Figure 2: Potential applications of Deep Learning in fluorescence microscopy and key concepts. (a) Learning to reduce scattered light ’haze’ in light-sheet microscopy. (b) Learning spectral unmixing of simultaneous multi-color acquisitions. (c) Learning to reconstruct super-resolved images from structured illumination acquisitions. (d) Learning to straighten live dynamic samples with weak supervision against a template shape. (e) Fluorescence optical flow with Deep Learning. Forward model simulations can produce time-lapse data for given vector fields, and a network can be trained to compute the inverse: vector fields from fluorescence spatio-temporal fluctuations. (f) Leveraging the time dimension by enforcing temporal consistency in predictions to further improve image quality – applicable to any image reconstruction task. (g) Transfer learning and other approaches to facilitate training and reducing the need of specialised curated training datasets will be key for the success of Deep Learning in microscopy. (h) Unsupervised learning is another research direction that will reduce the need for curation. For example, unpaired image translation (CycleGANs 47 ) are a promising approach. (i) Deep Reinforcement Learning could drive smart microscopes
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Abstract. An essential part of new physics searches at the Large Hadron Col- lider (LHC) at CERN involves event classification, or distinguishing potential signal events from those coming from background processes. Current machine learning techniques accomplish this using traditional hand-engineered features like particle 4-momenta, motivated by our understanding of particle decay phe- nomenology. While such techniques have proven useful for simple decays, they are highly dependent on our ability to model all aspects of the phenomenol- ogy and detector response. Meanwhile, powerful deep learning algorithms are capable of not only training on high-level features, but of performing feature extraction. In computer vision, convolutional neural networks have become the state-of-the-art for many applications. Motivated by their success, we apply deep learning algorithms to low-level detector data from the 2012 CMS Sim- ulated Open Data to directly learn useful features, in what we call, end-to-end event classification. We demonstrate the power of this approach in the context of a physics search and offer solutions to some of the inherent challenges, such as image construction, image sparsity, combining multiple sub-detectors, and de-correlating the classifier from the search observable, among others.
Zhou et al.  describe how a Deep Learning algorithm can be used for incremental feature learning on very large datasets, employing denoising autoencoders . Denoising autoencoders are a variant of autoencoders which extract features from corrupted input, where the extracted features are robust to noisy data and good for classification purposes. Deep Learning algorithms in general use hidden layers to contribute towards the extrac- tion of features or data representations. In a denoising autoencoder, there is one hidden layer which extracts features, with the number of nodes in this hidden layer initially being the same as the number of features that would be extracted. Incrementally, the samples that do not conform to the given objective function (for example, their classification error is more than a threshold, or their reconstruction error is high) are collected and are used for adding new nodes to the hidden layer, with these new nodes being initialized based on those samples. Subsequently, incoming new data samples are used to jointly retrain all the features. This incremental feature learning and mapping can improve the discrimina- tive or generative objective function; however, monotonically adding features can lead to having a lot of redundant features and overfitting of data. Consequently, similar features are merged to produce a more compact set of features. Zhou et al.  demonstrate that the incremental feature learning method quickly converges to the optimal number of fea- tures in a large-scale online setting. This kind of incremental feature extraction is useful in applications where the distribution of data changes with respect to time in massive online data streams. Incremental feature learning and extraction can be generalized for other Deep Learning algorithms, such as RBM , and makes it possible to adapt to new incom- ing stream of an online large-scale data. Moreover, it avoids expensive cross-validation analysis in selecting the number of features in large-scale datasets.
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Abstract: In this essay, the researcher is focusing on the deep learning systems and its major applications in various fields. Song Yukun uses the relu incentive algorithm and the convolution functions to make the program automatically recognize different things or same type of things with different features. Before actually processing the image recognition part, the researcher adds a transforming program which change all kinds of image into one small form. Then, using this modelled image, the program could delicately determine the type of the contents in the image. This technological program is automatic and performs as an essential part of artificial intelligences. The main work it does is imitating the learning process of human brain, which accumulate experiences from thousands of events. It realizes this function by adding different algorithms in the program including the relu incentive algorithm which “teaches” the program particular types of images. After massive input, this technological program could quickly solve current problems with the lack of human labor force doing repetitive but intelligent works like checking particular tumor in the X-ray films. Besides, learning by themselves, the programs could generate results more specific than humans do. This deep-learning principle could be widely utilized since everything in human lives are learning and accumulating experiences. It could change any previous mechanical program into “intelligent” programs which would have an acceleration in their delicacy of determination.
In this study, deep learning and AR issues are explained, and the research results of the combination of the two are examined. Despite the fact that people's ability to see and classify has not been passed for many years, new project issues have been started after the success of deep learning. The use of deep learning in important issues such as autonomous driving, which we can entrust to human life, shows how confident the method is. The fact that the number of AR applications that use deep learning will increase, and more and more artificial intelligence applications will process visual information and help us in the near future.
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The fact that deep learning algorithms performs well with large and diverse datasets which has two consequences. First, the dataset becomes critically important for the system design. Therefore, we focused our efforts on this criterion during our analysis. We found that, the scien- tific work, documented in 31 of the reviewed papers, was based on one or more freely available datasets. Therefore, we predict that the importance of these freely available public datasets may increase. The other consequence is that deep learning algorithms will perform well in practical settings, because clinical routine produces lots of data with large variations. However, none of the reviewed papers verified this in a practical setting. Another fundamental point is that, our literature survey yielded only 53 papers. The small number of studies imply that there is scope for future work. To be specific, 53 papers do not reflect the comprehensive healthcare applications based on the physiological signals. In future, there may be more advanced deep learning algorithms focused on the early detection of diseases using physiological signals.
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together form the M-RNN. They have tested the model on IAPR TC-12, Flickr 8K, Flickr 30K and MS COCO datasets. It has significant performance improvement over the others. In  they have proposed a new model which consists of a policy and value network. They do not follow the conventional architecture of encoder-decoder. The policy network provides guidance in predicting the next word and the value network works as a look-ahead guidance by evaluating all possible extensions of the current state. Its focus is to predict words around the ground truth. A reinforcement type of training is performed on the MS COCO dataset. In  the image captioning system can caption any type of images i.e. people, objects, places, etc. it is capable of handing out of the domain data like captioning celebrities name even if the picture is not included in the dataset. It out performs other deep learning models which are trained on datasets like (MS COCO, Flickr30K), etc. the dataset is trained on MSCOCO dataset and images crawled from commercial search engines. It uses regular CNN architecture. For caption generation we use MELM (maximum entropy language model) with DMSM (deep multimodal similarity model). In  a model which uses the standard CNN-LSTM model for capturing the images features and captioning them is proposed. The MSCOCO dataset is first split into test, train and validation classes, and then it is sent to CNN for generating the image features and then it is passes to LSTM for sentence generation. This is also known as “Show and Tell image captioning”. In  the model uses an end to end trainable bidirectional LSTM model for image captioning. This model consists of CNN and two bi-directional LSTMS. With the help of previous and future context information the captions are generated at high level semantic
system uptime and adaptive scheduling of maintenance. Given the surge in sensor influx, if there exists sufficient structured information in historical or transient data, accurate models describing the system evolution may be proposed. The general idea is that in such approaches, there is a point in the opera- tional cycle of a component beyond which it no longer delivers optimum performance. In this regard, the most widely used metric for determining the critical operational cycle is termed as the Remaining Useful Life (RUL), which is a measure of the time from measurement to the critical cycle beyond which sub-optimal performance is anticipated. Prognostic approaches may be divided into three categorizations: (a) Model-driven (b) Data-driven (c) Hybrid i.e. any combination of (a) and (b). The last three decades have seen extensive usage of model- driven approaches with Gaussian Processes and Sequential Monte-Carlo (SMC) methods which continue to be popular in capturing patterns in relatively simpler sensor data streams. However, one shortcoming of model driven approaches used till date happens to be their dependence on physical evolution equations recommended by an expert with problem-specific domain knowledge. For model-driven approaches to continue to perform as well when the problem complexity scales, the prior distribution (physical equations) needs to continue to capture the embedded causalities in the data accurately. However, it has been the observation that as sensor data scales, the ability of model-driven approaches to learn the inherent structures in the data has lagged. This is of course due to the use of simplistic priors and updates which are unable to capture the complex functional relationships from the high dimensional input data. With the introduction of self-regulated learning paradigms such as Deep Learning, this problem of learning the structure in sensor data was mitigated to a large extent because it was no longer necessary for an expert
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machine learning (ML) look into. It includes numerous shrouded layers of counterfeit neural systems. The profound get the hanging of system applies nonlinear changes and model deliberations of abnormal state in substantial databases. The ongoing headways in profound learning architectures inside various fields have just given noteworthy commitments in man-made brainpower. This article displays a best in class overview on the contributions and the novel uses of profound learning. The accompanying audit chronologically shows how and in what real applications profound realizing calculations have been used. Besides, the prevalent and helpful of the profound learning philosophy and its progression in layers and nonlinear activities are given and thought about the more traditional calculations in the basic applications. The best in class study further gives a general review on the novel idea and the consistently expanding preferences and ubiquity of profound learning.
G. Mask Region based Convolutional Networks Mask R-CNN has been the new condition of workmanship as far as occasion division. It is a significant neural framework planned to handle event division issue in machine learning or PC vision. Figuratively speaking, it can seclude unmistakable questions in a picture or a video. One can give it a picture; it gives question ricocheting boxes, classes and veils. Mask R-CNN, widens Faster R-CNN by including a branch for anticipating division veils on each Region of Interest (RoI), in parallel with the current branch for classiﬁcation and hopping box backslide. The mask branch is a little FCN associated with each ROIs, envisioning a division cover in a pixel-to pixel way. On a basic level Mask R-CNN is a natural extension of Faster R-CNN, yet building up the mask branch suitably is fundamental for good results . Most importantly, Faster RCNN was not planned for pixel-to-pixel course of action between system data sources and yields. This is most clear in how RoI Pool the genuine focus action for dealing with cases, performs coarse spatial quantization for highlight extraction. To ﬁx the misalignment, a fundamental, non-quantization layer, called RoIAlign, that reliably spares right spatial regions .
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Typically, automatic face detection is the first step towards face related applications and it is expected to identify faces under arbitrary image conditions. In real-world settings, the face detector should be robust enough to detect faces in low-resolution and low-quality images with occlusions, changes in pose/illumination and distortions, such as out-of-focus blur, noise and low contrast [14–16], which are commonly present in CSEM. However, automatic face detection a very challenging task in these conditions since performance degradation has been observed while testing detectors on low-quality images . There are basically two main approaches to address this problem : those based on hand-crafted descriptors and the ones based on trainable features with deep-learning techniques.
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E534 2019 Big Data Applications and Analytics Sports Informatics Part II (Unit 33) Section Summary (Parts I, II, III): Sports sees significant growth in analytics with pervasive statistics shifting to more sophisticated measures. We start with baseball as game is built around segments dominated by individuals where detailed (video/image) achievement measures including PITCHf/x and FIELDf/x are moving field into big data arena. There are interesting relationships between the economics of sports and big data analytics. We look at Wearables and consumer sports/recreation. The importance of spatial visualization is discussed. We look at other Sports: Soccer, Olympics, NFL Football, Basketball, Tennis and Horse Racing.
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Considering recent applications of deep learning models to the problem of matching sentences, our network is most similar to the models in (Hu et al., 2014) applied for computing sentence similarity and in (Yu et al., 2014) (answer sentence selection in QA) with the following difference. To compute the similarity between the vector representation of the input sentences, our network uses two methods: (i) computing the similarity score obtained using a sim- ilarity matrix M (explored in (Yu et al., 2014)), and (ii) directly modelling interactions between interme- diate vector representations of the input sentences via fully-connected hidden layers (used by (Hu et al., 2014)). This approach, as proposed in (Sev- eryn and Moschitti, 2015), results in a significant improvement in the task of question answer selec- tion over the two methods used separately. Differ- ently from the above models we do not add addi- tional features in the join layer.
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A primary challenge to machine learning is the lack of adequate training data to build accurate and reliable models in many realistic situations. When quality data are in short supply, the resulting models can perform very poorly on a new domain, even if the learning algorithms are best chosen. Unlabeled data is cheap and plentiful, unlike labeled data which is expensive to obtain. The promise of self-taught learning is that by exploiting the massive amount of unlabeled data, much better models can be learnt. By using unlabeled data to learn a good initial value for the weights in all the layers, the algorithm is able to learn and discover patterns from massive amounts of data than purely supervised approaches. This frequently results in much better classifiers being learned.
Since ML covers a wide scope of research, numerous methodologies have been built up. Bunching, Bayesian Network, Deep Learning and Decision Tree Learning are just piece of the methodologies. The accompanying audit predominantly centers on profound taking in, its fundamental ideas, past and these days applications in various fields. Furthermore, it shows a few figures depicting the quick improvement of profound learning research through barlications over the ongoing years in logical databases.
With the appearance of World Wide internet, quantity of information on internet inflated enormously. Although, such a large accumulation info of data of knowledge} is efficacious and most of this information is texts, it becomes a retardant or a challenge for humans to spot the foremost relevant data or information. Therefore text classification helps to beat this challenge. Text classification is that the act of dividing a group of input documents into 2 or a lot of categories wherever every document may be aforementioned to belong to 1 or multiple categories . Text Classification could be a text mining technique that is employed to classify the text documents into predefined categories. Classification may be manual or automated. not like manual classification, that consumes time and needs high accuracy, machine-driven Text Classification makes the classification method quick and a lot of economical since it automatically categorizes document Language is employed as medium for written moreover as spoken communication. With the utilization of Unicode encryption, text on internet could also be gift in several languages. This can add complexness of linguistic communication process to text classification. Text Classification is combination of Text Mining moreover as Natural Language process. it's several applications such as document indexing, document organization and ranked categorization of internet pages . This task is sometimes resolved by combining data Retrieval technology and Machine Learning (ML) technology that each work along to assign keywords to the documents and classify them into specific classes. Cubic centimetre helps to categorise the documents automatically and IR represents the text as a feature.
Realizing the benefits of autonomous robot exploration presents robotics researchers with many applications of considerable community and financial impact. Robotics research relies on perfect knowledge and control of the environment. The problems related to unstructured environments are an outcome of the high-dimensional state space as well as the inherent likelihood in mapping sensory perceptions on particular states. It should be noted that the high dimensionality of the state space is representative of the most basic difficulty since robots leave highly controlled environments of a laboratory and enter into unstructured surroundings. For example autonomous unmanned aerial vehicles used deep learning to classify terrain and solve any exploration shortcomings by generating control commands for its human operator so as to adapt to a certain tradeoff. The major hypothesis of this approach is therefore for mobile robots to succeed in unstructured surroundings such that they can carefully choose assignment specific attributes and identify the relevant real-time structures to lower their state space without impacting the performance of their exploration objectives. Robots perform assignments by exploring their surroundings. As such, given our focus on autonomous mobile exploration, we shall direct most attention to exploration in service of movement, that is to say collision-free movement for end-effector placement. The challenge of generating such movement is an example of the problem faced in motion planning. Motion planning for robotic systems with many levels of freedom is computationally challenging even in environments that are highly structured due to the increased-dimensional configuration space.
comparing school surveillance camera images to know child molesters and the same can be used for verifying the court records thereby minimizing victim trauma. Similarly, it can also be used for surveillance at banks and residential areas. The technologies used in the project are Machine Learning - supervised, Image Processing - Digital images of the face region, Deep Learning - Convolutional Neural Network and Deep Learning - Tensor Flow. Supervised learning is a machine learning algorithm wherein the input is mapped to the output with the help of training data consisting of input output pairs. TensorFlow, an open source library, is used for mathematical computation, dataflow programming and various machine learning applications. TensorFlow computations are expressed as stateful dataflow graphs. These arrays are referred to as tensors. Convolutional Neural Network (CNN) as one of the most prevalent algorithm has gained a high reputation in image features extraction .
The information process of human consciousness and thinking can be imitated by using AI. In actual, it is not human intelligence, but it thinks like a human. Machine Learning is the type of AI and is directly associated to computational statistics. It bears the responsibility of taking using computers. ML is comprised of mathematical optimization which assists in delivering the methods, applications and theory in certain domain to the industry. ML can be referred to as the amalgamation with data mining , however, the other subfields focus on investigative data analysis and is called to be unsupervised learning. Machine learning can be unsupervised which is furthermore used to study and develop profiles with baseline behavioral for a variety of entities and eventually, these profiles are used to locate meaningful anomalies .
are useful for prediction purpose with almost the same quality of prediction in terms of statistical error. Deep learning models like, AE method in risk management  and LSTM-SVR approach  in investment problem, showed that they enable agents to considerably maximize their revenue while taking care of risk constraints with reasonable high performance as it is quite important to the economic markets. In addition, reinforcement learning is able to simulate more efficient models with more realistic market constraints. While deep RL goes further that solve the scalability problem of RL algorithms, which is crucial with fast users and market growing, and that efficiently work with high- dimensional settings as it is highly desired in the financial market. Where DRL can give notable helps to design more efficient algorithms for forecasting and analyzing the market with real-word parameters. Deep Deterministic Policy Gradient (DDPG) method used by  in stock trading demonstrated that how the model is able to handle large setting concerning stability, improving data using, and equilibrating risk while optimizing the return with high performance guarantee. Another example in deep RL framework made use of DQN scheme  for meliorate the news recommendation accuracy dealing with huge users at the same time with considerably high performance guarantee of the method. Our review demonstrated that there is great potential of improving deep learning methods applied to the RL problems to better analyze the relevant problem for finding the best strategy in a wide range of economic domains. Furthermore, RL is highly growing research in DL. However, most of the well-known results in RL have been in single agent environments while the cumulative reward just involves the single state-action spaces. It would be interesting that considering multi-agent scenarios
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