Thermal annealing of GaAs concentrator solar cells

Isochronal and isothermal annealing tests were performed on GaAs concentrator cells which were irradiated with electrons of various energies to fluences up to 1 x 10(exp 16) e/sq cm. The results include: (1) For cells irradiated with electrons from 0.7 to 2.3 MeV, recovery decreases with increasing electron energy. (2) As determined by the un-annealed fractions, isothermal and isochronal annealing produce the same recovery. Also, cells irradiated to 3 x 10(exp 15) or 1 x 10(exp 16) e/sq cm recover to similar un-annealed fractions. (3) Some significant annealing is being seen at 150 C although very long times are required

Effect of dislocations on properties of heteroepitaxial InP solar cells

The apparently unrelated phenomena of temperature dependency, carrier removal and photoluminescence are shown to be affected by the high dislocation densities present in heteroepitaxial InP solar cells. Using homoepitaxial InP cells as a baseline, it is found that the relatively high dislocation densities present in heteroepitaxial InP/GaAs cells lead to increased volumes of dVoc/dt and carrier removal rate and substantial decreases in photoluminescence spectral intensities. With respect to dVoc/dt, the observed effect is attributed to the tendency of dislocations to reduce Voc. Although the basic cause for the observed increase in carrier removal rate is unclear, it is speculated that the decreased photoluminescence intensity is attributable to defect levels introduced by dislocations in the heteroepitaxial cells

Active Sampling-based Binary Verification of Dynamical Systems

Nonlinear, adaptive, or otherwise complex control techniques are increasingly relied upon to ensure the safety of systems operating in uncertain environments. However, the nonlinearity of the resulting closed-loop system complicates verification that the system does in fact satisfy those requirements at all possible operating conditions. While analytical proof-based techniques and finite abstractions can be used to provably verify the closed-loop system's response at different operating conditions, they often produce conservative approximations due to restrictive assumptions and are difficult to construct in many applications. In contrast, popular statistical verification techniques relax the restrictions and instead rely upon simulations to construct statistical or probabilistic guarantees. This work presents a data-driven statistical verification procedure that instead constructs statistical learning models from simulated training data to separate the set of possible perturbations into "safe" and "unsafe" subsets. Binary evaluations of closed-loop system requirement satisfaction at various realizations of the uncertainties are obtained through temporal logic robustness metrics, which are then used to construct predictive models of requirement satisfaction over the full set of possible uncertainties. As the accuracy of these predictive statistical models is inherently coupled to the quality of the training data, an active learning algorithm selects additional sample points in order to maximize the expected change in the data-driven model and thus, indirectly, minimize the prediction error. Various case studies demonstrate the closed-loop verification procedure and highlight improvements in prediction error over both existing analytical and statistical verification techniques.Comment: 23 page

The Importance of Importance: Self-Descriptors in Dysphoria

This study examined the relationship between importance ratings of positive and negative personal attributes and depressed mood. Undergraduate psychology students (n = 115) completed the Beck Depression Inventory-II and made self-referential ratings on several adjectives. Participants subsequently indicated how important it was for them to possess or fail to exhibit each of these traits. The results demonstrated that the perceived lack of important positive traits was related to increased depressed mood whereas not exhibiting important negative traits was associated with less depressed mood. Moreover, depressed mood was related to the degree to which respondents were certain about their endorsement of the traits. The implications of these results are discussed

Cryptic persistence and loss of local endemism in Lake Constance charr subject to anthropogenic disturbance

In the welcome circumstance that species believed extinct are rediscovered, it is often the case that biological knowledge acquired before the presumed extinction is limited. Efforts to address these knowledge gaps, in particular to assess the taxonomic integrity and conservation status of such species, can be hampered by a lack of genetic data and scarcity of samples in museum collections. Here, we present a proof-of-concept case study based on a multidisciplinary data evaluation approach to tackle such problems. The approach was developed after the rediscovery, 40 years after its presumed extinction, of the enigmatic Lake Constance deep-water charr Salvelinus profundus. Targeted surveys led to the capture of further species and additional sympatric normal charr, Salvelinus cf. umbla. Since the lake had been subject to massive stocking in the past, an evaluation of the genetic integrity of both extant forms was called for in order to assess possible introgression. A two-step genomic approach was developed based on restriction site associated DNA (RAD). Diagnostic population genomic (single nucleotide polymorphism [SNP]) data were harvested from contemporary samples and used for RNA bait design to perform target capture in DNA libraries of archival scale material, enabling a comparison between extant and historic samples. Furthermore, life history traits and morphological data for both extant forms were gathered and compared with historical data from the past 60–120 years. While extant deep-water charr matched historical deep-water specimens in body shape, gill raker count, and growth rates, significant differences were discovered between historical and extant normal charr. These resulted were supported by genomic analyses of contemporary samples, revealing the two extant forms to be highly divergent. The results of population assignment tests suggest that the endemic deep-water charr persisted in Lake Constance during the eutrophic phase, but not one of the historical genomic samples could be assigned to the extant normal charr taxon. Stocking with non-endemic charr seems to be the most likely reason for these changes. This proof-of-concept study presents a multidisciplinary data evaluation approach that simultaneously tests population genomic integrity and addresses some of the conservation issues arising from rediscovery of a species characterized by limited data availability.publishedVersio

Deep neural network or dermatologist?

Deep learning techniques have proven high accuracy for identifying melanoma in digitised dermoscopic images. A strength is that these methods are not constrained by features that are pre-defined by human semantics. A down-side is that it is difficult to understand the rationale of the model predictions and to identify potential failure modes. This is a major barrier to adoption of deep learning in clinical practice. In this paper we ask if two existing local interpretability methods, Grad-CAM and Kernel SHAP, can shed light on convolutional neural networks trained in the context of melanoma detection. Our contributions are (i) we first explore the domain space via a reproducible, end-to-end learning framework that creates a suite of 30 models, all trained on a publicly available data set (HAM10000), (ii) we next explore the reliability of GradCAM and Kernel SHAP in this context via some basic sanity check experiments (iii) finally, we investigate a random selection of models from our suite using GradCAM and Kernel SHAP. We show that despite high accuracy, the models will occasionally assign importance to features that are not relevant to the diagnostic task. We also show that models of similar accuracy will produce different explanations as measured by these methods. This work represents first steps in bridging the gap between model accuracy and interpretability in the domain of skin cancer classification

What do cyclists need to see to avoid single-bicycle crashes?

The number of single-bicycle crash victims is substantial in countries with high levels of cycling. To study the role of visual characteristics of the infrastructure, such as pavement markings, in single-bicycle crashes, a study in two steps was conducted. In Study 1, a questionnaire study was conducted among bicycle crash victims (n = 734). Logistic regression was used to study the relationship between the crashes and age, light condition, alcohol use, gaze direction and familiarity with the crash scene. In Study 2, the image degrading and edge detection method (IDED-method) was used to investigate the visual characteristics of 21 of the crash scenes. The results of the studies indicate that crashes, in which the cyclist collided with a bollard or road narrowing or rode off the road, were related to the visual characteristics of bicycle facilities. Edge markings, especially in curves of bicycle tracks, and improved conspicuity of bollards are recommended. Statement of Relevance: Elevated single-bicycle crash numbers are common in countries with high levels of cycling. No research has been conducted on what cyclists need to see to avoid this type of crash. The IDED-method to investigate crash scenes is new and proves to be a powerful tool to quantify 'visual accessibility'. © 2011 Taylor & Francis

Band Formation during Gaseous Diffusion in Aerogels

We study experimentally how gaseous HCl and NH_3 diffuse from opposite sides of and react in silica aerogel rods with porosity of 92 % and average pore size of about 50 nm. The reaction leads to solid NH_4Cl, which is deposited in thin sheet-like structures. We present a numerical study of the phenomenon. Due to the difference in boundary conditions between this system and those usually studied, we find the sheet-like structures in the aerogel to differ significantly from older studies. The influence of random nucleation centers and inhomogeneities in the aerogel is studied numerically.Comment: 7 pages RevTex and 8 figures. Figs. 4-8 in Postscript, Figs. 1-3 on request from author

Three-dimensional coherent X-ray diffraction imaging of a ceramic nanofoam: determination of structural deformation mechanisms

Ultra-low density polymers, metals, and ceramic nanofoams are valued for their high strength-to-weight ratio, high surface area and insulating properties ascribed to their structural geometry. We obtain the labrynthine internal structure of a tantalum oxide nanofoam by X-ray diffractive imaging. Finite element analysis from the structure reveals mechanical properties consistent with bulk samples and with a diffusion limited cluster aggregation model, while excess mass on the nodes discounts the dangling fragments hypothesis of percolation theory.Comment: 8 pages, 5 figures, 30 reference