title: An electrophoretic study of mutationally altered A proteins of the tryptophan synthetase of Escherichia coli.

abstract: The mutationally altered A proteins (tryptophan synthetase) of nine single mutants and three double mutants of Escherichia coli have been examined by electrophoresis in Polyacrylamide gel and on cellulose acetate. The A proteins from seven of the single mutants studied exhibited charge differences from the wild-type A protein. The mutationally induced amino acid substitution in one of these seven A proteins is known and the change in charge observed with this protein is consistent with the amino acid substitution. The A protein of one double mutant had a charge which was intermediate between that of the two corresponding single-mutant proteins. However, the mobilities observed with two other double-mutant proteins were not consistent with those expected on the basis of electrophoretic analyses with the corresponding single-mutant proteins. Furthermore, the electrophoretic mobilities of these two double-mutant proteins relative to the wild-type A protein were different on cellulose acetate and in Polyacrylamide gel. Possible explanations of the inconsistent mobilities are offered. The results obtained are correlated with the restricted location of the corresponding mutant sites on the genetic map of the A gene.


title: From Reeds and Shepp's to continuous-curvature paths

abstract: This paper presents Continuous Curvature (CC) Steer, a steering method for car-like vehicles, i.e., an algorithm planning paths in the absence of obstacles. CC Steer is the first to compute paths with: 1) continuous curvature; 2) upper-bounded curvature; and 3) upper-bounded curvature derivative. CC Steer also verifies a topological property that ensures that when it is used within a general motion-planning scheme, it yields a complete collision-free path planner. The coupling of CC Steer with a general planning scheme yields a path planner that computes collision-free paths verifying the properties mentioned above. Accordingly, a car-like vehicle can follow such paths without ever having to stop in order to reorient its front wheels. Besides, such paths can be followed with a nominal speed which is proportional to the curvature derivative limit. The paths computed by CC Steer are made up of line segments, circular arcs, and clothoid arcs. They are not optimal in length. However, it is shown that they converge toward the optimal "Reeds and Shepp" paths when the curvature derivative upper bound tends to infinity. The capabilities of CC Steer to serve as an efficient steering method within two general planning schemes are also demonstrated.


Machine Learning for Professional Tennis Match Prediction and Betting


title: Deception Detection in Videos

abstract: We present a system for covert automated deception detection in real-life courtroom trial videos. We study the importance of different modalities like vision, audio and text for this task. On the vision side, our system uses classifiers trained on low level video features which predict human micro-expressions. We show that predictions of high-level micro-expressions can be used as features for deception prediction. Surprisingly, IDT (Improved Dense Trajectory) features which have been widely used for action recognition, are also very good at predicting deception in videos. We fuse the score of classifiers trained on IDT features and high-level micro-expressions to improve performance. MFCC (Mel-frequency Cepstral Coefficients) features from the audio domain also provide a significant boost in performance, while information from transcripts is not very beneficial for our system. Using various classifiers, our automated system obtains an AUC of 0.877 (10-fold cross-validation) when evaluated on subjects which were not part of the training set. Even though state-of-the-art methods use human annotations of micro-expressions for deception detection, our fully automated approach outperforms them by 5%. When combined with human annotations of micro-expressions, our AUC improves to 0.922. We also present results of a user-study to analyze how well do average humans perform on this task, what modalities they use for deception detection and how they perform if only one modality is accessible. Our project page can be found at \url{this https URL}.


title: Dynamic relief mapping1

abstract: The best texture mapping techniques that focus on the microrelief effect use iterative algorithms. The number of iterations must be determined in advance, but for real-time rendering, the number of iterations must be chosen dynamically to take into account the change of the depth scale and the viewing angle (viewing direction). In this paper, we presented a real-time rendering technique that allows a combined synchronization between depth scaling, viewing angle and number of iterations.


title: Efficient subgraphisomorphism detection: a decomposition approach

abstract: Graphs are a powerful and universal data structure useful in various subfields of science and engineering. In this paper, we propose a new algorithm for subgraph isomorphism detection from a set of a priori known model graphs to an input graph that is given online. The new approach is based on a compact representation of the model graphs that is computed offline. Subgraphs that appear more than once within the same or within different model graphs are represented only once, thus reducing the computational effort to detect them in an input graph. In the extreme case where all model graphs are highly similar, the run-time of the new algorithm becomes independent of the number of model graphs. Both a theoretical complexity analysis and practical experiments characterizing the performance of the new approach are given.


title: A comparative experimental study of image feature detectors and descriptors

abstract: Feature detection and matching is a fundamental problem in many computer vision applications. In the past decades, various types of feature detectors and descriptors have been proposed in the literature. Although several comparative studies on feature detectors and descriptors have been performed in the past, few studies have been carried out concerning recently proposed descriptors such as BRISK, FREAK, etc. Also, previous comparisons were either application oriented or limited in experimentation or in the number of detectors and descriptors compared. This paper provides a comprehensive review of a large number of popular feature detectors developed in the last three decades. The study makes several contributions to the development of a generic comparison of feature detectors and descriptors. First, we conduct comparisons of invariance against image transformations such as illumination changes, blurring, rotation, scaling, viewpoint changes, exposure, JPEG compression, combined scaling and rotation, and combined viewpoint changes. Second, we provide a proper distinction between detectors and descriptors using separate comparisons. Third, a few detectors have been tested on the variation of parameter values. Fourth, we conduct a statistical analysis of invariance against four popular types of transformations: viewpoint changes, blurring, scaling, and rotation. Fifth, we carry out intuitive matching between detectors and descriptors, testing on simulated and practical scenarios. Last, we conduct exhaustive experiments on several datasets for each combination of detectors and descriptors to provide a ranking that can also be weighted to suit specific applications.


title: Integration of Balanced Scorecard (BSC), Strategy Map, and Fuzzy Analytic Hierarchy Process (FAHP) for a Sustainability Business Framework: A Case Study of a Spanish Software Factory in the Financial Sector

abstract: This paper presents a case study of how a Spanish financial software factory (FSF) has determined the weights of the indicators and objectives included in their strategy map with the aim of ensuring its business sustainability. A strategy map is a graphical representation of the cause-effect relationships between strategic objectives and indicators of a balanced scorecard (BSC). The detailed description of the strategy map development and deployment is not part of the aim of this work as it was described in a former paper. In this study, FAHP, a multicriteria decision-making (MCDM) method using the concepts of fuzzy set theory and hierarchical structure analysis, was used to calculate the weights. The analysis was carried out considering the points of view of different groups of stakeholders (shareholders, top management, middle managers, other employees, customers and some experts in the field of software factories) and the results are presented grouped by role to get a better understanding of the preferences of each kind of stakeholder. The conclusions of this study give a better insight of the corporative sustainability strategies of this kind of firms as well as the different vision of each stakeholder, what could be very valuable to the software factory managers for the decision-making and the strategic management of their organizations.


Numerically Stable Direct Least Squares Fitting Of Ellipses


title: Updatable Block-Level Message-Locked Encryption

abstract: Deduplication is a widely used technique for reducing storage space of cloud service providers. Yet, it is unclear how to support deduplication of encrypted data securely until the study of Bellareetal on message-locked encryption (Eurocrypt 2013). Since then, there are many improvements such as strengthening its security, reducing client storage, etc. While updating a (shared) file is common, there is little attention on how to efficiently update large encrypted files in a remote storage with deduplication. To modify even a single bit, existing solutions require the trivial and expensive way of downloading and decrypting the large ciphertext. We initiate the study of updatable block-level message-locked encryption. We propose a provably secure construction that is efficiently updatable with O(log|F|) computational cost, where |F| is the file size. It also supports proof-of-ownership, a nice feature which protects storage providers from being abused as a free content distribution network.


title: Temporal decomposition and semantic enrichment of mobility flows

abstract: Mobility data has increasingly grown in volume over the past decade as loc- ::: alisation technologies for capturing mobility ::: ows have become ubiquitous. ::: Novel analytical approaches for understanding and structuring mobility data ::: are now required to support the back end of a new generation of space-time GIS ::: systems. This data has become increasingly important as GIS is now an essen- ::: tial decision support platform in many domains that use mobility data, such ::: as ::: eet management, accessibility analysis and urban transportation planning. ::: This thesis applies the machine learning method of probabilistic topic mod- ::: elling to decompose and semantically enrich mobility ::: ow data. This process ::: annotates mobility ::: ows with semantic meaning by fusing them with geograph- ::: ically referenced social media data. This thesis also explores the relationship ::: between causality and correlation, as well as the predictability of semantic ::: decompositions obtained during a case study using a real mobility dataset.


title: Modulation of Visual Responses by Behavioral State in Mouse Visual Cortex

abstract: Studies of visual processing in rodents have conventionally been performed on anesthetized animals, precluding examination of the effects of behavior on visually evoked responses. We have now studied the response properties of neurons in primary visual cortex of awake mice that were allowed to run on a freely rotating spherical treadmill with their heads fixed. Most neurons showed more than a doubling of visually evoked firing rate as the animal transitioned from standing still to running, without changes in spontaneous firing or stimulus selectivity. Tuning properties in the awake animal were similar to those measured previously in anesthetized animals. Response magnitude in the lateral geniculate nucleus did not increase with locomotion, demonstrating that the striking change in responsiveness did not result from peripheral effects at the eye. Interestingly, some narrow-spiking cells were spontaneously active during running but suppressed by visual stimuli. These results demonstrate powerful cell-type-specific modulation of visual processing by behavioral state in awake mice.


title: Rosemary: A Robust, Secure, and High-performance Network Operating System

abstract: Within the hierarchy of the Software Defined Network (SDN) network stack, the control layer operates as the critical middleware facilitator of interactions between the data plane and the network applications, which govern flow routing decisions. In the OpenFlow implementation of the SDN model, the control layer, commonly referred to as a network operating system (NOS), has been realized by a range of competing implementations that offer various performance and functionality advantages: Floodlight, POX, NOX, and ONIX. In this paper we focus on the question of control layer resilience, when rapidly developed prototype network applications go awry, or third-party network applications incorporate unexpected vulnerabilities, fatal instabilities, or even malicious logic. We demonstrate how simple and common failures in a network application may lead to loss of the control layer, and in effect, loss of network control. To address these concerns we present the ROSEMARY controller, which implements a network application containment and resilience strategy based around the notion of spawning applications independently within a micro-NOS. ROSEMARY distinguishes itself by its blend of process containment, resource utilization monitoring, and an application permission structure, all designed to prevent common failures of network applications from halting operation of the SDN Stack. We present our design and implementation of ROSEMARY, along with an extensive evaluation of its performance relative to several of the mostly well-known and widely used controllers. Rather than imposing significant performance costs, we find that with the integration of two optimization features, ROSEMARY offers a competitive performance advantage over the majority of other controllers.


Enhancing the predictive power of cybersickness dose value (CSDV) to include effects of field-of-view and binocular views.


Hallam of children and young people The power of music : Its impact on the intellectual , social and personal development


title: Analysis of dynamic power management on multi-core processors

abstract: Power management of multi-core processors is extremely important because it allows power/energy savings when all cores are not used. OS directed power management according to ACPI (Advanced Power and Configurations Interface) specifications is the common approach that industry has adopted for this purpose. While operating systems are capable of such power management, heuristics for effectively managing the power are still evolving. The granularity at which the cores are slowed down/turned off should be designed considering the phase behavior of the workloads. Using 3-D, video creation, office and e-learning applications from the SYSmark benchmark suite, we study the challenges in power management of a multi-core processor such as the AMD Quad-Core Opteron" and Phenom". We unveil effects of the idle core frequency on the performance and power of the active cores. We adjust the idle core frequency to have the least detrimental effect on the active core performance. We present optimized hardware and operating system configurations that reduce average active power by 30% while reducing performance by an average of less than 3%. We also present complete system measurements and power breakdown between the various systems components using the SYSmark and SPEC CPU workloads. It is observed that the processor core and the disk consume the most power, with core having the highest variability.


title: Improved Localization Accuracy by LocNet for Faster R-CNN Based Text Detection

abstract: Although Faster R-CNN based approaches have achieved promising results for text detection, their localization accuracy is not satisfactory in certain cases. In this paper, we propose to use a LocNet to improve the localization accuracy of a Faster R-CNN based text detector. Given a proposal generated by region proposal network (RPN), instead of predicting directly the bounding box coordinates of the concerned text instance, the proposal is enlarged to create a search region so that conditional probabilities to each row and column of this search region can be assigned, which are then used to infer accurately the concerned bounding box. Experiments demonstrate that the proposed approach boosts the localization accuracy for Faster R-CNN based text detection significantly. Consequently, our new text detector has achieved superior performance on ICDAR-2011, ICDAR-2013 and MULTILIGUL text detection benchmark tasks.


title: RFID Backscattering in Long-Range Scenarios

abstract: This paper presents a 5.8-GHz RFID tag that, by exploiting the quantum tunneling effect, significantly increases the range of backscatter radio links. We present an electronically simple Tunneling RFID Tag characterized by return gains as high as 35 dB with link sensitivity as low as −81 dBm. Without relevant increase in power consumption, the tunneling tag enables a host of new wireless sensors and Internet of Things applications that require both the long range of conventional wireless links and the low power consumption of semi-passive RFID devices. Selected measurements demonstrate a reader-to-tag separation distance 10 times higher than the maximum range of ideal semi-passive tags. Moreover, the collected experimental results allowed to outline a mathematical model demonstrating how the long-range RFID tag prototype can achieve distances unusual for this technology.


title: A floating dummy trench gate IGBT (FDT-IGBT) for hybrid and electric vehicle (HEV/EV) applications

abstract: A floating dummy trench gate IGBT (FDT-IGBT) has been proposed through experimental demonstration. It is shown that the FDT-IGBT is very efficient in low stray inductance, high frequency and relatively high voltage operations which are desirable in HEV/EV system. The proposed chip shows improved trade-off relationship between turn-on di/dt and E on , and effective di/dt controllability with variations of R gon without sacrificing turn-off and on-state performance when compared to the conventional grounded dummy trench gate IGBT (GDT-IGBT). In addition, its much lower surge current during short circuit turn-on, enhances short circuit robustness.


title: Comparing Clinical and Social-Personality Conceptualizations of Narcissism

abstract: ABSTRACT There is a lack of consensus surrounding the conceptualization of narcissism. The present study compared two measures of narcissism—one used in clinical settings (Personality Diagnostic Questionnaire, PDQ-4+; Hyler, 1994) and one used in social-personality research (Narcissistic Personality Inventory, NPI; Raskin & Terry, 1988)—across two samples. Sample 1 (N=271) was composed of undergraduates, whereas Sample 2 (N=211) was composed of parents of the Sample 1 participants. The scales were significantly interrelated but manifested divergent relations with general personality traits, personality disorders (including expert prototypal ratings of narcissism), recollections of parenting received, and psychological distress and self-esteem. PDQ-4 narcissism captured an emotionally unstable, negative-affect-laden, and introverted variant of narcissism; NPI narcissism captured an emotionally resilient, extraverted form. The clinical and social-personality conceptualizations of narcissism primarily share a tendency to use an antagonistic interpersonal style. Implications for the DSM-V are discussed.


title: Correlation exploitation in error ranking

abstract: Static program checking tools can find many serious bugs in software, but due to analysis limitations they also frequently emit false error reports. Such false positives can easily render the error checker useless by hiding real errors amidst the false. Effective error report ranking schemes mitigate the problem of false positives by suppressing them during the report inspection process [17, 19, 20]. In this way, ranking techniques provide a complementary method to increasing the precision of the analysis results of a checking tool. A weakness of previous ranking schemes, however, is that they produce static rankings that do not adapt as reports are inspected, ignoring useful correlations amongst reports. This paper addresses this weakness with two main contributions. First, we observe that both bugs and false positives frequently cluster by code locality. We analyze clustering behavior in historical bug data from two large systems and show how clustering can be exploited to greatly improve error report ranking. Second, we present a general probabilistic technique for error ranking that (1) exploits correlation behavior amongst reports and (2) incorporates user feedback into the ranking process. In our results we observe a factor of 2-8 improvement over randomized ranking for error reports emitted by both intra-procedural and inter-procedural analysis tools.


title: Design and Simulation of 32-Point FFT Using Radix-2 Algorithm for FPGA Implementation

abstract: The Fast Fourier Transform (FFT) is one of the rudimentary operations in field of digital signal and image processing. Some of the very vital applications of the fast fourier transform include Signal analysis, Sound filtering, Data compression, Partial differential equations, Multiplication of large integers, Image filtering etc. Fast Fourier transform (FFT) is an efficient implementation of the discrete Fourier transform (DFT). This paper concentrates on the development of the Fast Fourier Transform (FFT), based on Decimation-In-Time (DIT) domain, Radix-2 algorithm, this paper uses VHDL as a design entity, and their Synthesis by Xilinx Synthesis Tool on Vertex kit has been done. The input of Fast Fourier transform has been given by a PS2 KEYBOARD using a test bench and output has been displayed using the waveforms on the Xilinx Design Suite 12.1. The synthesis results show that the computation for calculating the 32-point Fast Fourier transform is efficient in terms of speed.


title: Finding the self by losing the self: Neural correlates of ego‐dissolution under psilocybin

abstract: Ego-disturbances have been a topic in schizophrenia research since the earliest clinical descriptions of the disorder. Manifesting as a feeling that one's "self," "ego," or "I" is disintegrating or that the border between one's self and the external world is dissolving, "ego-disintegration" or "dissolution" is also an important feature of the psychedelic experience, such as is produced by psilo- cybin (a compound found in "magic mushrooms"). Fifteen healthy subjects took part in this placebo- controlled study. Twelve-minute functional MRI scans were acquired on two occasions: subjects received an intravenous infusion of saline on one occasion (placebo) and 2 mg psilocybin on the other. Twenty-two visual analogue scale ratings were completed soon after scanning and the first principal component of these, dominated by items referring to "ego-dissolution", was used as a primary mea- sure of interest in subsequent analyses. Employing methods of connectivity analysis and graph theory, an association was found between psilocybin-induced ego-dissolution and decreased functional con- nectivity between the medial temporal lobe and high-level cortical regions. Ego-dissolution was also associated with a "disintegration" of the salience network and reduced interhemispheric communica- tion. Addressing baseline brain dynamics as a predictor of drug-response, individuals with lower diversity of executive network nodes were more likely to experience ego-dissolution under psilocybin. These results implicate MTL-cortical decoupling, decreased salience network integrity, and reduced inter-hemispheric communication in psilocybin-induced ego disturbance and suggest that the mainte- nance of "self"or "ego," as a perceptual phenomenon, may rest on the normal functioning of these sys- tems. Hum Brain Mapp 00:000-000, 2015. V C 2015 Wiley Periodicals, Inc.


title: A critical evaluation of the trigger point phenomenon

abstract: The theory of myofascial pain syndrome (MPS) caused by trigger points (TrPs) seeks to explain the phenomena of muscle pain and tenderness in the absence of evidence for local nociception. Although it lacks external validity, many practitioners have uncritically accepted the diagnosis of MPS and its system of treatment. Furthermore, rheumatologists have implicated TrPs in the pathogenesis of chronic widespread pain (FM syndrome). We have critically examined the evidence for the existence of myofascial TrPs as putative pathological entities and for the vicious cycles that are said to maintain them. We find that both are inventions that have no scientific basis, whether from experimental approaches that interrogate the suspect tissue or empirical approaches that assess the outcome of treatments predicated on presumed pathology. Therefore, the theory of MPS caused by TrPs has been refuted. This is not to deny the existence of the clinical phenomena themselves, for which scientifically sound and logically plausible explanations based on known neurophysiological phenomena can be advanced. Rheumatology key messages . The theory of myofascial pain based on trigger points is conjecture that has been put forward as established


title: Revisiting Word Embedding for Contrasting Meaning

abstract: Contrasting meaning is a basic aspect of semantics. Recent word-embedding models based on distributional semantics hypothesis are known to be weak for modeling lexical contrast. We present in this paper the embedding models that achieve an F-score of 92% on the widely-used, publicly available dataset, the GRE “most contrasting word” questions (Mohammad et al., 2008). This is the highest performance seen so far on this dataset. Surprisingly at the first glance, unlike what was suggested in most previous work, where relatedness statistics learned from corpora is claimed to yield extra gains over lexicon-based models, we obtained our best result relying solely on lexical resources (Roget’s and WordNet)—corpora statistics did not lead to further improvement. However, this should not be simply taken as that distributional statistics is not useful. We examine several basic concerns in modeling contrasting meaning to provide detailed analysis, with the aim to shed some light on the future directions for this basic semantics modeling problem.


title: A prediction model for the purchase probability of anonymous customers to support real time web marketing: a case study

abstract: Abstract The rapid growth of e-commerce has provided both an opportunity to create new values in the online marketplace and dramatic competition to survive. To survive in a competitive environment, Internet shopping malls attempt to adopt and use Customer Relationship Management. However, previous researches focused on navigation patterns of customers with membership. Therefore, they failed to apply real time web marketing to anonymous customers who navigate web pages without personal login. To overcome the problems noted above, we propose a methodology for predicting the purchase probability of anonymous customers to support real time web marketing. The proposed methodology is composed of two phases: (1) extracting purchase patterns and (2) predicting purchase probability. Purchase pattern provides marketing implications to web marketers while the purchase probability provides an opportunity for real time web marketing by predicting the purchase probability of an anonymous customer. The proposed methodology can be applied to the real time web marketing such as navigation shortcuts, product recommendations and better customer inducement since anonymous customers are included in marketing target and significant navigation pattern for purchase is identified.


title: Affective social robots ✩

abstract: For human-robot interaction to proceed in a smooth, natural manner, robots must adhere to human social norms. One such human convention is the use of expressive moods and emotions as an integral part of social interaction. Such expressions are used to convey messages such as ''I'm happy to see you'' or ''I want to be comforted,'' and people's long-term relationships depend heavily on shared emotional experiences. Thus, we have developed an affective model for social robots. This generative model attempts to create natural, human-like affect and includes distinctions between immediate emotional responses, the overall mood of the robot, and long-term attitudes toward each visitor to the robot, with a focus on developing long-term human-robot relationships. This paper presents the general affect model as well as particular details of our implementation of the model on one robot, the Roboceptionist. In addition, we present findings from two studies that demonstrate the model's potential.


title: Contrastive Estimation: Training Log-Linear Models On Unlabeled Data

abstract: Conditional random fields (Lafferty et al., 2001) are quite effective at sequence labeling tasks like shallow parsing (Sha and Pereira, 2003) and named-entity extraction (McCallum and Li, 2003). CRFs are log-linear, allowing the incorporation of arbitrary features into the model. To train on unlabeled data, we require unsupervised estimation methods for log-linear models; few exist. We describe a novel approach, contrastive estimation. We show that the new technique can be intuitively understood as exploiting implicit negative evidence and is computationally efficient. Applied to a sequence labeling problem---POS tagging given a tagging dictionary and unlabeled text---contrastive estimation outperforms EM (with the same feature set), is more robust to degradations of the dictionary, and can largely recover by modeling additional features.


title: Midbrain circuits for defensive behaviour

abstract: Survival in threatening situations depends on the selection and rapid execution of an appropriate active or passive defensive response, yet the underlying brain circuitry is not understood. Here we use circuit-based optogenetic, in vivo and in vitro electrophysiological, and neuroanatomical tracing methods to define midbrain periaqueductal grey circuits for specific defensive behaviours. We identify an inhibitory pathway from the central nucleus of the amygdala to the ventrolateral periaqueductal grey that produces freezing by disinhibition of ventrolateral periaqueductal grey excitatory outputs to pre-motor targets in the magnocellular nucleus of the medulla. In addition, we provide evidence for anatomical and functional interaction of this freezing pathway with long-range and local circuits mediating flight. Our data define the neuronal circuitry underlying the execution of freezing, an evolutionarily conserved defensive behaviour, which is expressed by many species including fish, rodents and primates. In humans, dysregulation of this 'survival circuit' has been implicated in anxiety-related disorders.


title: Protein purification with vapor-phase carbon dioxide.

abstract: Gaseous CO2 was used as an antisolvent to induce the fractional precipitation of alkaline phosphatase, insulin, lysozyme, ribonuclease, trypsin, and their mixtures from dimethylsulfoxide (DMSO). Compressed CO2 was added continuously and isothermally to stationary DMSO solutions (gaseous antisolvent, GAS). Dissolution of CO2 was accompanied by a pronounced, pressure-dependent volumetric expansion of DMSO and a consequent reduction in solvent strength of DMSO towards dissolved proteins. View cell experiments were conducted to determine the pressures at which various proteins precipitate from DMSO. The solubility of each protein in CO2-expanded DMSO was different, illustrating the potential to separate and purify proteins using gaseous antisolvents. Polyacrylamide gel electrophoresis in sodium dodecyl sulfate (SDS-PAGE) was used to quantify the separation of lysozyme from ribonuclease, alkaline phosphatase from insulin, and trypsin from catalase. Lysozyme biological activity assays were also performed to determine the composition of precipitates from DMSO initially containing lysozyme and ribonuclease. SDS-PAGE characterizations suggest that the composition and purity of solid-phase precipitated from a solution containing multiple proteins may be accurately controlled through the antisolvent's pressure. Insulin, lysozyme, ribonuclease, and trypsin precipitates recovered substantial amounts of biological activity upon redissolution in aqueous media. Alkaline phosphatase, however, was irreversibly denaturated. Vapor-phase antisolvents, which are easily separated and recovered from proteins and liquid solvents upon depressurization, appear to be a reliable and effective means of selectively precipitating proteins.


title: K-Means-Type Algorithms: A Generalized Convergence Theorem and Characterization of Local Optimality

abstract: The K-means algorithm is a commonly used technique in cluster analysis. In this paper, several questions about the algorithm are addressed. The clustering problem is first cast as a nonconvex mathematical program. Then, a rigorous proof of the finite convergence of the K-means-type algorithm is given for any metric. It is shown that under certain conditions the algorithm may fail to converge to a local minimum, and that it converges under differentiability conditions to a Kuhn-Tucker point. Finally, a method for obtaining a local-minimum solution is given.


title: Improving Soft Pneumatic Actuator fingers through integration of soft sensors, position and force control, and rigid fingernails

abstract: Soft Pneumatic Actuators (SPAs) have recently become popular for use as fingers in robotic hands because of their inherent compliance, low cost, and ease of construction. We seek to overcome two key limitations which limit SPAs' abilities to grasp and manipulate objects: 1) Current SPAs lack position or force sensor feedback, which prevents controlling them precisely (e.g. to achieve a hand preshape or apply a specified pushing force), and 2) the tip of the SPA is compliant and has high friction against common surfaces, causing the SPA to stick against surfaces when grasping objects from above. To overcome the first limitation we propose methods to integrate soft eGaIn sensors into SPAs and controllers that use these sensors' feedback for position and force control. To overcome the second limitation, we explore embedding rigid fingernails into the tip of the SPA so that the finger does not stick against surfaces and can wedge under objects. Our experiments suggest that we can achieve low steady-state error and overshoot in position and force using feed-forward models that relate pressure, force, and curvature along with a PID controller. We also compare several fingernail designs and show that the best-performing design significantly outperforms having no fingernails when grasping a set of common objects from a table.


title: The one-time pad revisited

abstract: The one-time pad, the mother of all encryption schemes, is well known to be information-theoretically secure, in contrast to most encryption schemes used in practice, which are at most computationally secure. In this paper, we focus on another, completely different aspect in which the one-time pad is superior to normal encryption, and which surfaces only when the receiver (not only the eavesdropper) is considered potentially dishonest, as can be the case in a larger protocol context in which encryption is used as a sub-protocol. For example, such a dishonest receiver (who is, say, coerced by the eavesdropper) can in normal encryption verifiably leak the message to the eavesdropper by revealing the secret key. While this leakage feature can provably not be avoided completely, it is more limited if the one-time pad is used. We use the constructive cryptography framework to make these statements precise.


title: dfesnippets: An Open-Source Library for Dataflow Acceleration on FPGAs

abstract: Highly-tuned FPGA implementations can achieve significant performance and power efficiency gains over general purpose hardware. However the limited development productivity has prevented mainstream adoption of FPGAs in many areas such as High Performance Computing. High level standard development libraries are increasingly adopted in improving productivity. We propose an approach for performance critical applications including standard library modules, benchmarking facilities and application benchmarks to support a variety of use-cases. We implement the proposed approach as an open-source library for a commercially available FPGA system and highlight applications and productivity gains.


title: Rotor Dynamical Characteristics of Helicopter Start

abstract: Based on the theory of finite rotation beam, together with the finite element method, the rotor dynamic model is established, and the relationship of strain and displacement in the strain energy of blade is solved by introducing the generalized Danielson-Hodges strain and Green-Lagrange strain. The three-dimensional nonlinear analysis is decoupled into two-dimensional linear profile analysis and one-dimensional nonlinear blade analysis. Hartenberg-Denarit augmented transformation matrix is introduced in kinetic modeling. According to the Hamilton principle, the nonlinear implicit dynamic equations of the rotor are established. Blade flap under the influence of gust wind is calculated; the safety criterion of the ground starting of the helicopter is put forward. In the United States, there has been many accidents of such collision in the process of starting or stopping of the blades, which is called 'Strike Tunnel'. In order to avoid the occurrence of such accidents, this paper studies the flap response characteristics of the blades in the starting process of helicopter using the rotor starting model built, and provides a safe basis for the starting operation of the helicopter. Different from the traditional isotropic dynamics model which is based on the moderate deflection beam theory, this paper adopts the finite rotation beam theory to deal with the elastic deformation of blade, and sets up an accurate model of rotor dynamics with the method of finite element. The paper also discusses the characteristics of composite blade, the key structural design parameters (pre cone angle, torsion angle and sweep angle etc. of blade, the characteristics of typical hub configuration, and the coupling motion and nonlinear deformation of flap, shimmy and torsion in the collision. The definition of generalized Danielson-Hodges strain is introduced in strain energy modeling, and the three-dimensional nonlinear analysis of composite blade is decoupled into two-dimensional linear profile analysis and one-dimensional nonlinear blade analysis. Meanwhile, the Hartenberg-Denarit augmented transformation matrix is introduced in kinetic modeling. This paper creates a new recursive method of identifying the position vector of any point on the blade in inertial coordinates, and finally establishes the nonlinear implicit dynamic equations of the rotor according to the Hamilton principle. 1.1 Rotation beam Euler-Bernoulli beam assumption is used to model the blade. After deformation, the position coordinates of the origin in the undeformed coordinate system is


title: Race/ethnic differences in exposure to traumatic events, development of post-traumatic stress disorder, and treatment-seeking for post-traumatic stress disorder in the United States.

abstract: BACKGROUND ::: To identify sources of race/ethnic differences related to post-traumatic stress disorder (PTSD), we compared trauma exposure, risk for PTSD among those exposed to trauma, and treatment-seeking among Whites, Blacks, Hispanics and Asians in the US general population. ::: ::: ::: METHOD ::: Data from structured diagnostic interviews with 34 653 adult respondents to the 2004-2005 wave of the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) were analysed. ::: ::: ::: RESULTS ::: The lifetime prevalence of PTSD was highest among Blacks (8.7%), intermediate among Hispanics and Whites (7.0% and 7.4%) and lowest among Asians (4.0%). Differences in risk for trauma varied by type of event. Whites were more likely than the other groups to have any trauma, to learn of a trauma to someone close, and to learn of an unexpected death, but Blacks and Hispanics had higher risk of child maltreatment, chiefly witnessing domestic violence, and Asians, Black men, and Hispanic women had higher risk of war-related events than Whites. Among those exposed to trauma, PTSD risk was slightly higher among Blacks [adjusted odds ratio (aOR) 1.22] and lower among Asians (aOR 0.67) compared with Whites, after adjustment for characteristics of trauma exposure. All minority groups were less likely to seek treatment for PTSD than Whites (aOR range: 0.39-0.61), and fewer than half of minorities with PTSD sought treatment (range: 32.7-42.0%). ::: ::: ::: CONCLUSIONS ::: When PTSD affects US race/ethnic minorities, it is usually untreated. Large disparities in treatment indicate a need for investment in accessible and culturally sensitive treatment options.


Confidence Intervals and Prediction Intervals for Feed-Forward Neural Networks


title: Learning Efficient Convolutional Networks through Network Slimming

abstract: The deployment of deep convolutional neural networks (CNNs) in many real world applications is largely hindered by their high computational cost. In this paper, we propose a novel learning scheme for CNNs to simultaneously 1) reduce the model size; 2) decrease the run-time memory footprint; and 3) lower the number of computing operations, without compromising accuracy. This is achieved by enforcing channel-level sparsity in the network in a simple but effective way. Different from many existing approaches, the proposed method directly applies to modern CNN architectures, introduces minimum overhead to the training process, and requires no special software/hardware accelerators for the resulting models. We call our approach network slimming, which takes wide and large networks as input models, but during training insignificant channels are automatically identified and pruned afterwards, yielding thin and compact models with comparable accuracy. We empirically demonstrate the effectiveness of our approach with several state-of-the-art CNN models, including VGGNet, ResNet and DenseNet, on various image classification datasets. For VGGNet, a multi-pass version of network slimming gives a 20x reduction in model size and a 5x reduction in computing operations.


title: Automated classification of contact lens type in iris images

abstract: Textured cosmetic lenses have long been known to present a problem for iris recognition. It was once believed that clear, soft contact lenses did not impact iris recognition accuracy. However, it has recently been shown that persons wearing clear, soft contact lenses experience an increased false non-match rate relative to persons not wearing contact lenses. Iris recognition systems need the ability to automatically determine if a person is (a) wearing no contact lens, (b) wearing a clear prescription lens, or (c), wearing a textured cosmetic lens. This work presents results of the first attempt that we are aware of to solve this three-class classification problem. Results show that it is possible to identify with high accuracy (96.5%) the images in which a textured cosmetic contact lens is present, but that correctly distinguishing between no lenses and soft lenses is a challenging problem.


HEMORRHAGIC DIATHESIS WITH INCREASED CAPILLARY FRAGILITY CAUSED BY SALICYLATE THERAPY


title: Alpha-Concave Hull, a Generalization of Convex Hull

abstract: Bounding hull, such as convex hull, concave hull, alpha shapes etc. has vast applications in different areas especially in computational geometry. Alpha shape and concave hull are generalizations of convex hull. Unlike the convex hull, they construct non-convex enclosure on a set of points. In this paper, we introduce another generalization of convex hull, named alpha-concave hull, and compare this concept with convex hull and alpha shape. We show that the alpha-concave hull is also a generalization of an NP-complete problem named min-area TSP. We prove that computing the alpha-concave hull is NP-hard on a set of points.


title: Compressive phase retrieval via generalized approximate message passing

abstract: In this paper, we propose a novel approach to compressive phase retrieval based on loopy belief propagation and, in particular, on the generalized approximate message passing (GAMP) algorithm. Numerical results show that the proposed PR-GAMP algorithm has excellent phase-transition behavior, noise robustness, and runtime. In particular, for successful recovery of synthetic Bernoulli-circular-Gaussian signals, PR-GAMP requires ≈4 times the number of measurements as a phase-oracle version of GAMP and, at moderate to large SNR, the NMSE of PR-GAMP is only ≈3 dB worse than that of phase-oracle GAMP. A comparison to the recently proposed convex-relation approach known as “CPRL” reveals PR-GAMP's superior phase transition and orders-of-magnitude faster runtimes, especially as the problem dimensions increase. When applied to the recovery of a 65k-pixel grayscale image from 32k randomly masked magnitude measurements, numerical results show a median PR-GAMP runtime of only 13.4 seconds.


title: Bringing Big Data to Personalized Healthcare: A Patient-Centered Framework

abstract: Faced with unsustainable costs and enormous amounts of under-utilized data, health care needs more efficient practices, research, and tools to harness the full benefits of personal health and healthcare-related data. Imagine visiting your physician’s office with a list of concerns and questions. What if you could walk out the office with a personalized assessment of your health? What if you could have personalized disease management and wellness plan? These are the goals and vision of the work discussed in this paper. The timing is right for such a research direction—given the changes in health care, reimbursement, reform, meaningful use of electronic health care data, and patient-centered outcome mandate. We present the foundations of work that takes a Big Data driven approach towards personalized healthcare, and demonstrate its applicability to patient-centered outcomes, meaningful use, and reducing re-admission rates.


title: Few-Shot Generalization Across Dialogue Tasks

abstract: Machine-learning based dialogue managers are able to learn complex behaviors in order to complete a task, but it is not straightforward to extend their capabilities to new domains. We investigate different policies' ability to handle uncooperative user behavior, and how well expertise in completing one task (such as restaurant reservations) can be reapplied when learning a new one (e.g. booking a hotel). We introduce the Recurrent Embedding Dialogue Policy (REDP), which embeds system actions and dialogue states in the same vector space. REDP contains a memory component and attention mechanism based on a modified Neural Turing Machine, and significantly outperforms a baseline LSTM classifier on this task. We also show that both our architecture and baseline solve the bAbI dialogue task, achieving 100% test accuracy.


title: Application of Machine Learning Techniques to classify Fetal Hypoxia

abstract: Aim of this research is to classify fetal hypoxia using machine learning approach based on Cardiotocography (CTG) data and patient's previous complications records. Classification method is very popular in analysing new born baby's health in critical cases. CTG data had been used by obstetricians for analysing fetal well-being during pregnancy complications which provides fetus information in detail. CTG data consist of fetal heart rate (FHR) signals from mothers abdomen in the form of continuous electronic time varying pattern. CTG data provides information about FHR which is used for prior prediction and diagnosis of long term embryo impairment. In this study high dimensional CTG data is used to classify fetal hypoxia having attributes like FHR and uterine contraction (UC) in normal and pathological terms. CTG data has been taken from UCI machine learning repository consist of 1832 instances and 21 attributes used in this study out of which 8 are continuous and 13 are discrete. Research follows stacked generalised ensemble approach consist of 6 machine learning classifiers (Artificial Neural Network (ANN), Support Vector Machine (SVM), Random Forest (RF), C4.5 Decision Tree Classier (J48), Simple Logistics (SL), Adaptive Boosting (AdaBoost)) for CTG data classification. Robustness of the model has been evaluated by 10-fold cross validation technique with accuracy, sensitivity and specificity as evaluation features for model. F-measure, ROC (receiver operation characteristics) and Kappa Statistics also used as tools to study test performance. We also compared Model Testing Time for each individual model with accuracy to find out which model performs best. Highest accuracy of 98.79 % provided by Stacking 2 (SL+C4.5(J48)+RF) model. If experienced obstetricians are not available this research will help physicians in prior detection and diagnosis of fetal hypoxia.


title: Automatic query generation for patent search

abstract: Patent search is the task of finding relevant existing patents, which is an important part of the patent's examiner's process of validating a patent application. In this paper, we studied how to transform a query patent (the application) into search queries. Three types of search features are explored for automatic query generation for patent search. Furthermore, different types of features are combined with a learning to rank method. Experiments based on a USPTO patent collection demonstrate that the single best search feature is the combination of words and noun-phrases from the summary field and the retrieval performance can be significantly improved by combining three types of search features.


TRAFFIC STREAM CHARACTERISTICS 4


title: Early prediction of student success: Mining student enrollment data

abstract: This paper explores the socio-demographic variables (age, gender, ethnicity, education, work status, and disability) and study environment (course programme and course block), that may influence persistence or dropout of students at the Open Polytechnic of New Zealand. We examine to what extent these factors, i.e. enrolment data help us in pre-identifying successful and unsuccessful students. The data stored in the Open Polytechnic student management system from 2006 to 2009, covering over 450 students who enrolled to 71150 Information Systems course was used to perform a quantitative analysis of study outcome. Based on a data mining techniques (such as feature selection and classification trees), the most important factors for student success and a profile of the typical successful and unsuccessful students are identified. The empirical results show the following: (i) the most important factors separating successful from unsuccessful students are: ethnicity, course programme and course block; (ii) among classification tree growing methods Classification and Regression Tree (CART) was the most successful in growing the tree with an overall percentage of correct classification of 60.5%; and (iii) both the risk estimated by the cross-validation and the gain diagram suggests that all trees, based only on enrolment data are not quite good in separating successful from unsuccessful students. The implications of these results for academic and administrative staff are discussed.


title: Mutual information based radar waveform design for joint radar and cellular communication systems

abstract: A joint radar/communication system is considered, where the radar adaptively designs the transmitted waveform such that the interference caused to the cellular systems is strictly controlled. In this paper, different Mutual Information based criteria for radar waveform optimization are proposed and the corresponding waveform optimization problems are formulated and solved analytically. Radar performance trade-offs for the considered Mutual Information based criteria are presented and, using simulation results, it is shown that a larger maximized Mutual Information does not guarantee an optimal detection performance. It is also emphasized the importance of exploiting the communication signals scattered off the target for the detection task when dealing with weak radar returns.


title: Scalable RDF store based on HBase and MapReduce

abstract: The growing size of Resource Description Framework (RDF) dataset requires RDF repository to be excellent scalable and highly efficient. Distributed and parallel processing model meets the urgent needs naturally. In this paper, we propose a scalable RDF store based on HBase, which is a distributed, column-oriented database modeled after Google's Bigtable. Our approach adopts the idea of Hexastore and considers both RDF data model and HBase capability. We store RDF triples into six HBase tables (S_PO, P_SO, O_SP, PS_O, SO_P and PO_S) which covers all combinations of RDF triple patterns. And we index them with HBase provided index structure on row key. Besides presenting the storage schema, we also propose a MapReduce strategy for SPARQL Basic Graph Pattern (BGP) processing, which is suitable for our storage schema. It uses multiple MapReduce jobs to process a typical BGP. In each job, it uses a greedy method to select join key and eliminates multiple triple patterns. The evaluation result indicates that our approach works well against large RDF dataset.


title: WebVision Database: Visual Learning and Understanding from Web Data

abstract: In this paper, we present a study on learning visual recognition models from large scale noisy web data. We build a new database called WebVision, which contains more than $2.4$ million web images crawled from the Internet by using queries generated from the 1,000 semantic concepts of the benchmark ILSVRC 2012 dataset. Meta information along with those web images (e.g., title, description, tags, etc.) are also crawled. A validation set and test set containing human annotated images are also provided to facilitate algorithmic development. Based on our new database, we obtain a few interesting observations: 1) the noisy web images are sufficient for training a good deep CNN model for visual recognition; 2) the model learnt from our WebVision database exhibits comparable or even better generalization ability than the one trained from the ILSVRC 2012 dataset when being transferred to new datasets and tasks; 3) a domain adaptation issue (a.k.a., dataset bias) is observed, which means the dataset can be used as the largest benchmark dataset for visual domain adaptation. Our new WebVision database and relevant studies in this work would benefit the advance of learning state-of-the-art visual models with minimum supervision based on web data.


Chapter 2 The Sociocultural Aspects of HIV / AIDS in South Africa


Funology: designing enjoyment


Electronic Fuel Management–Fundamentals


title: Fast Copy-Move Detection of Digital Audio

abstract: Audio forgery is a critical part of digital forgery. There is a large variety of audio forgery forms, and copy-move is one of the most common but effective one. Merely one word's copy-move can turn the whole sentence into distinct meaning. So the way to detect this form of forgery precisely and effectively is especially useful and significant in the field of digital forensics. In this paper, we propose a fast method to detect audio copy-move forgery. At first, the audio is segmented into syllables. Then discrete Fourier transform (DFT) on each audio segment is applied for the sake of extracting the feature. In addition, every audio segment is sorted based on the features. Finally, unlike other mainstream algorithms which compare the similarity between every two segments of the audio file, we just compare one audio segment with some adjacent ones in the sorted list of audio segments, which can reduce the time complexity from O(n2) to O(nlogn) and cut down the time consumption of detection consequently. Experimental results demonstrate that our methods is faster and more effective than other state of the art ones.


title: Design of Small Smart Home system based on Arduino

abstract: Smart Home is applied in order to provide comfort, energy efficiency and better security. Smart Home System is still rarely used in Indonesia because of the cost and the difficulty of getting the device. The objective of this paper is to offer a Small Smart Home System designed and created by utilizing WLAN network based on Arduino microcontroller. The system is able to monitor and control lights, room temperature, alarms and other household appliances. Results from testing the system show proper control and control monitoring functions can be performed from a device connected to a network that supports HTML5.


title: Simulation tools for cloud computing: A survey and comparative study

abstract: Today, cloud computing has become a promising paradigm that aims at delivering computing resources and services on demand. The adoption of these services has been rapidly increasing. One of the main issues in this context is how to evaluate the ability of cloud systems to provide the desired services while respecting the QoS constraints. Experimentation in a real environment is a hard problem. In fact, the financial cost and the time required are very high. Also, the experiments are not repeatable, because a number of variables that are not under control of the tester may affect experimental results. Therefore, using simulation frameworks to evaluate cloud applications is preferred. This paper presents a survey of the existing simulation tools in cloud computing. It provides also a critical and comparative analysis of the studied tools. Finally, it stands out a major challenge to be addressed for further research.


title: A Connectionist Approach to Algorithmic Composition

abstract: With the advent of von Neumann-style computers, widespread exploration of new methods of music composition became possible. For the first time, complex sequences of carefully specified symbolic operations could be performed in a rapid fashion. Composers could develop algorithms embodying the compositional rules they were interested in and then use a computer to carry out these algorithms. In this way, composers could soon tell whether the results of their rules held artistic merit. This approach to algorithmic composition, based on the wedding between von Neumann computing machinery and rule-based software systems, has been prevalent for the past thirty years. The arrival of a new paradigm for computing has made a different approach to algorithmic composition possible. This new computing paradigm is called parallel distributed processing (PDP), also known as connectionism. Computation is performed by a collection of several simple processing units connected in a network and acting in cooperation (Rumelhart and McClelland 1986). This is in stark contrast to the single powerful central processor used in the von Neumann architecture. One of the major features of the PDP approach is that it replaces strict rule-following behavior with regularity-learning and generalization (Dolson 1989). This fundamental shift allows the development of new algorithmic composition methods that rely on learning the structure of existing musical examples and generalizing from these learned structures to compose new pieces. These methods contrast greatly with the majority of older schemes that simply follow a previously assembled set of compositional rules, resulting in brittle systems typically unable to appropriately handle unexpected musical situations. To be sure, other algorithmic composition methods in the past have been based on abstracting certain features from musical examples and using these to create new compositions. Techniques such as Markov modeling with transition probability analysis (Jones 1981), Mathews' melody interpolation method (Mathews and Rosler 1968), and Cope's EMI system (Cope 1987) can all be placed in this category. However, the PDP computational paradigm provides a single powerful unifying approach within which to formulate a variety of algorithmic composition methods of this type. These new learning methods combine many of the features of the techniques listed above and add a variety of new capabilities. Perhaps most importantly, though, they yield different and interesting musical results. This paper presents a particular type of PDP network for music composition applications. Various issues are discussed in designing the network, choosing the music representation used, training the network, and using it for composition. Comparisons are made to previous methods of algorithmic composition, and examples of the network's output are presented. This paper is intended to provide an indication of the power and range of PDP methods for algorithmic composition and to encourage others to begin exploring this new approach. Hence, rather than merely presenting a reduced compositional technique, alternative approaches and tangential ideas are included throughout as points of departure for further efforts.


title: What is normal in normal aging? Effects of aging, amyloid and Alzheimer's disease on the cerebral cortex and the hippocampus

abstract: What can be expected in normal aging, and where does normal aging stop and pathological neurodegeneration begin? With the slow progression of age-related dementias such as Alzheimer's disease (AD), it is difficult to distinguish age-related changes from effects of undetected disease. We review recent research on changes of the cerebral cortex and the hippocampus in aging and the borders between normal aging and AD. We argue that prominent cortical reductions are evident in fronto-temporal regions in elderly even with low probability of AD, including regions overlapping the default mode network. Importantly, these regions show high levels of amyloid deposition in AD, and are both structurally and functionally vulnerable early in the disease. This normalcy-pathology homology is critical to understand, since aging itself is the major risk factor for sporadic AD. Thus, rather than necessarily reflecting early signs of disease, these changes may be part of normal aging, and may inform on why the aging brain is so much more susceptible to AD than is the younger brain. We suggest that regions characterized by a high degree of life-long plasticity are vulnerable to detrimental effects of normal aging, and that this age-vulnerability renders them more susceptible to additional, pathological AD-related changes. We conclude that it will be difficult to understand AD without understanding why it preferably affects older brains, and that we need a model that accounts for age-related changes in AD-vulnerable regions independently of AD-pathology.


title: Extending Long Short-Term Memory for Multi-View Structured Learning

abstract: Long Short-Term Memory (LSTM) networks have been successfully applied to a number of sequence learning problems but they lack the design flexibility to model multiple view interactions, limiting their ability to exploit multi-view relationships. In this paper, we propose a Multi-View LSTM (MV-LSTM), which explicitly models the view-specific and cross-view interactions over time or structured outputs. We evaluate the MV-LSTM model on four publicly available datasets spanning two very different structured learning problems: multimodal behaviour recognition and image captioning. The experimental results show competitive performance on all four datasets when compared with state-of-the-art models.


title: Salvation in World Religions

abstract: This article is a plea for a sympathetic and empathetic understanding of salvation in the major faith traditions of the world. There is no such thing as Christian, Buddhist, Hindu, Muslim or Jewish salvation, H. S. Wilson insists--only human salvation. After discussing the biblical roots of the word "salvation," Wilson reflects on what Christians would call salvation in Judaism, Islam,, Hinduism, Buddhism, Sikhism, Confucianism and Taoism. Here he finds many similarities to the Christian notion, but also significant differences. Then, in the context of today's changing Christian attitude toward the possibility of salvation in other religious ways, Wilson finds paths toward possible progress in John B. Cobb, Jr.'s challenge of "mutual transformation," in Raimon Panikkar's call for intra-religious dialogue, and in Aloysius Pieris' notion of "enreligionization." The religions of the world will thrive in the future, argues Wilson, only if they grow towards one another and avoid isolation and fundamentalism.


title: Double Synchronous Reference Frame PLL for Power Converters Control

abstract: This paper deals with one of the most important issues in the control of grid-connected converters, which is the detection of the positive sequence fundamental component of the utility voltage. The study carried out in this paper conducts to a fast, precise, and robust positive sequence voltage detector offering a good behavior, even if unbalanced and distorted conditions are present in the grid. The proposed detector utilizes a new "double synchronous reference frame PLL" (DSRF-PLL), which completely eliminates the existing errors in conventional synchronous reference frame PLL systems (SRF-PLL) when operating under unbalanced utility voltages. In the study performed in this paper, the positive and negative sequence components of the unbalanced voltage vector are properly characterized. When this unbalanced vector is expressed on the DSRF, the analysis of the signals on the DSRF axes permits to design a decoupling network which isolates the positive and negative sequence components. This decoupling network gives rise to a new PLL structure which detects the positive sequence voltage component quickly and accurately. In this paper, conclusions of the analytical study are verified by simulation and experiment


title: I. Ion Channels in Human THP-1 Monocytes

abstract: The THP-1 human monocytic leukemia cell line is a useful model of macrophage differentiation. Patch clamp methods were used to identify five types of ion channels in undifferentiated THP-1 monocytes. (i) Delayed rectifier K+ current, IDR, was activated by depolarization to potentials positive to -50 mV, inactivated with a time constant of several hundred msec, and recovered from inactivation with a time constant approximately21 sec. IDR was inhibited by 4-aminopyridine (4-AP), tetraethylammonium (TEA+), and potently by charybdotoxin (ChTX). (ii) Ca-activated K+ current (ISK) dominated whole-cell currents in cells studied with 3-10 micron [Ca2+]i. ISK was at most weakly voltage-dependent, with reduced conductance at large positive potentials, and was inhibited by ChTX and weakly by TEA+, Cs+, and Ba2+, but not 4-AP or apamin. Block by Cs+ and Ba2+ was enhanced by hyperpolarization. (iii) Nonselective cation current, Icat, appeared at voltages above +20 mV. Little time-dependence was observed, and a panel of channel blockers was without effect. (iv) Chloride current, ICl, was present early in experiments, but disappeared with time. (v) Voltage-activated H+ selective current is described in detail in a companion paper (DeCoursey & Cherny, 1996. J. Membrane Biol. 152:2). The ion channels in THP-1 cells are compared with channels described in other macrophage-related cells. Profound changes in ion channel expression that occur during differentiation of THP-1 cells are described in a companion paper (DeCoursey et al., 1996. J. Membrane Biol. 152:2).


title: A direct link between MITF, innate immunity, and hair graying

abstract: Melanocyte stem cells (McSCs) and mouse models of hair graying serve as useful systems to uncover mechanisms involved in stem cell self-renewal and the maintenance of regenerating tissues. Interested in assessing genetic variants that influence McSC maintenance, we found previously that heterozygosity for the melanogenesis associated transcription factor, Mitf, exacerbates McSC differentiation and hair graying in mice that are predisposed for this phenotype. Based on transcriptome and molecular analyses of Mitfmi-vga9/+ mice, we report a novel role for MITF in the regulation of systemic innate immune gene expression. We also demonstrate that the viral mimic poly(I:C) is sufficient to expose genetic susceptibility to hair graying. These observations point to a critical suppressor of innate immunity, the consequences of innate immune dysregulation on pigmentation, both of which may have implications in the autoimmune, depigmenting disease, vitiligo.


title: Acquisition of Linguistic Patterns for Knowledge-based Information Extraction

abstract: In this paper we present a new method of automatic acquisition of linguistic patterns for Information Extraction, as implemented in the CICERO system. Our approach combines lexico-semantic information available from the WordNet database with collocating data extracted from training corpora. Due to the open-domain nature of the WordNet information and the immediate availability of large collections of texts, our method can be easily ported to open-domain Information Extraction.


Research of Decision Tree on YARN Using MapReduce and Spark


title: Compact Ultrawideband MIMO Antenna With Half-Slot Structure

abstract: In this letter, a multiple-input–multiple-output (MIMO) antenna with very compact size of only $\text{23}\times \text{18}\; \text{mm}^{2}$ is proposed for ultrawideband (UWB) applications. The proposed MIMO antenna consists of two symmetrical half-slot antenna elements with coplanar waveguide-fed structures and a Y-shaped slot that is cut at the bottom center of the common ground plane. The slot efficiently prevents the current from directly flowing between two ports at low UWB frequency. For such a compact-size antenna, the ground plane works as a radiator as well as a reflector that reflects the radiation from radiators at high frequency. The measured impedance bandwidth for $S_{11}, S_{22}$ $S_{12}, S_{21}$ $S_{12}, S_{21}$ < −20 dB is from 4 to 12.4 GHz. The proposed antenna also contains relatively stable radiation patterns and gains. These performances indicate that the proposed antenna is one competitive candidate for UWB applications.


title: Achieving Transmit Diversity in OFDM-IM by Utilizing Multiple Signal Constellations

abstract: Orthogonal frequency division multiplexing (OFDM) index modulation (IM) is a novel multicarrier modulation, which employs both the indices of the active subcarriers and conventional constellation symbols to convey information. Although OFDM-IM shows advantages in many aspects, it cannot provide transmit diversity (TD). To address this problem partly, in this paper, we propose a modified scheme, termed as OFDM-IM with TD (OFDM-IM-TD), through utilizing silent subcarriers and multiple signal constellations (SCs). Specifically, in OFDM-IM-TD, besides the active subcarriers, the same information is transmitted over the silent subcarriers, while using a different SC. The design of the SC for the silent subcarriers is further illustrated. Next, the union bound of the average bit error probability is derived, based on which the transmit diversity gain of the proposed scheme over OFDM-IM is theoretically verified. Moreover, a low-complexity log-likelihood ratio-based detector is developed. Finally, simulation results are given to demonstrate the performance of the proposed scheme.


Use of High Levels of Dietary Aluminum and Zinc for Inducing Pauses in Egg Production of Japanese Quail


title: Evaluation of convolutional neural network features for malware detection

abstract: Advances in machine learning technologies have provided that malicious programs can be detected based on static and dynamic features. Moreover, a crowded set of studies throughout literature indicates that malware detection can be handled with remarkable accuracy rate once converted into image domain. To realize this, some image based techniques have been developed together with feature extraction and classifiers in order to discover the relation between malware binaries in grayscale color representation. With a similar way, we have contributed the CNN features to overcome the malware detection problem. Findings of experimental research support that the malware types can be classified with 85% accuracy rate when applying the machine learning system on 36 (including benign type) malware families consisting of 12,279 malware samples. Moreover, we have achieved the 99% accuracy rate when conducting and experiment on 25 families having 9, 339 malware samples.


title: Color nonuniformity in projection-based displays: analysis and solutions

abstract: Large-area displays made up of several projectors show significant variation in color. Here, we identify different projector parameters that cause the color variation and study their effects on the luminance and chrominance characteristics of the display. This work leads to the realization that luminance varies significantly within and across projectors, while chrominance variation is relatively small, especially across projectors of same model. To address this situation, we present a method to achieve luminance matching across all pixels of a multiprojector display that results in photometrically uniform displays. We use a camera as a measurement device for this purpose. Our method comprises a one-time calibration step that generates a per channel per projector luminance attenuation map (LAM), which is then used to correct any image projected on the display at interactive rates on commodity graphics hardware. To the best of our knowledge, this is the first effort to match luminance across all the pixels of a multiprojector display.


title: Design, principles, and testing of a latching modular robot connector

abstract: Connection and disconnection occur often in modular robotics. Furthermore, position errors increase in chain-style modular robots as chains get longer. This paper presents a passive (unactuated) compliant two-layer latching mechanism compatible with planar docking mechanisms such as the X-Face, with design considerations and important design parameters empirically identified. Design parameters such as alignment, face curvature and overhang width are shown to have unintuitive effects on the behavior and strength of the connectors. The latch mechanism has a bonding ratio (force of the bond over force required to engage bond) in the range of 20 to 76, depending on the design parameters used. The paper also presents a reconfiguration control sequence that combines the module actuation forces with the connectors' natural forces to increase the reliability of the connection process and reduce the maximum force required. In addition, several types of reconfiguration are performed, including reconfiguration with a 12 module long chain that demonstrates the connectors' robustness to error.


title: Multi-body Motion Estimation from Monocular Vehicle-Mounted Cameras

abstract: This paper addresses the problem of simultaneous estimation of a vehicle's ego motion and motions of multiple moving objects in the scene–called eoru motions –through a monocular vehicle-mounted camera. Localization of multiple moving objects and estimation of their motions is crucial for autonomous vehicles. Conventional localization and mapping techniques (e.g., visual odometry and simultaneous localization and mapping) can only estimate the ego motion of the vehicle. The capability of a robot localization pipeline to deal with multiple motions has not been widely investigated in the literature. We present a theoretical framework for robust estimation of multiple relative motions in addition to the camera ego motion. First, the framework for general unconstrained motion is introduced and then it is adapted to exploit the vehicle kinematic constraints to increase efficiency. The method is based on projective factorization of the multiple-trajectory matrix . First, the ego motion is segmented and then several hypotheses are generated for the eoru motions . All the hypotheses are evaluated and the one with the smallest reprojection error is selected. The proposed framework does not need any a priori knowledge of the number of motions and is robust to noisy image measurements. The method with a constrained motion model is evaluated on a popular street-level image dataset collected in urban environments (the KITTI dataset), including several relative ego-motion and eoru-motion scenarios. A benchmark dataset ( Hopkins 155 ) is used to evaluate this method with a general motion model. The results are compared with those of the state-of-the-art methods considering a similar problem, referred to as multibody structure from motion in the computer vision community.


title: Identifying Unknown Unknowns in the Open World: Representations and Policies for Guided Exploration

abstract: Predictive models deployed in the real world may assign incorrect labels to instances with high confidence. Such errors or unknown unknowns are rooted in model incompleteness, and typically arise because of the mismatch between training data and the cases encountered at test time. As the models are blind to such errors, input from an oracle is needed to identify these failures. In this paper, we formulate and address the problem of informed discovery of unknown unknowns of any given predictive model where unknown unknowns occur due to systematic biases in the training data. We propose a model-agnostic methodology which uses feedback from an oracle to both identify unknown unknowns and to intelligently guide the discovery. We employ a two-phase approach which first organizes the data into multiple partitions based on the feature similarity of instances and the confidence scores assigned by the predictive model, and then utilizes an explore-exploit strategy for discovering unknown unknowns across these partitions. We demonstrate the efficacy of our framework by varying the underlying causes of unknown unknowns across various applications. To the best of our knowledge, this paper presents the first algorithmic approach to the problem of discovering unknown unknowns of predictive models.


title: Domain Specific Word Extraction from Hierarchical Web Documents: A First Step Toward Building Lexicon Trees from Web Corpora

abstract: Domain specific words and ontological information among words are important resources for general natural language applications. This paper proposes a statistical model for finding domain specific words (DSW s) in particular domains, and thus building the association among them. When applying this model to the hierarchical structure of the web directories node-by-node, the document tree can potentially be converted into a large semantically annotated lexicon tree. Some preliminary results show that the current approach is better than a conventional TF-IDF approach for measuring domain specificity. An average precision of 65.4% and an average recall of 36.3% are observed if the top-10% candidates are extracted as domain-specific words. 1 Domain Specific Words and Lexicon Trees as Important NLP Resources Domain specific words (DSW s) are important anchoring words for natural language processing applications that involve word sense disambiguation (WSD). It is appreciated that multi-sense words appearing in the same document tend to be tagged with the same word sense if they belong to the same common domain in the semantic hierarchy (Yarowsky, 1995). The existence of some DSW s in a document will therefore be a strong evidence of a specific sense for words within the document. For instance, the existence of basketball in a document would strongly suggest the sport sense of the word ( Pistons ), rather than its mechanics sense. It is also a personal belief that DSW-based sense disambiguation, document classification and many similar applications would be easier than sense-based models since sense-tagged documents are rare while domain-aware training documents are abundant on the Web. DSW identification is therefore an important issue. On the other hand, the semantics hierarchy among words (especially among sets of domain specific words) as well as the membership of domain specific words are also important resources for general natural language processing applications, since the hierarchy will provide semantic links and ontological information (such as is-A and part-of relationships) for words, and, domain specific words belonging to the same domain may have the synonym or antonym relationships. A hierarchical lexicon tree (or a network, in general) (Fellbaum, 1998; Jurafsky and Martin, 2000), indicative of sets of highly associated domain specific words and their hierarchy, is therefore invaluable for NLP applications. Manually constructing such a lexicon hierarchy and acquiring the associated words for each node in the hierarchy, however, is most likely unaffordable both in terms of time and cost. In addition, new words (or new usages of words) are dynamically produced day by day. For instance, the Chinese word (pistons) is more frequently used as the sport or basketball sense (referring to the Detroit


title: Technology in spiritual formation: an exploratory study of computer mediated religious communications

abstract: In this paper, we report findings from a study of American Christian ministers' uses of technologies in religious practices. We focus on the use of technologies for spiritual purposes as opposed to pragmatic and logistical, but report on all. We present results about the uses of technologies in three aspects of religious work: religious study and reflection, church services, and pastoral care. We end by examining how the collaborative religious uses of technologies cross and blend work and personal life.


title: Emotion Recognition of Affective Speech Based on Multiple Classifiers Using Acoustic-Prosodic Information and Semantic Labels

abstract: This work presents an approach to emotion recognition of affective speech based on multiple classifiers using acoustic-prosodic information (AP) and semantic labels (SLs). For AP-based recognition, acoustic and prosodic features are extracted from the detected emotional salient segments of the input speech. Three types of models GMMs, SVMs, and MLPs are adopted as the base-level classifiers. A Meta Decision Tree (MDT) is then employed for classifier fusion to obtain the AP-based emotion recognition confidence. For SL-based recognition, semantic labels are used to automatically extract Emotion Association Rules (EARs) from the recognized word sequence of the affective speech. The maximum entropy model (MaxEnt) is thereafter utilized to characterize the relationship between emotional states and EARs for emotion recognition. Finally, a weighted product fusion method is used to integrate the AP-based and SL-based recognition results for final emotion decision. For evaluation, 2,033 utterances for four emotional states were collected. The experimental results reveal that the emotion recognition performance for AP-based recognition using MDT achieved 80.00%. On the other hand, an average recognition accuracy of 80.92% was obtained for SL-based recognition. Finally, combining AP information and SLs achieved 83.55% accuracy for emotion recognition.


title: Oral manifestations of infectious diseases.

abstract: The oral manifestations of infectious diseases is a major topic since the prevalence rate has increased, and usually poses diagnostic and therapeutic dilemmas to the oral clinician. The clinical features of the most common and important oral infectious diseases are discussed.


Distributed representations of politicians


title: Large-scale Isolated Gesture Recognition using Convolutional Neural Networks

abstract: This paper proposes three simple, compact yet effective representations of depth sequences, referred to respectively as Dynamic Depth Images (DDI), Dynamic Depth Normal Images (DDNI) and Dynamic Depth Motion Normal Images (DDMNI). These dynamic images are constructed from a sequence of depth maps using bidirectional rank pooling to effectively capture the spatial-temporal information. Such image-based representations enable us to fine-tune the existing ConvNets models trained on image data for classification of depth sequences, without introducing large parameters to learn. Upon the proposed representations, a convolutional Neural networks (ConvNets) based method is developed for gesture recognition and evaluated on the Large-scale Isolated Gesture Recognition at the ChaLearn Looking at People (LAP) challenge 2016. The method achieved 55.57\% classification accuracy and ranked $2^{nd}$ place in this challenge but was very close to the best performance even though we only used depth data.


title: Explanations as Mechanisms for Supporting Algorithmic Transparency

abstract: Transparency can empower users to make informed choices about how they use an algorithmic decision-making system and judge its potential consequences. However, transparency is often conceptualized by the outcomes it is intended to bring about, not the specifics of mechanisms to achieve those outcomes. We conducted an online experiment focusing on how different ways of explaining Facebook's News Feed algorithm might affect participants' beliefs and judgments about the News Feed. We found that all explanations caused participants to become more aware of how the system works, and helped them to determine whether the system is biased and if they can control what they see. The explanations were less effective for helping participants evaluate the correctness of the system's output, and form opinions about how sensible and consistent its behavior is. We present implications for the design of transparency mechanisms in algorithmic decision-making systems based on these results.


title: Confidence-Weighted Local Expression Predictions for Occlusion Handling in Expression Recognition and Action Unit Detection

abstract: Fully-Automatic Facial Expression Recognition (FER) from still images is a challenging task as it involves handling large interpersonal morphological differences, and as partial occlusions can occasionally happen. Furthermore, labelling expressions is a time-consuming process that is prone to subjectivity, thus the variability may not be fully covered by the training data. In this work, we propose to train Random Forests upon spatially defined local subspaces of the face. The output local predictions form a categorical expression-driven high-level representation that we call Local Expression Predictions (LEPs). LEPs can be combined to describe categorical facial expressions as well as Action Units (AUs). Furthermore, LEPs can be weighted by confidence scores provided by an autoencoder network. Such network is trained to locally capture the manifold of the non-occluded training data in a hierarchical way. Extensive experiments show that the proposed LEP representation yields high descriptive power for categorical expressions and AU occurrence prediction, and leads to interesting perspectives towards the design of occlusion-robust and confidence-aware FER systems.


title: OLAPing social media: the case of Twitter

abstract: Social networks are platforms where millions of users interact frequently and share variety of digital content with each other. Users express their feelings and opinions on every topic of interest. These opinions carry import value for personal, academic and commercial applications, but the volume and the speed at which these are produced make it a challenging task for researchers and the underlying technologies to provide useful insights to such data. We attempt to extend the established OLAP(On-line Analytical Processing) technology to allow multidimensional analysis of social media data by integrating text and opinion mining methods into the data warehousing system and by exploiting various knowledge discovery techniques to deal with semi-structured and unstructured data from social media. The capabilities of OLAP are extended by semantic enrichment of the underlying dataset to discover new measures and dimensions for building data cubes and by supporting up-to-date analysis of the evolving as well as the historical social media data. The benefits of such an analysis platform are demonstrated by building a data warehouse for a social network of Twitter, dynamically enriching the underlying dataset and enabling multidimensional analysis.


title: Adaptive Robust Expansion Planning for a Distribution Network With DERs

abstract: Distribution expansion planning (DEP) is used to determine the best expansion plan of a distribution system. Considering the uncertainties of loads and of power productions of wind distributed energy resources (DERs) and incorporating AC power flow equations make the DEP problem increasingly challenging. In this context, this paper presents a new adaptive robust distribution expansion planning model to identify the timing of feeder reinforcements in addition to the location, capacity, and installation time of dispatchable and wind DERs. The proposed approach characterizes the uncertain nature of loads and power productions of wind DERs through polyhedral uncertainty sets and the robustness of the solution can be controlled by means of a budget of uncertainty. Since AC power flow equations rather than DC ones are considered, the proposed model is a nonlinear and non-convex min–max–min optimization problem, which cannot be solved directly by commercial optimization packages. Hence, a tri-level decomposition algorithm using both primal and dual cutting planes is introduced to solve the problem. Additionally, to attain a smoother optimization problem, the non-convex AC power flow equations are convexified. The working of the proposed model is illustrated using 33-bus and 123-bus distribution networks.


title: Improving the Reliability of Series Resonant Inverters for Induction Heating Applications

abstract: This paper analyzes a high-power (100-kW) high-frequency (50-kHz) voltage-fed inverter with a series resonant load circuit for industrial induction heating applications which is characterized by a full-bridge inverter composed of isolated-gate bipolar transistors and a new power control based on phase-shift (PS) control. This power control circuit incorporates a load-adaptive variable-frequency controller and automated blanking time management in order to allow the inverter to work in zero-voltage switching for all output power levels and load conditions. An important improvement of the inverter reliability is achieved by choosing an appropriate and novel switching sequence for the PS inverter. The results are verified experimentally using a prototype for induction hardening applications. A comparative study between the proposed and standard PS power control will be made.


title: The quantitative characterization of the distinctiveness and robustness of local image descriptors

abstract: We introduce a new method that characterizes quantitatively local image descriptors in terms of their distinctiveness and robustness to geometric transformations and brightness deformations. The quantitative characterization of these properties is important for recognition systems based on local descriptors because it allows for the implementation of a classifier that selects descriptors based on their distinctiveness and robustness properties. This classification results in: (a) recognition time reduction due to a smaller number of descriptors present in the test image and in the database of model descriptors; (b) improvement of the recognition accuracy since only the most reliable descriptors for the recognition task are kept in the model and test images; and (c) better scalability given the smaller number of descriptors per model. Moreover, the quantitative characterization of distinctiveness and robustness of local descriptors provides a more accurate formulation of the recognition process, which has the potential to improve the recognition accuracy. We show how to train a multi-layer perceptron that quickly classifies robust and distinctive local image descriptors. A regressor is also trained to provide quantitative models for each descriptor. Experimental results show that the use of these trained models not only improves the performance of our recognition system, but it also reduces significantly the computation time for the recognition process.


title: Educational Data Mining: a Case Study

abstract: In this paper, we show how using data mining algorithms can help discovering pedagogically relevant knowledge contained in databases obtained from Web-based educational systems. These findings can be used both to help teachers with managing their class, understand their students' learning and reflect on their teaching and to support learner reflection and provide proactive feedback to learners.


title: Concepts for mars on-orbit robotic sample capture and transfer

abstract: A potential Mars Sample Return (MSR) mission would require robotic autonomous capture and manipulation of an Orbital Sample (OS) before returning the samples to Earth. An orbiter would capture the OS, manipulate to a preferential orientation, transition it through the steps required to break-the-chain with Mars, stowing it in a containment vessel or an Earth Entry Vehicle (EEV) and providing redundant containment to the OS (for example by closing and sealing the lid of the EEV). In this paper, we discuss the trade-space of concepts generated for both the individual aspects of capture and manipulation of the OS, as well as concepts for the end-to-end system. Notably, we discuss concepts for OS capture, manipulation of the OS to orient it to a preferred configuration, and steps for transitioning the OS between different stages of manipulation, ultimately securing it in a containment vessel or Earth Entry Vehicle.


title: A method, system, and tools for intelligent interruption management

abstract: Interrupting users engaged in tasks typically has negative effects on their task completion time, error rate, and affective state. Empirical research has shown that these negative effects can be mitigated by deferring interruptions until more opportune moments in a user's task sequence. However, existing systems that reason about when to interrupt do not have access to task models that would allow for such finer-grained temporal reasoning. We outline our method of finding opportune moments that links a physiological measure of workload with task modeling techniques and theories of attention. We describe the design and implementation of our interruption management system, showing how it can be used to specify and monitor practical, representative user tasks. We discuss our ongoing empirical work in this area, and how the use of our framework may enable attention aware systems to consider a user's position in a task when reasoning about when to interrupt.


title: Multimedia questions and answering using web data mining

abstract: A large amount of information returned by the web search engines like google, yahoo etcUsers become overloaded to find the correct information from the web. Question Answering (QA)approach solves these problems. Instead of returning a list of document from the current search engine, QA system provides information from a comprehensive set of well answered question with appropriate media data. QA aims to leverage in depth linguistic and media content analysis as well as domain knowledge to return precise answers to natural language questions. This paper briefly surveys the progress of multimedia question answering (MMQA) research and details its future directions.


title: Anomaly Detection Using Autoencoders with Nonlinear Dimensionality Reduction

abstract: This paper proposes to use autoencoders with nonlinear dimensionality reduction in the anomaly detection task. The authors apply dimensionality reduction by using an autoencoder onto both artificial data and real data, and compare it with linear PCA and kernel PCA to clarify its property. The artificial data is generated from Lorenz system, and the real data is the spacecrafts' telemetry data. This paper demonstrates that autoencoders are able to detect subtle anomalies which linear PCA fails. Also, autoencoders can increase their accuracy by extending them to denoising autoenconders. Moreover, autoencoders can be useful as nonlinear techniques without complex computation as kernel PCA requires. Finaly, the authors examine the learned features in the hidden layer of autoencoders, and present that autoencoders learn the normal state properly and activate differently with anomalous input.


title: BAFS: Bundle Adjustment With Feature Scale Constraints for Enhanced Estimation Accuracy

abstract: We propose to incorporate within bundle adjustment (BA) a new type of constraint that uses feature scale information, leveraging the scale invariance property of typical image feature detectors (e.g., SIFT). While feature scales play an important role in image matching, they have not been utilized thus far for estimation purposes in a BA framework. Our approach exploits the already-available feature scale information and uses it to enhance the accuracy of BA, especially along the optical axis of the camera in a monocular setup. Importantly, the mentioned feature scale constraints can be formulated on a frame to frame basis and do not require loop closures. We study our approach in synthetic environments and the real-imagery KITTI dataset, demonstrating significant improvement in positioning error.


title: Key technology and application of millimeter wave communications for 5G: a survey

abstract: As mobile communication technology continues to develop, millimeter-wave communication for 5G mobile networks is attracting widespread public attention. Many studies have appeared in the literatures related to millimeter wave mobile broadband communication systems. This paper briefly introduces the development of 5G and millimeter wave communications, analyses several practical designs and implementations of millimeter wave communications for 5G, and proposes some future applications and challenges for 5G.