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It has been shown that unclocked, recurrent networks of Boolean gates in FPGAs can be used for low-SWaP reservoir computing. In such systems, topology and node functionality of the network are randomly initialized. To create a network that solves a task, weights are applied to output nodes and learning is achieved by adjusting those weights with conventional machine learning methods. However, performance is often limited compared to networks where all parameters are learned. Herein, we explore an alternative learning approach for unclocked, recurrent networks in FPGAs. We use evolutionary computation to evolve the Boolean functions of network nodes. In one type of implementation the output nodes are used directly to perform a task and all learning is via evolution of the network's node functions. In a second type of implementation a back-end classifier is used as in traditional reservoir computing. In that case, both evolution of node functions and adjustment of output node weights contribute to learning. We demonstrate the practicality of node function evolution, obtaining an accuracy improvement of ~30% on an image classification task while processing at a rate of over three million samples per second. We additionally demonstrate evolvability of network memory and dynamic output signals.

À chaque instant, la puissance solaire qui arrive sur la Terre représente plusieurs milliers de fois la consommation énergétique de l’humanité. L’effet photovoltaïque, qui convertit la lumière en électricité, permet de tirer parti de cette manne énergétique – un enjeu particulièrement important alors que nous touchons aux limites du système des énergies fossiles hérité des révolutions industrielles. En quelques années, les panneaux solaires ont connu une progression spectaculaire.Bien que l’énergie photovoltaïque soit décarbonée, son développement soulève cependant de nombreuses questions, sur le fonctionnement et la fabrication des dispositifs comme sur leur installation et leur utilisation.Ce livre propose un tour d’horizon de l’énergie solaire photovoltaïque, de son histoire, de sa dynamique et de ses perspectives. En s’appuyant sur des considérations physiques fondamentales, il établit les principaux ordres de grandeur et les principes de fonctionnement communs à toutes les cellules solaires. Il expose ensuite les différentes technologies actuellement disponibles sur le marché. Il aborde enfin les coûts, économiques et écologiques, du photovoltaïque.Ce livre s’adresse à un large public désireux de se faire une idée précise et documentée sur un volet essentiel de notre réponse aux défis climatiques et énergétiques du XXIe siècle.

The brain is arguably the most powerful computation system known. It is extremely efficient in processing large amounts of information and can discern signals from noise, adapt, and filter faulty information all while running on only 20 watts of power. The human brain's processing efficiency, progressive learning, and plasticity are unmatched by any computer system. Recent advances in stem cell technology have elevated the field of cell culture to higher levels of complexity, such as the development of three-dimensional (3D) brain organoids that recapitulate human brain functionality better than traditional monolayer cell systems. Organoid Intelligence (OI) aims to harness the innate biological capabilities of brain organoids for biocomputing and synthetic intelligence by interfacing them with computer technology. With the latest strides in stem cell technology, bioengineering, and machine learning, we can explore the ability of brain organoids to compute, and store given information (input), execute a task (output), and study how this affects the structural and functional connections in the organoids themselves. Furthermore, understanding how learning generates and changes patterns of connectivity in organoids can shed light on the early stages of cognition in the human brain. Investigating and understanding these concepts is an enormous, multidisciplinary endeavor that necessitates the engagement of both the scientific community and the public. Thus, on Feb 22-24 of 2022, the Johns Hopkins University held the first Organoid Intelligence Workshop to form an OI Community and to lay out the groundwork for the establishment of OI as a new scientific discipline. The potential of OI to revolutionize computing, neurological research, and drug development was discussed, along with a vision and roadmap for its development over the coming decade.

Alzheimer’s disease (AD) pathology develops many years before the onset of cognitive symptoms. Two pathological processes—aggregation of the amyloid- (A ) peptide into plaques and the microtubule protein tau into neurofibrillary tangles (NFTs)—are hallmarks of the disease. However, other pathological brain processes are thought to be key disease mediators of A plaque and NFT pathology. How these additional pathologies evolve over the course of the disease is currently unknown. Here we show that proteomic measurements in autosomal dominant AD cerebrospinal fluid (CSF) linked to brain protein coexpression can be used to characterize the evolution of AD pathology over a timescale spanning six decades. SMOC1 and SPON1 proteins associated with A plaques were elevated in AD CSF nearly 30 years before the onset of symptoms, followed by changes in synaptic proteins, metabolic proteins, axonal proteins, inflammatory proteins and finally decreases in neurosecretory proteins. The proteome discriminated mutation carriers from noncarriers before symptom onset as well or better than A and tau measures. Our results highlight the multifaceted landscape of AD pathophysiology and its temporal evolution. Such knowledge will be critical for developing precision therapeutic interventions and biomarkers for AD beyond those associated with A and tau.

The brain is arguably the most powerful computation system known. It is extremely efficient in processing large amounts of information and can discern signals from noise, adapt, and filter faulty information all while running on only 20 watts of power. The human brain's processing efficiency, progressive learning, and plasticity are unmatched by any computer system. Recent advances in stem cell technology have elevated the field of cell culture to higher levels of complexity, such as the development of three-dimensional (3D) brain organoids that recapitulate human brain functionality better than traditional monolayer cell systems. Organoid Intelligence (OI) aims to harness the innate biological capabilities of brain organoids for biocomputing and synthetic intelligence by interfacing them with computer technology. With the latest strides in stem cell technology, bioengineering, and machine learning, we can explore the ability of brain organoids to compute, and store given information (input), execute a task (output), and study how this affects the structural and functional connections in the organoids themselves. Furthermore, understanding how learning generates and changes patterns of connectivity in organoids can shed light on the early stages of cognition in the human brain. Investigating and understanding these concepts is an enormous, multidisciplinary endeavor that necessitates the engagement of both the scientific community and the public. Thus, on Feb 22–24 of 2022, the Johns Hopkins University held the first Organoid Intelligence Workshop to form an OI Community and to lay out the groundwork for the establishment of OI as a new scientific discipline. The potential of OI to revolutionize computing, neurological research, and drug development was discussed, along with a vision and roadmap for its development over the coming decade.

Despite the progress in deep learning networks, efficient learning at the edge (enabling adaptable, low-complexity machine learning solutions) remains a critical need for defense and commercial applications. We envision a pipeline to utilize large neuroimaging datasets, including maps of the brain which capture neuron and synapse connectivity, to improve machine learning approaches. We have pursued different approaches within this pipeline structure. First, as a demonstration of data-driven discovery, the team has developed a technique for discovery of repeated subcircuits, or motifs. These were incorporated into a neural architecture search approach to evolve network architectures. Second, we have conducted analysis of the heading direction circuit in the fruit fly, which performs fusion of visual and angular velocity features, to explore augmenting existing computational models with new insight. Our team discovered a novel pattern of connectivity, implemented a new model, and demonstrated sensor fusion on a robotic platform. Third, the team analyzed circuitry for memory formation in the fruit fly connectome, enabling the design of a novel generative replay approach. Finally, the team has begun analysis of connectivity in mammalian cortex to explore potential improvements to transformer networks. These constraints increased network robustness on the most challenging examples in the CIFAR-10-C computer vision robustness benchmark task, while reducing learnable attention parameters by over an order of magnitude. Taken together, these results demonstrate multiple potential approaches to utilize insight from neural systems for developing robust and efficient machine learning techniques., Comment: 11 pages, 4 figures

Optimal sensor placement enhances the efficiency of a variety of applications for monitoring dynamical systems. It has been established that deterministic solutions to the sensor placement problem are insufficient due to the many uncertainties in system input and parameters that affect system response sensor measurements. Accounting for the uncertainties in this typically expensive optimization is challenging due to computational intractability. This study proposes a stochastic environment in the form of a Markov Decision Process and a sensor placement agent that aims to maximize the information gain from placing a particular number of sensors of different types within the system. The agent is trained to maximize its reward based on an information-theoretic reward function. To verify the efficacy, the approach is applied to place a set of heterogeneous sensors in a shear building model. This methodology can be used to accommodate uncertainties in the sensor placement problem in real-world systems.

The brain is arguably the most powerful computation system known. It is extremely efficient in processing large amounts of information and can discern signals from noise, adapt, and filter faulty information all while running on only 20 watts of power. The human brain's processing efficiency, progressive learning, and plasticity are unmatched by any computer system. Recent advances in stem cell technology have elevated the field of cell culture to higher levels of complexity, such as the development of three-dimensional (3D) brain organoids that recapitulate human brain functionality better than traditional monolayer cell systems. Organoid Intelligence (OI) aims to harness the innate biological capabilities of brain organoids for biocomputing and synthetic intelligence by interfacing them with computer technology. With the latest strides in stem cell technology, bioengineering, and machine learning, we can explore the ability of brain organoids to compute, and store given information (input), execute a task (output), and study how this affects the structural and functional connections in the organoids themselves. Furthermore, understanding how learning generates and changes patterns of connectivity in organoids can shed light on the early stages of cognition in the human brain. Investigating and understanding these concepts is an enormous, multidisciplinary endeavor that necessitates the engagement of both the scientific community and the public. Thus, on Feb 22–24 of 2022, the Johns Hopkins University held the first Organoid Intelligence Workshop to form an OI Community and to lay out the groundwork for the establishment of OI as a new scientific discipline. The potential of OI to revolutionize computing, neurological research, and drug development was discussed, along with a vision and roadmap for its development over the coming decade.

Scientists are adopting new approaches to scale up their activities and goals. Progress in neurotechnologies, artificial intelligence, automation, and tools for collaboration promises new bursts of discoveries. However, compared to other disciplines and the industry, neuroscience laboratories have been slow to adopt key technologies to support collaboration, reproducibility, and automation. Drawing on progress in other fields, we define a roadmap for implementing automated research workflows for diverse research teams. We propose establishing a five-level capability maturity model for operations in neuroscience research. Achieving higher levels of operational maturity requires new technology-enabled methodologies, which we describe as ``SciOps''. The maturity model provides guidelines for evaluating and upgrading operations in multidisciplinary neuroscience teams., Comment: 10 pages, one figure

We propose a novel modular inference approach combining two different generative models -- generative adversarial networks (GAN) and normalizing flows -- to approximate the posterior distribution of physics-based Bayesian inverse problems framed in high-dimensional ambient spaces. We dub the proposed framework GAN-Flow. The proposed method leverages the intrinsic dimension reduction and superior sample generation capabilities of GANs to define a low-dimensional data-driven prior distribution. Once a trained GAN-prior is available, the inverse problem is solved entirely in the latent space of the GAN using variational Bayesian inference with normalizing flow-based variational distribution, which approximates low-dimensional posterior distribution by transforming realizations from the low-dimensional latent prior (Gaussian) to corresponding realizations of a low-dimensional variational posterior distribution. The trained GAN generator then maps realizations from this approximate posterior distribution in the latent space back to the high-dimensional ambient space. We also propose a two-stage training strategy for GAN-Flow wherein we train the two generative models sequentially. Thereafter, GAN-Flow can estimate the statistics of posterior-predictive quantities of interest at virtually no additional computational cost. The synergy between the two types of generative models allows us to overcome many challenges associated with the application of Bayesian inference to large-scale inverse problems, chief among which are describing an informative prior and sampling from the high-dimensional posterior. We demonstrate the efficacy and flexibility of GAN-Flow on various physics-based inverse problems of varying ambient dimensionality and prior knowledge using different types of GANs and normalizing flows.

There are multiple scales of abstraction from which we can describe the same image, depending on whether we are focusing on fine-grained details or a more global attribute of the image. In brain mapping, learning to automatically parse images to build representations of both small-scale features (e.g., the presence of cells or blood vessels) and global properties of an image (e.g., which brain region the image comes from) is a crucial and open challenge. However, most existing datasets and benchmarks for neuroanatomy consider only a single downstream task at a time. To bridge this gap, we introduce a new dataset, annotations, and multiple downstream tasks that provide diverse ways to readout information about brain structure and architecture from the same image. Our multi-task neuroimaging benchmark (MTNeuro) is built on volumetric, micrometer-resolution X-ray microtomography images spanning a large thalamocortical section of mouse brain, encompassing multiple cortical and subcortical regions. We generated a number of different prediction challenges and evaluated several supervised and self-supervised models for brain-region prediction and pixel-level semantic segmentation of microstructures. Our experiments not only highlight the rich heterogeneity of this dataset, but also provide insights into how self-supervised approaches can be used to learn representations that capture multiple attributes of a single image and perform well on a variety of downstream tasks. Datasets, code, and pre-trained baseline models are provided at: https://mtneuro.github.io/ ., Comment: 10 pages, 4 figures, Accepted at NeurIPS 2022

Learning new tasks and skills in succession without losing prior learning (i.e., catastrophic forgetting) is a computational challenge for both artificial and biological neural networks, yet artificial systems struggle to achieve parity with their biological analogues. Mammalian brains employ numerous neural operations in support of continual learning during sleep. These are ripe for artificial adaptation. Here, we investigate how modeling three distinct components of mammalian sleep together affects continual learning in artificial neural networks: (1) a veridical memory replay process observed during non-rapid eye movement (NREM) sleep; (2) a generative memory replay process linked to REM sleep; and (3) a synaptic downscaling process which has been proposed to tune signal-to-noise ratios and support neural upkeep. We find benefits from the inclusion of all three sleep components when evaluating performance on a continual learning CIFAR-100 image classification benchmark. Maximum accuracy improved during training and catastrophic forgetting was reduced during later tasks. While some catastrophic forgetting persisted over the course of network training, higher levels of synaptic downscaling lead to better retention of early tasks and further facilitated the recovery of early task accuracy during subsequent training. One key takeaway is that there is a trade-off at hand when considering the level of synaptic downscaling to use - more aggressive downscaling better protects early tasks, but less downscaling enhances the ability to learn new tasks. Intermediate levels can strike a balance with the highest overall accuracies during training. Overall, our results both provide insight into how to adapt sleep components to enhance artificial continual learning systems and highlight areas for future neuroscientific sleep research to further such systems., Comment: 9 pages, 12 figures, code available upon reasonable request. Corresponding author: William G. Coon (will.coon@jhuapl.edu)

Despite groundbreaking progress in reinforcement learning for robotics, gameplay, and other complex domains, major challenges remain in applying reinforcement learning to the evolving, open-world problems often found in critical application spaces. Reinforcement learning solutions tend to generalize poorly when exposed to new tasks outside of the data distribution they are trained on, prompting an interest in continual learning algorithms. In tandem with research on continual learning algorithms, there is a need for challenge environments, carefully designed experiments, and metrics to assess research progress. We address the latter need by introducing a framework for continual reinforcement-learning development and assessment using Lifelong Learning Explorer (L2Explorer), a new, Unity-based, first-person 3D exploration environment that can be continuously reconfigured to generate a range of tasks and task variants structured into complex and evolving evaluation curricula. In contrast to procedurally generated worlds with randomized components, we have developed a systematic approach to defining curricula in response to controlled changes with accompanying metrics to assess transfer, performance recovery, and data efficiency. Taken together, the L2Explorer environment and evaluation approach provides a framework for developing future evaluation methodologies in open-world settings and rigorously evaluating approaches to lifelong learning., Comment: 10 Pages submitted to AAAI AI for Open Worlds Symposium 2022

Capturing general deforming scenes from monocular RGB video is crucial for many computer graphics and vision applications. However, current approaches suffer from drawbacks such as struggling with large scene deformations, inaccurate shape completion or requiring 2D point tracks. In contrast, our method, Ub4D, handles large deformations, performs shape completion in occluded regions, and can operate on monocular RGB videos directly by using differentiable volume rendering. This technique includes three new in the context of non-rigid 3D reconstruction components, i.e., 1) A coordinate-based and implicit neural representation for non-rigid scenes, which in conjunction with differentiable volume rendering enables an unbiased reconstruction of dynamic scenes, 2) a proof that extends the unbiased formulation of volume rendering to dynamic scenes, and 3) a novel dynamic scene flow loss, which enables the reconstruction of larger deformations by leveraging the coarse estimates of other methods. Results on our new dataset, which will be made publicly available, demonstrate a clear improvement over the state of the art in terms of surface reconstruction accuracy and robustness to large deformations., Comment: 26 pages, 17 figures, 8 tables

Mass spectrometry (MS)-based proteomics is a powerful tool to explore pathogenic changes of a disease in an unbiased manner and has been used extensively in Alzheimer disease (AD) research. Here, by performing a meta-analysis of high-quality proteomic studies, we address which pathological changes are observed consistently and therefore most likely are of great importance for AD pathogenesis. We retrieved datasets, comprising a total of 21,588 distinct proteins identified across 857 postmortem human samples, from ten studies using labeled or label-free MS approaches. Our meta-analysis findings showed significant alterations of 757 and 1,195 proteins in AD in the labeled and label-free datasets, respectively. Only 33 proteins, some of which were associated with synaptic signaling, had the same directional change across the individual studies. However, despite alterations in individual proteins being different between the labeled and the label-free datasets, several pathways related to synaptic signaling, oxidative phosphorylation, immune response and extracellular matrix were commonly dysregulated in AD. These pathways represent robust changes in the human AD brain and warrant further investigation.

We report in situ synthesis of crystalline indium islands on InAs nanowires grown by molecular beam epitaxy. Structural analysis by transmission electron microscopy showed that In crystals grew in a tetragonal body-centered crystal structure within two families of orientations relative to wurtzite InAs. The crystalline islands had lengths < 500 nm and low-energy surfaces, suggesting that growth was driven mainly by surface energy minimization. Electrical transport through In/InAs devices exhibited Cooper pair charging, evidencing charge parity preservation and a pristine In/InAs interface, with an induced superconducting gap ∼0.45 meV. Cooper pair charging persisted to temperatures > 1.2 K and magnetic fields ∼0.7 T, demonstrating that In/InAs hybrids belong to an expanding class of semiconductor/superconductor hybrids operating over a wider parameter space than state-of-the-art Al-based hybrids. Engineering crystal morphology while isolating single islands using shadow epitaxy provides an interesting alternative to previous semiconductor/superconductor hybrid morphologies and device geometries.

We report in situ synthesis of crystalline indium islands on InAs nanowires grown by molecular beam epitaxy. Structural analysis by transmission electron microscopy showed that In crystals grew in a tetragonal body-centered crystal structure within two families of orientations relative to wurtzite InAs. The crystalline islands had lengths < 500 nm and low-energy surfaces, suggesting that growth was driven mainly by surface energy minimization. Electrical transport through In/InAs devices exhibited Cooper pair charging, evidencing charge parity preservation and a pristine In/InAs interface, with an induced superconducting gap similar to 0.45 meV. Cooper pair charging persisted to temperatures > 1.2 K and magnetic fields similar to 0.7 T, demonstrating that In/InAs hybrids belong to an expanding class of semiconductor/superconductor hybrids operating over a wider parameter space than state-of-the-art Al-based hybrids. Engineering crystal morphology while isolating single islands using shadow epitaxy provides an interesting alternative to previous semiconductor/superconductor hybrid morphologies and device geometries.

State-of-the-art methods for self-supervised learning (SSL) build representations by maximizing the similarity between different transformed "views" of a sample. Without sufficient diversity in the transformations used to create views, however, it can be difficult to overcome nuisance variables in the data and build rich representations. This motivates the use of the dataset itself to find similar, yet distinct, samples to serve as views for one another. In this paper, we introduce Mine Your Own vieW (MYOW), a new approach for self-supervised learning that looks within the dataset to define diverse targets for prediction. The idea behind our approach is to actively mine views, finding samples that are neighbors in the representation space of the network, and then predict, from one sample's latent representation, the representation of a nearby sample. After showing the promise of MYOW on benchmarks used in computer vision, we highlight the power of this idea in a novel application in neuroscience where SSL has yet to be applied. When tested on multi-unit neural recordings, we find that MYOW outperforms other self-supervised approaches in all examples (in some cases by more than 10%), and often surpasses the supervised baseline. With MYOW, we show that it is possible to harness the diversity of the data to build rich views and leverage self-supervision in new domains where augmentations are limited or unknown.

Data-driven analyses are increasingly valued in modern medicine. We integrate quantitative proteomics and transcriptomics from over 1,000 post-mortem brains from six cohorts representing Alzheimer's disease (AD), asymptomatic AD, progressive supranuclear palsy (PSP), and control patients from the Accelerating Medicines Partnership - Alzheimer's Disease consortium. We define robust co-expression trajectories related to disease progression, including early neuronal, microglial, astrocyte, and immune response modules, and later mRNA splicing and mitochondrial modules. The majority of, but not all, modules are conserved at the transcriptomic level, including module C3, which is only observed in proteome networks and enriched in mitogen-activated protein kinase (MAPK) signaling. Genetic risk enriches in modules changing early in disease and indicates that AD and PSP have distinct causal biological drivers at the pathway level, despite aspects of similar pathology, including synaptic loss and glial inflammatory changes. The conserved, high-confidence proteomic changes enriched in genetic risk represent targets for drug discovery.

Caenorhabditis elegans (C. elegans) lifespan assays constitute a broadly used approach for investigating the fundamental biology of longevity. Traditional C. elegans lifespan assays require labor-intensive microscopic monitoring of individual animals to evaluate life/death over a period of weeks, making large-scale high throughput studies impractical. The lifespan machine developed by Stroustrup et al. (2013) adapted flatbed scanner technologies to contribute a major technical advance in the efficiency of C. elegans survival assays. Introducing a platform in which large portions of a lifespan assay are automated enabled longevity studies of a scope not possible with previous exclusively manual assays and facilitated novel discovery. Still, as initially described, constructing and operating scanner-based lifespan machines requires considerable effort and expertise. Here we report on design modifications that simplify construction, decrease cost, eliminate certain mechanical failures, and decrease assay workload requirements. The modifications we document should make the lifespan machine more accessible to interested laboratories.

Caenorhabditis elegans (C. elegans) lifespan assays constitute a broadly used approach for investigating the fundamental biology of longevity. Traditional C. elegans lifespan assays require labor-intensive microscopic monitoring of individual animals to evaluate life/death over a period of weeks, making large-scale high throughput studies impractical. The lifespan machine developed by Stroustrup et al. (2013) adapted flatbed scanner technologies to contribute a major technical advance in the efficiency of C. elegans survival assays. Introducing a platform in which large portions of a lifespan assay are automated enabled longevity studies of a scope not possible with previous exclusively manual assays and facilitated novel discovery. Still, as initially described, constructing and operating scanner-based lifespan machines requires considerable effort and expertise. Here we report on design modifications that simplify construction, decrease cost, eliminate certain mechanical failures, and decrease assay workload requirements. The modifications we document should make the lifespan machine more accessible to interested laboratories.

Data-driven analyses are increasingly valued in modern medicine. We integrate quantitative proteomics and transcriptomics from over 1,000 post-mortem brains from six cohorts representing Alzheimer's disease (AD), asymptomatic AD, progressive supranuclear palsy (PSP), and control patients from the Accelerating Medicines Partnership - Alzheimer's Disease consortium. We define robust co-expression trajectories related to disease progression, including early neuronal, microglial, astrocyte, and immune response modules, and later mRNA splicing and mitochondrial modules. The majority of, but not all, modules are conserved at the transcriptomic level, including module C3, which is only observed in proteome networks and enriched in mitogen-activated protein kinase (MAPK) signaling. Genetic risk enriches in modules changing early in disease and indicates that AD and PSP have distinct causal biological drivers at the pathway level, despite aspects of similar pathology, including synaptic loss and glial inflammatory changes. The conserved, high-confidence proteomic changes enriched in genetic risk represent targets for drug discovery.

Uniform, defect-free crystal interfaces and surfaces are crucial ingredients for realizing high-performance nanoscale devices. A pertinent example is that advances in gate-tunable and topological superconductivity using semiconductor/superconductor electronic devices are currently built on the hard proximity-induced superconducting gap obtained from epitaxial indium arsenide/aluminum heterostructures. Fabrication of devices requires selective etch processes; these exist only for InAs/Al hybrids, precluding the use of other, potentially superior material combinations. This work introduces a crystal growth platform-based on 3D structuring of growth substrates-which enables synthesis of semiconductor nanowire hybrids with in situ patterned superconductor shells. The platform eliminates the need for etching, thereby enabling full freedom in the choice of hybrid constituents. All of the most frequently used superconducting hybrid device architectures are realized and characterized. These devices exhibit increased yield and electrostatic stability compared to etched devices, and evidence of ballistic superconductivity is observed. In addition to aluminum, hybrid structures based on tantalum, niobium, and vanadium are presented.

We report a method to locally deliver a chemical etchant at the nanoscale in the vapor phase by capillary condensation forming a meniscus at the nanoparticle/substrate interface. The process is simple, scalable and does not require functionalization of the nanoparticles. Furthermore, it does not rely on any specific chemical properties of the materials other than the solution being aqueous and the wettability properties of the surfaces involved, which should enable its application to other material and chemical combinations. In particular, in this work we demonstrate the proposed process by periodically pattering a SiO2 layer using a self-assembled monolayer of polystyrene particles exposed to HF vapors. The patterned SiO2 layer is then used as a mask to etch a pattern of inverted nanopyramids on Si. The silicon nanopatterning has been demonstrated for particles sizes ranging from 800 nm down to 100 nm, providing pyramids with a size down to 50 nm for 100 nm nanoparticles., This work has been financially supported by the following projects: ENE2015-74009-JIN funded by the Spanish Ministry of Economy and Competitiveness (MINECO) and the European Regional Development Fund (ERDF-EU), TEC2017-82305-R (MINECO), the InnoEnergy PhD School Programme and the European Institute of Technology (EIT)., Peer Reviewed, Postprint (author's final draft)

We report MBE synthesis of InAs/vanadium hybrid nanowires. The vanadium was deposited without breaking ultra-high vacuum after InAs nanowire growth, minimizing any effect of oxidation and contamination at the interface between the two materials. We investigated four different substrate temperatures during vanadium deposition, ranging from -150°C to 250°C. The structural relation between vanadium and InAs depended on the deposition temperature. The three lower temperature depositions gave vanadium shells with a polycrystalline, granular morphology and the highest temperature resulted in vanadium reacting with the InAs nanowire. We fabricated electronic devices from the hybrid nanowires and obtained a high out-of-plane critical magnetic field, exceeding the bulk value for vanadium. However, size effects arising from the nanoscale grains resulted in the absence of a well-defined critical temperature, as well as device-to-device variation in the resistivity vs. temperature dependence during the transition to the superconducting state.

Curated Heuristic Using a Network of Knowledge, A custom-built website used to provide students educational material, based on the student's capabilities, learning styles, and problem-solving abilities.

The combination of strong spin-orbit coupling, large g factors, and the coupling to a superconductor can be used to create a topologically protected state in a semiconductor nanowire. Here we report on growth and characterization of hybrid epitaxial InAsSb/Al nanowires, with varying composition and crystal structure. We find the strongest spin-orbit interaction at intermediate compositions in zinc-blende InAs1-xSbx nanowires, exceeding that of both InAs and InSb materials, confirming recent theoretical studies. We show that the epitaxial InAsSb/Al interface allows for a hard induced superconducting gap and 2e transport in Coulomb charging experiments, similarly to experiments on InAs/Al and InSb/Al materials, and find measurements consistent with topological phase transitions at low magnetic fields due to large effective g factors. Finally we present a method to grow pure wurtzite InAsSb nanowires which are predicted to exhibit even stronger spin-orbit coupling than the zinc-blende structure.

Evangelical Protestants are less likely than most other Americans to support environmental policies and spending to protect the natural environment. We use almost three decades of repeated cross-sectional data to examine the factors that promote evangelicals’ opposition to environmental spending. Mediation models with bootstrapped standard errors show that affiliation with the Republican Party, biblical literalism, and religious service attendance mediate differences in support for environmental spending between evangelical Protestants and other Americans. The importance of these mediating variables, however, varies over time and by the group evangelicals are being compared to. Differences in support for environmental spending between evangelical and mainline Protestants, for example, are primarily due to views of the Bible, but not at all to Republican identification. The results shed light on the causal effects of religion on views of the environment, temporal changes in the social and political implications of religiosity, the persistence of divisive issues that support the continued existence of culture wars, and the future of government spending on environmental problems in a social context where scientific evidence is filtered through political and religious ideology.

Evangelical Protestants are less likely than most other Americans to support environmental policies and spending to protect the natural environment. We use almost three decades of repeated cross-sectional data to examine the factors that promote evangelicals’ opposition to environmental spending. Mediation models with bootstrapped standard errors show that affiliation with the Republican Party, biblical literalism, and religious service attendance mediate differences in support for environmental spending between evangelical Protestants and other Americans. The importance of these mediating variables, however, varies over time and by the group evangelicals are being compared to. Differences in support for environmental spending between evangelical and mainline Protestants, for example, are primarily due to views of the Bible, but not at all to Republican identification. The results shed light on the causal effects of religion on views of the environment, temporal changes in the social and political implications of religiosity, the persistence of divisive issues that support the continued existence of culture wars, and the future of government spending on environmental problems in a social context where scientific evidence is filtered through political and religious ideology.

Epitaxially connected nanowires allow for the design of electron transport experiments and applications beyond the standard two terminal device geometries. In this Letter, we present growth methods of three distinct types of wurtzite structured InAs nanocrosses via the vapor-liquid-solid mechanism. Two methods use conventional wurtzite nanowire arrays as a 6-fold hexagonal basis for growing single crystal wurtzite nanocrosses. A third method uses the 2-fold cubic symmetry of (100) substrates to form well-defined coherent inclusions of zinc blende in the center of the nanocrosses. We show that all three types of nanocrosses can be transferred undamaged to arbitrary substrates, which allows for structural, compositional, and electrical characterization. We further demonstrate the potential for synthesis of as-grown nanowire networks and for using nanowires as shadow masks for in situ fabricated junctions in radial nanowire heterostructures.