Bank of China has launched an artificial intelligence-based forex trading signal prediction application through the Refinitiv Eikon desktop. Known as DeepFX, the tool was developed by the digital asset management division of Bank of China using deep learning technology to predict short-term price movements on major foreign exchange currency pairs. "With the unprecedented increase in market volatility across global financial markets in recent months, the Bank of China's DeepFX application is a timely and practical tool to empower users with the insights they need to navigate the turbulent FX landscape," said Nicole Chen, Head of China at Refinitiv. Bank of China said the'Lite' released version of the DeepFX app provides forecasting in real-time of FX trade signals in 5-minute intervals, while displaying back-test results within 10 days. Bank of China added the app is aimed at helping traders, quant developers, FinTech innovation heads, as well as data scientists.

Data Scientist is the most promising job in the U.S according to LinkedIn. Also, the demand for Data Scientists is growing exponentially in all the industries. Out of all the openings, 19% of data science professionals jobs are secured by the Finance Industry. Python statistics is one of the most important python built-in libraries developed for descriptive statistics. Python statistics is all about the ability to describe, summarize, and represent data visually through comprehensive python statistics libraries.

The algorithm, which is integrated with three new Canon CT scanners installed at Frimley Health NHS Foundation Trust, has been trained to differentiate'noise' from true signal, reducing distortions and maintaining details in image outputs.

Global technology solutions provider Avnet Asia and AI software innovator Mipsology announced that Avnet will promote and resell Mipsology's Zebra software platform to its APAC customer base. Zebra removes the technical complexity of FPGAs, making them plug-and-play with fast performance. This agreement extends Avnet's IoT ecosystem, bringing Mipsology's deep learning inference acceleration solution to its Asia customers. Companies looking to deploy AI can now seamlessly migrate to new FPGA-based acceleration technologies with no code change and enjoy a longer lifespan for software and hardware than they could with GPU-based solutions. Avnet's first product incorporating the solution will be the Zebra-powered Xilinx Alveo data center accelerator cards. The range of offerings is expected to expand in the future.

A number of studies claim that artificial intelligence (AI) does as well or better than doctors at interpreting images and diagnosing medical conditions. However, a recent study published in The BMJ in March 2020 reveals that most of this research is flawed, and the results exaggerated. The outcome could be that the decision to adopt AI as part of patient care is based upon faulty premises, compromising the quality of patient care for millions of people. AI is an advanced field of computing, with many discoveries and achievements to its credit. It is also remarkable for its level of innovation. With its flexibility and ability to'learn' from past experiences, it is touted as a solution to help improve patient care and to take off some of the work from the shoulders of healthcare professionals who have too much to do.

A number of studies claim that artificial intelligence (AI) does as well or better than doctors at interpreting images and diagnosing medical conditions. However, a recent study published in The BMJ in March 2020 reveals that most of this research is flawed, and the results exaggerated. The outcome could be that the decision to adopt AI as part of patient care is based upon faulty premises, compromising the quality of patient care for millions of people. AI is an advanced field of computing, with many discoveries and achievements to its credit. It is also remarkable for its level of innovation. With its flexibility and ability to'learn' from past experiences, it is touted as a solution to help improve patient care and to take off some of the work from the shoulders of healthcare professionals who have too much to do.

Recent works have demonstrated the existence of {\it adversarial examples} targeting a single machine learning system. In this paper we ask a simple but fundamental question of "selective fooling": given {\it multiple} machine learning systems assigned to solve the same classification problem and taking the same input signal, is it possible to construct a perturbation to the input signal that manipulates the outputs of these {\it multiple} machine learning systems {\it simultaneously} in arbitrary pre-defined ways? For example, is it possible to selectively fool a set of "enemy" machine learning systems but does not fool the other "friend" machine learning systems? The answer to this question depends on the extent to which these different machine learning systems "think alike". We formulate the problem of "selective fooling" as a novel optimization problem, and report on a series of experiments on the MNIST dataset. Our preliminary findings from these experiments show that it is in fact very easy to selectively manipulate multiple MNIST classifiers simultaneously, even when the classifiers are identical in their architectures, training algorithms and training datasets except for random initialization during training. This suggests that two nominally equivalent machine learning systems do not in fact "think alike" at all, and opens the possibility for many novel applications and deeper understandings of the working principles of deep neural networks.

This paper presents a new approach to classification of high dimensional spectroscopy data and demonstrates that it outperforms other current state-of-the art approaches. The specific task we consider is identifying whether samples contain chlorinated solvents or not, based on their Raman spectra. We also examine robustness to classification of outlier samples that are not represented in the training set (negative outliers). A novel application of a locally-connected neural network (NN) for the binary classification of spectroscopy data is proposed and demonstrated to yield improved accuracy over traditionally popular algorithms. Additionally, we present the ability to further increase the accuracy of the locally-connected NN algorithm through the use of synthetic training spectra and we investigate the use of autoencoder based one-class classifiers and outlier detectors. Finally, a two-step classification process is presented as an alternative to the binary and one-class classification paradigms. This process combines the locally-connected NN classifier, the use of synthetic training data, and an autoencoder based outlier detector to produce a model which is shown to both produce high classification accuracy, and be robust to the presence of negative outliers.

Recent theoretical studies proved that deep neural network (DNN) estimators obtained by minimizing empirical risk with a certain sparsity constraint can attain optimal convergence rates for regression and classification problems. However, the sparsity constraint requires to know certain properties of the true model, which are not available in practice. Moreover, computation is difficult due to the discrete nature of the sparsity constraint. In this paper, we propose a novel penalized estimation method for sparse DNNs, which resolves the aforementioned problems existing in the sparsity constraint. We establish an oracle inequality for the excess risk of the proposed sparse-penalized DNN estimator and derive convergence rates for several learning tasks. In particular, we prove that the sparse-penalized estimator can adaptively attain minimax convergence rates for various nonparametric regression problems. For computation, we develop an efficient gradient-based optimization algorithm that guarantees the monotonic reduction of the objective function.

FPGAs have shown great potential in providing low-latency and energy-efficient solutions for deep neural network (DNN) inference applications. Currently, the majority of FPGA-based DNN accelerators in the cloud run in a time-division multiplexing way for multiple users sharing a single FPGA, and require re-compilation with $\sim$100 s overhead. Such designs lead to poor isolation and heavy performance loss for multiple users, which are far away from providing efficient and flexible FPGA virtualization for neither public nor private cloud scenarios. To solve these problems, we introduce a novel virtualization framework for instruction architecture set (ISA) based on DNN accelerators by sharing a single FPGA. We enable the isolation by introducing a two-level instruction dispatch module and a multi-core based hardware resources pool. Such designs provide isolated and runtime-programmable hardware resources, further leading to performance isolation for multiple users. On the other hand, to overcome the heavy re-compilation overheads, we propose a tiling-based instruction frame package design and two-stage static-dynamic compilation. Only the light-weight runtime information is re-compiled with $\sim$1 ms overhead, thus the performance is guaranteed for the private cloud. Our extensive experimental results show that the proposed virtualization design achieves 1.07-1.69x and 1.88-3.12x throughput improvement over previous static designs using the single-core and the multi-core architectures, respectively.