Division of Advanced Cyberinfrastructure

COVID-19, the disease caused by SARS-CoV-2, has caused significant morbidity and mortality worldwide. The betacoronavirus continues to evolve with global health implications as we race to learn more to curb its transmission, evolution, and sequelae. The focus of this review, the second of a three-part series, is on the biological effects of the SARS-CoV-2 virus on post-acute disease in the context of tissue and organ adaptations and damage. We highlight the current knowledge and describe how virological, animal, and clinical studies have shed light on the mechanisms driving the varied clinical diagnoses and observations of COVID-19 patients. Moreover, we describe how investigations into SARS-CoV-2 effects have informed the understanding of viral pathogenesis and provide innovative pathways for future research on the mechanisms of viral diseases.

Ongoing experimental efforts in Antarctica seek to detect ultra-high energy neutrinos by measurement of radio-frequency (RF) Askaryan radiation generated by the collision of a neutrino with an ice molecule. An array of RF antennas, deployed either in-ice or in-air, is used to infer the properties of the neutrino. To evaluate their experimental sensitivity, such experiments

Ultra high energy neutrinos (E >10 16.5 eV) are efficiently measured via radio signals following a neutrino interaction in ice. An antenna placed O(15 m) below the ice surface will measure two signals for the vast majority of events (90% at E =10 18 eV): a direct pulse and a second delayed pulse from a reflection off the ice surface. This allows for a unique identification of neutrinos against backgrounds arriving from above. Furthermore, the time delay between the direct and reflected signal (D'n'R) correlates with the distance to the neutrino interaction vertex, a crucial quantity to determine the neutrino energy. In a simulation study, we derive the relation between time delay and distance and study the corresponding experimental uncertainties in estimating neutrino energies. We find that the resulting contribution to the energy resolution is well below the natural limit set by the unknown inelasticity in the initial neutrino interaction. We present an in-situ measurement that proves the experimental feasibility of this technique. Continuous monitoring of the local snow accumulation in the vicinity of the transmit and receive antennas using this technique provide a precision of O(1 mm) in surface elevation, which is much better than that needed to apply the D'n'R technique to neutrinos.

Abstract Following the Ridgecrest earthquake sequence, consisting of an M 6.4 foreshock and M 7.1 mainshock along with many other events, the Geotechnical Extreme Events Reconnaissance association deployed a team to gather perishable data. The team focused their efforts on documenting ground deformations including surface fault rupture south of the Naval Air Weapons Station China Lake, and liquefaction features in Trona and Argus. The team published a report within two weeks of the M 7.1 mainshock. This article presents data products gathered by the team, which are now published and publicly accessible. The data products presented herein include ground-based observations using Global Positioning System trackers, digital cameras, and hand-measuring devices, as well as unmanned aerial vehicle-based imaging products using Structure from Motion to create point clouds and digital surface models. The article describes the data products, as well as tools available for interacting with the products.

We investigate the effect of the pulse length on the two-photon double ionization (DI) of H${}_{2}$ in the direct domain, for a femtosecond (fs) laser with a polarization vector oriented along the molecular axis. In the fixed-nuclei approximation, we find that the doubly excited ${Q}_{1}\phantom{\rule{0.28em}{0ex}}{}^{1}{\ensuremath{\Sigma}}_{u}^{+}$ states manifest themselves as resonances in the angle-integrated cross sections if the laser interaction lasts longer than about 3 fs. Decay into single-ionization channels does not significantly affect the shape of the angular distribution. A sharp rise in the probability for DI, due to virtual sequential ionization, occurs when the photon energy approaches the threshold for sequential DI.

Abstract The TAROGE-M radio observatory is a self-triggered antenna array on top of the ∼2700 m high Mt. Melbourne in Antarctica, designed to detect impulsive geomagnetic emission from extensive air showers induced by ultra-high energy (UHE) particles beyond 10 17 eV, including cosmic rays, Earth-skimming tau neutrinos, and particularly, the “ANITA anomalous events” (AAE) from near and below the horizon. The six AAE discovered by the ANITA experiment have signal features similar to tau neutrinos but that hypothesis is in tension either with the interaction length predicted by Standard Model or with the flux limits set by other experiments. Their origin remains uncertain, requiring more experimental inputs for clarification. The detection concept of TAROGE-M takes advantage of a high altitude with synoptic view toward the horizon as an efficient signal collector, and the radio quietness as well as strong and near vertical geomagnetic field in Antarctica, enhancing the relative radio signal strength. This approach has a low energy threshold, high duty cycle, and is easy to extend for quickly enlarging statistics. Here we report experimental results from the first TAROGE-M station deployed in January 2020, corresponding to approximately one month of livetime. The station consists of six receiving antennas operating at 180–450 MHz, and can reconstruct source directions of impulsive events with an angular resolution of ∼0.3°, calibrated in situ with a drone-borne pulser system. To demonstrate TAROGE-M's ability to detect UHE air showers, a search for cosmic ray signals in 25.3-days of data together with the detection simulation were conducted, resulting in seven identified candidates. The detected events have a mean reconstructed energy of 0.95 -0.31 +0.46 EeV and zenith angles ranging from 25° to 82°, with both distributions agreeing with the simulations, indicating an energy threshold at about 0.3 EeV. The estimated cosmic ray flux at that energy is 1.2 -0.9 +0.7 × 10 -16 eV -1 km -2 yr -1 sr -1 , also consistent with results of other experiments. The TAROGE-M sensitivity to AAEs is approximated by the tau neutrino exposure with simulations, which suggests comparable sensitivity as ANITA's at around 1 EeV energy with a few station-years of operation. These first results verified the station design and performance in a polar and high-altitude environment, and are promising for further discovery of tau neutrinos and AAEs after an extension in the near future.

Abstract The ARIANNA experiment is an Askaryan detector designed to record radio signals induced by neutrino interactions in the Antarctic ice. Because of the low neutrino flux at high energies (E_ν> 10^16 eV), the physics output is limited by statistics. Hence, an increase in sensitivity significantly improves the interpretation of data and offers the ability to probe new parameter spaces. The amplitudes of the trigger threshold are limited by the rate of triggering on unavoidable thermal noise fluctuations. We present a real-time thermal noise rejection algorithm that enables the trigger thresholds to be lowered, which increases the sensitivity to neutrinos by up to a factor of two (depending on energy) compared to the current ARIANNA capabilities. A deep learning discriminator, based on a Convolutional Neural Network (CNN), is implemented to identify and remove thermal events in real time. We describe a CNN trained on MC data that runs on the current ARIANNA microcomputer and retains 95% of the neutrino signal at a thermal noise rejection factor of 10^5, compared to a template matching procedure which reaches only 10^2 for the same signal efficiency. Then the results are verified in a lab measurement by feeding in generated neutrino-like signal pulses and thermal noise directly into the ARIANNA data acquisition system. Lastly, the same CNN is used to classify cosmic-rays events to make sure they are not rejected. The network classified 102 out of 104 cosmic-ray events as signal.

Abstract The ARIANNA detector is designed to detect neutrinos with energies above 10 17 eV. Due to the similarities in generated radio signals, cosmic rays are often used as test beams for neutrino detectors. Some ARIANNA detector stations are equipped with antennas capable of detecting air showers. Since the radio emission properties of air showers are well understood, and the polarization of the radio signal can be predicted from the arrival direction, cosmic rays can be used as a proxy to assess the reconstruction capabilities of the ARIANNA neutrino detector. We report on dedicated efforts of reconstructing the polarization of cosmic-ray radio pulses. After correcting for difference in hardware, the two stations used in this study showed similar performance in terms of event rate and agreed with simulation. Subselecting high quality cosmic rays, the polarizations of these cosmic rays were reconstructed with a resolution of 2.5° (68% containment), which agrees with the expected value obtained from simulation. A large fraction of this resolution originates from uncertainties in the predicted polarization because of the contribution of the subdominant Askaryan effect in addition to the dominant geomagnetic emission. Subselecting events with a zenith angle greater than 70° removes most influence of the Askaryan emission, and, with limited statistics, we found the polarization uncertainty is reduced to 1.3° (68% containment).

Abstract. We investigate the elastic scattering of Bose-Einstein condensates at shallow periodic and disorder potentials. We show that the collective scattering of the macroscopic quantum object couples to internal degrees of freedom of the Bose-Einstein condensate such that the Bose-Einstein condensate gets depleted. As a precursor for the excitation of the Bose-Einstein condensate we observe wave chaos within a mean-field theory. 1.

International audience

While artificial intelligence and machine learning (AI/ML) frameworks gain prominence in science and engineering, most researchers face significant challenges in adopting complex AI/ML workflows to campus and national cyberinfrastructure (CI) environments. Data from the Texas A&M High Performance Computing (HPRC) researcher training program indicate that researchers increasingly want to learn how to migrate and work with their pre-existing AI/ML frameworks on large scale computing environments. Building on the continuing success of our work in developing innovative pedagogical approaches for CItraining approaches, we expand CI-infused pedagogical approaches to teach technology-based AI and data sciences. We revisit the pedagogical approaches used in the decades-old tradition of laboratories in the Physical Sciences that taught concepts via experiential learning. Here, we structure a series of exercises on interactive computing environments that give researchers immediate hands-on experience in AI/ML and data science technologies that they will use as they work on larger CI resources. These exercises, called "tech-labs," assume that participating researchers are familiar with AI/ML approaches and focus on hands-on exercises that teach researchers how to use these approaches on large-scale CI. The tech-labs offer four consecutive sessions, each introducing a learner to specific technologies offered in CI environments for AI/ML and data workflows. We report on our tech-lab offered for Python-based AI/ML approaches during which learners are introduced to Jupyter Notebooks followed by exercises using Pandas, Matplotlib, Scikit-learn, and Keras. The program includes a series of enhancements such as container support and easy launch of virtual environments in our Web-based computing interface. The approach is scalable to programs using a command line interface (CLI) as well. In all, the program offers a shift in focus from teaching AI/ML toward increasing adoption of AI/ML in large-scale CI.

TV band is a precious band from coverage point. A large portion of 1 GHz sub band has been allocated for TV broadcasting. It has been found that large chunk of TV band is unutilized at a given location and time. It is wastage of precious natural resource. The idea of exploitation of idle TV spectrum at any location emerges after introduction of cognitive radio technology and dynamic spectrum access. Most of countries has developed regulatory framework for unlicensed application in TV white space. The TV band has not yet been opened in India for unlicensed usage. This paper gives an idea about present scenario of allocation of TV spectrum in India and its utilization including requisite regulatory framework.

This paper examines scenarios in which science gateways can facilitate access to cloud computing resources to support scientific research using regulated or protected data stored on clouds. Specifically, we discuss the use of science gateways to access Controlled Unclassified Information (CUI), a US regulatory standard that covers a broad range of US federal government-owned or regulated data, and that also provides a useful proxy for other types of sensitive data, such as private sector intellectual property. We focus on the impact of CUI requirements on science gateway platforms that can be used to create and manage science gateway instances. Gateway platforms are centrally operated by gateway platform providers who create and control gateway instances on behalf of gateway providers. Broadly, platforms operate following either a multi-tenant or else a multi-instance pattern. Multi-tenanted science gateway platforms are designed to support multiple science gateways simultaneously, with each gateway as a tenant to a single operational instance of the platform middleware. Multi-instance platforms, on the other hand, provide and manage an entire instance of the science gateway software for each gateway. This paper reviews these two scenarios from the perspective of the Science Gateways Platform as a service (SciGaP), a multitenanted gateway platform based on the open-source Apache Airavata software. We examine requirements for providing multitenanted platforms for CUI gateways and also the requirements for providing the same software as a multi-instance platform. In both cases, we assume the use of CUI-compatible resources from commercial cloud providers. Both approaches are technically feasible but have trade-offs that must be considered.

The need for fast and accurate spectrum utilization characterization in three dimensional (3D) space, has been established as a significant topic in modern wireless communications. A critical consideration in this regard, is the reconstruction of radio environment maps (REMs) from a set of measurements, which is ordinarily much smaller than the number of samples necessary to achieve high fidelity. Consequently, 3D interpolation methods are widely utilized. This study compares the performance of established statistical and deep learning (DL) approaches for 3D REM reconstruction for three types of simulated and real- world measured spectrum data. More specifically, the effect of the received signal strength (RSS) range between non-normalized and normalized values, is assessed. Root mean square error (RMSE) of as low as -8 dB is achieved for sampling ratio of 10%. The results establish the normalization of the RSS data improves the accuracy for applications that do not require prediction of the exact RSS values such as identifying areas with under-utilized spectrum.

The Radio Access Network is evolving to AI- Native, and the driver for this innovation is O-RAN. This is because O-RAN integrates intelligence with the RAN through the RIC component. The RIC leverages machine learning for a wide variety of use cases, enabling advanced optimization and automation capabilities. This paper presents different uses cases where O-RAN is applicable. It also discusses the architectural improvements and challenges of integrating O-RAN into future network.

The recent advances in wireless communications toward upcoming services and infrastructure have spurred the development of unmanned aerial vehicle (UAV) assisted terrestrial networks. They offer the prospect of significant capacity improvement through the aerial base station (ABS), particularly when it leverages three-dimensional (3D) radio environment maps (REMs) that provide spectrum occupancy characterization. The heterogeneous network (Het-Net) with an ABS scenario consists of a stationary ground base station (GBS), an ABS and mobile users. Motivated by the 3D REMs’ potential and their limited application in the field, this work introduces a heuristic procedure for ABS positioning that integrates the collection of received signal strength (RSS) measurements to update the REM, Kriging-based 3D REM reconstruction, and user association with the ABS. The experiments determine the 3D REM sampling rate and, consequently, the amount of necessary measurements, based on the highest achievable system capacity. In addition, this novel solution is compared to reference methods for UAV positioning through extensive simulations, showing an increase of up to 15% compared to alternative algorithms, reaching a maximum capacity of 128 Mbps.

This paper presents a cyberinfrastructure (i.e., web-based platform) for geotechnical engineering for storing, computing, and managing capabilities of project data, analyses, and designs. This geotechnical engineering cyberinfrastructure (GTCI) hosts basic project data, analysis packages, and design results. GTCI consists of (1) a web portal enclosing the details of computation and data management, thereby minimizing the required learning effort, (2) a flexible framework that allows easy extension by integrating geotechnical analysis suites for analysis and visualization; this means users can add new tools/scripts as needed overtime, and (3) a common repository to manage data and results through a database that maintains metadata. Furthermore, in GTCI, all design data, models, and results live online, offering the ability to submit, review, exchange, and edit designs. GTCI, therefore, overcomes the status quo of relying on offline segregated data, analyses, and designs. Thus, GTCI increases the security, safety, and lifetime of critical project information.

The Radio Access Network is evolving to AI- Native, and the driver for this innovation is O-RAN. This is because O-RAN integrates intelligence with the RAN through the RIC component. The RIC leverages machine learning for a wide variety of use cases, enabling advanced optimization and automation capabilities. This paper presents different uses cases where O-RAN is applicable. It also discusses the architectural improvements and challenges of integrating O-RAN into future network.

The recent advances in wireless communications toward upcoming services and infrastructure have spurred the development of unmanned aerial vehicle (UAV) assisted terrestrial networks. They offer the prospect of significant capacity improvement through the aerial base station (ABS), particularly when it leverages three-dimensional (3D) radio environment maps (REMs) that provide spectrum occupancy characterization. The heterogeneous network (Het-Net) with an ABS scenario consists of a stationary ground base station (GBS), an ABS and mobile users. Motivated by the 3D REMs’ potential and their limited application in the field, this work introduces a heuristic procedure for ABS positioning that integrates the collection of received signal strength (RSS) measurements to update the REM, Kriging-based 3D REM reconstruction, and user association with the ABS. The experiments determine the 3D REM sampling rate and, consequently, the amount of necessary measurements, based on the highest achievable system capacity. In addition, this novel solution is compared to reference methods for UAV positioning through extensive simulations, showing an increase of up to 15% compared to alternative algorithms, reaching a maximum capacity of 128 Mbps.

The need for fast and accurate spectrum utilization characterization in three dimensional (3D) space, has been established as a significant topic in modern wireless communications. A critical consideration in this regard, is the reconstruction of radio environment maps (REMs) from a set of measurements, which is ordinarily much smaller than the number of samples necessary to achieve high fidelity. Consequently, 3D interpolation methods are widely utilized. This study compares the performance of established statistical and deep learning (DL) approaches for 3D REM reconstruction for three types of simulated and real- world measured spectrum data. More specifically, the effect of the received signal strength (RSS) range between non-normalized and normalized values, is assessed. Root mean square error (RMSE) of as low as -8 dB is achieved for sampling ratio of 10%. The results establish the normalization of the RSS data improves the accuracy for applications that do not require prediction of the exact RSS values such as identifying areas with under-utilized spectrum.