A modified commercial drone may have been responsible for an attempted attack on a Pennsylvania power substation last year, the first reported case of a drone assault on the U.S.'s energy infrastructure. Authorities believe a DJI Mavic 2 drone with a thick copper wire tethered to it was found in June 2020 was likely intended to disrupt operations'by creating a short circuit to cause damage to transformers or distribution lines,' according to a joint intelligence bulletin from the FBI, Department of Homeland Security, and the National Counterterrorism Center released October 28. If the wire had come into contact with any of the power plant's high-voltage equipment it could have resulted in a short circuit, power failure or even a fire, according to New Scientist. The Drive reported the drone was recovered by authorities from a substation near Hershey, Pennsylvania, about 100 miles from Philadelphia. No groups has claimed responsibility: The device's camera and internal memory card had been removed and identifying labels were removed, in a likely attempt to obscure its origins.

In July of last year, a DJI Mavic 2 drone approached a Pennsylvania power substation. Two 4-foot nylon ropes dangled from its rotors, a thick copper wire connected to the ends with electrical tape. The device had been stripped of any identifiable markings, as well as its onboard camera and memory card, in an apparent effort by its owner to avoid detection. Its likely goal, according to a joint security bulletin released by DHS, the FBI, and the National Counterterrorism Center, was to "disrupt operations by creating a short circuit." The drone crashed on the roof of an adjacent building before it reached its ostensible target, damaging a rotor in the process.

There's a massive announcement set to take place later this year, and it could change the $12 trillion healthcare industry forever. Over the last 2 years, we've seen a huge transformation as businesses across nearly every industry have gone digital. And with the health crisis sweeping the globe last year, the healthcare sector was no different. Mentioned in today's commentary includes: Brookfield Renewable Partners L.P. (NYSE: BEP), LifeStance Health Group, Inc. (NASDAQ: LFST), Teladoc Health, Inc. (NYSE: TDOC), Mind Medicine (MindMed) Inc. (NASDAQ: MNMD), American Well Corporation (NYSE: AMWL). That's why we're now on the verge of a healthcare revolution, set to leverage the latest technology to disrupt a bloated and complicated system.

A modified consumer drone was used in an attack on an electrical substation in the US last year, according to a report from the FBI, Department of Homeland Security and National Counterterrorism Center. The report, which is being circulated to law enforcement agencies in the US, highlights the incident at a substation in Pennsylvania last year as the first known use of a drone to target energy infrastructure in the US. The location isn't specifically identified, but the drone crashed without causing damage. The drone was modified with a trailing tether supporting a length of copper wire. If the wire had come into contact with high-voltage equipment it could have caused a short circuit, equipment failures and possibly fires.

Let's analyze our own bodies. We the humans begin to have six senses like touch, smell, vision, hearing, tasting, and the sixth sense. I'm not sure some researchers are saying the sixth sense is about sensing one's body in space. Our human body is fully connected with nerves whenever you touch or taste anything you will feel or sense something about that action you have done. How you are able to classify the sense whether that it is good or bad?

We consider the problem of controlling a Linear Quadratic Regulator (LQR) system over a finite horizon $T$ with fixed and known cost matrices $Q,R$, but unknown and non-stationary dynamics $\{A_t, B_t\}$. The sequence of dynamics matrices can be arbitrary, but with a total variation, $V_T$, assumed to be $o(T)$ and unknown to the controller. Under the assumption that a sequence of stabilizing, but potentially sub-optimal controllers is available for all $t$, we present an algorithm that achieves the optimal dynamic regret of $\tilde{\mathcal{O}}\left(V_T^{2/5}T^{3/5}\right)$. With piece-wise constant dynamics, our algorithm achieves the optimal regret of $\tilde{\mathcal{O}}(\sqrt{ST})$ where $S$ is the number of switches. The crux of our algorithm is an adaptive non-stationarity detection strategy, which builds on an approach recently developed for contextual Multi-armed Bandit problems. We also argue that non-adaptive forgetting (e.g., restarting or using sliding window learning with a static window size) may not be regret optimal for the LQR problem, even when the window size is optimally tuned with the knowledge of $V_T$. The main technical challenge in the analysis of our algorithm is to prove that the ordinary least squares (OLS) estimator has a small bias when the parameter to be estimated is non-stationary. Our analysis also highlights that the key motif driving the regret is that the LQR problem is in spirit a bandit problem with linear feedback and locally quadratic cost. This motif is more universal than the LQR problem itself, and therefore we believe our results should find wider application.

We present a model of sense-making that greatly facilitates the collaboration between an intelligent analyst and a knowledge-based agent. It is a general model grounded in the science of evidence and the scientific method of hypothesis generation and testing, where sense-making hypotheses that explain an observation are generated, relevant evidence is then discovered, and the hypotheses are tested based on the discovered evidence. We illustrate how the model enables an analyst to directly instruct the agent to understand situations involving the possible production of weapons (e.g., chemical warfare agents) and how the agent becomes increasingly more competent in understanding other situations from that domain (e.g., possible production of centrifuge-enriched uranium or of stealth fighter aircraft).

For the past two decades, there have been significant efforts to develop methods for object detection in Remote Sensing (RS) images. In most cases, the datasets for small object detection in remote sensing images are inadequate. Many researchers used scene classification datasets for object detection, which has its limitations; for example, the large-sized objects outnumber the small objects in object categories. Thus, they lack diversity; this further affects the detection performance of small object detectors in RS images. This paper reviews current datasets and object detection methods (deep learning-based) for remote sensing images. We also propose a large-scale, publicly available benchmark Remote Sensing Super-resolution Object Detection (RSSOD) dataset. The RSSOD dataset consists of 1,759 hand-annotated images with 22,091 instances of very high resolution (VHR) images with a spatial resolution of ~0.05 m. There are five classes with varying frequencies of labels per class. The image patches are extracted from satellite images, including real image distortions such as tangential scale distortion and skew distortion. We also propose a novel Multi-class Cyclic super-resolution Generative adversarial network with Residual feature aggregation (MCGR) and auxiliary YOLOv5 detector to benchmark image super-resolution-based object detection and compare with the existing state-of-the-art methods based on image super-resolution (SR). The proposed MCGR achieved state-of-the-art performance for image SR with an improvement of 1.2dB PSNR compared to the current state-of-the-art NLSN method. MCGR achieved best object detection mAPs of 0.758, 0.881, 0.841, and 0.983, respectively, for five-class, four-class, two-class, and single classes, respectively surpassing the performance of the state-of-the-art object detectors YOLOv5, EfficientDet, Faster RCNN, SSD, and RetinaNet.

In this paper, we propose a new end-to-end methodology to optimize the energy performance as well as comfort and air quality in large buildings without any renovation work. We introduce a metamodel based on recurrent neural networks and trained to predict the behavior of a general class of buildings using a database sampled from a simulation program. This metamodel is then deployed in different frameworks and its parameters are calibrated using the specific data of two real buildings. Parameters are estimated by comparing the predictions of the metamodel with real data obtained from sensors using the CMA-ES algorithm, a derivative free optimization procedure. Then, energy consumptions are optimized while maintaining a target thermal comfort and air quality, using the NSGA-II multi-objective optimization procedure. The numerical experiments illustrate how this metamodel ensures a significant gain in energy efficiency, up to almost 10%, while being computationally much more appealing than numerical models and flexible enough to be adapted to several types of buildings.

The 2011 Fukushima nuclear disaster will cost hundreds of billions of dollars to clean up when all is said and done, but the environmental cost could be significantly higher, with nearby lakes contaminated for decades, according to a new study. A group of researchers, led by those at the University of Tsukuba, have found that Lake Onuma on Mount Akagi could be contaminated with radioactive cesium-137 (137CS) for roughly 30 years after the disaster. The researchers used the fractional diffusional method and determined that radioactivity concentration will happen for up to 10,000 days following the accident. Just after the nuclear accident, the radioactivity concentration declined sharply, but that decline slows greatly in the months and years that follow. Lake Onuma is a closed lake and has a limited amount of inflow and runoff water. Japan's Lake Onuma could be contaminated with radioactive cesium-137 (137CS) for roughly 30 years after the Fukushima disaster, a new study has found'Previous investigations have used the two-component decay function model, which is the sum of two exponential functions, to fit the measured 137Cs radioactivity concentration,' one of the study's co-authors, Professor Yuko Hatano, said in a statement.