Query: Time-Agnostic Prediction: Predicting Predictable Video Frames

Relevant passages:
Activity Forecasting <=SEP=> We address the task of inferring the future actions of people from noisy visual input. We denote this task activity forecasting. To achieve accurate activity forecasting, our approach models the effect of the physical environment on the choice of human actions. This is accomplished by the use of state-of-the-art semantic scene understanding combined with ideas from optimal control theory. Our unified model also integrates several other key elements of activity analysis, namely, destination forecasting, sequence smoothing and transfer learning. As proof-of-concept, we focus on the domain of trajectory-based activity analysis from visual input. Experimental results demonstrate that our model accurately predicts distributions over future actions of individuals. We show how the same techniques can improve the results of tracking algorithms by leveraging information about likely goals and trajectories.

Anticipating Visual Representations from Unlabeled Video <=SEP=> Anticipating actions and objects before they start or appear is a difficult problem in computer vision with several real-world applications. This task is challenging partly because it requires leveraging extensive knowledge of the world that is difficult to write down. We believe that a promising resource for efficiently learning this knowledge is through readily available unlabeled video. We present a framework that capitalizes on temporal structure in unlabeled video to learn to anticipate human actions and objects. The key idea behind our approach is that we can train deep networks to predict the visual representation of images in the future. Visual representations are a promising prediction target because they encode images at a higher semantic level than pixels yet are automatic to compute. We then apply recognition algorithms on our predicted representation to anticipate objects and actions. We experimentally validate this idea on two datasets, anticipating actions one second in the future and objects five seconds in the future.

Autoencoding beyond pixels using a learned similarity metric <=SEP=> We present an autoencoder that leverages learned representations to better measure similarities in data space. By combining a variational autoencoder with a generative adversarial network we can use learned feature representations in the GAN discriminator as basis for the VAE reconstruction objective. Thereby, we replace element-wise errors with feature-wise errors to better capture the data distribution while offering invariance towards e.g. translation. We apply our method to images of faces and show that it outperforms VAEs with element-wise similarity measures in terms of visual fidelity. Moreover, we show that the method learns an embedding in which high-level abstract visual features (e.g. wearing glasses) can be modified using simple arithmetic.

Unsupervised Learning for Physical Interaction through Video Prediction <=SEP=> A core challenge for an agent learning to interact with the world is to predict how its actions affect objects in its environment. Many existing methods for learning the dynamics of physical interactions require labeled object information. However, to scale real-world interaction learning to a variety of scenes and objects, acquiring labeled data becomes increasingly impractical. To learn about physical object motion without labels, we develop an action-conditioned video prediction model that explicitly models pixel motion, by predicting a distribution over pixel motion from previous frames. Because our model explicitly predicts motion, it is partially invariant to object appearance, enabling it to generalize to previously unseen objects. To explore video prediction for real-world interactive agents, we also introduce a dataset of 50, 000 robot interactions involving pushing motions, including a test set with novel objects. In this dataset, accurate prediction of videos conditioned on the robot’s future actions amounts to learning a “visual imagination” of different futures based on different courses of action. Our experiments show that our proposed method not only produces more accurate video predictions, but also more accurately predicts object motion, when compared to prior methods.

Self-Supervised Visual Planning with Temporal Skip Connections <=SEP=> In order to autonomously learn wide repertoires of complex skills, robots must be able to learn from their own autonomously collected data, without human supervision. One learning signal that is always available for autonomously collected data is prediction. If a robot can learn to predict the future, it can use this predictive model to take actions to produce desired outcomes, such as moving an object to a particular location. However, in complex open-world scenarios, designing a representation for prediction is difficult. In this work, we instead aim to enable self-supervised robot learning through direct video prediction: instead of attempting to design a good representation, we directly predict what the robot will see next, and then use this model to achieve desired goals. A key challenge in video prediction for robotic manipulation is handling complex spatial arrangements such as occlusions. To that end, we introduce a video prediction model that can keep track of objects through occlusion by incorporating temporal skipconnections. Together with a novel planning criterion and action space formulation, we demonstrate that this model substantially outperforms prior work on video prediction-based control. Our results show manipulation of objects not seen during training, handling multiple objects, and pushing objects around obstructions. These results represent a significant advance in the range and complexity of skills that can be performed entirely with self-supervised robot learning.

Integrated staffing and scheduling for an aircraft line maintenance problem <=SEP=> This paper presents a method for constructing the workforce schedules of an aircraft maintenance company. The method integrates both the staffing and the scheduling decision. We formulate the optimization problem using a mixed integer linear programming approach and solve it heuristically using a branch-and-bound enumeration framework. Each node of the tree represents a combination of teams and team sizes. An extensive computational experiment is added to illustrate the algorithmic performance using a set of test instances that are derived from a real-life setting. We conclude this paper by discussing some results and by addressing interesting future extensions to the model.

Local pattern transformation based feature extraction techniques for classification of epileptic EEG signals <=SEP=> Background and objective: According to the World Health Organization (WHO) epilepsy affects approximately 45–50 million people. Electroencephalogram (EEG) records the neurological activity in the brain and it is used to identify epilepsy. Visual inspection of EEG signals is a time-consuming process and it may lead to human error. Feature extraction and classification are two main steps that are required to build an automated epilepsy detection framework. Feature extraction reduces the dimensions of the input signal by retaining informative features and the classifier assigns a proper class label to the extracted feature vector. Our aim is to present effective feature extraction techniques for automated epileptic EEG signal classification. Methods: In this study, two effective feature extraction techniques (Local Neighbor Descriptive Pattern [LNDP] and One-dimensional Local Gradient Pattern [1D-LGP]) have been introduced to classify epileptic EEG signals. The classification between epileptic seizure and non-seizure signals is performed using different machine learning classifiers. The benchmark epilepsy EEG dataset provided by the University of Bonn is used in this research. The classification performance is evaluated using 10-fold cross validation. The classifiers used are the Nearest Neighbor (NN), Support Vector Machine (SVM), Decision Tree (DT) and Artificial Neural Network (ANN). The experiments have been repeated for 50 times. Results: LNDP and 1D-LGP feature extraction techniques with ANN classifier achieved the average classification accuracy of 99.82% and 99.80%, respectively, for the classification between normal and epileptic EEG signals. Eight different experimental cases were tested. The classification results were better than those of some existing methods. Conclusions: This study suggests that LNDP and 1D-LGP could be effective feature extraction techniques for the classification of epileptic EEG signals. . Introduction Epilepsy is a central nervous system disorder and it has been eported that approximately 45–50 million people suffer from this isorder [1]. EEG captures the neurological activity inside the brain y placing electrodes on the scalp and helps in detection of epilepic seizure [2,3]. Epileptic seizure detection can be considered as classification problem where the task is to classify an input sigal either as an epileptic seizure signal or as a non-seizure signal. EG signals are usually recorded for long durations to carry out n analysis. Detection of epileptic seizure in these long duration EG signals requires expertise. In the absence of expert, particuarly in emergencies, seizure detection becomes a challenging task. herefore, providing a framework for automated epileptic seizure ∗ Corresponding author: E-mail address: abegiitdhanbad@gmail.com (A.K. Jaiswal). ttp://dx.doi.org/10.1016/j.bspc.2017.01.005 746-8094/© 2017 Elsevier Ltd. All rights reserved. © 2017 Elsevier Ltd. All rights reserved. detection is of great significance. A number of methods have been proposed in literature for the classification of epileptic EEG signals. The methods can be categorized into following domains of signal analysis: Time domain analysis: Some techniques in the field of epileptic EEG signal classification belong to this category. Techniques like linear prediction [4], Fractional linear prediction [5], Principal component analysis based radial basis function neural network [6], etc. have been proposed for epileptic seizure detection. Frequency domain analysis: With the assumption that the EEG signals are stationary signals, Polat and Gunes [7] introduced a hybrid framework based on frequency domain analysis with Fourier transform and decision tree. In the hybrid model, Fourier transform was used for feature extraction and decision tree was used for the classification. Srinivasan et al. [8] used features extracted from time domain and frequency domain for seizure detection in EEG signals. The extracted features were fed to the Elman neural network to detect epileptic seizure. 82 A.K. Jaiswal, H. Banka / Biomedical Signal Processing and Control 34 (2017) 81–92 0 50

Obtaining accurate trajectories from repeated coarse and inaccurate location logs <=SEP=> Context awareness is a key property for enabling context aware services. For a mobile device, the user's location or trajectory is one of the crucial contexts. One common challenge for detecting location or trajectory by mobile devices is to manage the tradeoff between accuracy and power consumption. Typical approaches are (1) controlling the frequency of usage of sensors and (2) sensor fusion technique. The algorithm proposed in this paper takes a different approach to improve the accuracy by merging repeatedly measured coarse and inaccurate location data from cell tower. The experimental result shows that the mean error distance between the detected trajectory and the ground truth is improved from 44m to 10.9m by merging data from 41 days' measurement.

Bayesian Learning for Neural Networks <=SEP=> 

A flame detection algorithm based on Bag-of-Features in the YUV color space <=SEP=> Computer vision-based fire detection involves flame detection and smoke detection. This paper proposes a new flame detection algorithm, which is based on a Bag-of-Features technique in the YUV color space. Inspired by that the color of flame in image and video will fall in certain regions in the color space, models of flame pixels and non-flame pixels are established based on code book in the training phase in our proposal. In the testing phase, the input image is split into some N×N blocks and each block is classified respectively. In each N×N block, the pixels values in the YUV color space are extracted as features, just as in the training phase. According to the experimental results, our proposed method can reduce the number of false alarms greatly compared with an alternative algorithm, while it also ensures the accurate classification of positive samples. The classification performance of our proposed method is better than that of alternative algorithms.

Multi sensor system for automatic fall detection <=SEP=> To reduce elderly falling risk in the area where video surveillance system is unavailable due to privacy reason and so on, we need a monitor system which does no recognize what they are doing. This paper presents an elderly-falling detection system using ultrasonic sensors. The ultrasonic technology-based multi sensors are couple of receiver and transmitter together, which are connected to Arduino microcontroller in order to send the elderly person's fall related signal using WiFi to the processing unit. The sensors are positioned as an array on the roof and wall in the room. The signal is analyzed to recognize human by sensing distance, and detect the action such as standing, sitting, and falling by pattern matching with the standard templates of top and side signal. In experiments, the proposed system can recognize accuracy of falling detection is 93% approximately.

Automatic music transcription: challenges and future directions <=SEP=> Automatic music transcription is considered by many to be a key enabling technology in music signal processing. However, the performance of transcription systems is still significantly below that of a human expert, and accuracies reported in recent years seem to have reached a limit, although the field is still very active. In this paper we analyse limitations of current methods and identify promising directions for future research. Current transcription methods use general purpose models which are unable to capture the rich diversity found in music signals. One way to overcome the limited performance of transcription systems is to tailor algorithms to specific use-cases. Semi-automatic approaches are another way of achieving a more reliable transcription. Also, the wealth of musical scores and corresponding audio data now available are a rich potential source of training data, via forced alignment of audio to scores, but large scale utilisation of such data has yet to be attempted. Other promising approaches include the integration of information from multiple algorithms and different musical aspects.

Millibot Trains for Enhanced Mobility <=SEP=> The objective of this work is to enhance the mobility of small mobile robots by enabling them to link into a train configuration capable of crossing relatively large obstacles. In particular, we are building on Millibots, semiautonomous, tracked mobile sensing/communication platforms at the 5-cm scale previously developed at Carnegie Mellon University. TheMillibot Train concept provides couplers that allow the Millibot modules to engage/disengage under computer control and joint actuators that allow lifting of one module by another and control of the whole train shape in two dimensions. A manually configurable train prototype demonstrated the ability to climb standard stairs and vertical steps nearly half the train length. A fully functional module with powered joints has been developed and several have been built and tested. Construction of a set of six modules is well underway and will allow testing of the complete train in the near future. This paper focuses on the development, design, and construction of the electromechanical hardware for the Millibot Train.

Learning Globally-Consistent Local Distance Functions for Shape-Based Image Retrieval and Classification <=SEP=> We address the problem of visual category recognition by learning an image-to-image distance function that attempts to satisfy the following property: the distance between images from the same category should be less than the distance between images from different categories. We use patch-based feature vectors common in object recognition work as a basis for our image-to-image distance functions. Our large-margin formulation for learning the distance functions is similar to formulations used in the machine learning literature on distance metric learning, however we differ in that we learn local distance functions¿a different parameterized function for every image of our training set¿whereas typically a single global distance function is learned. This was a novel approach first introduced in Frome, Singer, & Malik, NIPS 2006. In that work we learned the local distance functions independently, and the outputs of these functions could not be compared at test time without the use of additional heuristics or training. Here we introduce a different approach that has the advantage that it learns distance functions that are globally consistent in that they can be directly compared for purposes of retrieval and classification. The output of the learning algorithm are weights assigned to the image features, which is intuitively appealing in the computer vision setting: some features are more salient than others, and which are more salient depends on the category, or image, being considered. We train and test using the Caltech 101 object recognition benchmark.

A demonstration of the GUINNESS: A GUI based neural NEtwork SyntheSizer for an FPGA <=SEP=> The GUINNESS is a tool flow for the deep neural network toward FPGA implementation [3,4,5] based on the GUI (Graphical User Interface) including both the binarized deep neural network training on GPUs and the inference on an FPGA. It generates the trained the Binarized deep neural network [2] on the desktop PC, then, it generates the bitstream by using standard the FPGA CAD tool flow. All the operation is done on the GUI, thus, the designer is not necessary to write any scripts to descript the neural network structure, training behaviour, only specify the values for hyper parameters. After finished the training, it automatically generates C++ codes to synthesis the bitstream using the Xilinx SDSoC system design tool flow. Thus, our tool flow is suitable for the software programmers who are not familiar with the FPGA design.

DR(eye)VE: A Dataset for Attention-Based Tasks with Applications to Autonomous and Assisted Driving <=SEP=> Autonomous and assisted driving are undoubtedly hot topics in computer vision. However, the driving task is extremely complex and a deep understanding of drivers' behavior is still lacking. Several researchers are now investigating the attention mechanism in order to define computational models for detecting salient and interesting objects in the scene. Nevertheless, most of these models only refer to bottom up visual saliency and are focused on still images. Instead, during the driving experience the temporal nature and peculiarity of the task influence the attention mechanisms, leading to the conclusion that real life driving data is mandatory. In this paper we propose a novel and publicly available dataset acquired during actual driving. Our dataset, composed by more than 500,000 frames, contains drivers' gaze fixations and their temporal integration providing task-specific saliency maps. Geo-referenced locations, driving speed and course complete the set of released data. To the best of our knowledge, this is the first publicly available dataset of this kind and can foster new discussions on better understanding, exploiting and reproducing the driver's attention process in the autonomous and assisted cars of future generations.

Tooth structure removal associated with various preparation designs for anterior teeth. <=SEP=> STATEMENT OF PROBLEM
The conservation of sound tooth structure helps preserve tooth vitality and reduce postoperative sensitivity. Innovative preparation designs, like those for porcelain laminate veneers, are much less invasive than conventional complete-coverage crown preparations. However, no study has quantified the amount of tooth structure removed during these preparations.


PURPOSE
The purpose of this study was to quantify and compare the amount of tooth structure removed when various innovative and conventional tooth preparation designs were completed on different teeth.


MATERIAL AND METHOD
. A new comprehensive tooth preparation design classification system was introduced. Typodont resin teeth representing the maxillary left central incisor, maxillary left canine, and mandibular left central incisor were prepared with the following designs: partial (V1), traditional (V2), extended (V3), and complete (V4) porcelain laminate veneer preparations; resin-bonded retainer preparation with grooves (A1) and with wing/grooves (A2); all-ceramic crown preparation with 0.8 mm axial reduction and tapering chamfer finish line (F1), all-ceramic crown preparation with 1.0 mm axial reduction and rounded shoulder finish line (F2), and metal-ceramic crown with 1.4 mm axial reduction and facial shoulder finish line (F3). After tooth preparations (10 per group), the crown was separated from the root at the CEJ. The removed coronal tooth structure was measured with gravimetric analysis. Means and standard deviations for tooth structure removal with different preparation designs were calculated and analyzed with analysis of variance at a significance level of P<.05.


RESULTS
Significant differences in the amount of tooth structure removal were noted between preparation designs. Ceramic veneers and resin-bonded prosthesis retainers were the least invasive preparation designs, removing approximately 3% to 30% of the coronal tooth structure by weight. Approximately 63% to 72% of the coronal tooth structure was removed when teeth were prepared for all-ceramic and metal-ceramic crowns. For a single crown restoration, the tooth structure removal required for an F3 preparation (metal-ceramic crown) was 4.3 times greater than for a V2 preparation (porcelain laminate veneer, facial surface only) and 2.4 times greater than for a V4 preparation (more extensive porcelain laminate veneer).


CONCLUSION
Within the limitations of this study, tooth preparations for porcelain laminate veneers and resin-bonded prostheses required approximately one-quarter to one-half the amount of tooth reduction of conventional complete-coverage crowns.

InTouch Tactile Tales: Haptic Feedback and Long-Distance Storytelling <=SEP=> The squeeze of an arm during a tense moment in a story -- an absentminded caress on the back of the hand while listening to an engaging tale -- the physical presence and interpersonal touch of a loved one can be an important part of reading to a child. However the thrill of the tale is often lost and the intimacy diluted when separated families have to resort to the flatness of a video-call or mobile app to share a bedtime story with their loved one. This project is setting out to create a physical story portal, a magical object which uses technologies to link teller and listener through sound, light and touch to help bridge this gap when families are separated. While the development of the InTouch reading system is still in the preliminary stages, a prototype handheld device has been created to allow adults and children to share touch-based messages during story time. It has been trialled with several families in the lab, and the initial results have pointed to both guidance and design considerations that should be taken into account for such a system to be successfully deployed.

BabyTalk: Understanding and Generating Simple Image Descriptions <=SEP=> We present a system to automatically generate natural language descriptions from images. This system consists of two parts. The first part, content planning, smooths the output of computer vision-based detection and recognition algorithms with statistics mined from large pools of visually descriptive text to determine the best content words to use to describe an image. The second step, surface realization, chooses words to construct natural language sentences based on the predicted content and general statistics from natural language. We present multiple approaches for the surface realization step and evaluate each using automatic measures of similarity to human generated reference descriptions. We also collect forced choice human evaluations between descriptions from the proposed generation system and descriptions from competing approaches. The proposed system is very effective at producing relevant sentences for images. It also generates descriptions that are notably more true to the specific image content than previous work.

Quaternion Zernike moments and their invariants for color image analysis and object recognition <=SEP=> Moments and moment invariants have become a powerfu l tool in pattern recognition and image analysis. C onventional methods to deal with color images are based on RGB decomposition or graying, which may lose some signi ficant color information. In this paper, by using the algebra of quaternions, we introduce the quaternion Zernike m oments (QZMs) to deal with the color images in a holistic manner. It is s hown that the QZMs can be obtained from the convent ional Zernike moments of each channel. We also provide the theore tical framework to construct a set of combined inva riants with respect to rotation, scaling and translation (RST) transfor mation. Experimental results are provided to illust rate the efficiency of the proposed descriptors.

Efficient reinforcement learning using Gaussian processes <=SEP=> IX

Bilayer Phosphorene: Effect of Stacking Order on Bandgap and Its Potential Applications in Thin-Film Solar Cells. <=SEP=> Phosphorene, a monolayer of black phosphorus, is promising for nanoelectronic applications not only because it is a natural p-type semiconductor but also because it possesses a layer-number-dependent direct bandgap (in the range of 0.3 to 1.5 eV). On basis of the density functional theory calculations, we investigate electronic properties of the bilayer phosphorene with different stacking orders. We find that the direct bandgap of the bilayers can vary from 0.78 to 1.04 eV with three different stacking orders. In addition, a vertical electric field can further reduce the bandgap to 0.56 eV (at the field strength 0.5 V/Å). More importantly, we find that when a monolayer of MoS2 is superimposed with the p-type AA- or AB-stacked bilayer phosphorene, the combined trilayer can be an effective solar-cell material with type-II heterojunction alignment. The power conversion efficiency is predicted to be ∼18 or 16% with AA- or AB-stacked bilayer phosphorene, higher than reported efficiencies of the state-of-the-art trilayer graphene/transition metal dichalcogenide solar cells.

A General Empirical Solution to the Macro Software Sizing and Estimating Problem <=SEP=> Application software development has been an area of organizational effort that has not been amenable to the normal managerial and cost controls. Instances of actual costs of several times the initial budgeted cost, and a time to initial operational capability sometimes twice as long as planned are more often the case than not.

The Weibull-G Family of Probability Distributions <=SEP=> Abstract: The Weibull distribution is the most important distribution for problems in reliability. We study some mathematical properties of the new wider Weibull-G family of distributions. Some special models in the new family are discussed. The properties derived hold to any distribution in this family. We obtain general explicit expressions for the quantile function, ordinary and incomplete moments, generating function and order statistics. We discuss the estimation of the model parameters by maximum likelihood and illustrate the potentiality of the extended family with two applications to real data.

Transfer of mitochondria via tunneling nanotubes rescues apoptotic PC12 cells <=SEP=> Tunneling nanotubes (TNTs) are F-actin-based membrane tubes that form between cells in culture and in tissues. They mediate intercellular communication ranging from electrical signalling to the transfer of organelles. Here, we studied the role of TNTs in the interaction between apoptotic and healthy cells. We found that pheochromocytoma (PC) 12 cells treated with ultraviolet light (UV) were rescued when cocultured with untreated PC12 cells. UV-treated cells formed a different type of TNT with untreated PC12 cells, which was characterized by continuous microtubule localized inside these TNTs. The dynamic behaviour of mCherry-tagged end-binding protein 3 and the accumulation of detyrosinated tubulin in these TNTs indicate that they are regulated structures. In addition, these TNTs show different biophysical properties, for example, increased diameter allowing dye entry, prolonged lifetime and decreased membrane fluidity. Further studies demonstrated that microtubule-containing TNTs were formed by stressed cells, which had lost cytochrome c but did not enter into the execution phase of apoptosis characterized by caspase-3 activation. Moreover, mitochondria colocalized with microtubules in TNTs and transited along these structures from healthy to stressed cells. Importantly, impaired formation of TNTs and untreated cells carrying defective mitochondria were unable to rescue UV-treated cells in the coculture. We conclude that TNT-mediated transfer of functional mitochondria reverse stressed cells in the early stages of apoptosis. This provides new insights into the survival mechanisms of damaged cells in a multicellular context.

Business Analytics in the Context of Big Data: A Roadmap for Research <=SEP=> This paper builds on academic and industry discussions from the 2012 and 2013 pre-ICIS events: BI Congress III and the Special Interest Group on Decision Support Systems (SIGDSS) workshop, respectively. Recognizing the potential of “big data” to offer new insights for decision making and innovation, panelists at the two events discussed how organizations can use and manage big data for competitive advantage. In addition, expert panelists helped to identify research gaps. While emerging research in the academic community identifies some of the issues in acquiring, analyzing, and using big data, many of the new developments are occurring in the practitioner community. We bridge the gap between academic and practitioner research by presenting a big data analytics framework that depicts a process view of the components needed for big data analytics in organizations. Using practitioner interviews and literature from both academia and practice, we identify the current state of big data research guided by the framework and propose potential areas for future research to increase the relevance of academic research to practice.

A self-steering I/Q receiver array in 45-nm CMOS SOI <=SEP=> A novel I/Q receiver array is demonstrated that adapts phase shifts in each receive channel to point a receive beam toward an incident RF signal. The measured array operates at 8.1 GHz and covers steering angles of +/-35 degrees for a four element array. Additionally, the receiver incorporates an I/Q down-converter and demodulates 64QAM with EVM less than 4%. The chip is fabricated in 45 nm CMOS SOI process and occupies an area of 3.45 mm2 while consuming 143 mW dc power.

The Price of Anarchy in Supply Chains: Quantifying the Efficiency of Price-Only Contracts <=SEP=> I this paper, we quantify the efficiency of decentralized supply chains that use price-only contracts. With a price-only contract, a buyer and a seller agree only on a constant transaction price, without specifying the amount that will be transferred. It is well known that these contracts do not provide incentives to the parties to coordinate their inventory/capacity decisions. We measure efficiency with the price of anarchy (PoA), defined as the largest ratio of profits between the integrated supply chain (that is, fully coordinated) and the decentralized supply chain. We characterize the efficiency of various supply chain configurations: push or pull inventory positioning, two or more stages, serial or assembly systems, single or multiple competing suppliers, and single or multiple competing retailers.

Learning Semantic Script Knowledge with Event Embeddings <=SEP=> Induction of common sense knowledge about prototypical sequences of events has recently received much attention (e.g., (Chambers & Jurafsky, 2008; Regneri et al., 2010)). Instead of inducing this knowledge in the form of graphs, as in much of the previous work, in our method, distributed representations of event realizations are computed based on distributed representations of predicates and their arguments, and then these representations are used to predict prototypical event orderings. The parameters of the compositional process for computing the event representations and the ranking component of the model are jointly estimated from texts. We show that this approach results in a substantial boost in ordering performance with respect to previous methods.

Experimental study platform of the automatic transfer switch used to power supplies back-up <=SEP=> This paper presents the practical implementation of an experimental platform used for the automatic transfer switch to the back-up power source. The automatic transfer switch concept represents the procedure of fast connection of the electrical consumers to a back-up electric power source. The connection to the back-up power source is necessary in the event of failure of the main power source. The experimental platform is designed to operate in automatic standard mode, manual mode and through a programmable logic controller. The selection's mode of operation is determined by the operation's key. The simulation of grid faults on the power supply is done by means of the controls and the variable voltage regulators located on the experimental platform. The automatic transfer switch is conditioned by the effective status of the power supply and minimum voltage allowed by devices. The signaling status of the power supply is performed based on the information received from relays of interface and programmable logic controllers. The platform presents to users two technologies able to achieve the same function. Thus, in case of the classic mode of the automatic transfer switch, the wired technology is used and this involves many components and related links. In case of the automatic transfer switch based on programmable logic controller, a technology based on programming is also implemented. Using this platform, the advantage of implementation of programmable logic controllers versus wired technology in industrial applications can be observed.