Search

Employing Stochastic Nonlinear Model Predictive Control (SNMPC) for real-time applications is challenging due to the complex task of propagating uncertainties through nonlinear systems. This difficulty becomes more pronounced in high-dimensional systems with extended prediction horizons, such as autonomous vehicles. To enhance closed-loop performance in and feasibility in SNMPCs, we introduce the concept of the Uncertainty Propagation Horizon (UPH). The UPH limits the time for uncertainty propagation through system dynamics, preventing trajectory divergence, optimizing feedback loop advantages, and reducing computational overhead. Our SNMPC approach utilizes Polynomial Chaos Expansion (PCE) to propagate uncertainties and incorporates nonlinear hard constraints on state expectations and nonlinear probabilistic constraints. We transform the probabilistic constraints into deterministic constraints by estimating the nonlinear constraints' expectation and variance. We then showcase our algorithm's effectiveness in real-time control of a high-dimensional, highly nonlinear system-the trajectory following of an autonomous passenger vehicle, modeled with a dynamic nonlinear single-track model. Experimental results demonstrate our approach's robust capability to follow an optimal racetrack trajectory at speeds of up to 37.5m/s while dealing with state estimation disturbances, achieving a minimum solving frequency of 97Hz. Additionally, our experiments illustrate that limiting the UPH renders previously infeasible SNMPC problems feasible, even when incorrect uncertainty assumptions or strong disturbances are present.

The ongoing work to upgrade ALS to ALS-U demands strict RF requirements such as low jitter and low spurs frequency reference to meet its accelerator and science goals. A low phase noise dual frequency Master Oscillator (MO), where the two frequencies are related by a fractional ratio of 608/609 and flexible divide by four frequency outputs has been consolidated into a single chassis. Optical fiber clock distribution system has been selected over the old coax system used in ALS to distribute these signals to various clients across the facility, providing high electrical isolation between outputs and therefore lower phase errors. A Xilinx FPGA ties the MO chassis together by providing a RS-485 interface to monitor and control the system. The new system aims to deliver phase-continuous frequencies with a phase noise (integrated RMS jitter) from 1 Hz to 1 MHz of less than 200 femtosecond per output. This paper will discuss the design, implementation, performance and installation of the new MO generation and distribution system., Comment: Poster presented at LLRF Workshop 2023 (LLRF2023, arXiv: 2310.03199)

An ugly duckling is an obviously different skin lesion from surrounding lesions of an individual, and the ugly duckling sign is a criterion used to aid in the diagnosis of cutaneous melanoma by differentiating between highly suspicious and benign lesions. However, the appearance of pigmented lesions, can change drastically from one patient to another, resulting in difficulties in visual separation of ugly ducklings. Hence, we propose DMT-Quadruplet - a deep metric learning network to learn lesion features at two tiers - patient-level and lesion-level. We introduce a patient-specific quadruplet mining approach together with a tiered quadruplet network, to drive the network to learn more contextual information both globally and locally between the two tiers. We further incorporate a dynamic margin within the patient-specific mining to allow more useful quadruplets to be mined within individuals. Comprehensive experiments show that our proposed method outperforms traditional classifiers, achieving 54% higher sensitivity than a baseline ResNet18 CNN and 37% higher than a naive triplet network in classifying ugly duckling lesions. Visualisation of the data manifold in the metric space further illustrates that DMT-Quadruplet is capable of classifying ugly duckling lesions in both patient-specific and patient-agnostic manner successfully., Comment: 12 pages, 6 figures

Rule learning approaches for knowledge graph completion are efficient, interpretable and competitive to purely neural models. The rule aggregation problem is concerned with finding one plausibility score for a candidate fact which was simultaneously predicted by multiple rules. Although the problem is ubiquitous, as data-driven rule learning can result in noisy and large rulesets, it is underrepresented in the literature and its theoretical foundations have not been studied before in this context. In this work, we demonstrate that existing aggregation approaches can be expressed as marginal inference operations over the predicting rules. In particular, we show that the common Max-aggregation strategy, which scores candidates based on the rule with the highest confidence, has a probabilistic interpretation. Finally, we propose an efficient and overlooked baseline which combines the previous strategies and is competitive to computationally more expensive approaches., Comment: KLR Workshop@ICML2023

Skin lesions known as naevi exhibit diverse characteristics such as size, shape, and colouration. The concept of an "Ugly Duckling Naevus" comes into play when monitoring for melanoma, referring to a lesion with distinctive features that sets it apart from other lesions in the vicinity. As lesions within the same individual typically share similarities and follow a predictable pattern, an ugly duckling naevus stands out as unusual and may indicate the presence of a cancerous melanoma. Computer-aided diagnosis (CAD) has become a significant player in the research and development field, as it combines machine learning techniques with a variety of patient analysis methods. Its aim is to increase accuracy and simplify decision-making, all while responding to the shortage of specialized professionals. These automated systems are especially important in skin cancer diagnosis where specialist availability is limited. As a result, their use could lead to life-saving benefits and cost reductions within healthcare. Given the drastic change in survival when comparing early stage to late-stage melanoma, early detection is vital for effective treatment and patient outcomes. Machine learning (ML) and deep learning (DL) techniques have gained popularity in skin cancer classification, effectively addressing challenges, and providing results equivalent to that of specialists. This article extensively covers modern Machine Learning and Deep Learning algorithms for detecting melanoma and suspicious naevi. It begins with general information on skin cancer and different types of naevi, then introduces AI, ML, DL, and CAD. The article then discusses the successful applications of various ML techniques like convolutional neural networks (CNN) for melanoma detection compared to dermatologists' performance. Lastly, it examines ML methods for UD naevus detection and identifying suspicious naevi.

Resonances are essential for understanding the interactions between light and matter in photonic systems. The real frequency response of the non-Hermitian systems depends on the complex-valued resonance frequencies, which are the poles of electromagnetic response functions. The zeros of the response functions are often used for designing devices, since the zeros can be located close to the real axis, where they have significant impact on scattering properties. While methods are available to determine the locations of the poles, there is a lack of appropriate approaches to find the zeros in photonic systems. We present an approach to compute poles and zeros based on contour integration of electromagnetic quantities. This also allows to extract sensitivities with respect to geometrical or other parameters enabling efficient device design. The approach is applied to a topical example in nanophotonics, an illuminated metasurface, where the emergence of reflection zeros due to the underlying resonance poles is explored using residue-based modal expansions. The generality and simplicity of the theory allows straightforward transfer to other areas of physics. We expect that easy access to zeros will enable new computer-aided design methods in photonics and other fields.

Mutual coherence is a measure of similarity between two opinions. Although the notion comes from philosophy, it is essential for a wide range of technologies, e.g., the Wahl-O-Mat system. In Germany, this system helps voters to find candidates that are the closest to their political preferences. The exact computation of mutual coherence is highly time-consuming due to the iteration over all subsets of an opinion. Moreover, for every subset, an instance of the SAT model counting problem has to be solved which is known to be a hard problem in computer science. This work is the first study to accelerate this computation. We model the distribution of the so-called confirmation values as a mixture of three Gaussians and present efficient heuristics to estimate its model parameters. The mutual coherence is then approximated with the expected value of the distribution. Some of the presented algorithms are fully polynomial-time, others only require solving a small number of instances of the SAT model counting problem. The average squared error of our best algorithm lies below 0.0035 which is insignificant if the efficiency is taken into account. Furthermore, the accuracy is precise enough to be used in Wahl-O-Mat-like systems., Comment: Results from 2021

This paper investigates rate independent systems (RIS), where the dissipation functional depends not only on the rate but also on the history of the state. The latter is expressed in terms of a Volterra integral operator. We establish an existence result for the original problem and for the control thereof, without resorting to smallness assumptions. Under a smoothness condition, we prove the uniqueness of solutions to a certain class of history dependent RIS with unbounded dissipation potentials. In this context, we derive an essential estimate that opens the door to future research on the topic of optimization., Comment: 32 pages

We investigate the calculation of resonance modes of a VCSEL with a Riesz projection eigenvalue solver. The eigenvalue solver is based on the principle of contour integration where for the solution of scattering problems physical right sides are used. Here, it is investigated how numerical parameters impact the performance of the method, where we focus on the computation of the fundamental VCSEL mode., Comment: Proceedings article, SPIE conference "Integrated Optics: Design, Devices, Systems, and Applications VII"

Building a representative model of a complex system remains a highly challenging problem. While by now there is basic understanding of most physical domains, model design is often hindered by lack of detail, for example concerning model dimensions or its relevant constraints. Here we present a novel model-building approach -- adoptODE -- augmenting basic system descriptions, based on expert knowledge in the form of ordinary differential equations, with continuous adjoint sensitivity analysis related to artificial neural network principles, based on observable data. With this we have created a general tool, that can be applied to any physical system described by ordinary differential equations. AdoptODE allows validating or extending the initial description, for example with different variables and constraints. This way one arrives at a better-optimised, representative low-dimensional model, which can fit existing data and predict novel experimental outcomes. We validate our method on five, quite different problem domains. (1) Kolmogorov model: Lotka Volterra model where we show the application of adoptODE to continuous-time Markov processes and the performance of adoptODE when working with noisy data. (2) Particle model: Interactive N-body, is a demonstration of the scalability of adoptODE and that even interactive system with a high number of elements can be reconstructed with high precision. (3) Excitable media (heart dynamics) by the Bueno\-Orovio\-Cherry\-Fenton model: AdoptODE can reconstruct the parameters of a high-dimensional model and fields for diffusion driven system for chaotic behaviour. (4) Fluid dynamics: Rayleigh-B\'enard Convection where a complete unknown field, the temperature, can be extracted from only velocity data. (5) New experimental data of Zebrafish embryogenesis: This is a case where we extend existing models with new variables.

Autonomous vehicles demand high accuracy and robustness of perception algorithms. To develop efficient and scalable perception algorithms, the maximum information should be extracted from the available sensor data. In this work, we present our concept for an end-to-end perception architecture, named DeepSTEP. The deep learning-based architecture processes raw sensor data from the camera, LiDAR, and RaDAR, and combines the extracted data in a deep fusion network. The output of this deep fusion network is a shared feature space, which is used by perception head networks to fulfill several perception tasks, such as object detection or local mapping. DeepSTEP incorporates multiple ideas to advance state of the art: First, combining detection and localization into a single pipeline allows for efficient processing to reduce computational overhead and further improves overall performance. Second, the architecture leverages the temporal domain by using a self-attention mechanism that focuses on the most important features. We believe that our concept of DeepSTEP will advance the development of end-to-end perception systems. The network will be deployed on our research vehicle, which will be used as a platform for data collection, real-world testing, and validation. In conclusion, DeepSTEP represents a significant advancement in the field of perception for autonomous vehicles. The architecture's end-to-end design, time-aware attention mechanism, and integration of multiple perception tasks make it a promising solution for real-world deployment. This research is a work in progress and presents the first concept of establishing a novel perception pipeline., Comment: Accepted to be published as part of the 5th Workshop on 3D-Deep Learning for Automated Driving on the 34th IEEE Intelligent Vehicles Symposium (IV), Anchorage, Alaska, USA, June 4-7, 2023

We show that a properly scaled stretched long Brownian chain converges to a two-parametric stochastic process, given by the sum of an explicit deterministic continuous function and the solution of the stochastic heat equation with zero boundary conditions., Comment: 15pages