are briefly outlined.

Parenting stress in mothers and fathers of toddlers with autism spectrum disorders: associations with child characteristics. <=SEP=> Elevated parenting stress is observed among mothers of older children with autism spectrum disorders (ASD), but little is known about parents of young newly-diagnosed children. Associations between child behavior and parenting stress were examined in mothers and fathers of 54 toddlers with ASD (mean age = 26.9 months). Parents reported elevated parenting stress. Deficits/delays in children's social relatedness were associated with overall parenting stress, parent-child relationship problems, and distress for mothers and fathers. Regulatory problems were associated with maternal stress, whereas externalizing behaviors were associated with paternal stress. Cognitive functioning, communication deficits, and atypical behaviors were not uniquely associated with parenting stress. Clinical assessment of parental stress, acknowledging differences in parenting experiences for mothers and fathers of young children with ASD, is needed.

Dynamic time warping constraint learning for large margin nearest neighbor classification <=SEP=> Nearest neighbor (NN) classifier with dynamic time warping (DTW) is considered to be an effective method for time series classification. The performance of NN-DTW is dependent on the DTW constraints because the NN classifier is sensitive to the used distance function. For time series classification, the global path constraint of DTW is learned for optimization of the alignment of time series by maximizing the nearest neighbor hypothesis margin. In addition, a reduction technique is combined with a search process to condense the prototypes. The approach is implemented and tested on UCR datasets. Experimental results show the effectiveness of the proposed method. ! 2011 Elsevier Inc. All rights reserved.

DreamSketch: Early Stage 3D Design Explorations with Sketching and Generative Design <=SEP=> We present DreamSketch, a novel 3D design interface that combines the free-form and expressive qualities of sketching with the computational power of generative design algorithms. In DreamSketch, a user coarsely defines the problem by sketching the design context. Then, a generative design algorithm produces multiple solutions that are augmented as 3D objects in the sketched context. The user can interact with the scene to navigate through the generated solutions. The combination of sketching and generative algorithms enables designers to explore multiple ideas and make better informed design decisions during the early stages of design. Design study sessions with designers and mechanical engineers demonstrate the expressive nature and creative possibilities of DreamSketch.