Intel-owned company Mobileye has teamed up with Volkswagen to launch an autonomous EV ride-hailing service in its native country of Israel. Their joint venture called "New Mobility" will use electric Volkswagen vehicles powered by Mobileye's Level 4 autonomous driving system "AV Kit." A third partner, Israeli VW distributor Champion Motors, will manage the service's fleet and logistics. The Israeli government has promised to provide the venture with the regulatory and infrastructure support it needs, so the companies might not have to worry about dealing with red tape and bureaucracy. Intel purchased Israel-based Mobileye, which helped Tesla develop its Autopilot technology until they parted ways, for $15.3 billion in 2017.

Results from Fanuc Corp. and Komatsu were a mixed bag Monday. Factory-automation giant Fanuc reported an 8.4 percent drop in fiscal first-half operating income and saw its shares rise, while construction-equipment-maker Komatsu posted an 80 percent profit surge that was rewarded with a stock decline. Put that perplexing share reaction down to the topsy-turvy world of machinery-makers, where investors tend to view dismal earnings as a sign that a company is nearing the bottom, and good results as a warning that it's close to the top. The overall picture, though, is that concerns sparked by U.S. bellwether Caterpillar Inc. last week of late-cycle cost pressures and a deteriorating China outlook have been overdone, at least as far as the Japanese firms are concerned. China's faltering economy has been a key focus. Fanuc's sales in the country, already shrinking, fell a further 42 percent in the quarter through Sept. 30, compared with the previous three months.

Apple is investigating a factory in southwest China after a labour rights group said the tech giant's supplier forced student workers to work "like robots" to assemble its popular Apple Watch. Many were compelled to work in order to get their vocational degrees and had to do night shifts, according to an investigation by Hong Kong-based NGO Students and Scholars Against Corporate Misbehaviour (SACOM). SACOM interviewed 28 students at the plant in Chongqing municipality over the summer, and all of them said they had not voluntarily applied to work there, according to the report published last week. They worked under the guise of "internships", SACOM said, a practice rights groups say is widespread in China as manufacturers pair up with vocational schools to supply workers and fill labour shortages when they ramp up production for new models or the Christmas rush. "Our graduation certificate will be withheld by the school if we refuse to come," said one student majoring in e-commerce, according to SACOM.

Harry Lang: AI can't replace the counter-intuitive thinking behind great creativity The recent Winter Olympics in Pyeongchang was a marvel of human endeavour. Years of hurt, training and willpower culminating in all-too-brief moments of blood, sweat and tears.

Pseudo-relevance feedback (PRF) is commonly used to boost the performance of traditional information retrieval (IR) models by using top-ranked documents to identify and weight new query terms, thereby reducing the effect of query-document vocabulary mismatches. While neural retrieval models have recently demonstrated strong results for ad-hoc retrieval, combining them with PRF is not straightforward due to incompatibilities between existing PRF approaches and neural architectures. To bridge this gap, we propose an end-to-end neural PRF framework that can be used with existing neural IR models by embedding different neural models as building blocks. Extensive experiments on two standard test collections confirm the effectiveness of the proposed NPRF framework in improving the performance of two state-of-the-art neural IR models.

Transfer Learning (TL) in Deep Neural Networks is gaining importance because in most of the cases, the labeling of data is costly and time-consuming. Additionally, TL provides effective weight initialization. This paper introduces the idea of Adaptive Transfer Learning in Deep Neural Networks (ATL-DNN) for wind power prediction. Adaptive TL of Deep Neural Networks is proposed, which makes the proposed system an adaptive one as regards training on a different wind farm is concerned. The proposed ATL-DNN technique is tested for short-term wind power predictions, where continuously arriving information has to be exploited. Adaptive TL not only helps in providing good weight initialization, but is also helpful to utilize the online data that is continuously being generated by wind farms. Additionally, the proposed ATL-DNN technique is shown to transfer knowledge between different task domains (wind power to wind speed prediction) and from one region to another region. The simulation results show that proposed ATL-DNN technique achieves average values of 0.0637,0.0986, Keywords ---- Wind power prediction; Adaptive transfer learning; Deep learning; Ensemble learning 1. Introduction Many countries across the world use wind power as a renewable energy resource. Accurate prediction of wind power plays a significant role in generating smooth power from a turbine. There are numerous factors which affect the predicted power of a wind power prediction system, like fluctuation in speed of the wind with respect to time, geographical location, and climatic conditions.

Ensemble clustering has been a popular research topic in data mining and machine learning. Despite its significant progress in recent years, there are still two challenging issues in the current ensemble clustering research. First, most of the existing algorithms tend to investigate the ensemble information at the object-level, yet often lack the ability to explore the rich information at higher levels of granularity. Second, they mostly focus on the direct connections (e.g., direct intersection or pair-wise co-occurrence) in the multiple base clusterings, but generally neglect the multi-scale indirect relationship hidden in them. To address these two issues, this paper presents a novel ensemble clustering approach based on fast propagation of cluster-wise similarities via random walks. We first construct a cluster similarity graph with the base clusters treated as graph nodes and the cluster-wise Jaccard coefficient exploited to compute the initial edge weights. Upon the constructed graph, a transition probability matrix is defined, based on which the random walk process is conducted to propagate the graph structural information. Specifically, by investigating the propagating trajectories starting from different nodes, a new cluster-wise similarity matrix can be derived by considering the trajectory relationship. Then, the newly obtained cluster-wise similarity matrix is mapped from the cluster-level to the object-level to achieve an enhanced co-association (ECA) matrix, which is able to simultaneously capture the object-wise co-occurrence relationship as well as the multi-scale cluster-wise relationship in ensembles. Finally, two novel consensus functions are proposed to obtain the consensus clustering result. Extensive experiments on a variety of real-world datasets have demonstrated the effectiveness and efficiency of our approach.

Ideally, what confuses neural network should be confusing to humans. However, recent experiments have shown that small, imperceptible perturbations can change the network prediction. To address this gap in perception, we propose a novel approach for learning robust classifier. Our main idea is: adversarial examples for the robust classifier should be indistinguishable from the regular data of the adversarial target. We formulate a problem of learning robust classifier in the framework of Generative Adversarial Networks (GAN), where the adversarial attack on classifier acts as a generator, and the critic network learns to distinguish between regular and adversarial images. The classifier cost is augmented with the objective that its adversarial examples should confuse the adversary critic. To improve the stability of the adversarial mapping, we introduce adversarial cycle-consistency constraint which ensures that the adversarial mapping of the adversarial examples is close to the original. In the experiments, we show the effectiveness of our defense. Our method surpasses in terms of robustness networks trained with adversarial training. Additionally, we verify in the experiments with human annotators on MTurk that adversarial examples are indeed visually confusing. Codes for the project are available at https://github.com/aam-at/adversary_critic.

Convolutional neural networks (CNNs) attained a good performance in hyperspectral sensing image (HSI) classification, but CNNs consider spectra as orderless vectors. Therefore, considering the spectra as sequences, recurrent neural networks (RNNs) have been applied in HSI classification, for RNNs is skilled at dealing with sequential data. However, for a long-sequence task, RNNs is difficult for training and not as effective as we expected. Besides, spatial contextual features are not considered in RNNs. In this study, we propose a Shorten Spatial-spectral RNN with Parallel-GRU (St-SS-pGRU) for HSI classification. A shorten RNN is more efficient and easier for training than band-by-band RNN. By combining converlusion layer, the St-SSpGRU model considers not only spectral but also spatial feature, which results in a better performance. An architecture named parallel-GRU is also proposed and applied in St-SS-pGRU. With this architecture, the model gets a better performance and is more robust.

NEURAL SOURCE-FILTER-BASED WAVEFORM MODEL FOR STATISTICAL PARAMETRIC SPEECH SYNTHESIS Xin W ang 1, Shinji T akaki 1, Junichi Y amagishi 1 1 National Institute of Informatics, Japan wangxin@nii.ac.jp, takaki@nii.ac.jp, jyamagis@nii.ac.jp ABSTRACT Neural waveform models such as the WaveNet are used in many recent text-to-speech systems, but the original WaveNet is quite slow in waveform generation because of its autoregressive (AR) structure. Although faster non-AR models were recently reported, they may be prohibitively complicated due to the use of a distilling training method and the blend of other disparate training criteria. This study proposes a non-AR neural source-filter waveform model that can be directly trained using spectrum-based training criteria and the stochastic gradient descent method. Given the input acoustic features, the proposed model first uses a source module to generate a sine-based excitation signal and then uses a filter module to transform the excitation signal into the output speech waveform. Our experiments demonstrated that the proposed model generated waveforms at least 100 times faster than the AR WaveNet and the quality of its synthetic speech is close to that of speech generated by the AR WaveNet. Ablation test results showed that both the sine-wave excitation signal and the spectrum-based training criteria were essential to the performance of the proposed model. Index Terms -- speech synthesis, neural network, waveform modeling 1. INTRODUCTION Text-to-speech (TTS) synthesis, a technology that converts texts into speech waveforms, has been advanced by using end-to-end architectures [1] and neural-network-based waveform models [2, 3, 4]. Among those waveform models, the WaveNet [2] directly models the distributions of waveform sampling points and has demonstrated outstanding performance.