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Information processing in networks entails a dynamical transfer of information between stochastic variables. Transfer entropy is widely used for quantification of the directional transfer of information between input and output trajectories. However, currently there is no exact technique to quantify transfer entropy given the dynamical model of a general network. Here we introduce an exact computational algorithm, Transfer Entropy-Path Weight Sampling (TE-PWS), to quantify transfer entropy and its variants in an arbitrary network in the presence of multiple hidden variables, nonlinearity, transient conditions, and feedback. TE-PWS extends a recently introduced algorithm Path Weight Sampling (PWS) and uses techniques from the statistical physics of polymers and trajectory sampling. We apply TE-PWS to linear and nonlinear systems to reveal how transfer entropy can overcome naive applications of data processing inequalities in presence of feedback., Comment: 12 pages, 4 figures

For asynchronous transmission of short blocks, preambles for packet detection contribute a non-negligible overhead. To reduce the required preamble length, joint detection and decoding (JDD) techniques have been proposed that additionally utilize the payload part of the packet for detection. In this paper, we analyze two instances of JDD, namely hybrid preamble and energy detection (HyPED) and decoder-aided detection (DAD). While HyPED combines the preamble with energy detection for the payload, DAD also uses the output of a channel decoder. For these systems, we propose novel achievability and converse bounds for the rates over the binary-input additive white Gaussian noise (BI-AWGN) channel. Moreover, we derive a general bound on the required blocklength for JDD. Both the theoretical bound and the simulation of practical codebooks show that the rate of DAD quickly approaches that of synchronous transmission., Comment: 6 Pages, 3 Figures, submitted to IEEE for possible publication

The use of WiFi signals to sense the physical environment is gaining popularity, with some common applications being motion detection and transmitter localization. Standard-compliant WiFi provides a cost effective, easy and backward-compatible approach to Joint Communication and Sensing and enables a seamless transfer of results from experiments to practical applications. However, most WiFi sensing research is conducted on channel state information (CSI) data from current-generation devices, which are usually not meant for sensing applications and thus lack sufficient spatial diversity or phase synchronization. With ESPARGOS, we previously developed a phase-coherent, real-time capable many-antenna WiFi channel sounder specifically for wireless sensing. We describe how we use ESPARGOS to capture large CSI datasets that we make publicly available. The datasets are extensively documented and labeled, for example with information from reference positioning systems, enabling data-driven and machine learning-based research.

Semiconductor-superconductor hybrid materials are used as a platform to realise Andreev bound states, which hold great promise for quantum applications. These states require transparent interfaces between the semiconductor and superconductor, which are typically realised by in-situ deposition of an Al superconducting layer. Here we present a hybrid material based on an InAs two-dimensional electron gas (2DEG) combined with in-situ deposited Nb and NbTi superconductors, which offer a larger operating range in temperature and magnetic field due to their larger superconducting gap. We overcome the inherent difficulty associated with the formation of an amorphous interface between III-V semiconductors and Nb-based superconductors by introducing a 7 nm Al interlayer. The Al interlayer provides an epitaxial connection between an in-situ magnetron sputtered Nb or NbTi thin film and a shallow InAs 2DEG. This metal-to-metal epitaxy is achieved by optimization of the material stack and results in an induced superconducting gap of approximately 1 meV, determined from transport measurements of superconductor-semiconductor Josephson junctions. This induced gap is approximately five times larger than the values reported for Al-based hybrid materials and indicates the formation of highly-transparent interfaces that are required in high-quality hybrid material platforms.

As artificial intelligence (AI) technology advances, ensuring the robustness and safety of AI-driven systems has become paramount. However, varying perceptions of robustness among AI developers create misaligned evaluation metrics, complicating the assessment and certification of safety-critical and complex AI systems such as autonomous driving (AD) agents. To address this challenge, we introduce Simulation-Based Robustness Assessment Framework (S-RAF) for autonomous driving. S-RAF leverages the CARLA Driving simulator to rigorously assess AD agents across diverse conditions, including faulty sensors, environmental changes, and complex traffic situations. By quantifying robustness and its relationship with other safety-critical factors, such as carbon emissions, S-RAF aids developers and stakeholders in building safe and responsible driving agents, and streamlining safety certification processes. Furthermore, S-RAF offers significant advantages, such as reduced testing costs, and the ability to explore edge cases that may be unsafe to test in the real world. The code for this framework is available here: https://github.com/cognitive-robots/rai-leaderboard

MIRC-X and MYSTIC are six-telescope near-infrared beam (1.08-2.38 ${\mu}$m) combiners at the CHARA Array on Mt Wilson CA, USA. Ever since the commissioning of MIRC-X (J and H bands) in 2018 and MYSTIC (K bands) in 2021, they have been the most popular and over-subscribed instruments at the array. Observers have been able to image stellar objects with sensitivity down to 8.1 mag in H and 7.8 mag in K-band under the very best conditions. In 2022 MYSTIC was upgraded with a new ABCD mode using the VLTI/GRAVITY 4-beam integrated optics chip, with the goal of improving the sensitivity and calibration. The ABCD mode has been used to observe more than 20 T Tauri stars; however, the data pipeline is still being developed. Alongside software upgrades, we detail planned upgrades to both instruments in this paper. The main upgrades are: 1) Adding a motorized filter wheel to MIRC-X along with new high spectral resolution modes 2) Updating MIRC-X optics to allow for simultaneous 6T J+H observations 3) Removing the warm window between the spectrograph and the warm optics in MYSTIC 4) Adding a 6T ABCD mode to MIRC-X in collaboration with CHARA/SPICA 5) Updating the MIRC-X CRED-ONE camera funded by Prof. Kraus from U. Exeter 6) Carrying out science verification of the MIRC-X polarization mode 7) Developing new software for ABCD-mode data reduction and more efficient calibration routines. We expect these upgrades to not only improve the observing experience, but also increase the sensitivity by 0.4 mag in J+H-bands, and 1 mag in K-band., Comment: 11 pages, 9 figures, Submitted to SPIE in Yokohama 2024

Massive evolved stars such as red supergiants and hypergiants are potential progenitors of Type II supernovae, and they are known for ejecting substantial amounts of matter, up to half their initial mass, during their final evolutionary phases. The rate and mechanism of this mass loss play a crucial role in determining their ultimate fate and the likelihood of their progression to supernovae. However, the exact mechanisms driving this mass ejection have long been a subject of research. Recent observations, such as the Great Dimming of Betelgeuse, have suggested that the activity of large convective cells, combined with pulsation, could be a plausible explanation for such mass loss events. In this context, we conducted interferometric observations of the famous yellow hypergiant, $\rho$ Cassiopeiae using the CHARA Array in H and K-band wavelengths. $\rho$ Cas is well known for its recurrent eruptions, characterized by periods of visual dimming ($\sim$1.5-2 mag) followed by recovery. From our observations, we derived the diameter of the limb-darkened disk and found that this star has a radius of $1.04\pm0.01$ milliarcseconds (mas), or $564 - 700 R_\odot$. We performed image reconstructions with three different image reconstruction software packages, and they unveiled the presence of giant hot and cold spots on the stellar surface. We interpret these prominent hot spots as giant convection cells, suggesting a possible connection to mass ejections from the star's envelope. Furthermore, we detected spectral CO emission lines in the K-band ($\lambda=2.31-2.38 \mu$m), and the image reconstructions in these spectral lines revealed an extended circumstellar envelope with a radius of $1.45\pm0.10$ mas., Comment: 21 pages, 13 figures, 4 tables, Accepted by ApJ

Over the past few decades, numerous $N$-phase incompressible diffuse-interface flow models with non-matching densities have been proposed. Despite aiming to describe the same physics, these models are generally distinct, and an overarching modeling framework is absent. This paper provides a unified framework for $N$-phase incompressible Navier-Stokes Cahn-Hilliard Allen-Cahn mixture models with a single momentum equation. The framework naturally emerges from continuum mixture theory, exhibits an energy-dissipative structure, and is invariant to the choice of fundamental variables. This opens the door to exploring connections between existing $N$-phase models and facilitates the computation of $N$-phase flow models rooted in continuum mixture theory., Comment: Preprint, 45 pages

Simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) provides a promising way to expand coverage in wireless communications. However, limitation of single STAR-RIS inspire us to integrate the concept of multi-hop transmissions, as focused on RIS in existing research. Therefore, we propose the novel architecture of multi-hop STAR-RISs to achieve a wider range of full-plane service coverage. In this paper, we intend to solve active beamforming of the base station and passive beamforming of STAR-RISs, aiming for maximizing the energy efficiency constrained by hardware limitation of STAR-RISs. Furthermore, we investigate the impact of the on-off state of STAR-RIS elements on energy efficiency. To tackle the complex problem, a Multi-Agent Global and locAl deep Reinforcement learning (MAGAR) algorithm is designed. The global agent elevates the collaboration among local agents, which focus on individual learning. In numerical results, we observe the significant improvement of MAGAR compared to the other benchmarks, including Q-learning, multi-agent deep Q network (DQN) with golbal reward, and multi-agent DQN with local rewards. Moreover, the proposed architecture of multi-hop STAR-RISs achieves the highest energy efficiency compared to mode switching based STAR-RISs, conventional RISs and deployment without RISs or STAR-RISs., Comment: Accepted by Proc. IEEE VTC-fall

The International Muon Collider Collaboration (IMCC) [1] was established in 2020 following the recommendations of the European Strategy for Particle Physics (ESPP) and the implementation of the European Strategy for Particle Physics-Accelerator R&D Roadmap by the Laboratory Directors Group [2], hereinafter referred to as the the European LDG roadmap. The Muon Collider Study (MuC) covers the accelerator complex, detectors and physics for a future muon collider. In 2023, European Commission support was obtained for a design study of a muon collider (MuCol) [3]. This project started on 1st March 2023, with work-packages aligned with the overall muon collider studies. In preparation of and during the 2021-22 U.S. Snowmass process, the muon collider project parameters, technical studies and physics performance studies were performed and presented in great detail. Recently, the P5 panel [4] in the U.S. recommended a muon collider R&D, proposed to join the IMCC and envisages that the U.S. should prepare to host a muon collider, calling this their "muon shot". In the past, the U.S. Muon Accelerator Programme (MAP) [5] has been instrumental in studies of concepts and technologies for a muon collider., Comment: This document summarises the International Muon Collider Collaboration (IMCC) progress and status of the Muon Collider R&D programme

The ability of Language Models (LMs) to understand natural language makes them a powerful tool for parsing human instructions into task plans for autonomous robots. Unlike traditional planning methods that rely on domain-specific knowledge and handcrafted rules, LMs generalize from diverse data and adapt to various tasks with minimal tuning, acting as a compressed knowledge base. However, LMs in their standard form face challenges with long-horizon tasks, particularly in partially observable multi-agent settings. We propose an LM-based Long-Horizon Planner for Multi-Agent Robotics (LLaMAR), a cognitive architecture for planning that achieves state-of-the-art results in long-horizon tasks within partially observable environments. LLaMAR employs a plan-act-correct-verify framework, allowing self-correction from action execution feedback without relying on oracles or simulators. Additionally, we present MAP-THOR, a comprehensive test suite encompassing household tasks of varying complexity within the AI2-THOR environment. Experiments show that LLaMAR achieves a 30% higher success rate compared to other state-of-the-art LM-based multi-agent planners., Comment: 27 pages, 4 figures, 5 tables

Quantum dot-superconductor arrays have emerged as a new and promising material platform for realizing Kitaev chains with Majorana zero modes. So far, experiments have implemented a two-site chain with limited protection. We propose a protocol for scaling up the Kitaev chain that is accessible to current experiments and optimizes the Majorana protection. To this end, we make use of the fact that the relative sign of normal and superconducting hoppings mediated by an Andreev bound state can be changed by electrostatic gates. In this way, our method only relies on the use of individual electrostatic gates on hybrid regions, quantum dots, and tunnel barriers, respectively, without the need for individual magnetic flux control, greatly simplifying the device design. Our work provides guidance for realizing a topologically protected Kitaev chain, which is the building block of error-resilient topological quantum computation., Comment: 7+2 pages, 4+2 figures

The newly installed Silmaril beam combiner at the CHARA array is designed to observe previously inaccessible faint targets, including Active Galactic Nuclei and T-Tauri Young Stellar Objects. Silmaril leverages cutting-edge optical design, low readout noise, and a high-speed C-RED1 camera to realize its sensitivity objectives. In this presentation, we offer a comprehensive overview of the instrument's software, which manages critical functions, including camera data acquisition, fringe tracking, automatic instrument alignment, and observing interfaces, all aimed at optimizing on-sky data collection. Additionally, we offer an outline of the data reduction pipeline, responsible for converting raw instrument data products into the final OIFITS used by the standard interferometry modeling software. The purpose of this paper is to provide a solid reference for studies based on Silmaril data., Comment: Presented at 2024 SPIE Astronomical Telescopes + Instrumentation conference

The proliferation of Internet of Things (IoT) embedded devices is expected to reach 30 billion by 2030, creating a dynamic landscape where diverse devices must coexist. This presents challenges due to the rapid expansion of different architectures and platforms. Addressing these challenges requires a unifi ed solution capable of accommodating various devices while offering a broad range of services to connect them to the Internet effectively. This white paper introduces CROSSCON, a three-year Research and Innovation Action funded under Horizon Europe. CROSSCON aims to tackle current IoT challenges by developing a new open, modular, and universally compatible IoT security stack. This stack is designed to be highly portable and vendor-independent, enabling its deployment across different devices with heterogeneous embedded hardware architectures, including ARM and RISC-V. The CROSSCON consortium consists of 11 partners spanning 8 European countries. This consortium includes 4 academic institutions, 1 major industrial partner, and 5 small to medium-sized enterprises (SMEs).

We theoretically explore the emergence of strong zero modes in a two-site chain consisting of two quantum dots coupled due to a central dot that mediates electron hopping and singlet superconducting pairing. In the presence of time-reversal symmetry, the on-site Coulomb interaction leads to a three-fold ground-state degeneracy when tuning the system to a sweet spot as a function of the inter-dot couplings. This degeneracy is protected against changes of the dot energies in the same way as "poor man's'' Majorana bound states in short Kitaev chains. In the limit of strong interactions, this protection is maximal and the entire spectrum becomes triply degenerate, indicating the emergence of a ''poor man's'' version of a strong zero mode. We explain the degeneracy and protection by constructing corresponding Majorana Kramers-pair operators and $\mathbb{Z}_3$-parafermion operators. The strong zero modes share many properties of Majorana bound states in short Kitaev chains, including the stability of zero-bias peaks in the conductance and the behavior upon coupling to an additional quantum dot. However, they can be distinguished through finite-bias spectroscopy and the exhibit a different behavior when scaling to longer chains., Comment: 24 pages, 10 figures

Contemporary quantum devices are reaching new limits in size and complexity, allowing for the experimental exploration of emergent quantum modes. However, this increased complexity introduces significant challenges in device tuning and control. Here, we demonstrate autonomous tuning of emergent Majorana zero modes in a minimal realization of a Kitaev chain. We achieve this task using cross-platform transfer learning. First, we train a tuning model on a theory model. Next, we retrain it using a Kitaev chain realization in a two-dimensional electron gas. Finally, we apply this model to tune a Kitaev chain realized in quantum dots coupled through a semiconductor-superconductor section in a one-dimensional nanowire. Utilizing a convolutional neural network, we predict the tunneling and Cooper pair splitting rates from differential conductance measurements, employing these predictions to adjust the electrochemical potential to a Majorana sweet spot. The algorithm successfully converges to the immediate vicinity of a sweet spot (within 1.5 mV in 67.6% of attempts and within 4.5 mV in 80.9% of cases), typically finding a sweet spot in 45 minutes or less. This advancement is a stepping stone towards autonomous tuning of emergent modes in interacting systems, and towards foundational tuning machine learning models that can be deployed across a range of experimental platforms.

The goal of this project is to design a digital dice that displays dice numbers in real-time. The number is generated by a pseudo-random number generator (PRNG) using XORshift algorithm that is implemented in Verilog HDL on an FPGA. The digital dice is equipped with tilt sensor, display, power management circuit, and rechargeable battery hosted in a 3D printed dice casing. By shaking the digital dice, the tilt sensor signal produces a seed for the PRNG. This digital dice demonstrates a set of possible random numbers of 2, 4, 6, 8, 10, 12, 20, 100 that simulate the number of dice sides. The kit is named SUTDicey., Comment: 15 pages, 5 figures

Channel Charting is a dimensionality reduction technique that reconstructs a map of the radio environment from similarity relationships found in channel state information. Distances in the channel chart are often computed based on some dissimilarity metric, which can be derived from angular-domain information, channel impulse responses, measured phase differences or simply timestamps. Using such information implicitly makes strong assumptions about the level of phase and time synchronization between base station antennas or assumes approximately constant transmitter velocity. Many practical systems, however, may not provide phase and time synchronization and single-antenna base stations may not even have angular-domain information. We propose a Doppler effect-based loss function for Channel Charting that only requires frequency synchronization between spatially distributed base station antennas, which is a much weaker assumption. We use a dataset measured in an indoor environment to demonstrate that the proposed method is practically feasible with just four base station antennas, that it produces a channel chart that is suitable for localization in the global coordinate frame and that it outperforms other state-of-the-art methods under the given limitations.

In this paper, a novel cyber-insurance model design is proposed based on system risk evaluation with smart technology applications. The cyber insurance policy for power systems is tailored via cyber risk modeling, reliability impact analysis, and insurance premium calculation. A stochastic Epidemic Network Model is developed to evaluate the cyber risk by propagating cyberattacks among graphical vulnerabilities. Smart technologies deployed in risk modeling include smart monitoring and job thread assignment. Smart monitoring boosts the substation availability against cyberattacks with preventive and corrective measures. The job thread assignment solution reduces the execution failures by distributing the control and monitoring tasks to multiple threads. Reliability assessment is deployed to estimate load losses convertible to monetary losses. These monetary losses would be shared through a mutual insurance plan. To ensure a fair distribution of indemnity, a new Shapley mutual insurance principle is devised. Effectiveness of the proposed Shapley mutual insurance design is validated via case studies. The Shapley premium is compared with existent premium designs. It is shown that the Shapley premium has high indemnity levels closer to those of Tail Conditional Expectation premium. Meanwhile, the Shapley premium is nearly as affordable as the coalitional premium and keeps a relatively low insolvency probability., Comment: Power system reliability, cyber-insurance, power system security, cyber-physical systems, cyber risk modeling, actuarial design, tail risk

As a response to the Russian aggression against Ukraine, the European Union (EU), through the notion of "digital sovereignty", imposed sanctions on organizations and individuals affiliated with the Russian Federation that prohibit broadcasting content, including online distribution. In this paper, we interrogate the implementation of these sanctions and interpret them as a means to translate the union of states' governmental edicts into effective technical countermeasures. Through longitudinal traffic analysis, we construct an understanding of how ISPs in different EU countries attempted to enforce these sanctions, and compare these implementations to similar measures in other western countries. We find a wide variation of blocking coverage, both internationally and within individual member states. We draw the conclusion that digital sovereignty through sanctions in the EU has a concrete but distinctly limited impact on information flows., Comment: Accepted to Free and Open Communications on the Internet (FOCI) 2024

Wireless channel models are a commonly used tool for the development of wireless telecommunication systems and standards. The currently prevailing geometry-based stochastic channel models (GSCMs) were manually specified for certain environments in a manual process requiring extensive domain knowledge, on the basis of channel measurement campaigns. By taking into account the stochastic distribution of certain channel properties like Rician k-factor, path loss or delay spread, they model the distribution of channel realizations. Instead of this manual process, a generative machine learning model like a generative adversarial network (GAN) may be used to automatically learn the distribution of channel statistics. Subsequently, the GAN's generator may be viewed as a channel model that can replace conventional stochastic or raytracer-based models. We propose a GAN architecture for a massive MIMO channel model, and train it on measurement data produced by a distributed massive MIMO channel sounder.

We demonstrate microwave measurements of the Andreev bound state (ABS) spectrum in planar Josephson junctions (JJs) defined in Ge high mobility two-dimensional hole gases contacted by superconducting PtSiGe. The JJs and readout circuitry are located on separate chips and inductively coupled via flip-chip bonding. For a device with $350~\mathrm{nm}$ junction length, spectroscopic signatures were consistent with the short-junction limit, with an induced superconducting gap $\Delta^{*}\approx48~\mathrm{\mu eV}$ and transmission $\tau \approx 0.94$. The interaction between the highest-transmission ABS and the resonator was well described by a Jaynes-Cummings model with a vacuum Rabi splitting of approximately $6~\mathrm{MHz}$. A device with junction length of $1~\mu m$ showed an ABS spectrum consistent with a long junction model. Time-resolved monitoring of the readout resonator in the dispersive regime revealed gate-voltage tunable junction parity fluctuations on the timescale of seconds. Our work indicates a viable path towards hybrid quantum devices based on planar Ge.

This manuscript will unify inverse freeform reflector design and surface light scattering to design freeform reflectors with a scattering surface. We use microfacets, which are small, tilted mirrors superimposed on a smooth surface. We form a simple model of surface roughness and light scattering based on the orientations of the microfacets. Using a least-squares solver to compute the smooth reflector as a starting point, we can subsequently alter the surface using an optimization procedure to account for the scattering. After optimization, the resulting reflector surface produces the desired scattered light distribution. We verify the resulting reflector using raytracing.

Majorana zero modes (MZMs) are non-Abelian excitations predicted to emerge at the edges of topological superconductors. One proposal for realizing a topological superconductor in one dimension involves a chain of spinless fermions, coupled through $p$-wave superconducting pairing and electron hopping. This concept is also known as the Kitaev chain. A minimal two-site Kitaev chain has recently been experimentally realized using quantum dots (QDs) coupled through a superconductor. In such a minimal chain, MZMs are quadratically protected against global perturbations of the QD electrochemical potentials. However, they are not protected from perturbations of the inter-QD couplings. In this work, we demonstrate that extending the chain to three sites offers greater protection than the two-site configuration. The enhanced protection is evidenced by the stability of the zero-energy modes, which is robust against variations in both the coupling amplitudes and the electrochemical potential variations in the constituent QDs. While our device offers all the desired control of the couplings it does not allow for superconducting phase control. Our experimental observations are in good agreement with numerical simulated conductances with phase averaging. Our work pioneers the development of longer Kitaev chains, a milestone towards topological protection in QD-based chains.

The mitigation of clutter is an important research branch in Integrated Sensing and Communication (ISAC), one of the emerging technologies of future cellular networks. In this work, we extend our previously introduced method Clutter Removal with Acquisitions Under Phase Noise (CRAP) by means to track clutter over time. This is necessary in scenarios that require high reliability but can change dynamically, like safety applications in factory floors. To that end, exponential smoothing is leveraged to process new measurements and previous clutter information in a unique matrix using the singular value decomposition, allowing adaptation to changing environments in an efficient way.We further propose a singular value threshold based on the Marchenko-Pastur distribution to select the meaningful clutter components. Results from both simulations and measurements show that continuously updating the clutter components with new acquisitions according to our proposed algorithm Smoothed CRAP (SCRAP) enables coping with dynamic clutter environments and facilitates the detection of sensing targets., Comment: 6 pages, 5 figures. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible

Mono-static sensing operations in ISAC perform joint beamforming between transmitter and receiver. However, in contrast to pure radar systems, ISAC requires to fulfill communications tasks and to retain the corresponding design constraints for at least one half-duplex antenna array. This shifts the available degrees of freedom to the design of the second half-duplex array, that completes the mono-static sensing setup of the 6G ISAC system. Consequently, although it is still possible to achieve the gains foreseen by the radar sparse array literature, it is necessary to adapt these considerations to the new ISAC paradigm. In this work, we propose a model to evaluate the angular capabilities of a mono-static setup, constrained to the shape of the communications array and its topology requirements in wireless networks. Accordingly, we enhance the joint angular capabilities by utilizing a sparse element topology of the sensing array with the same number of elements. Our analysis is validated by simulation experiments, confirming the value of our model in providing system designers with a tool to drastically improve the trade-off between angular capabilities for sensing and the cost of the deployed hardware., Comment: 5 pages, 5 figures. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible

The macro-element variant of the hybridized discontinuous Galerkin (HDG) method combines advantages of continuous and discontinuous finite element discretization. In this paper, we investigate the performance of the macro-element HDG method for the analysis of compressible flow problems at moderate Reynolds numbers. To efficiently handle the corresponding large systems of equations, we explore several strategies at the solver level. On the one hand, we devise a second-layer static condensation approach that reduces the size of the local system matrix in each macro-element and hence the factorization time of the local solver. On the other hand, we employ a multi-level preconditioner based on the FGMRES solver for the global system that integrates well within a matrix-free implementation. In addition, we integrate a standard diagonally implicit Runge-Kutta scheme for time integration. We test the matrix-free macro-element HDG method for compressible flow benchmarks, including Couette flow, flow past a sphere, and the Taylor-Green vortex. Our results show that unlike standard HDG, the macro-element HDG method can operate efficiently for moderate polynomial degrees, as the local computational load can be flexibly increased via mesh refinement within a macro-element. Our results also show that due to the balance of local and global operations, the reduction in degrees of freedom, and the reduction of the global problem size and the number of iterations for its solution, the macro-element HDG method can be a competitive option for the analysis of compressible flow problems.

To successfully navigate chemical gradients, microorganisms need to predict how the ligand concentration changes in space. Due to their limited size, they do not take a spatial derivative over their body length but rather a temporal derivative, comparing the current signal with that in the recent past, over the so-called adaptation time. This strategy is pervasive in biology, but it remains unclear what determines the accuracy of such measurements. Using a generalized version of the previously established sampling framework, we investigate how resource limitations and the statistics of the input signal set the optimal design of a well-characterized network that measures temporal concentration changes: the Escherichia coli chemotaxis network. Our results show how an optimal adaptation time arises from the trade-off between the sampling error, caused by the stochastic nature of the network, and the dynamical error, caused by uninformative fluctuations in the input. A larger resource availability reduces the sampling error, which allows for a smaller adaptation time, thereby simultaneously decreasing the dynamical error. Similarly, we find that the optimal adaptation time scales inversely with the gradient steepness, because steeper gradients lift the signal above the noise and reduce the sampling error. These findings shed light on the principles that govern the optimal design of the E. coli chemotaxis network specifically, and any system measuring temporal changes more broadly.

This paper proposes to use graph neural networks (GNNs) for equalization, that can also be used to perform joint equalization and decoding (JED). For equalization, the GNN is build upon the factor graph representations of the channel, while for JED, the factor graph is expanded by the Tanner graph of the parity-check matrix (PCM) of the channel code, sharing the variable nodes (VNs). A particularly advantageous property of the GNN is the robustness against cycles in the factor graphs which is the main problem for belief propagation (BP)-based equalization. As a result of having a fully deep learning-based receiver, joint optimization instead of individual optimization of the components is enabled, so-called end-to-end learning. Furthermore, we propose a parallel flooding schedule that further reduces the latency, which turns out to improve also the error correcting performance. The proposed approach is analyzed and compared to state-of-the-art baselines in terms of error correcting capability and latency. At a fixed low latency, the flooding GNN for JED demonstrates a gain of 2.25 dB in bit error rate (BER) compared to an iterative Bahl--Cock--Jelinek--Raviv (BCJR)-BP baseline., Comment: Submitted to ISIT 2024

The challenging propagation environment, combined with the hardware limitations of mmWave systems, gives rise to the need for accurate initial access beam alignment strategies with low latency and high achievable beamforming gain. Much of the recent work in this area either focuses on one-sided beam alignment, or, joint beam alignment methods where both sides of the link perform a sequence of fixed channel probing steps. Codebook-based non-adaptive beam alignment schemes have the potential to allow multiple user equipment (UE) to perform initial access beam alignment in parallel whereas adaptive schemes are favourable in achievable beamforming gain. This work introduces a novel deep learning based joint beam alignment scheme that aims to combine the benefits of adaptive, codebook-free beam alignment at the UE side with the advantages of a codebook-sweep based scheme at the base station. The proposed end-to-end trainable scheme is compatible with current cellular standard signaling and can be readily integrated into the standard without requiring significant changes to it. Extensive simulations demonstrate superior performance of the proposed approach over purely codebook-based ones.

The conceptual design study of a Future Circular hadron-hadron Collider (FCC-hh) to be con-structed at CERN with a center-of-mass energy of the order of 100 TeV requires superconducting magnetic systems with a central magnetic flux density of an order of 4 T for the experimental detectors. The developed concept of the FCC-hh detector involves the use of an iron-free magnetic system consisting of three superconducting solenoids. A superconducting magnet with a minimal steel yoke is proposed as an alternative to the baseline iron-free design. In this study, both magnetic system options for the FCC-hh detector are modeled with the same electrical parameters using Cobham$'$s program TOSCA. All the main characteristics of both designs are compared and discussed., Comment: 1 pages, 10 figures, 2 equations, 14 references