In the present era of Artificial Intelligence, Deep Learning, advanced quantum computing we humans are surrounded by machines from everywhere. Many critics point to Artificial Intelligence as the main threat to humankind while on the other hand, the supporters of AI claim that humans can never be replaced by machines and would only compliment their abilities. Over the past decade, Artificial Intelligence has undoubtedly emerged as one of the technological successes and with the amount of research and investment going into this domain, it is nowhere near an end. The past decade has shown us that it is no longer a story or science fiction to make machines intelligent -- in terms of learning as we humans do. Artificial Intelligence has impacted our lives greatly, with so many services and products relying on it that it is irrevocably connected with our everyday world.

We consider a compartmental model from which we incorporate a time-dependent health care capacity having a logistic growth. This allows us to take into account the Senegalese authorities response in anticipating the growing number of infected cases. We highlight the importance of anticipation and timing to avoid overwhelming that could impact considerably the treatment of patients and the well-being of health care workers. A condition, depending on the health care capacity and the flux of new hospitalized individuals, to avoid possible overwhelming is provided. We also use machine learning approach to project forward the cumulative number of cases from March 02, 2020, until 1st December, 2020.

We study the algorithmic task of finding a large independent set in a sparse Erd\H{o}s-R\'{e}nyi random graph with $n$ vertices and average degree $d$. The maximum independent set is known to have size $(2 \log d / d)n$ in the double limit $n \to \infty$ followed by $d \to \infty$, but the best known polynomial-time algorithms can only find an independent set of half-optimal size $(\log d / d)n$. We show that the class of low-degree polynomial algorithms can find independent sets of half-optimal size but no larger, improving upon a result of Gamarnik, Jagannath, and the author. This generalizes earlier work by Rahman and Vir\'ag, which proved the analogous result for the weaker class of local algorithms.

Different theories have been proposed model's ability to rank a well-organized document to describe the properties that contribute to higher than its noisy counterparts created by discourse coherence and some have been integrated corrupting sentence order in the original document with computational models for empirical (binary discrimination task), and neural evaluation. A popular approach is the entitybased models have achieved remarkable accuracy on model which hypothesizes that coherence this task. Recent efforts have targeted additional can be assessed in terms of the distribution of tasks such as recovering the correct sentence and transitions between entities in a text - by order (Logeswaran et al., 2018; Cui et al., 2018), constructing an entity-grid (Egrid) representation evaluating on realistic data (Lai and Tetreault, (Barzilay and Lapata, 2005, 2008), building 2018; Farag and Yannakoudakis, 2019) and on Centering Theory (Grosz et al., 1995). Subsequent focusing on open-domain models of coherence work has adapted and further extended (Li and Jurafsky, 2017; Xu et al., 2019). Egrid representations (Filippova and Strube, However, less attention has been directed to 2007; Burstein et al., 2010; Elsner and Charniak, investigating and analyzing the properties of coherence 2011; Guinaudeau and Strube, 2013). Other that current models can capture, nor what research has focused on syntactic patterns knowledge is encoded in their representations and that cooccur in text (Louis and Nenkova, how it might relate to aspects of coherence.

Convolutional Neural Network (CNN) techniques have proven to be very useful in image-based anomaly detection applications. CNN can be used as deep features extractor where other anomaly detection techniques are applied on these features. For this scenario, using transfer learning is common since pretrained models provide deep feature representations that are useful for anomaly detection tasks. Consequentially, anomaly can be detected by applying similarly measure between extracted features and a defined model of normality. A key factor in such approaches is the decision threshold used for detecting anomaly. While most of the proposed methods focus on the approach itself, slight attention has been paid to address decision threshold settings. In this paper, we tackle this problem and propose a welldefined method to set the working-point decision threshold that improves detection accuracy. We introduce a transfer learning framework for anomaly detection based on similarity measure with a Model of Normality (MoN) and show that with the proposed threshold settings, a significant performance improvement can be achieved. Moreover, the framework has low complexity with relaxed computational requirements.

You play as Templars (the antagonists of the series) who are training by using the Animus, learning how to hunt and kill assassins, which is a neat twist. It's a game of cat and mouse: you're given a target to track and kill while other players are hunt you as well. The premise is so unique, and so satisfyingly tense. I can't think of any other multiplayer experience that put so many resources into building a compelling stealth experience to play with friends. I loved being able to sneak up to a target and poison them silently, then watch them collapse like a rag doll moments later.

Yesterday was all about Apple and its new family of Macs. Regardless of whether you're a Windows or macOS user, you should probably pay attention -- these are the first computers powered by silicon made by Apple itself. The first chip now has a name: the M1. It's an interesting array of devices, probably meant to trumpet the flexibility, already, of Apple's first PC chip. The MacBook Air, with claims of 18 hours' battery life, is the showcase for mobility (it's called the M1, after all), while a new MacBook Pro is a pitch to creatives that demand more power.

Our objective of advancing education in information technology to the African society on all matters related to the Internet. We endeavor to continue to be the leading initiative that provides internet literacy programs to the youths in Africa. With the exploding popularity of smartphones and tablets, mobile application development is becoming an increasingly popular medium of software creation. The "Internet of things" (IoT) is becoming an increasingly growing topic of conversation both in the workplace and outside of it Artificial Intelligence (AI) is impacting the business world more. AI is everywhere, from gaming stations to maintaining complex information at work.

"Given the large swathes of data we currently have access to, there are potential solutions to many deep-seated issues, including corruption," says Professor Tshilidzi Marwala,the UJ's Vice-Chancellor and Principal. In an extreme case, Zero Trust, an anti-corruption AI system in China, has been used to monitor and evaluate the lifestyles of government officials. It has access to more than 150 protected databases in central and local governments and, since 2012, has uncovered 8,721 government employees engaged in embezzlement, abuse of power, misuse of government resources and nepotism. Zero Trust has come under fire for not explaining the process behind identifying corrupt individuals. And it still heavily relies on humans, which could make much of its work invalid if these people are also unscrupulous.

Researchers currently use a number of approaches to predict and substantiate information-computation gaps in high-dimensional statistical estimation problems. A prominent approach is to characterize the limits of restricted models of computation, which on the one hand yields strong computational lower bounds for powerful classes of algorithms and on the other hand helps guide the development of efficient algorithms. In this paper, we study two of the most popular restricted computational models, the statistical query framework and low-degree polynomials, in the context of high-dimensional hypothesis testing. Our main result is that under mild conditions on the testing problem, the two classes of algorithms are essentially equivalent in power. As corollaries, we obtain new statistical query lower bounds for sparse PCA, tensor PCA and several variants of the planted clique problem.