The federal government’s wage subsidy scheme may be wound back before its promised six month life span.

Officers believe the man in the picture may be able to help with their investigation following two incidents on May7

Crews and officers from Surrey Fire and Rescue Service and Surrey Police were in attendance

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.

Get free quotes, run your own portfolio, see market summaries from the DOW, NASDAQ, and more when you visit the ALL ACCESS STOCK PAGE. … more

The nine-year-old was playing for Glentoran 2011s before football was suspended in Northern Ireland

It even involves some serious dance moves at the end

Research Highlights: People who had the best heart health, defined as having healthy sleep in addition to meeting the AHA Life Simple 7, were less likely to have a diagnosis of a heart disease and were less likely to develop heart disease in the ...

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 ...

A kinetic type story illustrating a common understanding within Ayurvedic practice. Created for my motion graphics class this semester at Ohio University.

Why asking Help from Allah is the ‘First Thing First’? As everything is in this universe has made by Allah, Almighty. He is the only One who has power over all. So, we should always ask help from the Almighty first to keep us away from these...

The post Coronavirus Alert! Precautionary Measures & Real-time Apps To Keep An Eye On Covid-19 Outbreak Situation appeared first on SmashingApps.com.

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...

Starting a website can be a fun journey for some of the tech wizards out there, and a relative nightmare for the rest of us. So before you take a leap of faith and jump-start this project, there are a few things you need to keep in mind. 1.   The aim matters This is where it all begins, your vision. What is your website about? What is it that you would like for your website to showcase? What is the call-to-action you hope your website’s visitors to make? As you answer the above questions, you will be able to utilize

The post 5 Things You Should Keep in Mind Before Starting a Website appeared first on Photoshop Lady.

Earlier this week I met up for coffee with a client of mine. The two of us originally met when his employeer was my client and after leaving that job he hired me to customize his personal blog and we formed our own client/designer relationship. I was excited when he emailed me last week with the […]

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 →

Michael Mahemoff writes: To be effective, Rails cache sweepers need to be more fully understood.  They know no standard, so you must employ art. He goes on: Sweepers observe both your models and your controllers, but most workarounds focus on their controller nature.  Importantly: the sweeper must be explicitly added as an observer. Even more Read the rest...

We kick things off talking about the Jewish holiday of Yom Kippur. We then discuss David D Rainman's recent request...

The post METAL INJECTION LIVECAST #536 - Sinema with Chase from GATECREEPER appeared first on Metal Injection.

By Julia Conley Common Dreams “Nature is not a ‘nice to have’—it is our life-support system.” Scientists from around the world issued a stark warning to humanity Tuesday in a semi-annual report on the Earth’s declining biodiversity, which shows that … Continue reading →

By Julia Conley Common Dreams “Nature is not a ‘nice to have’—it is our life-support system.” Scientists from around the world issued a stark warning to humanity Tuesday in a semi-annual report on the Earth’s declining biodiversity, which shows that … 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.

Funding the production of quality online content is a pressing problem for content producers. The most common funding method, online advertising, is rife with well-known performance and privacy harms, and an intractable subject-agent conflict: many users do not want to see advertisements, depriving the site of needed funding.

Because of these negative aspects of advertisement-based funding, paywalls are an increasingly popular alternative for websites. This shift to a "pay-for-access" web is one that has potentially huge implications for the web and society. Instead of a system where information (nominally) flows freely, paywalls create a web where high quality information is available to fewer and fewer people, leaving the rest of the web users with less information, that might be also less accurate and of lower quality. Despite the potential significance of a move from an "advertising-but-open" web to a "paywalled" web, we find this issue understudied.

This work addresses this gap in our understanding by measuring how widely paywalls have been adopted, what kinds of sites use paywalls, and the distribution of policies enforced by paywalls. A partial list of our findings include that (i) paywall use is accelerating (2x more paywalls every 6 months), (ii) paywall adoption differs by country (e.g. 18.75% in US, 12.69% in Australia), (iii) paywalls change how users interact with sites (e.g. higher bounce rates, less incoming links), (iv) the median cost of an annual paywall access is $108 per site, and (v) paywalls are in general trivial to circumvent.

Finally, we present the design of a novel, automated system for detecting whether a site uses a paywall, through the combination of runtime browser instrumentation and repeated programmatic interactions with the site. We intend this classifier to augment future, longitudinal measurements of paywall use and behavior.

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.

Competition is steep in games like football. The desire to win often trumps safety.

We've come a long way since the olden days before legalization, when basically the only product on the market was the flower you got from a dealer.…

Since late last year, a new coronavirus, now dubbed COVID-19, has been sweeping the globe, sickening more than 114,000 with flu- and cold-like symptoms and killing more than 4,000 so far.…

[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.…

A network is described for providing estimates of the current time. The network includes multiple computer systems each configured to provide an estimate of the current time in response to a received request. The computer systems are logically arranged to form a hierarchical structure, wherein the hierarchical structure includes multiple levels ranked with respect to one another. Each of the computer systems is assigned one of multiple levels of trust, and occupies one of the levels of the hierarchical structure dependent upon the assigned level of trust. The level of trust assigned to a given computer system is dependent upon a timekeeping dependability of the given computer system. The assigned level of trust may also be dependent upon a timekeeping security of the given computer system, where the timekeeping security is dependent upon a tamper resistance of the time clock of the given computer system. Methods for delegating a level of trust to a new computer system (i.e., a computer system not part of the network) and for adding a new computer system to the network are also described.

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.

Technologies are described herein for utilizing a head heater to test temperature stability of a head of a storage device and to prevent the head from operating in an unstable temperature condition. A first power level may be applied to a head heater of a head in a storage device, the first power level configured to simulate a temperature condition in the head. An instability of the head is determined and the temperature condition and the instability of the head are recorded in a memory. The process may be repeated to develop a range of temperature conditions in which the head exhibits instability. The range of temperature conditions and the head heater may then be utilized to prevent the head from operating in an unstable temperature condition during normal operation.

A sleep apnea prevention sleep-aid mask is described. The sleep-aid mask is configured to secure the lips of a user together in a natural state, in order to encourage the respiratory system of the user to employ the nasal passages rather than the mouth for respiration. A pad equipped with a perforated mesh or channel is positioned over the lips and mouth of the user, and secured in position on the user via an adhesive, which temporarily affixes the pad to the face of the user. The sleep-aid mask is preferably shaped and sized according to the specific size of the user's face, and is configured to limit the effective diameter of the mouth of the user, in order to cause continued use of respiration via the nasal passages.

The present invention relates generally to a home therapy system for aiding spinal cord decompression and for treating related health issues. More specifically, the present invention teaches a garment system combining an upper portion and lower portion. According to a preferred embodiment, the upper portion includes a head band and yoke for cervical vertebra disc decompression and for aiding in treating Obstructive Sleep Apnea by repositioning and opening up breathing airways. Further in accordance with a preferred embodiment, the lower portion includes vertical decompression straps attached to leg bands and extending over the shoulders for thoracic and lumbar disc decompression by arching the spinal column back and opening the vertebra.

An improved system and method are disclosed for peer-to-peer communications. In one example, the method enables an endpoint to sweep an ongoing communication session to another endpoint by transferring session information between the endpoints.