In this paper, we address the problem of answering complex information needs by conversing conversations with search engines, in the sense that users can express their queries in natural language, and directly receivethe information they need from a short system response in a conversational manner. Recently, there have been some attempts towards a similar goal, e.g., studies on Conversational Agents (CAs) and Conversational Search (CS). However, they either do not address complex information needs, or they are limited to the development of conceptual frameworks and/or laboratory-based user studies. We pursue two goals in this paper: (1) the creation of a suitable dataset, the Search as a Conversation (SaaC) dataset, for the development of pipelines for conversations with search engines, and (2) the development of astate-of-the-art pipeline for conversations with search engines, the Conversations with Search Engines (CaSE), using this dataset. SaaC is built based on a multi-turn conversational search dataset, where we further employ workers from a crowdsourcing platform to summarize each relevant passage into a short, conversational response. CaSE enhances the state-of-the-art by introducing a supporting token identification module and aprior-aware pointer generator, which enables us to generate more accurate responses. We carry out experiments to show that CaSE is able to outperform strong baselines. We also conduct extensive analyses on the SaaC dataset to show where there is room for further improvement beyond CaSE. Finally, we release the SaaC dataset and the code for CaSE and all models used for comparison to facilitate future research on this topic.

Welcome back to the "Life in 2050" series! So far, we've looked at how ongoing developments in science, technology, and geopolitics will be reflected in terms of warfare and the economy. Today, we are shifting gears a little and looking at how the turbulence of this century will affect the way people live from day to day. As noted in the previous two installments, changes in the 21st century will be driven by two major factors. These include the disruption caused by rapidly accelerating technological progress, and the disruption caused by rising global temperatures, and the environmental impact this will have (aka. These factors will be pulling the world in opposite directions, and simultaneously at that.

The graph represents a network of 3,600 Twitter users whose tweets in the requested range contained "InsurTech", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Friday, 14 May 2021 at 12:08 UTC. The requested start date was Friday, 14 May 2021 at 00:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 5-day, 18-hour, 4-minute period from Saturday, 08 May 2021 at 05:55 UTC to Friday, 14 May 2021 at 00:00 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.

In robotics, ergodic control extends the tracking principle by specifying a probability distribution over an area to cover instead of a trajectory to track. The original problem is formulated as a spectral multiscale coverage problem, typically requiring the spatial distribution to be decomposed as Fourier series. This approach does not scale well to control problems requiring exploration in search space of more than 2 dimensions. To address this issue, we propose the use of tensor trains, a recent low-rank tensor decomposition technique from the field of multilinear algebra. The proposed solution is efficient, both computationally and storage-wise, hence making it suitable for its online implementation in robotic systems. The approach is applied to a peg-in-hole insertion task requiring full 6D end-effector poses, implemented with a 7-axis Franka Emika Panda robot. In this experiment, ergodic exploration allows the task to be achieved without requiring the use of force/torque sensors.

Considering processes of a business in a recommender system is highly advantageous. Although most studies in the business process analysis domain are of descriptive and predictive nature, the feasibility of constructing a process-aware recommender system is assessed in a few works. One reason can be the lack of knowledge on process mining potential for recommendation problems. Therefore, this paper aims to identify and analyze the published studies on process-aware recommender system techniques in business process management and process mining domain. A systematic review was conducted on 33 academic articles published between 2008 and 2020 according to several aspects. In this regard, we provide a state-of-the-art review with critical details and researchers with a better perception of which path to pursue in this field. Moreover, based on a knowledge base and holistic perspective, we discuss some research gaps and open challenges in this field.

In many public health settings, it is important for patients to adhere to health programs, such as taking medications and periodic health checks. Unfortunately, beneficiaries may gradually disengage from such programs, which is detrimental to their health. A concrete example of gradual disengagement has been observed by an organization that carries out a free automated call-based program for spreading preventive care information among pregnant women. Many women stop picking up calls after being enrolled for a few months. To avoid such disengagements, it is important to provide timely interventions. Such interventions are often expensive and can be provided to only a small fraction of the beneficiaries. We model this scenario as a restless multi-armed bandit (RMAB) problem, where each beneficiary is assumed to transition from one state to another depending on the intervention. Moreover, since the transition probabilities are unknown a priori, we propose a Whittle index based Q-Learning mechanism and show that it converges to the optimal solution. Our method improves over existing learning-based methods for RMABs on multiple benchmarks from literature and also on the maternal healthcare dataset.

If you've never heard colours, you can now do so. Researchers planted tiny electrodes on the surface of the brain of a man paralysed from the neck down. As he imagined writing letters with his hand, the researchers analysed the neural patterns for each letter. They created an algorithm that transformed these neural patterns into words on a screen [Anushree Dave, ScienceNews]. From his brain activity alone, the participant produced 90 characters, or 15 words, per minute, Krishna Shenoy, a Howard Hughes Medical Institute investigator at Stanford University, and colleagues report May 12 in Nature.

A comprehensive analysis of the situation of Artificial Intelligence in Digital Labour market is been done in this intelligence report. It includes the investigations done of the past progress, ongoing market scenarios and future prospects. An accurate data of the products, strategies and market shares of leading companies in this particular market is mentioned. It elucidates a detailed outline of Artificial Intelligence in Digital Labour market depending on the important parameters. End users, products, regions and many other segments are studied and explained.

Comprehensive synaptic wiring diagrams or "connectomes" provide a detailed map of all the neurons and their interconnections in a brain region or even an entire organism. Since the connectome of the nematode C. elegans was obtained using electron microscopy methods in 1986 [1], methods for data acquisition and analysis have both been scaled up and improved significantly. Today, it has become possible to provide connectomes of much more complex systems such as various Drosophila melanogaster circuits [2, 3], or even a large part of its brain [4, 5]; the olfactory bulb of zebrafish [6]; and various pieces of the rodent retina [7-9], hippocampus [10], and cortex [11-14]. While there still remain a number of formidable challenges on the way to the complete connectome of a mammal or even human brain [15], the data sets available today already give rise to a number of intriguing questions. At the same time, it is becoming increasingly clear that new quantitative methods must be developed to fully exploit the new troves of data that connectomics provides [16]. Here, we focus on local neural networks that store information in their synaptic connectivity. It has been hypothesised that cortical networks with their extensive recurrent synaptic connectivity are optimised for this task [17]. A popular model for these networks are attractor neural networks such as the Hopfield's model [18] and various generalisations [19-22], where memories are stored as

While deep learning-based 3D face generation has made a progress recently, the problem of dynamic 3D (4D) facial expression synthesis is less investigated. In this paper, we propose a novel solution to the following question: given one input 3D neutral face, can we generate dynamic 3D (4D) facial expressions from it? To tackle this problem, we first propose a mesh encoder-decoder architecture (Expr-ED) that exploits a set of 3D landmarks to generate an expressive 3D face from its neutral counterpart. Then, we extend it to 4D by modeling the temporal dynamics of facial expressions using a manifold-valued GAN capable of generating a sequence of 3D landmarks from an expression label (Motion3DGAN). The generated landmarks are fed into the mesh encoder-decoder, ultimately producing a sequence of 3D expressive faces. By decoupling the two steps, we separately address the non-linearity induced by the mesh deformation and motion dynamics. The experimental results on the CoMA dataset show that our mesh encoder-decoder guided by landmarks brings a significant improvement with respect to other landmark-based 3D fitting approaches, and that we can generate high quality dynamic facial expressions. This framework further enables the 3D expression intensity to be continuously adapted from low to high intensity. Finally, we show our framework can be applied to other tasks, such as 2D-3D facial expression transfer.