Unsupervised clustering is widely used to explore large corpora, but existing formulations neither consider the users' goals nor explain clusters' meanings. We propose a new task formulation, "Goal-Driven Clustering with Explanations" (GoalEx), which represents both the goal and the explanations as free-form language descriptions. For example, to categorize the errors made by a summarization system, the input to GoalEx is a corpus of annotator-written comments for system-generated summaries and a goal description "cluster the comments based on why the annotators think the summary is imperfect.''; the outputs are text clusters each with an explanation ("this cluster mentions that the summary misses important context information."), which relates to the goal and precisely explain which comments should (not) belong to a cluster. To tackle GoalEx, we prompt a language model with "[corpus subset] + [goal] + Brainstorm a list of explanations each representing a cluster."; then we classify whether each sample belongs to a cluster based on its explanation; finally, we use integer linear programming to select a subset of candidate clusters to cover most samples while minimizing overlaps. Under both automatic and human evaluation on corpora with or without labels, our method produces more accurate and goal-related explanations than prior methods. We release our data and implementation at https://github.com/ZihanWangKi/GoalEx.

Current optical vegetation indices (VIs) for monitoring forest ecosystems are widely used in various applications. However, continuous monitoring based on optical satellite data can be hampered by atmospheric effects such as clouds. On the contrary, synthetic aperture radar (SAR) data can offer insightful and systematic forest monitoring with complete time series due to signal penetration through clouds and day and night acquisitions. The goal of this work is to overcome the issues affecting optical data with SAR data and serve as a substitute for estimating optical VIs for forests using machine learning. Time series of four VIs (LAI, FAPAR, EVI and NDVI) were estimated using multitemporal Sentinel-1 SAR and ancillary data. This was enabled by creating a paired multi-temporal and multi-modal dataset in Google Earth Engine (GEE), including temporally and spatially aligned Sentinel-1, Sentinel-2, digital elevation model (DEM), weather and land cover datasets (MMT-GEE). The use of ancillary features generated from DEM and weather data improved the results. The open-source Automatic Machine Learning (AutoML) approach, auto-sklearn, outperformed Random Forest Regression for three out of four VIs, while a 1-hour optimization length was enough to achieve sufficient results with an R2 of 69-84% low errors (0.05-0.32 of MAE depending on VI). Great agreement was also found for selected case studies in the time series analysis and in the spatial comparison between the original and estimated SAR-based VIs. In general, compared to VIs from currently freely available optical satellite data and available global VI products, a better temporal resolution (up to 240 measurements/year) and a better spatial resolution (20 m) were achieved using estimated SAR-based VIs. A great advantage of the SAR-based VI is the ability to detect abrupt forest changes with a sub-weekly temporal accuracy.

To form precipitation datasets that are accurate and, at the same time, have high spatial densities, data from satellites and gauges are often merged in the literature. However, uncertainty estimates for the data acquired in this manner are scarcely provided, although the importance of uncertainty quantification in predictive modelling is widely recognized. Furthermore, the benefits that machine learning can bring to the task of providing such estimates have not been broadly realized and properly explored through benchmark experiments. The present study aims at filling in this specific gap by conducting the first benchmark tests on the topic. On a large dataset that comprises 15-year-long monthly data spanning across the contiguous United States, we extensively compared six learners that are, by their construction, appropriate for predictive uncertainty quantification. These are the quantile regression (QR), quantile regression forests (QRF), generalized random forests (GRF), gradient boosting machines (GBM), light gradient boosting machines (LightGBM) and quantile regression neural networks (QRNN). The comparison referred to the competence of the learners in issuing predictive quantiles at nine levels that facilitate a good approximation of the entire predictive probability distribution, and was primarily based on the quantile and continuous ranked probability skill scores. Three types of predictor variables (i.e., satellite precipitation variables, distances between a point of interest and satellite grid points, and elevation at a point of interest) were used in the comparison and were additionally compared with each other. This additional comparison was based on the explainable machine learning concept of feature importance. The results suggest that the order from the best to the worst of the learners for the task investigated is the following: LightGBM, QRF, GRF, GBM, QRNN and QR...

Recent empirical studies found that arrivals in many real queueing systems exhibit a clustering or self-exciting behavior; that is, an arrival may increase the possibility of new arrivals. In some cases, such clustering behavior is intrinsic to the underlying system. For example, in the stock market, it is a common practice to split a large order into small child orders to reduce transaction cost. As a consequence, one observed arriving order may be followed by a sequence of other child orders (Abergel and Jedidi, 2015). As a natural extension of the classic Poisson process, Hawkes process has been used to model arrivals with self-excitement such as order flow in stock market (Abergel and Jedidi, 2015), infected patients during pandemic (Bertozzi et al., 2020), and the internet traffic in social media (Zhao et al., 2015). To understand the impact of self-excitement in the arrival process on the long-run performance of service systems, Koops et al. (2018) and Daw and Pender (2018) provided analytic solutions to steady-state moments on the number of people in system for different infinite-server systems with Hawkes arrivals.

NASA's Mars exploration robots will be on their own for the next two weeks while the space agency waits out a natural phenomenon that will prevent normal communications. Mars and Earth have reached positions in their orbits that put them on opposite sides of the sun, in an alignment known as solar conjunction. During this time, NASA says it's risky to try and send commands to its instruments on Mars because interference from the sun could have a detrimental effect. To prevent any issues, NASA is taking a planned break from giving orders until the planets move into more suitable positions. The pause started on Saturday and will go on until November 25.

Once enticed by the prospect that Trump would usher in a new, ultra-capitalist era in Republican politics, members of the right-leaning tech elite are now looking for allies to protect the industry from bruising attacks by both parties and champion its worth as the country's most dynamic economic engine. These views have been calcified by government efforts to regulate artificial intelligence, which the Silicon Valley figures see as a transformative technology that would suffer from government meddling.

California Gov. Gavin Newsom walked out of the Tesla gigafactory in China last month feeling jazzed about the future. A future where people do a lot less driving, instead being whisked around by autonomous cars and flying taxis. A future where, he said, the "entire transportation system is completely reorganized." "I think it's going to come very fast," Newsom said to reporters on the last day of his trip to China promoting clean energy partnerships with California. "With AI in particular aiding this advancement, I think it's just going to explode and you're going to start seeing driverless flying cars as well."

A U.S. military unit operating MQ-9 spy drones has completed its withdrawal from the Maritime Self-Defense Force's Kanoya air base in Kagoshima Prefecture for relocation to Okinawa Prefecture, the Japanese government said Sunday. The Defense Ministry's Kyushu Defense Bureau announced the unit's withdrawal from the Japanese base, where eight MQ-9 aircraft were operated for a limited period of one year from November last year. Up to 200 U.S. military personnel related to the operations were stationed there. The unit will be transferred to the U.S. military's Kadena Air Base in Okinawa Prefecture, near Kagoshima. The drones are set to be used to strengthen surveillance of Chinese military ships in the East China Sea.

AI expert Marva Bailer tells Fox News Digital how the open availability of artificial intelligence can have negative impacts and talks potential federal legislation to control it. European nations continue to jockey for leadership on artificial intelligence (AI), with Paris announcing it will host the next safety summit shortly after Britain hosted the first one. "The first edition of the Artificial Intelligence Security Summit, organized by the United Kingdom, provides an opportunity to develop international cooperation in the field of security, a crucial issue for the years to come. It was, therefore, natural for France to host the second edition of this summit," French Minister Delegate for the Digital Economy Jean-Noël Barrot said in a press release. The future of AI remains up for grabs, with many nations trying to position themselves at the forefront of the race.

The nation's first vehicle accident involving so-called Level 4 autonomous driving occurred in October due to a failure to visually recognize an object, a local government report said. The report was released recently by the town of Eiheiji, Fukui Prefecture, where Level 4 driving, or fully automated driving under certain conditions, received approval, the first local government to do so in the country. The town government plans to improve Level 4 vehicles' visual recognition performance to prevent any similar accidents. In the accident in the town on Oct. 29, the autonomous vehicle, traveling at about 4 kilometers per hour, hit a pedal of a parked bicycle. The vehicle detected the crash and made an emergency stop.