Consumer fashion may be among the most unpredictable markets on the planet, but one startup in India has created an AI-based demand sensing platform that combines the brilliance of data scientists with seasoned industry experts to ferret out trends with uncanny accuracy. The idea is to close the gap between supply and demand. Omni-channel retailers are using AI to synch design and merchandising decisions with breaking consumer demand trends for sustainable growth. "We help companies create demand-driven fashion forecasts from consumer data across a holistic value chain," Ganesh Subramanian, founder and CEO at Stylumia. "Our demand sensing engine collects and analyzes publicly available global data to rank product trends, providing fashion designers, retail buyers, and merchandisers with a much deeper understanding of real-time consumer demand signals."

With 30 years' experience in print and digital media, Helen has written articles for clients ranging from The Sydney Morning Herald, The Australian Financial Review and GQ Australia, to CommBank, Swissotels and Bunnings. Her father was a civil engineer and she remains fascinated by the use of scientific principles to design and build a better world.

Varroa destructor is a deadly stowaway that port authorities are determined to keep away from the bee population in the southeast Australian state of Victoria. Artificially intelligent beehives are being installed at Victorian ports to detect pests as they arrive at ships rapidly. "The Varroa mite is extremely destructive; it kills bees very rapidly," said Mary-Anne Thomas, the Victorian agriculture minister. "I would look forward to a project like the Purple Hive rolling out across the country. Purple Hive was launched on March 29 at the Port of Melbourne -- a solar-powered device that detects Varroa destructor, a mite that feeds on honey bees. Using artificial intelligence and cameras, Purple Hive provides alerts in real-time and has been trialed in New Zealand, where the mite is established. The technology scans each honey bee entering the Purple Hive to determine if Varroa mite is present. The hive is colored purple because it attracts bees. Thomas tweeted a picture of a hive being installed. "At #BegaCheese, we're absolutely buzzing with excitement to announce that B honey's Purple Hive has officially found its first home at the Port of Melbourne, as we join forces with @VicGovAg to help protect honey bee populations from Varroa destructor," read the tweet of Jimmy Coleman, marketing manager of digital and communications, Bega Cheese. "Varroa destructor is the world's most devastating pest of Western honey bees, Apis mellifera Linnaeus," as per the website of the University of Florida. "Accurate estimates of the effect of Varroa on the apiculture industry are hard to find, but it is safe to assume that the mites have killed hundreds of thousands of colonies worldwide, resulting in billions of dollars of economic loss." The adult female mites are reddish-brown to dark brown and oval. Adult males are yellowish with light tan legs and have a spherical body shape. Varroa destructor, the most significant single driver of the global honey bee health decline, was detected on a ship that entered the Port of Melbourne in 2018, but authorities stopped it from becoming an outbreak. "Australia is the only populated country in the world that the Varroa destructor hasn't impacted.

My primary interests are in the fields of spatial intelligence – how we can develop better navigation and positioning systems for robots and autonomous vehicles. My main research approach involves using a combination of traditional algorithmic approaches, modern deep learning and biologically-inspired approaches, both in terms of software and hardware. Spatial intelligence is one of the most tangible aspects of general intelligence, and hence it's a great gateway by which to progress our understanding and development of intelligence in robotics. For example, spatial intelligence can be directly observed in the brain, where multiple navigationally-relevant neurons like "place cells" can be observed, and modelled in software to create better performing robotic systems. From a technical point of view, autonomous vehicles are very good but not yet sufficiently perfect to be practicable.

End-to-end models reach state-of-the-art performance for speech recognition, but global soft attention is not monotonic, which might lead to convergence problems, to instability, to bad generalisation, cannot be used for online streaming, and is also inefficient in calculation. Monotonicity can potentially fix all of this. There are several ad-hoc solutions or heuristics to introduce monotonicity, but a principled introduction is rarely found in literature so far. In this paper, we present a mathematically clean solution to introduce monotonicity, by introducing a new latent variable which represents the audio position or segment boundaries. We compare several monotonic latent models to our global soft attention baseline such as a hard attention model, a local windowed soft attention model, and a segmental soft attention model. We can show that our monotonic models perform as good as the global soft attention model. We perform our experiments on Switchboard 300h. We carefully outline the details of our training and release our code and configs.

These subprocesses are interrelated, with mapping considered to be the pivotal process (Gentner, 1983). Mapping may play a role in retrieval, as mapping a target analog to multiple potential source analogs stored in memory can help identify one or more that seems promising; and the correspondences computed by mapping support subsequent inference and schema induction. Thus, because of its centrality to analogical reasoning, the present paper focuses on the process of mapping between two analogs. We also consider the possible role that mapping may play in analog retrieval. Computational Approaches to Analogy Computational models of analogy have been developed in both artificial intelligence (AI) and cognitive science over more than half a century (for a recent review and critical analysis, see Mitchell, 2021). These models differ in many ways, both in terms of basic assumptions about the constraints that define a "good" analogy for humans, and in the detailed algorithms that accomplish analogical reasoning. For our present purposes, two broad approaches can be distinguished. The first approach, which can be termed representation matching, combines mental representations of structured knowledge about each analog with a matching process that computes some form of relational similarity, yielding a set of correspondences between the elements of the two analogs. The structured knowledge about an analog is typically assumed to approximate the content of propositions expressed in predicate calculus; e.g., the instantiated relation "hammer hits nail" might be coded as hit (hammer, nail).

Before Brexit, one of the greatest causes of arguments amongst British families was the question of the nature of Jaffa Cakes. Some argue that their size and host environment (the biscuit aisle) should make them a biscuit in their own right. Others consider that their physical properties (e.g. they harden rather than soften on becoming stale) suggest that they are in fact cake. In order to finally put this debate to rest, we re-purpose technologies used to classify transient events. We train two classifiers (a Random Forest and a Support Vector Machine) on 100 recipes of traditional cakes and biscuits. Our classifiers have 95 percent and 91 percent accuracy respectively. Finally we feed two Jaffa Cake recipes to the algorithms and find that Jaffa Cakes are, without a doubt, cakes. Finally, we suggest a new theory as to why some believe Jaffa Cakes are biscuits.

The Flatland competition aimed at finding novel approaches to solve the vehicle re-scheduling problem (VRSP). The VRSP is concerned with scheduling trips in traffic networks and the re-scheduling of vehicles when disruptions occur, for example the breakdown of a vehicle. While solving the VRSP in various settings has been an active area in operations research (OR) for decades, the ever-growing complexity of modern railway networks makes dynamic real-time scheduling of traffic virtually impossible. Recently, multi-agent reinforcement learning (MARL) has successfully tackled challenging tasks where many agents need to be coordinated, such as multiplayer video games. However, the coordination of hundreds of agents in a real-life setting like a railway network remains challenging and the Flatland environment used for the competition models these real-world properties in a simplified manner. Submissions had to bring as many trains (agents) to their target stations in as little time as possible. While the best submissions were in the OR category, participants found many promising MARL approaches. Using both centralized and decentralized learning based approaches, top submissions used graph representations of the environment to construct tree-based observations. Further, different coordination mechanisms were implemented, such as communication and prioritization between agents. This paper presents the competition setup, four outstanding solutions to the competition, and a cross-comparison between them.

This paper explores Google's Edge TPU for implementing a practical network intrusion detection system (NIDS) at the edge of IoT, based on a deep learning approach. While there are a significant number of related works that explore machine learning based NIDS for the IoT edge, they generally do not consider the issue of the required computational and energy resources. The focus of this paper is the exploration of deep learning-based NIDS at the edge of IoT, and in particular the computational and energy efficiency. In particular, the paper studies Google's Edge TPU as a hardware platform, and considers the following three key metrics: computation (inference) time, energy efficiency and the traffic classification performance. Various scaled model sizes of two major deep neural network architectures are used to investigate these three metrics. The performance of the Edge TPU-based implementation is compared with that of an energy efficient embedded CPU (ARM Cortex A53). Our experimental evaluation shows some unexpected results, such as the fact that the CPU significantly outperforms the Edge TPU for small model sizes.

For unsupervised image-to-image translation, we propose a discriminator architecture which focuses on the statistical features instead of individual patches. The network is stabilized by distribution matching of key statistical features at multiple scales. Unlike the existing methods which impose more and more constraints on the generator, our method facilitates the shape deformation and enhances the fine details with a greatly simplified framework. We show that the proposed method outperforms the existing state-of-the-art models in various challenging applications including selfie-to-anime, male-to-female and glasses removal. The code will be made publicly available.