Multivariate geo-sensory time series prediction is challenging because of the complex spatial and temporal correlation. In urban water distribution systems (WDS), numerous spatial-correlated sensors have been deployed to continuously collect hydraulic data. Forecasts of monitored flow and pressure time series are of vital importance for operational decision making, alerts and anomaly detection. To address this issue, we proposed a hybrid dual-stage spatial-temporal attention-based recurrent neural networks (hDS-RNN). Our model consists of two stages: a spatial attention-based encoder and a temporal attention-based decoder. Specifically, a hybrid spatial attention mechanism that employs inputs along temporal and spatial axes is proposed. Experiments on a real-world dataset are conducted and demonstrate that our model outperformed 9 baseline models in flow and pressure series prediction in WDS.

Numerical Weather Prediction (NWP) can reduce human suffering by predicting disastrous precipitation in time. A commonly-used NWP in the world is the European Centre for medium-range weather forecasts (EC). However, it is necessary to correct EC forecast through Bias Correcting on Precipitation (BCoP) since we still have not fully understood the mechanism of precipitation, making EC often have some biases. The existing BCoPs suffers from limited prior data and the fixed Spatio-Temporal (ST) scale. We thus propose an end-to-end deep-learning BCoP model named Spatio-Temporal feature Auto-Selective (STAS) model to select optimal ST regularity from EC via the ST Feature-selective Mechanisms (SFM/TFM). Given different input features, these two mechanisms can automatically adjust the spatial and temporal scales for correcting. Experiments on an EC public dataset indicate that compared with 8 published BCoP methods, STAS shows state-of-the-art performance on several criteria of BCoP, named threat scores (TS). Further, ablation studies justify that the SFM/TFM indeed work well in boosting the performance of BCoP, especially on the heavy precipitation.

Unlike the traditional dock-based systems, dockless bike-sharing systems are more convenient for users in terms of flexibility. However, the flexibility of these dockless systems comes at the cost of management and operation complexity. Indeed, the imbalanced and dynamic use of bikes leads to mandatory rebalancing operations, which impose a critical need for effective bike traffic flow prediction. While efforts have been made in developing traffic flow prediction models, existing approaches lack interpretability, and thus have limited value in practical deployment. To this end, we propose an Interpretable Bike Flow Prediction (IBFP) framework, which can provide effective bike flow prediction with interpretable traffic patterns. Specifically, by dividing the urban area into regions according to flow density, we first model the spatio-temporal bike flows between regions with graph regularized sparse representation, where graph Laplacian is used as a smooth operator to preserve the commonalities of the periodic data structure. Then, we extract traffic patterns from bike flows using subspace clustering with sparse representation to construct interpretable base matrices. Moreover, the bike flows can be predicted with the interpretable base matrices and learned parameters. Finally, experimental results on real-world data show the advantages of the IBFP method for flow prediction in dockless bike sharing systems. In addition, the interpretability of our flow pattern exploitation is further illustrated through a case study where IBFP provides valuable insights into bike flow analysis.

With the popularity and development of the wearable devices such as smartphones, human activity recognition (HAR) based on sensors has become as a key research area in human computer interaction and ubiquitous computing. The emergence of deep learning leads to a recent shift in the research of HAR, which requires massive strictly labeled data. In comparison with video data, activity data recorded from an accelerometer or gyroscope is often more difficult to interpret and segment. Recently, several attention mechanisms are proposed to handle the weakly labeled human activity data, which do not require accurate data annotation. However, these attention-based models can only handle the weakly labeled dataset whose segment includes one labeled activity, as a result it limits efficiency and practicality. In the paper, we proposed a recurrent attention network to handle sequential activity recognition and location tasks. The model can repeatedly perform steps of attention on multiple activities of one segment and each step is corresponding to the current focused activity according to its previous observations. The effectiveness of the recurrent attention model is validated by comparing with a baseline CNN, on the UniMiB-SHAR dataset and a collected sequential weakly labeled multi-activity dataset. The experiment results show that our recurrent attention model not only can perform single activity recognition tasks, but also can recognize and locate sequential weakly labeled multi-activity data. Besides, the recurrent attention can greatly facilitate the process of sensor data accumulation by automatically segmenting the regions of interest.

This technical report aims at defining a formal framework for Deep Neural Network (DNN) layer-wise quantization, focusing in particular on the problems related to the final deployment. It also acts as a documentation for the NEMO (NEural Minimization for pytOrch) framework. It describes the four DNN representations used in NEMO (FullPrecision, FakeQuantized, QuantizedDeployable and IntegerDeployable), focusing in particular on a formal definition of the latter two. An important feature of this model, and in particular the IntegerDeployable representation, is that it enables DNN inference using purely integers - without resorting to real-valued numbers in any part of the computation and without relying on an explicit fixed-point numerical representation.

The vulnerability of automated fingerprint recognition systems to presentation attacks (PA), i.e., spoof or altered fingers, has been a growing concern, warranting the development of accurate and efficient presentation attack detection (PAD) methods. However, one major limitation of the existing PAD solutions is their poor generalization to new PA materials and fingerprint sensors, not used in training. In this study, we propose a robust PAD solution with improved cross-material and cross-sensor generalization. Specifically, we build on top of any CNN-based architecture trained for fingerprint spoof detection combined with cross-material spoof generalization using a style transfer network wrapper. We also incorporate adversarial representation learning (ARL) in deep neural networks (DNN) to learn sensor and material invariant representations for PAD. Experimental results on LivDet 2015 and 2017 public domain datasets exhibit the effectiveness of the proposed approach.

Just when I thought TensorFlow's market share would be eaten by the emergence (and rapid adoption) of PyTorch, Google has come roaring back. TensorFlow 2.0, recently released and open-sourced to the community, is a flexible and adaptable deep learning framework that has won back a lot of detractors. I love the ease with which even beginners can pick up TensorFlow 2.0 and start executing deep learning tasks. There are a plethora of offshoots that come with TensorFlow 2.0. You can read about them in this article that summarizes all the developments at the TensorFlow Dev Summit 2020.

There has been an exponential growth of data sets that measure cellular biology & the activity of compounds over the last 5 years; enough to feed and encourage the use of Machine Learning algorithms such as that of Deep Learning (DL). Whilst gaining impressive traction across a range of applications, DL is well known for its remarkable progress in image recognition. Specific to pharmaceutical research, Deep Learning provides an ability to mine through extensive biomedical data sets and is paving the way toward alleviating the low success rate in pharmaceutical R&D as well as shortening the tunnel process of drug development - leading to faster medicinal solutions from the very first diagnosis of disease. This blog, part of the DL Explained Series, explains some of the key areas of DL used in the advancement of drug discovery alongside video presentations and detailed diagrams. There's a fundamental difference in the physical appearance of cells in healthy individuals compared to those that have inherited rare diseases.

The Korea Advanced Institute of Science and Technology (KAIST) announced on April 6 that professor Yoo Hoi-jun and his research team have succeeded in developing a generative adversarial networks processing unit (GANPU) as an AI chip processing GAN with low power and high efficiency. The AI chip is capable of quickly processing arithmetic operations required for image synthesis and restoration on a mobile basis. The single chip is capable of realizing image recognition, inference, learning and determination with low power and high efficiency and is expected to contribute to the use of AI in mobile devices for more purposes. GAN can be defined as AI technology in which a deep learning network generating fake data and a deep learning network distinguishing the fake data learn while competing with each other. The technology, which results in hardly distinguishable fake data, is applied to deepfakes, which are becoming increasingly problematic these days.

Artificial intelligence is currently one of the hottest buzzwords in tech -- with good reason. In the last few years, we have seen several technologies previously in the realm of science fiction transform into reality. Experts look at artificial intelligence as a factor of production, that has the potential to introduce new sources of growth and change the way work is done across industries. In fact, AI technologies could increase labour productivity by 40% or more by 2035, according to a recent report by Accenture. This could double economic growth in 12 developed nations that continue to draw talented and experienced professionals to work in this field.