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.

Most of the existing single object trackers track the target in a unitary local search window, making them particularly vulnerable to challenging factors such as heavy occlusions and out-of-view movements. Despite the attempts to further incorporate global search, prevailing mechanisms that cooperate local and global search are relatively static, thus are still sub-optimal for improving tracking performance. By further studying the local and global search results, we raise a question: can we allow more dynamics for cooperating both results? In this paper, we propose to introduce more dynamics by devising a dynamic attention-guided multi-trajectory tracking strategy. In particular, we construct dynamic appearance model that contains multiple target templates, each of which provides its own attention for locating the target in the new frame. Guided by different attention, we maintain diversified tracking results for the target to build multi-trajectory tracking history, allowing more candidates to represent the true target trajectory. After spanning the whole sequence, we introduce a multi-trajectory selection network to find the best trajectory that delivers improved tracking performance. Extensive experimental results show that our proposed tracking strategy achieves compelling performance on various large-scale tracking benchmarks. The project page of this paper can be found at https://sites.google.com/view/mt-track/.

In the literature, many methods have been proposed The extended state observer (ESO) is an inherent component for tuning bandwidth-parameterized observers. In [49] of the robust control framework that relies on the cancellation and [38], the authors presented an analytical tuning method providing of disturbances using their lumped estimate in the feedforward the best performance of the ADRC structure expressed component of the robust control law. The general idea of solely upon the control-error-dependent criteria in a noiseless such control structure was utilized in many specific robust algorithms environment. In [39] and [6], the authors considered also the such as active disturbance rejection control (ADRC) control cost as a factor that needs to be minimized to reduce the [13, 54], disturbance observer based control (DOBC) [5, 23], energy consumption of the robust control process, while in [26] or robust observer based control [16], while its applicability the observation error of the measured signals was taken into has been proven in many fields including power electronics account. Tuning procedures described in [28] and [12] have [20, 25, 46], temperature control [53], motion control [32, 42], utilized prior knowledge about the plant structure and some and robotics [30, 22]. Besides the fact that there is a wide variety known or identified model parameters to obtain assumed control of ESO architectures that deal with disadvantages of a most performance requirements. In [28] and [14], the authors commonly used Luenberger-like extended high-gain observer presented an observer tuning method that is relative to gains (HGO) [52, 8] in terms of the general disturbance observation of the selected ADRC controller. Some methods consider automatic quality [34], transient performance [40], or the robustness to tools designed for tuning the overall ADRC structure, measurement noise [37, 21], the final characteristics of the control including observer gains, to satisfy some predefined criteria determining system performance depend greatly on the appropriate tuning the robustness of the control structure [36].

This is the land of Spotify and many tech-driven firms. Sweden is one of the countries where there's a large presence of multinational IT firms and start-up tech companies with constant work in the field of research and development. With such a focus on R&D, it has several technology parks as well and all of this calls for a huge number of qualified IT professionals and data scientists throughout the country. The major companies in the field of data science in Sweden are vert well aware of the importance for individuals skilled in data science, AI/ML and deep learning. As per the reports, Sweden continually keeps facing a shortage of IT and Data science professionals and this in the past led to a shortage of more than 30 thousand IT and practitioners in 2012 and the numbers are even higher now as the supply to their demands are never met. Rather the inflation keeps increasing each passing minute. As the Swedish customers are also gaining awareness and maturity in the IT and tech products or associated products and services, demands for the individuals skilled in same are very much of crucial importance here. Reports also suggest that by 2035 Sweden is going to have to face a major shortage of individuals skilled in IT and Engineering fields at both junior and senior levels of the company hierarchy.

Here on Earth, we have more detailed maps of Mars than of our own ocean, and that's a problem. A massive force for surviving climate change, the ocean absorbs 90% of the heat caused by emissions and generates 50% of the oxygen we breathe. "We have the ocean to thank for so many aspects of our safety and well-being," says Dawn Wright, oceanographer and chief scientist at geographic information system (GIS) provider Esri, who notes the ocean also provides renewable energy, a major food source, and a transportation corridor for not only ships but submarine internet cables. Now, the same type of smart maps and geospatial technology guiding outer space exploration support the quest to better understand and protect our ocean. "For the first time, our knowledge of the ocean can approach our knowledge of the land," Wright says.

The problem of code generation from textual program descriptions has long been viewed as a grand challenge in software engineering. In recent years, many deep learning based approaches have been proposed, which can generate a sequence of code from a sequence of textual program description. However, the existing approaches ignore the global relationships among API methods, which are important for understanding the usage of APIs. In this paper, we propose to model the dependencies among API methods as an API dependency graph (ADG) and incorporate the graph embedding into a sequence-to-sequence (Seq2Seq) model. In addition to the existing encoder-decoder structure, a new module named ``embedder" is introduced. In this way, the decoder can utilize both global structural dependencies and textual program description to predict the target code. We conduct extensive code generation experiments on three public datasets and in two programming languages (Python and Java). Our proposed approach, called ADG-Seq2Seq, yields significant improvements over existing state-of-the-art methods and maintains its performance as the length of the target code increases. Extensive ablation tests show that the proposed ADG embedding is effective and outperforms the baselines.