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Privacy-preserving voice conversion aims to remove only the attributes of speech audio that convey identity information, keeping other speech characteristics intact. This paper presents a mechanism for privacy-preserving voice conversion that allows controlling the leakage of identity-bearing information using adversarial information hiding. This enables a deliberate trade-off between maintaining source-speech characteristics and modification of speaker identity. As such, the approach improves on voice-conversion techniques like CycleGAN and StarGAN, which were not designed for privacy, meaning that converted speech may leak personal information in unpredictable ways. Our approach is also more flexible than ASR-TTS voice conversion pipelines, which by design discard all prosodic information linked to textual content. Evaluations show that the proposed system successfully modifies perceived speaker identity whilst well maintaining source lexical content., Comment: Accepted for publication in proceedings of 4th symposium on security and privacy in speech communication

Soft-tissue and bone tumours (STBT) are rare, diagnostically challenging lesions with variable clinical behaviours and treatment approaches. This systematic review provides an overview of Artificial Intelligence (AI) methods using radiological imaging for diagnosis and prognosis of these tumours, highlighting challenges in clinical translation, and evaluating study alignment with the Checklist for AI in Medical Imaging (CLAIM) and the FUTURE-AI international consensus guidelines for trustworthy and deployable AI to promote the clinical translation of AI methods. The review covered literature from several bibliographic databases, including papers published before 17/07/2024. Original research in peer-reviewed journals focused on radiology-based AI for diagnosing or prognosing primary STBT was included. Exclusion criteria were animal, cadaveric, or laboratory studies, and non-English papers. Abstracts were screened by two of three independent reviewers for eligibility. Eligible papers were assessed against guidelines by one of three independent reviewers. The search identified 15,015 abstracts, from which 325 articles were included for evaluation. Most studies performed moderately on CLAIM, averaging a score of 28.9$\pm$7.5 out of 53, but poorly on FUTURE-AI, averaging 5.1$\pm$2.1 out of 30. Imaging-AI tools for STBT remain at the proof-of-concept stage, indicating significant room for improvement. Future efforts by AI developers should focus on design (e.g. define unmet clinical need, intended clinical setting and how AI would be integrated in clinical workflow), development (e.g. build on previous work, explainability), evaluation (e.g. evaluating and addressing biases, evaluating AI against best practices), and data reproducibility and availability (making documented code and data publicly available). Following these recommendations could improve clinical translation of AI methods., Comment: 23 pages, 6 figures, 6 supplementary figures

In this paper, we report a shape synthesis method for multi-mode dielectric resonator antennas (DRA) using characteristic mode theory (CMT) and a binary genetic algorithm (BGA). By including the antenna's characteristic modal responses (resonance frequencies and quality factors) in the cost function, the shape synthesis process is conducted without including excitation feeds. Through the optimization procedure, a non-canonical dielectric body is formed from tetrahedral elements to support the required modal properties. As a demonstration of the proposed design approach, two three-mode MIMO DRAs are synthesized from both a rectangular and a cylindrical volume to operate at 2.45 GHz. The synthesized MIMO DRA's complex shape (based on rectangle) is then fabricated using Nanoparticle jetted zirconia. A combination of probe and slot feeds are employed to excite the desired modes. Due to the orthogonality of the characteristic modes and the careful design of the feeding network, isolation $>20$ dB is achieved between all ports., Comment: 6 pages, 9 figures

The integration of traditional fixed-route transit (FRT) and more flexible microtransit has been touted as a means of improving mobility and access to opportunity, increasing transit ridership, and promoting environmental sustainability. To help evaluate integrated FRT and microtransit public transit (PT) system (henceforth ``integrated fixed-flex PT system'') designs, we propose a high-fidelity modeling framework that provides reliable estimates for a wide range of (i) performance metrics and (ii) integrated fixed-flex PT system designs. We formulate the mode choice equilibrium problem as a fixed-point problem wherein microtransit demand is a function of microtransit performance, and microtransit performance depends on microtransit demand. We propose a detailed agent-based simulation modeling framework that includes (i) a binary logit mode choice model (private auto vs. transit), (ii) a supernetwork-based model and pathfinding algorithm for multi-modal transit path choice where the supernetwork includes pedestrian, FRT, and microtransit layers, (iii) a detailed mobility-on-demand fleet simulator called FleetPy to model the supply-demand dynamics of the microtransit service. In this paper, we illustrate the capabilities of the modeling framework by analyzing integrated fixed-flex PT system designs that vary the following design parameters: FRT frequencies and microtransit fleet size, service region structure, virtual stop coverage, and operating hours. We include case studies in downtown San Diego and Lemon Grove, California. The computational results show that the proposed modeling framework converges to a mode choice equilibrium. Moreover, the scenario results imply that introducing a new microtransit service decreases FRT ridership and requires additional subsidies, but it significantly increases job accessibility and slightly reduces total VMT., Comment: 49 pages, 25 figures, 8 tables; Submitted To: Transportation Research Part C: Emerging Technologies on May 1st, 2024

We present three novel types of discrete Lagrangian 2-form for the integrable quad equations of the ABS list. Two of our new Lagrangian 2-forms have the quad equations, or a system equivalent to the quad equations, as their Euler-Lagrange equations, whereas the third produces the tetrahedron equations. This is in contrast to the well-established Lagrangian 2-form for these equations, which produces equations that are weaker than the quad equations (they are equivalent to two octahedron equations). We use relations between the Lagrangian 2-forms to prove that the system of quad equations is equivalent to the combined system of tetrahedron and octahedron equations. Furthermore, for each of the Lagrangian 2-forms, existing and new, we study the double zero property of the exterior derivative. In particular, this gives a possible variational interpretation to the octahedron equations., Comment: 37 pages, 9 figures

Accurate diagnostics of the combustor region of ramjet engines can improve engine design and create benchmarks for computational fluid dynamics models. Previous works demonstrate that dual frequency comb spectroscopy can provide low uncertainty diagnostics of multiple flow parameters in the non-combusting regions of ramjets. However, the high temperatures present in the combustor present a challenge for broadband spectroscopic absorption models that are used to interpret measurements in these regions. Here, we utilize a new water vapor absorption database created for high temperature water-air mixtures to fit spectra measured in a ground-test ramjet engine with a broadband near-infrared dual comb absorption spectrometer. We extract 2D profiles of pressure, temperature, water mole fraction, and velocity using this new database. We demonstrate that the new database provides the lowest fit residuals compared to other water vapor absorption databases. We compare computational fluid dynamics simulations of the combustor with the measured data to demonstrate that the simulations overpredict heat release and water vapor production., Comment: This work has been cleared by the Air Force under case number AFRL-2023-4205

Laser absorption Doppler velocimeters use a crossed-beam configuration to cancel error due to laser frequency drift and absorption model uncertainty. This configuration complicates the spatial interpretation of the measurement since the two beams sample different volumes of gas. Here, we achieve single-beam velocimetry with a portable dual comb spectrometer (DCS) with high frequency accuracy and stability enabled by GPS-referencing, and a new high-temperature water vapor absorption database. We measure the inlet flow in a supersonic ramjet engine and demonstrate single-beam measurements that are on average within 19 m/s of concurrent crossed-beam measurements. We estimate that the DCS and the new database contribute 1.6 and 13 m/s to this difference respectively., Comment: This work has been cleared by the Air Force under case number AFRL-2023-4805

The sponge-like biomineralised calcite materials found in echinoderm skeletons are of interest in terms of both structure formation and biological function. Despite their crystalline atomic structure, they exhibit curved interfaces that have been related to known triply-periodic minimal surfaces. Here, we investigate the endoskeleton of the sea urchin Cidaris rugosa that has long been known to form a microstructure related to the Primitive surface. Using X-ray tomography, we find that the endoskeleton is organised as a composite material consisting of domains of bicontinuous microstructures with different structural properties. We describe, for the first time, the co-occurrence of ordered Primitive and Diamond structures and of a disordered structure within a single skeletal plate. We show that these structures can be distinguished by structural properties including solid volume fraction, trabeculae width, and to a lesser extent, interface area and mean curvature. In doing so, we present a robust method that extracts interface areas and curvature integrals from voxelized datasets using the Steiner polynomial for parallel body volumes. We discuss these very large scale bicontinuous structures in the context of their function, formation, and evolution., Comment: accepted for publication in Journal of the Royal Society Interface

Segmentations are crucial in medical imaging to obtain morphological, volumetric, and radiomics biomarkers. Manual segmentation is accurate but not feasible in the radiologist's clinical workflow, while automatic segmentation generally obtains sub-par performance. We therefore developed a minimally interactive deep learning-based segmentation method for soft-tissue tumors (STTs) on CT and MRI. The method requires the user to click six points near the tumor's extreme boundaries. These six points are transformed into a distance map and serve, with the image, as input for a Convolutional Neural Network. For training and validation, a multicenter dataset containing 514 patients and nine STT types in seven anatomical locations was used, resulting in a Dice Similarity Coefficient (DSC) of 0.85$\pm$0.11 (mean $\pm$ standard deviation (SD)) for CT and 0.84$\pm$0.12 for T1-weighted MRI, when compared to manual segmentations made by expert radiologists. Next, the method was externally validated on a dataset including five unseen STT phenotypes in extremities, achieving 0.81$\pm$0.08 for CT, 0.84$\pm$0.09 for T1-weighted MRI, and 0.88\pm0.08 for previously unseen T2-weighted fat-saturated (FS) MRI. In conclusion, our minimally interactive segmentation method effectively segments different types of STTs on CT and MRI, with robust generalization to previously unseen phenotypes and imaging modalities.

Let $B_3$ be the Artin braid group on 3 strands and $PB_3$ be the corresponding pure braid group. In this paper, we construct the groupoid $GTSh$ of GT-shadows for a (possibly more tractable) version $GT_0$ of the Grothendieck-Teichmueller group $GT$ introduced by D. Harbater and L. Schneps in 2000. We call this group the gentle version of $GT$ and denote it by $GT_{gen}$. The objects of $GTSh$ are finite index normal subgroups $N$ of $B_3$ satisfying the condition $N \subset PB_3$. Morphisms of $GTSh$ are called GT-shadows and they may be thought of as approximations to elements of $GT_{gen}$. We show how GT-shadows can be obtained from elements of $GT_{gen}$ and prove that $GT_{gen}$ is isomorphic to the limit of a certain functor defined in terms of the groupoid $GTSh$. Using this result, we get a criterion for identifying genuine GT-shadows.

CdTe is the most commercially successful thin-film photovoltaic technology to date. The recent development of Se-alloyed CdSe$_{x}$Te$_{1-x}$ layers in CdTe solar cells has led to higher device efficiencies, due to a lowered bandgap improving the photocurrent, improved voltage characteristics and longer carrier lifetimes. Evidence from cross-sectional electron microscopy is widely believed to indicate that Se passivates defects in CdSe$_{x}$Te$_{1-x}$ solar cells, and that this is the reason for better lifetimes and voltages in these devices. Here, we utilise spatially resolved photoluminescence measurements of CdSe$_{x}$Te$_{1-x}$ thin films on glass to study the effects of Se on carrier recombination in the material, isolated from the impact of conductive interfaces and without the need to prepare cross-sections through the samples. We find further evidence to support Se passivation of grain boundaries, but also identify an associated increase in below-bandgap photoluminescence that indicates the presence of Se-enhanced luminescent defects. Our results show that Se treatment, in tandem with Cl passivation, does increase radiative efficiencies. However, the simultaneous enhancement of defects within the grain interiors suggests that although it is overall beneficial, Se incorporation may still ultimately limit the maximum attainable efficiency of CdSe$_{x}$Te$_{1-x}$ solar cells.

Artificial intelligence (AI) and robotics research and implementation emerged in the architecture, engineering, and construction (AEC) industry to positively impact project efficiency and effectiveness concerns such as safety, productivity, and quality. This shift, however, warrants the need for ethical considerations of AI and robotics adoption due to its potential negative impacts on aspects such as job security, safety, and privacy. Nevertheless, this did not receive sufficient attention, particularly within the academic community. This research systematically reviews AI and robotics research through the lens of ethics in the AEC community for the past five years. It identifies nine key ethical issues namely job loss, data privacy, data security, data transparency, decision-making conflict, acceptance and trust, reliability and safety, fear of surveillance, and liability, by summarizing existing literature and filtering it further based on its AEC relevance. Furthermore, thirteen research topics along the process were identified based on existing AEC studies that had direct relevance to the theme of ethics in general and their parallels are further discussed. Finally, the current challenges and knowledge gaps are discussed and seven specific future research directions are recommended. This study not only signifies more stakeholder awareness of this important topic but also provides imminent steps towards safer and more efficient realization., Comment: 109 pages, 5 figures, submitted to Automation in Construction

Constrained robot motion planning is a ubiquitous need for robots interacting with everyday environments, but it is a notoriously difficult problem to solve. Many sampled points in a sample-based planner need to be rejected as they fall outside the constraint manifold, or require significant iterative effort to correct. Given this, few solutions exist that present a constraint-satisfying trajectory for robots, in reasonable time and of low path cost. In this work, we present a transformer-based model for motion planning with task space constraints for manipulation systems. Vector Quantized-Motion Planning Transformer (VQ-MPT) is a recent learning-based model that reduces the search space for unconstrained planning for sampling-based motion planners. We propose to adapt a pre-trained VQ-MPT model to reduce the search space for constraint planning without retraining or finetuning the model. We also propose to update the neural network output to move sampling regions closer to the constraint manifold. Our experiments show how VQ-MPT improves planning times and accuracy compared to traditional planners in simulated and real-world environments. Unlike previous learning methods, which require task-related data, our method uses pre-trained neural network models and requires no additional data for training and finetuning the model making this a \textit{one-shot} process. We also tested our method on a physical Franka Panda robot with real-world sensor data, demonstrating the generalizability of our algorithm. We anticipate this approach to be an accessible and broadly useful for transferring learned neural planners to various robotic-environment interaction scenarios.

Transient absorption (TA) spectroscopy has long been an invaluable tool for determining the energetics and dynamics of excited states in atomic, molecular, and solid-state systems. When pump pulse intensities are sufficiently high, the resulting TA spectra include both the generally desired third-order response of the studied material as well as responses that are higher order in the electric field amplitudes of the pulses. It has recently been shown that pump-intensity-dependent TA measurements allow separating the various orders of response of the TA signal, but the information content available in those higher orders has not been described. We give a general framework, intuition, and nomenclature for understanding the information contained in high-order TA spectra. Standard TA spectra are generally interpreted in terms of three fundamental processes: ground-state bleach (GSB), stimulated emission (SE), and excited state absorption (ESA), and we extend those concepts to higher order. Each order introduces two new processes: SE and ESA from highly excited states that were not accessible in lower orders. In addition, each order contain negations of lower-order processes, just as GSB is a negation of the linear absorption. We show the new spectral and dynamical information that is introduced at each order and show how the relative signs of the signals in different orders can be used to identify which processes are dominant., Comment: 15 pages, 7 figures

Abdominal wall defects often lead to pain, discomfort, and recurrence of incisional hernias, resulting in significant morbidity and repeated surgical repairs worldwide. Mesh repair for large hernias is usually based on the defect area with a fixed overlap, neglecting biomechanical factors such as muscle activation, intra-abdominal pressure, tissue elasticity, and abdominal wall distension. To address this issue, we present a biomechanical approach to incisional hernia repair that takes into account the unstable abdominal wall. Additionally, we introduce HEDI, a tool that uses computed tomography with Valsalva maneuver to automatically detect and assess hernia size, volume, and abdominal wall instability. Our first clinical application of HEDI in the preoperative evaluation of 31 patients shows significantly improved success rates compared to reported rates, with all patients remaining pain-free and experiencing no hernia recurrence after three years of follow-up.

Motion planning is integral to robotics applications such as autonomous driving, surgical robots, and industrial manipulators. Existing planning methods lack scalability to higher-dimensional spaces, while recent learning based planners have shown promise in accelerating sampling-based motion planners (SMP) but lack generalizability to out-of-distribution environments. To address this, we present a novel approach, Vector Quantized-Motion Planning Transformers (VQ-MPT) that overcomes the key generalization and scaling drawbacks of previous learning-based methods. VQ-MPT consists of two stages. Stage 1 is a Vector Quantized-Variational AutoEncoder model that learns to represent the planning space using a finite number of sampling distributions, and stage 2 is an Auto-Regressive model that constructs a sampling region for SMPs by selecting from the learned sampling distribution sets. By splitting large planning spaces into discrete sets and selectively choosing the sampling regions, our planner pairs well with out-of-the-box SMPs, generating near-optimal paths faster than without VQ-MPT's aid. It is generalizable in that it can be applied to systems of varying complexities, from 2D planar to 14D bi-manual robots with diverse environment representations, including costmaps and point clouds. Trained VQ-MPT models generalize to environments unseen during training and achieve higher success rates than previous methods.

Magnetic multilayers are a promising platform for storage and logic devices based on skyrmion spin textures, due to the large materials phase space for tuning properties. Epitaxial superlattice structures of [Pt/Co/Cu]n thin films were grown by molecular beam epitaxy at room temperature. Spin-polarized scanning tunneling microscopy (SP-STM) of these samples was used to probe the connection between surface structure and skyrmion morphology with nanoscale spatial resolution. Irregular-shaped skyrmion bubbles were observed, with effective diameters from 20-200 nm that are much larger than the nanoscale grain structure of the surface topography. Nucleation, annihilation, and motion of skyrmion bubbles could be driven using the stray field of the ferromagnetic tip in repeated imaging, and spin-polarized current/voltage pulses. Our detailed comparison of STM topography and differential conductance images shows that there are no surface defects or inhomogeneities at length scales that could account for the range in skyrmion bubble size or shape observed in the measurements., Comment: 11 pages, 5 figures

Presently, gamma-ray tracking in germanium segmented detectors is realised by applying two advanced, complex algorithms. While they have already triggered an intensive R&D, they are still subject to further improvements. Making such algorithms effective, online in real time conditions and/or offline for deeper analysis, in data pipelines do require many additional software developments. This review paper gives an overview of the various bricks of software produced so far by the AGATA collaboration. It provides hints of what is foreseen for the next phases of the project up to its full configuration namely with 180 capsules in the array.

Most state-of-the-art Text-to-Speech systems use the mel-spectrogram as an intermediate representation, to decompose the task into acoustic modelling and waveform generation. A mel-spectrogram is extracted from the waveform by a simple, fast DSP operation, but generating a high-quality waveform from a mel-spectrogram requires computationally expensive machine learning: a neural vocoder. Our proposed ``autovocoder'' reverses this arrangement. We use machine learning to obtain a representation that replaces the mel-spectrogram, and that can be inverted back to a waveform using simple, fast operations including a differentiable implementation of the inverse STFT. The autovocoder generates a waveform 5 times faster than the DSP-based Griffin-Lim algorithm, and 14 times faster than the neural vocoder HiFi-GAN. We provide perceptual listening test results to confirm that the speech is of comparable quality to HiFi-GAN in the copy synthesis task., Comment: Accepted to the 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2023)

Enceladus is a primary target for astrobiology due to the $\rm H_2O$ plume ejecta measured by the Cassini spacecraft and the inferred subsurface ocean sustained by tidal heating. Sourcing the plumes via a direct connection from the ocean to the surface requires a fracture through the entire ice shell ($\sim$10 km). Here we explore an alternative mechanism in which shear heating within shallower tiger stripe fractures produces partial melting in the ice shell and interstitial convection allows fluid to be ejected as geysers. We use an idealized two-dimensional multiphase reactive transport model to simulate the thermomechanics of a mushy region generated by an upper bound estimate for the localized shear heating rate in a salty ice shell. From our simulations, we predict the temperature, porosity, salt content, melting rate, and liquid volume of an intrashell mushy zone surrounding a fracture. We find that the rate of internal melting can match the observed $\rm H_2O$ eruption rate and that there is sufficient brine volume within the mushy zone to sustain the geysers for $\sim350$ kyr without additional melting. The composition of the liquid brine is, however, distinct from that of the ocean, due to partial melting. This shear heating mechanism for geyser formation applies to Enceladus and other icy moons and has implications for our understanding of the geophysical processes and astrobiological potential of icy satellites., Comment: Main text (11 pages, 3 figures), supplement (16 pages, 7 figures, 1 table). Added new figures and discussion to the supplement

Supersonic engine development requires accurate and detailed measurements of fluidic and thermodynamic parameters to optimize engine designs and benchmark computational fluid dynamic (CFD) simulations. Here, we demonstrate that dual frequency comb spectroscopy (DCS) with mode-locked frequency combs can provide simultaneous absolute measurements of several flow parameters with low uncertainty across a range of conditions owing to the broadband and ultrastable optical frequency output of the lasers. We perform DCS measurements across a 6800-7200 cm-1 bandwidth covering hundreds of H2O absorption features resolved with a spectral point spacing of 0.0067 cm-1 and point spacing precision of 1.68 x 10-10 cm-1. We demonstrate 2D profiles of velocity, temperature, pressure, water mole fraction, and air mass flux in a ground-test dual-mode ramjet at Wright-Patterson Air Force Base. The narrow angles of the measurement beams offer sufficient spatial resolution to resolve properties across an oblique shock train in the isolator and the thermal throat of the combustor. We determine that the total measurement uncertainties for the various parameters range from 1% for temperature to 9% for water vapor mole fraction, with the absorption database/model that is used to interpret the data typically contributing the most uncertainty (leaving the door open for even lower uncertainty in the future). CFD at the various measurement locations show good agreement, largely falling within the DCS measurement uncertainty for most profiles and parameters.

Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data. This is currently addressed by centrally sharing ample, and importantly diverse, data from multiple sites. However, such centralization is challenging to scale (or even not feasible) due to various limitations. Federated ML (FL) provides an alternative to train accurate and generalizable ML models, by only sharing numerical model updates. Here we present findings from the largest FL study to-date, involving data from 71 healthcare institutions across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, utilizing the largest dataset of such patients ever used in the literature (25,256 MRI scans from 6,314 patients). We demonstrate a 33% improvement over a publicly trained model to delineate the surgically targetable tumor, and 23% improvement over the tumor's entire extent. We anticipate our study to: 1) enable more studies in healthcare informed by large and diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further quantitative analyses for glioblastoma via performance optimization of our consensus model for eventual public release, and 3) demonstrate the effectiveness of FL at such scale and task complexity as a paradigm shift for multi-site collaborations, alleviating the need for data sharing., Comment: federated learning, deep learning, convolutional neural network, segmentation, brain tumor, glioma, glioblastoma, FeTS, BraTS

We explore the potential of highly mismatched alloys (HMAs) for realizing lossless plasmonics. Systems with a plasmon frequency at which there are no interband or intraband processes possible are called lossless, as there is no 2-particle loss channel for the plasmon. We find that the band splitting in HMAs with a conduction band anticrossing guarantees a lossless frequency window. When such a material is doped, producing plasmonic behavior, we study the conditions required for the plasmon frequency to fall in the lossless window, realizing lossless plasmons. Considering a generic class of HMAs with a conduction band anticrossing, we find universal contours in their parameter space within which lossless plasmons are possible for some doping range. Our analysis shows that HMAs with heavier effective masses are most promising for realizing a lossless plasmonic material., Comment: 6 pages including appendices and 3 figures

Providing an accurate, representative sample of mass flux across large open areas for atmospheric studies or the extreme conditions of a hypersonic engine is challenging for traditional intrusive or point-based sensors. Here, we demonstrate that laser absorption spectroscopy with mode-locked frequency combs can simultaneously measure all of the components of mass flux (velocity, temperature, pressure, and species mole fraction) with low uncertainty, spatial resolution corresponding to the laser line of sight, and no supplemental sensor readings. The low uncertainty is provided by the broad spectral bandwidth, high resolution, and extremely well-known and controlled frequency axis of stabilized, mode-locked frequency combs. We demonstrate these capabilities using dual frequency comb spectroscopy (DCS) in the isolator of a ground-test supersonic propulsion engine at Wright-Patterson Air Force Base. The mass flux measurements are consistent within 3.6% of the facility-level engine air supply values. A vertical scan of the laser beams in the isolator measures the spatially resolved mass flux, which is compared with computational fluid dynamics simulations. A rigorous uncertainty analysis demonstrates a instrument uncertainty of ~0.4%, and total uncertainty (including non-instrument sources) of ~7% for mass flux measurements. These measurements demonstrate DCS with mode-locked frequency combs as a low-uncertainty mass flux sensor for a variety of applications.

Highly mismatched alloys (HMA's) are a class of semiconductor alloys with large electronegativity differences between the alloying elements. We predict the absorption spectrum due to transitions between the split bands of a doped highly mismatched alloy with a conduction band anticrossing. We analyze the joint densities of states for both direct and indirect transitions between the split bands. The resulting spectrum has features that reveal the unusual state distribution that is characteristic of HMAs, hence providing valuable insight into their electronic structure. In particular, we predict a peak near the absorption edge, which arises due to the suppression of direct transitions at large momenta. We present analytic forms for the near-absorption-edge and large-energy spectra, showing that they are qualitatively different from those in standard parabolic semiconductors. In particular, as a result of suppressed direct transitions, indirect transitions dominate the spectrum away from the edge of absorption., Comment: 7 pages including one appendix and bibliography, 2 figures

This paper focuses on the development of a ground-based test-bed to analyze the complexities of contact dynamics between multibody systems in space. The test-bed consists of an air-bearing platform equipped with a 7 degrees-of-freedom (one degree per revolute joint) robotic arm which acts as the servicing satellite. The dynamics of the manipulator on the platform is modeled as an aid for the analysis and design of stabilizing control algorithms suited for autonomous on-orbit servicing missions. The dynamics are represented analytically using a recursive Newton-Euler multibody method with D-H parameters derived from the physical properties of the arm and platform. In addition, Product of Exponential (PoE) method is also employed to serve as a comparison with the D-H parameters approach. Finally, an independent numerical simulation created with the SimScape modeling environment is also presented as a means of verifying the accuracy of the recursive model and the PoE approach. The results from both models and SimScape are then validated through comparison with internal measurement data taken from the robotic arm itself.

Learning and interpreting the structure of the environment is an innate feature of biological systems, and is integral to guiding flexible behaviours for evolutionary viability. The concept of a cognitive map has emerged as one of the leading metaphors for these capacities, and unravelling the learning and neural representation of such a map has become a central focus of neuroscience. While experimentalists are providing a detailed picture of the neural substrate of cognitive maps in hippocampus and beyond, theorists have been busy building models to bridge the divide between neurons, computation, and behaviour. These models can account for a variety of known representations and neural phenomena, but often provide a differing understanding of not only the underlying principles of cognitive maps, but also the respective roles of hippocampus and cortex. In this Perspective, we bring many of these models into a common language, distil their underlying principles of constructing cognitive maps, provide novel (re)interpretations for neural phenomena, suggest how the principles can be extended to account for prefrontal cortex representations and, finally, speculate on the role of cognitive maps in higher cognitive capacities.

Cation site disorder provides a degree of freedom in the growth of ternary nitrides for tuning the technologically relevant properties of a material system. For example, the band gap of ZnGeN$_2$ changes when the ordering of the structure deviates from that of its ground state. By combining the perspectives of carrier localization and defect states, we analyze the impact of different degrees of disordering on electronic properties in ZnGeN$_2$, addressing a gap in current studies which focus on dilute or fully disordered systems. The present study demonstrates changes in the density of states and localization of carriers in ZnGeN$_2$ calculated using band gap-corrected density functional theory and hybrid calculations on partially disordered supercells generated using the Monte Carlo method. We use localization and density of states to discuss the ill-defined nature of a band gap in a disordered material, comparing multiple definitions of the energy gap in the context of theory and experiment. Decreasing the order parameter results in a large reduction of the band gap in disordered cases. The reduction in band gap is due in part to isolated, localized states that form above the valence band continuum and are associated with nitrogen coordinated by more zinc than germanium. The prevalence of defect states in all but the perfectly ordered structure creates challenges for incorporating disordered ZnGeN$_2$ into optical devices, but the localization associated with these defects provides insight into mechanisms of electron/hole recombination in the material.

Radiomics uses quantitative medical imaging features to predict clinical outcomes. Currently, in a new clinical application, finding the optimal radiomics method out of the wide range of available options has to be done manually through a heuristic trial-and-error process. In this study we propose a framework for automatically optimizing the construction of radiomics workflows per application. To this end, we formulate radiomics as a modular workflow and include a large collection of common algorithms for each component. To optimize the workflow per application, we employ automated machine learning using a random search and ensembling. We evaluate our method in twelve different clinical applications, resulting in the following area under the curves: 1) liposarcoma (0.83); 2) desmoid-type fibromatosis (0.82); 3) primary liver tumors (0.80); 4) gastrointestinal stromal tumors (0.77); 5) colorectal liver metastases (0.61); 6) melanoma metastases (0.45); 7) hepatocellular carcinoma (0.75); 8) mesenteric fibrosis (0.80); 9) prostate cancer (0.72); 10) glioma (0.71); 11) Alzheimer's disease (0.87); and 12) head and neck cancer (0.84). We show that our framework has a competitive performance compared human experts, outperforms a radiomics baseline, and performs similar or superior to Bayesian optimization and more advanced ensemble approaches. Concluding, our method fully automatically optimizes the construction of radiomics workflows, thereby streamlining the search for radiomics biomarkers in new applications. To facilitate reproducibility and future research, we publicly release six datasets, the software implementation of our framework, and the code to reproduce this study., Comment: 33 pages, 4 figures, 4 tables, 2 supplementary figures, 3 supplementary table, submitted to Medical Image Analysis; revision

This paper introduces Chance Constrained Gaussian Process-Motion Planning (CCGP-MP), a motion planning algorithm for robotic systems under motion and state estimate uncertainties. The paper's key idea is to capture the variations in the distance-to-collision measurements caused by the uncertainty in state estimation techniques using a Gaussian Process (GP) model. We formulate the planning problem as a chance constraint problem and propose a deterministic constraint that uses the modeled distance function to verify the chance-constraints. We apply Simplicial Homology Global Optimization (SHGO) approach to find the global minimum of the deterministic constraint function along the trajectory and use the minimum value to verify the chance-constraints. Under this formulation, we can show that the optimization function is smooth under certain conditions and that SHGO converges to the global minimum. Therefore, CCGP-MP will always guarantee that all points on a planned trajectory satisfy the given chance-constraints. The experiments in this paper show that CCGP-MP can generate paths that reduce collisions and meet optimality criteria under motion and state uncertainties. The implementation of our robot models and path planning algorithm can be found on GitHub., Comment: Paper published in CASE21

We show theoretically that the characteristic modes of dielectric resonator antennas (DRAs) must be capacitive in the low frequency limit, and show that as a consequence of this constraint and the Poincar\'{e} Separation Theorem, the modes of any DRA consisting of partial elements of an encompassing super-structure cannot resonate at a frequency that is lower than that of the encompassing structure. Thus, design techniques relying on complex sub-structures to miniaturize the antenna, including topology optimization and meandered windings, cannot apply to DRAs. Due to the capacitive nature of the DRA modes, it is also shown that the Q factor of any DRA sub-structure will be bounded from below by that of the super-structure at frequencies below the first self-resonance of the super-structure. We demonstrate these bounding relations with numerical examples., Comment: 10 pages, 6 figures

Fast and efficient sampling-based motion planning (SMP) is an integral component of many robotic systems, such as autonomous cars. A popular technique to improve the efficiency of these planners is to restrict search space in the planning domain. Existing algorithms define parametric functions to bound the search space, but these do not extend to non-holonomic robotic systems. Recent learning-based methods use a combination of convolutional and fully connected networks to encode the planning space. However, these methods are restricted to fixed map sizes, which are often not realistic in the real world. In this paper, we introduce a transformer-based approach, Motion Planning Transformer, to restrict the search space by learning to discern regions with a valid path from prior data. The model learns not only to restrict search spaces for simple 2D systems but also for non-holonomic robotic systems. We validate our method on various randomly generated environments with different map sizes and plan trajectories for a physical non-holonomic robot. We also provide a ROS2 plugin of our method for the Nav2 planning stack. The results show that our method reduces search space nodes by 2-12 times compared to traditional planners and has better generalizability than recent learning-based planners.

We present a global interpretation of Higgs, diboson, and top quark production and decay measurements from the LHC in the framework of the Standard Model Effective Field Theory (SMEFT) at dimension six. We constrain simultaneously 36 independent directions in its parameter space, and compare the outcome of the global analysis with that from individual and two-parameter fits. Our results are obtained by means of state-of-the-art theoretical calculations for the SM and the EFT cross-sections, and account for both linear and quadratic corrections in the $1/\Lambda^2$ expansion. We demonstrate how the inclusion of NLO QCD and $\mathcal{O}\left( \Lambda^{-4}\right)$ effects is instrumental to accurately map the posterior distributions associated to the fitted Wilson coefficients. We assess the interplay and complementarity between the top quark, Higgs, and diboson measurements, deploy a variety of statistical estimators to quantify the impact of each dataset in the parameter space, and carry out fits in BSM-inspired scenarios such as the top-philic model. Our results represent a stepping stone in the ongoing program of model-independent searches at the LHC from precision measurements, and pave the way towards yet more global SMEFT interpretations extended to other high-$p_T$ processes as well as to low-energy observables., Comment: 93 pages, 31 figures, results available from https://lhcfitnikhef.github.io/SMEFT/. v2: updated results. v3: version accepted for publication in JHEP. This paper is dedicated to the memory of our friend and collaborator Cen Zhang

New techniques for probing hyperbolic phonon polaritons (HPP) in 2D materials will support the development of the emerging technologies in this field. Previous reports have shown that it is possible for WSe2 monolayers in contact with the hexagonal boron nitride (hBN) to generate HPP in the hBN via Raman scattering. In this paper, we set out new results on HPP Raman scattering induced in hBN by WSe2 and MoSe2 monolayers including new resonances at which the Raman scattering is enhanced. Analysis of the observed Raman lineshapes demonstrates that Raman scattering allows HPP with wavevectors with magnitudes significantly in excess of 15000 cm-1 to be probed. We present evidence that the Raman scattering can probe HPP with frequencies less than the expected lower bound on the Reststrahlen band suggesting new HPP physics still waits to be discovered., Comment: 14 + 10 pages, 5 + 6 figures, in paper + supplementary info

The functional form of Coulomb interactions in the transition metal dichalcogenides and other van der Waals solids is critical to many of their unique properties, e.g. strongly-correlated electron states, superconductivity and emergent ferromagnetism. This paper presents measurements of key excitonic energy levels in MoSe2/WSe2 heterostructures. These measurements are obtained from resonance Raman experiments on specific Raman peaks only observed at excited states of the excitons. This data is used to validate a model of the Coulomb potential in these structures which predicts the exciton energies to within ~5 meV / 2.5%. This model is used to determine the effect of heterostructure formation on the single-particle band gaps of the layers and will have a wide applicability in designing the next generation of more complex transition metal dichalcogenide structures., Comment: 19 pages, 3 figures and an additional 14 pages, 7 figures of supplimentary info. Submitted to IOP 2D Materials

Urban scaling analysis, the study of how aggregated urban features vary with the population of an urban area, provides a promising framework for discovering commonalities across cities and uncovering dynamics shared by cities across time and space. Here, we use the urban scaling framework to study an important, but under-explored feature in this community - income inequality. We propose a new method to study the scaling of income distributions by analyzing total income scaling in population percentiles. We show that income in the least wealthy decile (10%) scales close to linearly with city population, while income in the most wealthy decile scale with a significantly superlinear exponent. In contrast to the superlinear scaling of total income with city population, this decile scaling illustrates that the benefits of larger cities are increasingly unequally distributed. For the poorest income deciles, cities have no positive effect over the null expectation of a linear increase. We repeat our analysis after adjusting income by housing cost, and find similar results. We then further analyze the shapes of income distributions. First, we find that mean, variance, skewness, and kurtosis of income distributions all increase with city size. Second, the Kullback-Leibler divergence between a city's income distribution and that of the largest city decreases with city population, suggesting the overall shape of income distribution shifts with city population. As most urban scaling theories consider densifying interactions within cities as the fundamental process leading to the superlinear increase of many features, our results suggest this effect is only seen in the upper deciles of the cities. Our finding encourages future work to consider heterogeneous models of interactions to form a more coherent understanding of urban scaling., Comment: 18 pages, 7 figures

We quantify the impact of unpolarized lepton-proton and lepton-nucleus inclusive deep-inelastic scattering (DIS) cross section measurements from the future Electron-Ion Collider (EIC) on the proton and nuclear parton distribution functions (PDFs). To this purpose we include neutral- and charged-current DIS pseudodata in a self-consistent set of proton and nuclear global PDF determinations based on the NNPDF methodology. We demonstrate that the EIC measurements will reduce the uncertainty of the light quark PDFs of the proton at large values of the momentum fraction $x$, and, more significantly, of the quark and gluon PDFs of heavy nuclei, especially at small and large $x$. We illustrate the implications of the improved precision of nuclear PDFs for the interaction of ultra-high energy cosmic neutrinos with matter., Comment: 11 pages, 5 figures, In the context of the Electron-Ion collider yellow report

We present a systematic interpretation of vector boson scattering (VBS) and diboson measurements from the LHC in the framework of the dimension-six Standard Model Effective Field Theory (SMEFT). We consider all available measurements of VBS fiducial cross-sections and differential distributions from ATLAS and CMS, in most cases based on the full Run II luminosity, and use them to constrain 16 independent directions in the dimension-six EFT parameter space. Compared to the diboson measurements, we find that VBS provides complementary information on several of the operators relevant for the description of the electroweak sector. We also quantify the ultimate EFT reach of VBS measurements via dedicated projections for the High Luminosity LHC. Our results motivate the integration of VBS processes in future global SMEFT interpretations of particle physics data., Comment: 56 pages. Version 2: Fixed normalisation factor in the definition of the Wilson coefficients. Figures 3.11 and 4.8 replaced. References added

We present Fourier-space based methods to calculate the electronic structure of wurtzite quantum dot systems with continuous alloy profiles. We incorporate spatially varying elastic and dielectric constants in strain and piezoelectric potential calculations. A method to incorporate smooth alloy profiles in all aspects of the calculations is presented. We demonstrate our methodology for the case of a 1D InGaN quantum dot array and show the importance of including these spatially varying parameters in the modeling of devices. We demonstrate that convergence of the lowest bound state energies is to good approximation determined by the largest wave vector used in constructing the states. We also present a novel approach of coupling strain into the $\mathbf{k}\cdot\mathbf{p}$ Hamiltonian, greatly reducing the computational cost of generating the Hamiltonian.

From orbit, the visibility of Titan's surface is limited to a handful of narrow spectral windows in the near-infrared (near-IR), primarily from the absorption of methane gas. This has limited the ability to identify specific compounds on the surface -- to date Titan's bulk surface composition remains unknown. Further, understanding of the surface composition would provide insight into geologic processes, photochemical production and evolution, and the biological potential of Titan's surface. One approach to obtain wider spectral coverage with which to study Titan's surface is by decreasing the integrated column of absorbers (primarily methane) and scatterers between the observer and the surface. This is only possible if future missions operate at lower altitudes in Titan's atmosphere. Herein, we use a radiative transfer model to measure in detail the absorption through Titan's atmosphere from different mission altitudes, and consider the impacts this would have for interpreting reflectance measurements of Titan's surface. Over our modeled spectral range of 0.4 - 10 micron, we find that increases in the width of the transmission windows as large as 317% can be obtained for missions performing remote observations at the surface. However, any appreciable widening of the windows requires onboard illumination. Further, we make note of possible surface compounds that are not currently observable from orbit, but could be identified using the wider windows at low altitudes. These range from simple nitriles such as cyanoacetylene, to building blocks of amino acids such as urea. Finally, we discuss the implications that the identifications of these compounds would have for Titan science., Comment: 52 pages, 6 figures, Icarus 2020

The Advanced GAmma Tracking Array (AGATA) multi detector spectrometer will provide precise information for the study of the properties of the exotic nuclear matter (very unbalanced proton (Z) and neutron (N) numbers) along proton- and neutron- drip lines and of super-heavy nuclei. This is done using the latest technology of particle accelerators. The AGATA spectrometer consists of 180 high purity Germanium detectors. Each detector is segmented into 38 segments. The very harsh project requirements are to measure gamma ray energies with very high resolution (< 1x 10 -3) at a high detector counting rate (50 Kevents / sec / crystal). This results in a very high data transfer rate per crystal (5 to 8 Gbps). The 38 segments are sampled @ 100 MHz with 14 bits of resolution. The samples are continuously transferred to the CAP module which reduces the data rate from 64 Gbps to 5 Gbps. The CAP module also adds continuous monitoring data which results in total outgoing data rate of 10 Gbps. The STARE module is designed to fit between the CAP module and the computer farm. It will package the data from the CAP module and transmit it to the server farm using a 10 Gbps UDP connection with a delivery insurance mechanism implemented to ensure that all data is transferred.

Highly mismatched alloys (HMAs) have band structures strongly modified due to the introduction of the alloying element. We consider HMAs where the isolated state of the alloying element is near the host conduction band, which causes the conduction band to split into two bands. We determine the bulk plasma frequency when the lower-energy band is partially occupied, as by doping, using a semi-analytical method based on a disorder-averaged Green's function. We include the nontrivial effects of interband transitions to the higher-energy band, which limit the plasma frequency to be less than an effective band gap. We show that the distribution of states in the split bands causes plasmons in HMAs to behave differently than plasmons in standard metals and semiconductors. The effective mass of the lower split band $m^*$ changes with alloy fraction, and we find that the plasmon frequency with small carrier concentration $n$ scales with $\sqrt{n}/m^*$ rather than the $\sqrt{n/m^*}$ that is expected in standard materials. We suggest experiments to observe these phenomena. Considering the typical range of material parameters in this group of alloys and taking a realistic example, we suggest that HMAs can serve as highly tunable low-frequency plasmonic materials., Comment: 10 pages, 6 figures

Distinguishing gastrointestinal stromal tumors (GISTs) from other intra-abdominal tumors and GISTs molecular analysis is necessary for treatment planning, but challenging due to its rarity. The aim of this study was to evaluate radiomics for distinguishing GISTs from other intra-abdominal tumors, and in GISTs, predict the c-KIT, PDGFRA,BRAF mutational status and mitotic index (MI). All 247 included patients (125 GISTS, 122 non-GISTs) underwent a contrast-enhanced venous phase CT. The GIST vs. non-GIST radiomics model, including imaging, age, sex and location, had a mean area under the curve (AUC) of 0.82. Three radiologists had an AUC of 0.69, 0.76, and 0.84, respectively. The radiomics model had an AUC of 0.52 for c-KIT, 0.56 for c-KIT exon 11, and 0.52 for the MI. Hence, our radiomics model was able to distinguish GIST from non-GISTS with a performance similar to three radiologists, but was not able to predict the c-KIT mutation or MI., Comment: Martijn P.A. Starmans and Milea J.M. Timbergen contributed equally

Hybridisation of electronic bands of two-dimensional materials, assembled into twistronic heterostructures, enables one to tune their optoelectronic properties by selecting conditions for resonant interlayer hybridisation. Resonant interlayer hybridisation qualitatively modifies the excitons in such heterostructures, transforming these optically active modes into superposition states of interlayer and intralayer excitons. For MoSe$_2$/WSe$_2$ heterostructures, strong hybridization occurs between the holes in the spin-split valence band of WSe$_2$ and in the top valence band of MoSe$_2$, especially when both are bound to the same electron in the lowest conduction band of WSe$_2$. Here we use resonance Raman scattering to provide direct evidence for the hybridisation of excitons in twistronic MoSe$_2$/WSe$_2$ structures, by observing scattering of specific excitons by phonons in both WSe$_2$ and MoSe$_2$. We also demonstrate that resonance Raman scattering spectroscopy opens up a wide range of possibilities for quantifying the layer composition of the superposition states of the exciton and the interlayer hybridisation parameters in heterostructures of two-dimensional materials., Comment: 32 pages, 5 figures, 8 supplementary figures, 8 supplementary tables. 2D Mater. (2021)

Moir\'e structures in van der Waals heterostructures lead to emergent phenomena including superconductivity in twisted bilayer graphene and optically accessible strongly-correlated electron states in transition metal dichalcogenide heterobilayers. Dual periodicity moir\'e structures (DPMS) formed in layered structures with more than two layers have been shown to lead to ferromagnetism and multiple secondary Dirac points in TBG. Whilst in principle it is possible to obtain DPMS in bilayers there has not been clear experimental evidence of this yet. In this paper we present signatures of DPMS in a twisted MoSe$_2$/WSe$_2$ bilayer revealed by resonance Raman spectroscopy. We observed zone-folded acoustic and optical phonon modes with a wavevector twice of the moir\'e wavevector, evidence of a dual periodicity moir\'e heterostructure. These results simultaneously open up opportunities for new emergent phenomena and an optical method for characterising DPMS in a wide range of van der Waals heterostructures., Comment: 16 pages, 3 Figures, 1 Table

We measure spin-orbit torques (SOTs) in a unique model system of all-epitaxial ferrite/Pt bilayers to gain insights into charge-spin interconversion in Pt. With negligible electronic conduction in the insulating ferrite, the crystalline Pt film acts as the sole source of charge-to-spin conversion. A small field-like SOT independent of Pt thickness suggests a weak Rashba-Edelstein effect at the ferrite/Pt interface. By contrast, we observe a sizable damping-like SOT that depends on the Pt thickness, from which we deduce the dominance of an extrinsic spin-Hall effect (skew scattering) and Dyakonov-Perel spin relaxation in the crystalline Pt film. Furthermore, our results point to a large internal spin-Hall ratio of $\approx$0.8 in epitaxial Pt. Our experimental work takes an essential step towards understanding the mechanisms of charge-spin interconversion and SOTs in Pt-based heterostructures, which are crucial for power-efficient spintronic devices.

Reliable real-time planning for robots is essential in today's rapidly expanding automated ecosystem. In such environments, traditional methods that plan by relaxing constraints become unreliable or slow-down for kinematically constrained robots. This paper describes the algorithm Dynamic Motion Planning Networks (Dynamic MPNet), an extension to Motion Planning Networks, for non-holonomic robots that address the challenge of real-time motion planning using a neural planning approach. We propose modifications to the training and planning networks that make it possible for real-time planning while improving the data efficiency of training and trained models' generalizability. We evaluate our model in simulation for planning tasks for a non-holonomic robot. We also demonstrate experimental results for an indoor navigation task using a Dubins car., Comment: Accepted for IROS 2020

Nonlinear optical spectroscopies are powerful tools for probing quantum dynamics in molecular and nanoscale systems. While intuition about ultrafast spectroscopies is often built by considering impulsive optical pulses, actual experiments have finite-duration pulses, which can be important for interpreting and predicting experimental results. We present a new freely available open source method for spectroscopic modeling, called Ultrafast Ultrafast (UF$^2$) Spectroscopy, which enables computationally efficient and convenient prediction of nonlinear spectra, including treatment of arbitrary finite duration pulse shapes. UF$^2$ is a Fourier-based method that requires diagonalization of the Liouvillian propagator of the system density matrix. We also present a Runge-Kutta Euler (RKE) direct propagation method. We include open-systems dynamics in the secular Redfield, full Redfield, and Lindblad formalisms with Markovian baths. For non-Markovian systems, the degrees of freedom corresponding to memory effects are brought into the system and treated nonperturbatively. We analyze the computational complexity of the algorithms and demonstrate numerically that, including the cost of diagonalizing the propagator, UF$^2$ is 20-200 times faster than the direct propagation method for secular Redfield models with arbitrary Hilbert space dimension; that it is similarly faster for full Redfield models at least up to system dimensions where the propagator requires more than 20 GB to store; and that for Lindblad models it is faster up to dimension near 100, with speedups for small systems by factors of over 500. UF$^2$ and RKE are part of a larger open source Ultrafast Software Suite, which includes tools for automatic generation and calculation of Feynman diagrams., Comment: 16 pages, 5 Figures

Perturbative nonlinear optical spectroscopies are powerful methods to understand the dynamics of excitonic and other condensed phase systems. Feynman diagrams have long provided the essential tool to understand and interpret experimental spectra and to organize the calculation of spectra for model systems. When optical pulses are strictly time ordered, only a small number of diagrams contribute, but in many experiments pulse-overlap effects are important for interpreting results. When pulses overlap, the number of contributing diagrams can increase rapidly, especially with higher order spectroscopies, and human error is especially likely when attempting to write down all of the diagrams. We present an automated Diagram Generator (DG) that generates all of the Feynman diagrams needed to calculate any $n^{\text{th}}$-order spectroscopic signal. We characterize all perturbative nonlinear spectroscopies by their associated phase-discrimination condition as well as the time intervals where pulse amplitudes are nonzero. Although the DG can be used to automate impulsive calculations, its greatest strength lies in automating finite pulse calculations where pulse overlaps are important. We consider third-order transient absorption spectroscopy and fifth-order exciton-exciton interaction 2D (EEI2D) spectroscopy, which are respectively described by 6 or 7 diagrams in the impulsive limit but 16 or 240 diagrams, respectively, when pulses overlap. The DG allows users to automatically include all relevant diagrams at relatively low computational cost, since the extra diagrams are only generated for the inter-pulse delays where they are relevant. For EEI2D spectroscopy, we show the important effects of including the overlap diagrams., Comment: 10 pages, 4 figures

Intermediate band solar cells hold the promise of ultrahigh power conversion efficiencies using a single semiconductor junction. Many current implementations use materials with bandgaps too small to achieve maximum efficiency or use cost-prohibitive substrates. Here we demonstrate a material system for intermediate band solar cells using InGaN/GaN quantum-dot-in-nanowire heterostructures grown directly on silicon to provide a lower cost, large-bandgap intermediate band solar cell platform. We demonstrate sequential two-photon current generation with sub-bandgap photons, the hallmark of intermediate band solar cell operation, through vertically stacked quantum dots in the nanowires. Near-infrared light biasing with an 850 nm laser intensity up to 200 W/cm2 increases the photocurrent above and below the bandgap by up to 19% at 78 K, and 44% at room temperature. The nanostructured III-nitride strategy provides a route towards realistic room temperature intermediate band solar cells while leveraging the cost benefits of silicon substrates.