"Banning killer robots is both politically savvy and morally necessary," said Mary Wareham, the Arms Division advocacy director at Human Rights Watch and coordinator of the Campaign to Stop Killer Robots. "European states should take the lead and open ban treaty negotiations if they are serious about protecting the world from this horrific development." Countries attending the annual meeting of states parties to the Convention on Conventional Weapons (CCW) at the United Nations in Geneva will decide on November 15 whether to continue diplomatic talks on killer robots, also known as lethal autonomous weapons systems or fully autonomous weapons. Since 2014, these states have held eight meetings on lethal autonomous weapons systems under the auspices of the Convention on Conventional Weapons (CCW), a major disarmament treaty. Over the course of those meetings, states have built a shared understanding of concern, but they have struggled to reach agreement on credible recommendations for multilateral action due to the objections of a handful of military powers, most notably Russia and the United States.

Streets swamped by muddy water with garbage floating by, roads impassable. As in previous years, Diamniadio Lake City has not escaped the series of floods that affect some cities in Senegal each rainy season. Indeed, this urban centre is preparing to test, thanks to Artificial Intelligence (AI), a new way of managing urban development. "By taking the Digital Technologies Park of Diamniadio as a reference site, we have carried out modelling and worked on water runoff scenarios in order to channel them and solve these flood problems," Bassirou Abdoul Ba, coordinator of the Digital Technologies Park, told Scidev.Net. This park, covering 25 hectares, is the first experimental phase of the "smart city" under construction 35km from Dakar, the Senegalese capital.

Four years ago, I stood in the darkened operations center in front of a wall of blinking screens, arms crossed and squinting at video footage on one of them. The commander asked me for the second time, signaling toward the figure on the screen. I looked over and reviewed a mental checklist of the individual's pattern of life over more than a decade. I weighed this against his latest movements, reflected on the screen in real time. The commander took a step toward me and started again, "Kara. We are running out of time. I had a decision to make. Using a machine to determine the validity of the target and take action is a nonstarter. But not everyone agrees on the details. Though the machines I dealt with that day were only semi-autonomous, it is not difficult to imagine a world where fully autonomous weapons are programmed to make a lethal decision. Institutions, countries, industry, and society must choose when and how to govern this technology in today's world, where semi-autonomous ...

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This work provides a review of this literature. The motivation for this summary arose from our companion paper Ruf and W ang [2019]. There we continue th e discussions of this note; in particular, of potentially problematic data leakage when training ANNs to historic financial data. This paper is organised in the following way. Section 2 featu res Table 1, a summary of the literature that concerns the use of ANNs for nonparametric pricing (and hedging) of options. Section 3 provides a list of recommended papers from Table 1. Section 4 provides a n overview of related work where ANNs are applied in the context of option pricing and hedging, but not necessarily as nonparametric estimation tools. Section 5 briefly discusses various regularisation techniq ues used in the reviewed literature.

Phenotyping electronic health records (EHR) focuses on defining meaningful patient groups (e.g., heart failure group and diabetes group) and identifying the temporal evolution of patients in those groups. Tensor factorization has been an effective tool for phenotyping. Most of the existing works assume either a static patient representation with aggregate data or only model temporal data. However, real EHR data contain both temporal (e.g., longitudinal clinical visits) and static information (e.g., patient demographics), which are difficult to model simultaneously. In this paper, we propose Temporal And Static TEnsor factorization (TASTE) that jointly models both static and temporal information to extract phenotypes. TASTE combines the PARAFAC2 model with non-negative matrix factorization to model a temporal and a static tensor. To fit the proposed model, we transform the original problem into simpler ones which are optimally solved in an alternating fashion. For each of the sub-problems, our proposed mathematical reformulations lead to efficient sub-problem solvers. Comprehensive experiments on large EHR data from a heart failure (HF) study confirmed that TASTE is up to 14x faster than several baselines and the resulting phenotypes were confirmed to be clinically meaningful by a cardiologist. Using 80 phenotypes extracted by TASTE, a simple logistic regression can achieve the same level of area under the curve (AUC) for HF prediction compared to a deep learning model using recurrent neural networks (RNN) with 345 features.

Motivation: MiRNAs are a kind of small non - coding RNAs that are not translated into proteins, and aberrant expression of miRNAs is associated with human diseases. Since miRNAs have different roles in diseases, the miRNA - disease associations are categorized into multiple types according to their roles. Predicting miRNA - disease associations and types is critical to understand the underlying patho genesis of human diseases from the molecular level . Results: In this paper, we formulate the problem as a link prediction in knowledge graphs. We use biomedical knowledge bases to build a knowledge graph of entities representing miRNAs and disease and mult i - relations, and we propose a tensor decomposition - based model named TDRC to predict miRNA - disease associations and their types from the knowledge graph. We have experimentally evaluated our method and compared it to several baseline methods. The results d emonstrate that the proposed method h as high - accuracy and high - efficiency performances.

We present the Compressive Transformer, an attentive sequence model which compresses past memories for long-range sequence learning. We find the Com-pressive Transformer obtains state-of-the-art language modelling results in the WikiText-103 and Enwik8 benchmarks, achieving 17. 1 ppl and 0. 97 bpc respectively. We also find it can model high-frequency speech effectively and can be used as a memory mechanism for RL, demonstrated on an object matching task. To promote the domain of long-range sequence learning, we propose a new open-vocabulary language modelling benchmark derived from books, PG-19. Humans have a remarkable ability to remember information over long time horizons. When reading a book, we build up a compressed representation of the past narrative, such as the characters and events that have built up the story so far. We can do this even if they are separated by thousands of words from the current text, or long stretches of time between readings. During daily life, we make use of memories at varying timescales: from locating the car keys, placed in the morning, to recalling the name of an old friend from decades ago. These feats of memorisation are not achieved by storing every sensory glimpse throughout one's lifetime, but via lossy compression. We aggressively select, filter, or integrate input stimuli based on factors of surprise, perceived danger, or repetition -- amongst other signals (Richards and Frankland, 2017). Memory systems in artificial neural networks began with very compact representations of the past. Recurrent neural networks (RNNs, Rumelhart et al. (1986)) learn to represent the history of observations in a compressed state vector. The state is compressed because it uses far less space than the history of observations -- the model only preserving information that is pertinent to the optimization of the loss.

The graph represents a network of 4,182 Twitter users whose recent tweets contained "#4IR", or who were replied to or mentioned in those tweets, taken from a data set limited to a maximum of 18,000 tweets. The network was obtained from Twitter on Saturday, 27 July 2019 at 21:52 UTC. The tweets in the network were tweeted over the 9-day, 8-hour, 38-minute period from Thursday, 18 July 2019 at 12:24 UTC to Saturday, 27 July 2019 at 21:03 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods. These tweets may expand the complete time period of the data.

Cisco recently announced how its three most recent acquisitions will add value to its Cisco Contact Center help desk solution that's currently used by more than three million agents in over 30,000 enterprises. The announcement was made at the 10th annual Cisco Contact Center Summit on September 19, 2019, in Hollywood, Florida, which was attended by 1,100 Cisco salespersons, partners, and customers. Cisco's Contact Center is an integrated communications application suite that delivers intelligent call routing, network-to-desktop computer telephony integration (CTI), and multi-channel contact management to contact center agents over an Internet Protocol (IP) network. Cisco has been enhancing the capabilities of its contact center offerings by acquiring various products over the past year or so, and integrating those offerings' features and functionalities to its feature stack. Beginning in May 2018, Cisco finished its acquisition of business intelligence (BI) specialist Accompany.