DURHAM, N.C. - Using drones and artificial intelligence to monitor large colonies of seabirds can be as effective as traditional on-the-ground methods, while reducing costs, labor and the risk of human error, a new study finds. Scientists at Duke University and the Wildlife Conservation Society (WCS) used a deep-learning algorithm--a form of artificial intelligence--to analyze more than 10,000 drone images of mixed colonies of seabirds in the Falkland Islands off Argentina's coast. The Falklands, also known as the Malvinas, are home to the world's largest colonies of black-browed albatrosses (Thalassarche melanophris) and second-largest colonies of southern rockhopper penguins (Eudyptes c. chrysocome). Hundreds of thousands of birds breed on the islands in densely interspersed groups. The deep-learning algorithm correctly identified and counted the albatrosses with 97% accuracy and the penguins with 87%.

In a wide variety of applications including online advertising, contractual hiring, and wireless scheduling, the controller is constrained by a stringent budget constraint on the available resources, which are consumed in a random amount by each action, and a stochastic feasibility constraint that may impose important operational limitations on decision-making. In this work, we consider a general model to address such problems, where each action returns a random reward, cost, and penalty from an unknown joint distribution, and the decision-maker aims to maximize the total reward under a budget constraint $B$ on the total cost and a stochastic constraint on the time-average penalty. We propose a novel low-complexity algorithm based on Lyapunov optimization methodology, named ${\tt LyOn}$, and prove that it achieves $O(\sqrt{B\log B})$ regret and $O(\log B/B)$ constraint-violation. The low computational cost and sharp performance bounds of ${\tt LyOn}$ suggest that Lyapunov-based algorithm design methodology can be effective in solving constrained bandit optimization problems.

MR Accuracy Reports recently introduced new title on "Global Cloud Machine Learning Market Report 2020 Market: Industry Analysis, Size, Share, Growth, Trends, and Forecasts 2021-2027" from its database utilizing diverse methodologies aims to examine and put forth in-depth and accurate data regarding the global Cloud Machine Learning Market Report 2020 market. The report provides study with in-depth overview, describing about the Product / Industry Scope and elaborates market outlook and status (2021-2026). Cloud Machine Learning Market Report 2020 Market research report which provides an in-depth examination of the market scenario regarding market size, share, demand, growth, trends, and forecast for 2020-2026. The report covers the impact analysis of the COVID-19 pandemic. The COVID-19 pandemic has affected export imports, demands, and industry trends and is expected to have an economic impact on the market. The report provides a comprehensive analysis of the impact of the pandemic on the entire industry and provides an overview of a post-COVID-19 market scenario.

This Machine Learning market report provides a thorough insight of the market, allowing key players to keep informed and keep their competitive advantage. It focuses on present trends by forecasting future trends, market size, and market features. Such meticulous Market Analysis creates a comprehensive picture of market policies and supports industries in making larger earnings than before. The greatest way to gain insight into the current market situation and take a position in it is to read this Machine Learning market Research Report. It strengthens corporate positions and assists various industry participants in understanding future and current market situations.

A newly published study on Global AI & Machine Learning Operationalization Software Market the report observes numerous in-depth, influential and inducing factors that outline the market and industry. All of the findings, data, and information provided in the report are validated and revalidated with the help of trustworthy sources. The analysts who have authored the report took a unique and industry-best research and analysis approach for an in-depth study of the global AI & Machine Learning Operationalization Software market. This report forecasts demands, Trends, and revenue growth at regional & country levels and provides an analysis of the industry trends in each of the sub-segments from 2021 to 2026. The global AI & Machine Learning Operationalization Software Market to grow with a CAGR of 44.2% over the forecast period of 2021-2026.

Although Artificial Intelligence (AI) has existed for more than sixty years, today, thanks to greater computing power, as well as the availability of large amounts of data and more sophisticated algorithms, the applications of this field of computing have proliferated considerably and have had a direct impact on the workplace . The need to meet the demand for products and services that integrate this technology has forced companies to recruit qualified professionals and experts in this field . That is why many companies are currently looking to fill positions related to Artificial Intelligence and Machine Learning programming, as well as Data Scientists . According to the latest report from the World Economic Forum, Jobs of Tomorrow: Mapping Opportunity in the New Economy (2020), by 2022 133 million new jobs will be created worldwide, of which 16% will be within of the Data and Artificial Intelligence sector . According to the latest analysis by the Federal Institute of Telecommunications, published in January 2020, the Internet is the most used Information and Communication Technology (ICT) in Mexico, since 66 out of 100 people aged 6 years and over use it; which accounts for the importance of this network in the lives of Mexicans.

Graph convolutional network (GCN) has become popular in various natural language processing (NLP) tasks with its superiority in long-term and non-consecutive word interactions. However, existing single-hop graph reasoning in GCN may miss some important non-consecutive dependencies. In this study, we define the spectral graph convolutional network with the high-order dynamic Chebyshev approximation (HDGCN), which augments the multi-hop graph reasoning by fusing messages aggregated from direct and long-term dependencies into one convolutional layer. To alleviate the over-smoothing in high-order Chebyshev approximation, a multi-vote-based cross-attention (MVCAttn) with linear computation complexity is also proposed. The empirical results on four transductive and inductive NLP tasks and the ablation study verify the efficacy of the proposed model. Our source code is available at https://github.com/MathIsAll/HDGCN-pytorch.

Object-based attention is a key component of the visual system, relevant for perception, learning, and memory. Neurons tuned to features of attended objects tend to be more active than those associated with non-attended objects. There is a rich set of models of this phenomenon in computational neuroscience. However, there is currently a divide between models that successfully match physiological data but can only deal with extremely simple problems and models of attention used in computer vision. For example, attention in the brain is known to depend on top-down processing, whereas self-attention in deep learning does not. Here, we propose an artificial neural network model of object-based attention that captures the way in which attention is both top-down and recurrent. Our attention model works well both on simple test stimuli, such as those using images of handwritten digits, and on more complex stimuli, such as natural images drawn from the COCO dataset. We find that our model replicates a range of findings from neuroscience, including attention-invariant tuning, inhibition of return, and attention-mediated scaling of activity. Understanding object based attention is both computationally interesting and a key problem for computational neuroscience.

Intelligent and adaptive online education systems aim to make high-quality education available for a diverse range of students. However, existing systems usually depend on a pool of hand-made questions, limiting how fine-grained and open-ended they can be in adapting to individual students. We explore targeted question generation as a controllable sequence generation task. We first show how to fine-tune pre-trained language models for deep knowledge tracing (LM-KT). This model accurately predicts the probability of a student answering a question correctly, and generalizes to questions not seen in training. We then use LM-KT to specify the objective and data for training a model to generate questions conditioned on the student and target difficulty. Our results show we succeed at generating novel, well-calibrated language translation questions for second language learners from a real online education platform.

Overlap functions are a class of aggregation functions that measure the overlapping degree between two values. Interval-valued overlap functions were defined as an extension to express the overlapping of interval-valued data, and they have been usually applied when there is uncertainty regarding the assignment of membership degrees. The choice of a total order for intervals can be significant, which motivated the recent developments on interval-valued aggregation functions and interval-valued overlap functions that are increasing to a given admissible order, that is, a total order that refines the usual partial order for intervals. Also, width preservation has been considered on these recent works, in an intent to avoid the uncertainty increase and guarantee the information quality, but no deeper study was made regarding the relation between the widths of the input intervals and the output interval, when applying interval-valued functions, or how one can control such uncertainty propagation based on this relation. Thus, in this paper we: (i) introduce and develop the concepts of width-limited interval-valued functions and width limiting functions, presenting a theoretical approach to analyze the relation between the widths of the input and output intervals of bivariate interval-valued functions, with special attention to interval-valued aggregation functions; (ii) introduce the concept of $(a,b)$-ultramodular aggregation functions, a less restrictive extension of one-dimension convexity for bivariate aggregation functions, which have an important predictable behaviour with respect to the width when extended to the interval-valued context; (iii) define width-limited interval-valued overlap functions, taking into account a function that controls the width of the output interval; (iv) present and compare three construction methods for these width-limited interval-valued overlap functions.