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.

Mathematicians formulate complex mathematical models based on user requirements to solve a diverse range of problems in different domains. These models are, in most cases, represented through several mathematical equations and constraints. This modelling task comprises several time-intensive processes that require both mathematical expertise and (problem) domain knowledge. In an attempt to automate these processes, we have developed an ontology for Mixed Integer Linear Programming (MILP) problems to formulate expert mathematician knowledge and in this paper, we show how this new ontology can be utilized for modelling a relatively straightforward MILP problem, a Machine Scheduling example. We also show that more complex MILP problems, such as the Asymmetric Travelling Salesman Problem (ATSP), however, are not readily amenable to simple elicitation of user requirements and the utilization of the proposed mathematical model ontology. Therefore, an automatic mathematical modelling framework is proposed for such complex MILP problems, which includes a problem (requirement) elicitation module connected to a model extraction module through a translation engine that bridges between the non-expert problem domain and the expert mathematical model domain. This framework is argued to have the necessary components to effectively tackle the automation of modelling task of the more intricate MILP problems such as the ATSP.

Uncertainty quantification (UQ) plays a pivotal role in reduction of uncertainties during both optimization and decision making processes. It can be applied to solve a variety of real-world applications in science and engineering. Bayesian approximation and ensemble learning techniques are two most widely-used UQ methods in the literature. In this regard, researchers have proposed different UQ methods and examined their performance in a variety of applications such as computer vision (e.g., self-driving cars and object detection), image processing (e.g., image restoration), medical image analysis (e.g., medical image classification and segmentation), natural language processing (e.g., text classification, social media texts and recidivism risk-scoring), bioinformatics, etc.This study reviews recent advances in UQ methods used in deep learning. Moreover, we also investigate the application of these methods in reinforcement learning (RL). Then, we outline a few important applications of UQ methods. Finally, we briefly highlight the fundamental research challenges faced by UQ methods and discuss the future research directions in this field.

It's been called'the future of warfare'. Off-the-shelf unmanned aerial systems (UAS), carrying a'payload' of explosives or biological material, flown by terrorists or enemy armed forces into a crowded building or military base. Now the University of Technology Sydney (UTS) and Sydney ASX-listed defence tech company DroneShield have produced next-generation drone technology to better identify threats from these aggressive UAS. In a partnership funded by the NSW and Australian Governments, UTS and DroneShield – an Australian developer of counter-UAS solutions – have produced an optical system for detection, identification and tracking of fast-moving threats such as nefarious UAS, comprised of a camera and Convolutional Neural Network (CNN). UTS and DroneShield began working together in October 2019 – just a month after one of the most recent examples of aggressive use of drones when the oil facilities at Abqaiq–Khurais in Saudi Arabia were attacked by a swarm of UAS.

A polar bear on melting ice: It's a favorite image of nature documentaries and charity ads alike, never failing to put you in the emotional dumps for a simple reason--it forces you to grapple with a changing world, a darker future. But that emotion is often temporary, replaced quickly by others, because its effects are not immediately or directly felt, explained Peter Schlosser, the vice president and vice provost of global futures at Arizona State University. Footage of houses on fire in California, Oregon, and Australia alarms us, but falls short of making us understand that our own home may be next. These "delusions of escape," in the words of science fiction author Kim Stanley Robinson, or "failures of imagination," in the words of Future Tense academic director Ed Finn, placate us into reactive, piecemeal, short-sighted decision-making. But storytelling lights the path forward, agreed Robinson, Finn, Schlosser, Future Tense fellow Alexandra Zapata Hojel, and Malka Older, also a sci-fi author.

For this week's ML practitioner's series, Analytics India Magazine got in touch with Hiroki Yamamoto (tereka), a Kaggle Master. Hiroki is currently working as a data scientist and is ranked in the top 100 of the world's largest platforms for data science competitions– Kaggle. In this interview, Hiroki shares his experience of competing on Kaggle and how it has helped in growing as a data scientist. Hiroki: I got a master's degree in information technology back in 2015. During my graduation, I have worked on image processing research using deep learning -- for example, autoencoders.

AI robots are still not sophisticated enough to understand humans or the complexity of social situations, says UNSW's Dr Masimiliano Cappuccio. "So we need to think about how we interact with social and companion robots to instead help us become more aware of our own behaviour, limitations, vices or bad habits," says Dr Cappuccio, the Deputy Director of Values in Defense and Security Technology at UNSW Canberra. "And this can be in the areas of greater self-discipline and self-control but also in learning virtues such as generosity and empathy." Dr Cappuccio is the lead author of Can Robots Make Us Better Humans? Virtuous Robotics and the Good Life with Artificial Agents which was written in collaboration with UNSW Art & Design's Dr Eduardo Sandoval and Professor Mari Velonaki along with academics from the University of Western Sydney and Chalmers University of Technology in Sweden It is also the first in a collection co-edited by Dr Cappuccio, Dr Sandoval and Prof. Velonaki and published in the International Journal of Robotics as a special issue titled Virtuous Robotics: Artificial Agents and the Good Life.

Australia Post is using machine learning to calculate the arrival time of parcels down to a two-hour window based on data from a new route planner that has just been rolled out to delivery drivers. Executive general manager for transformation and enablement John Cox on Tuesday said the new feature is currently being trialled to give customers a more accurate estimate on delivery times. "What we're trialling – and this is not out in the public yet – is what we call an estimated time of arrival," Cox told the Digital Transformation Agency's 2020 Digital Summit. "So based off when the postie scans the parcel in the morning to put in their van, we'll be able to notify the consumer that it will be delivered within that window of time." The estimate is calculated using the "single scanning platform" that all deliver drivers use to scan their parcels before their run, which also determines the "optimal" route for each delivery run.

We develop a general framework for constructing new information-theoretic divergences that rigorously interpolate between $f$-divergences and integral probability metrics (IPMs), such as the Wasserstein distance. These new divergences inherit features from IPMs, such as the ability to compare distributions which are not absolute continuous, as well as from $f$-divergences, for instance the strict concavity of their variational representations and the ability to compare heavy-tailed distributions. When combined, these features establish a divergence with improved convergence and estimation properties for statistical learning applications. We demonstrate their use in the training of generative adversarial networks (GAN) for heavy-tailed data and also show they can provide improved performance over gradient-penalized Wasserstein GAN in image generation.

In this paper, we study the Empirical Risk Minimization (ERM) problem in the non-interactive Local Differential Privacy (LDP) model. Previous research on this problem \citep{smith2017interaction} indicates that the sample complexity, to achieve error $\alpha$, needs to be exponentially depending on the dimensionality $p$ for general loss functions. In this paper, we make two attempts to resolve this issue by investigating conditions on the loss functions that allow us to remove such a limit. In our first attempt, we show that if the loss function is $(\infty, T)$-smooth, by using the Bernstein polynomial approximation we can avoid the exponential dependency in the term of $\alpha$. We then propose player-efficient algorithms with $1$-bit communication complexity and $O(1)$ computation cost for each player. The error bound of these algorithms is asymptotically the same as the original one. With some additional assumptions, we also give an algorithm which is more efficient for the server. In our second attempt, we show that for any $1$-Lipschitz generalized linear convex loss function, there is an $(\epsilon, \delta)$-LDP algorithm whose sample complexity for achieving error $\alpha$ is only linear in the dimensionality $p$. Our results use a polynomial of inner product approximation technique. Finally, motivated by the idea of using polynomial approximation and based on different types of polynomial approximations, we propose (efficient) non-interactive locally differentially private algorithms for learning the set of k-way marginal queries and the set of smooth queries.