Abstract Motivation There is a growing need for efficient imputation methods for high-dimensional DNA methylation (DNAm) datasets. Existing microarray imputation approaches, such as k-nearest neighbors (K-NN) or principal component analysis (PCA)-based methods, provide high accuracy but can be computationally intensive, while methods for whole-genome data are not designed for large cohorts. We developed slideimp, an R package that implements sliding window, groupable, parallelized K-NN and optimized PCA imputation to address these limitations. Results Benchmarks on microarray DNAm datasets demonstrate that slideimp achieves up to 150x faster runtime and 10x-100x lower memory usage while maintaining comparable or superior accuracy over existing methods and implementations. For K-NN and PCA imputation, slideimp supports grouped imputation which enhances imputation efficiency and accuracy. In a whole-genome dataset, sliding window K-NN imputation substantially increased the correlation of the Horvath 2013 clock with chronological age from 0.131 to 0.477. Additional features include targeted imputation of CpG subsets for K-NN and estimation of imputation accuracy via repeated cross-validation. The efficient and flexible DNAm imputation methods implemented by slideimp can easily be applied to other high-dimensional data types. Availability and Implementation The code to fully reproduce all analyses presented in this paper is available on GitHub at https://github.com/hhp94/slideimp_paper . The R package slideimp is also available on GitHub at https://github.com/hhp94/slideimp . Supplemental figures are available online.

Stream processing systems are important in modern applications in which data arrive continuously and need to be processed in real time. Because of their resource and scalability requirements, many of these systems run on the cloud, which is considered untrusted. Existing works on securing databases on the cloud focus on protecting the data, and most systems leverage trusted hardware for high performance. However, in stream processing systems, queries are as sensitive as the data because they contain the application logics. We demonstrate that it is practical to extract the queries from stream processing systems that use Intel SGX for securing the execution engine. The attack performed by a malicious cloud provider is based on timing side channels, and it works in two phases. In the offline phase, the attacker profiles the execution time of individual stream operators, based on synthetic data. This phase outputs a model that identifies individual stream operators. In the online phase, the attacker isolates the operators that make up the query, monitors its execution, and recovers the operators using the model in the previous phase. We implement the attack based on popular data stream benchmarks using SecureStream and NEXMark, and demonstrate attack success rates of up to 92%. We further discuss approaches that can harden streaming processing systems against our attacks without incurring high overhead.

Knee osteoarthritis (KOA) is a degenerative joint disease that significantly impacts quality of life. While transfer learning shows promise in medical imaging, its application to KOA diagnosis remains challenging due to medical data's unique characteristics. To address this, we propose TRIKOP, a framework leveraging Visual Prompting for KOA diagnosis on MRI. Our approach explores three prompt-generating strategies that extract clinically relevant information from input images. Each prompt type is encoded using a tailored method to integrate effectively into the Vision Transformer for optimal representation. Among them, the contrastive embedding prompting strategy achieves 63.04% accuracy on the OAI dataset, surpassing prior studies. Moreover, TRIKOP produces attention maps highlighting diagnostically significant regions, improving model interpretability. This work highlights TRIKOP’s potential to improve AI-driven KOA diagnosis and clinical support.

This paper explores the idea that skillful assembly is best represented as dynamic sequences of Manipulation Primitives, and that such sequences can be automatically discovered by Reinforcement Learning. Manipulation Primitives, such as "Move down until contact", "Slide along x while maintaining contact with the surface", have enough complexity to keep the search tree shallow, yet are generic enough to generalize across a wide range of assembly tasks. Moreover, the additional "semantics" of the Manipulation Primitives make them more robust in sim2real and against model/environment variations and uncertainties, as compared to more elementary actions. Policies are learned in simulation, and then transferred onto a physical platform. Direct sim2real transfer (without retraining in real) achieves excellent success rates on challenging assembly tasks, such as round peg insertion with 0.04mm clearance or square peg insertion with large hole position/orientation estimation errors.

In this paper, a Linear Parameter Varying (LPV) Unknown Input Observer (UIO) design for the attitude subsystem of a mass-varying quadcopter is proposed by using algebraic matrix manipulation. First, the design of UIO for LPV systems is discussed. Then, by applying the design process of UIO for the general LPV system, an LPV UIO is designed for the attitude of a mass-varying quadcopter. Simulation results in Matlab are conducted for illustrating the promising results of the paper.

Fast robotics pick-and-place with suction cups is a crucial component in the current development of automation in logistics (factory lines, e-commerce, etc.). By “critically fast” we mean the fastest possible movement for transporting an object such that it does not slip or fall from the suction cup. The main difficulties are: (i) handling the contact between the suction cup and the object, which fundamentally involves kinodynamic constraints; and (ii) doing so at a low computational cost, typically a few hundreds of milliseconds. To address these difficulties, we propose (a) a model for suction cup contacts, (b) a procedure to identify the contact stability constraint based on that model, and (c) a pipeline to parameterize, in a time-optimal manner, arbitrary geometric paths under the identified contact stability constraint. We experimentally validate the proposed pipeline on a physical robot system: the cycle time for a typical pick-and-place task was less than 5 seconds, planning and execution times included. The full pipeline is released as opensource for the robotics community.

Visual Servoing (VS), where images taken from a camera typically attached to the robot end-effector are used to guide the robot motions, is an important technique to tackle robotic tasks that require a high level of accuracy. We propose a new neural network, based on a Siamese architecture, for highly accurate camera pose estimation. This, in turn, can be used as a final refinement step following a coarse VS or, if applied in an iterative manner, as a standalone VS on its own. The key feature of our neural network is that it outputs the relative pose between any pair of images, and does so with sub-millimeter accuracy. We show that our network can reduce pose estimation errors to 0.6 mm in translation and 0.4 degrees in rotation, from initial errors of 10 mm/5 degrees if applied once, or of several cm/tens of degrees if applied iteratively. The network can generalize to similar objects, is robust against changing lighting conditions, and to partial occlusions (when used iteratively). The high accuracy achieved enables tackling low-tolerance assembly tasks downstream: using our network, an industrial robot can achieve 97.5% success rate on a VGA-connector insertion task without any force sensing mechanism.

This paper addresses the problem of multi-robot path coordination, considering specific features that arise in applications such as automatic aircraft taxiing or driver-less cars coordination. The first feature is departure events: when robots arrive at their destinations (e.g. the runway for takeoff), they can be removed from the coordination diagram. The second feature is the “no-backward-movement” constraint: the robots can only move forward on their assigned paths. These features can interact to give rise to complex conflict situations, which existing planners are unable to solve in practical time. We propose a set of algorithms to efficiently account for these features and validate these algorithms on a realistic model of Charles de Gaulle airport.

Given a geometric path, the Time-Optimal Path Tracking problem consists in finding the control strategy to traverse the path time-optimally while regulating tracking errors. A simple yet effective approach to this problem is to decompose the controller into two components: (i) a path controller, which modulates the parameterization of the desired path in an online manner, yielding a reference trajectory; and (ii) a tracking controller, which takes the reference trajectory and outputs joint torques for tracking. However, there is one major difficulty: the path controller might not find any feasible reference trajectory that can be tracked by the tracking controller because of torque bounds. In turn, this results in degraded tracking performances. Here, we propose a new path controller that is guaranteed to find feasible reference trajectories by accounting for possible future perturbations. The main technical tool underlying the proposed controller is Reachability Analysis, a new method for analyzing path parameterization problems. Simulations show that the proposed controller outperforms existing methods.

Time-optimal path parameterization (TOPP) is a well-studied problem in robotics and has a wide range of applications. There are two main families of methods to address TOPP: numerical integration (NI) and convex optimization (CO). The NI-based methods are fast but difficult to implement and suffer from robustness issues, while CO-based approaches are more robust but, at the same time, significantly slower. Here, we propose a new approach to TOPP based on reachability analysis. The key insight is to recursively compute reachable and controllable sets at discretized positions on the path by solving small linear programs. The resulting algorithm is faster than NI-based methods and as robust as CO-based ones (100% success rate), as confirmed by extensive numerical evaluations. Moreover, the proposed approach offers unique additional benefits: admissible velocity propagation and robustness to parametric uncertainty can be derived from it in a simple and natural way.

Finding the Time-Optimal Parameterization of a Path (TOPP) subject to second-order constraints (e.g. acceleration, torque, contact stability, etc.) is an important and well-studied problem in robotics. In comparison, TOPP subject to third-order constraints (e.g. jerk, torque rate, etc.) has received far less attention and remains largely open. In this paper, we investigate the structure of the TOPP problem with third-order constraints. In particular, we identify two major difficulties: (i) how to smoothly connect optimal profiles, and (ii) how to address singularities, which stop profile integration prematurely. We propose a new algorithm, TOPP3, which addresses these two difficulties and thereby constitutes an important milestone towards an efficient computational solution to TOPP with third-order constraints.