The consumption of microbial-contaminated food and water is responsible for the deaths of millions of people annually. Smartphone-based microscopy systems are portable, low-cost, and more accessible alternatives for the detection of Giardia and Cryptosporidium than traditional brightfield microscopes. However, the images from smartphone microscopes are noisier and require manual cyst identification by trained technicians, usually unavailable in resource-limited settings. Automatic detection of (oo)cysts using deep-learning-based object detection could offer a solution for this limitation. We evaluate the performance of three state-of-the-art object detectors to detect (oo)cysts of Giardia and Cryptosporidium on a custom dataset that includes both smartphone and brightfield microscopic images from vegetable samples. Faster RCNN, RetinaNet, and you only look once (YOLOv8s) deep-learning models were employed to explore their efficacy and limitations. Our results show that while the deep-learning models perform better with the brightfield microscopy image dataset than the smartphone microscopy image dataset, the smartphone microscopy predictions are still comparable to the prediction performance of non-experts.

The transmission of the contagious COVID-19 is known to be highly dependent on individual viral dynamics. Since the cycle threshold (Ct) is the only semi-quantitative viral measurement that could reflect infectivity, we utilized Ct values to forecast COVID-19 incidences. Our COVID-19 cohort (n 9531), retrieved from a single representative cross-sectional virology test center in Lebanon, revealed that low daily mean Ct values are followed by an increase in the number of national positive COVID-19 cases. A subset of the data was used to develop a deep neural network model, tune its hyperparameters, and optimize the weights for minimal mean square error of prediction. The final model's accuracy is reported by comparing its predictions with an unseen dataset.

Package Name: Small Cap Forecast Recommended Positions: Long Forecast Length: 3 Days (10/4/2020 – 10/8/2020) I Know First Average: 11.18% In this 3 Days forecast for the Small Cap Forecast Package, there were many high performing trades and the algorithm correctly predicted 10 out 10 trades. The highest trade return came from NTZ, at 45.79%. Other notable stocks were NMIH and HIBB with a return of 16.96% and 14.57%. The package itself saw an overall return of 11.18%, providing investors with a 8.24% premium above the S&P 500's return of 2.94% for the same time period.

They may seem tiny and fragile, but mosquitoes can be extremely dangerous. Malaria has been a notoriously life-threatening disease for people of all ages which is spread by mosquitoes. More so because during the initial stages, the symptoms could easily be mistaken for fever, flu, or the common cold. But, in the advanced stages, it could wreak havoc by infecting and rupturing cell structure which could be potentially life-threatening. And if left untreated, it could even result in death.

Earlier this year, I posted about our project KataGo and research to improve self-play learning in Go, with an initial one-week run showing highly promising results. Several months later in June, KataGo performed a second, longer 19-day run with some major bugfixes and minor optimizations. Starting from scratch and with slightly less hardware than before, up to 28 V100 GPUs, it reached and surpassed the earlier one-week run in barely more than the first three days. By the end of the 19 days, it had reached the strength of ELF OpenGo, Facebook AI Research's multi-thousand-GPU replication of one of AlphaZero's runs - equating to roughly a factor of 50 reduction in computation required. This version of KataGo has also been released to the Go player community for several months now.

This forecast is part of the Risk-Conscious Package, as one of I Know First's equity research solutions. We determine our aggressive stock picks by screening our algorithm daily for higher volatility stocks that present greater opportunities but are also riskier. Package Name: Aggressive Stocks Forecast Recommended Positions: Long Forecast Length: 3 Months (8/28/2019 – 11/28/2019) I Know First Average: 37.51% The algorithm correctly predicted 7 out 10 of the suggested trades in the Aggressive Stocks Forecast Package for this 3 Months forecast. Among the top-performing market predictions in this forecast was FRAN, which registered a return of 277.67%. MHLD and OMI also performed well for this time horizon with returns of 53.16% and 42.33%, respectively.

Getting young children to tidy up their rooms is often challenging. What I insist is messy, they will insist is clean enough. After all, all adjectives are subjective and I want my children to grow up respecting others' opinion in our inclusive society. How do you put some definition around differences in opinion? An objective way to achieve this distinction is using image classification to differentiate between a clean versus a messy room.

Deep-learning based daily financial predictors. The program is aimed to provide trending predictors for major stocks and ETFs. There are nine combinations of trending (Up, Flat, Down) and Volatility (Stable, Decreasing, Increasing), based on which optimal allocations are suggested for individual asset. Please become a patron to receive prompt updates.

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Several research groups worldwide are currently investigating how deep learning may advance the state-of-the-art in image and video coding. An open question is how to make deep neural networks work in conjunction with existing (and upcoming) video codecs, such as MPEG AVC/H.264, Such compatibility is a crucial aspect, as the video content industry and hardware manufacturers are expected to remain committed to supporting these standards for the foreseeable future. We propose deep neural networks as precoding components for current and future codec ecosystems. In our current deployments for DASH/HLS adaptive streaming, this comprises downscaling neural networks.