A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Intelligent control for greenhouses using deep reinforcement learning.
Effective plant growth and yield prediction is an essential task for greenhouse growers and for agriculture in general. Developing models which can effectively model growth and yield can help growers improve the environmental control for better production, match supply and market demand and lower costs. Recent developm…
Deep learning speeds up real-time emission monitoring.
Deep learning predicts road GHG emissions with speed, density, and past ERs.
Machine learning predicts greenhouse gas emissions for undisclosed companies.
Modern control theories such as systems engineering approaches try to solve nonlinear system problems by revelation of causal relationship or co-relationship among the components; most of those approaches focus on control of sophisticatedly modeled white-boxed systems. We suggest an application of actor-critic reinforc…
This research uses reinforcement learning to find optimal emission offsets in greenhouse gas markets.
Model estimates non-reported GHG emissions for companies using machine learning.
A new method uses variational autoencoders to speed up greenhouse gas sensitivity calculations.
AI analyzes corporate ESG filings to identify key dimensions and investor reactions.
This work models GHG offset credit markets to find optimal strategies for market participants.
Model predicts methane emissions from oil sands tailing ponds, suggesting significant environmental impact.
Challenge forecasts EV charging station usage accurately.
It will be difficult to gain the agreement of all the actors on any proposal for climate change management, if universality and fairness are not considered. In this work, a universal measure of emissions to be applied at the international level is proposed, based on a modification of the Greenhouse Gas Intensity (GHG-I…
Higher environmental performance linked to more tax avoidance, especially for financially constrained firms.
Climate change is one of the greatest challenges facing humanity, and we, as machine learning experts, may wonder how we can help. Here we describe how machine learning can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, w…
An innovative method optimizes engine calibration to improve efficiency and reduce emissions.
Electricity accounts for 25% of global greenhouse gas emissions. Reducing emissions related to electricity consumption requires accurate measurements readily available to consumers, regulators and investors. In this case study, we propose a new real-time consumption-based accounting approach based on flow tracing. This…
Green stocks show less factor exposure heterogeneity compared to brown stocks.
As global greenhouse gas emissions continue to rise, the use of stratospheric aerosol injection (SAI), a form of solar geoengineering, is increasingly considered in order to artificially mitigate climate change effects. However, initial research in simulation suggests that naive SAI can have catastrophic regional conse…
According to a recent investigation, an estimated 33-50% of the world's coral reefs have undergone degradation, believed to be as a result of climate change. A strong driver of climate change and the subsequent environmental impact are greenhouse gases such as methane. However, the exact relation climate change has to …
Underwater gas reservoirs are used in many situations. In particular, Carbon Capture and Storage (CCS) facilities that are currently being developed intend to store greenhouse gases inside geological formations in the deep sea. In these formations, however, the gas might percolate, leaking back to the water and eventua…
Investment managers face harder choices in green stocks due to reduced performance variability.
Recent changes to greenhouse gas emission policies are catalyzing the electric vehicle (EV) market making it readily accessible to consumers. While there are challenges that arise with dense deployment of EVs, one of the major future concerns is cyber security threat. In this paper, cyber security threats in the form o…
Scaling relations, such as the IPAT equation and the Kaya identity, are useful for quickly gauging the scale of economic, technological, and demographic changes required to reduce environmental impacts and pressures; in the case of the Kaya identity, the environmental pressure is greenhouse gas emissions. However, when…
Conventional economic analysis of stringent climate change mitigation policy generally concludes various levels of economic slowdown as a result of substantial spending on low carbon technology. Equilibrium economics however could not explain or predict the current economic crisis, which is of financial nature. Meanwhi…
Long-range climate forecasts use integrated assessment models to link the global economy to greenhouse gas emissions. This paper evaluates an alternative economic framework outlined in part 1 of this study (Garrett, 2014) that approaches the global economy using purely physical principles rather than explicitly resolve…
CAI automates extraction and validation of corporate GHG emission metrics.
To achieve the ambitious aims of the Paris climate agreement, the majority of fossil-fuel reserves needs to remain underground. As current national government commitments to mitigate greenhouse gas emissions are insufficient by far, actors such as institutional and private investors and the social movement on divestmen…
This study analyzes how weather impacts bike sharing usage in Washington D.C.
Successful implementation of California's Renewable Portfolio Standard (RPS) mandating 33 percent renewable energy generation by 2020 requires inclusion of a robust strategy to mitigate increased risk of energy deficits (blackouts) due to short time-scale (sub 1 hour) intermittencies in renewable energy sources. Of the…
Two algorithms improve Federated RL in diverse environments.
OBSER framework infers sub-environments from objects, outperforming scene-based methods.
UAED discovers adaptive environments for robust learning.
New approach handles stochastic and partially-observable environments using discrete autoencoders and Monte Carlo tree search.
Self-supervised policy adapts after deployment without rewards.
Bayesian model for multi-environment prediction with latent variable changes.
Robustly detects and attributes climate change impacts under interventions.
This paper introduces CENIE to quantify environment novelty for better UED.
We consider apprenticeship learning, i.e., having an agent learn a task by observing an expert demonstrating the task in a partially observable environment when the model of the environment is uncertain. This setting is useful in applications where the explicit modeling of the environment is difficult, such as a dialog…
Infinite hierarchical contrastive clustering identifies personal environments linked to health outcomes.
Research shows collective learning across diverse environments is hard due to privacy and security concerns.
LEADS improves model generalization across different environments.
A new method shapes reinforcement learning environments by abstracting large state spaces.
In reinforcement learning algorithms, it is a common practice to account for only a single view of the environment to make the desired decisions; however, utilizing multiple views of the environment can help to promote the learning of complicated policies. Since the views may frequently suffer from partial observabilit…
MiniHack simplifies creation of complex RL environments.
WILD-SCAV benchmarks AI in complex 3D FPS environments.
Reinforcement learning aims at searching the best policy model for decision making, and has been shown powerful for sequential recommendations. The training of the policy by reinforcement learning, however, is placed in an environment. In many real-world applications, however, the policy training in the real environmen…