title: Object tracking with 3D LIDAR via multi-task sparse learning

abstract: Moving object tracking is a fundamental task for autonomous vehicles operating in urban areas. In this paper, a novel sparse learning based object tracking algorithm utilizing 3D LIDAR data is proposed. The 3D point clouds acquired from HDL-64E 3D LIDAR are first resampled on a virtual image plane, where the hypothesis of the targets is generated under the particle filtering framework. Four complementary features, i.e., normal orientation, depth, LBP and HOG, are extracted on each particle to describe the appearance of the candidates. Then a multi-task multi-cue sparse learning algorithm is employed to select the best candidate and realize the tracking of the object. To improve the robustness of the algorithm, the sparse learning framework is further enhanced by a specifically designed background filtering and occlusion detection mechanism. The experiments carried out on KITTI benchmark show promising object tracking performance, especially when handling complex tracking situations such as occlusion and posture change.


title: UWB monopole antenna with circular polarization

abstract: In this article, ultra-wideband (UWB) monopole antenna with circular polarization (CP) is designed and implemented. The proposed antenna consists of an asymmetric shaped radiator fed by a microstrip line and a limited ground plane on the back side of the substrate. The overall size of proposed antenna is 28 mm × 29 mm, which is suitable for mobile devices. The antenna is fabricated on inexpensive FR4 a dielectric constant of, loss tangent tan = 0.019 with thickness of 1.6-mm. The measured results show that the proposed monopole antenna has a very wide bandwidth from 2.98 to 10.93 GHz that covers the UWB application range and defined by 10-dB return loss. Furthermore, a very wide CP bandwidth of 3-dB axial ratio bandwidth that ranges from 7.18 to 10.01 GHz is achieved. © 2012 Wiley Periodicals, Inc.


title: A Digital-to-Analog Converter Based on Differential-Quad Switching

abstract: A high-conversion-rate high-resolution oversampling digital-to-analog converter (DAC) for direct digital modulation is addressed in this paper. A new type of switching scheme, called differential-quad switching, is presented. To verify the feasibility of this scheme, essential parts with some auxiliary circuitry for interfacing were fabricated in a 0.8-/spl mu/m CMOS technology. Measured results show that the switching scheme provides 11-b resolution at 100 MSamples/s and 6-b at 1 GSamples/s. The degradation in signal-to-noise ratio is not observed for the variation of the supply voltage down to 1.5 V, which means the proposed scheme is suitable for low-voltage applications.


title: SVD-based on-line exercise ECG signal orthogonalization

abstract: An orthogonalization method to eliminate unwanted signal components in standard 12-lead exercise electrocardiograms (ECG's) is presented in this work. A singular-value-decomposition-based algorithm is proposed to decompose the signal into two time-orthogonal subspaces; one containing the ECG and the other containing artifacts like baseline wander and electromyogram. The method makes use of redundancy in 12-lead ECG. The same method is also tested for reconstruction of a completely lost channel. The on-line implementation of the method is given. It is observed that the first two decomposed channels with highest energy are sufficient to reconstruct the ST-segment and J-point. The dimension of the signal space, on the other hand, does not exceed three. Data from 23 patients, with duration ranging from 9 to 21 min, are used.


title: Barrier coverage in camera sensor networks

abstract: Barrier coverage has attracted much attention in the past few years. However, most of the previous works focused on traditional scalar sensors. We propose to study barrier coverage in camera sensor networks. One fundamental difference between camera and scalar sensor is that cameras from different positions can form quite different views of the object. As a result, simply combining the sensing range of the cameras across the field does not necessarily form an effective camera barrier since the face image (or the interested aspect) of the object may be missed. To address this problem, we use the angle between the object's facing direction and the camera's viewing direction to measure the quality of sensing. An object is full-view covered if there is always a camera to cover it no matter which direction it faces and the camera's viewing direction is sufficiently close to the object's facing direction. We study the problem of constructing a camera barrier, which is essentially a connected zone across the monitored field such that every point within this zone is full-view covered. We propose a novel method to select camera sensors from an arbitrary deployment to form a camera barrier, and present redundancy reduction techniques to effectively reduce the number of cameras used. We also present techniques to deploy cameras for barrier coverage in a deterministic environment, and analyze and optimize the number of cameras required for this specific deployment under various parameters.


title: Decision Tree and SVM-Based Data Analytics for Theft Detection in Smart Grid

abstract: Nontechnical losses, particularly due to electrical theft, have been a major concern in power system industries for a long time. Large-scale consumption of electricity in a fraudulent manner may imbalance the demand–supply gap to a great extent. Thus, there arises the need to develop a scheme that can detect these thefts precisely in the complex power networks. So, keeping focus on these points, this paper proposes a comprehensive top-down scheme based on decision tree (DT) and support vector machine (SVM). Unlike existing schemes, the proposed scheme is capable enough to precisely detect and locate real-time electricity theft at every level in power transmission and distribution (T&D). The proposed scheme is based on the combination of DT and SVM classifiers for rigorous analysis of gathered electricity consumption data. In other words, the proposed scheme can be viewed as a two-level data processing and analysis approach, since the data processed by DT are fed as an input to the SVM classifier. Furthermore, the obtained results indicate that the proposed scheme reduces false positives to a great extent and is practical enough to be implemented in real-time scenarios.


title: Comparison of Brazilian Spiritist mediumship and dissociative identity disorder

abstract: We studied the similarities and differences between Brazilian Spiritistic mediums and North American dissociative identity disorder (DID) patients. Twenty-four mediums selected among different Spiritistic organizations in Sao Paulo, Brazil, were interviewed using the Dissociative Disorder Interview Schedule, and their responses were compared with those of DID patients described in the literature. The results from Spiritistic mediums were similar to published data on DID patients only with respect to female prevalence and high frequency of Schneiderian first-rank symptoms. As compared with individuals with DID, the mediums differed in having better social adjustment, lower prevalence of mental disorders, lower use of mental health services, no use of antipsychotics, and lower prevalence of histories of physical or sexual childhood abuse, sleepwalking, secondary features of DID, and symptoms of borderline personality. Thus, mediumship differed from DID in having better mental health and social adjustment, and a different clinical profile.


title: Miniature Butler Matrix Design Using Glass-Based Thin-Film Integrated Passive Device Technology for 2.5-GHz Applications

abstract: In this paper, miniature branch-line coupler and Butler matrix designs for 2.5-GHz applications are proposed using the glass-based thin-film integrated passive device (TF-IPD) technology. The size reduction is achieved by replacing the quarter-wavelength transmission lines in a conventional branch-line coupler with the bridged-T coil. In this way, the circuit size can be largely reduced without sacrificing the operation bandwidth. The proposed miniature branch-line coupler is then applied to the design of a 4 × 4 Butler matrix centered at 2.5 GHz using the glass-based TF-IPD technology. The measured results show a bandwidth of 2.4-2.6 GHz for better than 13-dB return loss with a maximal dissipation of 4 dB, amplitude imbalance within 1.1 dB, and phase imbalance less than 13°. Notably, the proposed Butler matrix occupies a chip area of 3.13 mm × 3.3 mm, which is only about 0.026 λ0 × 0.027 λ0 at 2.5 GHz.


title: A General Alignment Repulsion Algorithm for Flocking of Multi-Agent Systems

abstract: A novel individual-based alignment/repulsion algorithm is proposed in this note for a flock of multiple agents. With this algorithm, each individual repels its sufficiently close neighbors and aligns to the average velocity of its neighbors with moderate distances. In both mathematical analysis and numerical simulation, we prove that the algorithm guarantees an uncrowded flocking behavior with asymptotic velocity synchronization when sufficiently intensive communication exists within the agents. Moreover, we provide the conditions for collision avoidance along the whole transient procedure. The proposed flocking model has its references in natural collective behaviors like escaping panic and traffic jam motions.


title: Impact of timing and frequency offsets on multicarrier waveform candidates for 5G

abstract: This paper presents a study of the candidate waveforms for 5G when they are subject to timing and carrier frequency offset. These waveforms are: orthogonal frequency division multiplexing (OFDM), generalized frequency division multiplexing (GFDM), universal filtered multicarrier (UFMC), circular filter bank multicarrier (C-FBMC), and linear filter bank multicarrier (FBMC). We are particularly interested in multiple access interference (MAI) when a number of users transmit their signals to a base station in an asynchronous or a quasi-synchronous manner. We identify the source of MAI in these waveforms and present some numerical analysis that confirm our findings. The goal of this study is to answer the following question,"Which one of the 5G candidate waveforms has more relaxed synchronization requirements?".


title: A Review of Feature Extraction in Sentiment Analysis

abstract: Abstract: Rapid increase in internet users along with growing power of online review sites and social media has given birth to Sentiment analysis or Opinion mining, which aims at determining what other people think and comment. Sentiments or Opinions contain public generated content about products, services, policies and politics. People are usually interested to seek positive and negative opinions containing likes and dislikes, shared by users for features of particular product or service. Therefore product features or aspects have got significant role in sentiment analysis. In addition to sufficient work being performed in text analytics, feature extraction in sentiment analysis is now becoming an active area of research. This review paper discusses existing techniques and approaches for feature extraction in sentiment analysis and opinion mining. In this review we have adopted a systematic literature review process to identify areas well focused by researchers, least addressed areas are also highlighted giving an opportunity to researchers for further work. We have also tried to identify most and least commonly used feature selection techniques to find research gaps for future work.


title: An Undergraduate Experiment Using Microwave-Assisted Synthesis of Metalloporphyrins: Characterization and Spectroscopic Investigations

abstract: The effectiveness of microwave-assisted synthesis of 5,10,15,20-tetraphenylporphyrin and its corresponding to metal complexes of (Bi (III), Zn (II) and Ni (II)) are described. Moreover, the synthesized metalloporphyrins that were obtained with reasonable yields were characterized using UV-vis, 1H-NMR and IR.


title: Understanding Fashionability: What drives sales of a style?

abstract: We use customer demand data for fashion articles on Myntra, and derive a fashionability or style quotient, which represents customer demand for the stylistic content of a fashion article, decoupled with its commercials (price, offers, etc.). We demonstrate learning for assortment planning in fashion that would aim to keep a healthy mix of breadth and depth across various styles, and we show the relationship between a customer's perception of a style vs a merchandiser's catalogue of styles. We also backtest our method to calculate prediction errors in our style quotient and customer demand, and discuss various implications and findings.


title: Different Techniques to Reduce the PAPR in OFDM System

abstract: In the modern wireless Communication system high data rate must require. Orthogonal Frequency Division Multiplexing (OFDM) is well known technique for transmitting large data over radio waves. In the OFDM system, orthogonally placed sub - carriers are used to carry the data from the transmitter end to the receiver end. More bandwidth is required for transmitting data at higher rate. Orthogonal frequency division multiplexing is a special form of multicarrier modulation which is particularly suited for transmission over a dispersive channel. OFDM has gained a tremendous interest in recent years because of its robustness in the presence of severe multipath channel conditions with simple equalization, robustness against Inter-symbol Interference (ISI); multipath fading in addition to its high spectral efficiency. OFDM system used in microwave communication due to high data rate. But main drawback of OFDM system is PAPR (Peak to average power ratio) nothing but several sinusoidal leads. It restricts the efficiency of the transmitter. Numbers of techniques have been proposed in the literature for reducing the PAPR in OFDM systems. All techniques has been discussed in this thesis report and from all techniques two techniques name Tone Reservation & Clipping has been discussed and simulated in this thesis for reducing PAPR in OFDM system.


title: Differential Privacy via Wavelet Transforms

abstract: Privacy preserving data publishing has attracted considerable research interest in recent years. Among the existing solutions, ∈-differential privacy provides one of the strongest privacy guarantees. Existing data publishing methods that achieve ∈-differential privacy, however, offer little data utility. In particular, if the output dataset is used to answer count queries, the noise in the query answers can be proportional to the number of tuples in the data, which renders the results useless. In this paper, we develop a data publishing technique that ensures ∈-differential privacy while providing accurate answers for range-count queries, i.e., count queries where the predicate on each attribute is a range. The core of our solution is a framework that applies wavelet transforms on the data before adding noise to it. We present instantiations of the proposed framework for both ordinal and nominal data, and we provide a theoretical analysis on their privacy and utility guarantees. In an extensive experimental study on both real and synthetic data, we show the effectiveness and efficiency of our solution.


title: Neural plasticity in the ageing brain

abstract: A decline in learning, memory and executive functions frequently occurs with advanced age. Barnes and Burke evaluate recent progress in our understanding of subtle changes affecting plasticity in medial temporal and prefrontal regions that contribute to age-related cognitive decline.


title: PULP: A System for Exploratory Search of Scientific Literature

abstract: Despite the growing importance of exploratory search, information retrieval (IR) systems tend to focus on lookup search. Lookup searches are well served by optimising the precision and recall of search results, however, for exploratory search this may be counterproductive if users are unable to formulate an appropriate search query. We present a system called PULP that supports exploratory search for scientific literature, though the system can be easily adapted to other types of literature. PULP uses reinforcement learning (RL) to avert the user from context traps resulting from poorly chosen search queries, trading off between exploration (presenting the user with diverse topics) and exploitation (moving towards more specific topics). Where other RL-based systems suffer from the "cold start" problem, requiring sufficient time to adjust to a user's information needs, PULP initially presents the user with an overview of the dataset using temporal topic models. Topic models are displayed in an interactive alluvial diagram, where topics are shown as ribbons that change thickness with a given topics relative prevalence over time. Interactive, exploratory search sessions can be initiated by selecting topics as a starting point.


title: A 12V-to-0.9V active-clamp forward converter power block with planar transformer, standing slab inductor and direct edge solder to motherboard

abstract: DC-to-DC power supplies for CPUs or GPUs are critical components on the motherboard of modern computer systems. Converting an intermediate bus voltage (e.g. 12 V) to the core voltage (∼0.9 V) of CPUs or GPUs must be efficient, compact and cost effective. This paper proposes an active-clamp forward converter (ACFC) power block to supply core voltage on a motherboard. The ACFC power block can be individually tested prior to assembly and vertically soldered onto the motherboard to save motherboard area. A low loss, compact planar transformer is designed into the ACFC power block PCB. A custom, standing slab inductor not only provides high inductance and high saturation current but also helps to mechanically support the power block. A one-piece copper winding connects the transformer to the inductor, thereby reducing the DC loss in the current path. Experimental results show a peak efficiency of 90.4% with a 12 V input and 0.9 V output for an output current of 25 A.


title: Parallel striatal and hippocampal systems for landmarks and boundaries in spatial memory

abstract: How the memory systems centered on the hippocampus and dorsal striatum interact to support behavior remains controversial. We used functional MRI while people learned the locations of objects by collecting and replacing them over multiple trials within a virtual environment comprising a landmark, a circular boundary, and distant cues for orientation. The relative location of landmark and boundary was occasionally changed, with specific objects paired with one or other cue, allowing dissociation of learning and performance relative to either cue. Right posterior hippocampal activation reflected learning and remembering of boundaryrelated locations, whereas right dorsal striatal activation reflected learning and remembering of landmark-related locations. Within the right hippocampus, anterior processing of environmental change (spatial novelty) was dissociated from posterior processing of location. Behavioral studies show that landmark-related learning obeys associative reinforcement, whereas boundary-related learning is incidental [Doeller CF, Burgess N (2008) Proc Natl Acad Sci USA 105:5909 –5914]. The distinct incidental hippocampal processing of boundaries is suggestive of a ‘‘geometric module’’ or ‘‘cognitive map’’ and may explain the hippocampal support of incidental/observational learning in ‘‘declarative’’ or ‘‘episodic’’ memory versus the striatal support of trial-and-error learning in ‘‘procedural’’ memory. Finally, the hippocampal and striatal systems appear to combine ‘‘bottom-up,’’ simply influencing behavior proportional to their activations, without direct interaction, with ‘‘top-down’’ ventromedial prefrontal involvement when both are similarly active. cognitive map functional MRI incidental learning procedural


title: Bayesian-Based Iterative Method of Image Restoration*

abstract: An iterative method of restoring degraded images was developed by treating images, point spread functions, and degraded images as probability-frequency functions and by applying Bayes’s theorem. The method functions effectively in the presence of noise and is adaptable to computer operation.


title: Application of Artificial Neural Networks to Multiple Criteria Inventory Classification

abstract: Inventory classification is very important part of inventory control which represents the technique of operational research discipline. A systematic approach to the inventory control and classification may have a significant influence on company competitiveness. The paper describes the results obtained by investigating the application of neural networks in multiple criteria inventory classification. Various structures of a back-propagation neural network have been analysed and the optimal one with the minimum Root Mean Square error selected. The predicted results are compared to those obtained by the multiple criteria classification using the analytical hierarchy process.


title: Task-Based Mirror Therapy Augmenting Motor Recovery in Poststroke Hemiparesis: A Randomized Controlled Trial.

abstract: BACKGROUND ::: To establish the effect of the task-based mirror therapy (TBMT) on the upper limb recovery in stroke. ::: ::: ::: METHODS ::: A pilot, randomized, controlled, assessor-blinded trial was conducted in a rehabilitation institute. A convenience sample of 33 poststroke (mean duration, 12.5 months) hemiparetic subjects was randomized into 2 groups (experimental, 17; control, 16). The subjects were allocated to receive either TBMT or standard motor rehabilitation-40 sessions (5/week) for a period of 8 weeks. The TBMT group received movements using various goal-directed tasks and a mirror box. The movements were performed by the less-affected side superimposed on the affected side. The main outcome measures were Brunnstrom recovery stage (BRS) and Fugl-Meyer assessment (FMA)-FMA of upper extremity (FMA-UE), including upper arm (FMA-UA) and wrist-hand (FMA-WH). ::: ::: ::: RESULTS ::: The TBMT group exhibited highly significant improvement on mean scores of FMA-WH (P < .001) and FMA-UE (P < .001) at postassessment in comparison to the control group. Furthermore, there was a 12% increase in the number of subjects at BRS stage 5 (out of synergy movement) in the experimental group as compared to a 0% rise at the same stage in the control group. ::: ::: ::: CONCLUSIONS ::: This pilot trial confirmed the role of TBMT in improving the wrist-hand motor recovery in poststroke hemiparesis. MT using tasks may be used as an adjunct in stroke rehabilitation.


title: Using Video to Automatically Detect Learner Affect in Computer-Enabled Classrooms

abstract: Affect detection is a key component in intelligent educational interfaces that respond to students’ affective states. We use computer vision and machine-learning techniques to detect students’ affect from facial expressions (primary channel) and gross body movements (secondary channel) during interactions with an educational physics game. We collected data in the real-world environment of a school computer lab with up to 30 students simultaneously playing the game while moving around, gesturing, and talking to each other. The results were cross-validated at the student level to ensure generalization to new students. Classification accuracies, quantified as area under the receiver operating characteristic curve (AUC), were above chance (AUC of 0.5) for all the affective states observed, namely, boredom (AUC e .610), confusion (AUC e .649), delight (AUC e .867), engagement (AUC e .679), frustration (AUC e .631), and for off-task behavior (AUC e .816). Furthermore, the detectors showed temporal generalizability in that there was less than a 2p decrease in accuracy when tested on data collected from different times of the day and from different days. There was also some evidence of generalizability across ethnicity (as perceived by human coders) and gender, although with a higher degree of variability attributable to differences in affect base rates across subpopulations. In summary, our results demonstrate the feasibility of generalizable video-based detectors of naturalistic affect in a real-world setting, suggesting that the time is ripe for affect-sensitive interventions in educational games and other intelligent interfaces.


title: Automated attendance management system based on face recognition algorithms

abstract: In this paper we propose an automated attendance management system. This system, which is based on face detection and recognition algorithms, automatically detects the student when he enters the class room and marks the attendance by recognizing him. The system architecture and algorithms used in each stage are described in this paper. Different real time scenarios are considered to evaluate the performance of various face recognition systems. This paper also proposes the techniques to be used in order to handle the threats like spoofing. When compared to traditional attendance marking this system saves the time and also helps to monitor the students.


title: Effect of Meditation on Cognitive Functions in Context of Aging and Neurodegenerative Diseases

abstract: Effect of different meditation practices on various aspects of mental and physical health is receiving growing attention. The present paper reviews evidence about effects of several mediation practices on cognitive functions in the context of aging and neurodegenerative diseases. The effect of meditation in this area is still poorly explored. Seven studies were detected through the databases search which explores the effect of meditation on attention, memory, executive functions and other miscellaneous measures of cognition in a sample of older people and people suffering from neurodegenerative diseases. Overall, reviewed studies suggested a positive effect of meditation techniques, particularly in the area of attention, as well as memory, verbal fluency and cognitive flexibility. These findings are discussed in the context of MRI studies suggesting structural correlates of the effects. Meditation can be a potentially suitable non-pharmacological intervention aimed at the prevention of cognitive decline in the elderly. However, the conclusions of these studies are limited by their methodological flaws and differences of various types of meditation techniques. Further research in this direction could help to verify the validity of the findings and clarify the problematic aspects.


title: Enhancement of Delivery of Warning Messages for Mobile Networks

abstract: Vehicle-to-vehicle (V2V) communication systems have been studied extensively in recent years by transportation experts worldwide. Such systems enable the transmission of warning messages (alarms) between vehicles without additional roadside infrastructure. Thus, messages can be sent faster than through base stations. Because of these advantages, researchers worldwide are exploring how this technology can improve traffic safety. However, the various V2V schemes proposed thus far share the same problem: (1) unnecessary repetition of warning messages and (2) transmission to inapplicable respondents. This study proposes a new method for delivering V2V warning messages which reduces the redundant messages common in current schemes. Here, three schemes to deliver alarms are considered. One is blind flooding to broadcast alarms. Warning messages are routed by AODV protocol. Another is called Two Lanes. The broadcasting region of alarms is limited in two traffic lanes nearby a traffic accident only. And the last one is the proposed strategy where a warning message received by a car will be simply checked the prime number carried within the alarm header and decided whether the car is responsible to deliver it. If necessary, it will be forwarded to the next nodes. Otherwise, the redundant warning message shall be discarded to release system load and avoid to suspend other normal transmission proceeding. The results reveal that minimizing the delays caused by such redundant messages enables warnings to be sent to drivers more rapidly. Also, it prioritizes warning messages so that drivers are apprised of the most serious and dangerous traffic accidents. Moreover, another key finding in this paper is that the radii of the broadcasting coverage shall be adjusted carefully and adaptively to optimize the system performance.


title: Women's experience of IVF: a follow-up study

abstract: The aim of this research was to increase understanding of how women feel about the experience of IVF 2–3 years after ceasing treatment. A questionnaire covering issues relating to infertility and the value of the experience of IVF together with three self-report measures [Satisfaction With Life Scale (SWLS), Golombok Rust Inventory of Marital State (GRIMS) and General Health Questionnaire (GHQ-12)] were mailed to all women (n 229) who had their last contact with the clinic in 1994. The response rate was 55%. Having a baby positively influenced the recall of the IVF experience. Women who did not have a baby were more critical about the clinic and more negative about the experience of treatment but did not regret having tried IVF. These women had statistically significantly lower scores on SWLS but did not differ from those with babies on GRIMS and GHQ-12 scales. The results give insight into how women look back on the IVF experience and what aspects of treatment they recall as particularly difficult. The findings can be used by providers of IVF to implement strategies that may reduce stress and improve the patients’ well-being.


title: User Behavior Modelling Approach for Churn Prediction in Online Games

abstract: Massively Multiplayer Online Role-Playing Games (MMORPGs) are persistent virtual environments where millions of players interact in an online manner. Game logs capture player activities in great detail and user behavior modeling approaches can help to build accurate models of player behavior from these logs. We are interested in modeling player churn behavior and we use a lifecycle-based approach for this purpose. In a player lifecycle-based approach, we analyze the activity traits of churners in the weeks leading up to their point of leaving the game and compare it with the activity traits of a regular player. We identify several intuitive yet distinct behavioral profiles associated with churners and active players which can discriminate between the two populations. We use these insights to propose three semantic dimensions of engagement, enthusiasm and persistence to construct derived features. Using three session-related variables and the features derived from them, we are able to achieve good classification performance with the churn prediction models. Finally, we propose a distance-based classification scheme, which we call wClusterDist, which benefits from these distinct behavioral profiles of the two populations. Experimental results show that the proposed classification scheme is well-suited for this problem formulation and its performance is better than or comparable to other traditional classification schemes.


title: Applications of CRISPR/Cas9 technology for targeted mutagenesis, gene replacement and stacking of genes in higher plants

abstract: Mutagenesis continues to play an essential role for understanding plant gene function and, in some instances, provides an opportunity for plant improvement. The development of gene editing technologies such as TALENs and zinc fingers has revolutionised the targeted mutation specificity that can now be achieved. The CRISPR/Cas9 system is the most recent addition to gene editing technologies and arguably the simplest requiring only two components; a small guide RNA molecule (sgRNA) and Cas9 endonuclease protein which complex to recognise and cleave a specific 20 bp target site present in a genome. Target specificity is determined by complementary base pairing between the sgRNA and target site sequence enabling highly specific, targeted mutation to be readily engineered. Upon target site cleavage, error-prone endogenous repair mechanisms produce small insertion/deletions at the target site usually resulting in loss of gene function. CRISPR/Cas9 gene editing has been rapidly adopted in plants and successfully undertaken in numerous species including major crop species. Its applications are not restricted to mutagenesis and target site cleavage can be exploited to promote sequence insertion or replacement by recombination. The multiple applications of this technology in plants are described.


title: YouthTube: Youth Video Authorship on YouTube and Vine

abstract: What kinds of content do children and teenagers author and share on public video platforms? We approached this question through a qualitative directed content analysis of over 250 youth-authored videos filtered by crowdworkers from public videos on YouTube and Vine. We found differences between YouTube and Vine platforms in terms of the age of the youth authors, the type of collaborations witnessed in the videos, and the significantly greater amount of violent, sexual, and obscene content on Vine. We also highlight possible differences in how adults and youths approach online video sharing. Specifically, we consider that adults may view online video as an archive to keep precious memories of everyday life with their family, friends, and pets, humorous moments, and special events, while children and teenagers treat online video as a stage to perform, tell stories, and express their opinions and identities in a performative way.


Countermeasures for Automatic Speaker Verification Replay Spoofing Attack : On Data Augmentation, Feature Representation, Classification and Fusion.


title: The Necessity of a Secondary System in Machine Consciousness

abstract: Abstract Our research purpose is to realize a consciousness system on computers. In this paper, we focus on the relation between a primary system that directly interacts and learns the input-output relation within an environment, and a secondary system that continuously monitors and is able to direct the primary system. We hold that consciousness is not composed of a primary system alone, but that the employment of a secondary system is essential. The purpose of this paper is therefore to clarify the importance of a secondary system. We show by numerical experiments that the addition of a secondary system provides adaptability to a wider range of environmental changes than a primary system alone. Alternatively, we present an extraordinary case where a customized primary system fully adapts to an environment undergoing a particular type of change. Far from refuting the value of a secondary system, this special case serves to point out the importance of the effective design of a secondary system. Therefore, we confirm the value and usefulness of a secondary system in a machine consciousness system through these numericalexperiments.


title: Revisiting the PnP Problem: A Fast, General and Optimal Solution

abstract: In this paper, we revisit the classical perspective-n-point (PnP) problem, and propose the first non-iterative O(n) solution that is fast, generally applicable and globally optimal. Our basic idea is to formulate the PnP problem into a functional minimization problem and retrieve all its stationary points by using the Gr¨obner basis technique. The novelty lies in a non-unit quaternion representation to parameterize the rotation and a simple but elegant formulation of the PnP problem into an unconstrained optimization problem. Interestingly, the polynomial system arising from its first-order optimality condition assumes two-fold symmetry, a nice property that can be utilized to improve speed and numerical stability of a Gr¨obner basis solver. Experiment results have demonstrated that, in terms of accuracy, our proposed solution is definitely better than the state-ofthe- art O(n) methods, and even comparable with the reprojection error minimization method. (Less)


title: Autonomous reconstruction of unknown indoor scenes guided by time-varying tensor fields

abstract: Autonomous reconstruction of unknown scenes by a mobile robot inherently poses the question of balancing between exploration efficacy and reconstruction quality. We present a navigation-by-reconstruction approach to address this question, where moving paths of the robot are planned to account for both global efficiency for fast exploration and local smoothness to obtain high-quality scans. An RGB-D camera, attached to the robot arm, is dictated by the desired reconstruction quality as well as the movement of the robot itself. Our key idea is to harness a time-varying tensor field to guide robot movement, and then solve for 3D camera control under the constraint of the 2D robot moving path. The tensor field is updated in real time, conforming to the progressively reconstructed scene. We show that tensor fields are well suited for guiding autonomous scanning for two reasons: first, they contain sparse and controllable singularities that allow generating a locally smooth robot path, and second, their topological structure can be used for globally efficient path routing within a partially reconstructed scene. We have conducted numerous tests with a mobile robot, and demonstrate that our method leads to a smooth exploration and high-quality reconstruction of unknown indoor scenes.


title: Channel bonding for Next Generation Passive Optical Network Stage 2 (NG-PON2)

abstract: Channel bonding is a key endeavor in the ITU Telecommunication Standardization Sector (ITU-T) to increase the peak rate of the Next Generation Passive Optical Network Stage 2 (NG-PON2) system. It manages several parallel wavelength channels in NG-PON2 as a single link. In this paper, we investigate the NG-PON2 layering structure and propose the sublayer changes to support channel bonding. The problem is formulated as an integer non-linear programming (INLP) problem, and is analyzed by using the water filling scheme. Two interleaving heuristic algorithms are proposed to solve this problem. Evaluation results show that full wavelength channel delivery algorithm provides the lowest jitter, and restricted wavelength channel delivery algorithm with a threshold 3 provides the lowest end-to-end delay by reducing envelope headers.


title: Wav2Letter: an End-to-End ConvNet-based Speech Recognition System

abstract: This paper presents a simple end-to-end model for speech recognition, combining a convolutional network based acoustic model and a graph decoding. It is trained to output letters, with transcribed speech, without the need for force alignment of phonemes. We introduce an automatic segmentation criterion for training from sequence annotation without alignment that is on par with CTC (Graves et al., 2006) while being simpler. We show competitive results in word error rate on the Librispeech corpus (Panayotov et al., 2015) with MFCC features, and promising results from raw waveform.


title: Person Search with Natural Language Description

abstract: Searching persons in large-scale image databases with the query of natural language description has important applications in video surveillance. Existing methods mainly focused on searching persons with image-based or attribute-based queries, which have major limitations for a practical usage. In this paper, we study the problem of person search with natural language description. Given the textual description of a person, the algorithm of the person search is required to rank all the samples in the person database then retrieve the most relevant sample corresponding to the queried description. Since there is no person dataset or benchmark with textual description available, we collect a large-scale person description dataset with detailed natural language annotations and person samples from various sources, termed as CUHK Person Description Dataset (CUHK-PEDES). A wide range of possible models and baselines have been evaluated and compared on the person search benchmark. An Recurrent Neural Network with Gated Neural Attention mechanism (GNA-RNN) is proposed to establish the state-of-the art performance on person search.


title: Workshop on Applications of Pattern Analysis Facial Expression Detection using Filtered Local Binary Pattern Features with ECOC Classifiers and Platt Scaling

abstract: We outline a design for a FACS-based facial expression recognition system and describe in more detail the implementation of two of its main components. Firstly we look at how features that are useful from a pattern analysis point of view can be extracted from a raw input image. We show that good results can be obtained by using the method of local binary patterns (LPB) to generate a large number of candidate features and then selecting from them using fast correlation-based filtering (FCBF). Secondly we show how Platt scaling can be used to improve the performance of an error-correcting output code (ECOC) classifier.


title: Planning and Integrating Deception into Computer Security Defenses

abstract: Deceptive techniques played a prominent role in many human conflicts throughout history. Digital conflicts are no different as the use of deception has found its way to computing since at least the 1980s. However, many computer defenses that use deception were ad-hoc attempts to incorporate deceptive elements. In this paper, we present a model that can be used to plan and integrate deception in computer security defenses. We present an overview of fundamental reasons why deception works and the essential principles involved in using such techniques. We investigate the unique advantages deception-based mechanisms bring to traditional computer security defenses. Furthermore, we show how our model can be used to incorporate deception in many part of computer systems and discuss how we can use such techniques effectively. A successful deception should present plausible alternative(s) to the truth and these should be designed to exploit specific adversaries' biases. We investigate these biases and discuss how can they be used by presenting a number of examples.


title: Extracting influential nodes for information diffusion on a social network

abstract: We consider the combinatorial optimization problem of finding the most influential nodes on a large-scale social network for two widely-used fundamental stochastic diffusion models. It was shown that a natural greedy strategy can give a good approximate solution to this optimization problem. However, a conventional method under the greedy algorithm needs a large amount of computation, since it estimates the marginal gains for the expected number of nodes influenced by a set of nodes by simulating the random process of each model many times. In this paper, we propose a method of efficiently estimating all those quantities on the basis of bond percolation and graph theory, and apply it to approximately solving the optimization problem under the greedy algorithm. Using real-world large-scale networks including blog networks, we experimentally demonstrate that the proposed method can outperform the conventional method, and achieve a large reduction in computational cost.


title: A Planar Magic-T Structure Using Substrate Integrated Circuits Concept and Its Mixer Applications

abstract: In this paper, a planar 180 phase-reversal T-junction and a modified magic-T using substrate integrated waveguide (SIW) and slotline are proposed and developed for RF/microwave applications on the basis of the substrate integrated circuits concept. In this case, slotline is used to generate the odd-symmetric field pattern of the SIW in the phase-reverse T-junction. Measured results indicate that 0.3-dB amplitude imbalance and 3 phase imbalance can be achieved for the proposed 180 phase-reversal T-junction over the entire -band. The modified narrowband and optimized wideband magic-T are developed and fabricated, respectively. Measured results of all those circuits agree well with their simulated ones. Finally, as an application demonstration of our proposed magic-T, a singly balanced mixer based on this structure is designed and measured with good performances.


title: The ‘when’ and ‘where’ of perceiving signals of threat versus non-threat

abstract: Abstract We tested the proposal that signals of potential threat are given precedence over positive and neutral signals, reflected in earlier and more pronounced changes in neural activity. The temporal sequence (‘when’) and source localization (‘where’) of event-related potentials (ERPs) elicited by fearful and happy facial expressions, compared to neutral control expressions, were examined for 219 healthy subjects. We scored ERPs over occipito-temporal sites (N80, 50–120 ms; P120, 80–180 ms; N170, 120–220 ms; P230, 180–290 ms; N250, 230–350 ms) and their polarity-reversed counterparts over medial sites (P80, 40–120 ms; N120, 80–150 ms; VPP, 120–220 ms; N200, 150–280 ms; P300, 280–450 ms). In addition to scoring peak amplitude and latency, the anatomical sources of activity were determined using low resolution brain electromagnetic tomography (LORETA). Fearful faces were distinguished by persistent increases in positivity, associated with a dynamical shift from temporo-frontal (first 120 ms) to more distributed cortical sources (120–220 ms) and back (220–450 ms). By contrast, expressions of happiness produced a discrete enhancement of negativity, later in the time course (230–350 ms) and localized to the fusiform region of the temporal cortex. In common, fear and happiness modulated the face-related N170, and produced generally greater right hemisphere activity. These findings support the proposal that fear signals are given precedence in the neural processing systems, such that processing of positive signals may be suppressed until vigilance for potential danger is completed. While fear may be processed via parallel pathways (one initiated prior to structural encoding), neural systems supporting positively valenced input may be more localized and rely on structural encoding.


title: Fine-Grained Proper Noun Ontologies For Question Answering

abstract: The WordNet lexical ontology, which is primarily composed of common nouns, has been widely used in retrieval tasks. Here, we explore the notion of a fine-grained proper noun ontology and argue for the utility of such an ontology in retrieval tasks. To support this claim, we build a fine-grained proper noun ontology from unrestricted news text and use this ontology to improve performance on a question answering task.


title: Serum magnesium: a biomarker of cardiovascular risk revisited?

abstract: mrh.2012.0309 Auteur(s) : Liana C. Del Gobbo1, Ronald J. Elin2, Paul Poirier3, Grace M Egeland1 grace.egeland@ mcgill.ca 1 Human Nutrition, McGill University, 21,111 Lakeshore Road, Ste. Anne de Bellevue, Quebec, Canada H9X 3V9. 2 Department of Pathology and Laboratory Medicine, University of Louisville, Louisville, Kentucky, United States 40292. 3 Public Health Research Unit, CHUL Research Center, Centre Hospitalier Universitaire de Quebec Sainte-Foy, Canada G1V 5B3 Correspondence: Grace M. Egeland, [...]


title: Abundance and Location of the Small Heat Shock Proteins HSP25 and αB-Crystallin in Rat and Human Heart

abstract: Background In the heart, there are high constitutive levels of the two related small heat shock proteins, HSP25 and αB-crystallin. To gain insight into their functional role, we have analyzed abundance and location of both proteins in rat and human hearts at different stages of development and in diseased state. Methods and Results Immunoblotting analysis of rat ventricular tissue at fetal, neonatal, and adult stages reveals the level of HSP25 to decline strongly during development, whereas the level of αB-crystallin remains nearly constant. In parallel, the portion of phosphorylated isoforms of HSP25 decreases as shown by two-dimensional polyacrylamide gel electrophoresis. HSP25 is detected in cardiomyocytes and endothelial and vascular smooth muscle cells, whereas αB-crystallin is detected in cardiomyocytes only by immunofluorescence and immunoelectron microscopy. Both proteins colocalize in the I-band and M-line region of myofibrils in cardiomyocytes. In diseased and transplanted adult human hearts, HS...


title: Improving accuracy for TVWS geolocation databases: Results from measurement-driven estimation approaches

abstract: Recent advances towards opening underutilized spectrum resources for secondary use rely on geolocation databases to determine protection requirements of primary users. This paper studies the achievable accuracy of signal strength estimation for a primary TV network with log-normal propagation models, linear interpolation through Delaunay triangulation, and ordinary kriging as means of including measurement data into a database's prediction process. Direct estimation methods are compared against hybrid mixture models of statistical propagation modelling with interpolation of the error surface. The results presented herein use measurements from a large-scale measurement campaign in the UK. They show that relatively small data sets suffice to achieve mean absolute prediction errors of 3.1–5.0dB compared to 5.9–6.9dB achieved by purely terrain-based estimation approaches.


title: A Review on Driver Face Monitoring Systems for Fatigue and Distraction Detection

abstract: Every year, many car accidents due to driver fatigue and distraction occur around the world and cause many casualties and injuries. Driver face monitoring systems is one of the main approaches for driver fatigue or distraction detection and accident prevention. Driver face monitoring systems capture the images from driver face and extract the symptoms of fatigue and distraction from eyes, mouth and head. These symptoms are usually percentage of eyelid closure over time (PERCLOS), eyelid distance, eye blink rate, blink speed, gaze direction, eye saccadic movement, yawning, head nodding and head orientation. The system estimates driver alertness based on extracted symptoms and alarms if needed. In this paper, after an introduction to driver face monitoring systems, the general structure of these systems is discussed. Then a comprehensive review on driver face monitoring systems for fatigue and distraction detection is presented.


title: Position and Force Sensitive N-Ary User Interface Framework for Wrist-Worn Wearables

abstract: In this paper, we describe and identify current limitations of wearable user interface to elicit requirements and design considerations of an effective wearable user interface framework. Based on the elicited criteria, we propose a novel wearable user interface framework that succinctly enables expressive physical contacts to be made on a sensor-embedded wristband of the wearable device. In the presented framework, multiple force-sensitive linear potentiometers enclosed in the wristband are exploited as tools of user input where an instance of user contact is represented as a pairwise set of a contacted position with an exerted force value at that position. Consequently, the proposed framework expands input expressivity of the wearable device by creating and recognizing various position-force combinations of user input. Moreover, to help user differentiate and navigate different inputs, two novel methods of instant N-ary input and indicative visualization are integrated into the framework, respectively. To demonstrate the feasibility of the proposed conceptual framework, a proof-of-concept text entry application is implemented and evaluated.


title: Cuckoo search implementation in LabVIEW

abstract: LabVIEW is one of the various programming platforms which is widely utilized by the researchers of fraternities form both industry and academia, due to its simple-to-use graphical programming environment and extra ordinary easy interface with the hardware. LabVIEW is equipped with various in-built toolkits to perform different measurement and control tasks. One of these tasks is solving an optimization problem which is often encountered by researchers from variety of fields viz. control engineering, civil design, machine design, digital signal processing, and economics etc. However, the standard LabVIEW package is equipped with only a single global optimization algorithm, i.e. Differential Evolution (DE) algorithm and there exists a need of other efficient global optimization techniques. This paper deals with same concern and an effort has been made to develop a widely established and practiced global meta-heuristic algorithm i.e. cuckoo search algorithm (CSA) in LabVIEW environment. To test the CSA implementation in LabVIEW environment, nine benchmark test functions have been used and a comparative study has been made with DE algorithm. It was found from the conducted studies that CSA was more efficient in solving optimization problems with better convergence rate and repeatability than DE.


title: Evaluation of Decision Tree Pruning Algorithms for Complexity and Classification Accuracy

abstract: ABSTRACT Classification is an important problem in data mining. Given a database of records, each with a class label, a classifier generates a concise and meaningful description for each class that can be used to classify subsequent records. A number of popular classifiers construct decision trees to generate class models. These classifiers first build a decision tree and then prune subtrees from the decision tree in a subsequent pruning phase to improve accuracy and prevent “overfitting”. In this paper, the different pruning methodologies available & their various features are discussed. Also the effectiveness of pruning is evaluated in terms of complexity and classification accuracy by applying C4.5 decision tree classification algorithm on Credit Card Database with pruning and without pruning. Instead of classifying the transactions either fraud or non-fraud the transactions are classified in four risk levels which is an innovative concept.


title: Project massive: self-regulation and problematic use of online gaming

abstract: A longitudinal design was employed to collect three waves of survey data over a 14 month period from 2790 online gamers. Respondents were asked questions about their gaming activity, motivations, personality, social and emotional environment, and the effect gaming has had on their lives. Prospective analysis was used to establish causal and temporal linkages among the repeatedly measured factors. While the data provide some indication that a player's reasons for playing do influence the development of problematic usage, these effects are overshadowed by the central importance of self-regulation in managing both the timing and amount of play. An individual's level of self-regulatory activity is shown to be very important in allowing them to avoid negative outcomes like problematic use. The role of depression is also discussed. With responsible use, online gaming appears to be a healthy recreational activity that provides millions of people with hours of social entertainment and adaptive diversion. However, failure to manage play behavior can lead to feelings of dependency.


title: Tutorial: Current state of P vs NP problem

abstract: Do I understand the problem of P vs. NP? The answer is a simple "no". If I were to understand the problem, I would've solved it as well" — This is the current state of many theoretical computer scientists around the world. Apart from a bag of laureates waiting for the person who successfully understands this most popular millennium prize riddle, this is also considered to be a game changer in both mathematics and computer science. According to Scott Aaronson, "If P = NP, then the world would be a profoundly different place than we usually assume it to be". The speaker intends to share the part that he understood on the problem, and about the efforts that were recently put-forth in cracking the same.


title: More than Just Two Sexes: The Neural Correlates of Voice Gender Perception in Gender Dysphoria

abstract: Gender dysphoria (also known as “transsexualism”) is characterized as a discrepancy between anatomical sex and gender identity. Research points towards neurobiological influences. Due to the sexually dimorphic characteristics of the human voice, voice gender perception provides a biologically relevant function, e.g. in the context of mating selection. There is evidence for a better recognition of voices of the opposite sex and a differentiation of the sexes in its underlying functional cerebral correlates, namely the prefrontal and middle temporal areas. This fMRI study investigated the neural correlates of voice gender perception in 32 male-to-female gender dysphoric individuals (MtFs) compared to 20 non-gender dysphoric men and 19 non-gender dysphoric women. Participants indicated the sex of 240 voice stimuli modified in semitone steps in the direction to the other gender. Compared to men and women, MtFs showed differences in a neural network including the medial prefrontal gyrus, the insula, and the precuneus when responding to male vs. female voices. With increased voice morphing men recruited more prefrontal areas compared to women and MtFs, while MtFs revealed a pattern more similar to women. On a behavioral and neuronal level, our results support the feeling of MtFs reporting they cannot identify with their assigned sex.


title: R.: Towards RealTime 3D US to CT Bone Image Registration Using Phase and Curvature Feature Based GMM Matching

abstract: In order to use pre-operatively acquired computed tomography (CT) scans to guide surgical tool movements in orthopaedic surgery, the CT scan must first be registered to the patient's anatomy. Threedimensional (3D) ultrasound (US) could potentially be used for this purpose if the registration process could be made sufficiently automatic, fast and accurate, but existing methods have difficultiesmeeting one or more of these criteria.We propose a near-real-time US-to-CT registration method that matches point clouds extracted from local phase images with points selected in part on the basis of local curvature. The point clouds are represented as Gaussian Mixture Models (GMM) and registration is achieved by minimizing the statistical dissimilarity between the GMMs using an L2 distance metric. We present quantitative and qualitative results on both phantom and clinical pelvis data and show a mean registration time of 2.11 s with a mean accuracy of 0.49 mm.


title: Soft-Legged Wheel-Based Robot with Terrestrial Locomotion Abilities

abstract: In recent years robotics has been influenced by a new approach, soft-robotics, bringing the idea that safe interaction with user and more adaptation to the environment can be achieved by exploiting easily deformable materials and flexible components in the structure of robots. In 2016, the soft-robotics community has promoted a new robotics challenge, named RoboSoft Grand Challenge, with the aim of bringing together different opinions on the usefulness and applicability of softness and compliancy in robotics. In this paper we describe the design and implementation of a terrestrial robot based on two soft legged wheels. The tasks predefined by the challenge were set as targets in the robot design, which finally succeeded to accomplish all the tasks. The wheels of the robot can passively climb over stairs and adapt to slippery grounds using two soft legs embedded in their structure. The soft legs, fabricated by integration of soft and rigid materials and mounted on the circumference of a conventional wheel, succeed to enhance its functionality and easily adapt to unknown grounds. The robot has a semi stiff tail that helps in the stabilization and climbing of stairs. An active wheel is embedded at the extremity of the tail in order to increase the robot maneuverability in narrow environments. Moreover two parallelogram linkages let the robot to reconfigure and shrink its size allowing entering inside gates smaller than its initial dimensions.


Automatic Drum Transcription Using the Student-Teacher Learning Paradigm with Unlabeled Music Data


title: Radio propagation path loss models for 5G cellular networks in the 28 GHZ and 38 GHZ millimeter-wave bands

abstract: This article presents empirically-based largescale propagation path loss models for fifthgeneration cellular network planning in the millimeter-wave spectrum, based on real-world measurements at 28 GHz and 38 GHz in New York City and Austin, Texas, respectively. We consider industry-standard path loss models used for today’s microwave bands, and modify them to fit the propagation data measured in these millimeter-wave bands for cellular planning. Network simulations with the proposed models using a commercial planning tool show that roughly three times more base stations are required to accommodate 5G networks (cell radii up to 200 m) compared to existing 3G and 4G systems (cell radii of 500 m to 1 km) when performing path loss simulations based on arbitrary pointing angles of directional antennas. However, when directional antennas are pointed in the single best directions at the base station and mobile, coverage range is substantially improved with little increase in interference, thereby reducing the required number of 5G base stations. Capacity gains for random pointing angles are shown to be 20 times greater than today’s fourth-generation Long Term Evolution networks, and can be further improved when using directional antennas pointed in the strongest transmit and receive directions with the help of beam combining techniques.


title: A new variable stiffness actuator (CompAct-VSA): Design and modelling

abstract: This paper describes the design and modelling of a new variable stiffness actuator (CompAct-VSA). The principle of operation of CompAct-VSA is based on a lever arm mechanism with a continuously regulated pivot point. The proposed concept allows for the development of an actuation unit with a wide range of stiffness and a fast stiffness regulation response. The implementation of the actuator makes use of a cam shaped lever arm with a variable pivot axis actuated by a rack and pinion transmission system. This realization results in a highly integrated and modular assembly. Size and weight are indeed an open issue in the VSAs design, which ultimately limit their implementation in multi-dof robotic systems. The paper introduces the mechanics, the principle of operation and the model of the actuator. Preliminary results are presented to demonstrate the fast stiffness regulation response and the wide range of stiffness achieved by the proposed CompAct-VSA design.


title: The filtered combination of the weighted edit distance and the Jaro-Winkler distance to improve spellchecking Arabic texts

abstract: The digital environments for human learning have been much evolving thanks to the incredible progress of information technologies. This is particularly the case for automatic correction of spelling errors requested by a large majority of people nowadays. Almost all of the current spellcheckers are semiautomatic, and they enable users to find the good solution for a committed error. The major shortcoming of the existing metric methods of correction lies in the bad scheduling of the solutions suggested to the spellchecking out of context of a detected error. To overcome this limitation, we have developed several approaches which suggest probability costs estimated from a learning test. It is attributed in various editing operations during calculating measure of similarity, case of the edit distance. The idea developed in this work was to know how to efficiently weigh these editing operations without resorting to a phase of learning. This is based only on the proximity and the similarity between Arabic keyboard keys. Additionally, we have suggested combining this measure with the distance of Jaro-Winkler in order to better filter, refine and weigh certain solutions compared to others. The experimental results stem from tests conducted on errors committed in a learning corpus, trying to validate the choices of conception and to prove the interest of both approaches.


title: Spontaneous Speech Corpus of Japanese

abstract: Design issues of a spontaneous speech corpus is described. The corpus under compilation will contain 800-1000 hour spontaneously uttered Common Japanese speech and the morphologically annotated transcriptions. Also, segmental and intonation labeling will be provided for a subset of the corpus. The primary application domain of the corpus is speech recognition of spontaneous speech, but we plan to make it useful for natural language processing and phonetic/linguistic studies also.


title: Imitation and Mechanisms of Joint Attention: A Developmental Structure for Building Social Skills on a Humanoid Robot

abstract: Adults are extremely adept at recognizing social cues, such as eye direction or pointing gestures, that establish the basis of joint attention. These skills serve as the developmental basis for more complex forms of metaphor and analogy by allowing an infant to ground shared experiences and by assisting in the development of more complex communication skills. In this chapter, we review some of the evidence for the developmental course of these joint attention skills from developmental psychology, from disorders of social development such as autism, and from the evolutionary development of these social skills. We also describe an on-going research program aimed at testing existing models of joint attention development by building a human-like robot which communicates naturally with humans using joint attention. ::: ::: Our group has constructed an upper-torso humanoid robot, called Cog, in part to investigate how to build intelligent robotic systems by following a developmental progression of skills similar to that observed in human development. Just as a child learns social skills and conventions through interactions with its parents, our robot will learn to interact with people using natural social communication. We further consider the critical role that imitation plays in bootstrapping a system from simple visual behaviors to more complex social skills. We will present data from a face and eye finding system that serves as the basis of this developmental chain, and an example of how this system can imitate the head movements of an individual.


title: Antenna system with active impedance matching

abstract: With the growing use of M2M [Machine to Machine] communication in the industry, it has become essential to have antennas in devices that cover a wide frequency range from 700MHz to 2700 MHz and work efficiently by being impedance matched under various environmental effects. This paper proposes a closed loop impedance matched antenna system solution to tackle this challenging issue. A novel and compact plug and play solution is proposed that can include an antenna with a dynamic tuning range of up to a few 100 MHz.


title: Weakly Supervised Memory Networks

abstract: In this paper we introduce a variant of Memory Networks that needs significantly less supervision to perform question and answering tasks. The original model requires that the sentences supporting the answer be explicitly indicated during training. In contrast, our approach only requires the answer to the question during training. We apply the model to the synthetic bAbI tasks, showing that our approach is competitive with the supervised approach, particularly when trained on a sufficiently large amount of data. Furthermore, it decisively beats other weakly supervised approaches based on LSTMs. The approach is quite general and can potentially be applied to many other tasks that require capturing long-term dependencies.


Word Embeddings for Multi-label Document Classification.


title: Automated Void Detection in Solder Balls in the Presence of Vias and Other Artifacts

abstract: Voids are one of the major defects in solder balls and their detection and assessment can help in reducing unit and board yield issues caused by excessive or very large voids. Voids are difficult to detect using manual inspection alone. 2-D X-ray machines are often used to make voids visible to an operator for manual inspection. Automated methods do not give good accuracy in void detection and measurement because of a number of challenges present in 2-D X-ray images. Some of these challenges include vias, plated-through holes, reflections from the the plating or vias, inconsistent lighting, background traces, noise, void-like artifacts, and parallax effects. None of the existing methods that has been researched or utilized in equipment could accurately and repeatably detect voids in the presence of these challenges. This paper proposes a robust automatic void detection algorithm that detects voids accurately and repeatably in the presence of the aforementioned challenges. The proposed method operates on the 2-D X-ray images by first segregating each individual solder ball, including balls that are overshadowed by components, in preparation for treating each ball independently for void detection. Feature parameters are extracted through different classification steps to classify each artifact detected inside the solder ball as a candidate or phantom void. Several classification steps are used to tackle the challenges exhibited in the 2-D X-ray images. The proposed method is able to detect different-sized voids inside the solder balls under different brightness conditions and voids that are partially obscured by vias. Results show that the proposed method achieves a correlation squared of 86% when compared with manually measured and averaged data from experienced operators from both 2-D and 3-D X-ray tools. The proposed algorithm is fully automated and benefits the manufacturing process by reducing operator inspection time and removing the manual measurement variability from the results, thus providing a cost-effective solution to improve output product quality.


title: A Demonstration of Rubato DB: A Highly Scalable NewSQL Database System for OLTP and Big Data Applications

abstract: We propose to demonstrate Rubato DB, a highly scalable NewSQL system, supporting various consistency levels from ACID to BASE for OLTP and big data applications. Rubato DB employs the staged grid architecture with a novel formula based protocol for distributed concurrency control. Our demonstration will present Rubato DB as one NewSQL database management system running on a collection of commodity servers against two of benchmark sets. The demo attendees can modify the configuration of system size, fine-tune the query workload, and visualize the performance on the fly by the graphical user interface. Attendees can experiment with various system scales, and thus grasp the potential scalability of Rubato DB, whose performance, with the increase of the number of servers used, can achieve a linear growth for both OLTP application with the strong consistency properties and key-value storage applications with the weak consistency properties.


title: Communicating emotions and mental states to robots in a real time parallel framework using Laban movement analysis

abstract: This paper presents a parallel real time framework for emotions and mental states extraction and recognition from video fragments of human movements. In the experimental setup human hands are tracked by evaluation of moving skin-colored objects. The tracking analysis demonstrates that acceleration and frequency characteristics of the traced objects are relevant for classification of the emotional expressiveness of human movements. The outcomes of the emotional and mental states recognition are cross-validated with the analysis of two independent certified movement analysts (CMA's) who use the Laban movement analysis (LMA) method. We argue that LMA based computer analysis can serve as a common language for expressing and interpreting emotional movements between robots and humans, and in that way it resembles the common coding principle between action and perception by humans and primates that is embodied by the mirror neuron system. The solution is part of a larger project on interaction between a human and a humanoid robot with the aim of training social behavioral skills to autistic children with robots acting in a natural environment.


title: MetaCost: a general method for making classifiers cost-sensitive

abstract: Research in machine learning, statistics and related fields has produced a wide variety of algorithms for classification. However, most of these algorithms assume that all errors have the same cost, which is seldom the case in KDD problems. Individually making each classification learner costsensitive is laborious, and often non-trivial. In this paper we propose a principled method for making an arbitrary classifier cost-sensitive by wrapping a cost-minimizing procedure around it. This procedure, called MetaCost, treats the underlying classifier as a black box, requiring no knowledge of its functioning or change to it. Unlike stratification, MetaCost, is applicable to any number of classes and to arbitrary cost matrices. Empirical trials on a large suite of benchmark databases show that MetaCost almost always produces large cost reductions compared to the cost-blind classifier used (C4.5RULES) and to two forms of stratification. Further tests identify the key components of MetaCost and those that can be varied without substantial loss. Experiments on a larger database indicate that MetaCost scales well.


title: Reconstruction of sparse signals from distorted randomized measurements

abstract: In this paper we show that, surprisingly, it is possible to recover sparse signals from nonlinearly distorted measurements, even if the nonlinearity is unknown. Assuming just that the nonlinearity is monotonic, we use the only reliable information in the distorted measurements: their ordering. We demonstrate that this information is sufficient to recover the signal with high precision and present two approaches to do so. The first uses order statistics to compute the minimum mean square (MMSE) estimate of the undistorted measurements and use it with standard compressive sensing (CS) reconstruction algorithms. The second uses the principle of consistent reconstruction to develop a deterministic nonlinear reconstruction algorithm that ensures that measurements of the reconstructed signal have ordering consistent with the ordering of the distorted measurements. Our experiments demonstrate the superior performance of both approaches compared to standard CS methods.


title: Hierarchies of Attention and Experimental Designs: Effects of Spatial and Intermodal Attention Revisited

abstract: When attention is directed to stimuli in a given modality and location, information processing in other irrelevant modalities at this location is affected too. This spread of attention to irrelevant stimuli is often interpreted as superiority of location selection over modality selection. However, this conclusion is based on experimental paradigms in which spatial attention was transient whereas intermodal attention was sustained. Furthermore, whether modality selection affects processing in the task-relevant modality at irrelevant locations remains an open question. Here, we addressed effects of simultaneous spatial and intermodal attention in an EEG study using a balanced design where spatial attention was transient and intermodal attention sustained or vice versa. Effects of spatial attention were not affected by which modality was attended and effects of intermodal attention were not affected by whether the stimuli were at the attended location or not. This suggests not only spread of spatial attention to task-irrelevant modalities but also spread of intermodal attention to task-irrelevant locations. Whether spatial attention was transient or sustained did not alter the effect of spatial attention on visual N1 and Nd1 responses. Prestimulus preparatory occipital alpha band responses were affected by both transient and sustained spatial cueing, whereas late post-stimulus responses were more strongly affected by sustained than by transient spatial attention. Sustained but not transient intermodal attention affected late responses >200 msec to visual stimuli. Together, the results undermine the universal superiority of spatial attention and suggest that the mode of attention manipulation is an important factor determining attention effects.


title: Noninvasive Prediction of Glucose by Near-Infrared Diffuse Reflectance Spectroscopy

abstract: Background: Self-monitoring of blood glucose by diabetics is crucial in the reduction of complications related to diabetes. Current monitoring techniques are invasive and painful, and discourage regular use. The aim of this study was to demonstrate the use of near-infrared (NIR) diffuse reflectance over the 1050–2450 nm wavelength range for noninvasive monitoring of blood glucose. ::: ::: Methods: Two approaches were used to develop calibration models for predicting the concentration of blood glucose. In the first approach, seven diabetic subjects were studied over a 35-day period with random collection of NIR spectra. Corresponding blood samples were collected for analyte analysis during the collection of each NIR spectrum. The second approach involved three nondiabetic subjects and the use of oral glucose tolerance tests (OGTTs) over multiple days to cause fluctuations in blood glucose concentrations. Twenty NIR spectra were collected over the 3.5-h test, with 16 corresponding blood specimens taken for analyte analysis. ::: ::: Results: Statistically valid calibration models were developed on three of the seven diabetic subjects. The mean standard error of prediction through cross-validation was 1.41 mmol/L (25 mg/dL). The results from the OGTT testing of three nondiabetic subjects yielded a mean standard error of calibration of 1.1 mmol/L (20 mg/dL). Validation of the calibration model with an independent test set produced a mean standard error of prediction equivalent to 1.03 mmol/L (19 mg/dL). ::: ::: Conclusions: These data provide preliminary evidence and allow cautious optimism that NIR diffuse reflectance spectroscopy using the 1050–2450 nm wavelength range can be used to predict blood glucose concentrations noninvasively. Substantial research is still required to validate whether this technology is a viable tool for long-term home diagnostic use by diabetics.


Multimodal Coreference Resolution for Exploratory Data Visualization Dialogue: Context-Based Annotation and Gesture Identification


title: Maximum margin planning

abstract: Imitation learning of sequential, goal-directed behavior by standard supervised techniques is often difficult. We frame learning such behaviors as a maximum margin structured prediction problem over a space of policies. In this approach, we learn mappings from features to cost so an optimal policy in an MDP with these cost mimics the expert's behavior. Further, we demonstrate a simple, provably efficient approach to structured maximum margin learning, based on the subgradient method, that leverages existing fast algorithms for inference. Although the technique is general, it is particularly relevant in problems where A* and dynamic programming approaches make learning policies tractable in problems beyond the limitations of a QP formulation. We demonstrate our approach applied to route planning for outdoor mobile robots, where the behavior a designer wishes a planner to execute is often clear, while specifying cost functions that engender this behavior is a much more difficult task.


title: The soft iron and hard iron calibration method using extended kalman filter for attitude and heading reference system

abstract: The calibration method of the soft iron and hard iron distortion based on attitude and heading reference system (AHRS) can boil down to the estimation of 12 parameters of magnetic deviation, normally using 12-state Kalman filter (KF) algorithm. The performance of compensation is limited by the accuracy of local inclination angle of magnetic field and initial heading. A 14-state extended Kalman filter (EKF) algorithm is developed to calibrate magnetic deviation, local magnetic inclination angle error and initial heading error all together. The calibration procedure is to change the attitude of AHRS and rotate it two cycles. As the strapdown matrix can hold high precision after initial alignment of AHRS in short time for the gyropsilas short-term precision, the magnetic field vector can be projected onto the body frame of AHRS. The experiment results demonstrate that 14-state EKF outperforms 12-state KF, with measurement errors exist in the initial heading and local inclination angle. The heading accuracy (variance) after compensation is 0.4 degree for tilt angle ranging between 0 and 60 degree.


title: Vectorized production path tracing

abstract: This paper presents MoonRay, a high performance production rendering architecture using Monte Carlo path tracing developed at DreamWorks Animation. MoonRay is the first production path tracer, to our knowledge, designed to fully leverage Single Instruction/Multiple Data (SIMD) vector units throughout. To achieve high SIMD efficiency, we employ Embree for tracing rays and vectorize the remaining compute intensive components of the renderer: the integrator, the shading system and shaders, and the texturing engine. Queuing is used to help keep all vector lanes full and improve data coherency. We use the ISPC programming language [Intel 2011; Pharr and Mark 2012] to achieve improved performance across SSE, AVX/AVX2 and AVX512 instruction sets. Our system includes two functionally equivalent uni-directional CPU path tracing implementations: a C++ scalar depth-first version and an ISPC vectorized breadth-first wavefront version. Using side by side performance comparisons on complex production scenes and assets we show our vectorized architecture, running on AVX2, delivers between a 1.3× to 2.3× speed-up in overall render time, and up to 3×, 6×, and 4×, speed-ups within the integration, shading, and texturing components, respectively.


CME Nasal Reconstruction


title: Using e-CRM for a unified view of the customer

abstract: Applying detailed knowledge of the customer to a larger business domain.


title: On-line fingerprint verification

abstract: We describe the design and implementation of an online fingerprint verification system which operates in two stages: (i) minutia extraction and (ii) minutia matching. An improved minutia extraction algorithm that is much faster and more accurate than our earlier algorithm has been implemented. For minutia matching, an alignment-based elastic matching algorithm has been developed. This algorithm is capable of finding the correspondences between input minutiae and the stored template without resorting to exhaustive search and has the ability to adaptively compensate for the nonlinear deformations and inexact pose transformations between finger prints. The system has been tested on two sets of finger print images captured with inkless scanners. The verification accuracy is found to be over 99% with a 15% reject rate. Typically, a complete fingerprint verification procedure takes, on an average, about 8 seconds on a SPARC 20 workstation. It meets the response time requirements of on-line verification with high accuracy.


title: Learning to Find Good Correspondences

abstract: We develop a deep architecture to learn to find good correspondences for wide-baseline stereo. Given a set of putative sparse matches and the camera intrinsics, we train our network in an end-to-end fashion to label the correspondences as inliers or outliers, while simultaneously using them to recover the relative pose, as encoded by the essential matrix. Our architecture is based on a multi-layer perceptron operating on pixel coordinates rather than directly on the image, and is thus simple and small. We introduce a novel normalization technique, called Context Normalization, which allows us to process each data point separately while embedding global information in it, and also makes the network invariant to the order of the correspondences. Our experiments on multiple challenging datasets demonstrate that our method is able to drastically improve the state of the art with little training data.


title: PME: Projected Metric Embedding on Heterogeneous Networks for Link Prediction

abstract: Heterogenous information network embedding aims to embed heterogenous information networks (HINs) into low dimensional spaces, in which each vertex is represented as a low-dimensional vector, and both global and local network structures in the original space are preserved. However, most of existing heterogenous information network embedding models adopt the dot product to measure the proximity in the low dimensional space, and thus they can only preserve the first-order proximity and are insufficient to capture the global structure. Compared with homogenous information networks, there are multiple types of links (i.e., multiple relations) in HINs, and the link distribution w.r.t relations is highly skewed. To address the above challenging issues, we propose a novel heterogenous information network embedding model PME based on the metric learning to capture both first-order and second-order proximities in a unified way. To alleviate the potential geometrical inflexibility of existing metric learning approaches, we propose to build object and relation embeddings in separate object space and relation spaces rather than in a common space. Afterwards, we learn embeddings by firstly projecting vertices from object space to corresponding relation space and then calculate the proximity between projected vertices. To overcome the heavy skewness of the link distribution w.r.t relations and avoid "over-sampling'' or "under-sampling'' for each relation, we propose a novel loss-aware adaptive sampling approach for the model optimization. Extensive experiments have been conducted on a large-scale HIN dataset, and the experimental results show superiority of our proposed PME model in terms of prediction accuracy and scalability.


title: DETC2004/MECH-57125 SEVEN-DOF CABLE-SUSPENDED ROBOT WITH INDEPENDENT METROLOGY

abstract: This paper presents a new cable-suspended robot. It is a 7-cable spatial design with a closed-form forward pose kinematics solution. Applications include automated machining, construction, and sculpting. This paper presents two new ideas. First, an independent, passive, six-string-pot-based Cartesian metrology system was built and tested since length measurements through the active drive system may be inaccurate for large-scale systems. Second, we introduce a new active cable tensioning approach wherein we control the displacement of a physical spring in-line with one of the active drive cables in attempt to ensure only positive cable tensions in all active cables. This is compared with an existing particular/homogeneous tensioning solution approach. Simulation examples are presented in this paper; we are currently building the proposed system for future evaluation work.Copyright © 2004 by ASME


title: Optional stopping: No problem for Bayesians

abstract: Optional stopping refers to the practice of peeking at data and then, based on the results, deciding whether or not to continue an experiment. In the context of ordinary significance-testing analysis, optional stopping is discouraged, because it necessarily leads to increased type I error rates over nominal values. This article addresses whether optional stopping is problematic for Bayesian inference with Bayes factors. Statisticians who developed Bayesian methods thought not, but this wisdom has been challenged by recent simulation results of Yu, Sprenger, Thomas, and Dougherty (2013) and Sanborn and Hills (2013). In this article, I show through simulation that the interpretation of Bayesian quantities does not depend on the stopping rule. Researchers using Bayesian methods may employ optional stopping in their own research and may provide Bayesian analysis of secondary data regardless of the employed stopping rule. I emphasize here the proper interpretation of Bayesian quantities as measures of subjective belief on theoretical positions, the difference between frequentist and Bayesian interpretations, and the difficulty of using frequentist intuition to conceptualize the Bayesian approach.


title: A blocker-tolerant wideband noise-cancelling receiver with a 2dB noise figure

abstract: As narrowband off-chip RF filtering is not compatible with the concept of software-defined radio (SDR), an SDR receiver must be designed to tolerate large out-of-band blockers with minimal gain compression and noise figure degradation. A recent circuit [1] tackles this problem by dispensing with the LNA entirely. This mixer-first approach achieves impressive linearity, but at the expense of noise figure and, since such a receiver has no gain prior to down-conversion, the flicker noise corner can be unacceptably high. Other SDR attempts invariably use a noise-cancelling LNA at the front end, which provides wideband matching, however such approaches have either inadequate linearity [2] or display too large a noise for our purposes [3]. In this work, we propose a hybrid frequency-translational, noise-cancelling(FTNC) receiver that employs two separate down-conversion paths to enable noise cancelling with no voltage gain prior to base-band filtering. The resulting design has a sub-2dB noise figure and tolerates 0dBm blockers with no gain back-off, breaking the traditional noise-linearity trade-off common in all receivers.


title: SPOOK: A System for Probabilistic Object-Oriented Knowledge Representation

abstract: In previous work, we pointed out the limitations of standard Bayesian networks as a modeling framework for large, complex domains. We proposed a new, richly structured modeling language, Object-oriented Bayesian Networks, that we argued would be able to deal with such domains. However, it turns out that OOBNs are not expressive enough to model many interesting aspects of complex domains: the existence of specific named objects, arbitrary relations between objects, and uncertainty over domain structure. These aspects are crucial in real-world domains such as battlefield awareness. In this paper, we present SPOOK, an implemented system that addresses these limitations. SPOOK implements a more expressive language that allows it to represent the battlespace domain naturally and compactly. We present a new inference algorithm that utilizes the model structure in a fundamental way, and show empirically that it achieves orders of magnitude speedup over existing approaches.


title: Galton board experiment and simulation in Excel

abstract: According to experimental principle of Galton nail Board, This paper uses several Excel functions tools to simulate the Galton Board Experiment in a simple, intuitive way. A dynamic demonstration using graphics for interpretation of the center limit theorem are given. Further, in an independent case, the result of the two-dimension above can be extended to three-dimensional situation. This reflects that the theoretical study should combine with modern educational technology.


title: Design and implementation of 64 bit multiplier using vedic algorithm

abstract: As floating point architecture is very hot topic for researchers so challenges are always there to design the efficient Floating point architecture. Out of other operations, Floating point multiplication is the most commonly used operation and it requires the multiplication of the mantissa of Floating point numbers. This paper presents the highly efficient 64 bit multiplier for the mantissa calculation using rule or sutra of Vedic mathematics called Urdhva Tiryakbhyam Sutra which deals with vertically and crosswise multiplication. Using this sutra in the computation algorithm of DSP processors, can enhance the efficiency and at the same time can reduce the complexity, area, power consumption and delay. Starting from the design of 2 bit Vedic multiplier we went up to design 64 bit Vedic multiplier as presented in this paper. Vedic multiplier is coded in Verilog HDL and targeted to three different families of FPGA Spartan6, Virtex5 and Virtex6 in Xilinx 13.1 ISE software. Result is compared with the Karatsuba, Vedic-Karatsuba and Optimized Vedic multiplier and found 33% reduction in delay.


Little Ben: The Ben Franklin Racing Team’s Entry in the 2007 DARPA Urban Challenge


Implementation of Water Quality Management by Fish School Detection Based on Computer Vision Technology


title: A survey on automatic image caption generation

abstract: Abstract Image captioning means automatically generating a caption for an image. As a recently emerged research area, it is attracting more and more attention. To achieve the goal of image captioning, semantic information of images needs to be captured and expressed in natural languages. Connecting both research communities of computer vision and natural language processing, image captioning is a quite challenging task. Various approaches have been proposed to solve this problem. In this paper, we present a survey on advances in image captioning research. Based on the technique adopted, we classify image captioning approaches into different categories. Representative methods in each category are summarized, and their strengths and limitations are talked about. In this paper, we first discuss methods used in early work which are mainly retrieval and template based. Then, we focus our main attention on neural network based methods, which give state of the art results. Neural network based methods are further divided into subcategories based on the specific framework they use. Each subcategory of neural network based methods are discussed in detail. After that, state of the art methods are compared on benchmark datasets. Following that, discussions on future research directions are presented.


title: Understanding my data, myself: supporting self-reflection with ubicomp technologies

abstract: We live in a world where many kinds of data about us can be collected and more will be collected as Ubicomp technologies mature. People reflect on this data using different tools for personal informatics. However, current tools do not have sufficient understanding of users' self-reflection needs to appropriately leverage Ubicomp technologies. To design tools that effectively assist self-reflection, we need to comprehensively understand what kinds of questions people have about their data, why they ask these questions, how they answer them with current tools, and what kinds of problems they encounter. To explore this, we conducted interviews with people who use various kinds of tools for personal informatics. We found six kinds of questions that people asked about their data. We also found that certain kinds of questions are more important at certain times, which we call phases. We identified two phases of reflection: Discovery and Maintenance. We discuss the kinds of questions and the phases in detail and identify features that should be supported in personal informatics tools for which Ubicomp technologies can play an important role.


title: Determinants of the variability of heart rate measures during a competitive period in young soccer players

abstract: Measurements of exercise heart rate (HRex), HR recovery (HRR) and HR variability (HRV) are used as indices of training status. However, the day-to-day variability of these indices throughout a competitive soccer period is unknown. On 14 occasions during a 3-week competition camp, 18 under 15 (U15) and 15 under 17 (U17) years soccer players performed a 5-min submaximal run, followed by a seated 5-min recovery period. HRex was determined during the last 30 s of exercise, while HRR and HRV were measured during the first and last 3 min of the post-exercise recovery period, respectively. U15 players displayed greater HRex (P = 0.02) and HRR (P = 0.004) compared with the U17 players, but there was no difference in HRV (P = 0.74). The mean coefficient of variation (CV) for HRex was lower than that for HRV [3.4 (90% CL, 3.1, 3.7) vs. 10.7 (9.6, 11.9)%, P < 0.001]; both were lower than that for HRR [13.3 (12.2, 14.3)%, P < 0.01]. In contrast to HRex and HRR, the CV for HRV was correlated to maximal aerobic speed (r = −0.52, P = 0.002). There was no correlation between total activity time (training sessions + matches) and CV of any of the quantified variables. The variability of each of these measures and player fitness levels should be considered when interpreting changes in training status.


title: Reconfiguring the Imaging Pipeline for Computer Vision

abstract: Advancements in deep learning have ignited an explosion of research on efficient hardware for embedded computer vision. Hardware vision acceleration, however, does not address the cost of capturing and processing the image data that feeds these algorithms. We examine the role of the image signal processing (ISP) pipeline in computer vision to identify opportunities to reduce computation and save energy. The key insight is that imaging pipelines should be be configurable: to switch between a traditional photography mode and a low-power vision mode that produces lower-quality image data suitable only for computer vision. We use eight computer vision algorithms and a reversible pipeline simulation tool to study the imaging system's impact on vision performance. For both CNN-based and classical vision algorithms, we observe that only two ISP stages, demosaicing and gamma compression, are critical for task performance. We propose a new image sensor design that can compensate for these stages. The sensor design features an adjustable resolution and tunable analog-to-digital converters (ADCs). Our proposed imaging system's vision mode disables the ISP entirely and configures the sensor to produce subsampled, lower-precision image data. This vision mode can save ~75% of the average energy of a baseline photography mode with only a small impact on vision task accuracy.