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The scientific debate following the initial formulation of the “bad luck” hypothesis in cancer development highlighted how measures based on analysis of variance are inappropriately used for risk communication. The notion of “explained” variance is not only used to quantify randomness, but also to quantify genetic and environmental contribution to disease in heritability coefficients. In this pape

The interaction of oppositely charged lock- and key-colloids is investigated using computer simulations. We show that indented spheres, i.e., lock-particles, can be specifically assembled with spherical key-particles using solely electrostatic interactions in addition to a hard overlap potential. An analytic description of the entropic and energetic contributions is derived and supported by simula

This work addresses the design of multi-agent coordination through high-order consensus protocols. While first-order consensus strategies are well-studied—with known robustness to uncertainties such as time delays, time-varying weights, and nonlinearities like saturations—the theoretical guarantees for high-order consensus are comparatively limited. We propose a compositional control framework tha

Neural implicit representations have recently become popular in simultaneous localization and mapping (SLAM), especially in dense visual SLAM. However, existing works either rely on RGB-D sensors or require a separate monocular SLAM approach for camera tracking, and fail to produce high-fidelity 3D dense reconstructions. To address these shortcomings, we present NICER-SLAM, a dense RGB SLAM system

Distributed optimal control is known to be challenging and can become intractable even for linear-quadratic regulator problems. In this work, we study a special class of such problems where distributed state feedback controllers can give near-optimal performance. More specifically, we consider networked linear-quadratic controllers with decoupled costs and spatially exponentially decaying dynamics

Certainty equivalence adaptive controllers are analysed using a “data-driven Riccati equation”, corresponding to the model-free Bellman equation used in Q-learning. The equation depends quadratically on data correlation matrices. This makes it possible to derive simple sufficient conditions for stability and robustness to unmodeled dynamics in adaptive systems. The paper is concluded by short rema

The strategy of pre-training a large model on a diverse dataset, then fine-tuning for a particular application has yielded impressive results in computer vision, natural language processing, and robotic control. This strategy has vast potential in adaptive control, where it is necessary to rapidly adapt to changing conditions with limited data. Toward concretely understanding the benefit of pre-tr

Frequent updates in IoT software are crucial for fixing security vulnerabilities, correcting bugs, and adding new features. However, for systems comprising geographically distributed devices, implementing updates is challenging. Such updates must be coordinated across multiple devices, automated without end-user involvement, adaptable to weak connectivity, and minimally disruptive to end users. In

In this work, we consider the problem of coordinating a collection of nth-order integrator systems. The coordination is achieved through the novel serial consensus design; this control design achieves a stable closed-loop system while adhering to the constraint of only using local and relative measurements. Earlier work has shown that second-order serial consensus can stabilize a collection of dou

The optimization of expensive-to-evaluate black-box functions is prevalent in various scientific disciplines. Bayesian optimization is an automatic, general and sample-efficient method to solve these problems with minimal knowledge of the underlying function dynamics. However, the ability of Bayesian optimization to incorporate prior knowledge or beliefs about the function at hand in order to acce

System logs are crucial for understanding the state and health of systems, yet manual inspection becomes impractical due to the high volume of messages. Consequently, machine learning-based log anomaly detection has emerged to automatically identify irregularities. This study investigates the effectiveness of log message embeddings, a novel parsing method, for anomaly detection in complex systems.

This paper deals with the centroid and formation control problem of multi–agent robotic systems. The proposed solution is based on a leader–follower scheme, where only a subset of agents, i.e., the leaders, knows the desired trajectories for the centroid and the formation of the system, while the other agents, i.e., the followers, are required to estimate them through a dynamic consensus scheme. T

This paper presents an in-depth analysis of the stability and performance of control systems experiencing sensor timing misalignments, a common challenge in practical applications such as autonomous vehicles and aerospace systems. We model multichannel sensor delays as independent random variables, capturing the variability of real-world systems where different sensors exhibit distinct and non-con

An optimal control problem on finite-dimensional positive cones is stated. Under a critical assumption on the cone, the corresponding Bellman equation is satisfied by a linear function, which can be computed by convex optimization. A separate theorem relates the assumption on the cone to the existence of minimal elements in certain subsets of the dual cone. Three special cases are derived as examp

Logs are prevalent in modern cloud systems and serve as a valuable source of information for system maintenance. Over the years, many supervised, semi-supervised, and unsupervised log analysis methods have been proposed to detect system anomalies. In particular, semi-supervised methods have garnered increasing attention as they balance reduced labeled data requirements and optimal detection perfor

Recent methods on privacy-preserving image-based localization use a random line parameterization to protect the privacy of query images and database maps. The lifting of points to lines effectively drops one of the two geometric constraints traditionally used with point-to-point correspondences in structure-based localization. This leads to a significant loss of accuracy for the privacy-preserving

We verify safety of a nonlinear continuous-time system controlled by a neural network controller. The system is decomposed into low-dimensional subsystems connected in a feedback loop. Our application is a rocket landing, and open-loop properties of the two-dimensional altitude subsystem are verified using worst-case simulations. Closed-loop safety properties (crash-avoidance) of the full system a

This work demonstrates the utility of dual-arm robots with dual-wrist force-torque sensors in manipulating a Deformable Linear Object (DLO) within an unknown environment that imposes constraints on the DLO’s movement through contacts and fixtures. We propose a strategy to estimate the pose of unknown environmental contacts encountered during the manipulation of a DLO, classifying the induced const

Motivated by empirical research on bias and opinion formation, we formulate a multidimensional nonlinear opinion-dynamical model where agents have individual biases, which are fixed, as well as opinions, which evolve. The dimensions represent competing options, of which each agent has a relative opinion, and are coupled through normalization of the opinion vector. This can capture, for example, an

The rapid development of wearable biomedical systems now enables real-time monitoring of electroencephalography (EEG) signals. Acquisition of these signals relies on electrodes. These systems must meet the design challenge of selecting an optimal set of electrodes that balances performance and usability constraints. The search for the optimal subset of electrodes from a larger set is a problem wit