Search

For the first time the neutron-rich hydrogen isotope $\rm ^{6}H$ was produced in an electron scattering experiment in the reaction $\rm ^{7}Li(e,~e'p\pi^{+})^{6}H$ using the spectrometer facility of the A1 Collaboration at the Mainz Microtron accelerator. By measuring the triple coincidence between the scattered electron, the produced proton, and $\pi^{+}$, the missing mass spectrum of $\rm ^{6}H$ was obtained. A clear peak above $^3$H+n+n+n energy threshold was seen resulting in a ground state energy of $\rm ^{6}H$ at $2.3\pm0.5({\rm stat.})\pm0.4({\rm syst.})$ MeV with a width of $1.9\pm1.0({\rm stat.})\pm0.4({\rm syst.})$ MeV. This work challenges the understandings of multi-nucleon interactions and presents a new method to study light neutron-rich nuclei with electron scattering experiments., Comment: 6 pages, 4 figures

Bayesian inference in function space has gained attention due to its robustness against overparameterization in neural networks. However, approximating the infinite-dimensional function space introduces several challenges. In this work, we discuss function space inference via particle optimization and present practical modifications that improve uncertainty estimation and, most importantly, make it applicable for large and pretrained networks. First, we demonstrate that the input samples, where particle predictions are enforced to be diverse, are detrimental to the model performance. While diversity on training data itself can lead to underfitting, the use of label-destroying data augmentation, or unlabeled out-of-distribution data can improve prediction diversity and uncertainty estimates. Furthermore, we take advantage of the function space formulation, which imposes no restrictions on network parameterization other than sufficient flexibility. Instead of using full deep ensembles to represent particles, we propose a single multi-headed network that introduces a minimal increase in parameters and computation. This allows seamless integration to pretrained networks, where this repulsive last-layer ensemble can be used for uncertainty aware fine-tuning at minimal additional cost. We achieve competitive results in disentangling aleatoric and epistemic uncertainty for active learning, detecting out-of-domain data, and providing calibrated uncertainty estimates under distribution shifts with minimal compute and memory.

Randomization is a powerful tool that endows algorithms with remarkable properties. For instance, randomized algorithms excel in adversarial settings, often surpassing the worst-case performance of deterministic algorithms with large margins. Furthermore, their success probability can be amplified by simple strategies such as repetition and majority voting. In this paper, we enhance deep neural networks, in particular transformer models, with randomization. We demonstrate for the first time that randomized algorithms can be instilled in transformers through learning, in a purely data- and objective-driven manner. First, we analyze known adversarial objectives for which randomized algorithms offer a distinct advantage over deterministic ones. We then show that common optimization techniques, such as gradient descent or evolutionary strategies, can effectively learn transformer parameters that make use of the randomness provided to the model. To illustrate the broad applicability of randomization in empowering neural networks, we study three conceptual tasks: associative recall, graph coloring, and agents that explore grid worlds. In addition to demonstrating increased robustness against oblivious adversaries through learned randomization, our experiments reveal remarkable performance improvements due to the inherently random nature of the neural networks' computation and predictions.

The emergence of spatial coherence in a confined two-dimensional Bose gas of exciton-polaritons with tuneable interactions offers a unique opportunity to explore the role of interactions in a phase transition in a driven-dissipative quantum system, where both the phase transition and thermalisation are mediated by interactions. We investigate, experimentally and numerically, the phase correlations and steady-state properties of the gas over a wide range of interaction strengths by varying the photonic/excitonic fraction of the polaritons and their density. We find that the first order spatial coherence function exhibits algebraic decay consistent with the Berezinskii-Kosterlitz-Thouless (BKT) phase transition. Surprisingly, the exponent of the algebraic decay is inversely proportional to the coherent density of polaritons, in analogy to equilibrium superfluids above the BKT transition, but with a different proportionality constant. Our work paves the way for future investigations of the phenomenon of phase transitions and superfluidity in a driven-dissipative setting, Comment: 11 pages, 6 figures

We present a careful calibration of the exciton fraction of polaritons in high-$Q$ ($\sim 300,000$), long-lifetime ($\sim 300$ ps), GaAs/AlGaAs microcavities.This is a crucial parameter for many-body theories which include the polariton-polariton interactions.It is much harder to establish this number in high-$Q$ structures compared to low-$Q$ structures, because the upper polariton is nearly invisible in high-$Q$ cavities.We present a combination of photoluminescence, photoluminescence excitation, and reflectivity measurements to highly constrain the fit model, and compare the results of this model to the results from low-$Q$ structures.We present a fitted curve of exciton fraction as a function of the lower polariton energy for multiple samples which have been used in prior experiments., Comment: 16 pages, 11 figures

Exciton-polariton laser is a promising source of coherent light for low-energy applications due to its low-threshold operation. However, a detailed experimental study of its spectral purity, which directly affects its coherence properties is still missing. Here}, we present a high-resolution spectroscopic investigation of the energy and linewidth of an exciton-polariton laser in the single-mode regime, which derives its coherent emission from an optically pumped and confined exciton-polariton condensate. We report an ultra-narrow linewidth of 56~MHz or 0.24~$\mu$eV, corresponding to a coherence time of 5.7~ns. The narrow linewidth is consistently achieved by using an exciton-polariton condensate with a high photonic content confined in an optically induced trap. Contrary to previous studies, we show that the excitonic reservoir created by the pump and responsible for creating the trap does not strongly affect the emission linewidth as long as the condensate is trapped and the pump power is well above the condensation (lasing) threshold. \red{The long coherence time of the exciton-polariton system uncovered here opens up opportunities for manipulating its macroscopic quantum state, which is essential for applications in classical and quantum computing.

Segmentation is a critical step in analyzing the developing human fetal brain. There have been vast improvements in automatic segmentation methods in the past several years, and the Fetal Brain Tissue Annotation (FeTA) Challenge 2021 helped to establish an excellent standard of fetal brain segmentation. However, FeTA 2021 was a single center study, and the generalizability of algorithms across different imaging centers remains unsolved, limiting real-world clinical applicability. The multi-center FeTA Challenge 2022 focuses on advancing the generalizability of fetal brain segmentation algorithms for magnetic resonance imaging (MRI). In FeTA 2022, the training dataset contained images and corresponding manually annotated multi-class labels from two imaging centers, and the testing data contained images from these two imaging centers as well as two additional unseen centers. The data from different centers varied in many aspects, including scanners used, imaging parameters, and fetal brain super-resolution algorithms applied. 16 teams participated in the challenge, and 17 algorithms were evaluated. Here, a detailed overview and analysis of the challenge results are provided, focusing on the generalizability of the submissions. Both in- and out of domain, the white matter and ventricles were segmented with the highest accuracy, while the most challenging structure remains the cerebral cortex due to anatomical complexity. The FeTA Challenge 2022 was able to successfully evaluate and advance generalizability of multi-class fetal brain tissue segmentation algorithms for MRI and it continues to benchmark new algorithms. The resulting new methods contribute to improving the analysis of brain development in utero., Comment: Results from FeTA Challenge 2022, held at MICCAI; Manuscript submitted to IEEE Transactions on Medical Imaging (2024). Supplementary Info (including submission methods descriptions) available here: https://zenodo.org/records/10628648

Many complex tasks can be decomposed into simpler, independent parts. Discovering such underlying compositional structure has the potential to enable compositional generalization. Despite progress, our most powerful systems struggle to compose flexibly. It therefore seems natural to make models more modular to help capture the compositional nature of many tasks. However, it is unclear under which circumstances modular systems can discover hidden compositional structure. To shed light on this question, we study a teacher-student setting with a modular teacher where we have full control over the composition of ground truth modules. This allows us to relate the problem of compositional generalization to that of identification of the underlying modules. In particular we study modularity in hypernetworks representing a general class of multiplicative interactions. We show theoretically that identification up to linear transformation purely from demonstrations is possible without having to learn an exponential number of module combinations. We further demonstrate empirically that under the theoretically identified conditions, meta-learning from finite data can discover modular policies that generalize compositionally in a number of complex environments., Comment: Published as a conference paper at ICLR 2024; Code available at https://github.com/smonsays/modular-hyperteacher

Battery management systems (BMS) are becoming increasingly important in the modern age, where clean energy awareness is getting more prominent. They are responsible for controlling large battery packs in modern electric vehicles. However, conventional solutions rely only on a wired design, which adds manufacturing cost and complexity. Recent research has considered wireless solutions for the BMS. However, it is still challenging to develop a solution that considers both the active in-vehicle and the external second-life applications. The battery passport initiative aims to keep track of the batteries, both during active and inactive use cases. There is a need to provide a secure design while considering energy and cost-efficient solutions. We aim to fill this gap by proposing a wireless solution based on near-field communication (NFC) that extends previous work and provides a unified architecture for both use cases. To provide protection against common wireless threats, an advanced security analysis is performed, as well as a system design analysis for the wake-up process that reduces the daily power consumption of the stored battery packs from milli- to microwatts., Comment: Accepted copy for Publication at the 8th IEEE International Conference on Smart and Sustainable Technologies, 2023

In modern systems that rely on the use of Battery Management Systems (BMS), longevity and the re-use of battery packs have always been important topics of discussion. These battery packs would be stored inside warehouses where they would need to be properly monitored and configured before their re-integration into the new systems. Traditional use of wired connections can be very cumbersome, and sometimes even impossible, due to the outer layers and packaging. To circumvent these issues, we propose an extension to the conventional BMS design that incorporates the use of Near Field Communication (NFC) for the purpose of wireless battery pack status readout. Additionally, to ensure that these packs are only managed by authenticated devices and that the data that is communicated with is protected against outside eavesdropping and tampering, we present a solution in the form of a lightweight security layer on top of the NFC protocol. To show the feasibility of our design, an accompanying prototype has been implemented and evaluated., Comment: Accepted copy for Publication at the 16th IEEE International Conference on RFID, 2022

Be it in the IoT or automotive domain, implicit certificates are gaining ever more prominence in constrained embedded devices. They present a resource-efficient security solution against common threat concerns. The computational requirements are not the main issue anymore. The focus is now placed on determining a good balance between the provided security level and the derived threat model. A security aspect that often gets overlooked is the establishment of secure communication sessions, as most design solutions are based only on the use of static key derivation, and therefore, lack the perfect forward secrecy. This leaves the transmitted data open for potential future exposures by having keys tied to the certificates rather than the communication sessions. We aim to patch this gap, by presenting a design that utilizes the Station to Station (STS) protocol with implicit certificates. In addition, we propose potential protocol optimization implementation steps and run a comprehensive study on the performance and security level between the proposed design and the state-of-the-art key derivation protocols. In our comparative study, we show that with a slight computational increase of 20\% compared to a static ECDSA key derivation, we are able to mitigate many session-related security vulnerabilities that would otherwise remain open., Comment: Accepted copy for Publication at the Design, Automation and Test in Europe (DATE) Conference 2023

With the advent of clean energy awareness and systems that rely on extensive battery usage, the community has seen an increased interest in the development of more complex and secure Battery Management Systems (BMS). In particular, the inclusion of BMS in modern complex systems like electric vehicles and power grids has presented a new set of security-related challenges. A concern is shown when BMS are intended to extend their communication with external system networks, as their interaction can leave many backdoors open that potential attackers could exploit. Hence, it is highly desirable to find a general design that can be used for BMS and its system inclusion. In this work, a security architecture solution is proposed intended for the communication between BMS and other system devices. The aim of the proposed architecture is to be easily applicable in different industrial settings and systems, while at the same time keeping the design lightweight in nature., Comment: Accepted copy for Publication at the 23rd International Conference on Industrial Technology (ICIT), IEEE, 2022

With the advent of modern embedded systems, logging as a process is becoming more and more prevalent for diagnostic and analytic services. Traditionally, storage and managing of the logged data are generally kept as a part of one entity together with the main logic components. In systems that implement network connections, this activity is usually handled over a remote device. However, enabling remote connection is still considered a limiting factor for many embedded devices due to the demanding production cost. A significant challenge is presented to vendors who need to decide how the data will be extracted and handled for an embedded platform during the design concept phase. It is generally desirable that logging memory modules are able to be addressed as separate units. These devices need to be appropriately secured and verifiable on a different system since data compromise can lead to enormous privacy and even financial losses. In this paper, we present two patterns. First, a pattern that allows flexible logging operation design in terms of module and interface responsibility separation. Second, a pattern for the design of secure logging processes during the utilization of constrained embedded devices. The introduced patterns fulfil the following conditions: (i) flexibility, design is independent of the chip vendors making the logging memory modules easily replaceable, (ii) self-sufficiency, every logging controller is maintained as a separate entity in a decentralized topology, (iii) security, through providing authenticity, confidentiality, and integrity by means of using a dedicated security module., Comment: Accepted copy for Publication at the 26th European Conference on Pattern Languages of Programs (EuroPLoP), ACM, 2021

Recent architectural developments have enabled recurrent neural networks (RNNs) to reach and even surpass the performance of Transformers on certain sequence modeling tasks. These modern RNNs feature a prominent design pattern: linear recurrent layers interconnected by feedforward paths with multiplicative gating. Here, we show how RNNs equipped with these two design elements can exactly implement (linear) self-attention, the main building block of Transformers. By reverse-engineering a set of trained RNNs, we find that gradient descent in practice discovers our construction. In particular, we examine RNNs trained to solve simple in-context learning tasks on which Transformers are known to excel and find that gradient descent instills in our RNNs the same attention-based in-context learning algorithm used by Transformers. Our findings highlight the importance of multiplicative interactions in neural networks and suggest that certain RNNs might be unexpectedly implementing attention under the hood.

Wireless Battery Management Systems (BMS) are increasingly being considered for modern applications. The ever-increasing complexity and production costs of BMS modules and wired connections resulted in a necessity for new ideas and approaches. Despite this growing trend, there is a lack of generic solutions focused on battery cells' sensor readout, where wireless communication allows for a more flexible and cost-efficient sensor installation in battery packs. Many wireless technologies, such as those that use the 2.4 GHz frequency band, suffer from interference and other limitations. In this article, we present an alternative approach to communication in BMS that relies on the use of Near Field Communication (NFC) technology for battery sensor readouts. As an answer to the rising concern over the counterfeited battery packs, we consider an authentication schema for battery pack validation. We further consider security measures for the processed and stored BMS status data. To show that a general BMS application can make use of our design, we implement a BMS demonstrator using the targeted components. We further test the demonstrator on the technical and functional level, by also performing evaluation on its performance, energy usage, and a security threat model., Comment: arXiv admin note: text overlap with arXiv:2308.09366

In the last several years, wireless Battery Management Systems (BMS) have slowly become a topic of interest from both academia and industry. It came from a necessity derived from the increased production and use in different systems, including the electrical vehicles. Wireless communication allows for a more flexible and cost-efficient sensor installation in battery packs. However, many wireless technologies, such as those that use the 2.4 GHz frequency band, suffer from the interference limitations that need to be addressed. In this paper we present an alternative approach to communication in BMS that relies on the use of Near Field Communication (NFC) technology for battery sensor readouts. Due to a vital concern over the counterfeited battery pack products, security measures are also considered. To this end, we propose the use of an effective, and easy to integrate, authentication schema that is supported by the dedicated NFC devices. To test the usability of our design, a demonstrator using the targeted devices was implemented and evaluated., Comment: Accepted copy for Publication at the IEEE International Conference on RFID-Technologies and Applications, 2021

Biomimetic, dexterous robotic hands have the potential to replicate much of the tasks that a human can do, and to achieve status as a general manipulation platform. Recent advances in reinforcement learning (RL) frameworks have achieved remarkable performance in quadrupedal locomotion and dexterous manipulation tasks. Combined with GPU-based highly parallelized simulations capable of simulating thousands of robots in parallel, RL-based controllers have become more scalable and approachable. However, in order to bring RL-trained policies to the real world, we require training frameworks that output policies that can work with physical actuators and sensors as well as a hardware platform that can be manufactured with accessible materials yet is robust enough to run interactive policies. This work introduces the biomimetic tendon-driven Faive Hand and its system architecture, which uses tendon-driven rolling contact joints to achieve a 3D printable, robust high-DoF hand design. We model each element of the hand and integrate it into a GPU simulation environment to train a policy with RL, and achieve zero-shot transfer of a dexterous in-hand sphere rotation skill to the physical robot hand., Comment: for project website, see https://srl-ethz.github.io/get-ball-rolling/ . for video, see https://youtu.be/YahsMhqNU8o . for code, see https://github.com/srl-ethz/faive_gym_oss . Published to the 2023 IEEE-RAS International Conference on Humanoid Robots

Internet measurements are a crucial foundation of IPv6-related research. Due to the infeasibility of full address space scans for IPv6 however, those measurements rely on collections of reliably responsive, unbiased addresses, as provided e.g., by the IPv6 Hitlist service. Although used for various use cases, the hitlist provides an unfiltered list of responsive addresses, the hosts behind which can come from a range of different networks and devices, such as web servers, customer-premises equipment (CPE) devices, and Internet infrastructure. In this paper, we demonstrate the importance of tailoring hitlists in accordance with the research goal in question. By using PeeringDB we classify hitlist addresses into six different network categories, uncovering that 42% of hitlist addresses are in ISP networks. Moreover, we show the different behavior of those addresses depending on their respective category, e.g., ISP addresses exhibiting a relatively low lifetime. Furthermore, we analyze different Target Generation Algorithms (TGAs), which are used to increase the coverage of IPv6 measurements by generating new responsive targets for scans. We evaluate their performance under various conditions and find generated addresses to show vastly differing responsiveness levels for different TGAs., Comment: Proceedings of the Network Traffic Measurement and Analysis Conference (TMA)

The Kalman filter (KF) is a widely-used algorithm for tracking dynamic systems that are captured by state space (SS) models. The need to fully describe a SS model limits its applicability under complex settings, e.g., when tracking based on visual data, and the processing of high-dimensional signals often induces notable latency. These challenges can be treated by mapping the measurements into latent features obeying some postulated closed-form SS model, and applying the KF in the latent space. However, the validity of this approximated SS model may constitute a limiting factor. In this work, we study tracking from high-dimensional measurements under complex settings using a hybrid model-based/data-driven approach. By gradually tackling the challenges in handling the observations model and the task, we develop Latent-KalmanNet, which implements tracking from high-dimensional measurements by leveraging data to jointly learn the KF along with the latent space mapping. Latent-KalmanNet combines a learned encoder with data-driven tracking in the latent space using the recently proposed-KalmanNet, while identifying the ability of each of these trainable modules to assist its counterpart via providing a suitable prior (by KalmanNet) and by learning a latent representation that facilitates data-aided tracking (by the encoder). Our empirical results demonstrate that the proposed Latent-KalmanNet achieves improved accuracy and run-time performance over both model-based and data-driven techniques by learning a surrogate latent representation that most facilitates tracking, while operating with limited complexity and latency., Comment: Under review for publication in the IEEE

Carbon nanotubes are excellent nano-electromechanical systems, combining high resonance frequency, low mass, and large zero-point motion. At cryogenic temperatures they display high mechanical quality factors. Equally they are outstanding single electron devices with well-known quantum levels and have been proposed for the implementation of charge or spin qubits. The integration of these devices into microwave optomechanical circuits is however hindered by a mismatch of scales, between typical microwave wavelengths, nanotube segment lengths, and nanotube deflections. As experimentally demonstrated recently in [Blien et al., Nat. Comm. 11, 1363 (2020)], coupling enhancement via the quantum capacitance allows to circumvent this restriction. Here we extend the discussion of this experiment. We present the subsystems of the device and their interactions in detail. An alternative approach to the optomechanical coupling is presented, allowing to estimate the mechanical zero point motion scale. Further, the mechanical damping is discussed, hinting at hitherto unknown interaction mechanisms., Comment: 17 pages, 13 figures, 3 tables; v3: minor corrections