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In recent years, the telecommunications field has experienced an unparalleled proliferation of wireless data traffic. Innovative solutions are imperative to circumvent the inherent limitations of the current technology, in particular in terms of capacity. Carrier frequencies in the sub-terahertz (sub-THz) range (~0.2-0.3 THz) can deliver increased capacity and low attenuation for short-range wireless applications. Here, we demonstrate a direct, passive and compact sub-THz receiver based on graphene, which outperforms state-of-the-art sub-THz receivers. These graphene-based receivers offer a cost-effective, CMOS-compatible, small-footprint solution that can fulfill the size, weight, and power consumption (SWaP) requirements of 6G technologies. We exploit a sub-THz cavity, comprising an antenna and a back mirror, placed in the vicinity of the graphene channel to overcome the low inherent absorption in graphene and the mismatch between the areas of the photoactive region and the incident radiation, which becomes extreme in the sub-THz range. The graphene receivers achieve a multigigabit per second data rate with a maximum distance of ~3 m from the transmitter, a setup-limited 3 dB bandwidth of 40 GHz, and a high responsivity of 0.16 A/W, enabling applications such as chip-to-chip communication and close proximity device-to-device communication., Comment: 13 pages, 4 figures

In the rapidly evolving landscape of veterinary healthcare, integrating machine learning (ML) clinical decision-making tools with electronic health records (EHRs) promises to improve diagnostic accuracy and patient care. However, the seamless integration of ML classifiers into existing EHRs in veterinary medicine is frequently hindered by the rigidity of EHR systems or the limited availability of IT resources. To address this shortcoming, we present Anna, a freely-available software solution that provides ML classifier results for EHR laboratory data in real-time.

Large language models (LLMs) can extract information from veterinary electronic health records (EHRs), but performance differences between models, the effect of temperature settings, and the influence of text ambiguity have not been previously evaluated. This study addresses these gaps by comparing the performance of GPT-4 omni (GPT-4o) and GPT-3.5 Turbo under different conditions and investigating the relationship between human interobserver agreement and LLM errors. The LLMs and five humans were tasked with identifying six clinical signs associated with Feline chronic enteropathy in 250 EHRs from a veterinary referral hospital. At temperature 0, the performance of GPT-4o compared to the majority opinion of human respondents, achieved 96.9% sensitivity (interquartile range [IQR] 92.9-99.3%), 97.6% specificity (IQR 96.5-98.5%), 80.7% positive predictive value (IQR 70.8-84.6%), 99.5% negative predictive value (IQR 99.0-99.9%), 84.4% F1 score (IQR 77.3-90.4%), and 96.3% balanced accuracy (IQR 95.0-97.9%). The performance of GPT-4o was significantly better than that of its predecessor, GPT-3.5 Turbo, particularly with respect to sensitivity where GPT-3.5 Turbo only achieved 81.7% (IQR 78.9-84.8%). Adjusting the temperature for GPT-4o did not significantly impact classification performance. GPT-4o demonstrated greater reproducibility than human pairs regardless of temperature, with an average Cohen's kappa of 0.98 (IQR 0.98-0.99) at temperature 0 compared to 0.8 (IQR 0.78-0.81) for humans. Most GPT-4o errors occurred in instances where humans disagreed (35/43 errors, 81.4%), suggesting that these errors were more likely caused by ambiguity of the EHR than explicit model faults. Using GPT-4o to automate information extraction from veterinary EHRs is a viable alternative to manual extraction., Comment: 24 pages, 3 figures, 8 supplementary figures

Accurate assessment of lymph node size in 3D CT scans is crucial for cancer staging, therapeutic management, and monitoring treatment response. Existing state-of-the-art segmentation frameworks in medical imaging often rely on fully annotated datasets. However, for lymph node segmentation, these datasets are typically small due to the extensive time and expertise required to annotate the numerous lymph nodes in 3D CT scans. Weakly-supervised learning, which leverages incomplete or noisy annotations, has recently gained interest in the medical imaging community as a potential solution. Despite the variety of weakly-supervised techniques proposed, most have been validated only on private datasets or small publicly available datasets. To address this limitation, the Mediastinal Lymph Node Quantification (LNQ) challenge was organized in conjunction with the 26th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2023). This challenge aimed to advance weakly-supervised segmentation methods by providing a new, partially annotated dataset and a robust evaluation framework. A total of 16 teams from 5 countries submitted predictions to the validation leaderboard, and 6 teams from 3 countries participated in the evaluation phase. The results highlighted both the potential and the current limitations of weakly-supervised approaches. On one hand, weakly-supervised approaches obtained relatively good performance with a median Dice score of $61.0\%$. On the other hand, top-ranked teams, with a median Dice score exceeding $70\%$, boosted their performance by leveraging smaller but fully annotated datasets to combine weak supervision and full supervision. This highlights both the promise of weakly-supervised methods and the ongoing need for high-quality, fully annotated data to achieve higher segmentation performance., Comment: Submitted to MELBA

Recent studies have underscored the capabilities of natural imaging foundation models to serve as powerful feature extractors, even in a zero-shot setting for medical imaging data. Most commonly, a shallow multi-layer perceptron (MLP) is appended to the feature extractor to facilitate end-to-end learning and downstream prediction tasks such as classification, thus representing the de facto standard. However, as graph neural networks (GNNs) have become a practicable choice for various tasks in medical research in the recent past, we direct attention to the question of how effective GNNs are compared to MLP prediction heads for the task of 3D medical image classification, proposing them as a potential alternative. In our experiments, we devise a subject-level graph for each volumetric dataset instance. Therein latent representations of all slices in the volume, encoded through a DINOv2 pretrained vision transformer (ViT), constitute the nodes and their respective node features. We use public datasets to compare the classification heads numerically and evaluate various graph construction and graph convolution methods in our experiments. Our findings show enhancements of the GNN in classification performance and substantial improvements in runtime compared to an MLP prediction head. Additional robustness evaluations further validate the promising performance of the GNN, promoting them as a suitable alternative to traditional MLP classification heads. Our code is publicly available at: https://github.com/compai-lab/2024-miccai-grail-kiechle, Comment: Accepted at MICCAI 2024 - GRAIL Workshop

In this work, we introduce Progressive Growing of Patch Size, a resource-efficient implicit curriculum learning approach for dense prediction tasks. Our curriculum approach is defined by growing the patch size during model training, which gradually increases the task's difficulty. We integrated our curriculum into the nnU-Net framework and evaluated the methodology on all 10 tasks of the Medical Segmentation Decathlon. With our approach, we are able to substantially reduce runtime, computational costs, and CO2 emissions of network training compared to classical constant patch size training. In our experiments, the curriculum approach resulted in improved convergence. We are able to outperform standard nnU-Net training, which is trained with constant patch size, in terms of Dice Score on 7 out of 10 MSD tasks while only spending roughly 50% of the original training runtime. To the best of our knowledge, our Progressive Growing of Patch Size is the first successful employment of a sample-length curriculum in the form of patch size in the field of computer vision. Our code is publicly available at https://github.com/compai-lab/2024-miccai-fischer., Comment: Accepted at MICCAI2024; Changes for Camera-Ready-Version for MICCAI2024 (missing in this arxiv submission): Replaced T-Test with Wilcoxon Signed Ranked Test, as DSC samples are not normally distributed => now only significant improvements and no significant decreases in performance for PGPS/PGPS+

The dielectric constant ($\varepsilon^{\prime}$) of interfacial water is an important parameter, but its measurement has posed challenges, and no consensus has been reached on a generalized expression. We derived a formula for $\varepsilon^{\prime}$ of a buried interface using the slab model for a half-solvated sphere: $ \varepsilon^{\prime}=\varepsilon_1 \varepsilon_2\left(\varepsilon_2-\varepsilon_1+6\right) / 2\left(2 \varepsilon_2+\varepsilon_1\right)$, where $\varepsilon_1$ and $\varepsilon_2$ are the dielectric constants of the solid and liquid phases, respectively. We experimentally validated this expression using vibrational sum frequency generation and Fresnel factor calculations for interfaces of alumina with water ($ \mathrm{H_2O} $ and $ \mathrm{D_2O} $) and acetonitrile. This fills an important knowledge gap in the description of the dielectric constant of interfaces.

Pathological lymph node delineation is crucial in cancer diagnosis, progression assessment, and treatment planning. The MICCAI 2023 Lymph Node Quantification Challenge published the first public dataset for pathological lymph node segmentation in the mediastinum. As lymph node annotations are expensive, the challenge was formed as a weakly supervised learning task, where only a subset of all lymph nodes in the training set have been annotated. For the challenge submission, multiple methods for training on these weakly supervised data were explored, including noisy label training, loss masking of unlabeled data, and an approach that integrated the TotalSegmentator toolbox as a form of pseudo labeling in order to reduce the number of unknown voxels. Furthermore, multiple public TCIA datasets were incorporated into the training to improve the performance of the deep learning model. Our submitted model achieved a Dice score of 0.628 and an average symmetric surface distance of 5.8~mm on the challenge test set. With our submitted model, we accomplished third rank in the MICCAI2023 LNQ challenge. A finding of our analysis was that the integration of all visible, including non-pathological, lymph nodes improved the overall segmentation performance on pathological lymph nodes of the test set. Furthermore, segmentation models trained only on clinically enlarged lymph nodes, as given in the challenge scenario, could not generalize to smaller pathological lymph nodes. The code and model for the challenge submission are available at \url{https://gitlab.lrz.de/compai/MediastinalLymphNodeSegmentation}., Comment: Accepted for publication at the Journal of Machine Learning for Biomedical Imaging (MELBA) https://melba-journal.org/2024:008

Recently, energy encryption for wireless power transfer has been developed for energy safety, which is important in public places to suppress unauthorized energy extraction. Most techniques vary the frequency so that unauthorized receivers cannot extract energy because of non-resonance. However, this strategy is unreliable. To stimulate the progress of energy encryption technology and point out security holes, this paper proposes a decryption method for the fundamental principle of encrypted frequency-varying wireless power transfer. The paper uses an auxiliary coil to detect the frequency and a switched-capacitor array to adaptively compensate the receiver for a wide frequency range. The switched-capacitor array contains two capacitors and one semi-conductor switch. One capacitor compensates the receiver all the time while the other's active time during one wireless power transfer cycle is regulated by the switch. Thus, the proposed hacking receiver controls the equivalent capacitance of the compensation and steals energy. Finally, a detailed simulation model and experimental results prove the effectiveness of the attack on frequency-hopping energy encryption. Although any nonnegligible energy extracted would be problematic, we achieved to steal 78% to 84% of the energy an authorized receiver could get. When the frequency changes, the interceptor is coarsely tuned very quickly, which can hack fast frequency-varying encrypted system., Comment: 10 pages, 17 figures

A/B testing methodology is generally performed by private companies to increase user engagement and satisfaction about online features. Their usage is far from being transparent and may undermine user autonomy (e.g. polarizing individual opinions, mis- and dis- information spreading). For our analysis we leverage a crucial case study dataset (i.e. Upworthy) where news headlines were allocated to users and reshuffled for optimizing clicks. Our centre of focus is to determine how and under which conditions A/B testing affects the distribution of content on the collective level, specifically on different social network structures. In order to achieve that, we set up an agent-based model reproducing social interaction and an individual decision-making model. Our preliminary results indicate that A/B testing has a substantial influence on the qualitative dynamics of information dissemination on a social network. Moreover, our modeling framework promisingly embeds conjecturing policy (e.g. nudging, boosting) interventions.

In this paper, we present a novel derivative-free optimization framework for solving unconstrained stochastic optimization problems. Many problems in fields ranging from simulation optimization to reinforcement learning involve settings where only stochastic function values are obtained via an oracle with no available gradient information, necessitating the usage of derivative-free optimization methodologies. Our approach includes estimating gradients using stochastic function evaluations and integrating adaptive sampling techniques to control the accuracy in these stochastic approximations. We consider various gradient estimation techniques including standard finite difference, Gaussian smoothing, sphere smoothing, randomized coordinate finite difference, and randomized subspace finite difference methods. We provide theoretical convergence guarantees for our framework and analyze the worst-case iteration and sample complexities associated with each gradient estimation method. Finally, we demonstrate the empirical performance of the methods on logistic regression and nonlinear least squares problems.

libEnsemble is a Python-based toolkit for running dynamic ensembles, developed as part of the DOE Exascale Computing Project. The toolkit utilizes a unique generator--simulator--allocator paradigm, where generators produce input for simulators, simulators evaluate those inputs, and allocators decide whether and when a simulator or generator should be called. The generator steers the ensemble based on simulation results. Generators may, for example, apply methods for numerical optimization, machine learning, or statistical calibration. libEnsemble communicates between a manager and workers. We overview the unique characteristics of libEnsemble as well as current and potential interoperability with other packages in the workflow ecosystem. We highlight libEnsemble's dynamic resource features: libEnsemble can detect system resources, such as available nodes, cores, and GPUs, and assign these in a portable way. These features allow users to specify the number of processors and GPUs required for each simulation; and resources will be automatically assigned on a wide range of systems, including Frontier, Aurora, and Perlmutter. Such ensembles can include multiple simulation types, some using GPUs and others using only CPUs, sharing nodes for maximum efficiency. We also describe the benefits of libEnsemble's generator--simulator coupling, which easily exposes to the user the ability to cancel, and portably kill, running simulations based on models that are updated with intermediate simulation output. We demonstrate libEnsemble's capabilities, scalability, and scientific impact via a Gaussian process surrogate training problem for the longitudinal density profile at the exit of a plasma accelerator stage. The study uses gpCAM for the surrogate model and employs either Wake-T or WarpX simulations, highlighting efficient use of resources that can easily extend to exascale.

There is currently a major trend to hairpin-winding motors for small and medium drives with increased power, specifically more torque density in the automotive industry. Practically all large players in the field either already use this winding technology or have announced doing so soon. However, hairpins, bar windings, and other segmented winding techniques are not purely a material and production issue. Instead their application to small drives influences all aspects of the design of machines, which are currently explored and studied by the industry. These range from not obvious gaps in the theory, parameter studies for maxima of efficiency, possible as well as advantageous winding schemes, thermal design, and ways to control ac losses to specific materials and process difficulties. Despite the apparent novelty of the trend, however, designers could revisit a widely forgotten knowledge base of more than 100 years for many of those questions. This old knowledge base and the understanding that many recently presented concepts have been developed earlier may speed up the technological development and appear to be a key to further innovation. Instead, many problems need to be solved again and technologies re-invented. Furthermore, as this technology has recently become merely industry-driven, a substantial portion of the information and technological developments are not available to the public -- a state that to our eyes may harm the innovation capacity of the drives community., Comment: 22 pages, 20 figures

This paper presents a novel direct-injection modular universal power flow and quality control topology exclusively using lower power components. In addition to conventional high-voltage applications, it is particularly attractive for the distribution and secondary grids, e.g., in soft open points, down to low voltage as it can exploit the latest developments in low-voltage high-current semiconductors. In contrast to other concepts that do not interface the grid through transformers, it does not need to convert the entire line power but only the injected or extracted power difference. The proposed power flow and quality (f/q) controller comprises a shunt active front end, together with high-frequency links serving as a power supply for a series floating module per phase. Each of the floating modules is in series with one phase of the line, floating with the electric potential of that particular phase, avoiding any ground connection. Omitting bulky and dynamically limited line transformers of conventional universal power flow controllers, the presented direct-injection f/q controller enables exceptionally small size and volume, high power density, high frequency content, and fast response. In contrast to direct-injection concepts with full back-to-back converters, it only needs to handle a fraction of the power. The circuit combines grid-voltage low-current electronics in the shunt unit and low-voltage high-current modules in the floating series injection units. Simulations and experiments demonstrate and validate the concept.

Automated vehicles operating in urban environments have to reliably interact with other traffic participants. Planning algorithms often utilize separate prediction modules forecasting probabilistic, multi-modal, and interactive behaviors of objects. Designing prediction and planning as two separate modules introduces significant challenges, particularly due to the interdependence of these modules. This work proposes a deep learning methodology to combine prediction and planning. A conditional GAN with the U-Net architecture is trained to predict two high-resolution image sequences. The sequences represent explicit motion predictions, mainly used to train context understanding, and pixel state values suitable for planning encoding kinematic reachability, object dynamics, safety, and driving comfort. The model can be trained offline on target images rendered by a sampling-based model-predictive planner, leveraging real-world driving data. Our results demonstrate intuitive behavior in complex situations, such as lane changes amidst conflicting objectives., Comment: This work has been submitted to the IEEE for possible publication

Major Depressive Disorder (MDD) is one of the most common causes of disability worldwide. Unfortunately, about one-third of patients do not benefit sufficiently from available treatments and not many new drugs have been developed in this area in recent years. We thus need better and faster ways to evaluate many different treatment options quickly. Platform trials are a possible remedy - they facilitate the evaluation of more investigational treatments in a shorter period of time by sharing controls, as well as reducing clinical trial activation and recruitment times. We discuss design considerations for a platform trial in MDD, taking into account the unique disease characteristics, and present the results of extensive simulations to investigate the operating characteristics under various realistic scenarios. To allow the testing of more treatments, interim futility analyses should be performed to eliminate treatments that have either no or negligible treatment effect. Furthermore, we investigate different randomisation and allocation strategies as well as the impact of the per-treatment arm sample size. We compare the operating characteristics of such platform trials to those of traditional randomised controlled trials and highlight the potential advantages of platform trials.