Query: Auto-Conditioned Recurrent Networks for Extended Complex Human Motion Synthesis

Relevant passages:
Gradient-based learning algorithms for recurrent networks and their computational complexity <=SEP=> 

Professor Forcing: A New Algorithm for Training Recurrent Networks <=SEP=> The Teacher Forcing algorithm trains recurrent networks by supplying observed sequence values as inputs during training and using the network’s own one-stepahead predictions to do multi-step sampling. We introduce the Professor Forcing algorithm, which uses adversarial domain adaptation to encourage the dynamics of the recurrent network to be the same when training the network and when sampling from the network over multiple time steps. We apply Professor Forcing to language modeling, vocal synthesis on raw waveforms, handwriting generation, and image generation. Empirically we find that Professor Forcing acts as a regularizer, improving test likelihood on character level Penn Treebank and sequential MNIST. We also find that the model qualitatively improves samples, especially when sampling for a large number of time steps. This is supported by human evaluation of sample quality. Trade-offs between Professor Forcing and Scheduled Sampling are discussed. We produce T-SNEs showing that Professor Forcing successfully makes the dynamics of the network during training and sampling more similar.

DRAW: A Recurrent Neural Network For Image Generation <=SEP=> This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural network architecture for image generation. DRAW networks combine a novel spatial attention mechanism that mimics the foveation of the human eye, with a sequential variational auto-encoding framework that allows for the iterative construction of complex images. The system substantially improves on the state of the art for generative models on MNIST, and, when trained on the Street View House Numbers dataset, it generates images that cannot be distinguished from real data with the naked eye.

Generating Sequences With Recurrent Neural Networks <=SEP=> This paper shows how Long Short-term Memory recurrent neural networks can be used to generate complex sequences with long-range structure, simply by predicting one data point at a time. The approach is demonstrated for text (where the data are discrete) and online handwriting (where the data are real-valued). It is then extended to handwriting synthesis by allowing the network to condition its predictions on a text sequence. The resulting system is able to generate highly realistic cursive handwriting in a wide variety of styles.

Style-based inverse kinematics <=SEP=> This paper presents an inverse kinematics system based on a learned model of human poses. Given a set of constraints, our system can produce the most likely pose satisfying those constraints, in real-time. Training the model on different input data leads to different styles of IK. The model is represented as a probability distribution over the space of all possible poses. This means that our IK system can generate any pose, but prefers poses that are most similar to the space of poses in the training data. We represent the probability with a novel model called a Scaled Gaussian Process Latent Variable Model. The parameters of the model are all learned automatically; no manual tuning is required for the learning component of the system. We additionally describe a novel procedure for interpolating between styles.Our style-based IK can replace conventional IK, wherever it is used in computer animation and computer vision. We demonstrate our system in the context of a number of applications: interactive character posing, trajectory keyframing, real-time motion capture with missing markers, and posing from a 2D image.

Impact of increased penetration of photovoltaic generation on power systems <=SEP=> Present renewable portfolio standards are changing power systems by replacing conventional generation with alternate energy resources such as photovoltaic (PV) systems. With the increase in penetration of PV resources, power systems are expected to experience a change in dynamic and operational characteristics. This paper studies the impact of increased penetration of PV systems on static performance as well as transient stability of a large power system, in particular the transmission system. Utility scale and residential rooftop PVs are added to the aforementioned system to replace a portion of conventional generation resources. While steady state voltages are observed under various PV penetration levels, the impact of reduced inertia on transient stability performance is also examined. The studied system is a large test system representing a portion of the Western U.S. interconnection. The simulation results obtained effectively identify both detrimental and beneficial impacts of increased PV penetration both for steady state stability and transient stability performance.

Bayesian Poisson Tensor Factorization for Inferring Multilateral Relations from Sparse Dyadic Event Counts <=SEP=> We present a Bayesian tensor factorization model for inferring latent group structures from dynamic pairwise interaction patterns. For decades, political scientists have collected and analyzed records of the form "country i took action a toward country j at time t" - known as dyadic events - in order to form and test theories of international relations. We represent these event data as a tensor of counts and develop Bayesian Poisson tensor factorization to infer a low-dimensional, interpretable representation of their salient patterns. We demonstrate that our model's predictive performance is better than that of standard non-negative tensor factorization methods. We also provide a comparison of our variational updates to their maximum likelihood counterparts. In doing so, we identify a better way to form point estimates of the latent factors than that typically used in Bayesian Poisson matrix factorization. Finally, we showcase our model as an exploratory analysis tool for political scientists. We show that the inferred latent factor matrices capture interpretable multilateral relations that both conform to and inform our knowledge of international a airs.

Optimizing Players' Expected Enjoyment in Interactive Stories <=SEP=> In interactive storytelling systems and other story-based computer games, a drama manager is a background agent that aims to bring about an enjoyable and coherent experience for the players. In this paper, we present a personalized drama manager that increases a player’s expected enjoyment without removing player agency. Our personalized drama manager models a player’s preference using data-driven techniques, predicts the probability the player transitioning to different story experiences, selects an objective experience that can maximize the player’s expected enjoyment, and guides the player to the selected story experience. Human study results show that our drama manager can significantly increase players’ enjoyment ratings in an interactive storytelling testbed, compared to drama managers in pre-

Improving Performances of MSER Features in Matching and Retrieval Tasks <=SEP=> MSER features are redefined to improve their performances in matching and retrieval tasks. The proposed SIMSER features (i.e. scale-insensitive MSERs) are the extremal regions which are maximally stable not only under the threshold changes (like MSERs) but, additionally, under image rescaling (smoothing). Theoretical advantages of such a modification are discussed. It is also preliminarily verified experimentally that such a modification preserves the fundamental properties of MSERs, i.e. the average numbers of features, repeatability, and computational complexity (which is only multiplicatively increased by the number of scales used), while performances (measured by typical CBVIR metrics) can be significantly improved. In particular, results on benchmark datasets indicate significant increments in recall values, both for descriptor-based matching and word-based matching. In general, SIMSERs seem particularly suitable for a usage with large visual vocabularies, e.g. they can be prospectively applied to improve quality of BoW pre-retrieval operations in large-scale databases.

Variational Inference for Data-Efficient Model Learning in POMDPs <=SEP=> Partially observable Markov decision processes (POMDPs) are a powerful abstraction for tasks that require decision making under uncertainty, and capture a wide range of real world tasks. Today, effective planning approaches exist that generate effective strategies given black-box models of a POMDP task. Yet, an open question is how to acquire accurate models for complex domains. In this paper we propose DELIP, an approach to model learning for POMDPs that utilizes structured, amortized variational inference. We empirically show that our model leads to effective control strategies when coupled with state-of-the-art planners. Intuitively, model-based approaches should be particularly beneficial in environments with changing reward structures, or where rewards are initially unknown. Our experiments confirm that DELIP is particularly effective in this setting.

Order-Preserving Encryption for Numeric Data <=SEP=> Encryption is a well established technology for protecting sensitive data. However, once encrypted, data can no longer be easily queried aside from exact matches. We present an order-preserving encryption scheme for numeric data that allows any comparison operation to be directly applied on encrypted data. Query results produced are sound (no false hits) and complete (no false drops). Our scheme handles updates gracefully and new values can be added without requiring changes in the encryption of other values. It allows standard databse indexes to be built over encrypted tables and can easily be integrated with existing database systems. The proposed scheme has been designed to be deployed in application environments in which the intruder can get access to the encrypted database, but does not have prior domain information such as the distribution of values and annot encrypt or decrypt arbitrary values of his choice. The encryption is robust against estimation of the true value in such environments.

Genetics and Marker-assisted Selection of the Chemotype in Cannabis sativa L. <=SEP=> Cannabis sativa is an interesting crop for several industrial uses, but the legislations in Europe and USA require a tight control of cannabinoid type and content for cultivation and subsidies release. Therefore, cannabinoid survey by gas chromatography of materials under selection is an important step in hemp breeding. In this paper, a number of Cannabis accessions were examined for their cannabinoid composition. Their absolute and relative content was examined, and results are discussed in the light of both the current genetic model for cannabinoid’s inheritance, and the legislation’s requirements. In addition, the effectiveness of two different types of markers associated to the locus determining the chemotype in Cannabis was evaluated and discussed, as possible tools in marker-assisted selection in hemp, but also for possible applications in the forensic and pharmaceutical fields.

Deep Context: End-to-end Contextual Speech Recognition <=SEP=> In automatic speech recognition (ASR) what a user says depends on the particular context she is in. Typically, this context is represented as a set of word n-grams. In this work, we present a novel, all-neural, end-to-end (E2E) ASR system that utilizes such context. Our approach, which we refer to as Contextual Listen, Attend and Spell (CLAS) jointly-optimizes the ASR components along with embeddings of the context n-grams. During inference, the CLAS system can be presented with context phrases which might contain-of-vocabulary (OOV) terms not seen during training. We compare our proposed system to a more traditional contextualization approach, which performs shallow-fusion between independently trained LAS and contextual n-gram models during beam search. Across a number of tasks, we find that the proposed CLAS system outperforms the baseline method by as much as 68% relative WER, indicating the advantage of joint optimization over individually trained components.

Balance control of two-wheeled self-balancing mobile robot based on TS fuzzy model <=SEP=> Two-wheeled self-balancing robot is a mechanically unstable, nonholonomic constraint robot. This paper has established a dynamics and kinematics for nonholonomic mobile robot. In order to control more effective for self-balancing robot's balance model, has designed fuzzy controller based on the T-S fuzzy model with the parallel distribution compensator (PDC) the structure. It realizes the robot balance and the movement control. Finally, validity and feasibility of the system modeling and the designed controller were confirmed through the actual test results and the simulation results contrast analysis.

A longitudinal study of follow predictors on twitter <=SEP=> Follower count is important to Twitter users: it can indicate popularity and prestige. Yet, holistically, little is understood about what factors -- like social behavior, message content, and network structure - lead to more followers. Such information could help technologists design and build tools that help users grow their audiences. In this paper, we study 507 Twitter users and a half-million of their tweets over 15 months. Marrying a longitudinal approach with a negative binomial auto-regression model, we find that variables for message content, social behavior, and network structure should be given equal consideration when predicting link formations on Twitter. To our knowledge, this is the first longitudinal study of follow predictors, and the first to show that the relative contributions of social behavior and mes-sage content are just as impactful as factors related to social network structure for predicting growth of online social networks. We conclude with practical and theoretical implications for designing social media technologies.

Can Invisible Watermarks Resolve Rightful Ownerships? <=SEP=> Digital watermarks have been proposed in recent literature as the means for copyright protection of multimedia data. In this paper we address the capability of invisible watermarking schemes to resolve copyright ownerships. We will show that rightful ownerships cannot be resolved by current watermarking schemes alone. In addition, in the absence of standardization of watermarking procedures, anyone can claim ownership of any watermarked image. Specifically, we provide counterfeit watermarking schemes that can be performed on a watermarked image to allow multiple claims of rightful ownerships. We also propose non-invertible watermarking schemes in this paper and discuss in general the usefulness of digital watermarks in identifying the rightful copyright owners. The results, coupled with the recent attacks on some image watermarks, further imply that we have to carefully re-think our approaches to invisible watermarking of images, and re-evaluate the promises, applications and limitations of such digital means of copyright protection.

A Compact, Wideband Circularly Polarized Co-designed Filtering Antenna and Its Application for Wearable Devices With Low SAR <=SEP=> A compact circularly polarized (CP) co-designed filtering antenna is reported. The device is based on a patch radiator seamlessly integrated with a bandpass filter composed of coupled stripline open-loop resonators, which are designed together as a system. In the proposed design, the patch functions simultaneously as the radiator and the last stage resonator of the filter, resulting in a low-profile integrated radiating and filtering module with a small overall form factor of 0.53λ0 × 0.53λ0 × 0.07λ0. It is shown that the filtering circuit not only ensures frequency selectivity but also provides impedance matching functionality, which serves to broaden both the impedance and axial ratio bandwidths. The designed filtering antenna was fabricated and measured, experimentally achieving an S11 <; -13.5 dB, an axial ratio of less than 3 dB and a gain higher than 5.2 dBi over a bandwidth from 3.77 to 4.26 GHz, i.e., around 12.2%, which makes it an excellent candidate for integration into a variety of wireless systems. A linearly polarized version of the integrated filtering antenna was also demonstrated. In addition, further full-wave simulations and experiments were carried out to verify that the designed CP filtering antenna maintains its properties even when mounted on different positions of the human body with various body gestures. The stable impedance and radiation properties also make it a suitable candidate as a wearable antenna for off-body wireless communications.

A service orchestration architecture for Fog-enabled infrastructures <=SEP=> The development of Fog Computing technology is crucial to address the challenges to come with the mass adoption of Internet Of Things technology, where the generation of data tends to grow at an unprecedented pace. The technology brings computing power to the surrounds of devices, to offer local processing, filtering, storage and analysis of data and control over actuators. Orchestration is a requirement of Fog Computing technology to deliver services, based on the composition of microservices. It must take into consideration the heterogeneity of the IoT environment and device's capabilities and constraints. This heterogeneity requires a different approach for orchestration, be it regarding infrastructure management, node selection and/or service placement. Orchestrations shall be manually or automatically started through event triggers. Also, the Orchestrator must be flexible enough to work in a centralized or distributed fashion. Orchestration is still a hot topic and can be seen in different areas, especially in the Service Oriented Architectures, hardware virtualization, in the Cloud, and in Network Virtualization Function. However, the architecture of these solutions is not enough to handle Fog Requirements, specially Fog's heterogeneity, and dynamics. In this paper, we propose an architecture for Orchestration for the Fog Computing environment. We developed a prototype to prof some concepts. We discuss in this paper the implementation, and the tools chose, and their roles. We end the paper with a discussion on performance indicators and future direction on the evaluation of non-functional aspects of the Architecture.

A Better Way to Pretrain Deep Boltzmann Machines <=SEP=> We describe how the pretraining algorithm for Deep Boltzmann Machines (DBMs) is related to the pretraining algorithm for Deep Belief Networks and we show that under certain conditions, the pretraining procedure improves the variational lower bound of a two-hidden-layer DBM. Based on this analysis, we develop a different method of pretraining DBMs that distributes the modelling work more evenly over the hidden layers. Our results on the MNIST and NORB datasets demonstrate that the new pretraining algorithm allows us to learn better generative models.

Age Estimation in Short Speech Utterances Based on LSTM Recurrent Neural Networks <=SEP=> Age estimation from speech has recently received increased interest as it is useful for many applications such as user-profiling, targeted marketing, or personalized call-routing. This kind of applications need to quickly estimate the age of the speaker and might greatly benefit from real-time capabilities. Long short-term memory (LSTM) recurrent neural networks (RNN) have shown to outperform state-of-the-art approaches in related speech-based tasks, such as language identification or voice activity detection, especially when an accurate real-time response is required. In this paper, we propose a novel age estimation system based on LSTM-RNNs. This system is able to deal with short utterances (from 3 to 10 s) and it can be easily deployed in a real-time architecture. The proposed system has been tested and compared with a state-of-the-art i-vector approach using data from NIST speaker recognition evaluation 2008 and 2010 data sets. Experiments on short duration utterances show a relative improvement up to 28% in terms of mean absolute error of this new approach over the baseline system.

Do hospital fall prevention programs work? A systematic review. <=SEP=> OBJECTIVES
To analyze published hospital fall prevention programs to determine whether there is any effect on fall rates. To review the methodological quality of those programs and the range of interventions used. To provide directions for further research.


DESIGN
Systematic review of published hospital fall prevention programs. Meta-analysis.


METHODS
Keyword searches of Medline, CINAHL, monographs, and secondary references. All papers were included that described fall rates before and during intervention. Risk ratios and 95% Confidence Intervals (95% CI) were estimated and random effects meta-analysis employed. Begg's test was applied to detect possible publication bias. Separate meta-analysis regressions were performed to determine whether individual components of multifaceted interventions were effective.


RESULTS
A total of 21 papers met the criteria (18 from North America), although only 10 contained sufficient data to allow calculation of confidence intervals. A rate ratio of <1 indicates a reduction in the fall rate, resulting from an intervention. Three were randomized controlled trials (pooled rate ratio 1.0 (CI 0.60, 1.68)), seven prospective studies with historical control (0.76 (CI 0.65, 0.88)). Pooled effect rate ratio from these 10 studies was 0.79 (CI 0.69, 0.89). The remaining 11 studies were prospective studies with historical control describing fall rates only. Individual components of interventions showed no significant benefit.


DISCUSSION
The pooled effect of about 25% reduction in the fall rate may be a result of intervention but may also be biased by studies that used historical controls not allowing for historical trends in the fall rate before and during the intervention. The randomized controlled trials apparent lack of effect might be due to a change in practice when patients and controls were in the same unit at the same time during a study. Studies did not analyze compliance with the intervention or opportunity costs resulting from the intervention. Research and clinical programs in hospital fall prevention should pay more attention to study design and the nature of interventions.

Asymptomatic vulvar pigmentation. <=SEP=> A 29-year-old woman presented with a 3-month history of a macular lesion on the vaginal introit and labia minor, which was causing concern because of its crescent-shaped enlargement. There was no itching or vaginal discharge. The patient’s medical history was unremarkable, particularly for dermatological diseases such as lichen planus or other inflammatory disorders. There was no family history of skin diseases or skin cancer, including melanoma. On physical examination, a dark-brown macule with irregular borders, measuring around 20 · 30 mm, was seen, localized to the vaginal introit and labia minora (Fig. 1). On palpation, the lesion was flat, having the contour of the normal vaginal mucosa, and was not indurated or tender. The patient had no oral mucosal, perioral or facial hyperpigmentation, and the remaining physical examination was normal. Dermatoscopy was performed, revealing diffuse pigmentation with a parallel pattern of partially linear and partially curvilinear brown streaks across the lesion.

Effective training of convolutional neural networks for face-based gender and age prediction <=SEP=> Convolutional Neural Networks (CNNs) have been proven very effective for human demographics estimation by a number of recent studies. However, the proposed solutions significantly vary in different aspects leaving many open questions on how to choose an optimal CNN architecture and which training strategy to use. In this work, we shed light on some of these questions improving the existing CNN-based approaches for gender and age prediction and providing practical hints for future studies. In particular, we analyse four important factors of the CNN training for gender recognition and age estimation: (1) the target age encoding and loss function, (2) the CNN depth, (3) the need for pretraining, and (4) the training strategy: mono-task or multi-task. As a result, we design the state-of-the-art gender recognition and age estimation models according to three popular benchmarks: LFW, MORPH-II and FGNET. Moreover, our best model won the ChaLearn Apparent Age Estimation Challenge 2016 significantly outperforming the solutions of other participants.

Inadvertent Harvest of the Median Nerve Instead of the Palmaris Longus Tendon. <=SEP=> BACKGROUND
The palmaris longus tendon is frequently used as a tendon graft or ligament replacement. In rare instances the median nerve has been inadvertently harvested instead of the palmaris longus for use as a tendon.


METHODS
Nineteen cases in which the median nerve had been mistakenly harvested instead of the palmaris longus tendon were collected from members of the American Society for Surgery of the Hand (ASSH) Listserve. Surgeons involved in the subsequent care of the subject who had had an inadvertent harvest were contacted or the chart was reviewed. The reason for the initial procedure, the skill level of the primary surgeon, and when the inadvertent harvest was recognized were documented. When possible, the method of harvest and subsequent treatment were also documented.


RESULTS
The most common initial procedure was a reconstruction of the elbow ulnar collateral ligament, followed by basal joint arthroplasty, tendon reconstruction, and reconstruction of the ulnar collateral ligament of the thumb metacarpophalangeal joint. Only 7 of the inadvertent harvests were recognized intraoperatively; in the remaining 12 cases the nerve was used as a tendon graft. The sensory loss was not recognized as being due to the inadvertent harvest until the first postoperative visit (2 subjects), 3 to 4 weeks (2 subjects), 2 to 3 months (2 subjects), 5 to 7 months (2 subjects), 1 year (1 subject), 3 years (1 subject), or 10 years (1 subject). Preoperative clinical identification of the presence or absence of a palmaris longus did not necessarily prevent an inadvertent harvest.


CONCLUSIONS
Knowledge of the relevant anatomy is crucial to avoiding inadvertent harvest of the median nerve instead of the palmaris longus tendon.

Semi-Supervised Learning for Relation Extraction <=SEP=> This paper proposes a semi-supervised learning method for relation extraction. Given a small amount of labeled data and a large amount of unlabeled data, it first bootstraps a moderate number of weighted support vectors via SVM through a co-training procedure with random feature projection and then applies a label propagation (LP) algorithm via the bootstrapped support vectors. Evaluation on the ACE RDC 2003 corpus shows that our method outperforms the normal LP algorithm via all the available labeled data without SVM bootstrapping. Moreover, our method can largely reduce the computational burden. This suggests that our proposed method can integrate the advantages of both SVM bootstrapping and label propagation.

Bayesian Point Cloud Reconstruction <=SEP=> In this paper, we propose a novel surface reconstruction technique based on Bayesian statistics: The measurement process as well as prior assumptions on the measured objects are modeled as probability distributions and Bayes’ rule is used to infer a reconstruction of maximum probability. The key idea of this paper is to define both measurements and reconstructions as point clouds and describe all statistical assumptions in terms of this finite dimensional representation. This yields a discretization of the problem that can be solved using numerical optimization techniques. The resulting algorithm reconstructs both topology and geometry in form of a well-sampled point cloud with noise removed. In a final step, this representation is then converted into a triangle mesh. The proposed approach is conceptually simple and easy to extend. We apply the approach to reconstruct piecewise-smooth surfaces with sharp features and examine the performance of the algorithm on different synthetic and real-world

The impact of website content dimension and e-trust on e-marketing effectiveness: The case of Iranian commercial saffron corporations <=SEP=> By considering the problems that commercial saffron companies have faced in international markets, the aim of this study is to investigate the impact of website content, including informational and design dimensions, on the effectiveness of e-marketing and e-trust as mediator variables. These aspects are examined with reference to sales and marketing division managers in a sample of 100 commercial saffron corporations in the Khorasan province. The findings support the ideas that website content has an effect on e-marketing and e-trust and that e-trust plays a mediating role in the relationship between etrust and e-marketing effectiveness. 2013 Elsevier B.V. All rights reserved. * Corresponding author. Tel.: +98 5118811240; fax: +98 5118811243. E-mail addresses: r-nia@ferdowsi.um.ac.ir, frahimnia@hotmail.com (F. Rahimnia).

Editorial: special issue on web content mining <=SEP=> With the phenomenal growth of the Web, there is an everincreasing volume of data and information published in numerous Web pages. The research in Web mining aims to develop new techniques to effectively extract and mine useful knowledge or information from these Web pages [8]. Due to the heterogeneity and lack of structure of Web data, automated discovery of targeted or unexpected knowledge/information is a challenging task. It calls for novel methods that draw from a wide range of fields spanning data mining, machine learning, natural language processing, statistics, databases, and information retrieval. In the past few years, there was a rapid expansion of activities in the Web mining field, which consists of Web usage mining, Web structure mining, and Web content mining. Web usage mining refers to the discovery of user access patterns from Web usage logs. Web structure mining tries to discover useful knowledge from the structure of hyperlinks. Web content mining aims to extract/mine useful information or knowledge from Web page contents. For this special issue, we focus on Web content mining.

How can machine learning help stock investment ? <=SEP=> The million-dollar question for stock investors is if the price of a stock will rise or not. The fluctuation of stock market is violent and there are many complicated financial indicators. Only people with extensive experience and knowledge can understand the meaning of the indicators, use them to make good prediction to get fortune. Most of other people can only rely on lucky to earn money from stock trading. Machine learning is an opportunity for ordinary people to gain steady fortune from stock market and also can help experts to dig out the most informative indicators and make better prediction.

An analysis of sources of risk in the consumer electronics industry <=SEP=> The consumer electronics industry is a $ 240 billion global industry with a small number of highly competitive global players. We describe many of the risks associated with any global supply chain in this industry. As illustration, we also list steps that Samsung Electronics and its subsidiary, Samsung Electronics UK, have taken to mitigate these risks. Our description of the risks and illustration of mitigation efforts provides the backdrop to identify areas of future research.