ROADS: Randomization for Obstacle Avoidance and Driving in Simulation

Various sensor network measurement studies have reported instances of transient faults in sensor readings. In this work, we seek to answer a simple question: How often are such faults observed in real deployments? To do this, we first explore and characterize three qualitatively different classes of fault detection methods. Rule-based methods leverage domain knowledge to develop heuristic rules for detecting and identifying faults. Estimation methods predict "normal" sensor behavior by leveraging sensor correlations, flagging anomalous sensor readings as faults. Finally, learning-based methods are trained to statistically identify classes of faults. We find that these three classes of methods sit at different points on the accuracy/robustness spectrum. Rule-based methods can be highly accurate, but their accuracy depends critically on the choice of parameters. Learning methods can be cumbersome, but can accurately detect and classify faults. Estimation methods are accurate, but cannot classify faults. We apply these techniques to four real-world sensor data sets and find that the prevalence of faults as well as their type varies with data sets. All three methods are qualitatively consistent in identifying sensor faults in real world data sets, lending credence to our observations. Our work is a first-step towards automated on-line fault detection and classification.

1024 Background: Trastuzumab deruxtecan (TDXd), an antibody drug conjugate, was first approved in the UK for the treatment of human epidermal growth factor receptor 2 positive (HER2+) metastatic breast cancer (mBC) in 2021. We aimed to review the efficacy and tolerability of TDXd in a real-world UK setting. Methods: We collected clinical and demographic data from medical records on HER2+ mBC patients treated with TDXd in the UK from Jan 2021 to Dec 2023 inclusive. This included: radiological response, the frequency of dose reductions and treatment delays, and reasons for discontinuation. Clinical trial patients were excluded. Descriptive statistical techniques were applied. Results: Data was collected from 26 UK cancer centres on 280 patients. The median age at TDXd initiation was 56 years (range 29-85). Median treatment duration was 9 cycles (range 1-40). 239 patients (85%) had visceral disease. 257 and 196 patients (92% and 70%, respectively) had received prior Trastuzumab and Pertuzumab, respectively. 233 patients (83%) had received prior TDM1. The median number of lines of treatment prior to TDXd was 3 (range 1 to 9). 20 patients (7%) were initiated on a reduced dose and 115 further patients (41%) required at least one dose reduction. 151 patients (54%) had at least one dose interruption due to: infection or neutropenia (n=61), other toxicity (n=85), interstitial lung disease (ILD) investigation (n=39), patient request (n=81), other concurrent illness (n=10), and cardiac investigation (n=6). Of these, 55 patients had more than one treatment break. 157 patients (56%) had discontinued TDXd treatment due to: disease progression or death (n=100), drug toxicity (n=39) of which 22 were due to ILD, and patient choice (n=8). 123 patients remained treatment at the time of analysis. The overall response rate of TDXd was 63% overall: stable disease 23%, partial response 56%, complete response 7%. Conclusions: The real-world data set is not directly comparable to either the DB02 or DB03 trials, as it included patients who had received TDXd in both the second line and later line settings. However, the majority (80%) of these patients were treated in the third line and beyond. The rates of drug interruption and reduction were higher in the real-world data set than in DB02, which may impact on efficacy in the real-world setting. Overall response rate is similar between trial data and our real-world data, although the latter was not measured by RECIST. Data collection is ongoing, including an analysis of real-world progression free survival. [Table: see text]

Extended-Reality Technologies: An Overview of Emerging Applications in Medical Education and Clinical Care.

This paper introduces a novel family of fourth-order hybrid beta polynomial kernels tailored for statistical analysis. The efficacy of these kernels is evaluated using two principal performance metrics: asymptotic mean integrated squared error (AMISE) and kernel efficiency. Comprehensive assessments were conducted using both simulated and real-world datasets, enabling a thorough comparison with conventional fourth-order polynomial kernels. The evaluation process entailed computing AMISE and efficiency metrics for both the hybrid and classical kernels. Consistently, the results illustrated the superior performance of the hybrid kernels over their classical counterparts across diverse datasets, underscoring the robustness and effectiveness of the hybrid approach. By leveraging these performance metrics and conducting evaluations on simulated and real world data, this study furnishes compelling evidence supporting the superiority of the proposed hybrid beta polynomial kernels. The heightened performance, evidenced by lower AMISE values and elevated efficiency scores, strongly advocates for the adoption of the proposed kernels in statistical analysis tasks, presenting a marked improvement over traditional kernels.

Accurate reconstruction of static environments from LiDAR scans of scenes containing dynamic objects, which we refer to as Dynamic to Static Translation (DST), is an important area of research in Autonomous Navigation. This problem has been recently explored for visual SLAM, but to the best of our knowledge no work has been attempted to address DST for LiDAR scans. The problem is of critical importance due to wide-spread adoption of LiDAR in Autonomous Vehicles. We show that state-of the art methods developed for the visual domain when adapted for LiDAR scans perform poorly. We develop DSLR, a deep generative model which learns a mapping between dynamic scan to its static counterpart through an adversarially trained autoencoder. Our model yields the first solution for DST on LiDAR that generates static scans without using explicit segmentation labels. DSLR cannot always be applied to real world data due to lack of paired dynamic-static scans. Using Unsupervised Domain Adaptation, we propose DSLR-UDA for transfer to real world data and experimentally show that this performs well in real world settings. Additionally, if segmentation information is available, we extend DSLR to DSLR-Seg to further improve the reconstruction quality. DSLR gives the state of the art performance on simulated and real-world datasets and also shows at least 4× improvement. We show that DSLR, unlike the existing baselines, is a practically viable model with its reconstruction quality within the tolerable limits for tasks pertaining to autonomous navigation like SLAM in dynamic environments.

Many scientific works on grouping mixed data have chosen different experimental scenarios to structure their findings, which involve deciding from the programming language to the use of real-world and/or simulated datasets and performance measures, for example. Due to these characteristics directly influence the conclusion of the studies and the way new scientific works are proposed, it would be useful to have a wide map with the main choices that have been done by the authors of high-impact scientific documents so that the community can reflect on the thematic direction, identify best practices, and propose new paths for future research. To the best of our knowledge, such a map does not exist, neither a methodological procedure to build it. Therefore, this paper proposes a systematic methodology to reach such maps and provides a wide and in-depth map of the main choices the authors of high-impact scientific documents on mixed data clustering and surrounding studies have done in their experiments. As a result, 160 documents were systematically selected and classified into one of the six class of data clustering approaches, besides individually tabulated. From the tables for each class, we found, for instance, that real-world datasets are used more frequently than simulated ones, the documents used more external indices, followed by internal and relative ones, it is not common for the authors to inform the programming language they have used, except in the partitional class. We also provided the address of the algorithms’ code when they are made available by the authors.

Robust detection of non-overlapping ellipses from points with applications to circular target extraction in images and cylinder detection in point clouds

PCN317 - THE CANCER DRUGS FUND: KEY UNCERTAINTIES, DATA COLLECTION PLANS, ANALYTICAL METHODS AND USE OF THE SYSTEMATIC ANTI-CANCER THERAPY (SACT) REAL WORLD DATA SET

It is expected that See-Through Head-Mounted Display (STHMD), which superimposes the virtual environment generated by computer graphics (CG) on the real world, can vividly execute various simulations and designs by using both of the real and virtual environment around us. In STHMDs, information given as a virtual environment has to exactly match with the real environment, because both environments are visible. This is one of the problems to be solved for practical use. Particularly for matching of locations and size between real and virtual objects, disaccordance is likely to occur between the world coordinate of the real environment where the user of STHMD actually exists and that of the virtual environment described as parameters of CG, which directly causes displacement of locations where virtual objects are superimposed. This must be calibrated so that the virtual environment is superimposed properly. Among causes of such errors, we focused both on systematic errors of visual parameters caused in manufacturing process and differences between actual and supposed location of user's eye on STHMD when in use. The former is required to be calibrated only once after the fabrication of STHMDs, whereas the latter has to be calibrated every time users start using STHMDs. We have proposed calibration methods which are suitable to properties of these causes of errors. In the method, the direct fitting of the virtual cursor drawn in the virtual environment onto targets in the real environment is performed. Then, based on the result of fitting, the least square method identifies values of the visual parameters which minimize differences between locations of virtual cursor in the virutal environment and targets in the real environment. Application of the method to the STHMD which we have made is also repored. The differences between the virtal cursor and targets in the real environment due to systematic errors caused in the manufacturing process were reduced to about 1 [mm] per target, which was less than one-thirtieth of that before the calibration. The differences between the virtual cursor and targets in the real environment due to the location of user's eyes were also reduced about 2 [mm] per target, which was a half of that before the calibration. This result was well enough to prove the effectiveness of the calibration methods.

Deep learning has become the main way of the object detection task for autonomous vehicles. Meanwhile, this method typically requires vast amounts of training data to reach their full potential. However, collecting the data from real world and labeling manually is an expensive, time-consuming and error-prone process. Synthetic image has the potential to replace real image for training neural networks, because image creation and labeling annotations are free in this way. For the network trained by synthetic images, the reality gap between real and synthetic images is the main obstacle to use it in the real world. And most previous works are only devoted to generate synthetic images with a good performance on model training, but lack of analysis of the domain gap that affects the performance. This work designs a method of generating the real and synthetic images to analyze and reduce the reality gap between synthetic and real images for car detection. Firstly, this work put one single car with no-background in a random background image to generate real single car and synthetic single car images. In order to further reduce the domain gap in content level, this method keeps the car distribution in synthetic images is similar with the distribution of car in real world. For the purpose of reducing the domain gap in appearance level, the parameters of camera model are same as the camera parameters of image collecting cars and the image is rendered by using the PBRT(Physically Based Ray Tracing) when we generated the synthetic images. Secondly, by training the neural network of instance segmentation with different datasets, the across validation result proves that the reality gap between synthetic and real images is no more than the domain gap between real images. Thirdly, the training results of datasets with different samples diversity show that the diversity of the samples yields better generalization between different datasets for car detection which can effectively reduce the domain gap.

Augmented regression models using neurochaos learning

A new biclustering technique based on crossing minimization

Recently, several deep-learning based navigation methods have been achieved because of a high quality dataset collected from high-quality simulated environments. However, the cost of creating high-quality simulated environments is high. In this paper, we present a concept of the reduced simulation, which can serve as a simplified version of a simulated environment yet be efficient enough for training deep-learning based UAV collision avoidance approaches. Our approach deals with the reality gap between a reduced simulation dataset and real world dataset and can provide a clear guideline for reduced simulation design. Our experimental result confirmed that the reduction in visual features provided by textures and lighting does not affect operating performance with the user study. Moreover, by conducting collision detection experiments, we verified that our reduced simulation outperforms the conventional cost-effective simulations in adaptation capability with respect to realistic simulation and real-world scenario.

A measurement of cluster quality is often needed for DNA microarray data analysis. In this paper, we introduce a new cluster validity index, which measures geometrical features of the data. The essential concept of this index is to evaluate the ratio between the squared total length of the data eigen-axes with respect to the between-cluster separation. We show that this cluster validity index works well for data that contain clusters closely distributed or with different sizes. We verify the method using three simulated data sets, two real world data sets and two microarray data sets. The experiment results show that the proposed index is superior to five other cluster validity indices, including partition coefficients (PC), General silhouette index (GS), Dunn’s index (DI), CH Index and I-Index. Also, we have given a theorem to show for what situations the proposed index works well.

Single Image Super-Resolution (SISR) using Convolutional Neural Networks (CNNs) for many applications in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">supervised</i> manner has resulted in state-of-the-art results. Such supervised models achieve remarkable accuracy; albeit their poor generalization ability for real-world Low-Resolution (LR) images. Supervised training in many SR works involves synthetically generated LR images from its corresponding High-Resolution (HR) images. As the distribution of such LR observation is relatively different from that of real LR image, the supervised training in SISR task results degradation when applied on real-world data. SISR has been scaled to real-world data recently by posing the unsupervised problem into a supervised one through learning the distribution of noisy LR observation first, following which supervised training is performed to obtain the SR image. It therefore involves two steps where the accuracy of SR image relies on how closely the LR’s distribution is learnt in the first step. In this work, we overcome such limitation by introducing unsupervised denoising network to transform real noisy LR image to clean image and then pre-trained SR network is utilised to increase the spatial resolution of cleaned LR image to generate SR image. Thus, instead of evaluating the denoised image in LR space to train the denoising network, we inspect the denoised image in SR space which allows to overcome the SR network’s generalization problem. The proposed Unsupervised Denoising framework for Super-Resolution ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">UDSR</i> ) is validated on real-world datasets (NTIRE-2020 Real-World SR Challenge validation and testing dataset (Track-1)) by comparing it with many recent unsupervised SISR methods. The performance of denoising and SR networks is superior in terms of various perceptual indices such as Perceptual Index (PI) and Ma Score in addition to numerous non-references metrics.