A United Arab Emirates (UAE) drone strike on a biscuit factory near the Libyan capital Tripoli on November 18 killed eight civilians and injured 27 others, Human Rights Watch (HRW) said. In a report released on Wednesday, the rights group said the UAE appeared to take little or no action to minimise civilian casualties and called on Emirati authorities to conduct a transparent investigation into the incident. "Since the current armed conflict in Tripoli erupted in April 2019, the UAE has been conducting air and drone strikes to support the Libyan Arab Armed forces, previously known as the Libyan National Army [LNA], one of two major parties to the conflict, some of which have resulted in civilian casualties," HRW said. "All causalities in the November incident were civilian factory workers, including seven Libyans and 28 foreign nationals, all of them men." Human Rights Watch visited the site and found remnants of at least four Blue Arrow-7 (BA-7) laser-guided missiles that were launched by a Wing Loong-II drone.

Following a full-day livestream, we now have a better idea of when and where the next game in Ubisoft's long-running Assassin's Creed franchise will take place. In Assassin's Creed: Valhalla, you'll play an assassin at some point during the Viking Age, which took place between 793 and 1066 CE. Publisher and developer Ubisoft picked the unconventional route of announcing the new game through a Photoshop livestream. An artist slowly and painstakingly built out the image you see above while fans speculated about the setting and classic songs from the series like "Ezio's Family" played in the background. At one point in the stream, more than 50,000 people across Twitch and YouTube tuned in to watch artist Kode Abdo work his craft.

Artificial intelligence has myriad use cases, but it turns out inventing devices isn't one of them -- at least in the eyes of the US Patent and Trademark Office. The agency issued a decision on two patent applications for devices created by an AI system, determining that only humans can legally be credited as inventors. The items in question -- an emergency flashlight and a shape-shifting drink container -- were the brainchildren of a system called DABUS. The Artificial Inventor Project filed the applications last year on behalf of the AI's creator, Stephen Thaler. AIP lawyers argued that, since Thaler didn't have any expertise in either of those types of products and couldn't have come up with them by himself, DABUS should be the credited inventor.

Peloton has added a new groups feature to allow people to exercise together despite being in lockdown. The company – which makes internet-enabled spinning bikes and treadmills, as well as running an app of online exercise classes – has seen a huge surge in users in recent months as people have looked for a way to work out at home. Now it has added a new groups feature, officially called "tags", allowing people to track their exercise alongside other people in communities they have formed off the bike. With the new feature, users are able to add hashtags to their profile, which designate certain groups: a certain set of people all from the same workplace, for instance, or one of the many "tribes" of users that have formed on other platforms such as Facebook and Reddit. If a user has a given hashtag in their profile, they will be able to see what classes other members have taken and when they are working out.

A recent source of concern for the security of neural networks is the emergence of clean-label dataset poisoning attacks, wherein correctly labeled poisoned samples are injected in the training dataset. While these poisons look legitimate to the human observer, they contain malicious characteristics that trigger a targeted misclassification during inference. We propose a scalable and transferable clean-label attack, Bullseye Polytope, which creates poison images centered around the target image in the feature space. Bullseye Polytope improves the attack success rate of the current state-of-the-art by 26.75% in end-to-end training, while increasing attack speed by a factor of 12. We further extend Bullseye Polytope to a more practical attack model by including multiple images of the same object (e.g., from different angles) in crafting the poisoned samples. We demonstrate that this extension improves attack transferability by over 16% to unseen images (of the same object) without increasing the number of poisons.

Recently, a race towards the simplification of deep networks has begun, showing that it is effectively possible to reduce the size of these models with minimal or no performance loss. However, there is a general lack in understanding why these pruning strategies are effective. In this work, we are going to compare and analyze pruned solutions with two different pruning approaches, one-shot and gradual, showing the higher effectiveness of the latter. In particular, we find that gradual pruning allows access to narrow, well-generalizing minima, which are typically ignored when using one-shot approaches. In this work we also propose PSP-entropy, a measure to understand how a given neuron correlates to some specific learned classes. Interestingly, we observe that the features extracted by iteratively-pruned models are less correlated to specific classes, potentially making these models a better fit in transfer learning approaches.

A large number of deep learning architectures use spatial transformations of CNN feature maps or filters to better deal with variability in object appearance caused by natural image transformations. In this paper, we prove that spatial transformations of CNN feature maps cannot align the feature maps of a transformed image to match those of its original, for general affine transformations, unless the extracted features are themselves invariant. Our proof is based on elementary analysis for both the single-and multi-layer network case. The results imply that methods based on spatial transformations of CNN feature maps or filters cannot replace image alignment of the input and cannot enable invariant recognition for general affine transformations, specifically not for scaling transformations or shear transformations. For rotations and reflections, spatially transforming feature maps or filters can enable invariance but only for networks with learnt or hardcoded rotation-or reflection-invariant features.

Automatic transistor sizing is a challenging problem in circuit design due to the large design space, complex performance trade-offs, and fast technological advancements. Although there has been plenty of work on transistor sizing targeting on one circuit, limited research has been done on transferring the knowledge from one circuit to another to reduce the re-design overhead. In this paper, we present GCN-RL Circuit Designer, leveraging reinforcement learning (RL) to transfer the knowledge between different technology nodes and topologies. Moreover, inspired by the simple fact that circuit is a graph, we learn on the circuit topology representation with graph convolutional neural networks (GCN). The GCN-RL agent extracts features of the topology graph whose vertices are transistors, edges are wires. Our learning-based optimization consistently achieves the highest Figures of Merit (FoM) on four different circuits compared with conventional black-box optimization methods (Bayesian Optimization, Evolutionary Algorithms), random search, and human expert designs. Experiments on transfer learning between five technology nodes and two circuit topologies demonstrate that RL with transfer learning can achieve much higher FoMs than methods without knowledge transfer. Our transferable optimization method makes transistor sizing and design porting more effective and efficient.

In work implicitly includes monolingual and natural language processing, the main promise of cross-lingual signals that constitute a departure multilingual learning is to bridge the digital language from the pure setting. We review existing training divide, to enable access to information and signals as well as other signals that may be technology for the world's 6,900 languages (Ruder of interest for future study (§4). We then discuss et al., 2019). For the purpose of this paper, we methodological issues in UCL (e.g., validation, hyperparameter define "multilingual learning" as learning a common tuning) and propose best evaluation model for two or more languages from raw practices (§5). Finally, we provide a unified outlook text, without any downstream task labels. Common of established research areas (cross-lingual use cases include translation as well as pretraining word embeddings, deep multilingual models and multilingual representations. We will use the term unsupervised machine translation) in UCL (§6), interchangeably with "cross-lingual learning".

We describe our submission to the Extreme Value Analysis 2019 Data Challenge in which teams were asked to predict extremes of sea surface temperature anomaly within spatio-temporal regions of missing data. We present a computational framework which reconstructs missing data using convolutional deep neural networks. Conditioned on incomplete data, we employ autoencoder-like models as multivariate conditional distributions from which possible reconstructions of the complete dataset are sampled using imputed noise. In order to mitigate bias introduced by any one particular model, a prediction ensemble is constructed to create the final distribution of extremal values. Our method does not rely on expert knowledge in order to accurately reproduce dynamic features of a complex oceanographic system with minimal assumptions. The obtained results promise reusability and generalization to other domains.