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The rapid expansion of Internet of Things (IoT), edge, and embedded devices in the past decade has introduced numerous challenges in terms of security and configuration management. Simultaneously, advances in cloud-native development practices have greatly enhanced the development experience and facilitated quicker updates, thereby enhancing application security. However, applying these advances to IoT, edge, and embedded devices remains a complex task, primarily due to the heterogeneous environments and the need to support devices with extended lifespans. WebAssembly and the WebAssembly System Interface (WASI) has emerged as a promising technology to bridge this gap. As WebAssembly becomes more popular on IoT, edge, and embedded devices, there is a growing demand for hardware interface support in WebAssembly programs. This work presents WASI proposals and proof-of-concept implementations to enable hardware interaction with I2C and USB, which are two commonly used protocols in IoT, directly from WebAssembly applications. This is achieved by running the device drivers within WebAssembly as well. A thorough evaluation of the proof of concepts shows that WASI-USB introduces a minimal overhead of at most 8% compared to native operating system USB APIs. However, the results show that runtime initialization overhead can be significant in low-latency applications., Comment: Preprint of article submitted to IEEE/IFIP Network Operations and Management Symposium 2025

The spherically bent crystal analyzer (SBCA) is a widely-adopted hard x-ray optic, renowned for its good energy resolution and large collection solid angle. It is frequently employed in synchrotron-based techniques like Resonant Inelastic X-ray Scattering (RIXS) and X-ray Emission Spectroscopy, as well as in the rebirth of laboratory-based X-ray Absorption Fine Strucure (XAFS) and XES, and its predominant use has been in 'symmetric' operation on the Rowland circle. The recent study of Gironda et al. (J. Anal. At. Spectrom., 2024, 39, 1375) emphasizes the benefits of 'asymmetric' SBCA operation, demonstrating the use of multiple crystal reflections from a single SBCA to broaden its accessible energy range. Furthermore, Gironda et al. demonstrate that asymmetric operation frequently mitigates energy broadening intrinsic to Johann optics and propose that under a specific Rowland circle configuration, designated here the Johann Normal Alignment (JNA), such broadening is eradicated altogether. We report extensive ray tracing simulations to scrutinize the impact of asymmetric configurations on energy broadening and detector plane defocusing. We find that the performance of asymmetric SBCA operation generally exceeds its symmetric counterpart in energy resolution when no analyzer masking is used and, with strategic detector placement, the decrease in detection efficiency due to defocusing can be minimized. Spectroscopic imaging is adversely affected by the detector plane blurring, but rejection of scattering from special environment windows in x-ray Raman imaging is still feasible. These results help inform future, more common implementation of asymmetric reflections with SBCA.

High resolution, hard x-ray spectroscopy at synchrotron x-ray light sources commonly uses spherically bent crystal analyzers (SBCAs) formed by shaping a single crystal wafer to a spherical backing. These Rowland circle optics are almost always used in a 'symmetric' (or nearly symmetric) configuration wherein the reciprocal lattice vector used for energy selectivity via diffraction is coincident with the normal vector to the curved wafer surface. However, Gironda, et al., recently proposed that asymmetric operation of SBCA, wherein the reciprocal lattice vector is no longer normal to the wafer surface, has significant operational benefits and has been an underutilized opportunity. First, those authors find improved energy resolution through decreased Johann error, or equivalently find increased solid angle at a chosen experimental tolerance for energy broadening. Second, they find productive, high-resolution use of a large number of reciprocal lattice vectors from a single SBCA, thus enabling operation over a wide energy range without need to exchange SBCA upon making large changes in desired photon energy. These observations hold the potential to improve performance, increase flexibility and decrease cost for both laboratory and synchrotron applications. Given these motivations, we report an open-source software package, hklhop, that enables exploration of the complex space of analyzer wafer choice, experimental energy range or ranges, and desired suppression of Johann error. This package can guide both the design and the day-to-day operations of Rowland spectrometers enabled for asymmetric use.

Atmospheres above lava-ocean planets (LOPs) hold clues as to the properties of their interiors, owing to the expectation that the two reservoirs are in chemical equilibrium. Here we consider `mineral' atmospheres produced in equilibrium with silicate liquids. We treat oxygen fugacity ($f$O$_2$) as an independent variable, together with temperature ($T$) and composition ($X$), to compute equilibrium partial pressures ($p$) of stable gas species at the liquid-gas interface. Above this boundary, the atmospheric speciation and the pressure-temperature structure are computed self-consistently to yield emission spectra. We explore a wide array of plausible compositions, oxygen fugacities (between 6 log$_{10}$ units below- and above the iron-w\"ustite buffer, IW) and irradiation temperatures (2000, 2500, 3000 and 3500 K) relevant to LOPs. We find that SiO(g), Fe(g) and Mg(g) are the major species below $\sim$IW, ceding to O$_2$(g) and O(g) in more oxidised atmospheres. The transition between the two regimes demarcates a minimum in total pressure ($P$). Because $p$ scales linearly with $X$, emission spectra are only modest functions of composition. By contrast, $f$O$_2$ can vary over orders of magnitude, thus causing commensurate changes in $p$. Reducing atmospheres show intense SiO emission, creating a temperature inversion in the upper atmosphere. Conversely, oxidised atmospheres have lower $p$SiO and lack thermal inversions, with resulting emission spectra that mimic that of a black body. Consequently, the intensity of SiO emission relative to the background, generated by MgO(g), can be used to quantify the $f$O$_2$ of the atmosphere. Depending on the emission spectroscopy metric of the target, deriving the $f$O$_2$ of known nearby LOPs is possible with a few secondary occultations observed by JWST.

Purpose: Federated training is often hindered by heterogeneous datasets due to divergent data storage options, inconsistent naming schemes, varied annotation procedures, and disparities in label quality. This is particularly evident in the emerging multi-modal learning paradigms, where dataset harmonization including a uniform data representation and filtering options are of paramount importance. Methods: DICOM structured reports enable the standardized linkage of arbitrary information beyond the imaging domain and can be used within Python deep learning pipelines with highdicom. Building on this, we developed an open platform for data integration and interactive filtering capabilities that simplifies the process of assembling multi-modal datasets. Results: In this study, we extend our prior work by showing its applicability to more and divergent data types, as well as streamlining datasets for federated training within an established consortium of eight university hospitals in Germany. We prove its concurrent filtering ability by creating harmonized multi-modal datasets across all locations for predicting the outcome after minimally invasive heart valve replacement. The data includes DICOM data (i.e. computed tomography images, electrocardiography scans) as well as annotations (i.e. calcification segmentations, pointsets and pacemaker dependency), and metadata (i.e. prosthesis and diagnoses). Conclusion: Structured reports bridge the traditional gap between imaging systems and information systems. Utilizing the inherent DICOM reference system arbitrary data types can be queried concurrently to create meaningful cohorts for clinical studies. The graphical interface as well as example structured report templates will be made publicly available.

Federated learning (FL) is a renowned technique for utilizing decentralized data while preserving privacy. However, real-world applications often involve inherent challenges such as partially labeled datasets, where not all clients possess expert annotations of all labels of interest, leaving large portions of unlabeled data unused. In this study, we conduct the largest federated cardiac CT imaging analysis to date, focusing on partially labeled datasets ($n=8,124$) of Transcatheter Aortic Valve Implantation (TAVI) patients over eight hospital clients. Transformer architectures, which are the major building blocks of current foundation models, have shown superior performance when trained on larger cohorts than traditional CNNs. However, when trained on small task-specific labeled sample sizes, it is currently not feasible to exploit their underlying attention mechanism for improved performance. Therefore, we developed a two-stage semi-supervised learning strategy that distills knowledge from several task-specific CNNs (landmark detection and segmentation of calcification) into a single transformer model by utilizing large amounts of unlabeled data typically residing unused in hospitals to mitigate these issues. This method not only improves the predictive accuracy and generalizability of transformer-based architectures but also facilitates the simultaneous learning of all partial labels within a single transformer model across the federation. Additionally, we show that our transformer-based model extracts more meaningful features for further downstream tasks than the UNet-based one by only training the last layer to also solve segmentation of coronary arteries. We make the code and weights of the final model openly available, which can serve as a foundation model for further research in cardiac CT imaging.

Silicon-Germanium heterostructures are a promising quantum circuit platform, but crucial aspects as the long-term charge dynamics and cooldown-to-cooldown variations are still widely unexplored quantitatively. In this letter we present the results of an extensive bias cooling study performed on gated silicon-germanium quantum dots with an Al2O3-dielectric. Over 80 cooldowns were performed in the course of our investigations. The performance of the devices is assessed by low-frequency charge noise measurements in the band of 200 micro Hertz to 10 milli Hertz. We measure the total noise power as a function of the applied voltage during cooldown in four different devices and find a minimum in noise at 0.7V bias cooling voltage for all observed samples. We manage to decrease the total noise power median by a factor of 6 and compute a reduced tunneling current density using Schr\"odinger-Poisson simulations. Furthermore, we show the variation in noise from the same device in the course of eleven different cooldowns performed under the nominally same conditions., Comment: 11 pages, 8 figures, silicon, SiGe, heterostructure, bias cooling, experiment, simulation

This review is focused on describing the logic by which we make predictions of exoplanetary compositions and mineralogies, and how these processes could lead to compositional diversity among rocky exoplanets. We use these predictions to determine the sensitivity of present-day and future observations to detecting compositional differences between rocky exoplanets and the four terrestrial planets. First, we review data on stellar abundances and infer how changes in composition may manifest themselves in the expected bulk compositions of rocky exoplanets (section 2). Converting this information in mass-radius relationships requires calculation of the stable mineral assemblages at a given temperature-pressure-composition (T-P-X), an exercise we describe in section 3. Should the planet be hot enough to engender partial melting of the mantle, then these liquids are likely to rise to the surface and erupt to form planetary crusts; the possible compositional and mineralogical variability of which we examine in section 4. Finally, the expected spectroscopic responses of such crusts are examined in section 5., Comment: Chapter 8 accepted for publication in the Reviews in Mineralogy and Geochemistry (RiMG) Volume 90 on "Exoplanets: Compositions, Mineralogy, and Evolution" edited by Natalie Hinkel, Keith Putirka, and Siyi Xu; 40 pages, 11 figures, and 3 equations

Optical amplification, crucial for modern communication and data center interconnects, primarily relies on erbium-doped fiber amplifiers (EDFAs) to enhance signals without distortion. While EDFAs were historically decisive for the introduction of dense wavelength-division multiplexing, they only cover a portion of the low-loss spectrum of optical fibers. Pioneering work on optical traveling-wave parametric amplifiers (TWPAs) utilizing intrinsic third-order optical nonlinearity has led to demonstrations of increased channel capacity and performance. TWPAs are unidirectional, offer high gain, and can reach the 3-dB quantum limit for phase-preserving amplifiers. Despite the use of highly nonlinear fibers or bulk waveguides, their power requirements and technical complexity have impeded adoption. In contrast, TWPAs based on photonic integrated circuits (PICs) offer the advantages of substantially increased mode confinement and optical nonlinearity but have been limited in bandwidth because of the trade-off with maintaining low propagation loss. We overcome this challenge by using low-loss gallium phosphide-on-silicon dioxide PICs and attain up to 35~dB of parametric gain with waveguides only a few centimeters long in a compact footprint of 0.25 square millimeters. Fiber-to-fiber net gain exceeding 10 dB across a bandwidth of approximately 140 nm is achieved, surpassing the gain window of a standard C-band EDFA. We furthermore demonstrate the capability to handle weak signals; input powers can range over six orders of magnitude while maintaining a low noise figure. We exploit these performance characteristics to amplify both optical frequency combs and coherent communication signals. This marks the first ultra-broadband, high-gain, continuous-wave amplification in a PIC, opening up new capabilities for next-generation optical communication, metrology, and sensing., Comment: 20 pages, 13 figures

The connectivity within single carrier information-processing devices requires transport and storage of single charge quanta. Our all-electrical Si/SiGe shuttle device, called quantum bus (QuBus), spans a length of 10 $\mathrm{\mu}$m and is operated by only six simply-tunable voltage pulses. It operates in conveyor-mode, i.e. the electron is adiabatically transported while confined to a moving QD. We introduce a characterization method, called shuttle-tomography, to benchmark the potential imperfections and local shuttle-fidelity of the QuBus. The fidelity of the single-electron shuttle across the full device and back (a total distance of 19 $\mathrm{\mu}$m) is $(99.7 \pm 0.3)\,\%$. Using the QuBus, we position and detect up to 34 electrons and initialize a register of 34 quantum dots with arbitrarily chosen patterns of zero and single-electrons. The simple operation signals, compatibility with industry fabrication and low spin-environment-interaction in $^{28}$Si/SiGe, promises spin-conserving transport of spin qubits for quantum connectivity in quantum computing architectures., Comment: 11 pages, 6 figures

Quantum processor architectures must enable scaling to large qubit numbers while providing two-dimensional qubit connectivity and exquisite operation fidelities. For microwave-controlled semiconductor spin qubits, dense arrays have made considerable progress, but are still limited in size by wiring fan-out and exhibit significant crosstalk between qubits. To overcome these limitations, we introduce the SpinBus architecture, which uses electron shuttling to connect qubits and features low operating frequencies and enhanced qubit coherence. Device simulations for all relevant operations in the Si/SiGe platform validate the feasibility with established semiconductor patterning technology and operation fidelities exceeding 99.9 %. Control using room temperature instruments can plausibly support at least 144 qubits, but much larger numbers are conceivable with cryogenic control circuits. Building on the theoretical feasibility of high-fidelity spin-coherent electron shuttling as key enabling factor, the SpinBus architecture may be the basis for a spin-based quantum processor that meets the scalability requirements for practical quantum computing., Comment: 15 pages, 9 figures

Recent advances using Density Functional Theory (DFT) to augment Multiplet Ligand Field Theory (MLFT) have led to ab-initio calculations of many formerly empirical parameters. This development makes MLFT more predictive instead of interpretive, thus improving its value for understanding highly correlated 3d, 4d, and f-electron systems. Here, we explore a DFT + MLFT based approach for core-to-core K{\alpha} x-ray emission spectra (XES) and evaluate its performance for a range of transition metal systems. We find good agreement between theory and experiment, as well as the ability to capture key spectral trends related to spin and oxidation state. We also discuss limitations of the model in the context of the remaining free parameters and suggest directions forward.

Photonic crystals, material structures in which the dielectric function varies periodically in one, two, or three dimensions, can provide exquisite control over the propagation and confinement of light. By tailoring their band structure, exceptional optical effects can be achieved, such as slow light propagation or, through the creation of photonic bandgaps, optical cavities with both a high quality factor and a small mode volume. Photonic crystal cavities have been used to realize compact nano-lasers and achieve strong coupling to quantum emitters, such as semiconductor quantum dots, color centers, or cold atoms. A useful attribute of photonic crystals is the ability to create chirped mirrors. Chirping has underpinned advances in ultra-fast lasers based on bulk mirrors, but has yet to be fully exploited in integrated photonics, where it could provide a means to engineer otherwise unattainable dispersion profiles for a range of nonlinear optical applications, including soliton frequency comb generation. The vast majority of integrated resonators for frequency combs make use of microring geometries, where only waveguide width and height are varied to engineer dispersion. Generation of frequency combs has been demonstrated with one-dimensional photonic crystal cavities made of silicon nitride, but the low index contrast prevents formation of broad soliton combs. We overcome these challenges by using a photonic-crystal Fabry-P\'erot resonator made of gallium phosphide, a material with a high refractive index and a Kerr nonlinearity 200 times larger than that of silicon nitride. We employ chirped photonic crystal mirrors to provide anomalous dispersion. With subharmonic pulsed pumping at an average power of 23.6 mW, we are able to access stable dissipative Kerr frequency combs. We demonstrate soliton formation with a 3-dB bandwidth of 3.0 THz, corresponding to a pulse duration of 60 fs., Comment: 22 pages, 16 figures

Gate-layouts of spin qubit devices are commonly adapted from previous successful devices. As qubit numbers and the device complexity increase, modelling new device layouts and optimizing for yield and performance becomes necessary. Simulation tools from advanced semiconductor industry need to be adapted for smaller structure sizes and electron numbers. Here, we present a general approach for electrostatically modelling new spin qubit device layouts, considering gate voltages, heterostructures, reservoirs and an applied source-drain bias. Exemplified by a specific potential, we study the influence of each parameter. We verify our model by indirectly probing the potential landscape of two design implementations through transport measurements. We use the simulations to identify critical design areas and optimize for robustness with regard to influence and resolution limits of the fabrication process., Comment: 10 pages, 6 figures

This paper consists of two independent, but related parts. In the first part we show how to use symbolic computation to derive explicit expressions for the first few moments of the number of implicants that a random Boolean function has, or equivalently the number of fixed-dimensional subcubes contained in a random subset of the $n$-dimensional cube. These explicit expressions suggest, but do not prove, that these random variables are always asymptotically normal. The second part presents a full, human-generated proof, of this asymptotic normality, first proved by Urszula Konieczna., Comment: 12 pages. For Maple code and other supporting information, see http://www.math.rutgers.edu/~zeilberg/mamarim/mamarimhtml/subcubes.html

Despite their remarkable performance, deep neural networks remain unadopted in clinical practice, which is considered to be partially due to their lack in explainability. In this work, we apply attribution methods to a pre-trained deep neural network (DNN) for 12-lead electrocardiography classification to open this "black box" and understand the relationship between model prediction and learned features. We classify data from a public data set and the attribution methods assign a "relevance score" to each sample of the classified signals. This allows analyzing what the network learned during training, for which we propose quantitative methods: average relevance scores over a) classes, b) leads, and c) average beats. The analyses of relevance scores for atrial fibrillation (AF) and left bundle branch block (LBBB) compared to healthy controls show that their mean values a) increase with higher classification probability and correspond to false classifications when around zero, and b) correspond to clinical recommendations regarding which lead to consider. Furthermore, c) visible P-waves and concordant T-waves result in clearly negative relevance scores in AF and LBBB classification, respectively. In summary, our analysis suggests that the DNN learned features similar to cardiology textbook knowledge., Comment: Accepted version including new experiments on additional database