In many unmanned aerial vehicle (UAV) applications for surveillance and data collection, it is not possible to reach all requested locations due to the given maximum flight time. Hence, the requested locations must be prioritized and the problem of selecting the most important locations is modeled as an Orienteering Problem (OP). To fully exploit the kinematic properties of the UAV in such scenarios, we combine the OP with the generation of time-optimal trajectories with bounds on velocity and acceleration. We define the resulting problem as the Kinematic Orienteering Problem (KOP) and propose an exact mixed-integer formulation together with a Large Neighborhood Search (LNS) as a heuristic solution method. We demonstrate the effectiveness of our approach based on Orienteering instances from the literature and benchmark against optimal solutions of the Dubins Orienteering Problem (DOP) as the state-of-the-art. Additionally, we show by simulation \color{black} that the resulting solutions can be tracked precisely by a modern MPC-based flight controller. Since we demonstrate that the state-of-the-art in generating time-optimal trajectories in multiple dimensions is not generally correct, we further present an improved analytical method for time-optimal trajectory generation.

In this work, we study the multi-agent decision problem where agents try to coordinate to optimize a given system-level objective. While solving for the global optimal is intractable in many cases, the greedy algorithm is a well-studied and efficient way to provide good approximate solutions - notably for submodular optimization problems. Executing the greedy algorithm requires the agents to be ordered and execute a local optimization based on the solutions of the previous agents. However, in limited information settings, passing the solution from the previous agents may be nontrivial, as some agents may not be able to directly communicate with each other. Thus the communication time required to execute the greedy algorithm is closely tied to the order that the agents are given. In this work, we characterize interplay between the communication complexity and agent orderings by showing that the complexity using the best ordering is O(n) and increases considerably to O(n^2) when using the worst ordering. Motivated by this, we also propose an algorithm that can find an ordering and execute the greedy algorithm quickly, in a distributed fashion. We also show that such an execution of the greedy algorithm is advantageous over current methods for distributed submodular maximization.

Generative Adversarial Networks (GANs) have received wide acclaim among the machine learning (ML) community for their ability to generate realistic 2D images. ML is being applied more often to complex problems beyond those of computer vision. However, current frameworks often serve as black boxes and lack physics embeddings, leading to poor ability in enforcing constraints and unreliable models. In this work, we develop physics embeddings that can be stringently imposed, referred to as hard constraints, in the neural network architecture. We demonstrate their capability for 3D turbulence by embedding them in GANs, particularly to enforce the mass conservation constraint in incompressible fluid turbulence. In doing so, we also explore and contrast the effects of other methods of imposing physics constraints within the GANs framework, especially penalty-based physics constraints popular in literature. By using physics-informed diagnostics and statistics, we evaluate the strengths and weaknesses of our approach and demonstrate its feasibility.

Forecasting the state of vegetation in response to climate and weather events is a major challenge. Its implementation will prove crucial in predicting crop yield, forest damage, or more generally the impact on ecosystems services relevant for socio-economic functioning, which if absent can lead to humanitarian disasters. Vegetation status depends on weather and environmental conditions that modulate complex ecological processes taking place at several timescales. Interactions between vegetation and different environmental drivers express responses at instantaneous but also time-lagged effects, often showing an emerging spatial context at landscape and regional scales. We formulate the land surface forecasting task as a strongly guided video prediction task where the objective is to forecast the vegetation developing at very fine resolution using topography and weather variables to guide the prediction. We use a Convolutional LSTM (ConvLSTM) architecture to address this task and predict changes in the vegetation state in Africa using Sentinel-2 satellite NDVI, having ERA5 weather reanalysis, SMAP satellite measurements, and topography (DEM of SRTMv4.1) as variables to guide the prediction. Ours results highlight how ConvLSTM models can not only forecast the seasonal evolution of NDVI at high resolution, but also the differential impacts of weather anomalies over the baselines. The model is able to predict different vegetation types, even those with very high NDVI variability during target length, which is promising to support anticipatory actions in the context of drought-related disasters.

There is international consensus on the need to respond to the global threat posed by climate change (Paris Accord, Article 2). Development funds are essential to finance climate change adaptation and are thus an important part of international climate policy. The 2009 Copenhagen Accord (UNFCCC, 2009) aimed to mobilize USD 100 billion by 2020. Implementation of climate change adaptation measures is one of five targets set to reach the 13th Sustainable Development Goal (SDG): "Take urgent action to combat climate change and its impacts". The Creditor Reporting System (CRS), maintained by the OECD Development Assistance Committee (DAC), monitors adaptation finance flows from OECD DAC member countries to developing countries. One of the challenges in ensuring valid reporting - or at least comparable figures - across reporting agencies is that the agreements mentioned above lack indicators. To this end, the OECD DAC established in 2009 the Rio markers on climate change adaptation (CCA). For each aid activity, donors report whether it contributes to CCA, i.e. reducing "the vulnerability of human or natural systems to the current and expected impacts of climate change, including climate variability, by maintaining or increasing resilience, through increased ability to adapt to, or absorb, climate change stresses, shocks and variability and/or by helping reduce exposure to them" (OECD DAC, 2022, p. 4). Activities are eligible for a marker if "a) the climate change adaptation objective is explicitly indicated in the activity documentation; and b) the activity contains specific measures targeting the definition above."

As large language models (LLMs) grow larger and more sophisticated, assessing their "reasoning" capabilities in natural language grows more challenging. Recent question answering (QA) benchmarks that attempt to assess reasoning are often limited by a narrow scope of covered situations and subject matters. We introduce WikiWhy, a QA dataset built around a novel auxiliary task: explaining why an answer is true in natural language. WikiWhy contains over 9,000 "why" question-answer-rationale triples, grounded on Wikipedia facts across a diverse set of topics. Each rationale is a set of supporting statements connecting the question to the answer. WikiWhy serves as a benchmark for the reasoning capabilities of LLMs because it demands rigorous explicit rationales for each answer to demonstrate the acquisition of implicit commonsense knowledge, which is unlikely to be easily memorized. GPT-3 baselines achieve only 38.7% human-evaluated correctness in the end-to-end answer & explain condition, leaving significant room for future improvements.

Rolf Schmitz is the Co-Founder & Co-CEO of CollectiveCrunch, a platform changing the world's understanding of forests by providing the most accurate, scalable, timely analytics globally and enabling sustainable forestry and bring transparency to carbon trading markets. Rolf is an Engineer by education and holds an MBA from Manchester Business School. He has deep experience in global Business Development and Sales, having built teams in Asia, USA and Europe. Could you share the genesis story behind CollectiveCrunch? We are steeped in handling large amounts of data and deriving insights from them.

Nanoengineers at the University of California San Diego's Jacobs School of Engineering have developed an AI algorithm that predicts the structure and dynamic properties of any material--whether existing or new--almost instantaneously. Known as M3GNet, the algorithm was used to develop matterverse.ai, The project is explored in the Nov. 28 issue of the journal Nature Computational Science. The properties of a material are determined by the arrangement of its atoms. However, existing approaches to obtain that arrangement are either prohibitively expensive or ineffective for many elements.

Approximation of entropies of various types using machine learning (ML) regression methods are shown for the first time. The ML models presented in this study define the complexity of the short time series by approximating dissimilar entropy techniques such as Singular value decomposition entropy (SvdEn), Permutation entropy (PermEn), Sample entropy (SampEn) and Neural Network entropy (NNetEn) and their 2D analogies. A new method for calculating SvdEn2D, PermEn2D and SampEn2D for 2D images was tested using the technique of circular kernels. Training and testing datasets on the basis of Sentinel-2 images are presented (two training images and one hundred and ninety-eight testing images). The results of entropy approximation are demonstrated using the example of calculating the 2D entropy of Sentinel-2 images and R^2 metric evaluation. The applicability of the method for the short time series with a length from N = 5 to N = 113 elements is shown. A tendency for the R^2 metric to decrease with an increase in the length of the time series was found. For SvdEn entropy, the regression accuracy is R^2 > 0.99 for N = 5 and R^2 > 0.82 for N = 113. The best metrics were observed for the ML_SvdEn2D and ML_NNetEn2D models. The results of the study can be used for fundamental research of entropy approximations of various types using ML regression, as well as for accelerating entropy calculations in remote sensing. The versatility of the model is shown on a synthetic chaotic time series using Planck map and logistic map.

"Testing two NIRs instruments to predict chicken breast meat quality and exploiting machine learning approaches to discriminate among genotypes and presence of …" LINK "Discrimination of Minced Mutton Adulteration Based on Sized-Adaptive Online NIRS Information and 2D Conventional Neural Network" LINK "Sequential data-fusion of near-infrared and mid-infrared spectroscopy data for improved prediction of quality traits in tuber flours" LINK "End-point determination of the extraction processes for Stevia rebaudiana Bertoni leaves by near-infrared spectroscopy" LINK "Rapid detection of adulteration in powder of ginger (Zingiber officinale Roscoe) by FT-NIR spectroscopy combined with chemometrics" LINK "Extended molar absorption analysis of confined states of water in reverse micelles using near-infrared spectroscopy" LINK "Online quantitative substrate, product, and cell concentration in citric acid fermentation using near-infrared spectroscopy combined with chemometrics" LINK "Discrimination of Chemical Oxygen Demand Pollution in Surface Water Based on Visible Near-Infrared Spectroscopy" LINK "Handheld NIR Spectral Sensor Module Based on a Fully-Integrated Detector Array" LINK "Spectra Transfer based Learning for Predicting and Classifying Soil Texture with Short-Ranged Vis-NIRS Sensor" LINK "AS-polRI: Analysis of apparent spectral polarization radiant intensity in the midwave infrared band for man-made object detection" LINK "Sensors: Single Seed Identification in Three Medicago Species via Multispectral Imaging Combined with Stacking Ensemble Learning" LINK "Transfer Strategy for Near Infrared Analysis Model of Holocellulose and Lignin Based on Improved Slope/Bias Algorithm" LINK "Flipped detection of psychoactive substances in complex mixtures using handheld Raman spectroscopy coupled to chemometrics" LINK "Adaptive Spectral Model for abnormality detection based on physiological status monitoring of dairy cows" LINK "Foods: A Rapid Prediction Method of Moisture Content for Green Tea Fixation Based on WOA-Elman" LINK "Fibers: Numerical Study of Mid-IR Ultrashort Pulse Reconstruction Based on Processing of Spectra Converted in Chalcogenide Fibers with High Kerr Nonlinearity" LINK "Evaluation of a digital micro-mirror device based near-infrared spectrometer for rapid and accurate prediction of quality attributes in poultry feed" LINK "Agriculture: Grazing Intensity Has More Effect on the Potential Nitrification Activity Than the Potential Denitrification Activity in An Alpine Meadow" LINK