A novel learning-optimization-combined 4D radar odometry model, named DNOI-4DRO, is proposed in this paper. The proposed model seamlessly integrates traditional geometric optimization with end-to-end neural network training, leveraging an innovative differentiable neural-optimization iteration operator. In this framework, point-wise motion flow is first estimated using a neural network, followed by the construction of a cost function based on the relationship between point motion and pose in 3D space. The radar pose is then refined using Gauss-Newton updates. Additionally, we design a dual-stream 4D radar backbone that integrates multi-scale geometric features and clustering-based class-aware features to enhance the representation of sparse 4D radar point clouds. Extensive experiments on the VoD and Snail-Radar datasets demonstrate the superior performance of our model, which outperforms recent classical and learning-based approaches. Notably, our method even achieves results comparable to A-LOAM with mapping optimization using LiDAR point clouds as input. Our models and code will be publicly released.

Recent studies have discovered that Chain-of-Thought prompting (CoT) can dramatically improve the performance of Large Language Models (LLMs), particularly when dealing with complex tasks involving mathematics or reasoning. Despite the enormous empirical success, the underlying mechanisms behind CoT and how it unlocks the potential of LLMs remain elusive. In this paper, we take a first step towards theoretically answering these questions. Specifically, we examine the expressivity of LLMs with CoT in solving fundamental mathematical and decision-making problems. By using circuit complexity theory, we first give impossibility results showing that bounded-depth Transformers are unable to directly produce correct answers for basic arithmetic/equation tasks unless the model size grows super-polynomially with respect to the input length. In contrast, we then prove by construction that autoregressive Transformers of constant size suffice to solve both tasks by generating CoT derivations using a commonly used math language format. Moreover, we show LLMs with CoT can handle a general class of decision-making problems known as Dynamic Programming, thus justifying its power in tackling complex real-world tasks. Finally, an extensive set of experiments show that, while Transformers always fail to directly predict the answers, they can consistently learn to generate correct solutions step-by-step given sufficient CoT demonstrations.

We present a novel framework for generating photorealistic Editing capabilities for 3D-aware GANs have also been 3D human head and subsequently manipulating achieved through latent space auto-decoding, altering a 2D and reposing them with remarkable flexibility. The proposed semantic segmentation [62, 63], or modifying the underlying approach leverages an implicit function representation geometry scaffold [64]. However, generation and editing of 3D human heads, employing 3D Gaussians anchored quality tends to be unstable and less diversified due to on a parametric face model. To enhance representational the inherent limitation of GANs, and detailed-level editing capabilities and encode spatial information, we is not well supported due to feature entanglement in the embed a lightweight tri-plane payload within each Gaussian compact latent space or tri-plane representations.

The high simulation cost has been a bottleneck of practical analog/mixed-signal design automation. Many learning-based algorithms require thousands of simulated data points, which is impractical for expensive to simulate circuits. We propose a learning-based algorithm that can be trained using a small amount of data and, therefore, scalable to tasks with expensive simulations. Our efficient algorithm solves the post-layout performance optimization problem where simulations are known to be expensive. Our comprehensive study also solves the schematic-level sizing problem. For efficient optimization, we utilize Bayesian Neural Networks as a regression model to approximate circuit performance. For layout-aware optimization, we handle the problem as a multi-fidelity optimization problem and improve efficiency by exploiting the correlations from cheaper evaluations. We present three test cases to demonstrate the efficiency of our algorithms. Our tests prove that the proposed approach is more efficient than conventional baselines and state-of-the-art algorithms.

With the emergence of deep learning in the last years, new opportunities arose in Earth observation research. Nevertheless, they also brought with them new challenges. The data-hungry training processes of deep learning models demand large, resource expensive, annotated datasets and partly replaced knowledge-driven approaches, so that model behaviour and the final prediction process became a black box. The proposed SyntEO approach enables Earth observation researchers to automatically generate large deep learning ready datasets and thus free up otherwise occupied resources. SyntEO does this by including expert knowledge in the data generation process in a highly structured manner. In this way, fully controllable experiment environments are set up, which support insights in the model training. Thus, SyntEO makes the learning process approachable and model behaviour interpretable, an important cornerstone for explainable machine learning. We demonstrate the SyntEO approach by predicting offshore wind farms in Sentinel-1 images on two of the worlds largest offshore wind energy production sites. The largest generated dataset has 90,000 training examples. A basic convolutional neural network for object detection, that is only trained on this synthetic data, confidently detects offshore wind farms by minimising false detections in challenging environments. In addition, four sequential datasets are generated, demonstrating how the SyntEO approach can precisely define the dataset structure and influence the training process. SyntEO is thus a hybrid approach that creates an interface between expert knowledge and data-driven image analysis.

Drug discovery and development is an extremely complex process, with high attrition contributing to the costs of delivering new medicines to patients. Recently, various machine learning approaches have been proposed and investigated to help improve the effectiveness and speed of multiple stages of the drug discovery pipeline. Among these techniques, it is especially those using Knowledge Graphs that are proving to have considerable promise across a range of tasks, including drug repurposing, drug toxicity prediction and target gene-disease prioritisation. In such a knowledge graph-based representation of drug discovery domains, crucial elements including genes, diseases and drugs are represented as entities or vertices, whilst relationships or edges between them indicate some level of interaction. For example, an edge between a disease and drug entity might represent a successful clinical trial, or an edge between two drug entities could indicate a potentially harmful interaction. In order to construct high-quality and ultimately informative knowledge graphs however, suitable data and information is of course required. In this review, we detail publicly available primary data sources containing information suitable for use in constructing various drug discovery focused knowledge graphs. We aim to help guide machine learning and knowledge graph practitioners who are interested in applying new techniques to the drug discovery field, but who may be unfamiliar with the relevant data sources. Overall we hope this review will help motivate more machine learning researchers to explore combining knowledge graphs and machine learning to help solve key and emerging questions in the drug discovery domain.

The dataset used for building a network intrusion detection classifier is the classic KDD you can download here, released as first version in the 1999 KDD Cup, with 125.973 records in the training set. It was built for DARPA Intrusion Detection Evaluation Program by MIT Lincoln Laboratory. The dataset is already split into training and test dataset. The sub-classes into training dataset are 22 for attacks, and one "normal" for traffic allowed. The list of attacks and the associations with the four categories reported above is hold in this file.

The Artificial Intelligence Inner Circle Partner program is designed for partners who provide custom services or enhanced AI product solutions utilizing Microsoft AI technologies. This program recognizes a partner's unique expertise in specific industries and their ability to drive business transformation using the power of AI and data. AI Inner Circle Partners champion Microsoft AI technologies and deliver cutting edge AI solutions for customers. In 2018 Velrada won the Microsoft Dynamics 365 for Field Service Global Partner of the Year Award. We were honored among a global field of top Microsoft partners for demonstrating excellence in innovation and implementation of customer solutions based on Microsoft technology.

Artificial intelligence, energy diversification and the transformation of the workforce will be amongst the major talking points at SPE Offshore Europe 2019. Senior international industry figures will co-chair the keynote sessions which also includes late life and decommissioning, underwater innovation, transformative technologies to lower the carbon footprint, digital security, integrated technologies, digitalisation, standardisation and finance. The event will take place from 3-6 September at the new £333million The Event Complex Aberdeen (TECA), under the theme: 'Breakthrough to Excellence – Our license to operate'. Michael Borrell, SPE Offshore Europe 2019 Conference Chair & Senior Vice President, North Sea and Russia at Total said: "Our committee is full of international oil and gas industry leaders and they have developed an excellent programme which gets to the heart of the main opportunities and challenges facing the region. "Offshore Europe 2019 is a great opportunity for us to challenge ourselves in the North Sea basin.

Gaussian Processes provide good prior models for spatial data, but can be too smooth. In many physical situations there are discontinuities along bounding surfaces, for example fronts in near-surface wind fields. We describe a modelling method for such a constrained discontinuity and demonstrate how to infer the model parameters in wind fields with MCMC sampling.