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Heidelberg University

Academic institutioneurope · de
Official website
Research library283linked papers
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Selected work

Representative Papers

Inference from High-Frequency Data: A Subsampling Approach

Apr 01, 2016

This study addresses volatility estimation in high-frequency financial data contaminated by market frictions or microstructure noise. The authors propose an adaptive subsampling approach that directly infers the asymptotic (conditional) covariance matrix of volatility estimators without explicitly modeling the noise structure. By employing time-rescaled statistics over local intervals to assess sampling variability, the method automatically selects tuning parameters while ensuring the resulting covariance matrix is positive semidefinite. Theoretical analysis, Monte Carlo simulations, and empirical applications demonstrate that the proposed estimator is consistent, exhibits strong finite-sample performance, and enables robust and feasible statistical inference.

28 citations2 influentialRead paper

Learning conformational ensembles of proteins based on backbone geometry

Feb 19, 2025arXiv.org

Existing protein conformational sampling methods—relying either on evolutionary information or pretrained folding models—suffer from limited applicability, low efficiency, and potential biases. To address these limitations, we propose BBFlow, the first flow-matching generative model that operates exclusively on backbone geometric structure, requiring neither evolutionary sequence information nor pretrained models, and directly learns a conformational ensemble consistent with the Boltzmann distribution from scratch. BBFlow innovatively employs equilibrium backbone geometry both to condition the vector field and to define a learnable SE(3)-equivariant prior distribution, enabling robust modeling of multi-chain proteins and de novo design. Compared to state-of-the-art methods, BBFlow achieves orders-of-magnitude faster training (converging in GPU-days) and significantly accelerated inference, while maintaining competitive performance on both native protein reconstruction and de novo design benchmarks.

2 citations1 influentialRead paper

Tumor Detection, Segmentation and Classification Challenge on Automated 3D Breast Ultrasound: The TDSC-ABUS Challenge

Jan 26, 2025

Automatic breast ultrasound (ABUS) tumor detection, segmentation, and classification are challenged by morphological heterogeneity, low signal-to-noise ratio, and scarcity of annotated 3D data. Method: We introduce the first publicly available, high-quality, multi-center ABUS tumor benchmark dataset and the TDSC-ABUS2023 international challenge platform—enabling the first unified three-task evaluation. Our proposed framework integrates multi-scale 3D CNNs, Transformers, semi-supervised learning, and boundary-aware loss to address ABUS-specific challenges including ill-defined tumor boundaries and low contrast. Contribution/Results: Our method achieves state-of-the-art performance: 82.3% mAP@0.5 for detection, 79.6% Dice for segmentation, and 91.4% accuracy for malignancy classification—significantly outperforming baselines. This work fills critical gaps in publicly accessible ABUS benchmarks and standardized multi-task evaluation, advancing intelligent early diagnosis of breast cancer.

2 citationsRead paper

A Convoy of Magnetic Millirobots Transports Endoscopic Instruments for Minimally-Invasive Surgery.

Jul 01, 2024Advancement of science

In minimally invasive surgery, microrobots suffer from insufficient traction on slippery, soft-tissue surfaces, hindering reliable transport of elongated instruments (e.g., endoscopes, catheters). To address this, we present TrainBot—a magnetically actuated millirobotic convoy system—where multiple millirobots cooperatively form a “train-like” configuration to enable stable, heavy-load instrument transport within narrow anatomical lumens (e.g., bile ducts, intestines). Key contributions include: (i) the first demonstration of millirobotic swarm-based cargo transport, achieving a twofold increase in output force; (ii) bioinspired, biocompatible microstructured feet that enhance individual propulsion force by threefold; and (iii) the world’s first millirobot-assisted electrodilatation procedure for biliary stricture relief. Integrated with wireless permanent-magnet actuation and multi-robot closed-loop control, TrainBot successfully validated biliary obstruction clearance, drainage tunnel creation, and targeted drug delivery in human-scale organ phantoms—significantly advancing precision instrument delivery in minimally invasive interventions.

2 citationsRead paper
Recent publications

Latest Papers

The Stochastic Deputy: Structural Tenant Isolation for Tool-Using LLM Agents

Sep 13, 2026

"This study addresses the issue of resource misallocation in multi-tenant environments due to attackers manipulating the context of language model agents. The proposed solution is a structured defense mechanism that removes tenant identity information from the model context protocol and enforces access controls by binding permissions to verified credentials, ensuring that only legitimate credentials can access specified resources. This approach innovatively integrates adjustments to the model context protocol, cryptographic protection, and JSON_TABLE lateral joins. Experimental results demonstrate that all out-of-bound requests were successfully intercepted in 373 test cases. Although setting value scopes introduces additional latency, this can be mitigated through query plan optimization and index improvements."

0 citationsRead paper