--Forest fires pose a significant threat to the environment, human life, and property. Early detection and response are crucial to mitigating the impact of these disasters. However, traditional forest fire detection methods are often hindered by our reliability on manual observation and satellite imagery with low spatial resolution. This paper emphasizes the role of transfer learning in enhancing forest fire detection in India, particularly in overcoming data collection challenges and improving model accuracy across various regions. We compare traditional learning methods with transfer learning, focusing on the unique challenges posed by regional differences in terrain, climate, and vegetation. Transfer learning can be categorized into several types based on the similarity between the source and target tasks, as well as the type of knowledge transferred. One key method is utilizing pre-trained models for efficient transfer learning, which significantly reduces the need for extensive labeled data. We outline the transfer learning process, demonstrating how researchers can adapt pre-trained models like MobileNetV2 for specific tasks such as forest fire detection. India is home to a vast and diverse range of forests, covering over 70 million hectares of land [1]. These forests are crucial not only for the country's ecosystem and biodiversity but also provide livelihoods for millions of people, particularly in rural areas. However, India's forests are facing a growing threat from forest fires, which can have devastating consequences for the environment, human life, and property [2]. Forest fires are a major concern in India, particularly during the summer months when temperatures are high and humidity is low. According to the Indian government, forest fires affect over 50, 000 hectares of land annually, causing significant economic losses and damage to the environment [3]. The country's forests are also home to a wide range of wildlife, including many endangered species which are threatened by fires. Figure 1 illustrates some images of the Uttarakhand, India, forest fire. Early detection and response are critical to mitigating the impact of forest fires. Traditional methods of forest fire detection, such as manual observation and satellite imagery with low spatial resolution, are often limited in their ability to detect fires quickly and accurately [4]. Manual observation is time-consuming and labour-intensive and may not be feasible in remote or inaccessible areas [5]. Satellite imagery with low spatial resolution may not be able to detect small fires or fires in areas with dense vegetation. In recent years, advances in deep learning and computer vision have enabled the development of more effective methods for forest fire detection. Convolutional neural networks (CNNs), in particular, have shown great promise in image classification tasks [6]-[10], including fire detection [4].

A novel method for autonomous localization of multiple sources of gamma radiation using a group of Micro Aerial Vehicles (MAVs) is presented in this paper. The method utilizes an extremely lightweight (44 g) Compton camera MiniPIX TPX3. The compact size of the detector allows for deployment onboard safe and agile small-scale Unmanned Aerial Vehicles (UAVs). The proposed radiation mapping approach fuses measurements from multiple distributed Compton camera sensors to accurately estimate the positions of multiple radioactive sources in real time. Unlike commonly used intensity-based detectors, the Compton camera reconstructs the set of possible directions towards a radiation source from just a single ionizing particle. Therefore, the proposed approach can localize radiation sources without having to estimate the gradient of a radiation field or contour lines, which require longer measurements. The instant estimation is able to fully exploit the potential of highly mobile MAVs. The radiation mapping method is combined with an active search strategy, which coordinates the future actions of the MAVs in order to improve the quality of the estimate of the sources' positions, as well as to explore the area of interest faster. The proposed solution is evaluated in simulation and real world experiments with multiple Cesium-137 radiation sources.

We live in a world that is experiencing an unprecedented boom of AI applications that increasingly penetrate and enhance all sectors of private and public life, from education, media, medicine, and mobility to the industrial and professional workspace, and -- potentially particularly consequentially -- robotics. As this world is simultaneously grappling with climate change, the climate and environmental implications of the development and use of AI have become an important subject of public and academic debate. In this paper, we aim to provide guidance on the climate-related regulation for data centers and AI specifically, and discuss how to operationalize these requirements. We also highlight challenges and room for improvement, and make a number of policy proposals to this end. In particular, we propose a specific interpretation of the AI Act to bring reporting on the previously unadressed energy consumption from AI inferences back into the scope. We also find that the AI Act fails to address indirect greenhouse gas emissions from AI applications. Furthermore, for the purpose of energy consumption reporting, we compare levels of measurement within data centers and recommend measurement at the cumulative server level. We also argue for an interpretation of the AI Act that includes environmental concerns in the mandatory risk assessment (sustainability risk assessment, SIA), and provide guidance on its operationalization. The EU data center regulation proves to be a good first step but requires further development by including binding renewable energy and efficiency targets for data centers. Overall, we make twelve concrete policy proposals, in four main areas: Energy and Environmental Reporting Obligations; Legal and Regulatory Clarifications; Transparency and Accountability Mechanisms; and Future Far-Reaching Measures beyond Transparency.

Feed-forward convolutional neural networks (CNNs) are currently state-of-the-art for object classification tasks such as ImageNet. Further, they are quantitatively accurate models of temporally-averaged responses of neurons in the primate brain's visual system. However, biological visual systems have two ubiquitous architectural features not shared with typical CNNs: local recurrence within cortical areas, and long-range feedback from downstream areas to upstream areas. We found that standard forms of recurrence (vanilla RNNs and LSTMs) do not perform well within deep CNNs on the ImageNet task. In contrast, novel cells that incorporated two structural features, bypassing and gating, were able to boost task accuracy substantially. We extended these design principles in an automated search over thousands of model architectures, which identified novel local recurrent cells and long-range feedback connections useful for object recognition.

The design of flow control systems remains a challenge due to the nonlinear nature of the equations that govern fluid flow. However, recent advances in computational fluid dynamics (CFD) have enabled the simulation of complex fluid flows with high accuracy, opening the possibility of using learning-based approaches to facilitate controller design. We present a method for learning the forced and unforced dynamics of airflow over a cylinder directly from CFD data. The proposed approach, grounded in Koopman theory, is shown to produce stable dynamical models that can predict the time evolution of the cylinder system over extended time horizons. Finally, by performing model predictive control with the learned dynamical models, we are able to find a straightforward, interpretable control law for suppressing vortex shedding in the wake of the cylinder.

To win the race, the US is going to need access to a lot more electric power to serve data centers. AI data centers could add the equivalent of three New York Cities' worth of load to the grid by 2026, and they could more than double their share of US electricity consumption--to 9%--by the end of the decade. Artificial intelligence will thus contribute to a spike in power demand that the US hasn't seen in decades; according to one recent estimate, that demand--previously flat--is growing by around 2.5% per year, with data centers driving as much as 66% of the increase. Energy-hungry advanced AI chips are behind this growth. Three watt-hours of electricity are required for a ChatGPT query, compared with just 0.3 watt-hours for a simple Google search.

The paper presents an approach, from an optimization perspective, to integrate both the controller and the model. I am glad to see that this work has abundant citations from both classical control theory community and reinforcement learning community. And the idea of formulating the problem is nice, however I have concerns about clarity and significance. First, the formulation of the problem does not take into account any randomness (those from environment) and does not even mention Markov decision process. In the equation 2, there is no expectation operator available.

This paper proposes Tensorized LSTMs for efficient sequence learning. It represents hidden layers as tensors, and employs cross-layer memory cell convolution for efficiency and effectiveness. The model is clearly formulated. Experimental results show the utility of the proposed method. Although the paper is well written, I still have some questions/confusion as follows.

Understanding molecular structure and related knowledge is crucial for scientific research. Recent studies integrate molecular graphs with their textual descriptions to enhance molecular representation learning. However, they focus on the whole molecular graph and neglect frequently occurring subgraphs, known as motifs,which are essential for determining molecular properties. Without such fine-grained knowledge, these models struggle to generalize to unseen molecules and tasks that require motif-level insights. To bridge this gap, we propose FineMolTex, a novel Fine-grained Molecular graph-Text pre-training framework to jointly learn coarse-grained molecule-level knowledge and fine-grained motif-level knowledge. Specifically, FineMolTex consists of two pre-training tasks: a contrastive alignment task for coarse-grained matching and a masked multi-modal modeling task for fine-grained matching. In particular, the latter predicts the labels of masked motifs and words, leveraging insights from each other, thereby enabling FineMolTex to understand the fine-grained matching between motifs and words. Finally, we conduct extensive experiments across three downstream tasks, achieving up to 230% improvement in the text-based molecule editing task. Additionally, our case studies reveal that FineMolTex successfully captures fine-grained knowledge, potentially offering valuable insights for drug discovery and catalyst design.

In Reinforcement Learning (RL), multi-armed Bandit (MAB) problems have found applications across diverse domains such as recommender systems, healthcare, and finance. Traditional MAB algorithms typically assume stationary reward distributions, which limits their effectiveness in real-world scenarios characterized by non-stationary dynamics. This paper addresses this limitation by introducing and evaluating novel Bandit algorithms designed for non-stationary environments. First, we present the Adaptive Discounted Thompson Sampling (ADTS) algorithm, which enhances adaptability through relaxed discounting and sliding window mechanisms to better respond to changes in reward distributions. We then extend this approach to the Portfolio Optimization problem by introducing the Combinatorial Adaptive Discounted Thompson Sampling (CADTS) algorithm, which addresses computational challenges within Combinatorial Bandits and improves dynamic asset allocation. Additionally, we propose a novel architecture called Bandit Networks, which integrates the outputs of ADTS and CADTS, thereby mitigating computational limitations in stock selection. Through extensive experiments using real financial market data, we demonstrate the potential of these algorithms and architectures in adapting to dynamic environments and optimizing decision-making processes. For instance, the proposed bandit network instances present superior performance when compared to classic portfolio optimization approaches, such as capital asset pricing model, equal weights, risk parity, and Markovitz, with the best network presenting an out-of-sample Sharpe Ratio 20% higher than the best performing classical model.