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This paper examines the role of 5G communication in the Mobilities for EU project, a collaborative initiative involving 29 partners and 11 pilots aimed at revolutionizing urban mobility through electrification, automation, and connectivity. Focusing on Dresden as a Lead City, we explore the integration of 27 innovative solutions, including autonomous freight transport, eBuses, and charging robots, using a 5G communication network as the central framework. We analyze how 5G enables seamless connectivity and real-time data processing across diverse technologies, fostering interdependencies and synergies. This approach not only provides a cohesive understanding of the project's scope but also demonstrates 5G's critical role in smart city infrastructure. We evaluate the anticipated impact on sustainability metrics such as air quality, noise levels, CO2 emissions, and traffic congestion. The paper concludes by discussing challenges and strategies in leveraging 5G for comprehensive urban mobility solutions and its potential impact on future smart city developments., Comment: 6 pages

As one of the most prevalent diseases worldwide, plaque formation in human arteries, known as atherosclerosis, is the focus of many research efforts. Previously, molecular communication (MC) models have been proposed to capture and analyze the natural processes inside the human body and to support the development of diagnosis and treatment methods. In the future, synthetic MC networks are envisioned to span the human body as part of the Internet of Bio-Nano Things (IoBNT), turning blood vessels into physical communication channels. By observing and characterizing changes in these channels, MC networks could play an active role in detecting diseases like atherosclerosis. In this paper, building on previous preliminary work for simulating an MC scenario in a plaque-obstructed blood vessel, we evaluate different analytical models for non-Newtonian flow and derive associated channel impulse responses (CIRs). Additionally, we add the crucial factor of flow pulsatility to our simulation model and investigate the effect of the systole-diastole cycle on the received particles across the plaque channel. We observe a significant influence of the plaque on the channel in terms of the flow profile and CIR across different emission times in the cycle. These metrics could act as crucial indicators for early non-invasive plaque detection in advanced future MC methods., Comment: 6 pages, 6 figures

Error estimation is an important step for error correction in quantum key distribution. Traditional error estimation methods require sacrificing a part of the sifted key, forcing a trade-off between the accuracy of error estimation and the size of the partial sifted key to be used and discarded. In this paper, we propose a hybrid approach that aims to preserve the entire sifted key after error estimation while preventing Eve from gaining any advantage. The entire sifted key, modified and extended by our proposed method, is sent for error estimation in a public channel. Although accessible to an eavesdropper, the modified and extended sifted key ensures that the number of attempts to crack it remains the same as when no information is leaked. The entire sifted key is preserved for subsequent procedures, indicating the efficient utilization of quantum resources.

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

One of the most captivating properties of polaritons is their capacity to confine light at the nanoscale. This confinement is even more extreme in two-dimensional (2D) materials. 2D polaritons have been investigated by optical measurements using an external photodetector. However, their effective spectrally resolved electrical detection via far-field excitation remains unexplored. This fact hinders their potential exploitation in crucial applications such as sensing molecules and gases, hyperspectral imaging and optical spectrometry, banking on their potential for integration with silicon technologies. Herein, we present the first electrical spectroscopy of polaritonic nanoresonators based on a high-quality 2D-material heterostructure, which serves at the same time as the photodetector and the polaritonic platform. We employ metallic nanorods to create hybrid nanoresonators within the hybrid plasmon-phonon polaritonic medium in the mid and long-wave infrared ranges. Subsequently, we electrically detect these resonators by near-field coupling to a graphene pn-junction. The nanoresonators simultaneously present a record of lateral confinement and high-quality factors of up to 200, exhibiting prominent peaks in the photocurrent spectrum, particularly at the underexplored lower reststrahlen band of hBN. We exploit the geometrical and gate tunability of these nanoresonators to investigate their impact on the photocurrent spectrum and the polaritonic's waveguided modes. This work opens a venue for studying this highly tunable and complex hybrid system, as well as for using it in compact platforms for sensing and photodetection applications., Comment: 34 pages, 4 main figures and 22 supplementary figures

Chirality pervades natural processes from the atomic to the cosmic scales, crucially impacting molecular chemistry and pharmaceutics. Traditional chirality sensing methods face challenges in sensitivity and efficiency, prompting the quest of novel chiral recognition solutions based on nanophotonics. In this work we theoretically investigate the possibility to carry out enantiomeric discrimination by measuring the spontaneous emission rate of chiral molecules on twisted two-dimensional materials. We first present a general theoretical framework based on dyadic Green's functions to calculate the chiral contribution to the decay rate in the presence of a generic chiral bilayer interface. We then combine this theory with density functional theory to obtain numerical estimates of the decay rate of helical bilayer nanographene molecules placed on top of twisted bilayer graphene., Comment: 9 pages, 2 figures, 6 appendices. Draft version. Comments are welcome!

Selective sensing of chiral molecules is a key aspect in fields spanning biology, chemistry, and pharmacology. However, conventional optical methods, such as circular dichroism (CD), encounter limitations owing to weak chiral light-matter interactions. Several strategies have been investigated to enhance CD or circularly polarised luminescence (CPL), including superchiral light, plasmonic nanoresonators and dielectric nanostructures. However, a compromise between spatial uniformity and high sensitivity, without requiring specific molecular functionalization, remains a challenge. In this work, we propose a novel approach using twisted bilayer graphene (TBG), a chiral 2D material with a strong CD peak which energy is tunable through the twist angle. By matching the CD resonance of TBG with the optical transition energy of the molecule, we achieve a decay rate enhancement mediated by resonant energy transfer that depends on the electric-magnetic interaction, that is, on the chirality of both the molecules and TBG. This leads to an enantioselective quenching of the molecule fluorescence, allowing to retrieve the molecule chirality from time-resolved photoluminescence measurements. This method demonstrates high sensitivity down to single layer of molecules, with the potential to achieve the ultimate goal of single-molecule chirality sensing, while preserving the spatial uniformity and integrability of 2D heterostructures.

Emerging 5G/6G use cases span various industries, necessitating flexible solutions that leverage emerging technologies to meet diverse and stringent application requirements under changing network conditions. The standard 5G RAN solution, retransmission, reduces packet loss but can increase transmission delay in the process. Random Linear Network Coding (RLNC) offers an alternative by proactively sending combinations of original packets, thus reducing both delay and packet loss. Current research often only simulates the integration of RLNC in 5G while we implement and evaluate our approach on real commercially available hardware in a real-world deployment. We introduce Flexible Network Coding (FlexNC), which enables the flexible fusion of several RLNC protocols by incorporating a forwarder with multiple RLNC nodes. Network operators can configure FlexNC based on network conditions and application requirements. To further boost network programmability, our Recoder in the Network (RecNet) leverages intermediate network nodes to join the coding process. Both the proposed algorithms have been implemented on OpenAirInterface and extensively tested with traffic from different applications in a real network. While FlexNC adapts to various application needs of latency and packet loss, RecNet significantly minimizes packet loss for a remote user with minimal increase in delay compared to pure RLNC.

Pancreatic cancer remains one of the leading causes of cancer-related mortality worldwide. Precise segmentation of pancreatic tumors from medical images is a bottleneck for effective clinical decision-making. However, achieving a high accuracy is often limited by the small size and availability of real patient data for training deep learning models. Recent approaches have employed synthetic data generation to augment training datasets. While promising, these methods may not yet meet the performance benchmarks required for real-world clinical use. This study critically evaluates the limitations of existing generative-AI based frameworks for pancreatic tumor segmentation. We conduct a series of experiments to investigate the impact of synthetic \textit{tumor size} and \textit{boundary definition} precision on model performance. Our findings demonstrate that: (1) strategically selecting a combination of synthetic tumor sizes is crucial for optimal segmentation outcomes, and (2) generating synthetic tumors with precise boundaries significantly improves model accuracy. These insights highlight the importance of utilizing refined synthetic data augmentation for enhancing the clinical utility of segmentation models in pancreatic cancer decision making including diagnosis, prognosis, and treatment plans. Our code will be available at https://github.com/lkpengcs/SynTumorAnalyzer., Comment: MICCAI Workshop AIPAD 2024

Moir\'e materials represent strongly interacting electron systems bridging topological and correlated physics. Despite significant advances, decoding wavefunction properties underlying the quantum geometry remains challenging. Here, we utilize polarization-resolved photocurrent measurements to probe magic-angle twisted bilayer graphene, leveraging its sensitivity to the Berry connection that encompasses quantum "textures" of electron wavefunctions. Using terahertz light resonant with optical transitions of its flat bands, we observe bulk photocurrents driven by broken symmetries and reveal the interplay between electron interactions and quantum geometry. We observe inversion-breaking gapped states undetectable through quantum transport, sharp changes in the polarization axes caused by interaction-induced band renormalization, and recurring photocurrent patterns at integer fillings of the moir\'e unit cell that track the evolution of quantum geometry through the cascade of phase transitions. The large and tunable terahertz response intrinsic to flat-band systems offers direct insights into the quantum geometry of interacting electrons and paves the way for innovative terahertz quantum technologies.

Automated volumetric segmentation of the pancreas on cross-sectional imaging is needed for diagnosis and follow-up of pancreatic diseases. While CT-based pancreatic segmentation is more established, MRI-based segmentation methods are understudied, largely due to a lack of publicly available datasets, benchmarking research efforts, and domain-specific deep learning methods. In this retrospective study, we collected a large dataset (767 scans from 499 participants) of T1-weighted (T1W) and T2-weighted (T2W) abdominal MRI series from five centers between March 2004 and November 2022. We also collected CT scans of 1,350 patients from publicly available sources for benchmarking purposes. We developed a new pancreas segmentation method, called PanSegNet, combining the strengths of nnUNet and a Transformer network with a new linear attention module enabling volumetric computation. We tested PanSegNet's accuracy in cross-modality (a total of 2,117 scans) and cross-center settings with Dice and Hausdorff distance (HD95) evaluation metrics. We used Cohen's kappa statistics for intra and inter-rater agreement evaluation and paired t-tests for volume and Dice comparisons, respectively. For segmentation accuracy, we achieved Dice coefficients of 88.3% (std: 7.2%, at case level) with CT, 85.0% (std: 7.9%) with T1W MRI, and 86.3% (std: 6.4%) with T2W MRI. There was a high correlation for pancreas volume prediction with R^2 of 0.91, 0.84, and 0.85 for CT, T1W, and T2W, respectively. We found moderate inter-observer (0.624 and 0.638 for T1W and T2W MRI, respectively) and high intra-observer agreement scores. All MRI data is made available at https://osf.io/kysnj/. Our source code is available at https://github.com/NUBagciLab/PaNSegNet., Comment: Peer-reviewer version

Twisted bilayer graphene (TBG) represents a highly tunable, strongly correlated electron system owed to its unique flat electronic bands. However, understanding the single-particle band structure alone has been challenging due to complex lattice reconstruction effects and a lack of spectroscopic measurements over a broad energy range. Here, we probe the band structure of TBG around the magic angle using infrared spectroscopy. Our measurements reveal spectral features originating from interband transitions whose energies are uniquely defined by the twist angle. By combining with quantum transport, we connect spectral features over a broad energy range (10 to 700 meV) spanning several superlattice minibands and track their evolution with twist angle. We compare our data with calculations of the band structures obtained via the continuum model and find good agreement only when considering a variation of interlayer/intralayer tunnelling parameters with the twist angle. Our analysis suggests that the magic angle also shifts due to lattice relaxation, and is better defined for a wide angular range from 0.9{\deg} to 1.1{\deg}. Our work provides spectroscopic insights into TBG's band structure and offers an optical fingerprint of the magic angle for screening heterostructures before nanofabrication.

Image sensors hold a pivotal role in society due to their ability to capture vast amounts of information. Traditionally, image sensors are opaque due to light absorption in both the pixels and the read-out electronics that are stacked on top of each other. Making image sensors visibly transparent would have a far-reaching impact in numerous areas such as human-computer interfaces, smart displays, and both augmented and virtual reality. In this paper, we present the development and analysis of the first semi-transparent image sensor and its applicability as an eye-tracking device. The device consists of an 8x8 array of semi-transparent photodetectors and electrodes disposed on a fully transparent substrate. Each pixel of the array has a size of 60 x 140 {\mu}m and an optical transparency of 85-95%. Pixels have a high sensitivity, with more than 90% of them showing a noise equivalent irradiance < 10-4 W/m2 for wavelengths of 637 nm. As the semi-transparent photodetectors have a large amount of built-in gain, the opaque read-out electronics can be placed far away from the detector array to ensure maximum transparency and fill factor. Indeed, the operation and appearance of transparent image sensors present a fundamental shift in how we think about cameras and imaging, as these devices can be concealed in plain sight.

Ever since the initial experimental observation of correlated insulators and superconductivity in the flat Dirac bands of magic angle twisted bilayer graphene, a search for the microscopic description that explains its strong electronic interactions has begun. While the seemingly disagreeing electronic transport and scanning tunneling microscopy experiments suggest a dichotomy between local and extended electronic orbitals, definitive experimental evidence merging the two patterns together has been much sought after. Here, we report on the local photothermoelectric measurements in the flat electronic bands of twisted symmetric trilayer graphene (TSTG). We use a cryogenic scanning near-field optical microscope with an oscillating atomic force microscopy (AFM) tip irradiated by the infrared photons to create a nanoscopic hot spot in the planar samples, which generates a photocurrent that we probe globally. We observe a breakdown of the non-interacting Mott formalism at low temperatures (10K), signaling the importance of the electronic interactions. Our measurements reveal an overall negative offset of the Seebeck coefficient and significant peaks of the local photovoltage values at all positive integer fillings of the TSTG's moir\'e superlattice, further indicating a substantial deviation from the classical two-band semiconductor Seebeck response. We explain these observations using the interacting topological heavy-fermion model. In addition, our data reveal a spatial variation of the relative interaction strength dependent on the measured local twist angle (1.2{\deg} - 1.6{\deg}). Our findings provide experimental evidence of heavy fermion behaviour in the topological flat bands of moir\'e graphene and epitomize an avenue to apply local thermoelectric measurements to other strongly correlated materials in the disorder-free limit.

We present a theoretical study of the intrinsic plasmonic properties of twisted bilayer graphene (TBG) as a function of the twist angle $\theta$ (and other microscopic parameters such as temperature and filling factor). Our calculations, which rely on the random phase approximation, take into account four crucially important effects, which are treated on equal footing: i) the layer-pseudospin degree of freedom, ii) spatial non-locality of the density-density response function, iii) crystalline local field effects, and iv) Hartree self-consistency. We show that the plasmonic spectrum of TBG displays a smooth transition from a strongly-coupled regime (at twist angles $\theta \lesssim 2^{\circ}$), where the low-energy spectrum is dominated by a weakly dispersive intra-band plasmon, to a weakly-coupled regime (for twist angles $\theta \gtrsim 2^{\circ}$) where an acoustic plasmon clearly emerges. This crossover offers the possibility of realizing tunable mid-infrared sub-wavelength cavities, whose vacuum fluctuations may be used to manipulate the ground state of strongly correlated electron systems., Comment: 11 pages, 4 figures + Supplemental Material

Chirality-induced spin selectivity (CISS) is an effect that has recently attracted a great deal of attention in chiral chemistry and that remains to be understood. In the CISS effect, electrons passing through chiral molecules acquire a large degree of spin polarization. In this work we study the case of atomically-thin chiral crystals created by van der Waals assembly. We show that this effect can be spectacularly large in systems containing just two monolayers, provided they are spin-orbit coupled. Its origin stems from the combined effects of structural chirality and spin-flipping spin-orbit coupling. We present detailed calculations for twisted homobilayer transition metal dichalcogenides, showing that the chirality-induced spin polarization can be giant, e.g. easily exceeding $50\%$ for ${\rm MoTe}_2$. Our results clearly indicate that twisted quantum materials can operate as a fully tunable platform for the study and control of the CISS effect in condensed matter physics and chiral chemistry., Comment: 6 pages, 4 figures + Supplemental Material

Control over the optical properties of atomically thin two-dimensional (2D) layers, including those of transition metal dichalcogenides (TMDs), is needed for future optoelectronic applications. Remarkable advances have been achieved through alloying, chemical and electrical doping, and applied strain. However, the integration of TMDs with other 2D materials in van der Waals heterostructures (vdWHs) to tailor novel functionalities remains largely unexplored. Here, the near-field coupling between TMDs and graphene/graphite is used to engineer the exciton lineshape and charge state. Fano-like asymmetric spectral features are produced in WS$_{2}$, MoSe$_{2}$ and WSe$_{2}$ vdWHs combined with graphene, graphite, or jointly with hexagonal boron nitride (h-BN) as supporting or encapsulating layers. Furthermore, trion emission is suppressed in h-BN encapsulated WSe$_{2}$/graphene with a neutral exciton redshift (44 meV) and binding energy reduction (30 meV). The response of these systems to electron-beam and light probes is well-described in terms of 2D optical conductivities of the involved materials. Beyond fundamental insights into the interaction of TMD excitons with structured environments, this study opens an unexplored avenue toward shaping the spectral profile of narrow optical modes for application in nanophotonic devices.

Topological nanophotonics presents the potential for cutting-edge photonic systems, with a core aim revolving around the emergence of topological edge states. These states are primed to propagate robustly while embracing deep subwavelength confinement that defies diffraction limits. Such attributes make them particularly appealing for nanoscale applications, where achieving these elusive states has remained challenging. We unveil the first experimental proof of deep subwavelength topological edge states by implementing periodic modulation of hyperbolic phonon polaritons within a Van der Waals heterostructure. This finding represents a significant milestone in the field of nanophotonics, and it can be directly extended to and hybridized with other Van der Waals materials in various applications. The extensive scope for material substitution facilitates broadened operational frequency ranges, streamlined integration of diverse polaritonic materials, and compatibility with electronic and excitonic systems.

Traffic congestion in dense urban centers presents an economical and environmental burden. In recent years, the availability of vehicle-to-anything communication allows for the transmission of detailed vehicle states to the infrastructure that can be used for intelligent traffic light control. The other way around, the infrastructure can provide vehicles with advice on driving behavior, such as appropriate velocities, which can improve the efficacy of the traffic system. Several research works applied deep reinforcement learning to either traffic light control or vehicle speed advice. In this work, we propose a first attempt to jointly learn the control of both. We show this to improve the efficacy of traffic systems. In our experiments, the joint control approach reduces average vehicle trip delays, w.r.t. controlling only traffic lights, in eight out of eleven benchmark scenarios. Analyzing the qualitative behavior of the vehicle speed advice policy, we observe that this is achieved by smoothing out the velocity profile of vehicles nearby a traffic light. Learning joint control of traffic signaling and speed advice in the real world could help to reduce congestion and mitigate the economical and environmental repercussions of today's traffic systems., Comment: 6 pages, 2 figures, accepted for publication at IEEE ICMLA 2023

The requirements for broadband photodetection are becoming exceedingly demanding in hyperspectral imaging. Whilst intrinsic photoconductor arrays based on mercury cadmium telluride represent the most sensitive and suitable technology, their optical spectrum imposes a narrow spectral range with a sharp absorption edge that cuts their operation to < 25 um. Here, we demonstrate a giant ultra-broadband photoconductivity in twisted double bilayer graphene heterostructures spanning a spectral range of 2 - 100 um with internal quantum efficiencies ~ 40 % at speeds of 100 kHz. The giant response originates from unique properties of twist-decoupled heterostructures including pristine, crystal field induced terahertz band gaps, parallel photoactive channels, and strong photoconductivity enhancements caused by interlayer screening of electronic interactions by respective layers acting as sub-atomic spaced proximity screening gates. Our work demonstrates a rare instance of an intrinsic infrared-terahertz photoconductor that is complementary metal-oxide-semiconductor compatible and array integratable, and introduces twist-decoupled graphene heterostructures as a viable route for engineering gapped graphene photodetectors with 3D scalability.

Scattering-type scanning near-field optical microscopy is a powerful imaging technique for studying materials beyond the diffraction limit. However, interpreting near-field measurements poses challenges in mapping the response of polaritonic structures to meaningful physical properties. To address this, we propose a theory based on the transfer matrix method to simulate the near-field response of 1D polaritonic structures. Our approach provides a computationally efficient and accurate analytical theory, relating the near-field response to well-defined physical properties. This work enhances the understanding of near-field images and complex polaritonic phenomena. Finally, this scattering theory can extend to other systems like atoms or nanoparticles near a waveguide.

This paper provides an overview of the hardware and software components used in our test bed project the NET Playground. All source information is stored in the GitLab repository (https://gitlab.com/Paulteck/net-playground). In the Hardware section, we present sketches and 3D views of mechanical parts and technical drawings of printed boards. The Software section discusses relay control using shell scripts and the utilization of Ansible for automation. We also introduce a C++ framework for connecting with the INA231 energy sensor. This paper serves as a reference for understanding and replicating our project's hardware and software components.

Phase retardation is a cornerstone of modern optics, yet, at mid-infrared (mid-IR) frequencies, it remains a major challenge due to the scarcity of simultaneously transparent and birefringent crystals. Most materials resonantly absorb due to lattice vibrations occurring at mid-IR frequencies, and natural birefringence is weak, calling for hundreds of microns to millimeters-thick phase retarders for sufficient polarization rotation. We demonstrate mid-IR phase retardation with flakes of $\alpha$-molybdenum trioxide ($\alpha$-MoO$_3$) that are more than ten times thinner than the operational wavelength, achieving 90 degrees polarization rotation within one micrometer of material. We report conversion ratios above 50% in reflection and transmission mode, and wavelength tunability by several micrometers. Our results showcase that exfoliated flakes of low-dimensional crystals can serve as a platform for mid-IR miniaturized integrated polarization control., Comment: 8 pages, 5 figures

Understanding the condensed-phase behavior of chiral molecules is important in biology, as well as in a range of technological applications, such as the manufacture of pharmaceuticals. Here, we use molecular dynamics simulations to study a chiral four-site molecular model that exhibits a second-order symmetry-breaking phase transition from a supercritical racemic liquid, into subcritical D-rich and L-rich liquids. We determine the infinite-size critical temperature using the fourth-order Binder cumulant, and we show that the finite-size scaling behavior of the order parameter is compatible with the 3D Ising universality class. We also study the spontaneous D-rich to L-rich transition at a slightly subcritical temperature $T\approx0.985 T_c$ and our findings indicate that the free energy barrier for this transformation increases with system size as $N^{2/3}$ where $N$ is the number of molecules, consistent with a surface-dominated phenomenon. The critical behavior observed herein suggests a mechanism for chirality selection in which a liquid of chiral molecules spontaneously forms a phase enriched in one of the two enantiomers as the temperature is lowered below the critical point. Furthermore, the increasing free energy barrier with system size indicates that fluctuations between the L-rich and D-rich phases are suppressed as the size of the system increases, trapping it in one of the two enantiomerically-enriched phases. Such a process could provide the basis for an alternative explanation for the origin of biological homochirality. We also conjecture the possibility of observing nucleation at subcritical temperatures under the action of a suitable chiral external field., Comment: 9 pages, 7 figures

High-quality low-dimensional layered and van der Waals materials are typically exfoliated, with sample cross sectional areas on the order of tens to hundreds of microns. The small size of flakes makes the experimental characterization of their dielectric properties unsuitable with conventional spectroscopic ellipsometry, due to beam-sample size mismatch and non-uniformities of the crystal axes. Previously, the experimental measurement of the dielectrirc permittivity of such microcrystals was carried out with near-field tip-based scanning probes. These measurements are sensitive to external conditions like vibrations and temperature, and require non-deterministic numerical fitting to some a priori known model. We present an alternative method to extract the in-plane dielectric permittivity of van der Waals microcrystals, based on identifying reflectance minima in spectroscopic measurements. Our method does not require complex fitting algorithms nor near field tip-based measurements and accommodates for small-area samples. We demonstrate the robustness of our method using hexagonal boron nitride and {\alpha}-MoO3, and recover their dielectric permittivities that are close to literature values., Comment: 10 pages, 4 figure and 3 tables

Accessing the low-energy non-equilibrium dynamics of materials and their polaritons with simultaneous high spatial and temporal resolution has been a bold frontier of electron microscopy in recent years. One of the main challenges lies in the ability to retrieve extremely weak signals while simultaneously disentangling amplitude and phase information. Here, we present Free-Electron Ramsey Imaging (FERI), a microscopy approach based on light-induced electron modulation that enables coherent amplification of optical near-fields in electron imaging. We provide simultaneous time-, space-, and phase-resolved measurements of a micro-drum made from a hexagonal boron nitride membrane visualizing the sub-cycle dynamics of 2D polariton wavepackets therein. The phase-resolved measurements reveals vortex-anti-vortex singularities on the polariton wavefronts, together with an intriguing phenomenon of a traveling wave mimicking the amplitude profile of a standing wave. Our experiments show a 20-fold coherent amplification of the near-field signal compared to conventional electron near-field imaging, resolving peak field intensities in the order of ~W/cm2, corresponding to field amplitudes of a few kV/m. As a result, our work paves the way for spatio-temporal electron microscopy of biological specimens and quantum materials, exciting yet delicate samples that are currently difficult to investigate.

Many applications involve humans in the loop, where continuous and accurate human motion monitoring provides valuable information for safe and intuitive human-machine interaction. Portable devices such as inertial measurement units (IMUs) are applicable to monitor human motions, while in practice often limited computational power is available locally. The human motion in task space coordinates requires not only the human joint motion but also the nonlinear coordinate transformation depending on the parameters such as human limb length. In most applications, measuring these kinematics parameters for each individual requires undesirably high effort. Therefore, it is desirable to estimate both, the human motion and kinematic parameters from IMUs. In this work, we propose a novel computational framework for dual estimation in real-time exploiting in-network computational resources. We adopt the concept of field Kalman filtering, where the dual estimation problem is decomposed into a fast state estimation process and a computationally expensive parameter estimation process. In order to further accelerate the convergence, the parameter estimation is progressively computed on multiple networked computational nodes. The superiority of our proposed method is demonstrated by a simulation of a human arm, where the estimation accuracy is shown to converge faster than with conventional approaches.

As cellular networks evolve towards the 6th generation, machine learning is seen as a key enabling technology to improve the capabilities of the network. Machine learning provides a methodology for predictive systems, which can make networks become proactive. This proactive behavior of the network can be leveraged to sustain, for example, a specific quality of service requirement. With predictive quality of service, a wide variety of new use cases, both safety- and entertainment-related, are emerging, especially in the automotive sector. Therefore, in this work, we consider maximum throughput prediction enhancing, for example, streaming or high-definition mapping applications. We discuss the entire machine learning workflow highlighting less regarded aspects such as the detailed sampling procedures, the in-depth analysis of the dataset characteristics, the effects of splits in the provided results, and the data availability. Reliable machine learning models need to face a lot of challenges during their lifecycle. We highlight how confidence can be built on machine learning technologies by better understanding the underlying characteristics of the collected data. We discuss feature engineering and the effects of different splits for the training processes, showcasing that random splits might overestimate performance by more than twofold. Moreover, we investigate diverse sets of input features, where network information proved to be most effective, cutting the error by half. Part of our contribution is the validation of multiple machine learning models within diverse scenarios. We also use explainable AI to show that machine learning can learn underlying principles of wireless networks without being explicitly programmed. Our data is collected from a deployed network that was under full control of the measurement team and covered different vehicular scenarios and radio environments., Comment: 18 pages, 12 Figures. Accepted on IEEE Access

We introduce a gauge-invariant model of planar, square molecules coupled to a quantized spatially-varying cavity electromagnetic vector potential A(r). Specifically, we choose a temporally chiral cavity hosting a uniform magnetic field B, as this is the simplest instance in which a transverse spatially-varying A(r) is at play. We show that when the molecules are in the Van Vleck paramagnetic regime, an equilibrium quantum phase transition to a photon condensate state occurs., Comment: 14 pages, 3 figures