If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Metastasis is the migration of cancerous cells from a primary tumor to other anatomical sites. Although metastasis was long thought to result from monoclonal seeding, or single cellular migrations, recent phylogenetic analyses of metastatic cancers have reported complex patterns of cellular migrations between sites, including polyclonal migrations and reseeding. However, accurate determination of migration patterns from somatic mutation data is complicated by intratumor heterogeneity and discordance between clonal lineage and cellular migration. We introduce MACHINA, a multi-objective optimization algorithm that jointly infers clonal lineages and parsimonious migration histories of metastatic cancers from DNA sequencing data. MACHINA analysis of data from multiple cancers shows that migration patterns are often not uniquely determined from sequencing data alone and that complicated migration patterns among primary tumors and metastases may be less prevalent than previously reported.
Animals are able to reach a desired state in an environment by controlling various behavioral patterns. Identification of the behavioral strategy used for this control is important for understanding animals' decision-making and is fundamental to dissect information processing done by the nervous system. However, methods for quantifying such behavioral strategies have not been fully established. In this study, we developed an inverse reinforcement-learning (IRL) framework to identify an animal's behavioral strategy from behavioral time-series data. We applied this framework to C. elegans thermotactic behavior; after cultivation at a constant temperature with or without food, fed worms prefer, while starved worms avoid the cultivation temperature on a thermal gradient.
The conversion of light into usable chemical energy by plants is enabled by the precise spatial arrangement of light-absorbing photosynthetic systems and associated molecular complexes (1). In organic solar cells, there is also the need to control intermolecular spacing and molecular orientation, as well as thin-film crystallinity and morphology, so as to enable efficient energy migration and photoconversion (2). In an organic solar cell, light absorption creates excitons, tightly bound electron-hole pairs that must be efficiently dissociated into their component charge carriers in order to create an electrical current. Thus, long-range exciton migration must occur from the point of photogeneration to a dissociating site. In comparison, organic solar cells are typically constructed with materials having exciton diffusion lengths one order of magnitude smaller than this value, which limits device thickness and optical absorption.
The mammalian neocortex is one of the most intricate entities found in nature, both in terms of structure and function. It is the brain region responsible for the execution of high-order functions, including sensory perception, motor control, cognition, and speech. Its development is equally complex because it requires that millions to billions (depending on the species) of neurons assemble in distinct layers and connect with exquisite precision to perform complicated information processing operations. During embryonic development, formation of the cerebral cortex involves the migration of excitatory neurons generated in the ventricular zone toward the cortical plate, where they establish their final position in six well-defined horizontal layers consisting of different types of neurons and architecture. Along this migratory phase, developing neurons undergo a morphological transition from multipolar shape to bipolar morphology.
A drone camera films a herd of caribou as they migrate in Western Canada. The footage offers a unique look at the behavior of individuals within the herd. Flying cameras are giving biologists an all-encompassing view of migration that reveals how social interactions motivate the animals' every move. Ecologists Andrew Berdahl, a Santa Fe Institute fellow, Colin Torney of the University of Glasgow, and colleagues flew drones to capture footage of Dolphin and Union caribou, a Canadian herd, as the animals crossed from Victoria Island to the Canadian mainland in the last stage of their fall migration. Scientists have long pondered the dynamics of animal migrations, but they've had limited ways to study them.
Those funds could form the basis for a future "investment budget" for the euro zone. Companies should no longer be able to play EU states off each other in terms of taxation, and tax dumping must be banned. Please verify you're not a robot by clicking the box. You must select a newsletter to subscribe to. Prepared to loosen sanctions if Russia implements terms of Minsk agreement aimed at ending fighting in Ukraine.
Unprecedented human mobility has driven the rapid urbanization around the world. In China, the fraction of population dwelling in cities increased from 17.9% to 52.6% between 1978 and 2012. Such large-scale migration poses challenges for policymakers and important questions for researchers. To investigate the process of migrant integration, we employ a one-month complete dataset of telecommunication metadata in Shanghai with 54 million users and 698 million call logs. We find systematic differences between locals and migrants in their mobile communication networks and geographical locations. For instance, migrants have more diverse contacts and move around the city with a larger radius than locals after they settle down. By distinguishing new migrants (who recently moved to Shanghai) from settled migrants (who have been in Shanghai for a while), we demonstrate the integration process of new migrants in their first three weeks. Moreover, we formulate classification problems to predict whether a person is a migrant. Our classifier is able to achieve an F1-score of 0.82 when distinguishing settled migrants from locals, but it remains challenging to identify new migrants because of class imbalance. This classification setup holds promise for identifying new migrants who will successfully integrate into locals (new migrants that misclassified as locals).
Terra-Neves, Miguel (INESC-ID / Instituto Superior Técnico, Universidade de Lisboa, Portugal) | Lynce, Inês (INESC-ID / Instituto Superior Técnico, Universidade de Lisboa, Portugal) | Manquinho, Vasco (INESC-ID / Instituto Superior Técnico, Universidade de Lisboa, Portugal)
Minimal Correction Subsets (MCSs) have been successfully applied to find approximate solutions to several real-world single-objective optimization problems. However, only recently have MCSs been used to solve Multi-Objective Combinatorial Optimization (MOCO) problems. In particular, it has been shown that all optimal solutions of MOCO problems with linear objective functions can be found by an MCS enumeration procedure. In this paper, we show that the approach of MCS enumeration can also be applied to MOCO problems where objective functions are divisions of linear expressions. Hence, it is not necessary to use a linear approximation of these objective functions. Additionally, we also propose the integration of diversification techniques on the MCS enumeration process in order to find better approximations of the Pareto front of MOCO problems. Finally, experimental results on the Virtual Machine Consolidation (VMC) problem show the effectiveness of the proposed techniques.
In the United States there is an operational network of weather radars providing freely accessible data for monitoring meteorological phenomena in the atmosphere. Individual radars are sensitive enough to detect birds, and can provide insight into migratory behaviors of birds at scales that are not possible using other sensors. Archived data from the WSR-88D network of U.S. weather radars hold valuable and detailed information about the continent-scale migratory movements of birds over the last 20 years. However, significant technical challenges must be overcome to understand this information and harness its potential for science and conservation. We describe recent work on an AI system to quantify bird migration using radar data, which is part of the larger BirdCast project to model and forecast bird migration at large scales using radar, weather, and citizen science data.
The processes that affect the distributions of animals and plants operate at multiple spatial and temporal scales, presenting a unique challenge for the development and coordination of effective conservation strategies, particularly for wide-ranging species. In order to study ecological systems across scales, data must be collected at fine resolutions across broad spatial and temporal extents. Crowdsourcing has emerged as an efficient way to gather these data by engaging large numbers of people to record observations. However, data gathered by crowdsourced projects are often biased due to the opportunistic approach of data collection. In this article, we propose a general class of models called AdaSTEM (for adaptive spatiotemporal exploratory models) that are designed to meet these challenges by adapting to multiple scales while exploiting variation in data density common with crowdsourced data.