Don't miss out on ET Prime stories! Get your daily dose of business updates on WhatsApp. The Central Bureau of Investigation cited two instances of "quid pro quo" while seeking the custody of former ICICI Bank chief Chanda Kochhar and her husband, who were arrested on Friday in a corruption case. The Centre's subsidy bill is expected to fall in the next fiscal year even after making foodgrain free for the poor as the additional allocation under the Covid-19 relief scheme will end in December this year. The Department of Telecommunications (DoT) is not in favour of reserving spectrum in the mid-band for captive private networks as suggested by the Telecom Regulatory Authority of India (Trai), dealing a possible setback to firms such as Infosys, GMR and Tata Communications.

This paper extends quantile factor analysis to a probabilistic variant that incorporates regularization and computationally efficient variational approximations. By means of synthetic and real data experiments it is established that the proposed estimator can achieve, in many cases, better accuracy than a recently proposed loss-based estimator. We contribute to the literature on measuring uncertainty by extracting new indexes of low, medium and high economic policy uncertainty, using the probabilistic quantile factor methodology. Medium and high indexes have clear contractionary effects, while the low index is benign for the economy, showing that not all manifestations of uncertainty are the same.

In a sequential decision-making problem, having a structural dependency amongst the reward distributions associated with the arms makes it challenging to identify a subset of alternatives that guarantees the optimal collective outcome. Thus, besides individual actions' reward, learning the causal relations is essential to improve the decision-making strategy. To solve the two-fold learning problem described above, we develop the 'combinatorial semi-bandit framework with causally related rewards', where we model the causal relations by a directed graph in a stationary structural equation model. The nodal observation in the graph signal comprises the corresponding base arm's instantaneous reward and an additional term resulting from the causal influences of other base arms' rewards. The objective is to maximize the long-term average payoff, which is a linear function of the base arms' rewards and depends strongly on the network topology. To achieve this objective, we propose a policy that determines the causal relations by learning the network's topology and simultaneously exploits this knowledge to optimize the decision-making process. We establish a sublinear regret bound for the proposed algorithm. Numerical experiments using synthetic and real-world datasets demonstrate the superior performance of our proposed method compared to several benchmarks.

ROUGE is a standard automatic evaluation metric based on n-grams for sequence-to-sequence tasks, while cross-entropy loss is an essential objective of neural network language model that optimizes at a unigram level. We present differentiable n-gram objectives, attempting to alleviate the discrepancy between training criterion and evaluating criterion. The objective maximizes the probabilistic weight of matched sub-sequences, and the novelty of our work is the objective weights the matched sub-sequences equally and does not ceil the number of matched sub-sequences by the ground truth count of n-grams in reference sequence. We jointly optimize cross-entropy loss and the proposed objective, providing decent ROUGE score enhancement over abstractive summarization dataset CNN/DM and XSum, outperforming alternative n-gram objectives.

Two key problems in India's water sector are the estimation of dry-spell vulnerability during kharif, the monsoon season, and the design of water and energy-planning inputs to help villages undertake demand-side management during rabi, the post-monsoon season. In this article, we report our joint work with the Government of Maharashtra's Department of Agriculture on a World Bank-assisted program called the Project on Climate Resilient Agriculture, or PoCRA. The project is spread over 5,000 villages in 15 districts of Maharashtra (see Figure 1). Its main objective is to make smallholder farmers resilient to climate variability through targeted interventions. A key strategy is to promote water and energy budgeting in these villages and to supplement the community infrastructure and the capabilities of individual farmers.

We at WIRED are winding down for the year and gearing up for what is sure to be an eventful 2023. This week, following a new surge in mayhem at Twitter, we dove into exactly why the public needs real-time flight tracking, even if Elon Musk claims it's the equivalent of doxing. The crucial transparency this publicly available data provides far outweighs the limited privacy value that censoring would give to the world's rich and powerful. Unfortunately, Musk's threats of legal action against the developer of the @ElonJet tracker are having broader chilling effects. Meanwhile, Iran's internet blackouts--a response to widespread civil rights protests--are sabotaging the country's economy, according to a new assessment from the US Department of State.

AI victories are finally not science fiction anymore. With the help of the surrounding technologies such as graphic cards, cloud, and simple languages, The integration of AI (Artificial Intelligence) and RPA (Robotic Process Automation) within the supply chain field is finally happening. In addition, governments and organizations have started to regulate the same. We might won't be able to dive in the technicality side of each. However, will explain the relationship between this triad and how they share the sustainable same impact in today's world.

The field of autonomous mobile robots has undergone dramatic advancements over the past decades. Despite achieving important milestones, several challenges are yet to be addressed. Aggregating the achievements of the robotic community as survey papers is vital to keep the track of current state-of-the-art and the challenges that must be tackled in the future. This paper tries to provide a comprehensive review of autonomous mobile robots covering topics such as sensor types, mobile robot platforms, simulation tools, path planning and following, sensor fusion methods, obstacle avoidance, and SLAM. The urge to present a survey paper is twofold. First, autonomous navigation field evolves fast so writing survey papers regularly is crucial to keep the research community well-aware of the current status of this field. Second, deep learning methods have revolutionized many fields including autonomous navigation. Therefore, it is necessary to give an appropriate treatment of the role of deep learning in autonomous navigation as well which is covered in this paper. Future works and research gaps will also be discussed.

Foundation Models (FMs) are models trained on large corpora of data that, at very large scale, can generalize to new tasks without any task-specific finetuning. As these models continue to grow in size, innovations continue to push the boundaries of what these models can do on language and image tasks. This paper aims to understand an underexplored area of FMs: classical data tasks like cleaning and integration. As a proof-of-concept, we cast five data cleaning and integration tasks as prompting tasks and evaluate the performance of FMs on these tasks. We find that large FMs generalize and achieve SoTA performance on data cleaning and integration tasks, even though they are not trained for these data tasks. We identify specific research challenges and opportunities that these models present, including challenges with private and domain specific data, and opportunities to make data management systems more accessible to non-experts. We make our code and experiments publicly available at: https://github.com/HazyResearch/fm_data_tasks.

In recent years, reinforcement learning (RL) has become increasingly successful in its application to science and the process of scientific discovery in general. However, while RL algorithms learn to solve increasingly complex problems, interpreting the solutions they provide becomes ever more challenging. In this work, we gain insights into an RL agent's learned behavior through a post-hoc analysis based on sequence mining and clustering. Specifically, frequent and compact subroutines, used by the agent to solve a given task, are distilled as gadgets and then grouped by various metrics. This process of gadget discovery develops in three stages: First, we use an RL agent to generate data, then, we employ a mining algorithm to extract gadgets and finally, the obtained gadgets are grouped by a density-based clustering algorithm. We demonstrate our method by applying it to two quantum-inspired RL environments. First, we consider simulated quantum optics experiments for the design of high-dimensional multipartite entangled states where the algorithm finds gadgets that correspond to modern interferometer setups. Second, we consider a circuit-based quantum computing environment where the algorithm discovers various gadgets for quantum information processing, such as quantum teleportation. This approach for analyzing the policy of a learned agent is agent and environment agnostic and can yield interesting insights into any agent's policy.