A startup is using artificial intelligence to find new sources of … EVs and renewable energy, and machine learning can help narrow the search.

Temporal grounding aims to predict a time interval of a video clip corresponding to a natural language query input. In this work, we present EVOQUER, a temporal grounding framework incorporating an existing text-to-video grounding model and a video-assisted query generation network. Given a query and an untrimmed video, the temporal grounding model predicts the target interval, and the predicted video clip is fed into a video translation task by generating a simplified version of the input query. EVOQUER forms closed-loop learning by incorporating loss functions from both temporal grounding and query generation serving as feedback. Our experiments on two widely used datasets, Charades-STA and ActivityNet, show that EVOQUER achieves promising improvements by 1.05 and 1.31 at R@0.7. We also discuss how the query generation task could facilitate error analysis by explaining temporal grounding model behavior.

Out of the numerous hazards posing a threat to sustainable environmental conditions in the 21st century, only a few have a graver impact than air pollution. Its importance in determining the health and living standards in urban settings is only expected to increase with time. Various factors ranging from emissions from traffic and power plants, household emissions, natural causes are known to be primary causal agents or influencers behind rising air pollution levels. However, the lack of large scale data involving the major factors has hindered the research on the causes and relations governing the variability of the different air pollutants. Through this work, we introduce a large scale city-wise dataset for exploring the relationships among these agents over a long period of time. We analyze and explore the dataset to bring out inferences which we can derive by modeling the data. Also, we provide a set of benchmarks for the problem of estimating or forecasting pollutant levels with a set of diverse models and methodologies. Through our paper, we seek to provide a ground base for further research into this domain that will demand critical attention of ours in the near future.

We consider an information elicitation game where the center needs the agent to self-report her actual usage of a service and charges her a payment accordingly. The center can only observe a partial signal, representing part of the agent's true consumption, that is generated randomly from a publicly known distribution. The agent can report any information, as long as it does not contradict the signal, and the center issues a payment based on the reported information. Such problems find application in prosumer pricing, tax filing, etc., when the agent's actual consumption of a service is masked from the center and verification of the submitted reports is impractical. The key difference between the current problem and classic information elicitation problems is that the agent gets to observe the full signal and act strategically, but the center can only see the partial signal. For this seemingly impossible problem, we propose a penalty mechanism that elicits truthful self-reports in a repeated game. In particular, besides charging the agent the reported value, the mechanism charges a penalty proportional to her inconsistent reports. We show how a combination of the penalty rate and the length of the game incentivizes the agent to be truthful for the entire game, a phenomenon we call "fear of tomorrow verification". We show how approximate results for arbitrary distributions can be obtained by analyzing Bernoulli distributions. We extend our mechanism to a multi-agent cost sharing setting and give equilibrium results.

While multilingual pretrained language models (LMs) fine-tuned on a single language have shown substantial cross-lingual task transfer capabilities, there is still a wide performance gap in semantic parsing tasks when target language supervision is available. In this paper, we propose a novel Translate-and-Fill (TaF) method to produce silver training data for a multilingual semantic parser. This method simplifies the popular Translate-Align-Project (TAP) pipeline and consists of a sequence-to-sequence filler model that constructs a full parse conditioned on an utterance and a view of the same parse. Our filler is trained on English data only but can accurately complete instances in other languages (i.e., translations of the English training utterances), in a zero-shot fashion. Experimental results on three multilingual semantic parsing datasets show that data augmentation with TaF reaches accuracies competitive with similar systems which rely on traditional alignment techniques.

A large class of decision making under uncertainty problems can be described via Markov decision processes (MDPs) or partially observable MDPs (POMDPs), with application to artificial intelligence and operations research, among others. Traditionally, policy synthesis techniques are proposed such that a total expected cost or reward is minimized or maximized. However, optimality in the total expected cost sense is only reasonable if system behavior in the large number of runs is of interest, which has limited the use of such policies in practical mission-critical scenarios, wherein large deviations from the expected behavior may lead to mission failure. In this paper, we consider the problem of designing policies for MDPs and POMDPs with objectives and constraints in terms of dynamic coherent risk measures, which we refer to as the constrained risk-averse problem. For MDPs, we reformulate the problem into a infsup problem via the Lagrangian framework and propose an optimization-based method to synthesize Markovian policies. For MDPs, we demonstrate that the formulated optimization problems are in the form of difference convex programs (DCPs) and can be solved by the disciplined convex-concave programming (DCCP) framework. We show that these results generalize linear programs for constrained MDPs with total discounted expected costs and constraints. For POMDPs, we show that, if the coherent risk measures can be defined as a Markov risk transition mapping, an infinite-dimensional optimization can be used to design Markovian belief-based policies. For stochastic finite-state controllers (FSCs), we show that the latter optimization simplifies to a (finite-dimensional) DCP and can be solved by the DCCP framework. We incorporate these DCPs in a policy iteration algorithm to design risk-averse FSCs for POMDPs.

As part of Probabilistic Risk Assessment studies, it is necessary to study the fragility of mechanical and civil engineered structures when subjected to seismic loads. This risk can be measured with fragility curves, which express the probability of failure of the structure conditionally to a seismic intensity measure. The estimation of fragility curves relies on time-consuming numerical simulations, so that careful experimental design is required in order to gain the maximum information on the structure's fragility with a limited number of code evaluations. We propose and implement an active learning methodology based on adaptive importance sampling in order to reduce the variance of the training loss. The efficiency of the proposed method in terms of bias, standard deviation and prediction interval coverage are theoretically and numerically characterized. Keywords: Computer experiments, probabilistic risk assessment, importance sampling, statistical learning 1. Introduction The notion of fragility curve was developed in the early 80s in the context of seismic probabilistic risk assesment (SPRA) [1, 2] or performance based earthquake engineering (PBEE) [3]. Fully documented templates are available in the elsarticle package on CTAN. Fragility curves are used in several domains: nuclear safety evaluation [4], estimation of the collapse risk of structures in seismic regions [5], design checking process [6]. Nonetheless, the use of fragility curves is not limited to seismic load but is extended to other loading sources such as wind and waves 10 [7]. For complex structures, fragility curve estimation requires a large number of numerical mechanical simulations, involving in most cases non linear computationally expensive calculations. Moreover, they should account for both the uncertainties due to the seismic demand and due to the lack of knowledge on the system itself, respectively called random and epistemic uncertainties [8, 9]. As 15 failure for a typical and reliable mechanical structure is a rare event, the crude Monte Carlo method cannot be applied because it would require too many numerical simulations to produce a sufficiently large number of failed states [10, p.27].

As an important step to fulfill the Paris Agreement and achieve net-zero emissions by 2050, the European Commission adopted the most ambitious package of climate impact measures in April 2021 to improve the flow of capital towards sustainable activities. For these and other international measures to be successful, reliable data is key. The ability to see the carbon footprint of companies around the world will be critical for investors to comply with the measures. However, with only a small portion of companies volunteering to disclose their greenhouse gas (GHG) emissions, it is nearly impossible for investors to align their investment strategies with the measures. By training a machine learning model on disclosed GHG emissions, we are able to estimate the emissions of other companies globally who do not disclose their emissions. In this paper, we show that our model provides accurate estimates of corporate GHG emissions to investors such that they are able to align their investments with the regulatory measures and achieve net-zero goals.

Artificial intelligence has "tremendous potential" to support and accelerate a reliable and lowest-cost energy transition, a new report by the World Economic Forum has revealed. Through its high-tech applications, AI can integrate renewable energy resources into the power grid, support an autonomous electricity distribution system and open up new revenue streams for demand-side flexibility, WEF said in its Harnessing Artificial Intelligence to Accelerate Energy Transition report compiled in collaboration with BloombergNEF and Deutsche Energie-Agentur (dena) – the German energy agency. AI can create substantial value for the global energy transition, the report said. Every 1 per cent of additional efficiency in demand will create $1.3 trillion in value between 2020 and 2050 due to reduced investment needs, according to BloombergNEF's net-zero scenario modelling. This could be achieved by enabling greater energy efficiency and flexing demand. "In energy, we are only seeing the beginning ...

Artificial intelligence (AI) will play a key role in intertwining the power, transport, industry and building sectors while helping to decentralize the power industry as energy systems across the world move toward decarbonization, according to a new report by the World Economic Forum in collaboration with BloombergNEF and Deutsche Energie-Agentur (dena). The report says AI has the potential to accelerate a reliable and low-cost energy transition, including integrating renewable energy resources into the power grid, supporting proactive and autonomous electricity distribution systems as well as opening revenue streams for demand-side flexibility. The technology can also help accelerate new clean energy and storage uses. AI can simplify the management and cost of energy sources for organizations while managing a large amount of data and increasing the efficiency of those sources. This new report highlights ways to implement the technology in a way to standardize a renewed energy production.