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.

This paper describes a machine learning (ML) framework for tropical cyclone intensity and track forecasting, combining multiple distinct ML techniques and utilizing diverse data sources. Our framework, which we refer to as Hurricast (HURR), is built upon the combination of distinct data processing techniques using gradient-boosted trees and novel encoder-decoder architectures, including CNN, GRU and Transformers components. We propose a deep-feature extractor methodology to mix spatial-temporal data with statistical data efficiently. Our multimodal framework unleashes the potential of making forecasts based on a wide range of data sources, including historical storm data, reanalysis atmospheric images, and operational forecasts. Evaluating our models with current operational forecasts in North Atlantic and Eastern Pacific basins on the last years of available data, results show our models consistently outperform statistical-dynamical models and, albeit less accurate than the best dynamical models, our framework computes forecasts in seconds. Furthermore, the inclusion of Hurricast into an operational forecast consensus model leads to a significant improvement of 5% - 15% over NHC's official forecast, thus highlighting the complementary properties with existing approaches. In summary, our work demonstrates that combining different data sources and distinct machine learning methodologies can lead to superior tropical cyclone forecasting.

Training a multimodal network is challenging and it requires complex architectures to achieve reasonable performance. We show that one reason for this phenomena is the difference between the convergence rate of various modalities. We address this by pre-training modality-specific sub-networks in multimodal architectures independently before end-to-end training of the entire network. Furthermore, we show that the addition of an attention mechanism between sub-networks after pre-training helps identify the most important modality during ambiguous scenarios boosting the performance. We demonstrate that by performing these two tricks a simple network can achieve similar performance to a complicated architecture that is significantly more expensive to train on multiple tasks including sentiment analysis, emotion recognition, and speaker trait recognition.

MediaTek has announced a pair of Chromebook chipsets it says can balance power with improved battery life performance. The company designed the 6nm MT8195 for premium Chromebooks and the 7nm MT8192 for broader use. The four Arm CortexA78 cores handle more resource-intensive apps, while the Arm Cortex-A55 cores take care of background tasks and maximize battery life through power efficiency. A five-core Arm-Mali G57 GPU powers the visuals and the MT8195 includes quad-channel 2133MHz LPDDR4X on the memory side. The chipset can run up to three displays simultaneously, and it supports Dolby Vision and 7.1 surround sound audio.