Recent research on Software-Defined Networking (SDN) strongly promotes the adoption of distributed controller architectures. To achieve high network performance, designing a scheduling function (SF) to properly dispatch requests from each switch to suitable controllers becomes critical. However, existing literature tends to design the SF targeted at specific network settings. In this paper, a reinforcement-learning-based (RL) approach is proposed with the aim to automatically learn a general, effective, and efficient SF. In particular, a new dispatching system is introduced in which the SF is represented as a neural network that determines the priority of each controller. Based on the priorities, a controller is selected using our proposed probability selection scheme to balance the trade-off between exploration and exploitation during learning. In order to train a general SF, we first formulate the scheduling function design problem as an RL problem. Then a new training approach is developed based on a state-of-the-art deep RL algorithm. Our simulation results show that our RL approach can rapidly design (or learn) SFs with optimal performance. Apart from that, the trained SF can generalize well and outperforms commonly used scheduling heuristics under various network settings.

The problem of explaining the behavior of deep neural networks has gained a lot of attention over the last years. While several attribution methods have been proposed, most come without strong theoretical foundations. This raises the question of whether the resulting attributions are reliable. On the other hand, the literature on cooperative game theory suggests Shapley values as a unique way of assigning relevance scores such that certain desirable properties are satisfied. Previous works on attribution methods also showed that explanations based on Shapley values better agree with the human intuition. Unfortunately, the exact evaluation of Shapley values is prohibitively expensive, exponential in the number of input features. In this work, by leveraging recent results on uncertainty propagation, we propose a novel, polynomial-time approximation of Shapley values in deep neural networks. We show that our method produces significantly better approximations of Shapley values than existing state-of-the-art attribution methods.

Alphabet's subsidiary Wing announced this week that it has officially launched a commercial drone delivery service "to a limited set of eligible homes in the suburbs of Crace, Palmerston and Franklin," which are just north of Canberra, in Australia. Wing's drones are able to drop a variety of small products, including coffee, food, and pharmacy items, shuttling them from local stores to customers' backyards within minutes. We've been skeptical about whether this kind of drone delivery makes sense for a long, long time, and while this is certainly a major milestone for Wing, I'm still not totally convinced that the use-cases that Wing is pushing here are going to be sustainable long term. I've still got a bunch of questions about these things. For example, does the drone have any kind of in-flight sense and avoid?

It is estimated that one in three rare and genetic diseases show up in these features, which could aid an earlier diagnosis. Researchers from Curtin University in Australia have developed a tool, as part of the Cliniface project, which scans the face, creating a 3D image. It then measures the distance between facial features and compares them with the average measurement for their ethnicity, sex and age according to their system. By way of example, they use Foetal Alcohol Spectrum Disorders (FASD), an umbrella term used to describe the range of effects caused by alcohol exposure in the uterus. Researchers have developed a too, called Cliniface, which scans the person's face and then creates a 3D image of it.

Virtual humans need to be persuasive in order to promote behaviour change in human users. While several studies have focused on understanding the numerous aspects that influence the degree of persuasion, most of them are limited to dyadic interactions. In this paper, we present an evaluation study focused on understanding the effects of multiple agents on user's persuasion. Along with gender and status (authoritative & peer), we also look at type of focus employed by the agent i.e., user-directed where the agent aims to persuade by addressing the user directly and vicarious where the agent aims to persuade the user, who is an observer, indirectly by engaging another agent in the discussion. Participants were randomly assigned to one of the 12 conditions and presented with a persuasive message by one or several virtual agents. A questionnaire was used to measure perceived interpersonal attitude, credibility and persuasion. Results indicate that credibility positively affects persuasion. In general, multiple agent setting, irrespective of the focus, was more persuasive than single agent setting. Although, participants favored user-directed setting and reported it to be persuasive and had an increased level of trust in the agents, the actual change in persuasion score reflects that vicarious setting was the most effective in inducing behaviour change. In addition to this, the study also revealed that authoritative agents were the most persuasive.

The problem of predicting a novel view of the scene using an arbitrary number of observations is a challenging problem for computers as well as for humans. This paper introduces the Generative Adversarial Query Network (GAQN), a general learning framework for novel view synthesis that combines Generative Query Network (GQN) and Generative Adversarial Networks (GANs). The conventional GQN encodes input views into a latent representation that is used to generate a new view through a recurrent variational decoder. The proposed GAQN builds on this work by adding two novel aspects: First, we extend the current GQN architecture with an adversarial loss function for improving the visual quality and convergence speed. Second, we introduce a feature-matching loss function for stabilizing the training procedure. The experiments demonstrate that GAQN is able to produce high-quality results and faster convergence compared to the conventional approach.

Unsupervised part of speech (POS) tagging is often framed as a clustering problem, but practical taggers need to ground their clusters as well. Grounding generally requires reference labeled data, a luxury a low-resource language might not have. In this work, we describe an approach for low-resource unsupervised POS tagging that yields fully grounded output and requires no labeled training data. We find the classic method of Brown et al. (1992) clusters well in our use case and employ a decipherment-based approach to grounding. This approach presumes a sequence of cluster IDs is a'ciphertext' and seeks a POS tag-tocluster ID mapping that will reveal the POS sequence. We show intrinsically that, despite the difficulty of the task, we obtain reasonable performance across a variety of languages. We also show extrinsically that incorporating our POS tagger into a name tagger leads to stateof-the-art tagging performance in Sinhalese and Kinyarwanda, two languages with nearly no labeled POS data available. We further demonstrate our tagger's utility by incorporating Figure 1: Overview of our approach to grounded POS it into a true'zero-resource' variant of the tagging. We use an unsupervised clustering method MALOPA(Ammar et al., 2016) dependency (Section 3.2) then reduce and ground the clusters using parser model that removes the current reliance a decipherment approach informed by POS tag sequence on multilingual resources and gold POS tags data from many languages (Section 3.3).

Multi-hop reasoning question answering requires deep comprehension of relationships between various documents and queries. We propose a Bi-directional Attention Entity Graph Convolutional Network (BAG), leveraging relationships between nodes in an entity graph and attention information between a query and the entity graph, to solve this task. Graph convolutional networks are used to obtain a relation-aware representation of nodes for entity graphs built from documents with multi-level features. Bidirectional attention is then applied on graphs and queries to generate a query-aware nodes representation, which will be used for the final prediction. Experimental evaluation shows BAG achieves state-of-the-art accuracy performance on the QAngaroo WIKIHOP dataset.

Children with rare conditions could be diagnosed quicker thanks to 3D facial analysis software. Richard Palmer at Curtin University in Western Australia and his colleagues have developed a tool that can spot subtle, but important, differences in facial geometry. Around one in three rare and genetic diseases show up in facial features.

Young isolated neutron stars (INS) most commonly manifest themselves as rotationally powered pulsars (RPPs) which involve conventional radio pulsars as well as gamma-ray pulsars (GRPs) and rotating radio transients (RRATs). Some other young INS families manifest themselves as anomalous X-ray pulsars (AXPs) and soft gamma-ray repeaters (SGRs) which are commonly accepted as magnetars, i.e.\ magnetically powered neutron stars with decaying super-strong fields. Yet some other young INS are identified as central compact objects (CCOs) and X-ray dim isolated neutron stars (XDINs) which are cooling objects powered by their thermal energy. Older pulsars, as a result of a previous long episode of accretion from a companion, manifest themselves as millisecond pulsars and more commonly appear in binary systems. We use Dirichlet process Gaussian mixture model (DPGMM), an unsupervised machine learning algorithm, for analyzing the distribution of these pulsar families in period $P$ and period derivative $\dot{P}$ parameter space. We compare the average values of the characteristic age, magnetic dipole field strength, surface temperature and proper motion of all discovered components. We verify that DPGMM is robust and provides hints for inferring relations between different classes of pulsars. We discuss the implications of our findings for the magnetothermal spin evolution models and fallback discs.