Conflicting information in an agent's knowledge base may lead to a semantical defect, that is, a situation where it is impossible to draw any plausible conclusion. Finding out the reasons for the observed inconsistency (so-called diagnoses) and/or restoring consistency in a certain minimal way (so-called repairs) are frequently occurring issues in knowledge representation and reasoning. In this article we provide a series of first results for these problems in the context of abstract argumentation theory regarding the two most important reasoning modes, namely credulous as well as sceptical acceptance. Our analysis includes the following problems regarding minimal repairs/diagnoses: existence, verification, computation of one and enumeration of all solutions. The latter problem is tackled with a version of the so-called hitting set duality first introduced by Raymond Reiter in 1987. It turns out that grounded semantics plays an outstanding role not only in terms of complexity, but also as a useful tool to reduce the search space for diagnoses regarding other semantics.

IMPROVED MUL TI-ST AGE TRAINING OF ONLINE A TTENTION-BASED ENCODER-DECODER MODELS Abhinav Garg, Dhananjaya Gowda, Ankur Kumar, Kwangyoun Kim, Mehul Kumar, Chanwoo Kim Speech Processing Lab, AI Center, Samsung Research, Korea ABSTRACT In this paper, we propose a refined multistage multi-task training strategy to improve the performance of online attention-based encoder-decoder (AED) models. A three-stage training based on three levels of architectural granularity namely, character encoder, byte pair encoding (BPE) based encoder, and attention decoder, is proposed. Also, multi-task learning based on two-levels of linguistic granularity namely, character and BPE, is used. We explore different pre-training strategies for the encoders including transfer learning from a bidirectional encoder. Our models achieve a word error rate (WER) of 5.04% and 4.48% on the Librispeech test-clean data for the smaller and bigger models respectively after fusion with long short-term memory (LSTM) based external language model (LM). Index T erms-- Attention based encoder-decoder models, online attention, multistage training, multi-task learning 1. INTRODUCTION Recently, attention-based encoder-decoder (AED) models have gained popularity for developing end-to-end neural network based automatic speech recognition (ASR) systems [1, 2, 3]. One of the primary advantages of AED models is that the language information is tightly coupled into the decoder, obviating the need for an external language model (LM). AED models have been shown to perform better than other end-to-end models, namely, connectionist temporal classification (CTC) and recurrent neural network transducer (RNN-T) models [4].

The development of algorithms for unsupervised pattern recognition by nonlinear clustering is a notable problem in data science. Markov clustering (MCL) is a renowned algorithm that simulates stochastic flows on a network of sample similarities to detect the structural organization of clusters in the data, but it has never been generalized to deal with data nonlinearity. Minimum Curvilinearity (MC) is a principle that approximates nonlinear sample distances in the high-dimensional feature space by curvilinear distances, which are computed as transversal paths over their minimum spanning tree, and then stored in a kernel. Here we propose MC-MCL, which is the first nonlinear kernel extension of MCL and exploits Minimum Curvilinearity to enhance the performance of MCL in real and synthetic data with underlying nonlinear patterns. MC-MCL is compared with baseline clustering methods, including DBSCAN, K-means and affinity propagation. We find that Minimum Curvilinearity provides a valuable framework to estimate nonlinear distances also when its kernel is applied in combination with MCL. Indeed, MC-MCL overcomes classical MCL and even baseline clustering algorithms in different nonlinear datasets.

We propose a projection pursuit (PP) algorithm based on Gaussian mixture models (GMMs). The negentropy obtained from a multivariate density estimated by GMMs is adopted as the PP index to be maximised. For a fixed dimension of the projection subspace, the GMM-based density estimation is projected onto that subspace, where an approximation of the negentropy for Gaussian mixtures is computed. Then, Genetic Algorithms (GAs) are used to find the optimal, orthogonal projection basis by maximising the former approximation. We show that this semi-parametric approach to PP is flexible and allows highly informative structures to be detected, by projecting multivariate datasets onto a subspace, where the data can be feasibly visualised. The performance of the proposed approach is shown on both artificial and real datasets.

WASHINGTON – All but the smallest civilian drones would have to broadcast radio tracking data to ensure greater safety and prevent terrorism under a sweeping proposal unveiled by U.S. regulators Thursday. The long-awaited draft rules call for a massive new tracking network for everything from toys to larger commercial drones so that law enforcement can spot the devices flying anywhere, from congested urban areas to the most rural zones. The controversial measure by the Federal Aviation Administration, which is subject to public comment and could change before it becomes final, is a key foundation to advance drone-driven commerce, including deliveries of consumer goods by companies such as Alphabet Inc.'s Wing and Amazon.com The rules would come into full force three years after being finalized. "Remote ID technologies will enhance safety and security by allowing the FAA, law enforcement and federal security agencies to identify drones flying in their jurisdiction," Transportation Secretary Elaine Chao said in a press release.

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Rest of Europe 8.4 Asia Pacific 8.4.1

NeurIPS 2019 was with around 13,000 attendees the largest ML conference of the year. The NeurIPS 2019 Program Chairs did a fantastic analysis of the reviewing process. NeurIPS has no free-loader problem: Most of the authors of submitted papers participate in reviewing. It is still unclear how to filter papers before the full review. Review quality (as measured by length) is not lower compared to smaller conferences.

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A simple device, just a heat and movement sensor attached to digital camera, has revolutionised the way that conservationists learn about animals in the wild. Camera traps are a very simple solution to the task of working out when, where and how wildlife interacts with its environment. Monitoring populations without damaging habitats, these relatively simple devices have provided some astonishing finds including revealing species previously hidden in the untouched depths of the forest. Elusive new creatures aren't their only speciality, however, as in 2015, similar devices helped reveal that the critically endangered Javan rhinoceros was breeding and significantly adding to its tiny population. After identifying a likely area for a sighting, usually with the help of local guides, traps are placed at animal height on trees and posts and left to wait until wildlife walks by.