Gas turbine engines are complex machines that typically generate a vast amount of data, and require careful monitoring to allow for cost-effective preventative maintenance. In aerospace applications, returning all measured data to ground is prohibitively expensive, often causing useful, high value, data to be discarded. The ability to detect, prioritise, and return useful data in real-time is therefore vital. This paper proposes that system output measurements, described by a convolutional neural network model of normality, are prioritised in real-time for the attention of preventative maintenance decision makers. Due to the complexity of gas turbine engine time-varying behaviours, deriving accurate physical models is difficult, and often leads to models with low prediction accuracy and incompatibility with real-time execution. Data-driven modelling is a desirable alternative producing high accuracy, asset specific models without the need for derivation from first principles. We present a data-driven system for online detection and prioritisation of anomalous data. Biased data assessment deriving from novel operating conditions is avoided by uncertainty management integrated into the deep neural predictive model. Testing is performed on real and synthetic data, showing sensitivity to both real and synthetic faults. The system is capable of running in real-time on low-power embedded hardware and is currently in deployment on the Rolls-Royce Pearl 15 engine flight trials.

Machine learning plays a crucial role in enhancing and accelerating the search for new fundamental physics. We review the state of machine learning methods and applications for new physics searches in the context of terrestrial high energy physics experiments, including the Large Hadron Collider, rare event searches, and neutrino experiments. While machine learning has a long history in these fields, the deep learning revolution (early 2010s) has yielded a qualitative shift in terms of the scope and ambition of research. These modern machine learning developments are the focus of the present review.

Tensor decomposition is one of the fundamental technique for model compression of deep convolution neural networks owing to its ability to reveal the latent relations among complex structures. However, most existing methods compress the networks layer by layer, which cannot provide a satisfactory solution to achieve global optimization. In this paper, we proposed a model reduction method to compress the pre-trained networks using low-rank tensor decomposition of the convolution layers. Our method is based on the optimization techniques to select the proper ranks of decomposed network layers. A new regularization method, called funnel function, is proposed to suppress the unimportant factors during the compression, so the proper ranks can be revealed much easier. The experimental results show that our algorithm can reduce more model parameters than other tensor compression methods. For ResNet18 with ImageNet2012, our reduced model can reach more than twi times speed up in terms of GMAC with merely 0.7% Top-1 accuracy drop, which outperforms most existing methods in both metrics.

The increase in people's use of mobile messaging services has led to the spread of social engineering attacks like phishing, considering that spam text is one of the main factors in the dissemination of phishing attacks to steal sensitive data such as credit cards and passwords. In addition, rumors and incorrect medical information regarding the COVID-19 pandemic are widely shared on social media leading to people's fear and confusion. Thus, filtering spam content is vital to reduce risks and threats. Previous studies relied on machine learning and deep learning approaches for spam classification, but these approaches have two limitations. Machine learning models require manual feature engineering, whereas deep neural networks require a high computational cost. This paper introduces a dynamic deep ensemble model for spam detection that adjusts its complexity and extracts features automatically. The proposed model utilizes convolutional and pooling layers for feature extraction along with base classifiers such as random forests and extremely randomized trees for classifying texts into spam or legitimate ones. Moreover, the model employs ensemble learning procedures like boosting and bagging. As a result, the model achieved high precision, recall, f1-score and accuracy of 98.38%.

It is not always the big stuff or the latest packages that help improve the accuracy or performance of our #machine learning models. At times we overlook the basics of Machine Learning and rush to higher order solutions. When the solution is just right there in front of us. Below are 10 simple things you should remember to try first before throwing in the towel and jumping straight to RNNs and CNNs (of course there are datasets which merit you to start straight from LSTMs and BERT).Let us remind ourselves of our checklist before bringing out our Calculus skills. Try to understand as much about the domain as you can.

'Watters' World' host lists the many domestic issues President Biden faces This is a rush transcript from "Watters' World," December 4, 2021. This copy may not be in its final form and may be updated. JESSE WATTERS, FOX NEWS HOST: Welcome to WATTERS' WORLD, I'm Jesse Watters. The Annual White House Christmas Tree lighting is always such a special event, except Joe Biden, the President seemingly forgot he was supposed to light it. Maybe he thought Barack was going to light it. These things just keep happening every single week. I kind of feel bad for LL, they needed to do a second take. Now, President Biden and First Lady, Dr. Jill Biden [CHEERING AND APPLAUSE] (END VIDEO CLIP) WATTERS: So, how are we supposed to feel confident the President can crush the virus when he can't even get it together for a Christmas Tree lighting? I'm not worried about the new variant. I'm worried about how the government is going to overreact to the new variant. Biden has got a new plan. More masks, more testing, but unvaxxed illegals can just pour across the Southern border without testing, without quarantining. And then Joe packs them onto planes and buses and sends them to your neighborhood. Does that make sense to anybody? (BEGIN VIDEO CLIP) PETER DOOCY, FOX NEWS CHANNEL WHITE HOUSE CORRESPONDENT: Dr. Fauci, as you advised the President about the possibility of new testing requirements for people coming into this country? ANTHONY FAUCI, DIRECTOR, NATIONAL INSTITUTE OF ALLERGY AND INFECTIOUS DISEASES: Everybody who is coming into the country needs to get a test within 24 hours of getting on the plane to come here. DOOCY: But what about people who don't take a plane and just these border crossers coming in in huge numbers?

Audrey Azoulay, Director-General of UNESCO, presented last week the first-ever global standard on the ethics of artificial intelligence, adopted by UNESCO's 193 Member States at the international organization's General Conference. UNESCO had highlighted back in November 2019 the need for regulatory frameworks at the national but also international level to ensure that innovative AI technologies can benefit all humanity. This recommendation, the result of the work of 24 international experts appointed on March 11, 2020, sets a global normative framework and gives its member states the responsibility to translate this framework at their level. Over the past decade, AI has experienced a considerable boom. Experts agree that humanity is on the threshold of a new era and that artificial intelligence will transform our lives in ways we cannot imagine.

A year ago I found out, from one of my direct reports, that I had apparently resigned. I had just been fired from Google in one of the most disrespectful ways I could imagine. Thanks to organizing done by former and current Google employees and many others, Google did not succeed in smearing my work or reputation, although they tried. My firing made headlines because of the worker organizing that has been building up in the tech world, often due to the labor of people who are already marginalized, many of whose names we do not know. Since I was fired last December, there have been many developments in tech worker organizing and whistleblowing.

The UK's newly launched national strategy will help keep the UK competitive as AI transforms businesses and jobs The power of AI to drive growth and innovation is clear, evidenced by the McKinsey Global Survey on AI that suggests that organisations are using AI as a tool for generating value, increasingly, in the form of revenues. But the black box approach taken by most AI companies comes with serious risks to scale bias like never before, intentional or not. As part of any AI strategy, diverse teams must be part of the process from the ground up to recognise biases in the data on which models are trained and to scenario plan how minorities may be impacted. Ensuring AI is explainable with a greater degree of transparency in training data, data gaps, and algorithmic logic can further reduce bias at scale. These are the key ways to mitigate this very real risk.

Effective water resource management requires information on water availability, both in terms of quality and quantity, spatially and temporally. In this paper, we study the methodology behind Transfer Learning (TL) through fine-tuning and parameter transferring for better generalization performance of streamflow prediction in data-sparse regions. We propose a standard recurrent neural network in the form of Long Short-Term Memory (LSTM) to fit on a sufficiently large source domain dataset and repurpose the learned weights to a significantly smaller, yet similar target domain datasets. We present a methodology to implement transfer learning approaches for spatiotemporal applications by separating the spatial and temporal components of the model and training the model to generalize based on categorical datasets representing spatial variability. The framework is developed on a rich benchmark dataset from the US and evaluated on a smaller dataset collected by The Nature Conservancy in Kenya. The LSTM model exhibits generalization performance through our TL technique. Results from this current experiment demonstrate the effective predictive skill of forecasting streamflow responses when knowledge transferring and static descriptors are used to improve hydrologic model generalization in data-sparse regions.