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This paper challenges the prevailing view that convolutional neural network (CNN) filters become increasingly specialized in deeper layers. Motivated by recent observations of clusterable repeating patterns in depthwise separable CNNs (DS-CNNs) trained on ImageNet, we extend this investigation across various domains and datasets. Our analysis of DS-CNNs reveals that deep filters maintain generality, contradicting the expected transition to class-specific filters. We demonstrate the generalizability of these filters through transfer learning experiments, showing that frozen filters from models trained on different datasets perform well and can be further improved when sourced from larger datasets. Our findings indicate that spatial features learned by depthwise separable convolutions remain generic across all layers, domains, and architectures. This research provides new insights into the nature of generalization in neural networks, particularly in DS-CNNs, and has significant implications for transfer learning and model design.

Recent advances in reinforcement learning have demonstrated the potential of quantum learning models based on parametrized quantum circuits as an alternative to deep learning models. On the one hand, these findings have shown the ultimate exponential speed-ups in learning that full-blown quantum models can offer in certain -- artificially constructed -- environments. On the other hand, they have demonstrated the ability of experimentally accessible PQCs to solve OpenAI Gym benchmarking tasks. However, it remains an open question whether these near-term QRL techniques can be successfully applied to more complex problems exhibiting high-dimensional observation spaces. In this work, we bridge this gap and present a hybrid model combining a PQC with classical feature encoding and post-processing layers that is capable of tackling Atari games. A classical model, subjected to architectural restrictions similar to those present in the hybrid model is constructed to serve as a reference. Our numerical investigation demonstrates that the proposed hybrid model is capable of solving the Pong environment and achieving scores comparable to the classical reference in Breakout. Furthermore, our findings shed light on important hyperparameter settings and design choices that impact the interplay of the quantum and classical components. This work contributes to the understanding of near-term quantum learning models and makes an important step towards their deployment in real-world RL scenarios., Comment: 10 + 13 pages

The exponential growth of IoT networks necessitates a paradigm shift towards architectures that offer high flexibility and learning capabilities while maintaining low energy consumption, minimal communication overhead, and low latency. Traditional IoT systems, particularly when integrated with machine learning approaches, often suffer from high communication overhead and significant energy consumption. This work addresses these challenges by proposing a neuromorphic architecture inspired by biological systems. To illustrate the practical application of our proposed architecture, we present a case study focusing on water management in the Carinthian community of Neuhaus. Preliminary results regarding water consumption prediction and anomaly detection in this community are presented. We also introduce a novel neuromorphic IoT architecture that integrates biological principles into the design of IoT systems. This architecture is specifically tailored for edge computing scenarios, where low power and high efficiency are crucial. Our approach leverages the inherent advantages of neuromorphic computing, such as asynchronous processing and event-driven communication, to create an IoT framework that is both energy-efficient and responsive. This case study demonstrates how the neuromorphic IoT architecture can be deployed in a real-world scenario, highlighting its benefits in terms of energy savings, reduced communication overhead, and improved system responsiveness., Comment: Submitted to Edge AI conference, Cagliari, Italy

Reinforcement learning algorithms for mean-field games offer a scalable framework for optimizing policies in large populations of interacting agents. Existing methods often depend on online interactions or access to system dynamics, limiting their practicality in real-world scenarios where such interactions are infeasible or difficult to model. In this paper, we present Offline Munchausen Mirror Descent (Off-MMD), a novel mean-field RL algorithm that approximates equilibrium policies in mean-field games using purely offline data. By leveraging iterative mirror descent and importance sampling techniques, Off-MMD estimates the mean-field distribution from static datasets without relying on simulation or environment dynamics. Additionally, we incorporate techniques from offline reinforcement learning to address common issues like Q-value overestimation, ensuring robust policy learning even with limited data coverage. Our algorithm scales to complex environments and demonstrates strong performance on benchmark tasks like crowd exploration or navigation, highlighting its applicability to real-world multi-agent systems where online experimentation is infeasible. We empirically demonstrate the robustness of Off-MMD to low-quality datasets and conduct experiments to investigate its sensitivity to hyperparameter choices., Comment: Submitted to AAMAS

The spinel Li4Ti5O12 (LTO) has emerged as a promising anode material for the next generation of all-solid-state Li-ion batteries (ASSB), primarily due to its characteristic "zero strain" charge/discharge behavior and exceptional cycling stability, which significantly prolongs battery lifespan. Pristine LTO, however, is hindered by poor ionic and electronic conductivity. By employing tailored sintering protocols that create oxygen vacancies, a high-performing, blue LTO material is achieved. It has been proposed that the increased electronic conductivity could stem from vacancy-induced polarons. Yet, detailed insights into polaron stability, distribution, and dynamics within both the LTO bulk and surface have remained elusive due to limited information on structural changes. Utilizing Positron Annihilation Lifetime Spectroscopy (PALS) and Coincidence Doppler Broadening Spectroscopy (CDBS), in conjunction with Two Component Density Functional Theory (TCDFT) with the on-site Hubbard U correction, enables us to probe the depth profile of defect species introduced by sintering in a reductive environment. Our research provides direct evidence of oxygen vacancy formation within the subsurface region, an inference drawn from the observation of \ch{Ti^{3+}}. Our investigation into Li16d vacancy formation within the bulk region uncovers the interactions between mobile species, namely Li-ions and polarons. Furthermore, we delve into the polaron stability on the LTO surface, offering an explanation for the superior performance of the (100) facet exposed LTO nanoparticle, as compared to its (111) exposed counterpart.

Dynamic Vision Sensors (DVS), offer a unique advantage in control applications, due to their high temporal resolution, and asynchronous event-based data. Still, their adoption in machine learning algorithms remains limited. To address this gap, and promote the development of models that leverage the specific characteristics of DVS data, we introduce the Multi-Modal Dynamic-Vision-Sensor Line Following dataset (MMDVS-LF). This comprehensive dataset, is the first to integrate multiple sensor modalities, including DVS recordings, RGB video, odometry, and Inertial Measurement Unit (IMU) data, from a small-scale standardized vehicle. Additionally, the dataset includes eye-tracking and demographic data of drivers performing a Line Following task on a track. With its diverse range of data, MMDVS-LF opens new opportunities for developing deep learning algorithms, and conducting data science projects across various domains, supporting innovation in autonomous systems and control applications.

Accurate segmentation of prostate tumours from PET images presents a formidable challenge in medical image analysis. Despite considerable work and improvement in delineating organs from CT and MR modalities, the existing standards do not transfer well and produce quality results in PET related tasks. Particularly, contemporary methods fail to accurately consider the intensity-based scaling applied by the physicians during manual annotation of tumour contours. In this paper, we observe that the prostate-localised uptake threshold ranges are beneficial for suppressing outliers. Therefore, we utilize the intensity threshold values, to implement a new custom-feature-clipping normalisation technique. We evaluate multiple, established U-Net variants under different normalisation schemes, using the nnU-Net framework. All models were trained and tested on multiple datasets, obtained with two radioactive tracers: [68-Ga]Ga-PSMA-11 and [18-F]PSMA-1007. Our results show that the U-Net models achieve much better performance when the PET scans are preprocessed with our novel clipping technique.

We analyzed the Hartree-Fock approximation for an electron system. The interaction between particles is modeled by a non-Coulombian potential. We analyzed both the three-dimensional and two-dimensional systems. We obtained accurate analytical results for the particle energy, the particle velocity, the ground state energy of the system as well as the momentum dependent density of states. The previous classical results for the Coulombian case were reobtained as particular cases., Comment: 23 pages, 3 figures

Acute Leukemia is the most common hematologic malignancy in children and adolescents. A key methodology in the diagnostic evaluation of this malignancy is immunophenotyping based on Multiparameter Flow Cytometry (FCM). However, this approach is manual, and thus time-consuming and subjective. To alleviate this situation, we propose in this paper the FCM-Former, a machine learning, self-attention based FCM-diagnostic tool, automating the immunophenotyping assessment in Childhood Acute Leukemia. The FCM-Former is trained in a supervised manner, by directly using flow cytometric data. Our FCM-Former achieves an accuracy of 96.5% assigning lineage to each sample among 960 cases of either acute B-cell, T-cell lymphoblastic, and acute myeloid leukemia (B-ALL, T-ALL, AML). To the best of our knowledge, the FCM-Former is the first work that automates the immunophenotyping assessment with FCM data in diagnosing pediatric Acute Leukemia., Comment: The paper has been accepted at IEEE EMBS 2024 (46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society)

Background: The aim of this study was to investigate the role of clinical, dosimetric and pretherapeutic magnetic resonance imaging (MRI) features for lesion-specific outcome prediction of stereotactic radiotherapy (SRT) in patients with brain metastases from malignant melanoma (MBM). Methods: In this multicenter, retrospective analysis, we reviewed 517 MBM from 130 patients treated with SRT (single fraction or hypofractionated). For each gross tumor volume (GTV) 1576 radiomic features (RF) were calculated (788 each for the GTV and for a 3 mm margin around the GTV). Clinical parameters, radiation dose and RF from pretherapeutic contrast-enhanced T1-weighted MRI from different institutions were evaluated with a feature processing and elimination pipeline in a nested cross-validation scheme. Results: Seventy-two (72) of 517 lesions (13.9%) showed a local failure (LF) after SRT. The processing pipeline showed clinical, dosimetric and radiomic features providing information for LF prediction. The most prominent ones were the correlation of the gray level co-occurrence matrix of the margin (hazard ratio (HR): 0.37, confidence interval (CI): 0.23-0.58) and systemic therapy before SRT (HR: 0.55, CI: 0.42-0.70). The majority of RF associated with LF was calculated in the margin around the GTV. Conclusions: Pretherapeutic MRI based RF connected with lesion-specific outcome after SRT could be identified, despite multicentric data and minor differences in imaging protocols. Image data analysis of the surrounding metastatic environment may provide therapy-relevant information with the potential to further individualize radiotherapy strategies.

The automated generation of diverse and complex training scenarios has been an important ingredient in many complex learning tasks. Especially in real-world application domains, such as autonomous driving, auto-curriculum generation is considered vital for obtaining robust and general policies. However, crafting traffic scenarios with multiple, heterogeneous agents is typically considered as a tedious and time-consuming task, especially in more complex simulation environments. In our work, we introduce MATS-Gym, a Multi-Agent Traffic Scenario framework to train agents in CARLA, a high-fidelity driving simulator. MATS-Gym is a multi-agent training framework for autonomous driving that uses partial scenario specifications to generate traffic scenarios with variable numbers of agents. This paper unifies various existing approaches to traffic scenario description into a single training framework and demonstrates how it can be integrated with techniques from unsupervised environment design to automate the generation of adaptive auto-curricula. The code is available at https://github.com/AutonomousDrivingExaminer/mats-gym., Comment: 7 Pages, Under Review

We introduce liquid-resistance liquid-capacitance neural networks (LRCs), a neural-ODE model which considerably improve the generalization, accuracy, and biological plausibility of electrical equivalent circuits (EECs), liquid time-constant networks (LTCs), and saturated liquid time-constant networks (STCs), respectively. We also introduce LRC units (LRCUs), as a very efficient and accurate gated RNN-model, which results from solving LRCs with an explicit Euler scheme using just one unfolding. We empirically show and formally prove that the liquid capacitance of LRCs considerably dampens the oscillations of LTCs and STCs, while at the same time dramatically increasing accuracy even for cheap solvers. We experimentally demonstrate that LRCs are a highly competitive alternative to popular neural ODEs and gated RNNs in terms of accuracy, efficiency, and interpretability, on classic time-series benchmarks and a complex autonomous-driving lane-keeping task.

Recent advances in depthwise-separable convolutional neural networks (DS-CNNs) have led to novel architectures, that surpass the performance of classical CNNs, by a considerable scalability and accuracy margin. This paper reveals another striking property of DS-CNN architectures: discernible and explainable patterns emerge in their trained depthwise convolutional kernels in all layers. Through an extensive analysis of millions of trained filters, with different sizes and from various models, we employed unsupervised clustering with autoencoders, to categorize these filters. Astonishingly, the patterns converged into a few main clusters, each resembling the difference of Gaussian (DoG) functions, and their first and second-order derivatives. Notably, we were able to classify over 95\% and 90\% of the filters from state-of-the-art ConvNextV2 and ConvNeXt models, respectively. This finding is not merely a technological curiosity; it echoes the foundational models neuroscientists have long proposed for the vision systems of mammals. Our results thus deepen our understanding of the emergent properties of trained DS-CNNs and provide a bridge between artificial and biological visual processing systems. More broadly, they pave the way for more interpretable and biologically-inspired neural network designs in the future.

In this paper we propose real-time recurrent reinforcement learning (RTRRL), a biologically plausible approach to solving discrete and continuous control tasks in partially-observable markov decision processes (POMDPs). RTRRL consists of three parts: (1) a Meta-RL RNN architecture, implementing on its own an actor-critic algorithm; (2) an outer reinforcement learning algorithm, exploiting temporal difference learning and dutch eligibility traces to train the Meta-RL network; and (3) random-feedback local-online (RFLO) learning, an online automatic differentiation algorithm for computing the gradients with respect to parameters of the network.Our experimental results show that by replacing the optimization algorithm in RTRRL with the biologically implausible back propagation through time (BPTT), or real-time recurrent learning (RTRL), one does not improve returns, while matching the computational complexity for BPTT, and even increasing complexity for RTRL. RTRRL thus serves as a model of learning in biological neural networks, mimicking reward pathways in the basal ganglia., Comment: 14 pages, 9 figures, includes Appendix

Understanding the physics of a three-phase contact line between gas, liquid, and solid is important for numerous applications. At the macroscale, the three-phase contact line response to an external force action is often characterized by a contact angle hysteresis, and several models are presented in the literature for its description. Yet, there is still a need for more information about such model applications at the nanoscale. In this study, a molecular dynamics approach was used to investigate the shape of a liquid droplet under an external force for different wetting regimes. In addition, an analytic model for describing the droplet shape was developed. It gives us the possibility to evaluate the receding and advancing wetting angle accurately. With our modeling, we found that the interplay between capillary forces and viscous forces is crucial to characterize the droplet shape at the nanoscale. In this frame, the importance of the rolling movement of the interface between liquid and vapor was pointed out. We also demonstrate that in the range of the external forces when capillary forces are most significant compared to others, hysteresis is well described by the macroscale Cox-Voinov model.