Query: Tracking Hands in Interaction with Objects: A Review

Relevant passages:
Human hand modelling: kinematics, dynamics, applications <=SEP=> An overview of mathematical modelling of the human hand is given. We consider hand models from a specific background: rather than studying hands for surgical or similar goals, we target at providing a set of tools with which human grasping and manipulation capabilities can be studied, and hand functionality can be described. We do this by investigating the human hand at various levels: (1) at the level of kinematics, focussing on the movement of the bones of the hand, not taking corresponding forces into account; (2) at the musculotendon structure, i.e. by looking at the part of the hand generating the forces and thus inducing the motion; and (3) at the combination of the two, resulting in hand dynamics as well as the underlying neurocontrol. Our purpose is to not only provide the reader with an overview of current human hand modelling approaches but also to fill the gaps with recent results and data, thus allowing for an encompassing picture.

Accurate, Robust, and Flexible Real-time Hand Tracking <=SEP=> We present a new real-time hand tracking system based on a single depth camera. The system can accurately reconstruct complex hand poses across a variety of subjects. It also allows for robust tracking, rapidly recovering from any temporary failures. Most uniquely, our tracker is highly flexible, dramatically improving upon previous approaches which have focused on front-facing close-range scenarios. This flexibility opens up new possibilities for human-computer interaction with examples including tracking at distances from tens of centimeters through to several meters (for controlling the TV at a distance), supporting tracking using a moving depth camera (for mobile scenarios), and arbitrary camera placements (for VR headsets). These features are achieved through a new pipeline that combines a multi-layered discriminative reinitialization strategy for per-frame pose estimation, followed by a generative model-fitting stage. We provide extensive technical details and a detailed qualitative and quantitative analysis.

Real-Time Hand Tracking under Occlusion from an Egocentric RGB-D Sensor <=SEP=> We present an approach for real-time, robust and accurate hand pose estimation from moving egocentric RGB-D cameras in cluttered real environments. Existing methods typically fail for hand-object interactions in cluttered scenes imaged from egocentric viewpoints—common for virtual or augmented reality applications. Our approach uses two subsequently applied Convolutional Neural Networks (CNNs) to localize the hand and regress 3D joint locations. Hand localization is achieved by using a CNN to estimate the 2D position of the hand center in the input, even in the presence of clutter and occlusions. The localized hand position, together with the corresponding input depth value, is used to generate a normalized cropped image that is fed into a second CNN to regress relative 3D hand joint locations in real time. For added accuracy, robustness and temporal stability, we refine the pose estimates using a kinematic pose tracking energy. To train the CNNs, we introduce a new photorealistic dataset that uses a merged reality approach to capture and synthesize large amounts of annotated data of natural hand interaction in cluttered scenes. Through quantitative and qualitative evaluation, we show that our method is robust to self-occlusion and occlusions by objects, particularly in moving egocentric perspectives.

Efficient model-based 3D tracking of hand articulations using Kinect <=SEP=> The 3D tracking of the articulation of human hands is a theoretically interesting and challenging problem with numerous and diverse applications [1]. Several attempts have been made to address the problem by considering markerless visual data. Existing approaches can be categorized into modeland appearance-based [1]. In this work, we propose a model-based approach to the problem (Fig. 1). We formulate it as an optimization problem that minimizes the discrepancy between the 3D structure and appearance of hypothesized 3D hand model instances, and actual visual observations. Optimization is performed with a variant of an existing stochastic optimization method (Particle Swarm Optimization PSO) [2]. The most computationally demanding parts of the process have been implemented to run efficiently on a GPU [3]. The resulting system performs hand articulations tracking at a rate of 15Hz.

Real-time Joint Tracking of a Hand Manipulating an Object from RGB-D Input <=SEP=> Real-time simultaneous tracking of hands manipulating and interacting with external objects has many potential applications in augmented reality, tangible computing, and wearable computing. However, due to difficult occlusions, fast motions, and uniform hand appearance, jointly tracking hand and object pose is more challenging than tracking either of the two separately. Many previous approaches resort to complex multi-camera setups to remedy the occlusion problem and often employ expensive segmentation and optimization steps which makes real-time tracking impossible. In this paper, we propose a real-time solution that uses a single commodity RGB-D camera. The core of our approach is a 3D articulated Gaussian mixture alignment strategy tailored to handobject tracking that allows fast pose optimization. The alignment energy uses novel regularizers to address occlusions and hand-object contacts. For added robustness, we guide the optimization with discriminative part classification of the hand and segmentation of the object. We conducted extensive experiments on several existing datasets and introduce a new annotated hand-object dataset. Quantitative and qualitative results show the key advantages of our method: speed, accuracy, and robustness.

Terminological ontology learning and population using latent Dirichlet allocation <=SEP=> The success of Semantic Web will heavily rely on the availability of formal ontologies to structure machine understanding data. However, there is still a lack of general methodologies for ontology automatic learning and population, i.e. the generation of domain ontologies from various kinds of resources by applying natural language processing and machine learning techniques In this paper, the authors present an ontology learning and population system that combines both statistical and semantic methodologies. Several experiments have been carried out, demonstrating the effectiveness of the proposed system. Keywords-Ontologies, Ontology Learning, Ontology Population, Latent Dirichlet Allocation

Evolutionary Competition in Platform Ecosystems <=SEP=> I competition has received scant attention in prior studies, which predominantly study interplatform competition. We develop a middle-range theory of how complementarity between input control and a platform extension’s modularization—by inducing evolution—influences its performance in a platform market. Primary and archival data spanning five years from 342 Firefox extensions show that such complementarity fosters performance by accelerating an extension’s perpetual evolution.

Cache in the air: exploiting content caching and delivery techniques for 5G systems <=SEP=> The demand for rich multimedia services over mobile networks has been soaring at a tremendous pace over recent years. However, due to the centralized architecture of current cellular networks, the wireless link capacity as well as the bandwidth of the radio access networks and the backhaul network cannot practically cope with the explosive growth in mobile traffic. Recently, we have observed the emergence of promising mobile content caching and delivery techniques, by which popular contents are cached in the intermediate servers (or middleboxes, gateways, or routers) so that demands from users for the same content can be accommodated easily without duplicate transmissions from remote servers; hence, redundant traffic can be significantly eliminated. In this article, we first study techniques related to caching in current mobile networks, and discuss potential techniques for caching in 5G mobile networks, including evolved packet core network caching and radio access network caching. A novel edge caching scheme based on the concept of content-centric networking or information-centric networking is proposed. Using trace-driven simulations, we evaluate the performance of the proposed scheme and validate the various advantages of the utilization of caching content in 5G mobile networks. Furthermore, we conclude the article by exploring new relevant opportunities and challenges.

The German Traffic Sign Recognition Benchmark: A multi-class classification competition <=SEP=> The “German Traffic Sign Recognition Benchmark” is a multi-category classification competition held at IJCNN 2011. Automatic recognition of traffic signs is required in advanced driver assistance systems and constitutes a challenging real-world computer vision and pattern recognition problem. A comprehensive, lifelike dataset of more than 50,000 traffic sign images has been collected. It reflects the strong variations in visual appearance of signs due to distance, illumination, weather conditions, partial occlusions, and rotations. The images are complemented by several precomputed feature sets to allow for applying machine learning algorithms without background knowledge in image processing. The dataset comprises 43 classes with unbalanced class frequencies. Participants have to classify two test sets of more than 12,500 images each. Here, the results on the first of these sets, which was used in the first evaluation stage of the two-fold challenge, are reported. The methods employed by the participants who achieved the best results are briefly described and compared to human traffic sign recognition performance and baseline results.

Leveraging graph topology and semantic context for pharmacovigilance through twitter-streams <=SEP=> Adverse drug events (ADEs) constitute one of the leading causes of post-therapeutic death and their identification constitutes an important challenge of modern precision medicine. Unfortunately, the onset and effects of ADEs are often underreported complicating timely intervention. At over 500 million posts per day, Twitter is a commonly used social media platform. The ubiquity of day-to-day personal information exchange on Twitter makes it a promising target for data mining for ADE identification and intervention. Three technical challenges are central to this problem: (1) identification of salient medical keywords in (noisy) tweets, (2) mapping drug-effect relationships, and (3) classification of such relationships as adverse or non-adverse. We use a bipartite graph-theoretic representation called a drug-effect graph (DEG) for modeling drug and side effect relationships by representing the drugs and side effects as vertices. We construct individual DEGs on two data sources. The first DEG is constructed from the drug-effect relationships found in FDA package inserts as recorded in the SIDER database. The second DEG is constructed by mining the history of Twitter users. We use dictionary-based information extraction to identify medically-relevant concepts in tweets. Drugs, along with co-occurring symptoms are connected with edges weighted by temporal distance and frequency. Finally, information from the SIDER DEG is integrate with the Twitter DEG and edges are classified as either adverse or non-adverse using supervised machine learning. We examine both graph-theoretic and semantic features for the classification task. The proposed approach can identify adverse drug effects with high accuracy with precision exceeding 85 % and F1 exceeding 81 %. When compared with leading methods at the state-of-the-art, which employ un-enriched graph-theoretic analysis alone, our method leads to improvements ranging between 5 and 8 % in terms of the aforementioned measures. Additionally, we employ our method to discover several ADEs which, though present in medical literature and Twitter-streams, are not represented in the SIDER databases. We present a DEG integration model as a powerful formalism for the analysis of drug-effect relationships that is general enough to accommodate diverse data sources, yet rigorous enough to provide a strong mechanism for ADE identification.

American Sign Language Translation through Sensory Glove ; SignSpeak <=SEP=> To make a communication bridge, a highly accurate, cost effective and an independent glove was designed for deaf/mute people to enable them to communicate. The glove translates the sign language gestures into speech according to the American Sign Language Standard. The glove contained flex and contact sensors to detect the movements of the fingers and bending of the palm. In addition an accelerometer was built in the glove to measure the acceleration produced by the changing positions of the hand. Principal Component Analysis (PCA) was used to train the glove into recognizing various gestures, and later classify the gestures into alphabets in real time. The glove then established a Bluetooth link with an Android phone, which was used to display the received letters and words and convert the text into speech. The glove was found to have an accuracy of 92%.

Extreme scale with full SQL language support in microsoft SQL Azure <=SEP=> Cloud SQL Server is an Internet scale relational database service which is currently used by Microsoft delivered services and also offered directly as a fully relational database service known as "SQL Azure". One of the principle design objectives in Cloud SQL Server was to provide true SQL support with full ACID transactions within controlled scale "consistency domains" and provide a relaxed degree of consistency across consistency domains that would be viable to clusters of 1,000's of nodes. In this paper, we describe the implementation of Cloud SQL Server with an emphasis on this core design principle.

Multiwave: Doppler Effect Based Gesture Recognition in Multiple Dimensions <=SEP=> We constructed an acoustic, gesture-based recognition system called Multiwave, which leverages the Doppler Effect to translate multidimensional movements into user interface commands. Our system only requires the use of two speakers and a microphone to be operational. Since these components are already built in to most end user systems, our design makes gesture-based input more accessible to a wider range of end users. By generating a known high frequency tone from multiple speakers and detecting movement using changes in the sound waves, we are able to calculate a Euclidean representation of hand velocity that is then used for more natural gesture recognition and thus, more meaningful interaction mappings. We present the results of a user study of Multiwave to evaluate recognition rates for different gestures and report accuracy rates comparable to or better than the current state of the art. We also report subjective user feedback and some lessons learned from our system that provide additional insight for future applications of multidimensional gesture recognition.

An inventory for measuring clinical anxiety: psychometric properties. <=SEP=> The development ofa 2 l-item self-report inventory for measuring the severity of anxiety in psychiatric populations is described. The initial item pool of 86 items was drawn from three preexisting scales: the Anxiety Checklist, the Physician's Desk Reference Checklist, and the Situational Anxiety Checklist. A series of analyses was used to reduce the item pool. The resulting Beck Anxiety Inventory (BAI) is a 21-item scale that showed high internal consistency (a = .92) and test-retest reliability over 1 week, r(81) = .75. The BAI discriminated anxious diagnostic groups (panic disorder, generalized anxiety disorder, etc.) from nonanxious diagnostic groups (major depression, dysthymic disorder, etc). In addition, the BAI was moderately correlated with the revised Hamilton Anxiety Rating Scale, r(150) = .51, and was only mildly correlated with the revised Hamilton Depression Rating Scale, r(l 53) = .25.

Manifold surface reconstruction of an environment from sparse Structure-from-Motion data <=SEP=> The majority of methods for the automatic surface reconstruction of an environment from an image sequence have two steps: Structure-from-Motion and dense stereo. From the computational standpoint, it would be interesting to avoid dense stereo and to generate a surface directly from the sparse cloud of 3D points and their visibility information provided by Structure-fromMotion. The previous attempts to solve this problem are currently very limited: the surface is non-manifold or has zero genus, the experiments are done on small scenes or objects using a few dozens of images. Our solution does not have these limitations. Furthermore, we experiment with hand-held or helmet-held catadioptric cameras moving in a city and generate 3D models such that the camera trajectory can be longer than one kilometer.

A coordinate gradient descent method for ℓ1-regularized convex minimization <=SEP=> In applications such as signal processing and statistics, many problems involve finding sparse solutions to under-determined linear systems of equations. These problems can be formulated as a structured nonsmooth optimization problems, i.e., the problem of minimizing `1-regularized linear least squares problems. In this paper, we propose a block coordinate gradient descent method (abbreviated as CGD) to solve the more general `1-regularized convex minimization problems, i.e., the problem of minimizing an `1-regularized convex smooth function. We establish a Q-linear convergence rate for our method when the coordinate block is chosen by a Gauss-Southwell-type rule to ensure sufficient descent. We propose efficient implementations of the CGD method and report numerical results for solving large-scale `1-regularized linear least squares problems arising in compressed sensing and image deconvolution as well as large-scale `1-regularized logistic regression problems for feature selection in data classification. Comparison with several state-of-the-art algorithms specifically designed for solving large-scale `1-regularized linear least squares or logistic regression problems suggests that an efficiently implemented CGD method may outperform these algorithms despite the fact that the CGD method is not specifically designed just to solve these special classes of problems.

Application of Computational Intelligence to Improve Education in Smart Cities <=SEP=> According to UNESCO, education is a fundamental human right and every nation's citizens should be granted universal access with equal quality to it. Because this goal is yet to be achieved in most countries, in particular in the developing and underdeveloped countries, it is extremely important to find more effective ways to improve education. This paper presents a model based on the application of computational intelligence (data mining and data science) that leads to the development of the student's knowledge profile and that can help educators in their decision making for best orienting their students. This model also tries to establish key performance indicators to monitor objectives' achievement within individual strategic planning assembled for each student. The model uses random forest for classification and prediction, graph description for data structure visualization and recommendation systems to present relevant information to stakeholders. The results presented were built based on the real dataset obtained from a Brazilian private k-9 (elementary school). The obtained results include correlations among key data, a model to predict student performance and recommendations that were generated for the stakeholders.

Multicenter randomized clinical trial evaluating the effectiveness of the Lokomat in subacute stroke. <=SEP=> OBJECTIVE
To compare the efficacy of robotic-assisted gait training with the Lokomat to conventional gait training in individuals with subacute stroke.


METHODS
A total of 63 participants<6 months poststroke with an initial walking speed between 0.1 to 0.6 m/s completed the multicenter, randomized clinical trial. All participants received twenty-four 1-hour sessions of either Lokomat or conventional gait training. Outcome measures were evaluated prior to training, after 12 and 24 sessions, and at a 3-month follow-up exam. Self-selected overground walking speed and distance walked in 6 minutes were the primary outcome measures, whereas secondary outcome measures included balance, mobility and function, cadence and symmetry, level of disability, and quality of life measures.


RESULTS
Participants who received conventional gait training experienced significantly greater gains in walking speed (P=.002) and distance (P=.03) than those trained on the Lokomat. These differences were maintained at the 3-month follow-up evaluation. Secondary measures were not different between the 2 groups, although a 2-fold greater improvement in cadence was observed in the conventional versus Lokomat group.


CONCLUSIONS
For subacute stroke participants with moderate to severe gait impairments, the diversity of conventional gait training interventions appears to be more effective than robotic-assisted gait training for facilitating returns in walking ability.

On self-aggrandizement and anger: a temporal analysis of narcissism and affective reactions to success and failure. <=SEP=> Narcissists are thought to display extreme affective reactions to positive and negative information about the self. Two experiments were conducted in which high- and low-narcissistic individuals, as defined by the Narcissistic Personality Inventory (NPI), completed a series of tasks in which they both succeeded and failed. After each task, participants made attributions for their performance and reported their moods. High-NPI participants responded with greater changes in anxiety, anger, and self-esteem. Low self-complexity was examined, but it neither mediated nor moderated affective responses. High-NPI participants tended to attribute initial success to ability, leading to more extreme anger responses and greater self-esteem reactivity to failure. A temporal sequence model linking self-attribution and emotion to narcissistic rage is discussed.

Gradient Flow in Recurrent Nets: the Difficulty of Learning Long-Term Dependencies <=SEP=> D3EGF(FIH)J KMLONPEGQSRPETN UCV.WYX(Z R.[ V R6\M[ X N@]_^O\`JaNcb V RcQ W d EGKeL(^(QgfhKeLOE?i)^(QSj ETNPfPQkRl[ V R)m"[ X ^(KeLOEG^ npo qarpo m"[ X ^(KeLOEG^tsAu EGNPb V ^ v wyx zlwO{(|(}<~OC}((xp{ay.~A}_~ Cl3#|<Azw#|l6 (|  JpfhL XV EG^O QgJ  ETFOR] ^O\JNPb V RcQ X E)ETR 6EGKeLOETNcKMLOE F ETN V RcQgJp^(^OE ZgZ E i ^(Qkj EGNPfhQSRO E OE2m1Jp^ RcNY E VZ sO! ¡ q.n sCD X KGKa8¢EG^ RPNhE¤£ ¥¦Q ZgZ Es m§J^ RPNO E VZ s( ̈ X  EG©#EKas# V ^ V  V s(H a «a¬3­ ®#|.Y ̄y} xa°OC}l{x  yxlY~3{|  ±2Pz   V J Z J U N V fhKTJp^(Q  ETFOR J\ D vYf3RPEGb ́f V ^(§JpbF X RPETN@D KTQEG^(KTE i ^(QSjpEGNPfhQSR4vμJ\ U¶Z JaNPEG^(K·E jYQ V (Q ̧D V ^ R V m V N3R V aOs#1 o ¡Ga r U QNhE^OoTE1⁄4»,] R VZ vC1⁄2 3⁄4  x ± x  #¿ }À 3t}lC}2P}<~ ¬t[ X NPE^§D KeL(b ́Qg(L X ©yETN ]  DY]_Á JNPfhJÃÂ Z j EToQ V a rpopo2Ä X  V ^(J(sCD Å)QSRPoTEGN ZgV ^( Æ #|{3 ̄|.(C}.C¿Y}p Pzw

Optimal and Low-Complexity Algorithms for Dynamic Spectrum Access in Centralized Cognitive Radio Networks with Fading Channels <=SEP=> In this paper, we develop a centralized spectrum sensing and Dynamic Spectrum Access (DSA) scheme for secondary users (SUs) in a Cognitive Radio (CR) network. Assuming that the primary channel occupancy follows a Markovian evolution, the channel sensing problem is modeled as a Partially Observable Markov Decision Process (POMDP). We assume that each SU can sense only one channel at a time by using energy detection, and the sensing outcomes are then reported to a central unit, called the secondary system decision center (SSDC), that determines the channel sensing/accessing policies. We derive both the optimal channel assignment policy for secondary users to sense the primary channels, and the optimal channel access rule. Our proposed optimal sensing and accessing policies alleviate many shortcomings and limitations of existing proposals: (a) ours allows fully utilizing all available primary spectrum white spaces, (b) our model, and thus the proposed solution, exploits the temporal and spatial diversity across different primary channels, and (c) is based on realistic local sensing decisions rather than complete knowledge of primary signalling structure. As an alternative to the high complexity of the optimal channel sensing policy, a suboptimal sensing policy is obtained by using the Hungarian algorithm iteratively, which reduces the complexity of the channel assignment from an exponential to a polynomial order. We also propose a heuristic algorithm that reduces the complexity of the sensing policy further to a linear order. The simulation results show that the proposed algorithms achieve a near-optimal performance with a significant reduction in computational time.

Compact Design of a Hydraulic Driving Robot for Intraoperative MRI-Guided Bilateral Stereotactic Neurosurgery <=SEP=> In this letter, we present an intraoperative magnetic resonance imaging (MRI)-guided robot for bilateral stereotactic procedures. Its compact design enables robot's operation within the constrained space of standard imaging head coil. magnetic resonance (MR) safe and high-performance hydraulic transmissions are incorporated. A maximum stiffness coefficient of 24.35 N/mm can be achieved with transmission fluid preloaded at 2 bar. Sufficient targeting accuracy (average within ≤1.73 mm) has been demonstrated in a simulated needle insertion task of deep brain stimulation. A novel MR-based wireless tracking technique is adopted. It is capable of offering real-time and continuous (30–40 Hz) three-dimensional (3-D) localization of robotic instrument under the proper MR tracking sequence. It outperforms the conventional method of using low-contrast passive fiducials that can only be revealed in the MR image domain. Two wireless tracking units/markers are integrated with the robot, which are two miniaturized coil circuits in size of 1.5 × 5 × 0.2 mm3, fabricated on flexible thin films. A navigation test was performed under the standard MRI settings in order to visualize the 3-D localization of the robotic instrument. MRI compatibility test was also carried out to prove the minimal interference to MR images of the presented hydraulic robotic platform.

The Cricket location-support system <=SEP=> This paper presents the design, implementation, and evaluation of Cricket, a location-support system for in-building, mobile, location-dependent applications. It allows applications running on mobile and static nodes to learn their physical location by using listeners that hear and analyze information from beacons spread throughout the building. Cricket is the result of several design goals, including user privacy, decentralized administration, network heterogeneity, and low cost. Rather than explicitly tracking user location, Cricket helps devices learn where they are and lets them decide whom to advertise this information to; it does not rely on any centralized management or control and there is no explicit coordination between beacons; it provides information to devices regardless of their type of network connectivity; and each Cricket device is made from off-the-shelf components and costs less than U.S. $10. We describe the randomized algorithm used by beacons to transmit information, the use of concurrent radio and ultrasonic signals to infer distance, the listener inference algorithms to overcome multipath and interference, and practical beacon configuration and positioning techniques that improve accuracy. Our experience with Cricket shows that several location-dependent applications such as in-building active maps and device control can be developed with little effort or manual configuration.

Breaking CAPTCHAs on the Dark Web 11 February , 2018 <=SEP=> On the Dark Web, several websites inhibit automated scraping attempts by employing CAPTCHAs. Scraping important content from a website is possible if these CAPTCHAs are solved by a web scraper. For this purpose, a Machine Learning tool is used, TensorFlow and an Optical Character Recognition tool, Tesseract to solve simple CAPTCHAs. Two sets of CATPCHAs, which are also used on some Dark Web websites, were generated for testing purposes. Tesseract achieved a success rate of 27.6% and 13.7% for set 1 and 2, respectively. A total of three models were created for TensorFlow. One model per set of CAPTCHAs and one model with the two sets mixed together. TensorFlow achieved a success rate of 94.6%, 99.7%, and 70.1% for the first, second, and mixed set, respectively. The initial investment to train TensorFlow can take up to two days to train for a single type of CAPTCHA, depending on implementation efficiency and hardware. The CAPTCHA images, including the answers, are also a requirement for training TensorFlow. Whereas Tesseract can be used on-demand without need for prior training.

Debunking the Myths of Influence Maximization: An In-Depth Benchmarking Study <=SEP=> Influence maximization (IM) on social networks is one of the most active areas of research in computer science. While various IM techniques proposed over the last decade have definitely enriched the field, unfortunately, experimental reports on existing techniques fall short in validity and integrity since many comparisons are not based on a common platform or merely discussed in theory. In this paper, we perform an in-depth benchmarking study of IM techniques on social networks. Specifically, we design a benchmarking platform, which enables us to evaluate and compare the existing techniques systematically and thoroughly under identical experimental conditions. Our benchmarking results analyze and diagnose the inherent deficiencies of the existing approaches and surface the open challenges in IM even after a decade of research. More fundamentally, we unearth and debunk a series of myths and establish that there is no single state-of-the-art technique in IM. At best, a technique is the state of the art in only one aspect.

Employee Engagement From a Self-Determination Theory Perspective <=SEP=> Macey and Schneider (2008) draw on numerous theories to explain what engagement is and how it is similar to and different from related constructs in the organizational behavior literature. As a result, we now have a better understanding of some of the key ‘‘components’’ of engagement. What appears to be missing, however, is a strong unifying theory to guide research and practice. We believe that such a theory exists in the form of self-determination theory (SDT; Deci & Ryan, 1985; Ryan & Deci, 2000) and its various corollaries, self-concordance theory (SCT; Sheldon & Elliot, 1999), hierarchical theory (Vallerand, 1997), and passion theory (Vallerand et al., 2003). Although Macey and Schneider acknowledged the relevance of SDT and SCT, we believe that much greater use of these theories could be made to justify and extend their conceptual model.

A 5.8-Gb/s Adaptive Integrating Duobinary DFE Receiver for Multi-Drop Memory Interface <=SEP=> This paper describes a 5.8 Gb/s adaptive integrating duobinary decision-feedback equalizer (DFE) for use in next-generation multi-drop memory interface. The proposed receiver combines traditional interface techniques like the integrated signaling and the duobinary signaling, in which the duobinary signal is generated by current integration in the receiver. It can address issues such as input data dependence during integration, need for precursor equalization, high equalizer gain boosting, and sensitivity to high-frequency noise. The proposed receiver also alleviates DFE critical timing to provide gain in speed, and embed DFE taps in duobinary decoding to provide gain in power and area. The adaptation for adjusting the equalizer common-mode level, duobinary zero level, tap coefficient values, and timing recovery is incorporated. The proposed DFE receiver was fabricated in a 45 nm CMOS process, whose measurement results indicated that it worked at 5.8 Gb/s speed in a four-drop channel configuration with seven slave ICs, and the bathtub curve shows 36% open for  $10^{-10}$  bit error rate.

A Relatively Small Turing Machine Whose Behavior Is Independent of Set Theory <=SEP=> Since the definition of the busy beaver function by Rado in 1962, an interesting open question has been the smallest value of n for which BB(n) is independent of Zermelo–Fraenkel set theory with the axiom of choice (ZFC). Is this n approximately 10, or closer to 1 000 000, or is it even larger? In this paper, we show that it is at most 7910 by presenting an explicit description of a 7910-state Turing machine Z with one tape and a two-symbol alphabet that cannot be proved to run forever in ZFC (even though it presumably does), assuming ZFC is consistent. The machine is based on work of Harvey Friedman on independent statements involving order-invariant graphs. In doing so, we give the first known upper bound on the highest provable busy beaver number in ZFC. To create Z, we develop and use a higher-level language, Laconic, which is much more convenient than direct state manipulation. We also use Laconic to design two Turing machines, G and R, that halt if and only if there are counterexamples to Goldbach’s conjecture and the Riemann hypothesis, respectively.

Graph analytics using vertica relational database <=SEP=> Graph analytics is becoming increasingly popular, with a number of new applications and systems developed in the past few years. In this paper, we study Vertica relational database as a platform for graph analytics. We show that vertex-centric graph analysis can be translated to SQL queries, typically involving table scans and joins, and that modern column-oriented databases are very well suited to running such queries. Furthermore, we show how developers can trade memory footprint for significantly reduced I/O costs in Vertica. We present an experimental evaluation of the Vertica relational database system on a variety of graph analytics, including iterative analysis, a combination of graph and relational analyses, and more complex 1-hop neighborhood graph analytics, showing that it is competitive to two popular vertex-centric graph analytics systems, namely Giraph and GraphLab.

On the Reuse of Past Optimal Queries <=SEP=> Vijay V. Raghavan Hayri Sever The Center for Advanced Computer Studies The Department of Computer Science University of Southwestern Louisiana University of Southwestern Louisiana Lafayette, LA 70504, USA Lafayette, LA 70504, USA e-mail: raghavan@cacs.usl. edu Information Retrieval (IR) systems exploit user feedback by generating an optimal query with respect to a particular information need. Since obtaining an optimal query is an expensive process, the need for mechanisms to save and reuse past optimal queries for future queries is obvions. In this article, we propose the use of a query base, a set of persistent past optimal queries, and investigate similarity measures between queries. The query base can be used either to answer user queries or to formulate optimal queries. We justify the former case analytically and the latter case by experiment.