Query: Making 3D Eyeglasses Try-on practical

Relevant passages:
Active Appearance Models Revisited <=SEP=> Active Appearance Models (AAMs) and the closely related concepts of Morphable Models and Active Blobs are generative models of a certain visual phenomenon. Although linear in both shape and appearance, overall, AAMs are nonlinear parametric models in terms of the pixel intensities. Fitting an AAM to an image consists of minimising the error between the input image and the closest model instance; i.e. solving a nonlinear optimisation problem. We propose an efficient fitting algorithm for AAMs based on the inverse compositional image alignment algorithm. We show that the effects of appearance variation during fitting can be precomputed (“projected out”) using this algorithm and how it can be extended to include a global shape normalising warp, typically a 2D similarity transformation. We evaluate our algorithm to determine which of its novel aspects improve AAM fitting performance.

Virtual try-on of eyeglasses using 3D model of the head <=SEP=> This work presents a system for virtual try-on of eyeglasses using a 3D model of user's face and head. The 3D head model is reconstructed using only one image of the user. The 3D glasses model are then fitted onto this head model, and the user's head movement is tracked in real-time to rotate the 3D head model with glasses accordingly.

A mixed reality system for virtual glasses try-on <=SEP=> In this paper we present an augmented reality system for automatic try-on of 3D virtual eyeglasses. The user can select from various virtual models of eyeglasses for trying-on and the system will automatically fit the selected virtual glasses on the user's face. The user can see his/her face as in a mirror with the 3D virtual glasses fitted on it. We also propose a method for handling the occlusion problem, to display only those parts of the glasses that are not occluded by the face. This system can be used for online shopping, or short listing a large set of available models to a few before physical try-on at a retailer's site.

Linear Object Classes and Image Synthesis From a Single Example Image <=SEP=> The need to generate new views of a 3D object from a single real image arises in several fields, including graphics and°object recognition. While the traditional approach relies on the use of 3D models, we have recently introduced [11, 6, 5] techniques that are applicable under restricted conditions but simpler. The approach exploits image transformations that are specific to the relevant object class and learnable from example views of other "prototypical" objects of the same class. In this paper, we introduce such a new technique by extending the notion of linear class first proposed by Poggio and Vetter [12]. For linear object classes it is shown that linear transformations can be learned exactly from a basis set of 2D prototypical views. We demonstrate the approach on artificial objects and then show preliminary evidence that the technique can effectively "rotate" high-resolution face images from a single 2D view.

Rapid Object Detection using a Boosted Cascade of Simple Features <=SEP=> This paper describes a machine learning approach for visual object detection which is capable of processing images extremely rapidly and achieving high detection rates. This work is distinguished by three key contributions. The first is the introduction of a new image representation called the “Integral Image” which allows the features used by our detector to be computed very quickly. The second is a learning algorithm, based on AdaBoost, which selects a small number of critical visual features from a larger set and yields extremely efficient classifiers[6]. The third contribution is a method for combining increasingly more complex classifiers in a “cascade” which allows background regions of the image to be quickly discarded while spending more computation on promising object-like regions. The cascade can be viewed as an object specific focus-of-attention mechanism which unlike previous approaches provides statistical guarantees that discarded regions are unlikely to contain the object of interest. In the domain of face detection the system yields detection rates comparable to the best previous systems. Used in real-time applications, the detector runs at 15 frames per second without resorting to image differencing or skin color detection.

An Efficient Active Learning Framework for New Relation Types <=SEP=> Supervised training of models for semantic relation extraction has yielded good performance, but at substantial cost for the annotation of large training corpora. Active learning strategies can greatly reduce this annotation cost. We present an efficient active learning framework that starts from a better balance between positive and negative samples, and boosts training efficiency by interleaving self-training and co-testing. We also studied the reduction of annotation cost by enforcing argument type constraints. Experiments show a substantial speed-up by comparison to the previous state-of-the-art pure co-testing active learning framework. We obtain reasonable performance with only 150 labels for individual ACE 2004 relation

Multi-vehicle detection with identity awareness using cascade Adaboost and Adaptive Kalman filter for driver assistant system <=SEP=> Vision-based vehicle detection is an important issue for advanced driver assistance systems. In this paper, we presented an improved multi-vehicle detection and tracking method using cascade Adaboost and Adaptive Kalman filter(AKF) with target identity awareness. A cascade Adaboost classifier using Haar-like features was built for vehicle detection, followed by a more comprehensive verification process which could refine the vehicle hypothesis in terms of both location and dimension. In vehicle tracking, each vehicle was tracked with independent identity by an Adaptive Kalman filter in collaboration with a data association approach. The AKF adaptively adjusted the measurement and process noise covariance through on-line stochastic modelling to compensate the dynamics changes. The data association correctly assigned different detections with tracks using global nearest neighbour(GNN) algorithm while considering the local validation. During tracking, a temporal context based track management was proposed to decide whether to initiate, maintain or terminate the tracks of different objects, thus suppressing the sparse false alarms and compensating the temporary detection failures. Finally, the proposed method was tested on various challenging real roads, and the experimental results showed that the vehicle detection performance was greatly improved with higher accuracy and robustness.

Temporal Pattern Attention for Multivariate Time Series Forecasting <=SEP=> Forecasting of multivariate time series data, for instance the prediction of electricity consumption, solar power production, and polyphonic piano pieces, has numerous valuable applications. However, complex and non-linear interdependencies between time steps and series complicate this task. To obtain accurate prediction, it is crucial to model long-term dependency in time series data, which can be achieved by recurrent neural networks (RNNs) with an attention mechanism. The typical attention mechanism reviews the information at each previous time step and selects relevant information to help generate the outputs; however, it fails to capture temporal patterns across multiple time steps. In this paper, we propose using a set of filters to extract time-invariant temporal patterns, similar to transforming time series data into its “frequency domain”. Then we propose a novel attention mechanism to select relevant time series, and use its frequency domain information for multivariate forecasting. We apply the proposed model on several real-world tasks and achieve state-of-the-art performance in all of these with a single exception. Our source code is available at https://github.com/gantheory/TPA-LSTM. * indicates equal contribution. This work was financially supported by the Ministry of Science and Technology of Taiwan. Shun-Yao Shih National Taiwan University E-mail: shunyaoshih@gmail.com Fan-Keng Sun National Taiwan University E-mail: b03901056@ntu.edu.tw Hung-yi Lee National Taiwan University E-mail: hungyilee@ntu.edu.tw ar X iv :1 80 9. 04 20 6v 2 [ cs .L G ] 2 7 N ov 2 01 8 2 Shun-Yao Shih* et al. Fig. 1 Historical prices of crude oil, gasoline, and lumber. Units are omitted and scales are normalized for simplicity.

An innovative cost-effective smart meter with embedded non intrusive load monitoring <=SEP=> Most of the time, domestic energy usage is invisible to the users. Households have only a vague idea about the amount of energy they are using for different purposes during the day, and the electric bill is usually the only monthly/bimonthly feedback. Making the energy consumption promptly visible can make the difference and foster a really efficient usage of the energy in a city. The emerging smart metering infrastructure has the potential to address some of these goals, by providing both households and electric utilities useful information about energy consumption, with a detailed breakdown to single appliance contribution. This paper presents a novel ultra-low-cost smart metering system capable of providing domestic AC load identification, by implementing both basic/typical smart meter measurement and a simple yet effective load disaggregation algorithm, which can detect and monitor domestic loads starting from a single point of measurement.

Seven element wideband planar log-periodic antenna for TVWS base station <=SEP=> This paper presents design and simulation of a wide band antenna for base station in the TV White Space (TVWS) spectrum band operating between 470 MHz-700 MHz. Concept of printed Log Periodic Dipole Array (LPDA), which provides wide bandwidth, has been used to realize wide band antenna. The antenna elements are printed on low cost FR4 substrate with εr = 4.4 and tan δ = 0.02. These elements are printed alternatively on both side of the substrate. Total volume of the antenna is 303 × 162.3 × 1.6 mm3. To reduce the size, scaling factor (τ) for this design is considered as 0.89 and the relative spacing (σ) is chosen as 0.054. The antenna is fed at the base of smallest element. The antenna shows an impedance bandwidth for VSWR ≤ 2 in the frequency range of 470 MHz–700 MHz. The gain of this antenna is between 5.3 dB to 6.5 dB in the entire band of operation. The radiation pattern shows end fire behavior with uniform radiation pattern in both E and H-planes with maximum front to back lobe ratio (F/B) of 30 dB.

An ontology mindset for system engineering <=SEP=> This paper presents an approach to support model-based system engineering (MBSE) which combines a standard metamodel such as UML, SysML, or NAF, with dedicated project ontologies that ease the understanding and creation of models. The approach is formalized using category theory to determine the conditions for use of project ontologies while ensuring the resulting models comply with the selected metamodel. The paper exposes the advantages of the proposed approach over a direct metamodel-based approach or an ontology-based approach using a domain specific language (DSL).

A new multiclass SVM algorithm and its application to crowd density analysis using LBP features <=SEP=> Crowd density analysis is a crucial component in visual surveillance for security monitoring. In this paper, we propose to estimate crowd density at patch level, where the size of each patch varies in such way to compensate the effects of perspective distortions. The main contribution of this paper is two-fold: First, we propose to learn a discriminant subspace of the high-dimensional Local Binary Pattern (LBP) instead of using raw LBP feature vector. Second, an alternative algorithm for multiclass SVM based on relevance scores is proposed. The effectiveness of the proposed approach is evaluated on PETS dataset, and the results demonstrate the effect of low-dimensional compact representation of LBP on the classification accuracy. Also, the performance of the proposed multiclass SVM algorithm is compared to other frequently used algorithms for multi-classification problem and the proposed algorithm gives good results while reducing the complexity of the classification.

Moving towards a new vision: implementation of a public health policy intervention <=SEP=> BACKGROUND
Public health systems in Canada have undergone significant policy renewal over the last decade in response to threats to the public's health, such as severe acute respiratory syndrome. There is limited research on how public health policies have been implemented or what has influenced their implementation. This paper explores policy implementation in two exemplar public health programs -chronic disease prevention and sexually-transmitted infection prevention - in Ontario, Canada. It examines public health service providers', managers' and senior managements' perspectives on the process of implementation of the Ontario Public Health Standards 2008 and factors influencing implementation.


METHODS
Public health staff from six health units representing rural, remote, large and small urban settings were included. We conducted 21 focus groups and 18 interviews between 2010 (manager and staff focus groups) and 2011 (senior management interviews) involving 133 participants. Research assistants coded transcripts and researchers reviewed these; the research team discussed and resolved discrepancies. To facilitate a breadth of perspectives, several team members helped interpret the findings. An integrated knowledge translation approach was used, reflected by the inclusion of academics as well as decision-makers on the team and as co-authors.


RESULTS
Front line service providers often were unaware of the new policies but managers and senior management incorporated them in operational and program planning. Some participants were involved in policy development or provided feedback prior to their launch. Implementation was influenced by many factors that aligned with Greenhalgh and colleagues' empirically-based Diffusion of Innovations in Service Organizations Framework. Factors and related components that were most clearly linked to the OPHS policy implementation were: attributes of the innovation itself; adoption by individuals; diffusion and dissemination; the outer context - interorganizational networks and collaboration; the inner setting - implementation processes and routinization; and, linkage at the design and implementation stage.


CONCLUSIONS
Multiple factors influenced public health policy implementation. Results provide empirical support for components of Greenhalgh et al's framework and suggest two additional components - the role of external organizational collaborations and partnerships as well as planning processes in influencing implementation. These are important to consider by government and public health organizations when promoting new or revised public health policies as they evolve over time. A successful policy implementation process in Ontario has helped to move public health towards the new vision.

ICDAR 2013 Chinese Handwriting Recognition Competition <=SEP=> This paper describes the Chinese handwriting recognition competition held at the 12th International Conference on Document Analysis and Recognition (ICDAR 2013). This third competition in the series again used the CASIA-HWDB/OLHWDB databases as the training set, and all the submitted systems were evaluated on closed datasets to report character-level correct rates. This year, 10 groups submitted 27 systems for five tasks: classification on extracted features, online/offline isolated character recognition, online/offline handwritten text recognition. The best results (correct rates) are 93.89% for classification on extracted features, 94.77% for offline character recognition, 97.39% for online character recognition, 88.76% for offline text recognition, and 95.03% for online text recognition, respectively. In addition to the test results, we also provide short descriptions of the recognition methods and brief discussions on the results.

Hunting for Spammers: Detecting Evolved Spammers on Twitter <=SEP=> Once an email problem, spam has nowadays branched into new territories with disruptive effects. In particular, spam has established itself over the recent years as a ubiquitous, annoying, and sometimes threatening aspect of online social networks. Due to its prevalent existence, many works have tackled spam on Twitter from different angles. Spam is, however, a moving target. The new generation of spammers on Twitter has evolved into online creatures that are not easily recognizable by old detection systems. With the strong tangled spamming community, automatic tweeting scripts, and the ability to massively create Twitter accounts with a negligible cost, spam on Twitter is becoming smarter, fuzzier and harder to detect. Our own analysis of spam content on Arabic trending hashtags in Saudi Arabia results in an estimate of about three quarters of the total generated content. This alarming rate makes the development of adaptive spam detection techniques a very real and pressing need. In this paper, we analyze the spam content of trending hashtags on Saudi Twitter, and assess the performance of previous spam detection systems on our recently gathered dataset. Due to the escalating manipulation that characterizes newer spamming accounts, simple manual labeling currently leads to inaccurate results. In order to get reliable ground-truth data, we propose an updated manual classification algorithm that avoids the deficiencies of older manual approaches. We also adapt the previously proposed features to respond to spammers evading techniques, and use these features to build a new data-driven detection system.

Real-Time Keyword Extraction from Conversations <=SEP=> We introduce a novel, fully unsupervised method to extract keywords from meeting speech in real-time. Our approach represents text as a word co-occurrence network and leverages the k-core graph decomposition algorithm and properties of submodular functions. We outperform multiple baselines in a real-time scenario emulated from the AMI and ICSI meeting corpora. Evaluation is conducted against both extractive and abstractive gold standard using two standard performance metrics and a newer one based on word embeddings.

Building the Next Generation of Cyber Security Professionals <=SEP=> Cyber security is an area of strategic and policy interest to governments and enterprises globally, which results in an increase in the demand for cyber security professionals. However, there is a lack of education based on sound theories, standards and practices. In this paper, we adapted the Situational Crime Prevention Theory and the NICE National Cybersecurity Workforce Framework in the design and delivery of our courses, particularly in the Cyber Security Exercise (CSE) which forms an integral part of the courses. The CSE is an attack/defence environment where students are grouped and given a virtual machine with which to host a number of services (e.g. HTTP(S), FTP and SSH) for access by other groups. The CSE is designed to mirror real-world environments where the students’ skills will be applied. An overview of the CSE architecture was also provided for readers interested in replicating the exercise in their institutions. Based on student assessment and feedback, we found that our approach was useful in transferring theoretical knowledge to practical skills suitable for the cyber security workforce.

Contextual Consent: Ethical Mining of Social Media for Health Research <=SEP=> Social media are a rich source of insight for data mining and u sercentred research, but the question of consent arises when st udying such data without the express knowledge of the creator. Case studies that mine social data from users of online services s uch as Facebook and Twitter are becoming increasingly common. Thi s has led to calls for an open discussion into how researchers c an best use these vast resources to make innovative findings whi le still respecting fundamental ethical principles. In this positi n paper we highlight some key considerations for this topic and argu e that the conditions of informed consent are often not being met, a nd that using social media data that some deem free to access and analyse may result in undesirable consequences, particula ly within the domain of health research and other sensitive topics. We posit that successful exploitation of online personal data, part icularly for health and other sensitive research, requires new and us able methods of obtaining consent from the user.

Engineering Fast Route Planning Algorithms <=SEP=> Algorithms for route planning in transportation networks have recently undergone a rapid development, leading to methods that are up to one million times faster than Dijkstra’s algorithm. We outline ideas, algorithms, implementations, and experimental methods behind this development. We also explain why the story is not over yet because dynamically changing networks, flexible objective functions, and new applications pose a lot of interesting challenges.

Privacy awareness about information leakage: who knows what about me? <=SEP=> The task of protecting users' privacy is made more difficult by their attitudes towards information disclosure without full awareness and the economics of the tracking and advertising industry. Even after numerous press reports and widespread disclosure of leakages on the Web and on popular Online Social Networks, many users appear not be fully aware of the fact that their information may be collected, aggregated and linked with ambient information for a variety of purposes. Past attempts at alleviating this problem have addressed individual aspects of the user's data collection. In this paper we move towards a comprehensive and efficient client-side tool that maximizes users' awareness of the extent of their information leakage. We show that such a customizable tool can help users to make informed decisions on controlling their privacy footprint.

An Open Source Matlab/Simulink Toolbox for Interval Type-2 Fuzzy Logic Systems <=SEP=> In the last two decades, we have witnessed that Interval Type-2 Fuzzy Logic Systems (IT2-FLSs) have been successfully implemented in various engineering areas. In this paper, we will introduce a free open source Mat lab/Simulink toolbox for the development of IT2-FLSs for a wider accessibility to users beyond the type-2 fuzzy logic community. The presented IT2-FLS toolbox allows intuitive implementation of IT2-FLSs where it is capable to cover all the phases of its design. In order to allow users to easily construct IT2-FISs, a GUI is developed which is similar to that of Matlab® Fuzzy Logic Toolbox. We have embedded various Type Reduction (TR) methods in the toolbox since the TR method is the most important operation that must be taken into consideration. This gives the opportunity to the user to examine the performance of his/her IT2-FLS design with respect to the TR methods. We have also developed an IT2-FLS Simulink library so that the designer can perform various simulation analyses and can also investigate the effect of TR method on the performance of the IT2-FLS. Moreover, the developed IT2-FLS Mat lab/Simulink toolbox contains an automatic connection between Matlab and Simulink environments. Once the user has finished the design of the IT2-FLS via the GUI, it is possible to export his/her design directly to Simulink via an automatic Simulink file generation. We believe that the availability of the developed free and open-source IT2-FLS Mat lab/Simulink toolbox will be an important step for a wider deployment and development of IT2-FLSs.

MonoPerfCap : Human Performance Capture from Monocular Video WEIPENGXU , AVISHEKCHATTERJEE , MICHAELZOLLHÖFER , HELGERHODIN , DUSHYANTMEHTA <=SEP=> We present the first marker-less approach for temporally coherent 3D performance capture of a human with general clothing from monocular video. Our approach reconstructs articulated human skeleton motion as well as medium-scale non-rigid surface deformations in general scenes. Human performance capture is a challenging problem due to the large range of articulation, potentially fast motion, and considerable non-rigid deformations, even from multi-view data. Reconstruction from monocular video alone is drastically more challenging, since strong occlusions and the inherent depth ambiguity lead to a highly ill-posed reconstruction problem. We tackle these challenges by a novel approach that employs sparse 2D and 3D human pose detections from a convolutional neural network using a batch-based pose estimation strategy. Joint recovery of per-batch motion allows to resolve the ambiguities of the monocular reconstruction problem based on a low dimensional trajectory subspace. In addition, we propose refinement of the surface geometry based on fully automatically extracted silhouettes to enable medium-scale non-rigid alignment. We demonstrate state-of-the-art performance capture results that enable exciting applications such as video editing and free viewpoint video, previously infeasible from monocular video. Our qualitative and quantitative evaluation demonstrates that our approach significantly outperforms previous monocular methods in terms of accuracy, robustness and scene complexity that can be handled.

Tensors for Data Mining and Data Fusion: Models, Applications, and Scalable Algorithms <=SEP=> Tensors and tensor decompositions are very powerful and versatile tools that can model a wide variety of heterogeneous, multiaspect data. As a result, tensor decompositions, which extract useful latent information out of multiaspect data tensors, have witnessed increasing popularity and adoption by the data mining community. In this survey, we present some of the most widely used tensor decompositions, providing the key insights behind them, and summarizing them from a practitioner’s point of view. We then provide an overview of a very broad spectrum of applications where tensors have been instrumental in achieving state-of-the-art performance, ranging from social network analysis to brain data analysis, and from web mining to healthcare. Subsequently, we present recent algorithmic advances in scaling tensor decompositions up to today’s big data, outlining the existing systems and summarizing the key ideas behind them. Finally, we conclude with a list of challenges and open problems that outline exciting future research directions.

Swarm Intelligence Approaches for Grid Load Balancing <=SEP=> With the rapid growth of data and computational needs, distributed systems and computational Grids are gaining more and more attention. The huge amount of computations a Grid can fulfill in a specific amount of time cannot be performed by the best supercomputers. However, Grid performance can still be improved by making sure all the resources available in the Grid are utilized optimally using a good load balancing algorithm. This research proposes two new distributed swarm intelligence inspired load balancing algorithms. One algorithm is based on ant colony optimization and the other algorithm is based on particle swarm optimization. A simulation of the proposed approaches using a Grid simulation toolkit (GridSim) is conducted. The performance of the algorithms are evaluated using performance criteria such as makespan and load balancing level. A comparison of our proposed approaches with a classical approach called State Broadcast Algorithm and two random approaches is provided. Experimental results show the proposed algorithms perform very well in a Grid environment. Especially the application of particle swarm optimization, can yield better performance results in many scenarios than the ant colony approach.

Cross-cultural adaptation of health-related quality of life measures: literature review and proposed guidelines. <=SEP=> Clinicians and researchers without a suitable health-related quality of life (HRQOL) measure in their own language have two choices: (1) to develop a new measure, or (2) to modify a measure previously validated in another language, known as a cross-cultural adaptation process. We propose a set of standardized guidelines for this process based on previous research in psychology and sociology and on published methodological frameworks. These guidelines include recommendations for obtaining semantic, idiomatic, experiential and conceptual equivalence in translation by using back-translation techniques and committee review, pre-testing techniques and re-examining the weight of scores. We applied these guidelines to 17 cross-cultural adaptation of HRQOL measures identified through a comprehensive literature review. The reporting standards varied across studies but agreement between raters in their ratings of the studies was substantial to almost perfect (weighted kappa = 0.66-0.93) suggesting that the guidelines are easy to apply. Further research is necessary in order to delineate essential versus optional steps in the adaptation process.

Routing Around Congestion: Defeating DDoS Attacks and Adverse Network Conditions via Reactive BGP Routing <=SEP=> In this paper, we present Nyx, the first system to both effectively mitigate modern Distributed Denial of Service (DDoS) attacks regardless of the amount of traffic under adversarial control and function without outside cooperation or an Internet redesign. Nyx approaches the problem of DDoS mitigation as a routing problem rather than a filtering problem. This conceptual shift allows Nyx to avoid many of the common shortcomings of existing academic and commercial DDoS mitigation systems. By leveraging how Autonomous Systems (ASes) handle route advertisement in the existing Border Gateway Protocol (BGP), Nyx allows the deploying AS to achieve isolation of traffic from a critical upstream AS off of attacked links and onto alternative, uncongested, paths. This isolation removes the need for filtering or de-prioritizing attack traffic. Nyx controls outbound paths through normal BGP path selection, while return paths from critical ASes are controlled through the use of specific techniques we developed using existing traffic engineering principles and require no outside coordination. Using our own realistic Internet-scale simulator, we find that in more than 98% of cases our system can successfully route critical traf?c around network segments under transit-link DDoS attacks; a new form of DDoS attack where the attack traf?c never reaches the victim AS, thus invaliding defensive filtering, throttling, or prioritization strategies. More significantly, in over 95% of those cases, the alternate path provides complete congestion relief from transit-link DDoS. Nyx additionally provides complete congestion relief in over 75% of cases when the deployer is being directly attacked.

Building Natural Language Interfaces to Web APIs <=SEP=> As the Web evolves towards a service-oriented architecture, application program interfaces (APIs) are becoming an increasingly important way to provide access to data, services, and devices. We study the problem of natural language interface to APIs (NL2APIs), with a focus on web APIs for web services. Such NL2APIs have many potential benefits, for example, facilitating the integration of web services into virtual assistants.
 We propose the first end-to-end framework to build an NL2API for a given web API. A key challenge is to collect training data, i.e., NL command-API call pairs, from which an NL2API can learn the semantic mapping from ambiguous, informal NL commands to formal API calls. We propose a novel approach to collect training data for NL2API via crowdsourcing, where crowd workers are employed to generate diversified NL commands. We optimize the crowdsourcing process to further reduce the cost. More specifically, we propose a novel hierarchical probabilistic model for the crowdsourcing process, which guides us to allocate budget to those API calls that have a high value for training NL2APIs. We apply our framework to real-world APIs, and show that it can collect high-quality training data at a low cost, and build NL2APIs with good performance from scratch. We also show that our modeling of the crowdsourcing process can improve its effectiveness, such that the training data collected via our approach leads to better performance of NL2APIs than a strong baseline.

Machine Learning for High-Throughput Stress Phenotyping in Plants. <=SEP=> Advances in automated and high-throughput imaging technologies have resulted in a deluge of high-resolution images and sensor data of plants. However, extracting patterns and features from this large corpus of data requires the use of machine learning (ML) tools to enable data assimilation and feature identification for stress phenotyping. Four stages of the decision cycle in plant stress phenotyping and plant breeding activities where different ML approaches can be deployed are (i) identification, (ii) classification, (iii) quantification, and (iv) prediction (ICQP). We provide here a comprehensive overview and user-friendly taxonomy of ML tools to enable the plant community to correctly and easily apply the appropriate ML tools and best-practice guidelines for various biotic and abiotic stress traits.

Controllable Image-to-Video Translation: A Case Study on Facial Expression Generation <=SEP=> The recent advances in deep learning have made it possible to generate photorealistic images by using neural networks and even to extrapolate video frames from an input video clip. In this paper, for the sake of both furthering this exploration and our own interest in a realistic application, we study image-to-video translation and particularly focus on the videos of facial expressions. This problem challenges the deep neural networks by another temporal dimension comparing to the imageto-image translation. Moreover, its single input image fails most existing video generation methods that rely on recurrent models. We propose a user-controllable approach so as to generate video clips of various lengths from a single face image. The lengths and types of the expressions are controlled by users. To this end, we design a novel neural network architecture that can incorporate the user input into its skip connections and propose several improvements to the adversarial training method for the neural network. Experiments and user studies verify the effectiveness of our approach. Especially, we would like to highlight that even for the face images in the wild (downloaded from the Web and the authors’ own photos), our model can generate high-quality facial expression videos of which about 50% are labeled as real by Amazon Mechanical Turk workers. Please see our project page1 for more demonstrations.

A deep learning based stock trading model with 2-D CNN trend detection <=SEP=> The success of convolutional neural networks in the field of computer vision has attracted the attention of many researchers from other fields. One of the research areas in which neural networks is actively used is financial forecasting. In this paper, we propose a novel method for predicting stock price movements using CNN. To avoid the high volatility of the market and to maximize the profit, ETFs are used as primary financial assets. We extract commonly used trend indicators and momentum indicators from financial time series data and use these as our features. Adopting a sliding window approach, we generate our images by taking snapshots that are bounded by the window over a daily period. We then perform daily predictions, namely, regression for predicting the ETF prices and classification for predicting the movement of the prices on the next day, which can be modified to estimate weekly or monthly trends. To increase the number of images, we use numerous ETFs. Finally, we evaluate our method by performing paper trading and calculating the final capital. We also compare our method's performance to commonly used classical trading strategies. Our results indicate that we can predict the next day's prices with 72% accuracy and end up with 5:1 of our initial capital, taking realistic values of transaction costs into account.