Fields such as phylogenetics often work with a sort of abstracted family tree, called a phylogenetic tree, frequently abbreviated here as tree. These trees have different members of a population as their tips, and their branching points describe the relations between the tips and how recently they had a common ancestor. If some of the tips are censored, the tree topology simplifies in a process we refer to as restriction. If one has multiple trees restricted from the same original, uncensored tree, one may wish to reconstruct the original supertree. Suppose instead one has multiple probability distributions of restricted trees, then one may be interested in reconstructing the supertree probability distribution.

In this paper, a novel cognitive architecture for action recognition is developed by applying layers of growing grid neural networks.Using these layers makes the system capable of automatically arranging its representational structure. In addition to the expansion of the neural map during the growth phase, the system is provided with a prior knowledge of the input space, which increases the processing speed of the learning phase. Apart from two layers of growing grid networks the architecture is composed of a preprocessing layer, an ordered vector representation layer and a one-layer supervised neural network. These layers are designed to solve the action recognition problem. The first-layer growing grid receives the input data of human actions and the neural map generates an action pattern vector representing each action sequence by connecting the elicited activation of the trained map. The pattern vectors are then sent to the ordered vector representation layer to build the time-invariant input vectors of key activations for the second-layer growing grid. The second-layer growing grid categorizes the input vectors to the corresponding action clusters/sub-clusters and finally the one-layer supervised neural network labels the shaped clusters with action labels. Three experiments using different datasets of actions show that the system is capable of learning to categorize the actions quickly and efficiently. The performance of the growing grid architecture is com-pared with the results from a system based on Self-Organizing Maps, showing that the growing grid architecture performs significantly superior on the action recognition tasks.

This opinion piece is forthcoming in Il Sole 24 Ore. The Muller-Lyer optical illusion consists of two lines of equal length that differ only in the direction of arrowheads at either end. Yet, to most observers, the line with arrowheads pointing outwards looks longer than the other. If you grew up in and among buildings with straight walls and 90 degree angles, you have learned to perceive lines according to geometric patterns. Your view of the Muller-Lyer lines is biased.

PUNE AI-enabled radiology platform DeepTek is playing an important role in precise diagnosis of diseases like TB and Covid-19. The Pune-based startup has received strategic investment from a clutch of investors so far, and is eying another VC round in next six months. Patil completed his schooling from SSPMS school and engineering from COEP in 1992. He has a Master's degree from IIT-Kharagpur in Industrial Engineering and Operations Research. Amit Kharat, with a DNB and PhD in Radiology, has been engaged in the radiology space for the last 17 years.

Autonomous driving systems need to handle complex scenarios such as lane following, avoiding collisions, taking turns, and responding to traffic signals. In recent years, approaches based on end-to-end behavioral cloning have demonstrated remarkable performance in point-to-point navigational scenarios, using a realistic simulator and standard benchmarks. Offline imitation learning is readily available, as it does not require expensive hand annotation or interaction with the target environment, but it is difficult to obtain a reliable system. In addition, existing methods have not specifically addressed the learning of reaction for traffic lights, which are a rare occurrence in the training datasets. Inspired by the previous work on multi-task learning and attention modeling, we propose a novel multi-task attention-aware network in the conditional imitation learning (CIL) framework. This does not only improve the success rate of standard benchmarks, but also the ability to react to traffic lights, which we show with standard benchmarks.

Recent machine-learning approaches to deterministic search and domain-independent planning employ policy learning to speed up search. Unfortunately, when attempting to solve a search problem by successively applying a policy, no guarantees can be given on solution quality. The problem of how to effectively use a learned policy within a bounded-suboptimal search algorithm remains largely as an open question. In this paper, we propose various ways in which such policies can be integrated into Focal Search, assuming that the policy is a neural network classifier. Furthermore, we provide mathematical foundations for some of the resulting algorithms. To evaluate the resulting algorithms over a number of policies with varying accuracy, we use synthetic policies which can be generated for a target accuracy for problems where the search space can be held in memory. We evaluate our focal search variants over three benchmark domains using our synthetic approach, and on the 15-puzzle using a neural network learned using 1.5 million examples. We observe that \emph{Discrepancy Focal Search}, which we show expands the node which maximizes an approximation of the probability that its corresponding path is a prefix of an optimal path, obtains, in general, the best results in terms of runtime and solution quality.

The investment of time and resources for better strategies and methodologies to tackle a potential pandemic is key to deal with potential outbreaks of new variants or other viruses in the future. In this work, we recreated the scene of a year ago, 2020, when the pandemic erupted across the world for the fifty countries with more COVID-19 cases reported. We performed some experiments in which we compare state-of-the-art machine learning algorithms, such as LSTM, against online incremental machine learning algorithms to adapt them to the daily changes in the spread of the disease and predict future COVID-19 cases. To compare the methods, we performed three experiments: In the first one, we trained the models using only data from the country we predicted. In the second one, we use data from all fifty countries to train and predict each of them. In the first and second experiment, we used a static hold-out approach for all methods. In the third experiment, we trained the incremental methods sequentially, using a prequential evaluation. This scheme is not suitable for most state-of-the-art machine learning algorithms because they need to be retrained from scratch for every batch of predictions, causing a computational burden. Results show that incremental methods are a promising approach to adapt to changes of the disease over time; they are always up to date with the last state of the data distribution, and they have a significantly lower computational cost than other techniques such as LSTMs.

Question answering from semi-structured tables can be seen as a semantic parsing task and is significant and practical for pushing the boundary of natural language understanding. Existing research mainly focuses on understanding contents from unstructured evidence, e.g., news, natural language sentences, and documents. The task of verification from structured evidence, such as tables, charts, and databases, is still less explored. This paper describes sattiy team's system in SemEval-2021 task 9: Statement Verification and Evidence Finding with Tables (SEM-TAB-FACT). This competition aims to verify statements and to find evidence from tables for scientific articles and to promote the proper interpretation of the surrounding article. In this paper, we exploited ensemble models of pre-trained language models over tables, TaPas and TaBERT, for Task A and adjust the result based on some rules extracted for Task B. Finally, in the leaderboard, we attain the F1 scores of 0.8496 and 0.7732 in Task A for the 2-way and 3-way evaluation, respectively, and the F1 score of 0.4856 in Task B.

A collaboration from UoC Berkeley, Stanford University and Facebook offers a deeper and more granular picture of the actual state of poverty in and across nations, through the use of machine learning. The research, entitled Micro-Estimates of Wealth for all Low-and Middle-Income Countries, is accompanied by a beta website that allows users to interactively explore the absolute and relative economic state of fine-grained areas and pockets of poverty in low and middle-income countries. The framework incorporates data from satellite imagery, topographic maps, mobile phone networks and aggregated anonymized data from Facebook, and is verified against extensive face-to-face surveys, for purposes of reporting relative wealth disparity in a region, rather than absolute estimates of income. A map of global poverty, weighted towards the most affected areas. The system has been adopted by the government of Nigeria as a basis for administering social protection programs, and runs in tandem with the existing framework from the World Bank, the National Social Safety nets Project (NASSP).

A new analysis of the upper body of famed fossil'Little Foot,' a near-complete skeleton of a hominin that lived 3.67 million years ago, reveals she was a creature adapted to living in trees. Scientists at the University of Southern California (USC) examined Little Foot's shoulder assembly, showing it supported arms well suited for hanging from branches and moving up and down trees – similar to that of apes. The latest analysis'provides the best evidence yet of how human ancestors used their arms more than 3 million years ago,' said Kristian J. Carlson, lead author of the study. The findings also suggests the structural similarities in the shoulder between humans and African apes are much more recent, and persisted much longer, than previously believed. A new analysis of the upper body of famed fossil'Little Foot,' a near-complete skeleton of a hominin that lived 3.67 million years ago, reveals she was a creature adapted to living in trees Little Foot was discovered in South Africa in 1994 and has allowed scientists to travel back in time to learn more about the evolution of humans.