Optical fiber amplifiers are key elements in present optical networks. Failures of these components result in high financial loss of income of the network operator as the communication traffic over an affected link is interrupted. Applying Remaining useful lifetime (RUL) prediction in the context of Predictive Maintenance (PdM) to optical fiber amplifiers to predict upcoming system failures at an early stage, so that network outages can be minimized through planning of targeted maintenance actions, ensures reliability and safety. Optical fiber amplifier are complex systems, that work under various operating conditions, which makes correct forecasting a difficult task. Increased monitoring capabilities of systems results in datasets that facilitate the application of data-driven RUL prediction methods. Deep learning models in particular have shown good performance, but generalization based on comparatively small datasets for RUL prediction is difficult. In this paper, we propose Sparse Low-ranked self-Attention Transformer (SLAT) as a novel RUL prediction method. SLAT is based on an encoder-decoder architecture, wherein two parallel working encoders extract features for sensors and time steps. By utilizing the self-attention mechanism, long-term dependencies can be learned from long sequences. The implementation of sparsity in the attention matrix and a low-rank parametrization reduce overfitting and increase generalization. Experimental application to optical fiber amplifiers exemplified on EDFA, as well as a reference dataset from turbofan engines, shows that SLAT outperforms the state-of-the-art methods.

The development of audio event recognition systems require labeled training data, which are generally hard to obtain. One promising source of recordings of audio events is the large amount of multimedia data on the web. In particular, if the audio content analysis must itself be performed on web audio, it is important to train the recognizers themselves from such data. Training from these web data, however, poses several challenges, the most important being the availability of labels: labels, if any, that may be obtained for the data are generally weak, and not of the kind conventionally required for training detectors or classifiers. We propose that learning algorithms that can exploit weak labels offer an effective method to learn from web data. We then propose a robust and efficient deep convolutional neural network (CNN) based framework to learn audio event recognizers from weakly labeled data. The proposed method can train from and analyze recordings of variable length in an efficient manner and outperforms a network trained with strongly labeled web data by a considerable margin. Moreover, even though we learn from weakly labeled data, where event time stamps within the recording are not available during training, our proposed framework is able to localize events during the inference stage.

Lutron makes one of our favorite motorized shades, but the company also offers motorized blinds. Window blinds are considered "hard" window coverings because they consist of slats--wooden, in this case--that drop down from the top of the window (or that slide left or right, in the case of vertical blinds). The motor mounted in the headrail of the Serena blinds tilts the 2-inch slats for privacy and light control. The accumulated weight of the slats, however, makes them too heavy for the motor to lift--even though Lutron fabricates the slats from a soft, fine-grained timber called North American basswood. If you want to fully expose the window, you will need to lift the blinds by hand and pull them back down to close.

Motorized window treatments that can open and close on command, on a schedule, or even based on room occupancy are the ultimate finishing touch for any smart home. Like smart lighting, smart window treatments offer a host of benefits in terms of convenience, security, and energy conservation. There's a safety angle, too: There are no pull cords that pose a strangulation risk to children and pets. But the wow factor they deliver also renders them a luxury item--even deploying them one room at a time can cost thousands of dollars if each room has a lot of windows. Shades are a soft window covering, typically made of fabric.

If your child isn't a straight-A student today, don't worry, take the long view. In the future, artificial intelligence (AI) will automate many jobs and disrupt industries, outperforming people in many areas. In generations prior, college degrees and post-graduate degrees were a path toward having careers with higher than average income-earning potential. Automation due to AI will impact both white collar and blue collar jobs alike. Presently AI is already beginning to make inroads in the areas of medicine, legal, marketing, customer service, bookkeeping, financial services, business analytics, transportation, publishing, and others.

This story was originally published by CityLab and appears here as part of the Climate Desk collaboration. What's 1.6 million square kilometers, weighs 80,000 metric tons, and is three times the size of continental France? That would be the Great Pacific Garbage Patch--the enormous collection of detritus that floats in the Pacific Ocean, halfway between Hawaii and California. Also known as the "GPGP," the patch's sprawl has made it notoriously difficult to measure. But a new study in the journal Scientific Reports has gathered the most comprehensive measurement yet.