title: Generative Event Schema Induction with Entity Disambiguation

text: This paper presents a generative model to event schema induction. Previous methods in the literature only use head words to represent entities. However, elements other than head words contain useful information. For instance, an armed man is more discriminative than man. Our model takes into account this information and precisely represents it using probabilistic topic distributions. We illustrate that such information plays an important role in parameter estimation. Mostly, it makes topic distributions more coherent and more discriminative. Experimental results on benchmark dataset empirically confirm this enhancement.


title: A Unified Probabilistic Framework for Name Disambiguation in Digital Library

text: Despite years of research, the name ambiguity problem remains largely unresolved. Outstanding issues include how to capture all information for name disambiguation in a unified approach, and how to determine the number of people K in the disambiguation process. In this paper, we formalize the problem in a unified probabilistic framework, which incorporates both attributes and relationships. Specifically, we define a disambiguation objective function for the problem and propose a two-step parameter estimation algorithm. We also investigate a dynamic approach for estimating the number of people K. Experiments show that our proposed framework significantly outperforms four baseline methods of using clustering algorithms and two other previous methods. Experiments also indicate that the number K automatically found by our method is close to the actual number.


title: Broad expertise retrieval in sparse data environments

text: Expertise retrieval has been largely unexplored on data other than the W3C collection. At the same time, many intranets of universities and other knowledge-intensive organisations offer examples of relatively small but clean multilingual expertise data, covering broad ranges of expertise areas. We first present two main expertise retrieval tasks, along with a set of baseline approaches based on generative language modeling, aimed at finding expertise relations between topics and people. For our experimental evaluation, we introduce (and release) a new test set based on a crawl of a university site. Using this test set, we conduct two series of experiments. The first is aimed at determining the effectiveness of baseline expertise retrieval methods applied to the new test set. The second is aimed at assessing refined models that exploit characteristic features of the new test set, such as the organizational structure of the university, and the hierarchical structure of the topics in the test set. Expertise retrieval models are shown to be robust with respect to environments smaller than the W3C collection, and current techniques appear to be generalizable to other settings.


title: Relevance-Based Language Models

text: We explore the relation between classical probabilistic models of information retrieval and the emerging language modeling approaches. It has long been recognized that the primary obstacle to effective performance of classical models is the need to estimate a relevance model: probabilities of words in the relevant class. We propose a novel technique for estimating these probabilities using the query alone. We demonstrate that our technique can produce highly accurate relevance models, addressing important notions of synonymy and polysemy. Our experiments show relevance models outperforming baseline language modeling systems on TREC retrieval and TDT tracking tasks. The main contribution of this work is an effective formal method for estimating a relevance model with no training data.


title: Voting for candidates: adapting data fusion techniques for an expert search task

text: In an expert search task, the users' need is to identify people who have relevant expertise to a topic of interest. An expert search system predicts and ranks the expertise of a set of candidate persons with respect to the users' query. In this paper, we propose a novel approach for predicting and ranking candidate expertise with respect to a query. We see the problem of ranking experts as a voting problem, which we model by adapting eleven data fusion techniques.We investigate the effectiveness of the voting approach and the associated data fusion techniques across a range of document weighting models, in the context of the TREC 2005 Enterprise track. The evaluation results show that the voting paradigm is very effective, without using any collection specific heuristics. Moreover, we show that improving the quality of the underlying document representation can significantly improve the retrieval performance of the data fusion techniques on an expert search task. In particular, we demonstrate that applying field-based weighting models improves the ranking of candidates. Finally, we demonstrate that the relative performance of the adapted data fusion techniques for the proposed approach is stable regardless of the used weighting models.


title: A wirelessly controlled implantable LED system for deep brain optogenetic stimulation

text: In recent years optogenetics has rapidly become an essential technique in neuroscience. Its temporal and spatial specificity, combined with efficacy in manipulating neuronal activity, are especially useful in studying the behavior of awake behaving animals. Conventional optogenetics, however, requires the use of lasers and optic fibers, which can place considerable restrictions on behavior. Here we combined a wirelessly controlled interface and small implantable light-emitting diode (LED) that allows flexible and precise placement of light source to illuminate any brain area. We tested this wireless LED system in vivo, in transgenic mice expressing channelrhodopsin-2 in striatonigral neurons expressing D1-like dopamine receptors. In all mice tested, we were able to elicit movements reliably. The frequency of twitches induced by high power stimulation is proportional to the frequency of stimulation. At lower power, contraversive turning was observed. Moreover, the implanted LED remains effective over 50 days after surgery, demonstrating the long-term stability of the light source. Our results show that the wireless LED system can be used to manipulate neural activity chronically in behaving mice without impeding natural movements.


title: Optimal detection of fetal chromosomal abnormalities by massively parallel DNA sequencing of cell-free fetal DNA from maternal blood.

text:
BACKGROUND
Massively parallel DNA sequencing of cell-free fetal DNA from maternal blood can detect fetal chromosomal abnormalities. Although existing algorithms focus on the detection of fetal trisomy 21 (T21), these same algorithms have difficulty detecting trisomy 18 (T18).

METHODS
Blood samples were collected from 1014 patients at 13 US clinic locations before they underwent an invasive prenatal procedure. All samples were processed to plasma, and the DNA extracted from 119 samples underwent massively parallel DNA sequencing. Fifty-three sequenced samples came from women with an abnormal fetal karyotype. To minimize the intra- and interrun sequencing variation, we developed an optimized algorithm by using normalized chromosome values (NCVs) from the sequencing data on a training set of 71 samples with 26 abnormal karyotypes. The classification process was then evaluated on an independent test set of 48 samples with 27 abnormal karyotypes.

RESULTS
Mapped sites for chromosomes of interest in the sequencing data from the training set were normalized individually by calculating the ratio of the number of sites on the specified chromosome to the number of sites observed on an optimized normalizing chromosome (or chromosome set). Threshold values for trisomy or sex chromosome classification were then established for all chromosomes of interest, and a classification schema was defined. Sequencing of the independent test set led to 100% correct classification of T21 (13 of 13) and T18 (8 of 8) samples. Other chromosomal abnormalities were also identified.

CONCLUSION
Massively parallel sequencing is capable of detecting multiple fetal chromosomal abnormalities from maternal plasma when an optimized algorithm is used.


title: A paradigmatic and methodological examination of knowledge management research: 2000 to 2004

text: Knowledge management (KM) research has been evolving for more than a decade, yet little is known about KM theoretical perspectives, research paradigms, and research methods. This paper explores KM research in influential journals for the period 2000–2004. A total of 160 KM articles in ten top-tier information systems and management journals are analyzed. Articles that may serve as useful exemplars of KM research from positivist, interpretivist, and critical pluralist paradigms are selected. We find that KM research in information systems journals differs from that in management journals, but neither makes a balanced use of positivist and non-positivist research approaches. © 2007 Elsevier B.V. All rights reserved.


title: A fast and memory-efficient Discrete Focal Stack Transform for plenoptic sensors

text: Article history: Available online 23 December 2014


title: How to Choose the Best Pivot Language for Automatic Translation of Low-Resource Languages

text: Recent research on multilingual statistical machine translation focuses on the usage of pivot languages in order to overcome language resource limitations for certain language pairs. Due to the richness of available language resources, English is, in general, the pivot language of choice. However, factors like language relatedness can also effect the choice of the pivot language for a given language pair, especially for Asian languages, where language resources are currently quite limited. In this article, we provide new insights into what factors make a pivot language effective and investigate the impact of these factors on the overall pivot translation performance for translation between 22 Indo-European and Asian languages. Experimental results using state-of-the-art statistical machine translation techniques revealed that the translation quality of 54.8% of the language pairs improved when a non-English pivot language was chosen. Moreover, 81.0% of system performance variations can be explained by a combination of factors such as language family, vocabulary, sentence length, language perplexity, translation model entropy, reordering, monotonicity, and engine performance.


title: Sistemas de reconocimiento basados en la imagen facial

text: ─ This article summarizes the most important research projects developed in thearea of recognitions y stemsusing facial images. It presents the description of the principal lines of study about people identification systems using face recognition. Furthermore, it contains a synthesis of recent mathematical techniques for pattern sextraction in these systems.


title: An integrated approach to characterize genetic interaction networks in yeast metabolism

text: Although experimental and theoretical efforts have been applied to globally map genetic interactions, we still do not understand how gene-gene interactions arise from the operation of biomolecular networks. To bridge the gap between empirical and computational studies, we i, quantitatively measured genetic interactions between ∼185,000 metabolic gene pairs in Saccharomyces cerevisiae, ii, superposed the data on a detailed systems biology model of metabolism and iii, introduced a machine-learning method to reconcile empirical interaction data with model predictions. We systematically investigated the relative impacts of functional modularity and metabolic flux coupling on the distribution of negative and positive genetic interactions. We also provide a mechanistic explanation for the link between the degree of genetic interaction, pleiotropy and gene dispensability. Last, we show the feasibility of automated metabolic model refinement by correcting misannotations in NAD biosynthesis and confirming them by in vivo experiments.


title: Sequence of central nervous system myelination in human infancy. II. Patterns of myelination in autopsied infants.

text: The timing and synchronization of postnatal myelination in the human central nervous system (CNS) are complex. We found eight time-related patterns of CNS myelination during the first two postnatal years in autopsied infants. The intensity of myelination was graded in 162 infants with diverse diseases on an ordinal scale of degrees 0-4. The Ayer method for maximum likelihood estimates for censored data was utilized to generate curves of the temporal changes in the percent of infants with degrees 0 through 4 of myelin in 62 white matter sites. These sites fall into eight subgroups determined by the presence or absence of microscopic myelin (degree 1) at birth and the median age at which mature myelin (degree 3) is reached. There is variability in the timing of myelination within and across axonal systems, and early onset of myelination is not always followed by early myelin maturation. We reexamined general rules governing the timing of myelination proposed by previous investigators, and found that those rules are neither complete nor inviolate, and that there is a complex interplay among them. This study specifies distinct periods of maturation in which myelinating pathways are potentially vulnerable to insult during the first two postnatal years.


title: Machine Invention of First Order Predicates by Inverting Resolution

text: - lwx z¡c¢¤£x¥¦x§ ̈¡a©l«N¬r©l©«N«w¦x¡a©l ¡Q¢¤£¡.¡Q¢w© ®5© ̄a§°¦x ̄Q±_£2«¤3©;¦2§a© ́|μ¥¶¡aμ«w·_ ̧©l£x ̄a«wμ«w·A¥a1|¥»o ¡Q©±_¥_μ¥A¥¶¡a ̄Q¦x«w· ̧[1~¬wμ£x¥a©l1⁄4¬ 1~¡a¢¤©N1⁄2x¦|3£x¬w3⁄4w ̧£2 ̄Q1~®w ̄a¦1⁄2 μ|©l μ[«¿¡Q¢w©N®w ̄Q¦x¬w ̧©±w© o ¥Q3 ̄Qμ®|¡aμ¦x«( ̧£x«w·x3⁄4r£2·x©À ÁL« μ|©f£2 ̧"¥a1 ¥¶¡a©l±Â¥a¢w¦x3⁄4w ̧A¬5© 3£x®¤£2¬¤ ̧[© ¦2§ ¦1⁄2x© ̄Ã3 ¦±4μ[«w·3¡a¢wμ¥ ̄Q©l¥¶¡a ̄Qμ3 ¡aμ¦x«¬ 1w© Ä¤«wμ«w·Nμ[¡Q¥;¦Å«1⁄2¦ 3l£2¬w3⁄4¤ ̧£x ̄a1À-Æ 3⁄4¤3Ã¢W£N¥a1 ¥¶¡a©l±ÇÅ)¦x3⁄4w ̧È¬r©_ ̧©l¥Q¥ ̄Q© ̧μ£2«q¡ ¦« ¡Q¢w©)¡a©l£3Ã¢w© ̄fÉ ¥?μ«w·x©l« 3⁄4wμY¡»1 μ« ¥a3⁄4w®w®¤ ̧[1 μ«w· £x« £x®w®w ̄Q¦x®w ̄Qμ£2¡a©/®w ̄a¦¬w ̧©±Ê ̄Q©®|o ̄Q©l¥a©«q¡Ã£¡aμ¦x«?À Ë¤¦x ̄ ¡a¢¤μ¥;®¤3⁄4w ̄a®5¦¥a© Å/©4®w ̄Q©l¥a©«q¡ £(± ©f3Ã¢¤£2«¤μ¥a±&§°¦x ̄ £x3⁄4|¡a¦±_£¡aμ3£x ̧[ ̧1 μ« 1⁄2x©l«¡Qμ[«¤·3£2«¤4·©«w©l ̄Q£x ̧[μ¥aμ[«w· Är ̄Q¥¶¡¶oz¦x ̄Ã|© ̄/Ì.¦x ̄Q«A3 ̧£23⁄4r¥¶©.®¤ ̄a©f|μ3l£¡Q©l¥lÀ ÍÎ¢w©L± ©¡a¢w¦| μ¥ ¬¤£x¥a©lK¦x«Iμ« 1⁄2x©l ̄¶¡Qμ[«w·-¡a¢w© ±4©l3Ã¢¤£x«wμ¥¶±+¦2§/ ̄a©f¥¶¦ ̧[3⁄4|¡Qμ[¦«"À ÍÎ¢w© £x®w®w ̄Q¦£3Ã¢N¢¤£x¥ μ[¡Q¥ ̄Q¦ ¦2¡Ã¥Îμ[«(¡a¢¤© ÏL3⁄4¤3© ¥¶1|¥¶¡a©l±1§°¦x ̄μ«¤|3⁄4r3 ¡aμ¦x« ¦x§ ®w ̄Q¦x®5¦¥aμ[¡aμ¦x«¤£x ̧%ÌL¦x ̄Q«(3 ̧£x3⁄4¤¥a©l¥lÀ ÐÈ© ¢r£m1⁄2x©.μ±4®w ̧[©l±4©«q¡a©f4¡a¢w©c«w©ÅÑ±4©f3Ã¢¤£2«wμ¥a±Òμ«-£3¥a1|¥»¡Q©±Ó3£x ̧[ ̧©l Ô/ ¶Õ Ö × À Ô/ ¶Õ Ö × 3⁄4r¥¶©f¥ μ«¤3 ̄Q©±4©l«¡Ã£2 ̧rμ[«¤w3⁄4¤3 ¡Qμ[¦«_¡a¦3£x3⁄4w·x±4©l«¡/μ[«¤3¦x±4®w ̧© ¡Q©c3 ̧£23⁄4r¥a£x ̧w¡a¢¤©¦x ̄Qμ©l¥lÀ ÁØ¥¶μ«|o · ̧[©Ù?3⁄4w«¤μY§°¦ ̄a±ÛÚq«¤¦Å ̧[©f|·x©4 ̄Q©®w ̄Q©l¥a©«q¡Q£2¡aμ¦x«È£2 ̧ ̧[¦Å.¥ © ́|μ¥»¡Qμ[«¤·K3 ̧£23⁄4¤¥a©l¥ ¡Q¦N¬r©_3⁄4¤¥a©l £¥ ¬¤£x3ÃÚ ·x ̄Q¦x3⁄4¤«¤Ú «w¦Å ̧[©f|·x© μ[«1⁄4¡a¢¤© 3¦x«¤¥¶¡a ̄Q3⁄4¤3 ¡aμ¦x«W¦x§L«w©ÅÜ®w ̄Q©l|μ3£2¡a©l¥lÀ Õ μ1⁄2x©« © ́w£2±4®w ̧©l¥/¦2§ £3¢wμ[·¢|oz ̧[©l1⁄2x© ̧r®w ̄Q©l|μ3£2¡a©xÙ¤Ô/ ¶Õ Ö ×I·x©«¤© ̄Ã£¡a©f¥ ̄Q© ̧£¡Q©lA¥a3⁄4w¬|oÝ3 ¦«¤3 ©l®|¡Q¥ Å¢¤μ3Ã¢ÈμY¡ ¡Q¢w©«£x¥aÚ|¥cμ[¡Q¥ ¢ 3⁄4w±_£x«K¡a©f£x3Ã¢w©l ̄ ¡a¦ «¤£2±4©À Õ ©«¤© ̄Ã£2 ̧μ¥Q£¡Qμ[¦«¤¥L¦x§ ®w ̄Q©l|μ[o 3l£¡Q©l¥;£2 ̄Q© ¡Q©l¥¶¡a©fI¬ 1I£¥¶Ú μ«w·(Þq3⁄4w©f¥»¡Qμ[¦«¤¥ ¦2§/¡a¢w©4¢ 3⁄4w±_£2«¡a©f£x3Ã¢w©l ̄lÀ Ô/ ¶Õ Ö ×¿·©«|o ©l ̄Q£2¡a©f¥ «w©ÅÓ3 ¦«¤3 ©l®|¡Q¥ £x«¤È·x©l«w© ̄Ã£2 ̧μ¥Q£¡Qμ[¦«¤¥ ÅμY¡Q¢~£ ®w ̄a©§°© ̄Q©«¤3© §°¦x ̄3¥aμ[±4®w ̧μ3 μ[¡»1xÀ ÐÈ© μ[ ̧ ̧3⁄4¤¥»¡Q ̄Q£2¡a©c¡a¢w© ¦®r©l ̄Q£2¡aμ¦x«-¦x§aÔ/ ¶Õ Ö ×¬q1-Å)£m14¦2§ 1⁄2£2 ̄Qμ[¦3⁄4¤¥/¥a©l¥Q¥aμ[¦«¤¥ μ«(Å¢wμ3Ã¢ £x3⁄4| ́|μ[ ̧μ£2 ̄Q1_®w ̄a©f|μ3£¡Q©l¥£x ̄a©;£23⁄4w¡a¦x±_£2¡aμ3£2 ̧ ̧1_μ[«q¡a ̄Q¦||3⁄4¤3©l £2«¤ ·x©l«w© ̄Ã£2 ̧μ¥¶©f!À


title: DALEX: explainers for complex predictive models

text: Predictive modeling is invaded by elastic, yet complex methods such as neural networks or ensembles (model stacking, boosting or bagging). Such methods are usually described by a large number of parameters or hyper parameters a price that one needs to pay for elasticity. The very number of parameters makes models hard to understand. This paper describes a consistent collection of explainers for predictive models, a.k.a. black boxes. Each explainer is a technique for exploration of a black box model. Presented approaches are model-agnostic, what means that they extract useful information from any predictive method despite its internal structure. Each explainer is linked with a specific aspect of a model. Some are useful in decomposing predictions, some serve better in understanding performance, while others are useful in understanding importance and conditional responses of a particular variable. Every explainer presented in this paper works for a single model or for a collection of models. In the latter case, models can be compared against each other. Such comparison helps to find strengths and weaknesses of different approaches and gives additional possibilities for model validation. Presented explainers are implemented in the DALEX package for R. They are based on a uniform standardized grammar of model exploration which may be easily extended. The current implementation supports the most popular frameworks for classification and regression.


title: Visualizing Semantic Table Annotations with TableMiner+

text: This paper describes an extension of the TableMiner system, an open source Semantic Table Interpretation system that annotates Web tables using Linked Data in an effective and efficient approach. It adds a graphical user interface to TableMiner, to facilitate the visualization and correction of automatically generated annotations. This makes TableMiner an ideal tool for the semi-automatic creation of high-quality semantic annotations on tabular data, which facilitates the publication of Linked Data on the Web.


title: Adaptive sequential Monte Carlo by means of mixture of experts

text: Selecting appropriately the proposal kernel of particle filters is an issue of significant importance, since a bad choice may lead to deterioration of the particle sample and, consequently, waste of computational power. In this paper we introduce a novel algorithm approximating adaptively the so-called optimal proposal kernel by a mixture of integrated curved exponential distributions with logistic weights. This family of distributions, referred to as mixtures of experts, is broad enough to be used in the presence of multi-modality or strongly skewed distributions. The mixtures are fitted, via Monte Carlo EM or online-EM methods, to the optimal kernel through minimization of the Kullback-Leibler divergence between the auxiliary target and instrumental distributions of the particle filter. At each iteration of the particle filter, the algorithm is required to solve only a single optimization problem for the whole particle sample, as opposed to existing methods solving one problem per particle. In addition, we illustrate in a simulation study how the method can be successfully applied to optimal filtering in nonlinear state-space models.


title: Preservation of nostril morphology in nasal base reduction

text: Asian patients often desire reduction of the base and alar lobules of the Asian mesorrhine nose. Sill excision is commonly used, but may result in an angular or notched nostril rim. We developed an internal method of alar base reduction involving triangle flaps for sill resection. This method avoids alar rim notching and teardrop deformity. Cinching sutures and double-layer closure avoid tension on the wound. We categorized the results in 50 patients (4 men, 46 women) who underwent surgery between November 2012 and August 2015 and who could be followed up for more than 3 months. The mean age of the subjects was 26.3 years and the mean follow-up period was 8.9 months. Forty patients underwent base reduction with the internal method, while ten with alar flare were treated with additional external resection. The mean reduction of the nostril sill width was 4.8 mm for both methods. In the subjects receiving flare resection, the mean reduction of the lateral alar width was 4.4 mm. There was no notching at the suture site. Complications included a short scar running obliquely under the sill in 13 patients and a trap door deformity in one patient. Nasal base reduction is widely performed, but subject to outcomes with abnormal nostril contour. We used triangle flaps to narrow the sill, and cinching sutures to prevent tension on the wound. Our methods prevent nostril notching and/or teardrop deformity. Scarring can occur, but can be reduced using cinching sutures for wound relaxation. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266.


title: A localization and tracking scheme for target gangs based on big data of Wi-Fi locations

text: The modeling and analysis of target gangs’ usual haunts plays a very important role in law enforcement and supervision. Existing localization and tracking schemes usually need to deploy a large number of monitoring devices or continue to move with the target, which lead to high cost. In this paper, a localization and tracking scheme based on big data of Wi-Fi locations is proposed. Firstly, the characteristic of the smart mobile device that continuously broadcasts probe request frames is used to obtain its MAC address and Wi-Fi connection history. Secondly, the service set identifier (SSID) in the Wi-Fi connection history of smart mobile devices held by the target gangs are queried from the Wi-Fi location database, and the target gangs’ usual haunts are gained by statistical analysis. Lastly, monitoring devices are deployed in these places, and most of the target gangs’ activity pattern are known with only a small number of monitoring devices. The results of the related experimental tests demonstrate the feasibility of the proposed scheme.


title: DO INVESTMENT-CASH FLOW SENSITIVITIES PROVIDE USEFUL MEASURES OF FINANCING CONSTRAINTS?*

text: No. This paper investigates the relationship between financing constraints and investment-cash flow sensitivities by analyzing the firms identified by Fazzari, Hubbard, and Petersen as having unusually high investment-cash flow sensitivities. We find that firms that appear less financially constrained exhibit significantly greater sensitivities than firms that appear more financially constrained. We find this pattern for the entire sample period, subperiods, and individual years. These results (and simple theoretical arguments) suggest that higher sensitivities cannot be interpreted as evidence that firms are more financially constrained. These findings call into question the interpretation of most previous research that uses this methodology.


title: The Role of Gender Stereotypes in Perceptions of Entrepreneurs and Intentions to Become an Entrepreneur

text: In this study we examine the role of socially constructed gender stereotypes in entrepreneurship and their influence on men and women’s entrepreneurial intentions. Data on characteristics of males, females, and entrepreneurs were collected from young adults in three countries. As hypothesized, entrepreneurs were perceived to have predominantly masculine characteristics. Additional results revealed that although both men and women perceive entrepreneurs to have characteristics similar to those of males (masculine genderrole stereotype), only women also perceived entrepreneurs and females as having similar characteristics (feminine gender-role stereotype). Further, though men and women did not differ in their entrepreneurial intentions, those who perceived themselves as more similar to males (high on male gender identification) had higher entrepreneurial intentions than those who saw themselves as less similar to males (low male gender identification). No such difference was found for people who saw themselves as more or less similar to females (female gender identification). The results were consistent across the three countries. Practical implications and directions for future research are discussed.


title: Diced cartilage grafts in rhinoplasty surgery: current techniques and applications.

text:
BACKGROUND
The author has used diced cartilage grafts in nasal surgery for more than 30 years. However, the number of cases and the variety of techniques have increased dramatically over the past 6 years.

METHODS
The author uses three methods of diced cartilage in rhinoplasty surgery: diced cartilage, diced cartilage wrapped in fascia (with fascia then sewn closed), and diced cartilage covered with fascia (with the recipient area covered with fascia after graft placement). The constructs are highly varied to fit the specific defect. Pieces of diced cartilage without any fascia can be placed in pockets in the peripyriform area, radix, or alongside structural rib grafts.

RESULTS
Over a 2-year period, the author treated 546 rhinoplasty cases in which 79 patients (14 percent) had a total of 91 diced cartilage grafts. There were 34 primary and 45 secondary operations involving the radix (n = 11), half-length grafts (n = 14), full-length dorsum (n = 43), peripyriform (n = 16), infralobule (n = 4), and lateral wall (n = 3). All charts were reviewed for the 256 rhinoplasties performed in 2006 of which 30 patients had 35 diced cartilage grafts. With a median follow-up of 19 months (range, 13 to 25 months), two patients had had revisions unrelated to their diced cartilage grafts. The three most common technical problems were overcorrection, visibility, and junctional step-offs.

CONCLUSIONS
Diced cartilage grafts are a valuable addition to rhinoplasty surgery. They are highly flexible and useful throughout the nose. Their use simplifies one of the greatest challenges in all of rhinoplasty--dorsal augmentation. Complications have been relatively minor and their correction relatively simple.


title: STRADS: a distributed framework for scheduled model parallel machine learning

text: Machine learning (ML) algorithms are commonly applied to big data, using distributed systems that partition the data across machines and allow each machine to read and update all ML model parameters --- a strategy known as data parallelism. An alternative and complimentary strategy, model parallelism, partitions the model parameters for non-shared parallel access and updates, and may periodically repartition the parameters to facilitate communication. Model parallelism is motivated by two challenges that data-parallelism does not usually address: (1) parameters may be dependent, thus naive concurrent updates can introduce errors that slow convergence or even cause algorithm failure; (2) model parameters converge at different rates, thus a small subset of parameters can bottleneck ML algorithm completion. We propose scheduled model parallelism (SchMP), a programming approach that improves ML algorithm convergence speed by efficiently scheduling parameter updates, taking into account parameter dependencies and uneven convergence. To support SchMP at scale, we develop a distributed framework STRADS which optimizes the throughput of SchMP programs, and benchmark four common ML applications written as SchMP programs: LDA topic modeling, matrix factorization, sparse least-squares (Lasso) regression and sparse logistic regression. By improving ML progress per iteration through SchMP programming whilst improving iteration throughput through STRADS we show that SchMP programs running on STRADS outperform non-model-parallel ML implementations: for example, SchMP LDA and SchMP Lasso respectively achieve 10x and 5x faster convergence than recent, well-established baselines.


title: Image Stylization using Depth Information for Hatching and Engraving effects by

text: In this thesis inspired by artists such as Gustave Doré and copperplate engravers, we present a new approach for producing a sketch-type hatching effect as well as engraving art seen in the US dollar bills using depth maps as input. The approach uses the acquired depth to generate pen-and-ink type of strokes that exaggerate surface characteristics of faces and objects. The background on this area is presented with an emphasis on line drawing, pen-and-ink illustrations, engraving art and other artistic styles. The patterns generated are composed of parallel lines that change their directionality smoothly while avoiding intersections between them. The style communicates tone and surface characteristics using curve deformation, thickness, density, and spacing while at the same time adding crosshatching effects. This is achieved with our algorithmic approach which uses the data to perform operations that deforms patterns. The components of our methodology and algorithms are detailed, as well as the equations that govern these effects. This work also presents an array of different results by varying parameters or by rendering variations.


title: Evaluating Personalization and Persuasion in E-Commerce

text: The use of personalization and persuasion has been shown to optimize customers’ shopping experience in e-commerce. This study aims to identify the personalization methods and persuasive principles that make an ecommerce company successful. Using Amazon as a case study, we evaluated the personalization methods implemented using an existing process framework. We also applied the PSD model to Amazon to evaluate the persuasive principles it uses. Our results show that all the principles of the PSD model were implemented in Amazon. This study can serve as a guide to e-commerce businesses and software developers for building or improving existing e-commerce platforms.


title: DBpedia: A Multilingual Cross-domain Knowledge Base

text: The DBpedia project extracts structured information from Wikipedia editions in 97 different languages and combines this information into a large multi-lingual knowledge base covering many specific domains and general world knowledge. The knowledge base contains textual descriptions (titles and abstracts) of concepts in up to 97 languages. It also contains structured knowledge that has been extracted from the infobox systems of Wikipedias in 15 different languages and is mapped onto a single consistent ontology by a community effort. The knowledge base can be queried using a structured query language and all its data sets are freely available for download. In this paper, we describe the general DBpedia knowledge base and extended data sets that specifically aim at supporting computational linguistics tasks. These task include Entity Linking, Word Sense Disambiguation, Question Answering, Slot Filling and Relationship Extraction. These use cases are outlined, pointing at added value that the structured data of DBpedia provides.


title: Dynamic Pooling and Unfolding Recursive Autoencoders for Paraphrase Detection

text: Paraphrase detection is the task of examining two sentences and determining whether they have the same meaning. In order to obtain high accuracy on this task, thorough syntactic and semantic analysis of the two statements is needed. We introduce a method for paraphrase detection based on recursive autoencoders (RAE). Our unsupervised RAEs are based on a novel unfolding objective and learn feature vectors for phrases in syntactic trees. These features are used to measure the wordand phrase-wise similarity between two sentences. Since sentences may be of arbitrary length, the resulting matrix of similarity measures is of variable size. We introduce a novel dynamic pooling layer which computes a fixed-sized representation from the variable-sized matrices. The pooled representation is then used as input to a classifier. Our method outperforms other state-of-the-art approaches on the challenging MSRP paraphrase corpus.


title: A Comparison of Vector-based Representations for Semantic Composition

text: In this paper we address the problem of modeling compositional meaning for phrases and sentences using distributional methods. We experiment with several possible combinations of representation and composition, exhibiting varying degrees of sophistication. Some are shallow while others operate over syntactic structure, rely on parameter learning, or require access to very large corpora. We find that shallow approaches are as good as more computationally intensive alternatives with regards to two particular tests: (1) phrase similarity and (2) paraphrase detection. The sizes of the involved training corpora and the generated vectors are not as important as the fit between the meaning representation and compositional method.


title: Learning Discriminative Projections for Text Similarity Measures

text: Traditional text similarity measures consider each term similar only to itself and do not model semantic relatedness of terms. We propose a novel discriminative training method that projects the raw term vectors into a common, low-dimensional vector space. Our approach operates by finding the optimal matrix to minimize the loss of the pre-selected similarity function (e.g., cosine) of the projected vectors, and is able to efficiently handle a large number of training examples in the highdimensional space. Evaluated on two very different tasks, cross-lingual document retrieval and ad relevance measure, our method not only outperforms existing state-of-the-art approaches, but also achieves high accuracy at low dimensions and is thus more efficient.


title: Ground-Penetrating Radar Theory and Application of Thin-Bed Offset-Dependent Reflectivity

text: Offset-dependent reflectivity or amplitude-variationwith-offset AVO analysis of ground-penetrating radar GPR data may improve the resolution of subsurface dielectric permittivity estimates. A horizontally stratified medium has a limiting layer thickness below which thin-bed AVO analysis is necessary. For a typical GPR signal, this limit is approximately 0.75 of the characteristic wavelength of the signal. Our approach to modeling the GPR thin-bed response is a broadband, frequency-dependent computation that utilizes an analytical solution to the three-interface reflectivity and is easy to implement for either transverse electric TE or transverse magnetic TM polarizations. The AVO curves for TE and TM modes differ significantly. In some cases, constraining the interpretation using both TE and TM data is critical. In two field examples taken from contaminated-site characterization data, we find quantitative thin-bed modeling agrees with the GPR field data and available characterization data.


title: Multi-fiber Networks for Video Recognition

text: In this paper, we aim to reduce the computational cost of spatio-temporal deep neural networks, making them run as fast as their 2D counterparts while preserving state-of-the-art accuracy on video recognition benchmarks. To this end, we present the novel Multi-Fiber architecture that slices a complex neural network into an ensemble of lightweight networks or fibers that run through the network. To facilitate information flow between fibers we further incorporate multiplexer modules and end up with an architecture that reduces the computational cost of 3D networks by an order of magnitude, while increasing recognition performance at the same time. Extensive experimental results show that our multi-fiber architecture significantly boosts the efficiency of existing convolution networks for both image and video recognition tasks, achieving state-of-the-art performance on UCF-101, HMDB-51 and Kinetics datasets. Our proposed model requires over 9× and 13× less computations than the I3D [1] and R(2+1)D [2] models, respectively, yet providing higher accuracy.


title: Evolved GANs for generating pareto set approximations

text: In machine learning, generative models are used to create data samples that mimic the characteristics of the training data. Generative adversarial networks (GANs) are neural-network based generator models that have shown their capacity to produce realistic samples in different domains. In this paper we propose a neuro-evolutionary approach for evolving deep GAN architectures together with the loss function and generator-discriminator synchronization parameters. We also propose the problem of Pareto set (PS) approximation as a suitable benchmark to evaluate the quality of neural-network based generators in terms of the accuracy of the solutions they generate. The covering of the Pareto front (PF) by the generated solutions is used as an indicator of the mode-collapsing behavior of GANs. We show that it is possible to evolve GANs that generate good PS approximations. Our method scales to up to 784 variables and that it is capable to create architecture transferable across dimensions and functions.


title: Where Do Creative Interactions Come From? The Role of Tie Content and Social Networks

text: U the determinants of creativity at the individual and organizational level has been the focus of a long history of research in various disciplines from the social sciences, but little attention has been devoted to studying creativity at the dyadic level. Why are some dyadic interactions more likely than others to trigger the generation of novel and useful ideas in organizations? As dyads conduit both knowledge and social forces, they offer an ideal setting to disentangle the effects of knowledge diversity, tie strength, and network structure on the generation of creative thoughts. This paper not only challenges the current belief that sporadic and distant dyadic relationships (weak ties) foster individual creativity but also argues that diverse and strong ties facilitate the generation of creative ideas. From a knowledge viewpoint, our results suggest that ties that transmit a wide (rather than narrow) set of knowledge domains (within the same tie) favor creative idea generation if exchanges occur with sufficient frequency. From a social perspective, we find that strong ties serve as effective catalysts for the generation of creative ideas when they link actors who are intrinsically motivated to work closely together. Finally, this paper also shows that dyadic network cohesion (i.e., the connections from the focal dyad to common contacts) does not always hinder the generation of creative ideas. Our empirical evidence suggests that when cohesion exceeds its average levels, it becomes detrimental to creative idea generation. Hypotheses are tested in a sociometric study conducted within the development department of a software firm.


title: Validation, verification, and testing techniques throughout the life cycle of a simulation study

text: Life cycle validation, verification, and testing (VV&T) is extremely important for the success of a simulation study. This paper surveys current software VV&T techniques and current simulation model VV&T techniques and describes how they can all be applied throughout the life cycle of a simulation study. The processes and credibility assessment stages of the life cycle are described and the applicability of the VV&T techniques for each stage is stated.


title: Perceived qualities of smart wearables: determinants of user acceptance

text: Wearable computers are one of the new technologies that are expected to be a part of users' lives extensively in near future. While some of the users have positive attitudes towards these new products, some users may reject to use them due to different reasons. User experience is subjective, and effected by various parameters. Among these the first impression, namely the perceived qualities has an important impact on product acceptance. This paper aims to explore the perceived qualities of wearables and define the relations between them. An empirical study is conducted, to find out the hierarchy and meaningful relationships between the perceived qualities of smart wearables. The study is based on personal construct theory and data is presented by Cross-Impact Analysis. The patterns behind affection and affected qualities are explored to understand the design requirements for the best integration of wearables into daily lives.


title: Let's talk about it: evaluating contributions through discussion in GitHub

text: Open source software projects often rely on code contributions from a wide variety of developers to extend the capabilities of their software. Project members evaluate these contributions and often engage in extended discussions to decide whether to integrate changes. These discussions have important implications for project management regarding new contributors and evolution of project requirements and direction. We present a study of how developers in open work environments evaluate and discuss pull requests, a primary method of contribution in GitHub, analyzing a sample of extended discussions around pull requests and interviews with GitHub developers. We found that developers raised issues around contributions over both the appropriateness of the problem that the submitter attempted to solve and the correctness of the implemented solution. Both core project members and third-party stakeholders discussed and sometimes implemented alternative solutions to address these issues. Different stakeholders also influenced the outcome of the evaluation by eliciting support from different communities such as dependent projects or even companies. We also found that evaluation outcomes may be more complex than simply acceptance or rejection. In some cases, although a submitter's contribution was rejected, the core team fulfilled the submitter's technical goals by implementing an alternative solution. We found that the level of a submitter's prior interaction on a project changed how politely developers discussed the contribution and the nature of proposed alternative solutions.


To Kill a Centrifuge A Technical Analysis of What Stuxnet ’ s Creators Tried to Achieve


title: Fixed-Rate Compressed Floating-Point Arrays

text: Current compression schemes for floating-point data commonly take fixed-precision values and compress them to a variable-length bit stream, complicating memory management and random access. We present a fixed-rate, near-lossless compression scheme that maps small blocks of 4d values in d dimensions to a fixed, user-specified number of bits per block, thereby allowing read and write random access to compressed floating-point data at block granularity. Our approach is inspired by fixed-rate texture compression methods widely adopted in graphics hardware, but has been tailored to the high dynamic range and precision demands of scientific applications. Our compressor is based on a new, lifted, orthogonal block transform and embedded coding, allowing each per-block bit stream to be truncated at any point if desired, thus facilitating bit rate selection using a single compression scheme. To avoid compression or decompression upon every data access, we employ a software write-back cache of uncompressed blocks. Our compressor has been designed with computational simplicity and speed in mind to allow for the possibility of a hardware implementation, and uses only a small number of fixed-point arithmetic operations per compressed value. We demonstrate the viability and benefits of lossy compression in several applications, including visualization, quantitative data analysis, and numerical simulation.


title: Literature Review of Teamwork Models

text: Both human collaboration and software agent collaboration have been thoroughly studied, but there is relatively little research on hybrid human-agent teamwork. Some research has identified the roles that agents could play in hybrid teams: supporting individual team members, being a teammate, or supporting the team as a whole [99]. Some other work [57] has investigated trust concepts as the fundamental building block for effective human-agent teamwork or posited the types of shared knowledge that promote mutual understanding between cooperating humans and agents [9, 68]. However, many of the facets of human agent teamwork models, such as communication protocols for forming mutual intelligibility, performing team monitoring to assess progress, forming joint goals, addressing task interdependencies in hybrid teamwork are still unexplored. In this report, we address the following questions: 1. what factors affect human team task performance and cognition? 2. how can agent coordination mechanisms be adapted for human-agent teams? 3. with current technologies, what roles can agents successfully fill in hybrid human-agent teams? 4. what are the barriers to human-agent interaction?


title: Transformational leadership in nursing: a concept analysis.

text:
AIM
To analyse the concept of transformational leadership in the nursing context.

BACKGROUND
Tasked with improving patient outcomes while decreasing the cost of care provision, nurses need strategies for implementing reform in health care and one promising strategy is transformational leadership. Exploration and greater understanding of transformational leadership and the potential it holds is integral to performance improvement and patient safety.

DESIGN
Concept analysis using Walker and Avant's (2005) concept analysis method.

DATA SOURCES
PubMed, CINAHL and PsychINFO.

METHODS
This report draws on extant literature on transformational leadership, management, and nursing to effectively analyze the concept of transformational leadership in the nursing context.

IMPLICATIONS FOR NURSING
This report proposes a new operational definition for transformational leadership and identifies model cases and defining attributes that are specific to the nursing context. The influence of transformational leadership on organizational culture and patient outcomes is evident. Of particular interest is the finding that transformational leadership can be defined as a set of teachable competencies. However, the mechanism by which transformational leadership influences patient outcomes remains unclear.

CONCLUSION
Transformational leadership in nursing has been associated with high-performing teams and improved patient care, but rarely has it been considered as a set of competencies that can be taught. Also, further research is warranted to strengthen empirical referents; this can be done by improving the operational definition, reducing ambiguity in key constructs and exploring the specific mechanisms by which transformational leadership influences healthcare outcomes to validate subscale measures.


OF THE DISSERTATION Statistical Nested Sensor Array Signal Processing by Keyong Han Doctor of Philosophy in Electrical Engineering Washington University in St. Louis, 2015 Professor Arye Nehorai, Chair Source number detection and direction-of-arrival (DOA) estimation are two major applications of sensor arrays. Both applications are often confined to the use of uniform linear arrays (ULAs), which is expensive and difficult to yield wide aperture. Besides, a ULA with N scalar sensors can resolve at most N − 1 sources. On the other hand, a systematic approach was recently proposed to achieve O(N) degrees of freedom (DOFs) using O(N) sensors based on a nested array, which is obtained by combining two or more ULAs with successively increased spacing. This dissertation will focus on a fundamental study of statistical signal processing of nested arrays. Five important topics are discussed, extending the existing nested-array strategies to more practical scenarios. Novel signal models and algorithms are proposed. First, based on the linear nested array, we consider the problem for wideband Gaussian sources. To employ the nested array to the wideband case, we propose effective strategies to apply nested-array processing to each frequency component, and combine all the spectral xi information of various frequencies to conduct the detection and estimation. We then consider the practical scenario with distributed sources, which considers the spreading phenomenon of sources. Next, we investigate the self-calibration problem for perturbed nested arrays, for which existing works require certain modeling assumptions, for example, an exactly known array geometry, including the sensor gain and phase. We propose corresponding robust algorithms to estimate both the model errors and the DOAs. The partial Toeplitz structure of the covariance matrix is employed to estimate the gain errors, and the sparse total least squares is used to deal with the phase error issue. We further propose a new class of nested vector-sensor arrays which is capable of significantly increasing the DOFs. This is not a simple extension of the nested scalar-sensor array. Both the signal model and the signal processing strategies are developed in the multidimensional sense. Based on the analytical results, we consider two main applications: electromagnetic (EM) vector sensors and acoustic vector sensors. Last but not least, in order to make full use of the available limited valuable data, we propose a novel strategy, which is inspired by the jackknifing resampling method. Exploiting numerous iterations of subsets of the whole data set, this strategy greatly improves the results of the existing source number detection and DOA estimation methods.


title: PZT-Actuated and -Sensed Resonant Micromirrors with Large Scan Angles Applying Mechanical Leverage Amplification for Biaxial Scanning

text: This article presents design, fabrication and characterization of lead zirconate titanate (PZT)-actuated micromirrors, which enable extremely large scan angle of up to 106° and high frequency of 45 kHz simultaneously. Besides the high driving torque delivered by PZT actuators, mechanical leverage amplification has been applied for the micromirrors in this work to reach large displacements consuming low power. Additionally, fracture strength and failure behavior of poly-Si, which is the basic material of the micromirrors, have been studied to optimize the designs and prevent the device from breaking due to high mechanical stress. Since comparing to using biaxial micromirror, realization of biaxial scanning using two independent single-axial micromirrors shows considerable advantages, a setup combining two single-axial micromirrors for biaxial scanning and the results will also be presented in this work. Moreover, integrated piezoelectric position sensors are implemented within the micromirrors, based on which closed-loop control has been developed and studied.


title: Streaming Hierarchical Video Segmentation

text: The use of video segmentation as an early processing step in video analysis lags behind the use of image segmentation for image analysis, despite many available video segmentation methods. A major reason for this lag is simply that videos are an order of magnitude bigger than images; yet most methods require all voxels in the video to be loaded into memory, which is clearly prohibitive for even medium length videos. We address this limitation by proposing an approximation framework for streaming hierarchical video segmentation motivated by data stream algorithms: each video frame is processed only once and does not change the segmentation of previous frames. We implement the graph-based hierarchical segmentation method within our streaming framework; our method is the first streaming hierarchical video segmentation method proposed. We perform thorough experimental analysis on a benchmark video data set and longer videos. Our results indicate the graph-based streaming hierarchical method outperforms other streaming video segmentation methods and performs nearly as well as the full-video hierarchical graph-based method.


title: Design and Implementation of Power Converters for Hybrid Wind-Solar Energy Conversion System with an Implementation of MPPT

text: This paper presents the design and implementation of power converters for wind conversion systems. The power converter can not only transfer the power from a wind generator, but also improve the stability and safety of the system. The proposed system consists of a Permanent magnet synchronous generator (PMSG); a DC/DC boosts converter, a bi-directional DC/DC converter and a full-bridge DC/AC inverter. The wind generator is the main power source of the system, and the battery is used for energy storage and power compensation to recover the natural irregularity of the wind power. In this paper presents a new system configuration of the front-end rectifier stage for a hybrid wind or photo voltaic energy system. The configuration allows the two sources to supply the load separately or simultaneously, depending on the availability of energy sources. The inherent nature of this cuk-scpic fused converter, additional input filters are not necessary to filter out high frequency harmonic content is determinant for the generator life span, heating issue and efficiency. The fused multi-input rectifier stage also allows maximum power from the wind and sun. When it is available an adaptive MPPT algorithm will be used for photo voltaic (PV) system. Operational analysis of the proposed system, will discoursed in this paper simulation results are given to highlight the merit of the proposed circuit Index terms —Wind generator, PV and Fuel Cells, Bi-directional DC/DC converter, full-bridge DC/AC inverter, MPPT.


title: A Novel, Planar, and Compact Crossover Design for Dual-Band Applications

text: This paper presents, for the first time, the novel design of a dual-band crossover. The proposed circuit features planar implementation with moderate line impedance, compact layout, and widely separated frequency bands. Moreover, simple and explicit closed-form expressions are available for the exact evaluation of the line impedances. For verification, both simulated and measured results of a microstrip crossover operating at 800 MHz and 1.2 GHz are given.


title: Practical condition synchronization for transactional memory

text: Few transactional memory implementations allow for condition synchronization among transactions. The problems are many, most notably the lack of consensus about a single appropriate linguistic construct, and the lack of mechanisms that are compatible with hardware transactional memory. In this paper, we introduce a broadly useful mechanism for supporting condition synchronization among transactions. Our mechanism supports a number of linguistic constructs for coordinating transactions, and does so without introducing overhead on in-flight hardware transactions. Experiments show that our mechanisms work well, and that the diversity of linguistic constructs allows programmers to chose the technique that is best suited to a particular application.


title: Wasserstein Generative Adversarial Network

text: Recent advances in deep generative models give us new perspective on modeling highdimensional, nonlinear data distributions. Especially the GAN training can successfully produce sharp, realistic images. However, GAN sidesteps the use of traditional maximum likelihood learning and instead adopts an two-player game approach. This new training behaves very differently compared to ML learning. There are still many remaining problem of GAN training. In this thesis, we gives a comprehensive review of recently published methods or analysis on GAN training, especially the Wasserstein GAN and FlowGAN model. We also discuss the limitation of the later model and use this as the motivation to propose a novel generator architecture using mixture models. Furthermore, we also modify the discriminator architecture using similar ideas to allow ’personalized’ guidance. We refer the generator mixture model as Mixflow and mixture of discriminators as ’personalized GAN’ (PGAN). In experiment chapter, we demonstrate their performance advantages using toy examples compared to single flow model. In the end, we test their performance on MNIST dataset and the Mixflow model not only achieves the best log likelihood but also produce reasonable images compared to state-of-art DCGAN generation.


title: SparkClouds: Visualizing Trends in Tag Clouds

text: Tag clouds have proliferated over the web over the last decade. They provide a visual summary of a collection of texts by visually depicting the tag frequency by font size. In use, tag clouds can evolve as the associated data source changes over time. Interesting discussions around tag clouds often include a series of tag clouds and consider how they evolve over time. However, since tag clouds do not explicitly represent trends or support comparisons, the cognitive demands placed on the person for perceiving trends in multiple tag clouds are high. In this paper, we introduce SparkClouds, which integrate sparklines into a tag cloud to convey trends between multiple tag clouds. We present results from a controlled study that compares SparkClouds with two traditional trend visualizations-multiple line graphs and stacked bar charts-as well as Parallel Tag Clouds. Results show that SparkClouds' ability to show trends compares favourably to the alternative visualizations.


title: A survey of glove-based input

text: Clumsy intermediary devices constrain our interaction with computers and their applications. Glove-based input devices let us apply our manual dexterity to the task. We provide a basis for understanding the field by describing key hand-tracking technologies and applications using glove-based input. The bulk of development in glove-based input has taken place very recently, and not all of it is easily accessible in the literature. We present a cross-section of the field to date. Hand-tracking devices may use the following technologies: position tracking, optical tracking, marker systems, silhouette analysis, magnetic tracking or acoustic tracking. Actual glove technologies on the market include: Sayre glove, MIT LED glove, Digital Data Entry Glove, DataGlove, Dexterous HandMaster, Power Glove, CyberGlove and Space Glove. Various applications of glove technologies include projects into the pursuit of natural interfaces, systems for understanding signed languages, teleoperation and robotic control, computer-based puppetry, and musical performance.<<ETX>>


title: "Put-that-there": Voice and gesture at the graphics interface

text:
Recent technological advances in connected-speech recognition and position sensing in space have encouraged the notion that voice and gesture inputs at the graphics interface can converge to provide a concerted, natural user modality.
 The work described herein involves the user commanding simple shapes about a large-screen graphics display surface. Because voice can be augmented with simultaneous pointing, the free usage of pronouns becomes possible, with a corresponding gain in naturalness and economy of expression. Conversely, gesture aided by voice gains precision in its power to reference.


title: The See-Through System: A VANET-enabled assistant for overtaking maneuvers

text: The use of wireless technology based on Vehicular Ad hoc Networks (VANETs) for information exchange can influence the drivers' behavior towards improving driving performance and reducing road accidents. This information can even be more relevant if it is presented as a video stream. In this paper we propose a system that relies on VANET and video-streaming technology: the See-Through System (STS). The system enhances driver's visibility and supports the driver's overtaking decision in challenging situations, such as overtaking a vision-obstructing vehicle. The use of the See-Through System provides the driver with an additional tool for determining if traffic conditions permit starting an overtaking maneuver thus reducing the risk of overtaking. We tested the See-Through System on an experimental vehicle on the road as well as in the context of a driving simulator for real world environment. Results are promising, since the use of the 802.11p standard wireless communication protocol allows a vehicle-to-vehicle connection without significant delay and the totality of the participants regarded the information provided by the STS as useful.


title: The Collateral Damage of Internet Censorship by DNS Injection

text: Some ISPs and governments (most notably the Great Firewall of China) use DNS injection to block access to “unwanted” websites. The censorship tools inspect DNS queries near the ISP’s boundary routers for sensitive domain keywords and injecting forged DNS responses, blocking the users from accessing censored sites, such as twitter.com and facebook. com. Unfortunately this causes large scale collateral damage, affecting communication beyond the censored networks when outside DNS traffic traverses censored links. In this paper, we analyze the causes of the collateral damages comprehensively and measure the Internet to identify the injecting activities and their effect. We find 39 ASes in China injecting forged replies even for transit DNS traffic, and 26% of 43,000 measured open resolvers outside China, distributed in 109 countries, may suffer some collateral damage. Different from previous work, we find that most collateral damage arises from resolvers querying TLD name servers who’s transit passes through China rather than effects due to root servers (F, I, J) located in China.


title: The Dreaming Variational Autoencoder for Reinforcement Learning Environments

text: Reinforcement learning has shown great potential in generalizing over raw sensory data using only a single neural network for value optimization. There are several challenges in the current state-of-the-art reinforcement learning algorithms that prevent them from converging towards the global optima. It is likely that the solution to these problems lies in shortand long-term planning, exploration and memory management for reinforcement learning algorithms. Games are often used to benchmark reinforcement learning algorithms as they provide a flexible, reproducible, and easy to control environment. Regardless, few games feature a state-space where results in exploration, memory, and planning are easily perceived. This paper presents The Dreaming Variational Autoencoder (DVAE), a neural network based generative modeling architecture for exploration in environments with sparse feedback. We further present Deep Maze, a novel and flexible maze engine that challenges DVAE in partial and fully-observable state-spaces, long-horizon tasks, and deterministic and stochastic problems. We show initial findings and encourage further work in reinforcement learning driven by generative exploration.


title: The Double Dance of Agency : a socio-theoretic account of how machines and humans interact

text: The nature of the relationship between information technology (IT) and organizations has been a long-standing debate in the Information Systems literature. Does IT shape organizations, or do people in organisations control how IT is used? To formulate the question a little differently: does agency (the capacity to make a difference) lie predominantly with machines (computer systems) or humans (organisational actors)? Many proposals for a middle way between the extremes of technological and social determinism have been put advanced; in recent years researchers oriented towards social theories have focused on structuration theory and (lately) actor network theory. These two theories, however, adopt different and incompatible views of agency. Thus, structuration theory sees agency as exclusively a property of humans, whereas the principle of general symmetry in actor network theory implies that machines may also be agents. Drawing on critiques of both structuration theory and actor network theory, this paper develops a theoretical account of the interaction between human and machine agency: the double dance of agency. The account seeks to contribute to theorisation of the relationship between technology and organisation by recognizing both the different character of human and machine agency, and the emergent properties of their interplay.


title: MobileFaceNets: Efficient CNNs for Accurate Real-time Face Verification on Mobile Devices

text: We present a class of extremely efficient CNN models, MobileFaceNets, which use less than 1 million parameters and are specifically tailored for high-accuracy real-time face verification on mobile and embedded devices. We first make a simple analysis on the weakness of common mobile networks for face verification. The weakness has been well overcome by our specifically designed MobileFaceNets. Under the same experimental conditions, our MobileFaceNets achieve significantly superior accuracy as well as more than 2 times actual speedup over MobileNetV2. After trained by ArcFace loss on the refined MS-Celeb-1M, our single MobileFaceNet of 4.0MB size achieves 99.55% accuracy on LFW and 92.59% TAR@FAR1e-6 on MegaFace, which is even comparable to state-of-the-art big CNN models of hundreds MB size. The fastest one of MobileFaceNets has an actual inference time of 18 milliseconds on a mobile phone. For face verification, MobileFaceNets achieve significantly improved efficiency over previous state-of-the-art mobile CNNs.


title: Forensic Image Inspection Assisted by Deep Learning

text:
Investigations on the charge of possessing child pornography usually require manual forensic image inspection in order to collect evidence. When storage devices are confiscated, law enforcement authorities are hence often faced with massive image datasets which have to be screened within a limited time frame. As the ability to concentrate and time are highly limited factors of a human investigator, we believe that intelligent algorithms can effectively assist the inspection process by rearranging images based on their content. Thus, more relevant images can be discovered within a shorter time frame, which is of special importance in time-critical investigations of triage character.
 While currently employed techniques are based on black- and whitelisting of known images, we propose to use deep learning algorithms trained for the detection of pornographic imagery, as they are able to identify new content. In our approach, we evaluated three state-of-the-art neural networks for the detection of pornographic images and employed them to rearrange simulated datasets of 1 million images containing a small fraction of pornographic content. The rearrangement of images according to their content allows a much earlier detection of relevant images during the actual manual inspection of the dataset, especially when the percentage of relevant images is low. With our approach, the first relevant image could be discovered between positions 8 and 9 in the rearranged list on average. Without using our approach of image rearrangement, the first relevant image was discovered at position 1,463 on average.


title: EgoCap: Egocentric Marker-less Motion Capture with Two Fisheye Cameras

text: Marker-based and marker-less optical skeletal motion-capture methods use an outside-in arrangement of cameras placed around a scene, with viewpoints converging on the center. They often create discomfort with marker suits, and their recording volume is severely restricted and often constrained to indoor scenes with controlled backgrounds. Alternative suit-based systems use several inertial measurement units or an exoskeleton to capture motion with an inside-in setup, i.e. without external sensors. This makes capture independent of a confined volume, but requires substantial, often constraining, and hard to set up body instrumentation. Therefore, we propose a new method for real-time, marker-less, and egocentric motion capture: estimating the full-body skeleton pose from a lightweight stereo pair of fisheye cameras attached to a helmet or virtual reality headset - an optical inside-in method, so to speak. This allows full-body motion capture in general indoor and outdoor scenes, including crowded scenes with many people nearby, which enables reconstruction in larger-scale activities. Our approach combines the strength of a new generative pose estimation framework for fisheye views with a ConvNet-based body-part detector trained on a large new dataset. It is particularly useful in virtual reality to freely roam and interact, while seeing the fully motion-captured virtual body.


title: Deep Cosine Metric Learning for Person Re-identification

text: Metric learning aims to construct an embedding where two extracted features corresponding to the same identity are likely to be closer than features from different identities. This paper presents a method for learning such a feature space where the cosine similarity is effectively optimized through a simple re-parametrization of the conventional softmax classification regime. At test time, the final classification layer can be stripped from the network to facilitate nearest neighbor queries on unseen individuals using the cosine similarity metric. This approach presents a simple alternative to direct metric learning objectives such as siamese networks that have required sophisticated pair or triplet sampling strategies in the past. The method is evaluated on two large-scale pedestrian re-identification datasets where competitive results are achieved overall. In particular, we achieve better generalization on the test set compared to a network trained with triplet loss.


title: Grail: context-aware fixing of concurrency bugs

text: Writing efficient synchronization for multithreaded programs is notoriously hard. The resulting code often contains subtle concurrency bugs. Even worse, many bug fixes introduce new bugs. A classic example, seen widely in practice, is deadlocks resulting from fixing of an atomicity violation. These complexities have motivated the development of automated fixing techniques. Current techniques generate fixes that are typically conservative, giving up on available parallelism. Moreover, some of the techniques cannot guarantee the correctness of a fix, and may introduce deadlocks similarly to manual fix, whereas techniques that ensure correctness do so at the expense of even greater performance loss. We present Grail, a novel fixing algorithm that departs from previous techniques by simultaneously providing both correctness and optimality guarantees. Grail synthesizes bug-free yet optimal lock-based synchronization. To achieve this, Grail builds an analysis model of the buggy code that is both contextual, distinguishing different aliasing contexts to ensure efficiency, and global, accounting for the entire synchronization behavior of the involved threads to ensure correctness. Evaluation of Grail on 12 bugs from popular codebases confirms its practical advantages, especially compared with existing techniques: Grail patches are, in general, >=40% more efficient than the patches produced by other techniques, and incur only 2% overhead.


title: Multiple Trench Split-gate SOI LDMOS Integrated With Schottky Rectifier

text: In this paper, a multiple trench split-gate silicon-on-insulator (SOI) lateral double-diffused MOSFET with a Schottky rectifier (MTS-SG-LDMOS) is proposed and its characteristics are studied using 2-D simulations. The new structure features double oxide trenches, a floating polysilicon split-gate, and a Schottky rectifier. Each oxide trench includes a vertical field plate which enhances the depletion of the drift region and modulates the bulk electric field. As the simulation results, when compared to the conventional SOI LDMOS (C-LDMOS), the breakdown voltage in the MTS-SG-LDMOS increases from 297 to 350 V, the specific on-state resistance (<inline-formula> <tex-math notation="LaTeX">${R} _{ \mathrm{\scriptscriptstyle ON},\mathsf {sp}}$ </tex-math></inline-formula>) decreases from 142.9 to 48.2 <inline-formula> <tex-math notation="LaTeX">$\text{m}\Omega \cdot \mathsf {cm}^{{\mathsf {2}}}$ </tex-math></inline-formula>, and the gate–drain charge (<inline-formula> <tex-math notation="LaTeX">${Q} _{{\mathsf {gd}}}$ </tex-math></inline-formula>) decreases from 19 to 9 pC. Moreover, the reverse recovery time of the proposed structure shows a 73.6% reduction as compared to the C-LDMOS device.


title: Crafting Adversarial Examples For Speech Paralinguistics Applications

text: Computational paralinguistic analysis is increasingly being used in a wide range of applications, including securitysensitive applications such as speaker verification, deceptive speech detection, and medical diagnostics. While state-ofthe-art machine learning techniques, such as deep neural networks, can provide robust and accurate speech analysis, they are susceptible to adversarial attacks. In this work, we propose a novel end-to-end scheme to generate adversarial examples by perturbing directly the raw waveform of an audio recording rather than specific acoustic features. Our experiments show that the proposed adversarial perturbation can lead to a significant performance drop of state-of-the-art deep neural networks, while only minimally impairing the audio quality.


title: Wideband Patch Antenna for HPM Applications

text: This paper presents the design, fabrication, and characterization of a compact wideband antenna for high-power microwave applications. The antennas proposed are array of high-power wideband patches with high compactness and less than λ/10 thick. The concept developed can be fed by high-voltage signals (up to 60 kV) in repetitive operation. Two designs are produced at central frequencies of 350 MHz and 1 GHz. Their relative bandwidth is larger than 40% at 350 MHz and 25% at 1 GHz for S11 <; - 10 dB, respectively. The arrays studied produce a gain of more than 14 dB.


title: Aligning multiple genomic sequences with the threaded blockset aligner.

text: We define a "threaded blockset," which is a novel generalization of the classic notion of a multiple alignment. A new computer program called TBA (for "threaded blockset aligner") builds a threaded blockset under the assumption that all matching segments occur in the same order and orientation in the given sequences; inversions and duplications are not addressed. TBA is designed to be appropriate for aligning many, but by no means all, megabase-sized regions of multiple mammalian genomes. The output of TBA can be projected onto any genome chosen as a reference, thus guaranteeing that different projections present consistent predictions of which genomic positions are orthologous. This capability is illustrated using a new visualization tool to view TBA-generated alignments of vertebrate Hox clusters from both the mammalian and fish perspectives. Experimental evaluation of alignment quality, using a program that simulates evolutionary change in genomic sequences, indicates that TBA is more accurate than earlier programs. To perform the dynamic-programming alignment step, TBA runs a stand-alone program called MULTIZ, which can be used to align highly rearranged or incompletely sequenced genomes. We describe our use of MULTIZ to produce the whole-genome multiple alignments at the Santa Cruz Genome Browser.


title: Low-Voltage Wilson Current Mirrors in CMOS

text: In this paper, we describe three simple low-voltage CMOS analogs of the Wilson current mirror that function well at all current levels, ranging from weak inversion to strong inversion. Each of these current mirrors can operate on a low power-supply voltage of a diode drop plus two saturation voltages and features a wide output-voltage swing with a cascode-type incremental output impedance. Two of the circuits requires an input voltage of a diode drop plus a saturation voltage while the third one features a low input voltage of a saturation voltage. We present experimental results from versions of these three current mirrors that were fabricated in a 0.5-mum CMOS process through MOSIS, comparing them with CMOS implementations of the conventional Wilson and super-Wilson current mirrors.


title: Trend Recalling Algorithm for Automated Online Trading in Stock Market

text: Unlike financial forecasting, a type of mechanical trading technique called Trend Following (TF) doesn’t predict any market movement; instead it identifies a trend at early time of the day, and trades automatically afterwards by a pre-defined strategy regardless of the moving market directions during run time. Trend following trading has a long and successful history among speculators. The traditional TF trading method is by human judgment in setting the rules (aka the strategy). Subsequently the TF strategy is executed in pure objective operational manner. Finding the correct strategy at the beginning is crucial in TF. This usually involves human intervention in first identifying a trend, and configuring when to place an order and close it out, when certain conditions are met. In this paper, we presented a new type of TF, namely Trend Recalling algorithm that operates in a totally automated manner. It works by partially matching the current trend with one of the proven successful patterns from the past. Our experiments based on real stock market data show that this algorithm has an edge over the other trend following methods in profitability. The new algorithm is also compared to time-series forecasting type of stock trading, and it can even outperform the best forecasting type in a


title: Illuminant Aware Gamut-Based Color Transfer

text: This paper proposes a new approach for color transfer between two images. Our method is unique in its consideration of the scene illumination and the constraint that the mapped image must be within the color gamut of the target image. Specifically, our approach first performs a white-balance step on both images to remove color casts caused by different illuminations in the source and target image. We then align each image to share the same ‘white axis’ and perform a gradient preserving histogram matching technique along this axis to match the tone distribution between the two images. We show that this illuminant-aware strategy gives a better result than directly working with the original source and target image’s luminance channel as done by many previous methods. Afterwards, our method performs a full gamut-based mapping technique rather than processing each channel separately. This guarantees that the colors of our transferred image lie within the target gamut. Our experimental results show that this combined illuminant-aware and gamut-based strategy produces more compelling results than previous methods. We detail our approach and demonstrate its effectiveness on a number of examples.


title: Locate the Hate: Detecting Tweets against Blacks

text: Although the social medium Twitter grants users freedom of speech, its instantaneous nature and retweeting features also amplify hate speech. Because Twitter has a sizeable black constituency, racist tweets against blacks are especially detrimental in the Twitter community, though this effect may not be obvious against a backdrop of half a billion tweets a day. We apply a supervised machine learning approach, employing inexpensively acquired labeled data from diverse Twitter accounts to learn a binary classifier for the labels “racist” and “nonracist.” The classifier has a 76% average accuracy on individual tweets, suggesting that with further improvements, our work can contribute data on the sources of anti-black hate speech.


title: General 3D modelling of novel objects from a single view

text: In this paper we present a method for building models for grasping from a single 3D snapshot of a scene composed of objects of daily use in human living environments. We employ fast shape estimation, probabilistic model fitting and verification methods capable of dealing with different kinds of symmetries, and combine these with a triangular mesh of the parts that have no other representation to model previously unseen objects of arbitrary shape. Our approach is enhanced by the information given by the geometric clues about different parts of objects which serve as prior information for the selection of the appropriate reconstruction method. While we designed our system for grasping based on single view 3D data, its generality allows us to also use the combination of multiple views. We present two application scenarios that require complete geometric models: grasp planning and locating objects in camera images.


title: Graph Drawing by Stress Majorization

text: One of the most popular graph drawing methods is based of achieving graphtheoretic target ditsances. This method was used by Kamada and Kawai [15], who formulated it as an energy optimization problem. Their energy is known in the multidimensional scaling (MDS) community as the stress function. In this work, we show how to draw graphs by stress majorization, adapting a technique known in the MDS community for more than two decades. It appears that majorization has advantages over the technique of Kamada and Kawai in running time and stability. We also present a few extensions to the basic energy model which can improve layout quality and computation speed in practice. Majorization-based optimization is essential to these extensions.


title: Eigensolver Methods for Progressive Multidimensional Scaling of Large Data

text: We present a novel sampling-based approximation technique for classical multidimensional scaling that yields an extremely fast layout algorithm suitable even for very large graphs. It produces layouts that compare favorably with other methods for drawing large graphs, and it is among the fastest methods available. In addition, our approach allows for progressive computation, i.e. a rough approximation of the layout can be produced even faster, and then be refined until satisfaction.


title: Accelerating Large Graph Algorithms on the GPU Using CUDA

text: Large graphs involving millions of vertices are common in many practical applications and are challenging to process. Practical-time implementations using high-end computers are reported but are accessible only to a few. Graphics Processing Units (GPUs) of today have high computation power and low price. They have a restrictive programming model and are tricky to use. The G80 line of Nvidia GPUs can be treated as a SIMD processor array using the CUDA programming model. We present a few fundamental algorithms – including breadth first search, single source shortest path, and all-pairs shortest path – using CUDA on large graphs. We can compute the single source shortest path on a 10 million vertex graph in 1.5 seconds using the Nvidia 8800GTX GPU costing $600. In some cases optimal sequential algorithm is not the fastest on the GPU architecture. GPUs have great potential as high-performance co-processors.


title: Self-monitoring: appraisal and reappraisal.

text: Theory and research on self-monitoring have accumulated into a sizable literature on the impact of variation in the extent to which people cultivate public appearances in diverse domains of social functioning. Yet self-monitoring and its measure, the Self-Monitoring Scale, are surrounded by controversy generated by conflicting answers to the critical question, Is self-monitoring a unitary phenomenon? A primary source of answers to this question has been largely neglected--the Self-Monitoring Scale's relations with external criteria. We propose a quantitative method to examine the self-monitoring literature and thereby address major issues of the controversy. Application of this method reveals that, with important exceptions, a wide range of external criteria tap a dimension directly measured by the Self-Monitoring Scale. We discuss what this appraisal reveals about with self-monitoring is and is not.


title: Low cost high performance uncertainty quantification

text: Uncertainty quantification in risk analysis has become a key application. In this context, computing the diagonal of inverse covariance matrices is of paramount importance. Standard techniques, that employ matrix factorizations, incur a cubic cost which quickly becomes intractable with the current explosion of data sizes. In this work we reduce this complexity to quadratic with the synergy of two algorithms that gracefully complement each other and lead to a radically different approach. First, we turned to stochastic estimation of the diagonal. This allowed us to cast the problem as a linear system with a relatively small number of multiple right hand sides. Second, for this linear system we developed a novel, mixed precision, iterative refinement scheme, which uses iterative solvers instead of matrix factorizations. We demonstrate that the new framework not only achieves the much needed quadratic cost but in addition offers excellent opportunities for scaling at massively parallel environments. We based our implementation on BLAS 3 kernels that ensure very high processor performance. We achieved a peak performance of 730 TFlops on 72 BG/P racks, with a sustained performance 73% of theoretical peak. We stress that the techniques presented in this work are quite general and applicable to several other important applications.


title: ViP-CNN: A Visual Phrase Reasoning Convolutional Neural Network for Visual Relationship Detection

text: As the intermediate level task connecting image captioning and object detection, visual relationship detection started to catch researchers’ attention because of its descriptive power and clear structure. It localizes the objects and captures their interactions with a subject-predicateobject triplet, e.g. 〈person-ride-horse〉. In this paper, the visual relationship is considered as a phrase with three components. So we formulate the visual relationship detection as three inter-connected recognition problems and propose a Visual Phrase reasoning Convolutional Neural Network (ViP-CNN) to address them simultaneously. In ViP-CNN, we present a Visual Phrase Reasoning Structure (VPRS) to set up the connection among the relationship components and help the model consider the three problems jointly. Corresponding non-maximum suppression method and model training strategy are also proposed. Experimental results show that our ViP-CNN outperforms the stateof-art method both in speed and accuracy. We further pretrain our model on our cleansed Visual Genome Relationship dataset, which is found to perform better than the pretraining on the ImageNet for this task.


title: Goal Setting and Self-Efficacy During Self-Regulated Learning

text: This article focuses on the self-regulated learning processes of goal setting and perceived self-efficacy. Students enter learning activities with goals and self-efficacy for goal attainment. As learners work on tasks, they observe their own performances and evaluate their own goal progress. Self-efficacy and goal setting are affected by selfobservation, self-judgment, and self-reaction. When students perceive satisfactory goal progress, they feel capable of improving their skills; goal attainment, coupled with high self-efficacy, leads students to set new challenging goals. Research is reviewed on goal properties (specificity, proximity, difficulty), self-set goals, progress feedback, contracts and conferences, and conceptions of ability. Ways of teaching students to set realistic goals and evaluate progress include establishing upper and lower goal limits and employing games, contracts, and conferences. Future research might clarify the relation of goal setting and self-efficacy to transfer, goal orientations, and affective reactions. Article: Self-regulated learning occurs when students activate and sustain cognitions and behaviors systematically oriented toward attainment of learning goals. Self-regulated learning processes involve goal-directed activities that students instigate, modify, and sustain (Zimmerman, 1989). These activities include attending to instruction, processing and integrating knowledge, rehearsing information to be remembered, and developing and maintaining positive beliefs about learning capabilities and anticipated outcomes of actions (Schunk, 1989). This article focuses on two self-regulated learning processes: goal setting and perceived self-efficacy. As used in this article, a goal is what an individual is consciously trying to accomplish, goal setting involves establishing a goal and modifying it as necessary, and perceived self-efficacy refers to beliefs concerning one's capabilities to attain designated levels of performance (Bandura, 1986, 1988). I initially present a theoretical overview of self-regulated learning to include the roles of goal setting and self-efficacy. I discuss research bearing on these processes, and conclude with implications for educational practice and future research suggestions. THEORETICAL OVERVIEW Subprocesses of Self-Regulated Learning Investigators working within a social cognitive learning theory framework view self-regulation as comprising three subprocesses: self-observation, self-judgment, and self-reaction (Bandura, 1986; Kanfer & Gaelick, 1986; Schunk, 1989). A model highlighting goal setting and self-efficacy is portrayed in Figure 1. Students enter learning activities with such goals as acquiring knowledge, solving problems, and finishing workbook pages. Self-efficacy for goal attainment is influenced by abilities, prior experiences, attitudes toward learning, instruction, and the social context. As students work on tasks, they observe their performances, evaluate goal progress, and continue their work or change their task approach. Self-evaluation of goal progress as satisfactory enhances feelings of efficacy; goal attainment leads students to set new challenging goals. Self-observation. Self-observation, or deliberate attention to aspects of one's behaviors, informs and motivates. Behaviors can be assessed on such dimensions as quality, rate, quantity, and originality. The information gained is used to gauge goal progress. Self-observation also can motivate behavioral change. Many students with poor study habits are surprised to learn that they waste much study time on nonacademic activities. Sustained motivation depends on students believing that if they change their behavior they will experience better outcomes, valuing those outcomes, and feeling they can change those habits (high self-efficacy). Self-observation is aided with self-recording, where behavior instances are recorded along with such features as time, place, and duration of occurrence (Mace, Belfiore, & Shea, 1989). Without recording, observations may not faithfully reflect behaviors due to selective memory. Behaviors should be observed close in time to their occurrence and on a continuous basis rather than intermittently. Self-judgment. Self-judgment involves comparing present performance with one's goal. Self-judgments are affected by the type of standards employed, goal properties (discussed in next section), importance of goal attainment, and performance attributions. Learning goals may reflect absolute or normative standards (Bandura, 1986). Absolute standards are fixed (e.g., complete six workbook pages in 30 min). Grading systems often are based on absolute standards (A = 90-100, B = 80-89). Normative standards employ performances by others. Social comparison of one's performances with those of peers helps one determine behavioral appropriateness. Standards are informative; comparing one's performance with standards informs one of goal progress. Standards also can motivate when they show that goal progress is being made. Self-judgments can be affected by the importance of goal attainment. When individuals care little about how they perform, they may not assess their performance or expend effort to improve (Bandura, 1986). Judgments of goal progress are more likely to be made for goals one personally values. Attributions, or perceived causes of outcomes (successes, failures), influence achievement beliefs and behaviors (Weiner, 1985). Achievement outcomes often are attributed to such causes as ability, effort, task difficulty, and luck (Frieze, 1980; Weiner, 1979). Children view effort as the prime cause of outcomes (Nicholls, 1984). With development, ability attributions become increasingly important. Whether goal progress is judged as acceptable depends on its attribution. Students who attribute successes to teacher assistance may hold low self-efficacy for good performance if they believe they cannot succeed on their own. If they believe they lack ability, they may judge learning progress as deficient and be unmotivated to work harder. Self-reaction. Self-reactions to goal progress motivate behavior (Bandura, 1986). The belief that one's progress is acceptable, along with anticipated satisfaction of goal accomplishment, enhances self-efficacy and motivation. Negative evaluations will not decrease motivation if individuals believe they are capable of improving (Schunk, 1989). Motivation will not improve if students believe they lack the ability to succeed and increased effort will not help. Individuals routinely make such rewards as work breaks, new clothes, and nights on the town contingent on task progress or goal attainment. Anticipation of rewards enhances motivation and self-efficacy. Compensations raise efficacy when they are tied to accomplishments. If students are told that they will earn rewards based on what they accomplish, they become instilled with a sense of efficacy for learning. Self-efficacy is validated as students work at a task and note their own progress; receipt of the reward then becomes a symbol of the progress made. Goal Setting The effects of goals on behavior depend on their properties: specificity, proximity, and difficulty level (Bandura, 1988; Locke, Shaw, Saari, & Latham, 1981). Goals incorporating specific performance standards are more likely to enhance learning and activate self-evaluations than general goals (i.e., "Do your best"). Specific goals boost performance by greater specification of the amount of effort required for success and the selfsatisfaction anticipated. Specific goals promote self-efficacy because progress is easy to gauge. Proximal goals result in greater motivation than distant goals. It is easier to gauge progress toward a proximal goal, and the perception of progress raises self-efficacy. Proximal goals are especially influential with young children, who do not represent distant outcomes in thought. Goal difficulty, or the level of task proficiency required as assessed against a standard, influences the effort learners expend to attain a goal. Assuming requisite skills, individuals expend greater effort to attain difficult goals than when standards are lower. Learners initially may doubt whether they can attain difficult goals, but working toward them builds self-efficacy. Self-Efficacy Self-efficacy is hypothesized to influence choice of activities, effort expended, and persistence (Bandura, 1986). Students who hold low self-efficacy for learning may avoid tasks; those who judge themselves efficacious are more likely to participate. When facing difficulties, self-efficacious learners expend greater effort and persist longer than students who doubt their capabilities. Students acquire information about their self-efficacy in a given domain from their performances, observations of models (i.e., vicarious experiences), forms of social persuasion, and physiological indexes (e.g., heart rate, sweating). Information acquired from these sources does not influence efficacy automatically but is cognitively appraised. Efficacy appraisal is an inferential process; persons weigh and combine the contributions of personal and situational factors. In assessing self-efficacy, students take into account such factors as perceived ability, expended effort, task difficulty, teacher assistance, other situational factors, and patterns of successes and failures. The notion that personal expectations influence behavior is not unique to self-efficacy theory. Self-efficacy is conceptually similar to such other constructs as perceived competence, expectations of success, and selfconfidence. One means of distinguishing constructs involves the generality of the constructs. Some constructs (e.g., self-concept, self-esteem) are hypothesized to affect diverse areas of human functioning. Though perceptions of efficacy can generalize, they offer the best prediction of behavior within specific domains (e.g., selfefficacy for acquiring fraction


title: Learning to predict by the methods of temporal differences

text: This article introduces a class of incremental learning procedures specialized for prediction-that is, for using past experience with an incompletely known system to predict its future behavior. Whereas conventional prediction-learning methods assign credit by means of the difference between predicted and actual outcomes, the new methods assign credit by means of the difference between temporally successive predictions. Although such temporal-difference methods have been used in Samuel's checker player, Holland's bucket brigade, and the author's Adaptive Heuristic Critic, they have remained poorly understood. Here we prove their convergence and optimality for special cases and relate them to supervised-learning methods. For most real-world prediction problems, temporal-difference methods require less memory and less peak computation than conventional methods and they produce more accurate predictions. We argue that most problems to which supervised learning is currently applied are really prediction problems of the sort to which temporal-difference methods can be applied to advantage.


title: A review on methods and classifiers in lip reading

text: The idea of lip reading as a visual technique which people may use to translate lip movement into phrases without relying on speech itself is fascinating. There are numerous application areas in which lip reading could provide full assistance. Although there may be a downside to using the lip reading system, whether it may range from problems such as time constraint to minor word recognition mistakes, further development of the system should be an active cycle. Ongoing research into improvement of the lip reading system performance by way of suitable choices of feature extraction and classifiers is essential to track the developing trend in both fields of technology as well as in pattern recognition. This paper discusses a short review on the existing methods and classifiers which have been used in previous work in the field of lip reading.