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As wireless researchers are tasked to enable wireless communication as infrastructure in more dynamic aerial settings, there is a growing need for large-scale experimental platforms that provide realistic, reproducible, and reliable experimental validation. To bridge the research-to-implementation gap, the Aerial Experimentation and Research Platform for Advanced Wireless (AERPAW) offers open-source tools, reference experiments, and hardware to facilitate and evaluate the development of wireless research in controlled digital twin environments and live testbed flights. The inaugural AERPAW Challenge, "Find a Rover," was issued to spark collaborative efforts and test the platform's capabilities. The task involved localizing a narrowband wireless signal, with teams given ten minutes to find the "rover" within a twenty-acre area. By engaging in this exercise, researchers can validate the platform's value as a tool for innovation in wireless communications research within aerial robotics. This paper recounts the methods and experiences of the top three teams in automating and rapidly locating a wireless signal by automating and controlling an aerial drone in a realistic testbed scenario., Comment: submitted to IEEE magazine paper

A novel stochastic optimization method called MAC was suggested. The method is based on the calculation of the objective function at several random points and then an empirical expected value and an empirical covariance matrix are calculated. The empirical expected value is proven to converge to the optimum value of the problem. The MAC algorithm was encoded in Matlab and the code was tested on 20 test problems. Its performance was compared with those of the interior point method (Matlab name: fmincon), simplex, pattern search (PS), simulated annealing (SA), particle swarm optimization (PSO), and genetic algorithm (GA) methods. The MAC method failed two test functions and provided inaccurate results on four other test functions. However, it provided accurate results and required much less CPU time than the widely used optimization methods on the other 14 test functions.

In this paper, we present the design, implementation, and evaluation of SINATRA, which supports instantaneous browser updates that do not result in any data loss through a novel Multi-Version eXecution (MVX) approach for JavaScript programs. SINATRA works in pure JavaScript, does not require any browser support, thus works on closed-source browsers, and requires trivial changes to each target page, that can be automated. First, SINATRA captures all the non-determinism available to a JavaScript program (e.g., event handlers executed, expired timers, invocations of Math.random). Our evaluation shows that SINATRA requires 5MB to store such events, and the memory grows at a modest rate of 23.1KB/s as the user keeps interacting with each page. When an update becomes available, SINATRA transfer the state by re-executing the same set of non-deterministic events on the new browser. During this time, which can be as long as 13 seconds, SINATRA uses MVX to allow the user to keep interacting with the old browser. Finally, SINATRA changes the roles in 353ms, and the user starts interacting with the new browser, effectively performing a browser update with zero downtime and no loss of state., Comment: 23 pages, 5 tables, 8 figures

It has been shown that the performance of neural machine translation (NMT) drops starkly in low-resource conditions, often requiring large amounts of auxiliary data to achieve competitive results. An effective method of generating auxiliary data is back-translation of target language sentences. In this work, we present a case study of Tigrinya where we investigate several back-translation methods to generate synthetic source sentences. We find that in low-resource conditions, back-translation by pivoting through a higher-resource language related to the target language proves most effective resulting in substantial improvements over baselines., Comment: Accepted at AfricaNLP Workshop, EACL 2021

Non-collinear antiferromagnets, with either an L1$_{2}$ cubic crystal lattice (e.g. Mn$_{3}$Ir and Mn$_{3}$Pt) or a D0$_{19}$ hexagonal structure (e.g. Mn$_{3}$Sn and Mn$_{3}$Ge), exhibit a number of novel phenomena of interest to topological spintronics. Amongst the cubic systems, for example, tetragonally distorted Mn$_{3}$Pt exhibits an intrinsic anomalous Hall effect (AHE). However, Mn$_{3}$Pt only enters a non-collinear magnetic phase close to the stoichiometric composition and at suitably large thicknesses. Therefore, we turn our attention to Mn$_{3}$Ir, the material of choice for use in exchange bias heterostructures. In this paper, we investigate the magnetic and electrical transport properties of epitaxially grown, face-centered-cubic $\gamma$-Mn$_{3}$Ir thin films with (111) crystal orientation. Relaxed films of 10 nm thickness exhibit an ordinary Hall effect, with a hole-type carrier concentration of (2.24 $\pm$ 0.08) $\times$ 10$^{23}$ cm$^{-3}$. On the other hand, TEM characterization demonstrates that ultrathin 3 nm films grow with significant in-plane tensile strain. This may explain a small remanent moment, observed at low temperatures, shown by XMCD spectroscopy to arise from uncompensated Mn spins. Of the order 0.02 $\mu_{B}$ / atom, this dominates electrical transport behavior, leading to a small AHE and negative magnetoresistance. These results are discussed in terms of crystal microstructure and chiral domain behavior, with spatially resolved XML(C)D-PEEM supporting the conclusion that small antiferromagnetic domains, < 20 nm in size, of differing chirality account for the absence of observed Berry curvature driven magnetotransport effects.

Antiferromagnetic materials are of great interest for spintronics. Here we present a comprehensive study of the growth, structural characterization, and resulting magnetic properties of thin films of the non-collinear antiferromagnet Mn$_{3}$Ir. Using epitaxial engineering on MgO (001) and Al$_{2}$O$_{3}$ (0001) single crystal substrates, we control the growth of cubic ${\gamma}$-Mn$_{3}$Ir in both (001) and (111) crystal orientations, and discuss the optimization of growth conditions to achieve high-quality crystal structures with low surface roughness. Exchange bias is studied in bilayers, with exchange bias fields as large as -29 mT (equivalent to a unidirectional anisotropy constant of 11.5 nJ cm$^{-2}$) measured in Mn$_{3}$Ir (111) / permalloy heterostructures at room temperature. In addition, a distinct dependence of blocking temperature on in-plane crystallographic direction in Mn$_{3}$Ir (001) / Py bilayers is observed. These findings are discussed in the context of chiral antiferromagnetic domain structures, and will inform progress towards topological antiferromagnetic spintronic devices., Comment: 15 pages, 10 figures

Images from Positron Emission Tomography (PET) deliver functional data such as perfusion and metabolism. On the other hand, images from Magnetic Resonance Imaging (MRI) provide information describing anatomical structures. Fusing the complementary information from the two modalities is helpful in oncology. In this project, we implemented a complete tool allowing semi-automatic MRI-PET registration for small animal imaging in the preclinical studies. A two-stage hierarchical registration approach is proposed. First, a global affine registration is applied. For robust and fast registration, principal component analysis (PCA) is used to compute the initial parameters for the global affine registration. Since, only the low intensities in the PET volume reveal the anatomic information on the MRI scan, we proposed a non-uniform intensity transformation to the PET volume to enhance the contrast of the low intensity. This helps to improve the computation of the centroid and principal axis by increasing the contribution of the low intensities. Then, the globally registered image is given as input to the second stage which is a local deformable registration (B-spline registration). Mutual information is used as metric function for the optimization. A multi-resolution approach is used in both stages. The registration algorithm is supported by graphical user interface (GUI) and visualization methods so that the user can interact easily with the process. The performance of the registration algorithm is validated by two medical experts on seven different datasets on abdominal and brain areas including noisy and difficult image volumes., Comment: 8 pages

Classification of Visual Object Classes represents one of the most elaborated areas of interest in Computer Vision. It is always challenging to get one specific detector, descriptor or classifier that provides the expected object classification result. Consequently, it critical to compare the different detection, descriptor and classifier methods available and chose a single or combination of two or three to get an optimal result. In this paper, we have presented a comparative survey of different feature descriptors and classifiers. From feature descriptors, SIFT (Sparse & Dense) and HeuSIFT combination colour descriptors; From classification techniques, Support Vector Classifier, K-Nearest Neighbor, ADABOOST, and fisher are covered in comparative practical implementation survey., Comment: 9 pages

Tracking people in a video sequence is one of the fields of interest in computer vision. It has broad applications in motion capture and surveillance. However, due to the complexity of human dynamic structure, detecting and tracking are not straightforward. Consequently, different detection and tracking techniques with different applications and performance have been developed. To minimize the noise between the prediction and measurement during tracking, Kalman filter has been used as a filtering technique. At the same time, in most cases, detection and tracking results from a single sensor is not enough to detect and track a person. To avoid this problem, using a multi-sensor fusion technique is indispensable. In this paper, a comparative survey of detection, tracking and multi-sensor fusion methods are presented., Comment: 7 pages, 9 figures

The viscoelastic properties of arterial tissue dictate vessel behavior in certain disease states, injury modalities, and during some endovascular procedures. In this study, we characterized the viscoelastic mechanical response of porcine abdominal aortic tissue via uniaxial mechanical experiments on vascular tissue samples coupled with full-field surface strain measurements using the 2-D digital image correlation (DIC) technique. The measured stress relaxation response and intimal surface strain field were used to identify material parameters for a proposed viscoelastic anisotropic (VA) constitutive model of the passive arterial wall. The obtained results show that the VA constitutive model is able to capture the viscoelastic mechanical behavior of the arterial tissue over clinically-relevant time scales. The identified material model and numerical simulations provide a comprehensive description of the passive viscoelastic tissue properties of the arterial wall, and a quantitative understanding of the spatial and directional variability underlying arterial tissue mechanical behavior., Comment: 42 pages, 7 figures

We report thermal control of mode hybridization between the ferromagnetic resonance (FMR) and a planar resonator (notch filter) working at 4.74 GHz. The chosen magnetic material is a ferrimagnetic insulator (Yttrium Iron Garnet: YIG) covered by 6 nm of platinum (Pt). A current induced heating method has been used in order to enhance the temperature of the YIG/Pt system. The device permits us to control the transmission spectra and the magnon-photon coupling strength at room temperature. These experimental findings reveal potentially applicable tunable microwave filtering function., Comment: 5 pages, 4 figures

In literature, several approaches are trying to make the UAVs fly autonomously i.e., by extracting perspective cues such as straight lines. However, it is only available in well-defined human made environments, in addition to many other cues which require enough texture information. Our main target is to detect and avoid frontal obstacles from a monocular camera using a quad rotor Ar.Drone 2 by exploiting optical flow as a motion parallax, the drone is permitted to fly at a speed of 1 m/s and an altitude ranging from 1 to 4 meters above the ground level. In general, detecting and avoiding frontal obstacle is a quite challenging problem because optical flow has some limitation which should be taken into account i.e. lighting conditions and aperture problem.

This paper discusses characterization approaches for the evaluation of potential defects that may occur during the automated fiber placement (AFP) process. Two nondestructive evaluation techniques were utilized simultaneously, acoustic emission (AE) and digital image correlation (DIC), to investigate correlations between mechanical properties and damage propagation during tension tests. The layup configuration is designed with the defect running across the width, and the stacking sequence with seven layers is produced: [−45/0/45/̅9̅̅0̅]s. A total of 3 panels were manufactured: one containing a gap defect in the middle layer, another one with an overlap defect, and a pristine panel for baseline comparison. Each panel accommodates six coupons having dimensions of 20” x 2”. The results indicate that defects caused during the manufacturing process affect damage initiation and growth.

Adhesively bonded carbon fiber-reinforced epoxy composite laminates are widely used in aerospace applications. During a high energy impact event, these laminates are often subjected to high strain rate loading. However, the influence of high strain rate loading on the response of these composite joints is not well understood. Computational finite element (FE) modeling and simulations are conducted to guide the design of high strain rate experiments. Two different experimental designs based on split Hopkinson bar were numerically modeled to simulate Mode I and Mode II types loading in the composite. In addition, the computational approach adopted in this study helps in understanding the high strain rate response of adhesively bonded composite joints subjected to nominally Mode I and Mode II loading. The modeling approach consists of a ply-level 3D FE model, a progressive damage constitutive model for the composite material behavior and a cohesive tie-break contact element for interlaminar delamination.

Accurate assessment of risk factors for long-term space missions is critical for human space exploration: therefore it is essential to have a detailed understanding of the biological effects on humans living and working in deep space. Ionizing radiation from Galactic Cosmic Rays (GCR) is one of the major risk factors factor that will impact health of astronauts on extended missions outside the protective effects of the Earth's magnetic field. Currently there are gaps in our knowledge of the health risks associated with chronic low dose, low dose rate ionizing radiation, specifically ions associated with high (H) atomic number (Z) and energy (E). The GeneLab project (genelab.nasa.gov) aims to provide a detailed library of Omics datasets associated with biological samples exposed to HZE. The GeneLab Data System (GLDS) currently includes datasets from both spaceflight and ground-based studies, a majority of which involve exposure to ionizing radiation. In addition to detailed information for ground-based studies, we are in the process of adding detailed, curated dosimetry information for spaceflight missions. GeneLab is the first comprehensive Omics database for space related research from which an investigator can generate hypotheses to direct future experiments utilizing both ground and space biological radiation data. In addition to previously acquired data, the GLDS is continually expanding as Omics related data are generated by the space life sciences community. Here we provide a brief summary of space radiation related data available at GeneLab.

Biological risks associated with microgravity is a major concern for space travel. Although determination of risk has been a focus for NASA research, data examining systemic (i.e., multi- or pan-tissue) responses to space flight are sparse. The overall goal of our work is to identify potential master regulators responsible for such responses to microgravity conditions. To do this we utilized the NASA GeneLab database which contains a wide array of omics experiments, including data from: 1) different flight conditions (space shuttle (STS) missions vs. International Space Station (ISS); 2) different tissues; and 3) different types of assays that measure epigenetic, transcriptional, and protein expression changes. We have performed meta-analysis identifying potential master regulators involved with systemic responses to microgravity. The analysis used 7 different murine and rat data sets, examining the following tissues: liver, kidney, adrenal gland, thymus, mammary gland, skin, and skeletal muscle (soleus, extensor digitorum longus, tibialis anterior, quadriceps, and gastrocnemius). Using a systems biology approach, we were able to determine that p53 and immune related pathways appear central to pan-tissue microgravity responses. Evidence for a universal response in the form of consistency of change across tissues in regulatory pathways was observed in both STS and ISS experiments with varying durations; while degree of change in expression of these master regulators varied across species and strain, some change in these master regulators was universally observed. Interestingly, certain skeletal muscle (gastrocnemius and soleus) show an overall down-regulation in these genes, while in other types (extensor digitorum longus, tibialis anterior and quadriceps) they are up-regulated, suggesting certain muscle tissues may be compensating for atrophy responses caused by microgravity. Studying these organtissue-specific perturbations in molecular signaling networks, we demonstrate the value of GeneLab in characterizing potential master regulators associated with biological risks for spaceflight.

The NASA Space Radiation Risk project is responsible for integrating new experimental and computational results into models to predict risk of cancer and acute radiation syndrome (ARS) for use in mission planning and systems design, as well as current space operations. The project has several parallel efforts focused on proving NASA's radiation risk projection capability in both the near and long term. This presentation will give an overview, with select results from these efforts including the following topics: verification, validation, and streamlining the transition of models to use in decision making; relative biological effectiveness and dose rate effect estimation using a combination of stochastic track structure simulations, DNA damage model calculations and experimental data; ARS model improvements; pathway analysis from gene expression data sets; solar particle event probabilistic exposure calculation including correlated uncertainties for use in design optimization.

In this work, we evaluate the differential effects of low- and high-LET radiation on 3-D organotypic cultures in order to investigate radiation quality impacts on gene expression and cellular responses. Reducing uncertainties in current risk models requires new knowledge on the fundamental differences in biological responses (the so-called radiation quality effects) triggered by heavy ion particle radiation versus low-LET radiation associated with Earth-based exposures. We are utilizing novel 3-D organotypic human tissue models that provide a format for study of human cells within a realistic tissue framework, thereby bridging the gap between 2-D monolayer culture and animal models for risk extrapolation to humans. To identify biological pathway signatures unique to heavy ion particle exposure, functional gene set enrichment analysis (GSEA) was used with whole transcriptome profiling. GSEA has been used extensively as a method to garner biological information in a variety of model systems but has not been commonly used to analyze radiation effects. It is a powerful approach for assessing the functional significance of radiation quality-dependent changes from datasets where the changes are subtle but broad, and where single gene based analysis using rankings of fold-change may not reveal important biological information. We identified 45 statistically significant gene sets at 0.05 q-value cutoff, including 14 gene sets common to gamma and titanium irradiation, 19 gene sets specific to gamma irradiation, and 12 titanium-specific gene sets. Common gene sets largely align with DNA damage, cell cycle, early immune response, and inflammatory cytokine pathway activation. The top gene set enriched for the gamma- and titanium-irradiated samples involved KRAS pathway activation and genes activated in TNF-treated cells, respectively. Another difference noted for the high-LET samples was an apparent enrichment in gene sets involved in cycle cycle/mitotic control. It is plausible that the enrichment in these particular pathways results from the complex DNA damage resulting from high-LET exposure where repair processes are not completed during the same time scale as the less complex damage resulting from low-LET radiation.

The Moon's surface is covered by a layer of fine, potential reactive dust. Lunar dust contain about 1‐2% respirable very fine dust (less than 3 micrometers). The habitable area of any lunar landing vehicle and outpost would inevitably be contaminated with lunar dust that could pose a health risk. The purpose of the study is to analyze the dynamics of global gene expression changes in lung tissues of rats exposed to lunar dust particles. F344 rats were exposed for 4 weeks (6h/d; 5d/wk) in nose‐only inhalation chambers to concentrations of 0 (control air), 2.1, 6.8, 21, and 61 mg/m3 of lunar dust. Animals were euthanized at 1 day and 13 weeks after the last inhalation exposure. After being lavaged, lung tissue from each animal was collected and total RNA was isolated. Four samples of each dose group were analyzed using Agilent Rat GE v3 microarray to profile global gene expression of 44K transcripts. After background subtraction, normalization, and log transformation, t tests were used to compare the mean expression levels of each exposed group to the control group. Correction for multiple testing was made using the method of Benjamini, Krieger, and Yekuteli (1) to control the false discovery rate. Genes with significant changes of at least 1.75 fold were identified as genes of interest. Both low and high doses of lunar dust caused dramatic, dose‐dependent global gene expression changes in the lung tissues. However, the responses of lung tissue to low dose lunar dust are distinguished from those of high doses, especially those associated with 61mg/m3 dust exposure. The data were further integrated into the Ingenuity system to analyze the gene ontology (GO), pathway distribution and putative upstream regulators and gene targets. Multiple pathways, functions, and upstream regulators have been identified in response to lunar dust induced damage in the lung tissue.

The purpose of the study is to analyze the dynamics of global gene expression changes in the lung tissue of rats exposed to lunar dust particles. Multiple pathways and transcription factors were identified using the Ingenuity Pathway Analysis tool, showing the potential networks of these signaling regulations involved in lunar dust‐induced prolonged proflammatory response and toxicity. The data presented in this study, for the first time, explores the molecular mechanisms of lunar dust induced toxicity. This work contributes not only to the risk assessment for future space exploration, but also to the understanding of the dust‐induced toxicity to humans on earth.

The Moon's surface is covered by a layer of fine, reactive dust. Lunar dust contain about 1-2% of very fine dust (< 3 micron), that is respirable. The habitable area of any lunar landing vehicle and outpost would inevitably be contaminated with lunar dust that could pose a health risk. The purpose of the study is to analyze the dynamics of global gene expression changes in lung tissues from rats exposed to lunar dust particles. F344 rats were exposed for 4 weeks (6h/d; 5d/wk) in nose-only inhalation chambers to concentrations of 0 (control air), 2.1, 6.8, 21, and 61 mg/m(exp 3) of lunar dust. Five rats per group were euthanized 1 day, and 3 months after the last inhalation exposure. The total RNAs were isolated from lung tissues after being lavaged. The Agilent Rat GE v3 microarray was used to profile global gene expression (44K). The genes with significant expression changes are identified and the gene expression data were further analyzed using various statistical tools.

In this work, we evaluated the differential effects of low- and high-LET radiation on 3-D organotypic cultures in order to investigate radiation quality impacts on gene expression and cellular responses. Current risk models for assessment of space radiation-induced cancer have large uncertainties because the models for adverse health effects following radiation exposure are founded on epidemiological analyses of human populations exposed to low-LET radiation. Reducing these uncertainties requires new knowledge on the fundamental differences in biological responses (the so-called radiation quality effects) triggered by heavy ion particle radiation versus low-LET radiation associated with Earth-based exposures. In order to better quantify these radiation quality effects in biological systems, we are utilizing novel 3-D organotypic human tissue models for space radiation research. These models hold promise for risk assessment as they provide a format for study of human cells within a realistic tissue framework, thereby bridging the gap between 2-D monolayer culture and animal models for risk extrapolation to humans. To identify biological pathway signatures unique to heavy ion particle exposure, functional gene set enrichment analysis (GSEA) was used with whole transcriptome profiling. GSEA has been used extensively as a method to garner biological information in a variety of model systems but has not been commonly used to analyze radiation effects. It is a powerful approach for assessing the functional significance of radiation quality-dependent changes from datasets where the changes are subtle but broad, and where single gene based analysis using rankings of fold-change may not reveal important biological information.

The goal of the NASA Human Research Program's Space Radiation Program Element is to ensure that crews can safely live and work in the space radiation environment. Current work is focused on developing the knowledge base and tools required for accurate assessment of health risks resulting from space radiation exposure including cancer and circulatory and central nervous system diseases, as well as acute risks from solar particle events. Division of Space Life Sciences (DSLS) Space Radiation Team scientists work at multiple levels to advance this goal, with major projects in biological risk research; epidemiology; and physical, biophysical, and biological modeling.