To leverage this potential, effective forest and consumption, improve urban air quality, reduce urban tree management is essential. This requires detailed wind speeds, and mitigating the urban heat information about tree species, composition, health and geographical island effect. Urban trees also play a key role in location of each tree in order to create a long term climate change mitigation and global warming by sustainable plan for plantation and forestation sites, pruning capturing and storing atmospheric carbon-dioxide schedules and mitigation of potential problems (Baeten & which is the largest contributor to greenhouse Bruelheide, 2018). It also helps to monitor tree species diversity gases. Automated tree detection and species classification and track health and growth rate to creates a more using aerial imagery can be a powerful robust ecosystem with better productivity and greater resilience tool for sustainable forest and urban tree management.

Facebook's Prophet package aims to provide a simple, automated approach to the prediction of a large number of different time series. The package employs an easily interpreted, three-component additive model whose Bayesian posterior is sampled using STAN. In contrast to some other approaches, the user of Prophet might hope for good performance without tweaking a lot of parameters. Instead, hyper-parameters control how likely those parameters are a priori, and the Bayesian sampling tries to sort things out when data arrives. Judged by popularity, this is surely a good idea. Facebook's prophet package has been downloaded 13,698,928 times according to pepy. It tops the charts, or at least the one I compiled here where hundreds of Python time series packages were ranked by monthly downloads. Download numbers are easily gamed and deceptive but nonetheless, the Prophet package is surely the most popular standalone Python library for automated time series analysis. The funny thing is though, that if you poke around a little you'll quickly come to the conclusion that few people who have taken the trouble to assess Prophet's accuracy are gushing about its performance. The article by Hideaki Hayashi is somewhat typical, insofar as it tries to say nice things but struggles. Yahashi notes that out-of-the-box, "Prophet is showing a reasonable seasonal trend unlike auto.arima, even though the absolute values are kind of off from the actual 2007 data." However, in the same breath, the author observes that telling ARIMA to include a yearly cycle turns the tables. With that hint, ARIMA easily beats prophet in accuracy -- at least on the one example he looked at.

We describe a system for visually guided autonomous navigation of under-canopy farm robots. Low-cost under-canopy robots can drive between crop rows under the plant canopy and accomplish tasks that are infeasible for over-the-canopy drones or larger agricultural equipment. However, autonomously navigating them under the canopy presents a number of challenges: unreliable GPS and LiDAR, high cost of sensing, challenging farm terrain, clutter due to leaves and weeds, and large variability in appearance over the season and across crop types. We address these challenges by building a modular system that leverages machine learning for robust and generalizable perception from monocular RGB images from low-cost cameras, and model predictive control for accurate control in challenging terrain. Our system, CropFollow, is able to autonomously drive 485 meters per intervention on average, outperforming a state-of-the-art LiDAR based system (286 meters per intervention) in extensive field testing spanning over 25 km.

Inverse problems are notoriously difficult to solve because they can have no solutions, multiple solutions, or have solutions that vary significantly in response to small perturbations in measurements. Bayesian inference, which poses an inverse problem as a stochastic inference problem, addresses these difficulties and provides quantitative estimates of the inferred field and the associated uncertainty. However, it is difficult to employ when inferring vectors of large dimensions, and/or when prior information is available through previously acquired samples. In this paper, we describe how deep generative adversarial networks can be used to represent the prior distribution in Bayesian inference and overcome these challenges. We apply these ideas to inverse problems that are diverse in terms of the governing physical principles, sources of prior knowledge, type of measurement, and the extent of available information about measurement noise. In each case we apply the proposed approach to infer the most likely solution and quantitative estimates of uncertainty.

In the context of the introduction of intermittent renewable energies, we propose to optimize the main variables of the control rods of a nuclear power plant to improve its capability to load-follow. The design problem is a black-box combinatorial optimization problem with expensive evaluation based on a multi-physics simulator. Therefore, we use a parallel asynchronous master-worker Evolutionary Algorithm scaling up to thousand computing units. One main issue is the tuning of the algorithm parameters. A fitness landscape analysis is conducted on this expensive real-world problem to show that it would be possible to tune the mutation parameters according to the low-cost estimation of the fitness landscape features.

Hyperspectral (HS) images are characterized by approximately contiguous spectral information, enabling the fine identification of materials by capturing subtle spectral discrepancies. Owing to their excellent locally contextual modeling ability, convolutional neural networks (CNNs) have been proven to be a powerful feature extractor in HS image classification. However, CNNs fail to mine and represent the sequence attributes of spectral signatures well due to the limitations of their inherent network backbone. To solve this issue, we rethink HS image classification from a sequential perspective with transformers, and propose a novel backbone network called \ul{SpectralFormer}. Beyond band-wise representations in classic transformers, SpectralFormer is capable of learning spectrally local sequence information from neighboring bands of HS images, yielding group-wise spectral embeddings. More significantly, to reduce the possibility of losing valuable information in the layer-wise propagation process, we devise a cross-layer skip connection to convey memory-like components from shallow to deep layers by adaptively learning to fuse "soft" residuals across layers. It is worth noting that the proposed SpectralFormer is a highly flexible backbone network, which can be applicable to both pixel- and patch-wise inputs. We evaluate the classification performance of the proposed SpectralFormer on three HS datasets by conducting extensive experiments, showing the superiority over classic transformers and achieving a significant improvement in comparison with state-of-the-art backbone networks. The codes of this work will be available at \url{https://sites.google.com/view/danfeng-hong} for the sake of reproducibility.

In this paper, we propose a novel approach to support domain-specific Model-Driven Software Engineering (MDSE) for the real-world use-case scenarios of smart Cyber-Physical Systems (CPS) and the Internet of Things (IoT). We argue that the majority of available data in the nature for Artificial Intelligence (AI), specifically Machine Learning (ML) are unlabeled. Hence, unsupervised and/or semi-supervised ML approaches are the practical choices. However, prior work in the literature of MDSE has considered supervised ML approaches, which only work with labeled training data. Our proposed approach is fully implemented and integrated with an existing state-of-the-art MDSE tool to serve the CPS/IoT domain. Moreover, we validate the proposed approach using a portion of the open data of the REFIT reference dataset for the smart energy systems domain. Our model-to-code transformations (code generators) provide the full source code of the desired IoT services out of the model instances in an automated manner. Currently, we generate the source code in Java and Python. The Python code is responsible for the ML functionalities and uses the APIs of several ML libraries and frameworks, namely Scikit-Learn, Keras and TensorFlow. For unsupervised and semi-supervised learning, the APIs of Scikit-Learn are deployed. In addition to the pure MDSE approach, where certain ML methods, e.g., K-Means, Mini-Batch K-Means, DB-SCAN, Spectral Clustering, Gaussian Mixture Model, Self-Training, Label Propagation and Label Spreading are supported, a more flexible, hybrid approach is also enabled to support the practitioner in deploying a pre-trained ML model with any arbitrary architecture and learning algorithm.

Models are used in both the Software Engineering (SE) and the Artificial Intelligence (AI) communities. In the former case, models of software, which may specify the software system architecture on different levels of abstraction could be used in various stages of the Software Development Life-Cycle (SDLC), from early conceptualization and design, to verification, implementation, testing and evolution. However, in the latter case, i.e., AI, models may provide smart capabilities, such as prediction and decision making support. For instance, in Machine Learning (ML), which is the most popular sub-discipline of AI at the present time, mathematical models may learn useful patterns in the observed data instances and can become capable of making better predictions or recommendations in the future. The goal of this work is to create synergy by bringing models in the said communities together and proposing a holistic approach. We illustrate how software models can become capable of producing or dealing with data analytics and ML models. The main focus is on the Internet of Things (IoT) and smart Cyber-Physical Systems (CPS) use cases, where both ML and model-driven (model-based) SE play a key role. In particular, we implement the proposed approach in an open source prototype and validate it using two use cases from the IoT/CPS domain.

On edge devices, data scarcity occurs as a common problem where transfer learning serves as a widely-suggested remedy. Nevertheless, transfer learning imposes a heavy computation burden to resource-constrained edge devices. Existing task allocation works usually assume all submitted tasks are equally important, leading to inefficient resource allocation at a task level when directly applied in Multi-task Transfer Learning (MTL). To address these issues, we first reveal that it is crucial to measure the impact of tasks on overall decision performance improvement and quantify \emph{task importance}. We then show that task allocation with task importance for MTL (TATIM) is a variant of the NP-complete Knapsack problem, where the complicated computation to solve this problem needs to be conducted repeatedly under varying contexts. To solve TATIM with high computational efficiency, we propose a Data-driven Cooperative Task Allocation (DCTA) approach. Finally, we evaluate the performance of DCTA by not only a trace-driven simulation, but also a new comprehensive real-world AIOps case study that bridges model and practice via a new architecture and main components design within the AIOps system. Extensive experiments show that our DCTA reduces 3.24 times of processing time, and saves 48.4\% energy consumption compared with the state-of-the-art when solving TATIM.

According to Google Flights' estimate, a round trip of a fully loaded passenger jet between San Francisco and New York would release 180 tonnes of carbon dioxide equivalent (CO2e). Meanwhile, the training emissions of Google's 11 billion parameter T5 language model and OpenAI's GPT-3(175 billion parameters) stands 26%, 305% of the round trip, respectively. The "state-of-the-art" models require a substantial amount of computational resources and energy, leading to high environmental costs. Deep learning models are getting larger by the day. Such large models are routinely trained for thousands of hours on specialised hardware accelerators in data centers.