DeepTarget Poised for 2020 Expansion and Growth Acceleration

Four DeepTarget Clients Honored for January and February Performance

SmartyAds are teaming up with Protected Media within SmartHub, a white-label ad network to combat ad fraud.

Would you like to know what your customers, users, contacts, or relatives really think? NuWave Solutions' Executive Vice President, Brian Frutchey, leads participants as they build their own sentiment analysis application with KNIME Analytics.

Livestream Deep Learning World Munich 2020 from the comfort and safety of your home on 11-12 May 2020.

4 Steps to ensure your AI/Machine Learning system survives COVID-19; State of the Machine Learning and AI Industry; A Key Missing Part of the Machine Learning Stack; 5 Papers on CNNs Every Data Scientist Should Read

This hands-on book bridges the gap between theory and practice, showing you the math of deep learning algorithms side by side with an implementation in PyTorch. You can save 40% off Math and Architectures of Deep Learning until May 13! Just enter the code nlkdarch40 at checkout when you buy from manning.com.

This post will cover how testing is done for the coronavirus, why it's important in battling the pandemic, and how deep learning tools for medical imaging can help us improve the quality of COVID-19 testing.

"Deep Learning" is the quintessential book for understanding deep learning theory, and you can still read it freely online.

Optimus, FQ-GAN and Prevalent bring new ideas to apply generative models at large scale.

It’s one step closer to the podcast analytics advertisers have been waiting for.

Please visit Marketing Land for the full article.

Fellow Traveler managing director Chris Wright breaks down the data behind LudoNarraCon 2020 on Steam. ...

Volition is looking for an experienced Environment Artist to help us build the world for the next Saints Row title! You will work with a tight, collaborative team of artists and designers to create environments that are both beautiful and fun to play in.  You will be making major contributions to our world using our unique approach to world building with our proprietary world editor.  Responsibilities: Create environments from initial rough layout to completed polished spaces Build urban, interior, and natural spaces using our in-house world editor Author game ready assets that work well within given technical limitations Collaborate closely with level designers to create spaces that adhere to gameplay requirements Focus on the "big picture." Understand when details matter, and when broad strokes are more important Qualifications: 3+ years of experience in game development creating environments Strong communication skills. Must be able to be a master collaborator Ability to work with a library of assets to build visually interesting, dynamic gameplay spaces Strong understanding of form, shape, structure, and silhouette in regards to scene composition A keen eye for developing a visual mood as it relates to storytelling Comfortable working within technical limitations such as vertex and object counts, materials (draw calls), streaming conditions, etc. Fluent knowledge of at least one major 3d package (3dsmax preferred) Experience building environments in a game engine (Unreal4, Unity) Well-versed in PBR workflow Pluses: Strong foundation in the traditional arts, including but not limited to figure drawing, environmental development and/or architectural illustration Familiarity with procedural content authoring tools (Substance Designer, World Machine, Houdini) Basic Level Design Knowledge Sample Work Required: Portfolio should demonstrate ability to build fully realized environments. Fully fleshed out spaces are more helpful than examples of individual assets To apply, please send your resume, portfolio/sample work, and a cover letter to jobs@dsvolition.com with “Environment Artist” in the subject line. At Deep Silver Volition we believe in fostering an open, collaborative and diverse environment, and we are proud to be an equal opportunity employer. We will consider all qualified applicants without attention to race, color, national origin, religion, gender, gender identity, sexual orientation, genetic information, disability, age, veteran status, or military status.

The latest piece rounding up the week's notable writing and videos in games includes pieces on new games from Cloudpunk to Industries Of Titan, plus a bunch of neat historical writing & research. ...

Lots of great things to see this week, from Streets Of Rage's surprising comeback to an Animal Crossing talk show & far beyond. ...

Fellow Traveler managing director Chris Wright breaks down the data behind LudoNarraCon 2020 on Steam. ...

Developer and SEO/marketing teams can proactively test web pages for SEO impact

Brand Equity: In conversation Deepak Iyer, MD Mondelez India

Not only Kumari's but samples of four other Indians, including 2017 Asian champion quarter-miler Nirmala Sheoran's had returned negative at NDTL but were found positive when tested in Montreal. Jhuma Khatun, one of them, was also handed a four-year ban, last month.

By all accounts, the second division of Saturday's Grade 1 Arkansas Derby from Oaklawn in Hot Springs, Ark., going as the 13th race on the 14-race closing-day program, is much deeper in quality than the first division, which is dominated by the Speightstown colt Charlatan, likely an odds-on favorite. Nadal, who like Charlatan is an […]

The post Triple Crown News Minute Presented By Kentucky Equine Research: Arkansas Derby’s Deeper Division appeared first on Horse Racing News | Paulick Report.

Grand Canyon National Park and the entire National Park Service join the nation in mourning the tragic loss of 19 firefighters, including 18 elite firefighters from the Granite Mountain Hotshot Crew based in Prescott, AZ. https://www.nps.gov/grca/learn/news/grand-canyon-national-park-expresses-deepest-condolences-on-19-firefighter-deaths.htm

This guide presents a classification of the deep canyon and subalpine riparian and wetland vegetation types of the Malheur, Umatilla, and Wallowa-Whitman National Forests. A primary goal of the deep canyon and subalpine riparian and wetland classification was a seamless linkage with the midmontane northeastern Oregon riparian and wetland classification provided by Crowe and Clausnitzer in 1997. The classification is based on potential natural vegetation and follows directly from the plant association concept for riparian zones. The 95 vegetation types classified across the three national forests were organized into 16 vegetation series, and included some 45 vegetation types not previously classified for northeastern Oregon subalpine and deep canyon riparian and wetland environments. The riparian and wetland vegetation types developed for this guide were compared floristically and environmentally to riparian and wetland classifications in neighboring geographic regions. For each vegetation type, a section was included describing the occurrence#40;s#41; of the same or floristically similar vegetation types found in riparian and wetland classifications developed for neighboring geographic regions. Lastly, this guide was designed to be used in conjunction with the midmontane guide to provide a comprehensive look at the riparian and wetland vegetation of northeastern Oregon.

Research Highlights: The Vein-Chip device, a miniaturized version of a large human vein, allowed scientists to study changes in vein wall cells, blood flow and other functions that lead to deep vein thrombosis in humans. The device focused on venous ...

Deep Learning is a branch of artificial intelligence and a subset of machine learning that focuses on networks capable of, usually, unsupervised learning from unstructured and other forms of data. It is also known as deep structured learning or differential programming. Architectures inspired by deep learning find use in a range of fields, such as...

By Julie Cohen The UC Santa Barbara Current Oceanographer Uta Passow demonstrates that contaminants from Deepwater Horizon lingered for months in subsurface water before sinking to the seafloor Between April 20 and July 15, 2010, millions of barrels of crude … Continue reading →

Mirage JS is helping simplify modern front-end development by providing the ability for front-end engineers to craft applications without relying on an actual back-end service. In this article, I’ll be taking a framework-agnostic approach to show you Mirage JS models and associations. If you haven’t heard of Mirage JS, you can read my previous article in which I introduce it and also integrate it with the progressive framework Vue.js.

In this work, we investigate multi-task learning as a way of pre-training models for classification tasks in digital pathology. It is motivated by the fact that many small and medium-size datasets have been released by the community over the years whereas there is no large scale dataset similar to ImageNet in the domain. We first assemble and transform many digital pathology datasets into a pool of 22 classification tasks and almost 900k images. Then, we propose a simple architecture and training scheme for creating a transferable model and a robust evaluation and selection protocol in order to evaluate our method. Depending on the target task, we show that our models used as feature extractors either improve significantly over ImageNet pre-trained models or provide comparable performance. Fine-tuning improves performance over feature extraction and is able to recover the lack of specificity of ImageNet features, as both pre-training sources yield comparable performance.

With the increase in use of Unmanned Aerial Vehicles (UAVs)/drones, it is important to detect and identify causes of failure in real time for proper recovery from a potential crash-like scenario or post incident forensics analysis. The cause of crash could be either a fault in the sensor/actuator system, a physical damage/attack, or a cyber attack on the drone's software. In this paper, we propose novel architectures based on deep Convolutional and Long Short-Term Memory Neural Networks (CNNs and LSTMs) to detect (via Autoencoder) and classify drone mis-operations based on sensor data. The proposed architectures are able to learn high-level features automatically from the raw sensor data and learn the spatial and temporal dynamics in the sensor data. We validate the proposed deep-learning architectures via simulations and experiments on a real drone. Empirical results show that our solution is able to detect with over 90% accuracy and classify various types of drone mis-operations (with about 99% accuracy (simulation data) and upto 88% accuracy (experimental data)).

Lake ice, as part of the Essential Climate Variable (ECV) lakes, is an important indicator to monitor climate change and global warming. The spatio-temporal extent of lake ice cover, along with the timings of key phenological events such as freeze-up and break-up, provide important cues about the local and global climate. We present a lake ice monitoring system based on the automatic analysis of Sentinel-1 Synthetic Aperture Radar (SAR) data with a deep neural network. In previous studies that used optical satellite imagery for lake ice monitoring, frequent cloud cover was a main limiting factor, which we overcome thanks to the ability of microwave sensors to penetrate clouds and observe the lakes regardless of the weather and illumination conditions. We cast ice detection as a two class (frozen, non-frozen) semantic segmentation problem and solve it using a state-of-the-art deep convolutional network (CNN). We report results on two winters ( 2016 - 17 and 2017 - 18 ) and three alpine lakes in Switzerland. The proposed model reaches mean Intersection-over-Union (mIoU) scores >90% on average, and >84% even for the most difficult lake. Additionally, we perform cross-validation tests and show that our algorithm generalises well across unseen lakes and winters.

Introduction of spectrum-sharing in 5G and subsequent generation networks demand base-station(s) with the capability to characterize the wideband spectrum spanned over licensed, shared and unlicensed non-contiguous frequency bands. Spectrum characterization involves the identification of vacant bands along with center frequency and parameters (energy, modulation, etc.) of occupied bands. Such characterization at Nyquist sampling is area and power-hungry due to the need for high-speed digitization. Though sub-Nyquist sampling (SNS) offers an excellent alternative when the spectrum is sparse, it suffers from poor performance at low signal to noise ratio (SNR) and demands careful design and integration of digital reconstruction, tunable channelizer and characterization algorithms. In this paper, we propose a novel deep-learning framework via a single unified pipeline to accomplish two tasks: 1)~Reconstruct the signal directly from sub-Nyquist samples, and 2)~Wideband spectrum characterization. The proposed approach eliminates the need for complex signal conditioning between reconstruction and characterization and does not need complex tunable channelizers. We extensively compare the performance of our framework for a wide range of modulation schemes, SNR and channel conditions. We show that the proposed framework outperforms existing SNS based approaches and characterization performance approaches to Nyquist sampling-based framework with an increase in SNR. Easy to design and integrate along with a single unified deep learning framework make the proposed architecture a good candidate for reconfigurable platforms.

Histopathology is a reflection of the molecular changes and provides prognostic phenotypes representing the disease progression. In this study, we introduced feature scores generated from hematoxylin and eosin histology images based on deep learning (DL) models developed for prostate pathology. We demonstrated that these feature scores were significantly prognostic for time to event endpoints (biochemical recurrence and cancer-specific survival) and had simultaneously molecular biologic associations to relevant genomic alterations and molecular subtypes using already trained DL models that were not previously exposed to the datasets of the current study. Further, we discussed the potential of such feature scores to improve the current tumor grading system and the challenges that are associated with tumor heterogeneity and the development of prognostic models from histology images. Our findings uncover the potential of feature scores from histology images as digital biomarkers in precision medicine and as an expanding utility for digital pathology.

The objective of deep metric learning (DML) is to learn embeddings that can capture semantic similarity and dissimilarity information among data points. Existing pairwise or tripletwise loss functions used in DML are known to suffer from slow convergence due to a large proportion of trivial pairs or triplets as the model improves. To improve this, ranking-motivated structured losses are proposed recently to incorporate multiple examples and exploit the structured information among them. They converge faster and achieve state-of-the-art performance. In this work, we unveil two limitations of existing ranking-motivated structured losses and propose a novel ranked list loss to solve both of them. First, given a query, only a fraction of data points is incorporated to build the similarity structure. To address this, we propose to build a set-based similarity structure by exploiting all instances in the gallery. The learning setting can be interpreted as few-shot retrieval: given a mini-batch, every example is iteratively used as a query, and the rest ones compose the galley to search, i.e., the support set in few-shot setting. The rest examples are split into a positive set and a negative set. For every mini-batch, the learning objective of ranked list loss is to make the query closer to the positive set than to the negative set by a margin. Second, previous methods aim to pull positive pairs as close as possible in the embedding space. As a result, the intraclass data distribution tends to be extremely compressed. In contrast, we propose to learn a hypersphere for each class in order to preserve useful similarity structure inside it, which functions as regularisation. Extensive experiments demonstrate the superiority of our proposal by comparing with the state-of-the-art methods on the fine-grained image retrieval task.

In recent years, deep neural networks have been applied to obtain high performance of prediction, classification, and pattern recognition. However, the weights in these deep neural networks are difficult to be explained. Although a linear regression method can provide explainable results, the method is not suitable in the case of input interaction. Therefore, an explainable deep neural network (ExpDNN) with explainable layers is proposed to obtain explainable results in the case of input interaction. Three cases were given to evaluate the proposed ExpDNN, and the results showed that the absolute value of weight in an explainable layer can be used to explain the weight of corresponding input for feature extraction.

Automated person re-identification in a multi-camera surveillance setup is very important for effective tracking and monitoring crowd movement. In the recent years, few deep learning based re-identification approaches have been developed which are quite accurate but time-intensive, and hence not very suitable for practical purposes. In this paper, we propose an efficient hierarchical re-identification approach in which color histogram based comparison is first employed to find the closest matches in the gallery set, and next deep feature based comparison is carried out using Siamese network. Reduction in search space after the first level of matching helps in achieving a fast response time as well as improving the accuracy of prediction by the Siamese network by eliminating vastly dissimilar elements. A silhouette part-based feature extraction scheme is adopted in each level of hierarchy to preserve the relative locations of the different body structures and make the appearance descriptors more discriminating in nature. The proposed approach has been evaluated on five public data sets and also a new data set captured by our team in our laboratory. Results reveal that it outperforms most state-of-the-art approaches in terms of overall accuracy.

Chest computed tomography (CT) becomes an effective tool to assist the diagnosis of coronavirus disease-19 (COVID-19). Due to the outbreak of COVID-19 worldwide, using the computed-aided diagnosis technique for COVID-19 classification based on CT images could largely alleviate the burden of clinicians. In this paper, we propose an Adaptive Feature Selection guided Deep Forest (AFS-DF) for COVID-19 classification based on chest CT images. Specifically, we first extract location-specific features from CT images. Then, in order to capture the high-level representation of these features with the relatively small-scale data, we leverage a deep forest model to learn high-level representation of the features. Moreover, we propose a feature selection method based on the trained deep forest model to reduce the redundancy of features, where the feature selection could be adaptively incorporated with the COVID-19 classification model. We evaluated our proposed AFS-DF on COVID-19 dataset with 1495 patients of COVID-19 and 1027 patients of community acquired pneumonia (CAP). The accuracy (ACC), sensitivity (SEN), specificity (SPE) and AUC achieved by our method are 91.79%, 93.05%, 89.95% and 96.35%, respectively. Experimental results on the COVID-19 dataset suggest that the proposed AFS-DF achieves superior performance in COVID-19 vs. CAP classification, compared with 4 widely used machine learning methods.

Water quality has a direct impact on industry, agriculture, and public health. Algae species are common indicators of water quality. It is because algal communities are sensitive to changes in their habitats, giving valuable knowledge on variations in water quality. However, water quality analysis requires professional inspection of algal detection and classification under microscopes, which is very time-consuming and tedious. In this paper, we propose a novel multi-target deep learning framework for algal detection and classification. Extensive experiments were carried out on a large-scale colored microscopic algal dataset. Experimental results demonstrate that the proposed method leads to the promising performance on algal detection, class identification and genus identification.

Spiking neural networks (SNNs) are considered as a potential candidate to overcome current challenges such as the high-power consumption encountered by artificial neural networks (ANNs), however there is still a gap between them with respect to the recognition accuracy on practical tasks. A conversion strategy was thus introduced recently to bridge this gap by mapping a trained ANN to an SNN. However, it is still unclear that to what extent this obtained SNN can benefit both the accuracy advantage from ANN and high efficiency from the spike-based paradigm of computation. In this paper, we propose two new conversion methods, namely TerMapping and AugMapping. The TerMapping is a straightforward extension of a typical threshold-balancing method with a double-threshold scheme, while the AugMapping additionally incorporates a new scheme of augmented spike that employs a spike coefficient to carry the number of typical all-or-nothing spikes occurring at a time step. We examine the performance of our methods based on MNIST, Fashion-MNIST and CIFAR10 datasets. The results show that the proposed double-threshold scheme can effectively improve accuracies of the converted SNNs. More importantly, the proposed AugMapping is more advantageous for constructing accurate, fast and efficient deep SNNs as compared to other state-of-the-art approaches. Our study therefore provides new approaches for further integration of advanced techniques in ANNs to improve the performance of SNNs, which could be of great merit to applied developments with spike-based neuromorphic computing.

Bayesian predictive coding is a putative neuromorphic method for acquiring higher-level neural representations to account for sensory input. Although originating in the neuroscience community, there are also efforts in the machine learning community to study these models. This paper reviews some of the more well known models. Our review analyzes module connectivity and patterns of information transfer, seeking to find general principles used across the models. We also survey some recent attempts to cast these models within a deep learning framework. A defining feature of Bayesian predictive coding is that it uses top-down, reconstructive mechanisms to predict incoming sensory inputs or their lower-level representations. Discrepancies between the predicted and the actual inputs, known as prediction errors, then give rise to future learning that refines and improves the predictive accuracy of learned higher-level representations. Predictive coding models intended to describe computations in the neocortex emerged prior to the development of deep learning and used a communication structure between modules that we name the Rao-Ballard protocol. This protocol was derived from a Bayesian generative model with some rather strong statistical assumptions. The RB protocol provides a rubric to assess the fidelity of deep learning models that claim to implement predictive coding.

Segmentation is a prerequisite yet challenging task for medical image analysis. In this paper, we introduce a novel deeply supervised active learning approach for finger bones segmentation. The proposed architecture is fine-tuned in an iterative and incremental learning manner. In each step, the deep supervision mechanism guides the learning process of hidden layers and selects samples to be labeled. Extensive experiments demonstrated that our method achieves competitive segmentation results using less labeled samples as compared with full annotation.

Smart meters enable remote and automatic electricity, water and gas consumption reading and are being widely deployed in developed countries. Nonetheless, there is still a huge number of non-smart meters in operation. Image-based Automatic Meter Reading (AMR) focuses on dealing with this type of meter readings. We estimate that the Energy Company of Paran'a (Copel), in Brazil, performs more than 850,000 readings of dial meters per month. Those meters are the focus of this work. Our main contributions are: (i) a public real-world dial meter dataset (shared upon request) called UFPR-ADMR; (ii) a deep learning-based recognition baseline on the proposed dataset; and (iii) a detailed error analysis of the main issues present in AMR for dial meters. To the best of our knowledge, this is the first work to introduce deep learning approaches to multi-dial meter reading, and perform experiments on unconstrained images. We achieved a 100.0% F1-score on the dial detection stage with both Faster R-CNN and YOLO, while the recognition rates reached 93.6% for dials and 75.25% for meters using Faster R-CNN (ResNext-101).

Coronavirus disease 2019 (Covid-19) is highly contagious with limited treatment options. Early and accurate diagnosis of Covid-19 is crucial in reducing the spread of the disease and its accompanied mortality. Currently, detection by reverse transcriptase polymerase chain reaction (RT-PCR) is the gold standard of outpatient and inpatient detection of Covid-19. RT-PCR is a rapid method, however, its accuracy in detection is only ~70-75%. Another approved strategy is computed tomography (CT) imaging. CT imaging has a much higher sensitivity of ~80-98%, but similar accuracy of 70%. To enhance the accuracy of CT imaging detection, we developed an open-source set of algorithms called CovidCTNet that successfully differentiates Covid-19 from community-acquired pneumonia (CAP) and other lung diseases. CovidCTNet increases the accuracy of CT imaging detection to 90% compared to radiologists (70%). The model is designed to work with heterogeneous and small sample sizes independent of the CT imaging hardware. In order to facilitate the detection of Covid-19 globally and assist radiologists and physicians in the screening process, we are releasing all algorithms and parametric details in an open-source format. Open-source sharing of our CovidCTNet enables developers to rapidly improve and optimize services, while preserving user privacy and data ownership.

[IMAGE-1] I started sobbing from the opening moments of A Beautiful Day in the Neighborhood, and I didn't stop crying for two hours. And then after I left the cinema and ran into a fellow film critic who had also just seen it, I literally could not manage a word of discussion without bursting into tears again.…

Virtually every musician starts out trying to copy their heroes.…

The invention discloses a deep-ultraviolet chemically-amplified positive photoresist. The deep-ultraviolet chemically-amplified positive photoresist according to one embodiment of the invention includes a cyclopentenyl pimaric acid, a divinyl ether, a photoacid generator and an organic solvent. The deep-ultraviolet chemically-amplified positive photoresist according to the invention has a good sensitivity and a good transparency.

Disclosed is a mold hanger for supporting a bottle mold in a blow molding station, the mold hanger comprising a piston and piston sleeve fully contained within the mold hanger configured to push a moveable insert into the mold. Also disclosed is a method of retrofitting an original rotatable blow molding module having multiple existing blow molding stations, each existing mold hanger defining an existing outer envelope. The disclosed method may include providing an improved mold hanger substantially contained within the respective existing outer envelope and including low-profile drive mechanisms configured opposably to drive moveable inserts into the mold. Further disclosed is a method of manufacturing a blow molded bottle with a deep pinch grip, the method including providing within a mold hanger a drive mechanism to drive a moveable insert into the mold. A bottle made by such methods is also disclosed.

The present invention relates to the description of machine-assisted production assemblies and to the optimization thereof, said assemblies having, built therein, a patented device for folding into deep coated folds, thus making it possible to create paper packaging for fast food, said assemblies all comprising a folded element from a folded paper strip 31. Said machines are modular and all comprise, upstream, an assembly of modules 5, 6, and 7 that supply a folded paper strip 31 that is then converted in a specific finishing module 8 for making, from said lidded paper strip, a packaging for a sandwich or loose product such as fries or chicken pieces.

A borehole pump system includes an immersion pump (20) and a riser (15) accommodated in the borehole (10). A water treatment system for cleaning pumped water is disposed in the riser.

The present disclosure relates to a grouting and welding combined connection joint and an offshore installation method thereof, characterized in that: it comprises a set of grouting systems disposed inside of the annular space formed by an inner shell of the central pore canal of a column with large cross section and an outer wall of a column with small cross section, as well as a group of brackets welded on a top deck of a column with small cross section and an inner shell of the central pore canal of the column with large cross section. The grouting and welding combined connection joint provided by the present disclosure has advantages of being able to adapt to the connection of different dimensions of columns, and having simple offshore installation, highly reliable structural safety and low cost.

A system comprising a surface vessel floating on a body of water; an oil leak located in the body of water; a remotely operated vehicle located near the oil leak; a connection between the surface vessel and the remotely operated vehicle; wherein the remotely operated vehicle comprises a mixer and a dispersant injector.

News you don't want to miss for April 17-19:

1. "The good news: California has an $18 billion rainy day fund, the biggest in its history.…