Distributed pose graph optimization (DPGO) is one of the fundamental techniques of swarm robotics. Currently, the sub-problems of DPGO are built on the native poses. Our validation proves that this approach may introduce an imbalance in the sizes of the sub-problems in real-world scenarios, which affects the speed of DPGO optimization, and potentially increases communication requirements. In addition, the coherence of the estimated poses is not guaranteed when the robots in the swarm fail, or partial robots are disconnected. In this paper, we propose BDPGO, a balanced distributed pose graph optimization framework using the idea of decoupling the robot poses and DPGO. BDPGO re-distributes the poses in the pose graph to the robot swarm in a balanced way by introducing a two-stage graph partitioning method to build balanced subproblems. Our validation demonstrates that BDPGO significantly improves the optimization speed without changing the specific algorithm of DPGO in realistic datasets. What's more, we also validate that BDPGO is robust to robot failure, changes in the wireless network. BDPGO has capable of keeps the coherence of the estimated poses in these situations. The framework also has the potential to be applied to other collaborative simultaneous localization and mapping (CSLAM) problems involved in distributedly solving the factor graph.

In this paper we motivate the causal mechanisms behind sample selection induced collider bias (selection collider bias) that can cause Large Language Models (LLMs) to learn unconditional dependence between entities that are unconditionally independent in the real world. We show that selection collider bias can become amplified in underspecified learning tasks, and although difficult to overcome, we describe a method to exploit the resulting spurious correlations for determination of when a model may be uncertain about its prediction. We demonstrate an uncertainty metric that matches human uncertainty in tasks with gender pronoun underspecification on an extended version of the Winogender Schemas evaluation set, and we provide an online demo where users can apply our uncertainty metric to their own texts and models.

Climate change will have significant impacts on environmental, social, political, and economic systems around the world. Climate change mitigation, along with adaptation and resilience, is therefore crucial. Efforts to achieve net-zero emissions by 2050 will be essential, as will efforts to prepare for the consequences of climate change and to minimize the resulting harm. Applying advanced analytics and artificial intelligence (AI) to climate challenges provides a vital way to make meaningful change at this critical moment. According to a new report from the AI for the Planet Alliance, produced in collaboration with Boston Consulting Group (BCG) and BCG GAMMA, 87% of public- and private-sector leaders who oversee climate and AI topics believe that AI is a valuable asset in the fight against climate change.

DUBAI: Climate change will have significant impacts on environmental, social, political, and economic systems around the world. Climate change mitigation, along with adaptation and resilience, is therefore crucial. Efforts to achieve net-zero emissions by 2050 will be essential, as will efforts to prepare for the consequences of climate change and to minimize the resulting harm. Applying advanced analytics and artificial intelligence (AI) to climate challenges provides a vital way to make meaningful change at this critical moment. According to a new report from the AI for the Planet Alliance, produced in collaboration with Boston Consulting Group (BCG) and BCG GAMMA, 87% of public- and private-sector leaders who oversee climate and AI topics believe that AI is a valuable asset in the fight against climate change.

Whether you like them or not, Artificial Intelligence (AI) image generators have exploded in popularity this year and the technology shows no signs of stopping. So if you're feeling confused about which AI Image generator you should use in 2022, this is a complete guide to the best options out there. A product of the Elon Musk co-founded research lab OpenAI, DALL-E 2, which we'll refer to as simply DALL-E, is the software most people can name when you ask them about AI text-to-image generators. When it launched in April, DALL-E stunned social media with its ability to turn a brief description into a photo-realistic image. For the few people with privileged access to the closed-off tool, DALL-E was so exceptional that it almost felt like magic -- whether that involved generating pictures of "a raccoon astronaut with the cosmos reflecting on the glass of his helmet" or "teddy bears shopping for groceries in Ancient Egypt," all from a simple text prompt.

NIR Calibration-Model Services Services for Professional Development of NIRS Calibrations NIR Near-Infrared-Spectroscopy QA QC QAQC Laboratory LINK Spectroscopy and Chemometrics News Weekly 35, 2022 NIRS NIR Spectroscopy MachineLearning Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software IoT Sensors QA QC Testing Quality LINK Near-Infrared Spectroscopy (NIRS) "Comparing Calibration Algorithms for the Rapid Characterization of Pretreated Corn Stover Using Near-Infrared Spectroscopy" LINK "Indirect Measurement of -Glucan Content in Barley Grain with Near-Infrared Reflectance Spectroscopy" LINK "Foods: Markov Transition Field Combined with Convolutional Neural Network Improved the Predictive Performance of Near-Infrared Spectroscopy Models for Determination of Aflatoxin B1 in Maize" LINK "Determination of Fruit Freshness Using Near-Infrared Spectroscopy and Machine Learning Techniques" LINK "Extensive evaluation of prediction ...

Iran's recent seizure of unmanned US Navy boats shined a light on a pioneering Pentagon program to develop networks of air, surface, and underwater drones for patrolling large regions, meshing their surveillance with artificial intelligence. The year-old program operates numerous unmanned surface vessels, or USVs, in the waters around the Arabian peninsula, gathering data and images to be beamed back to collection centers in the Gulf. The program operated without incident until Iranian forces tried to grab three seven-meter Saildrone Explorer USVs in two incidents, on August 29-30 and September 1. In the first, a ship of Iran's Islamic Revolutionary Guard Corps hooked a line to a Saildrone in the Gulf and began towing it away, only releasing it when a US Navy Patrol boat and helicopter sped to the scene. In the second, an Iranian destroyer picked up two Saildrones in the Red Sea, hoisting them aboard.

Whether you're on the'AI art is art' camp or the other, this is a tool that will forever change the creative industry for better or worse, and it has arrived. If you're fortunate enough to not know anything about this technology, a number of machine learning / AI research labs have created AI systems that allow computers to generate images. These tools are mostly experimental and nothing close to being able to create what humans can create -- until now. It's something that the digital art field has paid close attention to and is going to change art in ways we could never expect. From abstract art, digital painting, complex sculpture, architectural visualisation or 5 years old hand drawing, whatever you ask for, the AI makes it, or at least tries its best to.

The energy transition requires critical minerals. Though the U.S. has plentiful resources of its own, the country has largely relied on foreign sources. That's in part because one major roadblock to accessing American critical mineral deposits is that they remain largely unmapped. That may be about to change, though. The Department of Defense and the U.S. Geological Survey have issued two separate challenges to explore using artificial intelligence and machine learning to expedite USGS' task of assessing the availability and mining potential of 50 critical minerals.

A promising way to improve the sample efficiency of reinforcement learning is model-based methods, in which many explorations and evaluations can happen in the learned models to save real-world samples. However, when the learned model has a non-negligible model error, sequential steps in the model are hard to be accurately evaluated, limiting the model's utilization. This paper proposes to alleviate this issue by introducing multi-step plans to replace multi-step actions for model-based RL. We employ the multi-step plan value estimation, which evaluates the expected discounted return after executing a sequence of action plans at a given state, and updates the policy by directly computing the multi-step policy gradient via plan value estimation. The new model-based reinforcement learning algorithm MPPVE (Model-based Planning Policy Learning with Multi-step Plan Value Estimation) shows a better utilization of the learned model and achieves a better sample efficiency than state-of-the-art model-based RL approaches.